CN113632101A - Method for predicting atmospheric pollution through vectorization analysis - Google Patents

Method for predicting atmospheric pollution through vectorization analysis Download PDF

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CN113632101A
CN113632101A CN201980089866.5A CN201980089866A CN113632101A CN 113632101 A CN113632101 A CN 113632101A CN 201980089866 A CN201980089866 A CN 201980089866A CN 113632101 A CN113632101 A CN 113632101A
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刘一平
付祥梅
吴吉鹏
吕峰
何新
许军
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Nova Fitness Co Ltd
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Abstract

According to the scheme, one or more similar grid intervals with the highest matching degree are used, the current air quality data are used as a reference (the current air quality data are known), pollution drift vectors at subsequent moments of known historical matching grid moments are used (the calculation mode of the pollution drift vectors at the subsequent moments is still a pollution drift vector calculation method), and the pollution drift vector calculation method is reversely applied to calculate and predict future air quality data to obtain the required future air quality data.

Description

Method for predicting atmospheric pollution through vectorization analysis Technical Field
The invention relates to a method for predicting atmospheric pollution through vectorization analysis, and belongs to the field of environmental monitoring.
Background
In the industrial process, the natural environment is seriously damaged by human beings, various pollutions are generated, and the atmospheric pollution is one of the main pollutions. Air pollution directly endangers people's health, and therefore there is a need for people to be able to know the air quality at a more accurate future date in order to arrange their work and life, and this need is strong.
Air quality data generally comes mainly from measured values of environmental protection departments. The existing technologies also include technologies for predicting air quality, which are mainly divided into two types: one method is to forecast based on chemical forecasting model calculation, namely, to establish a diffusion model, and to forecast based on chemical forecasting model calculation, which needs extremely high calculation resources and is difficult to realize; the other method is that the air quality data at the local prediction time is predicted based on local historical meteorological data and historical air quality data and combined with meteorological data at the prediction time, and when the air quality data at the local prediction time is predicted, the selected historical meteorological data and historical air quality data are data at the same time as the prediction time in the historical date, for example, the prediction time is 15: 00, the selected historical meteorological data and historical air quality data are historical data of one or more dates 15: the data at 00 hours, the accuracy of the air quality data predicted by the method is low.
The two air quality prediction modes have many disadvantages, the first mode needs a large amount of parameter data input and has extremely high requirements on computing resources and a meteorological monitoring network; the second method is based on historical meteorological data and historical air quality data, needs two kinds of data at the same time, and the simple prediction method based on the historical meteorological data and the historical air quality data has low accuracy. Both methods require meteorological data and cannot accurately predict small and medium scales in real time; the direction and trajectory of the pollution drift cannot be accurately predicted.
There have been some studies on the prediction of contaminants by relevant personnel.
Chinese patent application No.: 201510287229.2, title of the invention: a method and apparatus for predicting air quality. The method selects certain historical meteorological data and historical air quality data from historical meteorological data and historical air quality data of all regions within a prediction region and a preset distance range around the prediction region, uses the data, current meteorological data and air quality data as input parameters, and inputs a function to predict the air quality. The invention still requires meteorological data and cannot predict the path and trend of the pollutant drift.
Chinese patent application No.: 201710818682.0, title of the invention: an air quality prediction method and device. The invention uses historical data of air quality and target date to predict the possible air condition of the historical air quality on the target date simply by analyzing the probability. The lack of use of existing monitoring data at all results in a significant reduction in prediction accuracy and a high probability of a prediction not matching the current situation.
Disclosure of Invention
The prior application: PCT/CN2019/051245
Term(s) for
1. Historical air quality data: air contaminant concentrations at a certain time of history.
2. Historical air quality data characteristics: and during a certain two historical moments, expressing the vector of the contribution of the previous moment of the monitored area to the pollutants in the surrounding area at the later moment.
3. Historical air quality data feature library: a database consisting of vectors characterizing historical air quality data.
4. Air quality data frame: the method comprises the steps of dividing an area to be predicted into grids with set density according to prediction requirements, giving historical air quality data into the grids according to time and geographic positions, and generating an air quality data set with geographic position information based on time series.
5. Air quality data matrix: and expressing the data contained in the data frame in a matrix form to obtain a matrix, namely the air quality data matrix.
6. Recent air quality data: and air quality data obtained by statistics before the pollution process to be predicted is compared with the historical data.
7. Recent air quality data characteristics: and expressing the vector of the contribution of the previous moment of the monitored area to the pollutants in the surrounding area at the later moment between the moment when the pollution process to be predicted is cut off and the moment before the comparison with the historical data is carried out.
8. Matching degree coefficient: and the coefficient represents the matching degree of the recent air quality data characteristic of the pollution process to be predicted and the historical air quality data characteristic to be matched.
9. Vector group: and calculating a vector group containing the characteristic vector characterization pollutant diffusion path in each grid according to the air quality data frames at the two moments.
10. Time-to-time air quality data characteristics: and characterizing the vector of pollutant diffusion information between two moments.
11. Ratio comparison matrix: after the data of the historical air quality data frame and the current air quality data frame are compared to form a ratio comparison data frame, a matrix corresponding to the ratio comparison data frame is a ratio comparison matrix.
12. Similar grid intervals: and obtaining a section of historical air quality data similar to the recent air quality data development process by a feature vector matching method.
13. Difference comparison matrix: and after a difference value comparison data frame is formed by the difference value of the data of the historical air quality data frame and the current air quality data frame, a matrix corresponding to the difference value comparison data frame is a difference value comparison matrix.
The method aims to overcome the defect that the air quality prediction in the prior art requires high requirements on computing resources by meteorological parameters such as wind speed and wind direction, or the defect of simple probability prediction by means of historical data in the prior art. The invention provides a method for predicting atmospheric pollution through vectorization analysis, which can accurately predict air pollution and pollution drift path trend under the conditions of utilizing less computing resources and not using near-ground meteorological data which is not easy to obtain, and can simplify vector calculation and realize more rapid and accurate prediction. In order to achieve the purpose, the invention provides the following technical scheme:
establishing historical air quality database
Obtaining historical air quality data, geographical position information, information of an area to be predicted and the like, and establishing a historical air quality database, wherein the specific method comprises the following steps:
determining a geographical area range of an area to be predicted; historical air quality data with geographical position information in the area range to be predicted are obtained, and the historical air quality data with the geographical position information can be data collected by a state control station, a super station, an air quality monitoring micro station, a mobile station and the like.
Establishing a gridded database
And dividing the area to be predicted into grids with set density according to prediction requirements, giving historical air quality data into the grids according to time and geographic positions, and generating an air quality data frame with two-dimensional geographic position information and time stamp information.
And establishing a three-dimensional space-time grid by taking the time information as an axis, and arranging data frames with two-dimensional geographic position information and air quality data to generate the three-dimensional space-time grid.
The method for endowing air quality with geographical position information and time stamp information in the grid comprises the following steps:
1) only one air quality information falls into one grid at the same time, and the air quality data of the grid is the one air quality information.
2) A plurality of air quality information fall into a grid in the same time, and the air quality data of the grid is the average value of the air quality information.
3) And the grid with no air quality information falling into the grid can be obtained by performing mathematical methods such as interpolation, diffusion model and the like on grid data of adjacent spaces at the same time.
4) The grid with no air quality information falling into can also be obtained by interpolation, diffusion model and other mathematical methods through the grid data of the same space and adjacent moments.
5) The side length of each two-dimensional geographic grid may be 10 meters to 1000 meters.
6) The same time refers to air quality data of 1 minute to 1 hour before and after the time point.
Vectorized analysis gridding database
Analyzing characteristics of a historical air quality database
Analyzing historical air quality database characteristics, analyzing an air quality data frame with geographical position information, analyzing pollution contribution of each grid in a data frame at a previous moment to adjacent positions of the grids in a data frame at a later moment to obtain historical air quality data characteristics between the two moments, wherein the characteristics are that the pollution contribution of each grid to the periphery between the two moments represents the direction of pollution drift between the two moments, and continuous characteristics between multiple moments can represent the path of the pollution drift.
The method for obtaining the air quality data matrix comprises the following steps:
will be at T1Filling historical air quality data of the moment into the divided grids according to the geographical position information to obtain T1A temporal air quality data frame. The data frame is an air quality data plane with two-dimensional geographic position information at one moment in a three-dimensional space-time network.
Figure PCTCN2019102418-APPB-000001
Will T1Temporal air quality data frame transformation according to gridIs an A matrix, and the value of the element in the first row and the first column in the A matrix is T1The air quality of the first row and the first column in the air quality data frame at the moment, and the value of the element of the mth row and the nth column in the A matrix is T1The air quality of the mth row and the nth column in the air quality data frame at the moment, and the matrix A represents T1Air quality data frames in the time zone.
Figure PCTCN2019102418-APPB-000002
T 2The time air quality data matrix B is obtained by the method in the same way, and the matrix B represents T2Air quality data frames in the time zone.
Figure PCTCN2019102418-APPB-000003
Figure PCTCN2019102418-APPB-000004
The method for obtaining the historical air quality data characteristics comprises the following steps:
and respectively calculating the pollution contribution of each grid to the adjacent grid at the next moment in the previous moment, wherein the pollution contribution of each grid to the adjacent grid is obtained in a vector calculation mode.
B (m-1)(n-1) B (m-1)n B (m-1)(n+1)
B m(n-1) A mn B m(n+1)
B (m+1)(n-1) B (m+1)n B (m+1)(n+1)
Obtaining an analysis matrix
Figure PCTCN2019102418-APPB-000005
A mnAs air quality data of the preceding moment, BmnAir quality data at a later time. Above T1For air quality data at a previous moment, T2Air quality data at the latter time.
Vector characteristics of historical air quality data: t is1In time AmnRegion pair T2The pollution drift of the grid around the moment can be expressed in a vector mode, and the pollution drift direction can be artificially divided into three modes.
The first method is as follows: t is1In time AmnTo the upper, lower, left and right directions in T2The time of day has a drift contribution. That is, AmnTo B(m-1)n、B m(n-1)、B m(n+1)And B(m+1)n)It is helpful. Pollution drift vector of
Figure PCTCN2019102418-APPB-000006
Figure PCTCN2019102418-APPB-000007
Then the pollution drift vector calculation method of the first mode is as follows:
Figure PCTCN2019102418-APPB-000008
Figure PCTCN2019102418-APPB-000009
Figure PCTCN2019102418-APPB-000010
Figure PCTCN2019102418-APPB-000011
and (3) synthesizing the feature vectors:
Figure PCTCN2019102418-APPB-000012
Figure PCTCN2019102418-APPB-000013
the second method comprises the following steps: t is1In time AmnTo the left upper, the left lower, the right upper and the right lower in the T direction2The time of day has a drift contribution. That is, AmnTo B(m-1)(n-1)、B (m-1)(n+1)、B (m+1)(n-1)、B (m+1)(n+1)It is helpful. Pollution drift vector of
Figure PCTCN2019102418-APPB-000014
Then the pollution drift vector calculation method of the second mode is as follows:
Figure PCTCN2019102418-APPB-000015
Figure PCTCN2019102418-APPB-000016
Figure PCTCN2019102418-APPB-000017
Figure PCTCN2019102418-APPB-000018
and (3) synthesizing the feature vectors:
Figure PCTCN2019102418-APPB-000019
Figure PCTCN2019102418-APPB-000020
the third method comprises the following steps: t is1In time AmnTo the eight directions of the upper part, the lower part, the left part, the upper left part, the lower left part, the upper right part and the lower right part in the T direction2Tribute with drift at all timesA document is presented. That is, AmnTo B(m-1)(n-1)、B (m-1)n、B (m-1)(n+1)、B m(n-1)、B m(n+1)、B (m+1)(n-1)、B (m+1)n)、B (m+1)(n+1)It is helpful. Pollution drift vector of
Figure PCTCN2019102418-APPB-000021
Figure PCTCN2019102418-APPB-000022
Figure PCTCN2019102418-APPB-000023
Then the method for calculating the pollution drift vector in the third mode is as follows:
Figure PCTCN2019102418-APPB-000024
Figure PCTCN2019102418-APPB-000025
Figure PCTCN2019102418-APPB-000026
Figure PCTCN2019102418-APPB-000027
Figure PCTCN2019102418-APPB-000028
Figure PCTCN2019102418-APPB-000029
Figure PCTCN2019102418-APPB-000030
Figure PCTCN2019102418-APPB-000031
and (3) synthesizing the feature vectors:
Figure PCTCN2019102418-APPB-000032
Figure PCTCN2019102418-APPB-000033
Figure PCTCN2019102418-APPB-000034
is represented by AmnThe contribution to the periphery, i.e. the direction of the pollution drift between these two moments, i.e. AmnAt T1Time to T2Air quality data characteristics between moments; the continuous characteristic at a plurality of time points can represent the drift path of the pollutant. Separately calculating the T of each region in the grid1Time to T2Vector of time is obtained, T1Time to T2The vector group of the time is the characteristic of the pollution condition between the two time; and respectively calculating pollutant vector groups at all times in the historical air quality data to obtain the air quality historical data characteristics in the historical time period. Archiving and storing the historical air quality data characteristicsNamely the historical air quality data characteristic library. The pollution drift vector is also referred to herein as a feature vector.
Analyzing near term air quality data features
The method of analyzing recent air quality data characteristics is similar to the way a historical air quality data characteristic library is built. Firstly, assigning air quality data with geographical position information of an air quality monitoring station to a grid of a to-be-predicted area which is divided, generating a current air quality data frame and one or more groups of data frames which are forward by a certain time by taking the current time as a reference, and further generating an air quality data matrix of the current air quality and the forward certain time.
By using the method for obtaining the characteristics of the historical air quality data and the method for obtaining the air quality data matrix mentioned in the historical air quality data characteristic library, a vector group of recent air quality data changes can be obtained, and the vector group is the recent air quality data characteristics.
A cRepresenting current pollution data matrix
Figure PCTCN2019102418-APPB-000035
B cIndicating a certain moment t ahead1Pollution data matrix of
Figure PCTCN2019102418-APPB-000036
Figure PCTCN2019102418-APPB-000037
Indicating the direction of recent pollution excursions, i.e.
Figure PCTCN2019102418-APPB-000038
Region at t1The effect on current air quality over time, the continuous characterization of multiple time instants, can indicate recent pollutant drift paths.
Grid database for numerical analysis
And analyzing the recent air quality data and the historical air quality data by using a ratio or difference analysis method.
Comparing the recent air quality data unit with the historical air quality data unit, and finding the historical air quality data frame which is closest to the selected recent air quality data frame or is enlarged or reduced in a certain proportion according to each data in the recent air quality data frame, so that the historical air quality motion process or the pollution process which is closest to the current air quality or the current pollution condition and the motion path is found.
Examples of recent and historical air quality data analysis:
comparing the historical air quality data frame with the recent air quality data frame to determine whether the historical pollution process of the data frame A can predict the development trend of the recent pollution process.
Figure PCTCN2019102418-APPB-000039
Historical air quality data frame
Figure PCTCN2019102418-APPB-000040
Recent air quality data frame
Obtaining corresponding matrix A and matrix A from historical air quality data frame and current air quality data framecAnd (4) matrix.
Figure PCTCN2019102418-APPB-000041
Figure PCTCN2019102418-APPB-000042
Ratio analysis method
ε represents the ratio of recent data units to historical data units for the same location. Firstly, selecting single data A in recent data framec 11Geographic location data A corresponding to history11Calculating to obtain the ratio
Figure PCTCN2019102418-APPB-000043
Similarly, obtaining all grid data in the area
Figure PCTCN2019102418-APPB-000044
The values of the elements in the matrix of values epsilon are individually statistically calculated as the average value of epsilon,
Figure PCTCN2019102418-APPB-000045
may be a ratio feature value.
Figure PCTCN2019102418-APPB-000046
A-A cRatio-contrast data frame
Ratio contrast matrix can be obtained from ratio data frame
Figure PCTCN2019102418-APPB-000047
Contrast matrix of epsilon
Difference value analysis method
Delta represents the near termThe absolute value of the difference between the data unit and the same position history data unit. Firstly, selecting single data A in recent data framec 11Geographic location data A corresponding to history11Calculating to obtain the difference delta between the current data and the historical data11＝A c 11-A 11Similarly, the delta of all the grid data in the area is obtainedmn＝A c mn-A mnAnd respectively carrying out statistical calculation on the numerical values of the elements in the matrix of the values delta to calculate the average value of the delta, wherein the delta is the characteristic value of the difference.
Figure PCTCN2019102418-APPB-000048
A-A cDifferential contrast data frame
The difference value contrast matrix can be obtained from the difference values
Figure PCTCN2019102418-APPB-000049
Difference value comparison matrix
Matching recent air quality data with historical air quality data
Feature vector matching method
And after the recent air quality data characteristics are determined, comparing the recent air quality data characteristics with the historical air quality data characteristics to obtain a matching coefficient eta. According to mathematical methods such as the shortest vector distance, the cosine of an included angle, the Mahalanobis distance, the vector similarity and the like, the historical air quality data characteristics closest to the recent air quality data characteristics can be found, and the historical air quality motion process or the pollution process closest to the current air quality or the current pollution condition and the motion path is also found.
In the matching process, other impact factors f should be considered as follows:
setting a temperature influencing factor(factor temperature)：f tWith temperature parameter (t)temp) In this regard, when the temperature is higher than a certain temperature or lower than a certain temperature, the temperature is extremely high or low, which is not suitable for prediction.
Set humidity influence factor (factor humidity): f. ofhWith the humidity parameter (h)humid) In this regard, above a certain humidity, it is not suitable for prediction.
Seasonal influence factor (factor season): f. ofsAnd seasonal parameters(s)season) If the historical air quality data and the recent air quality data are close to or the same in season and temperature and humidity, the matching degree is high; if the difference between the seasons and the temperatures and the humidity of the historical air quality data and the recent air quality data is large, the influence factor value is small.
Setting extreme weather factor (factor weather): f. ofwAnd weather condition parameter (w)weather) In relation to the historical air quality data, extreme weather exists at the time when the historical air quality data and the recent air quality data are located, the value of an extreme weather influence factor is very low, the data cannot be used as prediction reference at that time, and the current situation is not suitable for prediction.
Set path influence factor (factor path): f. ofpAnd a pollution path parameter (p)path) In relation to historical air quality data and recent air quality data, the distance of the pollution moving path and the pollution source should be considered. If the recent air quality data characteristic has a higher matching degree with the pollutant drift paths of the plurality of historical air quality data characteristics, the historical air quality data characteristic path which is closer to the path in the recent air quality data characteristic has a higher matching degree.
Setting a diffusion degree influence factor (factor dispersion): f. ofdAnd a parameter of degree of diffusion (d)diffusion) In connection with this, in the case of similar contaminant diffusion paths, the degree of diffusion of the two contamination processes should be taken as a parameter. If the diffusion degree and the area level of the influence range of the two pollution processes are similar, the matching degree coefficient should beThe degree coefficient of the matching system is correspondingly reduced if the diffusion degree and the regional surface level difference of the influence range of the two pollution processes are larger;
setting a reproducibility influence factor (factor reproducibility): f. ofrAnd the repetition number parameter (r)repeatability) Is related to (r)repeatability) The number of times a historically similar contamination process occurs should be taken as a parameter. If the similar historical pollution process model matched with the pollution process to be predicted occurs for many times, the reliability of prediction performed by using the historical air quality data is considered to be high, and a high matching degree coefficient is set;
setting a terrain influence factor (factor geo): f. ofgAnd topographic parameters (g)geo) In connection with this, the history and the topographical factors at which the site of the production of the polluting process to be predicted is located should also be taken as parameters. If the terrain produced by two pollution processes differs greatly, for example, if there are not many buildings in place several years ago, and if there are many buildings in the past, the matching degree coefficient of the buildings not found several years ago is reduced accordingly.
Other impact factor determination methods table:
influencing factor Influencing parameter Condition Influencing factor
Humidity h humid Humidity > 90％ f h→0
Humidity h humid The humidity is less than 90 percent f h→1
Temperature of t temp The temperature is more than 40 ℃ or less than-20 DEG C f t→0
Temperature of t temp The temperature is more than 20 ℃ below zero and less than 40 DEG C f t→1
Extreme weather w weather Sandstorm, rainstorm, snowstorm, etc f w→0
Route of travel p path The path repetition times are more than 5 f p→1
Extent of diffusion, influence d diffusion Similar diffusion and influence range f d→1
Topography g geo High building shelter around f g→0
The method for matching cosine of included angle comprises the following steps:
comparing the recent air quality data characteristic with the historical air quality data characteristic is required for performing characteristic comparison matching, namely
Figure PCTCN2019102418-APPB-000050
And
Figure PCTCN2019102418-APPB-000051
and (6) carrying out comparison.
the matching coefficient of the angle cosine method is applied to the mth row and the nth column of squares at the moment t
Figure PCTCN2019102418-APPB-000052
Figure PCTCN2019102418-APPB-000053
Vector for cosine similarityThe cosine value of the included angle of the two vectors in the space is used as the measure of the difference between the two individuals. The closer the cosine value is to 1, the closer the angle is to 0 degrees. Similarly, vectors characterizing recent and historical air quality characteristics of the monitored area at a particular time
Figure PCTCN2019102418-APPB-000054
And
Figure PCTCN2019102418-APPB-000055
the comparison can also be done by the angle cosine method, the calculation process is as above (note: all air quality data obtained for the monitored period of time.
Applying an included angle cosine method matching coefficient eta in the whole grid range at the time tt：
Figure PCTCN2019102418-APPB-000056
Applying matching coefficient of included angle cosine method to mth row and nth column of squares at certain time within a period of time T
Figure PCTCN2019102418-APPB-000057
q represents the number of matches made within the T period:
Figure PCTCN2019102418-APPB-000058
matching coefficient eta of applying included angle cosine method in whole grid range within a period of time TTQ represents the number of matches made within the T period:
Figure PCTCN2019102418-APPB-000059
considering other influence factors, the correction formula of the matching coefficient is as follows:
η′＝f×η
eta' is the matching coefficient after correction
The closer the matching coefficient is to 1, the better the matching performance is, the more similar the current characteristic is to a certain section of characteristic of the historical air quality data characteristic library, and the higher the possibility of future occurrence is; the closer the matching coefficient is to 0, the poorer the matching, and the less similar and unlikely the current feature is to a certain segment of the historical air quality data feature library.
Matching coefficient Degree of matching
η′→0 The lower
η′→1 The higher the
Selection of historical air quality data characteristics: the selection of the historical air quality data characteristics can be determined by directly ranking the matching coefficients and can also be determined by ranking after screening.
The direct ranking mode is that the matching coefficients or the modified matching coefficients are ranked from large to small, the closer the matching coefficients or the modified matching coefficients are to 1, the earlier the ranking is, the finally, the matching coefficients which are 10% or 20% of the top ranking or a certain proportion of the top ranking are selected, and the historical air quality data characteristics corresponding to the matching coefficients can be used for predicting the future air quality.
For example: there are 10 total matching coefficients, and these matching coefficients and ranking cases are shown in the following table:
matching coefficient η 5 η 3 η 1 η 4 η 2 η 7 η 6 η 9 η 8 η 10
Value of match coefficient 0.98 0.97 0.95 0.9 0.89 0.8 0.8 0.75 0.72 0.7
Ranking 1 2 3 4 5 6 7 8 9 10
According to the above table, the matching coefficients of the top 20% of the ranking are selected, i.e. eta5And η3And selecting, wherein the historical air quality data characteristics corresponding to the selected matching coefficients can be used for predicting future air quality data.
The ranking mode after screening is a mode of firstly carrying out one round of screening on the condition of the historical air quality characteristics through objective factors and then ranking the screened matching coefficients to determine the required matching coefficients and the corresponding historical air quality data characteristics. The objective factors for screening include the weather condition of the historical air quality data characteristic at the time (if extreme weather conditions such as heavy rain, extreme cold and high temperature occur at the historical time, the historical time is excluded), and the time of the historical air quality data is far away from the current time (such as historical data of more than 3 years). After screening and exclusion, ranking is carried out and a matching coefficient is selected. The ranking mode can be arranged from big to small, the closer the matching coefficient is to 1, the higher the ranking is, the finally selected matching coefficient is 10%, 20% of the top ranking or a certain proportion of the top ranking, and the historical air quality data characteristics corresponding to the matching coefficient can be used for predicting the future air quality.
For example: there are 10 total matching coefficients, and these matching coefficients and ranking cases are shown in the following table:
matching coefficient η 5 η 3 η 1 η 4 η 2 η 7 η 6 η 9 η 8 η 10
Value of match coefficient 0.98 0.97 0.95 0.9 0.89 0.8 0.8 0.75 0.72 0.7
Objective factors of history High temperature - Data 3 years ago - Heavy Rain - - - - -
Ranking - 1 - 2 - 3 4 5 6 7
According to the above table, the matching coefficients of the top 20% of the ranking are selected, i.e. eta3And η4And selecting, wherein the historical air quality data characteristics corresponding to the selected matching coefficients can be used for predicting future air quality data.
Ratio characteristic value matching method
After the ratio comparison matrix is obtained, according to the mathematical methods of average value, mode, data distribution and linear regression, etc. the epsilon meeting the set requirements is countedmnThe ratio of the elements.
When the proportion of the elements of epsilon reaching the set standard to the total quantity of the elements of epsilon exceeds a certain limit, the historical air quality data frame A and the recent air quality data frame A are considered to becSimilarly.
Here, the average value method is exemplified: set a desired epsilon range to
Figure PCTCN2019102418-APPB-000060
In which the average of the ratio data frame elements is first calculated
Figure PCTCN2019102418-APPB-000061
If epsilonmnIs given by
Figure PCTCN2019102418-APPB-000062
Within the range, epsilon is consideredmnCompliance with the regulations. The range of epsilon that meets the requirement can be set artificially, can be set as a multiple of the average value of the ratio, or can be a fixed range.
Difference value characteristic value matching method
After the difference comparison matrix is obtained, the delta meeting the set requirement is counted according to the mathematical methods such as average value, mode, data distribution, linear regression and the likemnThe ratio of the elements.
Respectively counting and calculating the values of the elements in the matrix of the difference value deltaIf the element proportion of the set standard in the total element proportion exceeds a certain limit, the historical air quality data frame A and the recent air quality data frame A are consideredcSimilarly.
Here, the average value method is exemplified: setting the delta range to meet the requirement
Figure PCTCN2019102418-APPB-000063
In which the mean value of the difference data frame is calculated first
Figure PCTCN2019102418-APPB-000064
If deltamnIs given by
Figure PCTCN2019102418-APPB-000065
Within, then, consider δmnCompliance with the regulations. The delta range meeting the requirement can be set artificially, can be set as a multiple of the average value of the difference values, or can be set in a fixed range.
And defining lambda as a similarity coefficient, wherein the lambda is the ratio of the elements meeting the specification in the contrast matrix to the total number of the elements in the contrast matrix. K is a similarity parameter, which represents a set similarity criterion, and different values can be set artificially, for example, if k is set to 0.8, two data frames with λ exceeding k to 0.8 are considered to be similar, that is, the two data frames are matched, and if λ is greater than zero and smaller than k, the two data frames are not similar, that is, the two data frames are not matched. λ reflects the accuracy of the prediction, with higher λ representing higher prediction accuracy.
Figure PCTCN2019102418-APPB-000066
Figure PCTCN2019102418-APPB-000067
Kappa is similarity parameter, k is 0.8, 0.9, etc
Predicting future air quality based on matching results and current air quality conditions
Vector feature prediction
After one or more similar grid intervals with the highest matching degree are obtained, the current air quality data are used as a reference (the current air quality data are known), pollution drift vectors at subsequent moments of known historical matching grid moments are used (the calculation mode of the pollution drift vectors at the subsequent moments is still a pollution drift vector calculation method), the pollution drift vector calculation method is reversely applied to calculate and predict future air quality data, the required future air quality data are obtained, namely matrix operation is carried out on elements of a current air quality data frame and corresponding characteristic vectors to obtain air quality data information at positions around the current element position in the future, and the elements of each data frame are calculated in the same way, so that the future air quality data can be obtained.
When the vector features are used for prediction, if multiple similar sections can be used for predicting the future air quality, the matching coefficients or the percentage of the corrected matching coefficients can be used as the accuracy of prediction to give corresponding prediction conditions.
In the prediction, it is necessary to calculate the contribution value of the target grid to the surrounding grid, but the data (grid data) at the boundary of the statistical region may cause a calculation obstacle due to incomplete surrounding data, and for this case, the following two processing methods are used:
improved boundary processing method 1: data boundary inlining method
And (3) contracting the outermost circle of unit cells of the boundary of the statistical area to form a new statistical area, wherein the original boundary area grid data is not used as a prediction basis, and the original boundary area grid data is used for calculating vector features. All the mesh data of the new statistical region can be used as the basis data for the prediction and to participate in the vector feature calculation.
Figure PCTCN2019102418-APPB-000068
A cExample of data frame interpolation
For example, in this case, the data of the first row, the first column, the last row, and the last column are not used as the basic data of prediction, but may be used as the basic data of vector calculation. After the inlining, the basic data area that can be used for prediction is Ac 22、A c 2(n-1)、A c (m-1)2、A c (m-1)(n-1)Data within the grid (containing the just four coordinates).
Improved boundary processing method 2: data boundary extension method
And expanding the boundary cells of the statistical region by one circle to form a new statistical region, wherein the expansion method can be directly copying the original boundary numerical value as the numerical value of the new boundary, or expanding the original boundary numerical value by one circle in a difference mode. The raw data area can be used for prediction basis and vector feature calculation. The data of the new outward expansion boundary may not be used as a basis for prediction, but the data of the new outward expansion boundary may be used for calculation of vector features.
Figure PCTCN2019102418-APPB-000069
Figure PCTCN2019102418-APPB-000070
For example, in this case, the data of the first row, the first column, the last row and the last column are expanded and do not serve as the basic data of prediction, but all can serve as the basic data of vector calculation. The original data area is Ac 11、A c 1n、A c m1、A c mnData within the grid: (Containing the just four coordinates), the data in this region can also be used as the data basis for the prediction.
Ratio feature prediction
When one or more frames of historical air quality data are obtained that match recent air quality data based on the similarity coefficient. the future air quality prediction method after t time is based on the historical air quality data frame after t time and the ratio characteristic after the historical air quality data frame time point of the ratio characteristic is calculated, and the future air quality data after t time is obtained by reversely applying a ratio characteristic analysis method. The matrix multiplication operation is carried out by using the matrix of the historical air quality data frame at the time t after the time point of the historical air quality data frame and the ratio eigenvalue to obtain the future air quality data.
And lambda is the percentage of the similarity coefficient, and can be used as the accuracy of prediction to be endowed to a corresponding prediction situation.
Figure PCTCN2019102418-APPB-000071
A c+t: air quality data matrix at time t in future
A +t: matrix of historical air quality data at time t after historical air quality data frame time point
Figure PCTCN2019102418-APPB-000072
Characteristic value of ratio
Differential feature prediction
When one or more frames of historical air quality data are obtained that match recent air quality data based on the similarity coefficient. the future air quality prediction method after t time is based on the historical air quality data frame after t time and the difference characteristic after the time point of the historical air quality data frame with the difference characteristic is calculated, and the future air quality data after t time is obtained by reversely applying a difference characteristic analysis method. The matrix addition operation is carried out by using the matrix of the historical air quality data frame at the time t after the time point of the historical air quality data frame and the characteristic value of the difference value to obtain the future air quality data.
And lambda is the percentage of the similarity coefficient, and can be used as the accuracy of prediction to be endowed to a corresponding prediction situation.
Figure PCTCN2019102418-APPB-000073
A c+t: air quality data matrix at time t in future
A +t: matrix of historical air quality data at time t after historical air quality data frame time point
Figure PCTCN2019102418-APPB-000074
Characteristic value of ratio
Brief description of the drawings
FIG. 1 is a schematic view of an air quality prediction process;
FIG. 2 is a vector
Figure PCTCN2019102418-APPB-000075
A schematic diagram;
FIG. 3 is T1Time to T2Historical air quality data feature vector schematic;
FIG. 4 is a vector
Figure PCTCN2019102418-APPB-000076
A schematic diagram;
fig. 5 is 10: time 40 to 10: a characteristic diagram of air quality data of each grid at 50 moments;
fig. 6 is 10: time 40 to 10: a schematic diagram of air quality data in the region to be measured at 50 moments;
FIG. 7 is a drawing showing
Figure PCTCN2019102418-APPB-000077
And
Figure PCTCN2019102418-APPB-000078
angle theta.
Detailed Description
In this embodiment, the predicted area is 150m and 150m around a subway station in Jinan city and 22500m in area2The contamination process continued from 9 am, 23 d.23 d.2019 to 5 pm, 1 d.23 d.2019, taking 10 am: 40-10: and (4) carrying out pollutant drift path monitoring analysis on 50 air quality data frames, wherein the resolution of the air quality data frames is 50M multiplied by 50M. The present embodiment describes a method of vector prediction by taking a data unit as an example.
And dividing the area into grids by taking 50M multiplied by 50M as a unit, so that the area comprises 9 grids, and taking the grid at the lower left corner as an example to analyze the drift path of the pollutants. The number of the year 2019, 1, 23, morning 10: 40 and 10: and filling the historical air quality data at two times of 50 into the divided grids according to the geographic position information to obtain 10: time 40 air quality data frame and 10: air quality data frame at time 50. The geographical position of the air pollution data is often not continuous, and the air quality data in the grid without the data can be obtained by utilizing mathematical algorithms such as interpolation and the like according to the existing air quality data, and finally the data are obtained in the following steps of 1 month, 23 days and 10 am in 2019: 40 and 10: two time point of 50 pollution statistics (PM)2.5Concentration values) are shown in the following table:
10: 40 minute air quality data table
100 75 50
135 110 85
170 145 120
10: 50 minute air quality data table
150 135 120
165 150 135
180 165 150
And (3) mixing the components in a ratio of 10: and converting the air quality data frame at the moment 40 into an A matrix according to the grids, wherein the value of the element in the first row and the first column in the A matrix is 10: the air quality in the first row and the first column in the data frame at time 40, and so on, the value of the element in the mth row and the nth column in the matrix a is 10: air quality in the mth row and nth column of the data frame at time 40, and the A matrix represents 10: and (4) a pollution level data frame in the area at the moment of 40. Similarly, 10: the contaminated data frame at time 50 is converted into a B matrix.
Figure PCTCN2019102418-APPB-000079
Figure PCTCN2019102418-APPB-000080
Respectively calculating 10: time 40 each grid pair 10: and (3) pollution contribution of adjacent grids at 50 moments, wherein the pollution contribution of each grid to the adjacent grids is obtained by means of vector calculation. The following table:
B (m-1)(n-1) B (m-1)n B (m-1)(n+1)
B m(n-1) A mn B m(n+1)
B (m+1)(n-1) B (m+1)n) B (m+1)(n+1)
according to the numbering rule pair 10: 40 and 10: the 50 grid regions are numbered (the top left vertex of the grid at the top left corner of the grid region is used as the origin, the grid is transversely an x-axis, a coordinate grid is established for a y-axis longitudinally (the grid numbering direction is consistent with the matrix arrangement direction, the y-axis is downward in the forward direction), the grid is numbered according to the arrangement order of the grid in the x-axis direction and the y-axis direction), and the coordinate grid table is as follows:
10: time 40 air quality coordinate grid
A 11 100 A 12 75 A 13 50
A 21 135 A 22 110 A 23 85
A 31 170 A 32 145 A 33 120
10: 50 moment air quality coordinate grid
B 11 150 B 12 135 B 13 120
B 21 165 B 22 150 B 23 135
B 31 180 B 32 165 B 33 150
Then 10: lower left corner grid area pair 10 at time 40: the pollution contribution of the peripheral area at the moment of 50 is
Figure PCTCN2019102418-APPB-000081
Figure PCTCN2019102418-APPB-000082
The vector calculation is as follows:
Figure PCTCN2019102418-APPB-000083
(Vector)
Figure PCTCN2019102418-APPB-000084
represents 10: 40 to 10: time period A of 5031Zone for B22The contribution of the region is the same as the following.
Figure PCTCN2019102418-APPB-000085
Figure PCTCN2019102418-APPB-000086
Figure PCTCN2019102418-APPB-000087
(Vector)
Figure PCTCN2019102418-APPB-000088
Represents 10: 40 to 10: time A within 50 time period31Regional contamination spread information. FIG. 4 is a vector
Figure PCTCN2019102418-APPB-000089
Schematic representation.
Respectively calculating vectors by the same principle
Figure PCTCN2019102418-APPB-000090
The calculations are performed separately and fig. 5 shows the vectors described above, which are characteristic of the air quality data for each grid.
Figure PCTCN2019102418-APPB-000091
Characterization of this region 10: time 40 to 10: and obtaining a vector value by the air quality historical data characteristic at 50 moments.
Figure PCTCN2019102418-APPB-000092
Is 10: 40 to 10: air quality data characteristic of the area at time 50.
After the recent air quality data characteristics of the predicted area are determined, comparing the air quality data characteristics with historical air quality data characteristics in a database, finding out the historical air quality data characteristics closest to the recent air quality data characteristics, after finding out the matched historical air quality data characteristics, inputting current air quality data serving as basic data, and performing vector operation and fitting to predict the future air quality.
The current time is 2019, 1, 28, 8: 00, example analysis of pollutant prediction with bottom left corner grid predicts 2019, 1, 28, 8: 10 air pollution situation. The first step of prediction is to compare and fit the recent vector with the historical vector, and according to the historical air quality data feature library, the air quality data feature of the historical same-period time is obtained
Figure PCTCN2019102418-APPB-000093
Figure PCTCN2019102418-APPB-000094
Using the same calculation, we get the number of times 07 of year 1, month 28, 2019: 50 to 2019, 1, 28, 8: air quality data characteristic between 00. Will be provided with
Figure PCTCN2019102418-APPB-000095
And
Figure PCTCN2019102418-APPB-000096
and (6) carrying out comparison.
According to the cosine formula of the included angle:
Figure PCTCN2019102418-APPB-000097
x 1，y 1is composed of
Figure PCTCN2019102418-APPB-000098
Coordinate values of the vectors; x is the number of2，y 2Is composed of
Figure PCTCN2019102418-APPB-000099
Coordinate values of the vector.
Then
Figure PCTCN2019102418-APPB-000100
0.9998 ≈ 1, so the degree of matching of the two vectors is considered to be extremely high, and the historical data can be used for predicting pollutants, known as and
Figure PCTCN2019102418-APPB-000101
are matched with each otherAir quality data characteristics of the prepared historical air quality data during the time of the next 10-minute gradient
Figure PCTCN2019102418-APPB-000102
Wherein the component information of the vector is
Figure PCTCN2019102418-APPB-000103
Figure PCTCN2019102418-APPB-000104
By monitoring data known 08: time 00B31And if the pollutant concentration of the grid is 180, predicting 11 according to the information: 00 contamination concentration of each grid B22＝160，B 21＝174，B 32＝174。
8: prediction results of air pollution conditions around the lower left grid:
174 160
174

Claims (10)

  1. a method of predicting atmospheric pollution by vectorized analysis, comprising the steps of:
    1) establishing a historical air quality database: the historical air quality database comprises historical air quality data acquired by various air quality monitoring devices; the historical air quality data comprises two-dimensional geographic position information and timestamp information;
    2) establishing a three-dimensional space-time grid: the three-dimensional space-time grid takes a two-dimensional geographic position and time as an axis;
    3) establishing a gridding database: generating a unique data unit in each grid; the data cells are calculated from historical air quality data falling into the grid; or derived from neighboring grids of the grid;
    4) vectorizing analysis is carried out on the data of the gridding database to obtain the characteristic vectors of all or part of data units;
    5) comparing and matching the characteristic vector of the data unit in the gridding database at the current period of time with the characteristic vector of the data unit in the gridding database at the non-current period of time; obtaining one or more similar grid intervals with the highest matching degree;
    6) predicting the value of the data unit in the future time interval by using the characteristic vector of the data unit in a period of time after the similar grid interval;
    the vectorization analysis refers to:
    respectively calculating the pollution contribution of each grid to the next period of the adjacent grid by a vector calculation mode to obtain:
    1) firstly, an analysis matrix is established
    Figure PCTCN2019102418-APPB-100001
    2) Calculating a feature vector:
    Figure PCTCN2019102418-APPB-100002
  2. the method of claim 1, wherein when calculating the feature vector, the pollution drift contributions of the target grid to the upper, lower, left and right directions are calculated by:
    Figure PCTCN2019102418-APPB-100003
    wherein A ismnData representing a data unit with coordinates (m, n) at a certain time;
    B m(n-1)data representing a data unit with coordinates (m, n-1) at the next time instant;
    Figure PCTCN2019102418-APPB-100004
    is represented by AmnA contribution to contamination of the surrounding grid at the next time.
  3. The method of claim 1, wherein when calculating the eigenvector, the pollution drift contribution of the target grid to four directions of the target grid, namely the upper left direction, the lower left direction, the upper right direction and the lower right direction, is calculated by:
    Figure PCTCN2019102418-APPB-100005
  4. the method of claim 1, wherein when calculating the eigenvector, the pollution drift contribution of the target grid to four directions of the target grid, namely the upper left direction, the lower left direction, the upper right direction and the lower right direction, is calculated by:
    Figure PCTCN2019102418-APPB-100006
  5. the method of claim 1, wherein the feature vectors are matched in a manner that:
    Figure PCTCN2019102418-APPB-100007
    wherein the content of the first and second substances,
    Figure PCTCN2019102418-APPB-100008
    a feature vector representing recent pollution excursions,
    Figure PCTCN2019102418-APPB-100009
    is the matching coefficient for the grid (m, n) at time t.
  6. The method of claim 1, wherein the feature vectors are matched in a manner that:
    Figure PCTCN2019102418-APPB-100010
    wherein eta isTApplying a matching coefficient eta of an included angle cosine method in the whole grid range within a period of time TTAnd q represents the number of times matching is performed within the T period.
  7. The method of claim 6, wherein the matching coefficients are modified by:
    η′＝f×η
    f＝f(t temp,h humid,s season,w weather,p path,d diffusion,r repeatability,g geo)
    eta' is the modified matching coefficient, f is the influence factor
  8. The method of claim 7, wherein the impact factor is determined by:
    influencing factor Influencing parameter Condition Influencing factor Humidity h humid The humidity is more than 90 percent f h→0 Humidity h humid Humidity<90％ f h→1 Temperature of t temp Temperature of>40 ℃ or temperature<-20℃ f t→0 Temperature of t temp -20℃<Temperature of<40℃ f t→1 Extreme weather w weather Sandstorm, rainstorm, snowstorm, etc f w→0 Route of travel p path The path repetition times are more than 5 f p→1 Extent of diffusion, influence d diffusion Similar diffusion and influence range f d→1 Topography g geo High building shelter around f g→0
  9. The method of claim 5, wherein the matching coefficients are selected by filtering and then ranking, the filtering excluding historical air quality moments in extreme weather conditions, such as rainstorms, extreme coldness, or high temperatures; the screening mode further comprises the step of excluding historical air quality time which exceeds a certain time from the current time, wherein the certain time is 1 year, 2 years or 3 years.
  10. The method of claim 1, wherein the prediction method is:
    and calculating and predicting future air quality data by using the current air quality data as a reference and utilizing a pollution drift vector at the subsequent moment of the matching grid and reversely applying a pollution drift vector calculation method to obtain the future air quality data.
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