Urban near-surface atmospheric pollution data analysis method and system

Abstract

The invention relates to the technical field of atmospheric pollution treatment, and discloses an urban near-surface atmospheric pollution data analysis method, which comprises the following steps: acquiring atmospheric particulate concentration time sequence data and carrying out multi-time scale component separation to obtain a first feature sequence of the atmospheric particulate concentration time sequence data under different time scales; performing dynamic time warping processing on the first feature sequence among the different monitoring stations to obtain a second feature sequence for eliminating dynamic asynchronism among the different monitoring stations; performing agglomeration hierarchical clustering processing on the second feature sequence to obtain background atmospheric particulate concentration fluctuation features; according to the background atmospheric particulate concentration fluctuation characteristics, the urban near-surface pollution process of the atmospheric particulates is analyzed. According to the method, detailed analysis of urban near-surface atmospheric particulate matter concentration change characteristics on a minute-level time scale is realized, a reliable technical basis is provided for screening an exogenous particulate matter pollution process, and the efficiency and accuracy of atmospheric pollution analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air pollution control, and more specifically, to a method and system for analyzing urban near-ground air pollution data. Background Art

[0002] With the large-scale energy conservation and emission reduction work carried out in my country over the years, the emission of atmospheric pollutants from industrial sources has dropped significantly, and the overall urban air quality has improved. However, the proportion of regional pollution sources has increased, and coupled with the influence of unfavorable meteorological conditions, heavy pollution weather still occurs from time to time. In this context, the focus of atmospheric pollution control has gradually shifted from controlling the total amount of local pollutant emissions to controlling the concentration of atmospheric pollutants in the affected area. In order to achieve refined governance of atmospheric pollution and improve the atmospheric environment in urban space, it is urgent to accurately grasp the movement and distribution of particulate matter near the ground in cities under heavy pollution weather, distinguish between exogenous and local pollution processes, and capture and characterize the impact of local pollution sources.

[0003] At present, densely arranged high-frequency sampling networks of atmospheric pollutants near the ground in cities are the main means of monitoring and analyzing urban near-ground atmospheric pollution processes. However, due to the simultaneous effects of atmospheric pollution processes of multiple different sources and properties near the ground in cities, as well as interference from meteorological processes at different scales, conventional time series signal processing methods are difficult to effectively analyze the data generated by such monitoring networks. Existing analysis methods, such as methods based on segmented statistical features, frequency domain operations based on Fourier transforms, and global time-frequency analysis methods such as empirical mode decomposition, all have their own limitations. For example, CN118070110A discloses a method for monitoring atmospheric pollutants based on time domain data analysis, which obtains historical data of pollutants to be monitored in continuous periods of sampling period, divides the range of pollutant content into k state intervals by clustering method, obtains the time domain sequence D of pollutant content to be monitored, performs correlation analysis on the influencing factor data related to the pollutant content to be monitored, screens out the types of positive influencing factors and reverse influencing factors, classifies the sampling period according to the influencing factor types contained, calculates the content state transition probability, obtains the content state transition probability matrix under different period states, selects the corresponding state transition probability matrix to predict the pollutant content to be monitored according to the period state and the pollutant content to be monitored at the current time t, and obtains the predicted value of the pollutant content to be monitored in the next period. However, the above method either cannot effectively decompose the composite changes caused by different emission sources and background factors, or cannot simultaneously localize the signal in the time domain and frequency domain, or is disturbed by the dynamic asynchrony between sites and is difficult to effectively compare. These limitations cause interference in monitoring data analysis, which not only affects the monitoring accuracy, but also increases the difficulty of extracting effective information from it to form accurate early warning indicators, and has the defects of low efficiency and low accuracy. Summary of the invention

[0004] In order to overcome the defects of low efficiency and low accuracy in the existing air pollution analysis technology, the present invention proposes the following technical solutions:

[0005] In a first aspect, the present invention provides a method for analyzing urban near-surface air pollution data, comprising:

[0006] Obtain time series data on atmospheric particulate matter concentration at multiple monitoring stations near the ground in the city.

[0007] The multi-time scale components of the atmospheric particulate matter concentration time series data are separated to obtain the first characteristic sequence of the atmospheric particulate matter concentration time series data at different time scales.

[0008] The first characteristic sequence between different monitoring sites is subjected to dynamic time warping processing to obtain a second characteristic sequence that eliminates the dynamic asynchrony between different monitoring sites.

[0009] The second characteristic sequence is subjected to agglomerative hierarchical clustering processing to obtain the background atmospheric particulate matter concentration fluctuation characteristics.

[0010] Based on the background atmospheric particulate matter concentration fluctuation characteristics, the pollution process of atmospheric particulate matter on the urban near-surface is analyzed.

[0011] As a preferred technical solution, after obtaining the time series data of atmospheric particulate matter concentration at multiple monitoring stations near the ground in the city, the method further includes:

[0012] The atmospheric particulate matter concentration time series data segments with more than M consecutive zero values, null values ​​or unchanged values ​​are marked as abnormal segments and removed. M is a positive integer

[0013] The outliers of the atmospheric particulate matter concentration time series data are obtained by using the absolute median difference between the original value of the atmospheric particulate matter concentration time series data and the differential integrated moving average autoregressive predicted value of the sequence, and the outliers are replaced by the differential integrated moving average autoregressive predicted value. The expression is as follows:

[0014]

[0015] Where c≈1.4826 is a constant, erfcinv() represents the inverse function of the complementary error number, spred(t) is the predicted value of s(t) under ARIMA(p,q), p is the order of the autoregressive term of the ARIMA model, q is the order of the difference term of the ARIMA model, and when res(t)>3, s(t) is considered to be an outlier and is replaced by spred(t).

[0016] As a preferred technical solution, the atmospheric particulate matter concentration time series data is subjected to multi-time scale component separation to obtain the first characteristic sequence of the atmospheric particulate matter concentration time series data at different time scales, including:

[0017] The atmospheric particulate matter concentration time series data is decomposed by using a discrete wavelet decomposition method based on Meyer wavelet to obtain a wavelet approximate sequence of the atmospheric particulate matter concentration time series data on different time scales, and its expression is as follows:

[0018]

[0019] Where ψ(ω) is the Meyer wavelet basis function, j is the number of layers of the decomposed time scale, ω is the variable in the time domain representing the time point, and v(·) = a 4 (35-84a+70a 2 -20a 3 ), a∈[0,1] is the auxiliary function, a is the scale parameter in wavelet decomposition, z j is the low-frequency component of the atmospheric particle concentration time series data s, which represents the wavelet approximation sequence of the atmospheric particle concentration time series data s at time scale j, d n is the high-frequency component of the atmospheric particulate matter concentration time series data s at the decomposition level n.

[0020] The wavelet approximation sequence is converted into a discrete first characteristic sequence, and its expression is as follows:

[0021]

[0022] Among them, fp(·) is the discrete first characteristic sequence, z (n) (·) is the nth-order derivative of the first characteristic sequence z(·), t represents time, n0 is the number of elements contained in the atmospheric particulate matter concentration time series data, and N is a positive integer greater than 0.

[0023] As a preferred technical solution, dynamic time warping is performed on the first characteristic sequence between different monitoring sites to obtain a second characteristic sequence that eliminates the dynamic asynchrony between different monitoring sites, including:

[0024] The first characteristic sequences of the two monitoring sites are selected respectively.

[0025] Construct a distance matrix for storing the difference between any elements of two first feature sequences. The expression is as follows:

[0026] D(t1, t2)=z1(t1)-z2(t2), t1∈[1,m]∩N, t2∈[1,n]∩N

[0027] Wherein, D(t1, t2) is the distance matrix of t1×t2, z1(t1) is the element of the first feature sequence z1 of monitoring site 1 at time t1, z2(t2) is the element of the first feature sequence z2 of monitoring site 2 at time t2, m and n are the number of elements in the first feature sequence z1 and the first feature sequence z2 respectively, and N is a positive integer greater than 0.

[0028] Use Floyd's algorithm to calculate the shortest path from the upper left corner to the lower right corner of the distance matrix.

[0029] The corresponding relationship between the elements in the two first feature sequences is determined according to the shortest path.

[0030] According to the corresponding relationship, the two first characteristic sequences are aligned including local stretching or compression to obtain a second characteristic sequence that eliminates the dynamic asynchronism between the two monitoring sites.

[0031] Repeat the above steps to obtain the second characteristic sequence between any two monitoring sites at the same time scale.

[0032] As a preferred technical solution, the second characteristic sequence is subjected to agglomerative hierarchical clustering to obtain the background atmospheric particulate matter concentration fluctuation characteristics, including:

[0033] For the second characteristic sequence of any monitoring site, obtain the second characteristic sequence corresponding to any other monitoring site.

[0034] The time positions and atmospheric particle concentration values ​​in the second characteristic sequences of other monitoring sites are counted, and the second characteristic sequences whose time positions and atmospheric particle concentration values ​​do not meet the preset conditions are eliminated.

[0035] The time position mean and the atmospheric particulate matter concentration mean of the remaining second characteristic sequence are calculated, and the characteristic sequence is constructed using the time position mean and the atmospheric particulate matter concentration mean. The constructed characteristic sequence is then maximum-standardized to obtain a new second characteristic sequence for the current monitoring site.

[0036] Repeat the above steps to construct a new second feature sequence for each monitoring site.

[0037] Calculate the distance matrix between the new second feature sequences.

[0038] According to the distance matrix between the new second characteristic sequences, the second characteristic sequences are clustered using agglomerative hierarchical clustering to obtain K types of clustering features, where K is the average length of the new second characteristic sequences of all monitoring sites.

[0039] The intra-category similarity of all types of clustering features is evaluated, and the clustering features whose intra-category similarity meets the preset conditions are taken as the final background atmospheric particulate matter concentration fluctuation characteristics.

[0040] As a preferred technical solution, according to the fluctuation characteristics of the background atmospheric particulate matter concentration, the pollution process of atmospheric particulate matter on the urban near-surface is analyzed, including:

[0041] According to the background atmospheric particulate matter concentration fluctuation characteristics, the atmospheric particulate matter concentration fluctuation process is divided into a number of pollution process segments.

[0042] The pollution process fragments are subjected to time series clustering processing to obtain different pollution process patterns.

[0043] Calculate the characteristic indicators of each monitoring station under the same pollution process mode, and analyze the pollution process of atmospheric particulate matter on the near-surface of the city based on the calculation results of the characteristic indicators.

[0044] As a preferred technical solution, the background atmospheric particulate matter concentration fluctuation characteristics are divided into several pollution process segments, including:

[0045] According to the peak and valley values ​​of the atmospheric particle concentration fluctuation, the atmospheric particle concentration fluctuation is divided into rising and falling segments. Each individual rising or falling segment represents a process of increase or diffusion of the atmospheric particle concentration.

[0046] As a preferred technical solution, after segmenting the background atmospheric particulate matter concentration fluctuation characteristics into a plurality of pollution process segments, the method further comprises performing minimum value normalization processing on the atmospheric particulate matter concentration in each pollution process segment.

[0047] As a preferred technical solution, the pollution process fragments are subjected to time series clustering processing to obtain different pollution process patterns, including:

[0048] Calculate the time series difference D(s′1||s′2) between any two pollution process segments, and its expression is as follows:

[0049] D(s′1||s′2)=D DTW (s′1||s′2)×D shape (s′1||s′2)×D stretch (s′1||s′2)

[0050] Among them, s′1 and s′2 are any two pollution process fragments on the same time scale after minimum value normalization, D DTW (s′1||s′2) and D shape(s′1||s′2) are the warping distance and shape distance after dynamic time warping, respectively. stretch (s′1||s′2) is the extension ratio of the original sequence on the time axis during the dynamic time warping process.

[0051] According to the time series differences, a distance matrix between two pollution process segments is constructed.

[0052] According to the distance matrix between the two pollution process fragments, the two pollution process fragments are subjected to agglomerative hierarchical clustering to obtain cluster fragments.

[0053] The above steps are repeated until the cluster segments reach the optimal number of classifications, and each cluster segment represents a pollution process pattern.

[0054] As a preferred technical solution, the method further includes calculating the maximum value of the silhouette coefficient SC of the agglomerative hierarchical clustering as the optimal number of classifications, and its expression is as follows:

[0055]

[0056] in, The profile value s(i) is the mean of the profile values ​​for agglomerative hierarchical clustering of all pollution process fragments. The profile value s(i) is defined as:

[0057]

[0058] in, is the pollution process segment i and cluster segment c i The average distance of other contaminated process fragments in the cluster indicates that the contaminated process fragment i is assigned to the cluster fragment c. i The degree of quality. is the pollution process segment i and the clustering segment c i Other cluster fragments c k The average distance of the contaminated process fragments in , and the average dissimilarity between fragments in different clusters.

[0059] The beneficial effects of the present invention include at least:

[0060] The present invention separates the multi-time scale components of the atmospheric particulate matter concentration time series data to obtain the first characteristic sequence at different time scales, effectively identifies the multi-scale components of the single-site monitoring data, and improves the analysis ability of complex pollution processes. The dynamic time warping process is used to eliminate the dynamic asynchrony between different monitoring sites, solves the problem that the traditional method is difficult to effectively compare multi-site data, and improves the accuracy of data analysis. The background atmospheric particulate matter concentration fluctuation characteristics are extracted using agglomerative hierarchical clustering processing, which effectively eliminates the influence of irrelevant interference factors and improves the extraction efficiency of the characteristics of the exogenous particulate matter pollution process. The atmospheric particulate matter pollution process is analyzed based on the extracted background fluctuation characteristics, which provides a more accurate method for distinguishing exogenous and local pollution processes and improves the analysis accuracy of the urban near-surface pollution process. In summary, the present invention realizes a detailed analysis of the characteristics of the change of urban near-ground atmospheric particulate matter concentration on a minute-level time scale by integrating the analysis methods of time domain, frequency domain and time-frequency combination, provides a reliable technical basis for screening exogenous particulate matter pollution processes, and improves the efficiency and accuracy of atmospheric pollution analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the flow chart of the urban near-surface air pollution data analysis method provided in Example 1.

[0062] Figure 2 This is a logical architecture diagram of the time series characteristics of urban near-ground PM2.5 concentration data in Example 2.

[0063] Figure 3 This is a schematic diagram of the results of multi-scale autocorrelation feature analysis of monitoring data in Example 2.

[0064] Figure 4 This is the power spectrum density estimation diagram of the monitoring data in Example 2.

[0065] Figure 5 Schematic diagram of the stationarity characteristics of monitoring data segments at different sample lengths and wavelet decomposition levels in Example 2.

[0066] Figure 6 This is a graph showing the continuous wavelet decomposition results of the atmospheric particulate matter monitoring data at the monitoring site in Example 2.

[0067] Figure 7 The graph showing the variation of the numerical difference and time series morphological difference between the monitoring data segments of each monitoring station in Example 2 over the time scale

[0068] Figure 8 This is a logical architecture diagram for processing the monitoring data of each monitoring site in Example 3.

[0069] Fig. 9Schematic diagram of the results of monitoring data outlier marking and replacement examples in Example 3

[0070] Fig.10 This is a schematic diagram of the principle of dynamic time warping in Example 3.

[0071] Fig.11 Schematic diagram of the relationship between the asynchrony index and the severity index and the change in PM2.5 concentration in Example 3. DETAILED DESCRIPTION

[0072] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred technical solutions. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred technical solutions are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0073] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0074] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0075] Example 1

[0076] This embodiment proposes a method for analyzing urban near-surface air pollution data, such as Figure 1 As shown, Figure 1 A schematic diagram of a flow chart of a method for analyzing urban near-surface air pollution data provided by this embodiment, the method comprising the following steps:

[0077] S1: Obtain time series data on atmospheric particulate matter concentration at multiple monitoring stations near the ground in the city.

[0078] During the specific implementation process, the concentration of atmospheric particulate matter is usually dominated by PM2.5 concentration.

[0079] S2: Perform multi-time scale component separation on the atmospheric particulate matter concentration time series data to obtain the first characteristic sequence of the atmospheric particulate matter concentration time series data at different time scales.

[0080] In the specific implementation process, discrete wavelet transform is applied to the atmospheric particulate matter concentration time series data of each monitoring station. Appropriate wavelet basis functions and decomposition levels are selected to decompose the original time series data into approximate coefficients and detail coefficients at different time scales. These coefficients constitute the first characteristic sequence at different time scales.

[0081] S3: Perform dynamic time warping on the first characteristic sequence between different monitoring sites to obtain a second characteristic sequence that eliminates the dynamic asynchrony between different monitoring sites.

[0082] In the specific implementation process, dynamic time warping (DTW) is performed on the first characteristic sequence of different monitoring sites at the same time scale. The DTW algorithm eliminates the dynamic asynchrony between different sites by finding the best alignment between the two time series, and the sequence obtained after processing is the second characteristic sequence.

[0083] S4: performing agglomerative hierarchical clustering processing on the second characteristic sequence to obtain background atmospheric particulate matter concentration fluctuation characteristics.

[0084] In the specific implementation process, the second characteristic sequence is processed by agglomerative hierarchical clustering (AHC). First, the distance matrix between sequences is calculated, and then the AHC algorithm is used for clustering. During the clustering process, the optimal number of clusters is determined by the silhouette coefficient. The clustering results represent the fluctuation characteristics of the background atmospheric particulate matter concentration.

[0085] S5: Analyze the pollution process of atmospheric particulate matter on the urban near-surface according to the background atmospheric particulate matter concentration fluctuation characteristics.

[0086] In the specific implementation process, based on the obtained background fluctuation characteristics, the spatial and temporal relationship of each monitoring station is analyzed to distinguish between exogenous and local pollution processes and describe the pollution process of atmospheric particulate matter on the near-surface of the city.

[0087] It can be understood that this embodiment effectively identifies the multi-scale components of single-site monitoring data by separating the atmospheric particulate matter concentration time series data at multiple time scales to obtain the first characteristic sequence at different time scales, thereby improving the ability to analyze complex pollution processes. Dynamic time warping is used to eliminate the dynamic asynchrony between different monitoring sites, solve the problem that traditional methods are difficult to effectively compare multi-site data, and improve the accuracy of data analysis. Agglomerative hierarchical clustering is used to extract the background atmospheric particulate matter concentration fluctuation characteristics, effectively eliminate the influence of irrelevant interference factors, and improve the efficiency of extracting the characteristics of the exogenous particulate matter pollution process. The atmospheric particulate matter pollution process is analyzed based on the extracted background fluctuation characteristics, which provides a more accurate method for distinguishing exogenous and local pollution processes and improves the analysis accuracy of urban near-surface pollution processes. In summary, this embodiment realizes a detailed analysis of the characteristics of the change of urban near-ground PM2.5 concentration on a minute-level time scale by integrating the analysis methods of time domain, frequency domain and time-frequency combination, provides a reliable technical basis for screening exogenous particulate matter pollution processes, and improves the efficiency and accuracy of atmospheric pollution analysis.

[0088] Example 2

[0089] Based on the urban near-surface air pollution data analysis method proposed in Example 1, this embodiment makes a detailed analysis of the time series characteristics of urban near-surface PM2.5 concentration data. Figure 2 As shown, Figure 2 This is a logical architecture diagram of the time series characteristics of urban near-ground PM2.5 concentration data in this embodiment.

[0090] For the time series data characteristics of a single monitoring site, this embodiment analyzes the time series data of each site from the perspective of autocorrelation, local stationarity, and frequency component characteristics in the time domain, clarifies the mathematical characteristics of the single site data sequence, and provides a basis for establishing its appropriate characterization method. This analysis method helps to deeply understand the inherent characteristics and changing laws of PM2.5 concentration data.

[0091] As an example, the autocorrelation function is used to analyze the periodic characteristics of PM2.5 concentration time series data in the autocorrelation feature analysis. The results show that there is no significant daily and weekly periodicity, but there are signs of periodicity in the long time series. This finding is of great significance for understanding the long-term trend of PM2.5 concentration.

[0092] Autocorrelation is the degree of correlation between a time series and itself lagged by a certain continuous period of time. The autocorrelation sequence of a periodic signal has the same periodic characteristics as the signal itself. Measurement uncertainty and noise sometimes make it difficult to detect periodic oscillation behavior in the original signal, even if the periodic behavior is expected; autocorrelation can help verify the existence of the cycle and determine its length. This method is particularly useful when dealing with complex environmental data because it can reveal potential periodic patterns that may be masked by random fluctuations.

[0093] For the time series {Xt} (t = 1, 2, ...), its autocorrelation function is expressed as:

[0094] R(Δt)=E(X t ×X t +Δt)

[0095] The minute-by-minute fixed monitoring data of each station is taken as Xt, and Rx(Δt) is calculated (Δt∈[1, 60×24×365×1.5]), and the following is obtained: Figure 3 As a result, Figure 3 This is a schematic diagram of the results of multi-scale autocorrelation feature analysis of monitoring data in this embodiment. This calculation process covers a time range from 1 minute to 1.5 years, providing a comprehensive time scale analysis of PM2.5 concentration changes.

[0096] The results show that the autocorrelation number of the original signal does not show obvious daily or weekly periodicity, but starts from 1, drops rapidly to about 0.4 within 4,000 minutes (about 3 days), then oscillates and drops and stabilizes at about -0.08 at 200,000 minutes (about 140 days). This pattern shows that PM2.5 concentration has a strong autocorrelation in the short term (within 3 days), but this correlation weakens rapidly over time. In the long time series, there are signs of a cycle of about 7 months, but the value of Rx is not significant. This finding suggests that PM2.5 concentration may be affected by seasonal factors, but the effect is not very strong.

[0097] Similar conclusions can be obtained through power spectral density estimation, such as Figure 4 As shown, Figure 4 This is the power spectrum density estimation diagram of the monitoring data in this embodiment. Power spectrum density estimation provides another way to represent the signal in the frequency domain, which can reveal the periodic components in the time series. The specific analysis results are as follows:

[0098] A clear minimum can be observed at 0.30-0.80πrad / sample, corresponding to a significant seasonal oscillation, indicating that PM2.5 concentrations vary significantly on a seasonal scale.

[0099] There are maximum values ​​near 0.57πrad / sample and 0.71πrad / sample, corresponding to weaker interannual characteristics. This shows that PM2.5 concentration also has certain periodic changes on an annual scale, but not as significant as seasonal changes.

[0100] There are maximum values ​​at 0.013πrad / sample, 0.026πrad / sample, 0.037πrad / sample, 0.048πrad / sample, etc., corresponding to weak weekly oscillations. These higher frequency oscillations may reflect the periodic changes in PM2.5 concentrations on a shorter time scale, which may be related to weekly human activity patterns.

[0101] As an example, in the local stationary feature analysis, discrete wavelet decomposition and ADF unit root test (Augmented Dickey-Fuller test, ADF) are used to analyze the stationarity of PM2.5 concentration time series with different frequency components and different lengths, and to find the time scales at which different frequency components act on the PM2.5 concentration time series changes. This analysis method can help to better understand the changing characteristics of PM2.5 concentration on different time scales.

[0102] Specifically, stationarity reflects whether the process of generating time series will change over time. For the time series {Xt} (t = 1, 2, ...), when its moving mean E(Xt), variance var(Xt) and covariance cov(Xt, Xt+k) are all independent of time t, Xt can be considered to be a weakly stationary time series. If this time series is generated by a random process, then this random process is a stationary random process, that is, the statistical properties of the process generating the time series will not change over time. That is, if Xt is a sequence of PM2.5 concentrations in a certain place that changes over time within a certain time range, then it can be inferred that the factors affecting the PM2.5 concentration in that place have not changed significantly during this period of time. This kind of stationarity analysis is crucial to understanding the long-term trend and influencing factors of PM2.5 concentrations.

[0103] First, the different frequency components of each site are extracted by discrete wavelet decomposition, and the frequency component at decomposition level j is set as dj; dj can be regarded as the oscillation characteristics of the original time series on the 2j scale, where the unit of the time scale is consistent with the interval of the practice series, that is, 1 minute. Discrete wavelet decomposition allows complex time series to be decomposed into components of different frequencies, so as to better understand the characteristics of changes on different time scales.

[0104] Secondly, dj is divided into data segments of n hours (n∈[1, 24]∩N) in time order. Suppose dj is divided into time segments of n hours, and the mth segment is segj,n,m. This segmentation method enables the analysis of the stationary characteristics of PM2.5 concentration in different time windows.

[0105] ADF is used to examine whether any segj, n, m is a stationary time series. The principle is that if Xt is autoregressive, then a point in the sequence can be expressed as:

[0106]

[0107] Its characteristic equation is:

[0108]

[0109] If all the characteristic roots of the equation are within the unit circle, that is:

[0110] λ p |<1,i∈[1,p]∩N

[0111] If , the original time series is stationary, otherwise the series is non-stationary.

[0112] For each decomposition level j and data segment length (60×n), calculate the proportion of non-stationary segments in all segments that meet the conditions in segj,n,m and organize them into Figure 5 , Figure 5 Schematic diagram of the stationary characteristics of monitoring data segments under different sample lengths and wavelet decomposition levels in this embodiment.

[0113] like Figure 5 As shown in the figure, 1) when j = 1 or j = 2, almost all segj, n, m are time-series stationary regardless of the length of the data segment constructed. 2) When j ≥ 3 and j ≤ 5, most of the shorter data segments are non-stationary, but the proportion of non-stationary data segments to all segments decreases as the length of the data segment increases until it drops to nearly 0. 3) When j ≥ 6, most of the data segments are non-stationary and the proportion of non-stationary data segments hardly changes with the length of the data segment. These results reveal the stationary characteristics of PM2.5 concentrations on different time scales and frequency components, providing important clues for understanding the dynamic characteristics of the pollution process.

[0114] The results show that the fluctuation pattern of PM2.5 concentration at the site at high frequency (such as 4 minutes) is relatively stable. The fluctuation pattern at general frequency (such as 832 minutes) will change significantly on the 116-hour scale. Short-term pollution processes or pollution events will be reflected in these frequency components. If the local variation characteristics of such pollution processes in the time domain are to be fully characterized, the sampling frequency of the sensor should at least reach this level. The fluctuation pattern at low frequency (such as 64 minutes) is almost obviously changing no matter what time scale is observed. Therefore, if the long-term local variation characteristics of atmospheric particulate matter are to be fully characterized, the sensor should at least reach a sampling frequency of about 1 hour. These findings have important practical significance for the design and optimization of PM2.5 monitoring systems, and can guide the selection of appropriate sampling frequencies and data analysis methods.

[0115] As an example, continuous wavelet transform was used in the analysis of local frequency component characteristics to examine the relationship between the time-frequency characteristics of time series data and specific events. The results showed that the oscillation of PM2.5 concentration on a longer time scale is related to the exogenous pollution process, while high-frequency oscillations are often related to local pollution events. This analysis method can help distinguish different types of pollution events and provide a scientific basis for the formulation of targeted pollution control strategies.

[0116] For the time series {Xt} (t = 1, 2, ...), the result of its continuous wavelet transform can be expressed as a binary function, and there is:

[0117]

[0118] Among them, a is the scale variable of the frequency component, b represents the translation factor of the position in the time domain, Represents the cardinality of the wavelet transform. The modulus and sign of the real part of ψf(a, b) express the oscillation intensity and direction of the original signal at a certain time point and frequency.

[0119] like Figure 6 As shown, Figure 6The continuous wavelet decomposition result diagram of the atmospheric particulate matter monitoring data of the monitoring station in this embodiment shows that 1) For most of the time, the oscillation of PM2.5 concentration is mainly manifested in a few hours and larger scales, and the intensity of high-frequency oscillation is relatively small. This phenomenon reflects that the main characteristics of PM2.5 concentration changes usually occur on a longer time scale. At the same time, the oscillation characteristics of more than a few hours show obvious time domain locality, and higher intensity oscillations can be observed before and after the known exogenous particulate matter pollution process. This finding shows that the impact of exogenous pollution events on PM2.5 concentration is not only significant during the event, but also produces obvious fluctuations before and after the event. 2) In addition to being related to the increase in atmospheric particulate matter concentration in the morning under the daily periodic changes in the atmospheric boundary layer, high-frequency oscillations also have a clear corresponding relationship with short-term events that cause increased atmospheric particulate matter concentrations. This observation reveals that high-frequency oscillations may reflect two different phenomena: one is the regular changes caused by the daily changes in the atmospheric boundary layer, and the other is the sudden changes caused by local short-term events (such as traffic peaks, industrial activities, etc.).

[0120] For the comparison of time series data between multiple monitoring sites, this embodiment processes the time series data of each site into approximate sequence fragments on different time scales, and compares the approximate sequences of different sites and the same time fragment range on each time scale, and analyzes their similarity and asynchrony. The results show that the PM2.5 concentration time series changes at different sites are controlled by the same exogenous pollution process on a larger time scale, but the heterogeneity caused by local pollution events is large. This multi-site, multi-time scale comparative analysis method provides a new perspective for a comprehensive understanding of the spatial distribution characteristics of urban PM2.5 pollution.

[0121] This embodiment compares two similar sequence fragments based on and The value difference and shape difference between are defined as follows:

[0122]

[0123] Where N is the number of elements contained in the two fragments. value and D shape The distribution range of Figure 7 As shown, Figure 7 This is a graph showing the changes in the numerical differences and temporal morphological differences between the monitoring data segments of each monitoring station over time scale.

[0124] It is understandable that through the overall characteristic analysis of the time series data of this embodiment, it is found that the time series changes of the urban near-ground PM2.5 concentration have the characteristics of weak periodicity, strong locality, multi-scale and dynamic asynchrony between sites, and are also disturbed by some short-term and occasional events. These characteristics reflect the complexity and diversity of PM2.5 pollution, indicating that it is affected by multiple factors, including but not limited to meteorological conditions, human activities, geographical features, etc.

[0125] Weak periodicity indicates that although PM2.5 concentration may be affected by certain periodic factors (such as daily or weekly human activity patterns), this periodicity is not significant or consistent. Strong locality means that PM2.5 concentrations may vary significantly in different locations, which may be due to differences in local pollution sources or microenvironmental conditions. Multiscale indicates that changes in PM2.5 concentrations occur on different time scales, ranging from short-term fluctuations to long-term trends. Dynamic asynchrony between sites reflects the time lag or lead relationship of PM2.5 concentration changes between different monitoring sites, which may be related to the transport and diffusion process of pollutants.

[0126] The oscillation frequencies of different time series components can distinguish long-term, background changes from short-term, local changes to a certain extent, but there is still a large overlap between the two in the frequency domain. This overlap means that frequency analysis alone may not be able to fully distinguish different types of pollution processes, and other analysis methods need to be combined to obtain a more comprehensive understanding.

[0127] Therefore, to effectively extract the characteristics of exogenous particulate pollution processes near the urban surface, it is necessary to comprehensively utilize the time domain and frequency domain information such as the multi-scale components of a single site, the similarity and asynchronous relationship between sites, and try to eliminate the influence of irrelevant interference factors. This comprehensive analysis method can help more accurately identify and characterize exogenous pollution events, and also distinguish them from the impact of local pollution sources.

[0128] This embodiment proposes a method of comprehensive time domain, frequency domain and time-frequency joint analysis, which thoroughly analyzes the characteristics of urban near-ground PM2.5 concentration changes on a minute-level time scale, and provides a technical basis for screening exogenous particulate matter pollution processes. The innovation of this method is that it combines multiple data analysis techniques, including but not limited to time series analysis, spectrum analysis, wavelet analysis, etc., to comprehensively capture all aspects of PM2.5 concentration changes. By analyzing on a minute-level time scale, this method is able to capture short-term and rapidly changing pollution events, which is particularly important for understanding and predicting short-term fluctuations in urban air quality.

[0129] Example 3

[0130] This embodiment makes improvements on the urban near-surface air pollution data analysis method proposed in the above embodiment. Figure 8 As shown, Figure 8 This is a logical architecture diagram for processing the monitoring data of each monitoring site in this embodiment.

[0131] In this embodiment, after obtaining the atmospheric particulate matter concentration time series data of multiple monitoring stations near the ground in the city, the atmospheric particulate matter concentration time series data segments with more than 4 consecutive 0 values, null values ​​or unchanged values ​​are marked as abnormal segments and removed. The outliers of the atmospheric particulate matter concentration time series data are obtained by using the absolute median difference between the original value of the atmospheric particulate matter concentration time series data and the differential integrated moving average autoregressive predicted value of the sequence, and the outliers are replaced by the differential integrated moving average autoregressive predicted value. The expression is as follows:

[0132]

[0133] Among them, c≈1.4826 is a constant, erfcinv() represents the inverse function of the complementary error number, spred(t) is the predicted value of s(t) under ARIMA(p,q), p is the order of the autoregressive term of the ARIMA model, q is the order of the difference term of the ARIMA model, when res(t)>3, s(t) is considered to be an outlier and is replaced by spred(t). Fig. 9 As shown, Fig. 9 Schematic diagram of the results of monitoring data outlier marking and replacement examples in this embodiment.

[0134] In this embodiment, multi-time scale component separation is performed on the atmospheric particulate matter concentration time series data to obtain the first characteristic sequence of the atmospheric particulate matter concentration time series data at different time scales.

[0135] The atmospheric particulate matter concentration time series data is decomposed by using a discrete wavelet decomposition method based on Meyer wavelet to obtain wavelet approximate sequences of the atmospheric particulate matter concentration time series data at different time scales. Where ψ(ω) is the expression of the Meyer wavelet basis function as follows:

[0136]

[0137] Where ψ(ω) is the Meyer wavelet basis function, j is the number of layers of the decomposed time scale, ω is the variable in the time domain representing the time point, and v(·) = a 4 (35-84a+70a 2 -20a 3 ), a∈[0,1] is the auxiliary function, and a is the scale parameter in wavelet decomposition.

[0138] At time scale j, the atmospheric particulate matter concentration time series data s will be decomposed into a low-frequency component (wavelet approximation) through the Mallat Tower Decomposing Algorithm, expressed as z j , and j high-frequency components (wavelet details) d n , whose expression is as follows:

[0139]

[0140] Among them, z j is the low-frequency component of the atmospheric particle concentration time series data s, which represents the wavelet approximation sequence of the atmospheric particle concentration time series data s at time scale j, d n is the high-frequency component of the atmospheric particulate matter concentration time series data s at the decomposition level n, and j∈[4, 12]∩N.

[0141] In order to align the time series data of different sites and offset the dynamic asynchronous characteristics between sites, the dynamic time warping method needs to be used to process each pair of sites. However, the minimum time complexity of this algorithm is O(m×n), and the time consumed by its calculation will increase quadratically with the length of the original time series. For the fine-grained and long time series used in this embodiment, this exceeds the processing capacity of a general desktop workstation. To address this problem, this embodiment simplifies the characteristics of each wavelet approximation sequence by using the approximate 0 point of its multi-order derivative, that is, for the wavelet approximation sequence z j , and obtain the discrete first feature sequence fp(t). By testing the information loss of the feature point sequence compared to the original wavelet approximation sequence when n0 takes different values, this embodiment determines n0=3. Its expression is as follows:

[0142]

[0143] Among them, fp(·) is the discrete first characteristic sequence, z (n) (·) is the nth-order derivative of the first characteristic sequence z(·), t represents time, n0 is the number of elements contained in the atmospheric particulate matter concentration time series data, and N is a positive integer greater than 0.

[0144] In this embodiment, dynamic time warping is performed on the first characteristic sequence between different monitoring sites to obtain a second characteristic sequence that eliminates the dynamic asynchrony between different monitoring sites, including:

[0145] The first characteristic sequences of the two monitoring sites are selected respectively.

[0146] Construct a distance matrix for storing the difference between any elements of two first feature sequences. The expression is as follows:

[0147] D(t1, t2)=z1(t1)-z2(t2), t1∈[1,m]∩N, t2∈[1,n]∩N

[0148] Wherein, D(t1, t2) is the distance matrix of t1×t2, z1(t1) is the element of the first feature sequence z1 of monitoring site 1 at time t1, z2(t2) is the element of the first feature sequence z2 of monitoring site 2 at time t2, m and n are the number of elements in the first feature sequence z1 and the first feature sequence z2 respectively, and N is a positive integer greater than 0.

[0149] Use Floyd's algorithm to calculate the shortest path from the upper left corner to the lower right corner of the distance matrix.

[0150] The corresponding relationship between the elements in the two first feature sequences is determined according to the shortest path.

[0151] According to the corresponding relationship, the two first characteristic sequences are aligned including local stretching or compression to obtain a second characteristic sequence that eliminates the dynamic asynchronism between the two monitoring sites.

[0152] Repeat the above steps to obtain the second characteristic sequence between any two monitoring sites at the same time scale.

[0153] As an exemplary explanation, the Floyd algorithm is used to solve the shortest path from the upper left corner of the distance matrix D to the lower right corner of the matrix. If this shortest path completely coincides with the diagonal line in that direction, it means that the two sequences are completely aligned. A path that deviates from the diagonal line means a local stretch or compression to align the two sequences. If this path passes through a point D(t, t′) on the matrix, it means that the element s1(t) is aligned to the element s2(t′). Fig.10 As shown, Fig.10 This is a schematic diagram of the principle of dynamic time warping in this embodiment.

[0154] It should be noted that after regularization, the sum of the absolute values ​​of the point-by-point differences between the new sequences (which can be called the regularization distance) represents the similarity of the original sequences after alignment. In this embodiment, it is the similarity of the wavelet approximation sequences of the two sites after correcting the dynamic asynchrony between the sites. Its expression is as follows:

[0155]

[0156] The path sequence in the distance matrix D represents the corresponding relationship between the data points in the original sequence, and its expression is as follows:

[0157] P DTW(s1||s2)={(t1, t′1), (t2, t′2),..., (t m , t′ m )}

[0158] In this embodiment, the above correspondence relationship shows that for monitoring site 1, if there is a certain change feature at time t on a certain time scale, then at monitoring site 2, there is a similar change feature at time t′ on the same time scale. The wavelet approximate feature point sequence fp of each site at the same time scale is processed by the above method, and an alignment relationship between the wavelet approximate feature point sequences of any two sites at the same time scale is formed.

[0159] In this embodiment, the second feature sequence is subjected to agglomerative hierarchical clustering to obtain background atmospheric particulate matter concentration fluctuation characteristics, including:

[0160] For any monitoring site m0, the second characteristic sequence Get the second characteristic sequence corresponding to any other monitoring station, such as

[0161] Count the time positions t in the second characteristic sequence of other monitoring stations m and atmospheric particulate matter concentration value. In this embodiment, the atmospheric particulate matter concentration value is PM2.5 concentration value p m , and according to the 3σ principle, the second characteristic sequence whose time position and atmospheric particulate matter concentration value do not meet the conditions is eliminated.

[0162] Calculate the time position mean and the atmospheric particle concentration mean of the remaining second characteristic sequence, and use the time position mean and the atmospheric particle concentration mean to construct the characteristic sequence The vector is used to express (t m , p m ), and then the constructed feature sequence is normalized to the maximum value to obtain a new second feature sequence for the current monitoring site.

[0163] Repeat the above steps to construct a new second feature sequence for each monitoring site.

[0164] Calculate the distance matrix between the new second feature sequences.

[0165] According to the distance matrix between the new second characteristic sequences, the second characteristic sequences are clustered using agglomerative hierarchical clustering (AHC) to obtain K types of clustering features, where K is the average length of the new second characteristic sequences of all monitoring sites, which is the optimal number of classifications for agglomerative hierarchical clustering in this embodiment.

[0166] The intra-class similarity of all types of cluster features is evaluated, and the cluster features whose intra-class similarity meets the preset conditions are used as the final background atmospheric particulate matter concentration fluctuation features. In this embodiment, the cluster centers of the remaining clusters and the distribution range of each class are used as the extraction results of the background atmospheric particulate matter concentration fluctuation features at this time scale.

[0167] In this embodiment, according to the background atmospheric particulate matter concentration fluctuation characteristics, the pollution process of atmospheric particulate matter on the urban near-surface is analyzed, including:

[0168] According to the background atmospheric particulate matter concentration fluctuation characteristics, the atmospheric particulate matter concentration fluctuation process is divided into a number of pollution process segments.

[0169] The pollution process fragments are subjected to time series clustering processing to obtain different pollution process patterns.

[0170] Calculate the characteristic indicators of each monitoring station under the same pollution process mode, and analyze the pollution process of atmospheric particulate matter on the near-surface of the city based on the calculation results of the characteristic indicators.

[0171] In this embodiment, the background atmospheric particulate matter concentration fluctuation characteristics are segmented into several pollution process segments, including:

[0172] According to the peak and valley values ​​of the atmospheric particle concentration fluctuation, the atmospheric particle concentration fluctuation is divided into rising and falling segments. Each individual rising or falling segment represents a process of increase or diffusion of the atmospheric particle concentration.

[0173] In the specific implementation process, the background atmospheric particulate matter concentration fluctuation characteristics of the entire monitoring network under the influence of foreign particles are divided into separate rising and falling segments according to the peak and valley values. A single rising or falling segment represents the accumulation, increase or diffusion process of PM2.5 concentration at a corresponding time scale.

[0174] In this embodiment, after segmenting the background atmospheric particulate matter concentration fluctuation characteristics into a plurality of pollution process segments, the method further includes performing minimum value normalization processing on the atmospheric particulate matter concentration in each pollution process segment, and the expression thereof is as follows:

[0175] s′=s-min(s)

[0176] In this embodiment, the pollution process fragments are subjected to time series clustering processing to obtain different pollution process patterns, including:

[0177] Calculate the time series difference D(s′1||s′2) between any two pollution process segments, and its expression is as follows:

[0178] D(s′1||s′2)=D DTW (s′1||s′2)×D shape (s′1||s′2)×D stretch (s′1||s′2)

[0179] Among them, s′1 and s′2 are any two pollution process fragments on the same time scale after minimum value normalization, D DTW (s′1||s′2) and D shape (s′1||s′2) are the warping distance and shape distance after dynamic time warping, respectively. stretch (s′1||s′2) is the extension ratio of the original sequence on the time axis during the dynamic time warping process.

[0180] According to the time series differences, a distance matrix between two pollution process segments is constructed.

[0181] According to the distance matrix between the two pollution process fragments, the two pollution process fragments are subjected to agglomerative hierarchical clustering to obtain cluster fragments.

[0182] The above steps are repeated until the cluster segments reach the optimal number of classifications, and each cluster segment represents a pollution process pattern.

[0183] In this embodiment, the method further includes calculating the maximum value of the silhouette coefficient SC of the agglomerative hierarchical clustering as the optimal number of classifications, and its expression is as follows:

[0184]

[0185] in, The profile value s(i) is the mean of the profile values ​​for agglomerative hierarchical clustering of all pollution process fragments. The profile value s(i) is defined as:

[0186]

[0187] in, is the pollution process segment i and cluster segment c i The average distance of other contaminated process fragments in the cluster indicates that the contaminated process fragment i is assigned to the cluster fragment c. i The degree of quality. is the pollution process segment i and the clustering segment c i Other cluster fragments c k The average distance of the contaminated process fragments in , and the average dissimilarity between fragments in different clusters.

[0188] Table 1 Description of asynchrony indicators

[0189]

[0190] Table 2 Description of severity indicators

[0191]

[0192] like Fig.11 As shown in Table 1 and Table 2, in this embodiment, for a given pollution process pattern, the key feature points of the background fragments and the fragments of different sites in the pattern are compared. This comparison allows quantification of the difference in the response of different sites to the same pollution event. For a peak or valley value, the difference between the two is described by the asynchrony index and the severity index:

[0193] Asynchrony indicators include the advance or delay of the peak, the advance or delay of the start time of the rising phase, and the advance or delay of the end time of the falling phase. These indicators reflect the difference in the propagation time of pollutants between different stations, which may be related to factors such as wind direction and terrain.

[0194] Severity indicators include the peak concentration difference and the total concentration-time product of the segment under the corresponding wavelet approximation curve. These indicators reflect the intensity differences of pollution events at different sites, which may be related to the distance of the site from the pollution source or local conditions.

[0195] The calculation of these indices provides a basis for quantitatively comparing pollution characteristics of different sites and helps to more accurately describe and analyze the spatial characteristics of pollution events.

[0196] In this embodiment, the characteristic indicators of each station at different elevations, micro-environments and relative positions are examined, and the mechanism knowledge of the relevant exogenous particulate matter pollution process is combined to establish a characterization of the dynamic changes of near-surface particulate matter under this atmospheric particulate matter pollution process. This step combines the quantitative analysis results with the actual geographical environment and pollution mechanism, aiming to build a comprehensive pollution process model. By analyzing the response characteristics of the stations under different environmental conditions, we can better understand the impact of factors such as terrain and building distribution on the diffusion of pollutants.

[0197] It is understandable that this embodiment processes the PM2.5 concentration time series by discrete wavelet decomposition to obtain a multi-time scale approximate series to preliminarily distinguish between exogenous pollution processes and local disturbances. The use of discrete wavelet decomposition allows the analysis of pollution data at different time scales, which helps to identify long-term trends and short-term fluctuations. Subsequently, the dynamic time warping method is used to eliminate the dynamic asynchrony between different sites, and to establish a basis for comparing the response characteristics of different sites to the same exogenous pollution process. The application of dynamic time warping solves the problem of time delay that may exist in data from different sites, making the comparison between sites more accurate. Next, a background time series that shows consistency between different sites is found as a representation of the exogenous pollution process. This step helps identify common change patterns that may be caused by regional or large-scale pollution events. Finally, by combining the geographical location and response characteristics of each site, the impact characteristics of exogenous particulate pollution on near-surface pollution processes are inferred. This comprehensive analysis method not only takes into account time series data, but also incorporates spatial information and environmental factors, thereby providing a more comprehensive understanding of the pollution process.

[0198] Example 4

[0199] Based on the analysis results of urban near-surface air pollution data obtained in Example 3, this embodiment further analyzes the pollution process of atmospheric particulate matter on the urban near-surface.

[0200] In this embodiment, the background particle concentration rising segment change pattern obtained by time series clustering is compared with the actual exogenous atmospheric pollution mechanism to identify the main exogenous particle pollution patterns: north-side external air mass-dominated type, south-side urban pollution-dominated type, southwest-side external air mass-dominated type, sedimentation process-dominated type, surrounding traffic pollution-dominated type, and surrounding traffic pollution-steady diffusion type. These patterns account for 78.0% of the total duration of the pollution process and 76.3% of the total PM2.5 concentration-time product. The three main frequency components of the time series fluctuations of particle concentration: sub-hourly fluctuations, hourly fluctuations, and sky fluctuations. These frequency components are closely related to factors such as meteorological conditions, particulate matter input and diffusion during the pollution process.

[0201] In this embodiment, according to the geographical location of different stations in the monitoring network, the stations are divided into the north mountain group, the mountain group, the south mountain group, the northeast interface group, the southwest interface group, the urban road group, the lakeside road group, the non-urban road group, the mountain top group, the mountainside group, the mountain bottom group and the sea level group. By analyzing the response differences of these groups in each pollution process, the spatiotemporal characteristics of the particulate matter pollution process can be inferred and the corresponding scene mode can be established.

[0202] This embodiment applies the extraction results of these exogenous pollution processes to the urban near-surface atmospheric pollution data analysis method to improve the ability to identify and analyze atmospheric pollution processes. By combining the multi-time scale component separation, dynamic time regularization processing, agglomerative hierarchical clustering processing and pollution process pattern identification of monitoring data, this method can more accurately characterize the pollution process of atmospheric particulate matter on the urban near-surface, providing strong technical support for atmospheric pollution control and environmental optimization design.

[0203] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0204] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0205] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0206] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0207] A person of ordinary skill in the art may understand that all or part of the steps of the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0208] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for analyzing urban near-surface air pollution data, characterized in that: include: Obtain time series data on atmospheric particulate matter concentration at multiple monitoring stations near the ground in the city; The atmospheric particulate matter concentration time series data is separated into multiple time scale components to obtain the first characteristic sequence of the atmospheric particulate matter concentration time series data at different time scales; Performing dynamic time warping on the first characteristic sequence between different monitoring sites to obtain a second characteristic sequence that eliminates the dynamic asynchrony between different monitoring sites; Performing agglomerative hierarchical clustering on the second characteristic sequence to obtain background atmospheric particulate matter concentration fluctuation characteristics; Based on the background atmospheric particulate matter concentration fluctuation characteristics, the pollution process of atmospheric particulate matter on the urban near-surface is analyzed.

2. The method for analyzing urban near-surface air pollution data according to claim 1, characterized in that: After obtaining the time series data of atmospheric particulate matter concentration at multiple monitoring stations near the ground in the city, the method further includes: The atmospheric particulate matter concentration time series data segments with more than M consecutive 0 values, null values ​​or unchanged values ​​are marked as abnormal segments and removed; M is a positive integer The outliers of the atmospheric particulate matter concentration time series data are obtained by using the absolute median difference between the original value of the atmospheric particulate matter concentration time series data and the differential integrated moving average autoregressive predicted value of the sequence, and the outliers are replaced by the differential integrated moving average autoregressive predicted value. The expression is as follows: Where c≈1.4826 is a constant, erfcinv() represents the inverse function of the complementary error number, spred(t) is the predicted value of s(t) under ARIMA(p,q), p is the order of the autoregressive term of the ARIMA model, q is the order of the difference term of the ARIMA model, and when res(t)>3, s(t) is considered to be an outlier and is replaced by spred(t).

3. The method for analyzing urban near-surface air pollution data according to claim 1, characterized in that: The atmospheric particulate matter concentration time series data is separated into multiple time scale components to obtain the first characteristic sequence of the atmospheric particulate matter concentration time series data at different time scales, including: The atmospheric particulate matter concentration time series data is decomposed by using a discrete wavelet decomposition method based on Meyer wavelet to obtain a wavelet approximate sequence of the atmospheric particulate matter concentration time series data on different time scales, and its expression is as follows: Where ψ(ω) is the Meyer wavelet basis function, j is the number of layers of the decomposed time scale, ω is the variable in the time domain representing the time point, and v(·) = a 4 (35-84a+70a 2 -20a 3 ), a∈[0,1] is the auxiliary function, a is the scale parameter in wavelet decomposition, z j is the low-frequency component of the atmospheric particle concentration time series data s, which represents the wavelet approximation sequence of the atmospheric particle concentration time series data s at time scale j, d n is the high-frequency component of the atmospheric particulate matter concentration time series data s at the decomposition level n; The wavelet approximation sequence is converted into a discrete first characteristic sequence, and its expression is as follows: Among them, fp(·) is the discrete first characteristic sequence, z (n) (·) is the nth-order derivative of the first characteristic sequence z(·), t represents time, n0 is the number of elements contained in the atmospheric particulate matter concentration time series data, and N is a positive integer greater than 0.

4. The method for analyzing urban near-surface air pollution data according to claim 1, characterized in that: The first characteristic sequence between different monitoring sites is subjected to dynamic time warping to obtain a second characteristic sequence that eliminates the dynamic asynchrony between different monitoring sites, including: Select the first characteristic sequences of two monitoring sites respectively; Construct a distance matrix for storing the difference between any elements of two first feature sequences. The expression is as follows: D(t1,t2)=z1(t1)-z2(t2),t1∈[1,m]∩N,t2∈[1,n]∩N Where D(t1,t2) is the distance matrix of t1×t2, z1(t1) is the element of the first characteristic sequence z1 of monitoring site 1 at time t1, z2(t2) is the element of the first characteristic sequence z2 of monitoring site 2 at time t2, m and n are the number of elements in the first characteristic sequence z1 and the first characteristic sequence z2 respectively, and N is a positive integer greater than 0; Use Floyd's algorithm to calculate the shortest path from the upper left corner to the lower right corner of the distance matrix; Determine the corresponding relationship between the elements in the two first characteristic sequences according to the shortest path; According to the corresponding relationship, the two first characteristic sequences are aligned including local stretching or compression to obtain a second characteristic sequence that eliminates the dynamic asynchronism between the two monitoring sites; Repeat the above steps to obtain the second characteristic sequence between any two monitoring sites at the same time scale.

5. The method for analyzing urban near-surface air pollution data according to claim 4, characterized in that: The second characteristic sequence is subjected to agglomerative hierarchical clustering to obtain the background atmospheric particulate matter concentration fluctuation characteristics, including: For the second characteristic sequence of any monitoring site, obtain the second characteristic sequence corresponding to any other monitoring site; Counting the time positions and atmospheric particle concentration values ​​in the second characteristic sequences of other monitoring stations, and eliminating the second characteristic sequences whose time positions and atmospheric particle concentration values ​​do not meet the preset conditions; Calculate the time position mean and the atmospheric particulate matter concentration mean of the remaining second characteristic sequence, and construct a characteristic sequence using the time position mean and the atmospheric particulate matter concentration mean, and then perform maximum value standardization on the constructed characteristic sequence to obtain a new second characteristic sequence for the current monitoring site; Repeat the above steps to construct a new second characteristic sequence for each monitoring site; Calculate the distance matrix between the new second feature sequences; According to the distance matrix between the new second characteristic sequences, the second characteristic sequences are clustered using agglomerative hierarchical clustering to obtain K types of clustering features; where K is the average length of the new second characteristic sequences of all monitoring stations; The intra-category similarity of all types of clustering features is evaluated, and the clustering features whose intra-category similarity meets the preset conditions are taken as the final background atmospheric particulate matter concentration fluctuation characteristics.

6. The method for analyzing urban near-surface air pollution data according to claim 1, characterized in that: According to the fluctuation characteristics of the background atmospheric particulate matter concentration, the pollution process of atmospheric particulate matter on the urban near-surface is analyzed, including: According to the background atmospheric particulate matter concentration fluctuation characteristics, the atmospheric particulate matter concentration fluctuation process is divided into a plurality of pollution process segments; Performing time series clustering processing on the pollution process fragments to obtain different pollution process patterns; Calculate the characteristic indicators of each monitoring station under the same pollution process mode, and analyze the pollution process of atmospheric particulate matter on the near-surface of the city based on the calculation results of the characteristic indicators.

7. The method for analyzing urban near-surface air pollution data according to claim 6, characterized in that: The background atmospheric particulate matter concentration fluctuation characteristics are divided into several pollution process segments, including: According to the peak and valley values ​​of the atmospheric particle concentration fluctuation, the atmospheric particle concentration fluctuation is divided into rising and falling segments. Each individual rising or falling segment represents a process of increase or diffusion of the atmospheric particle concentration.

8. The method for analyzing urban near-surface air pollution data according to claim 6 or 7, characterized in that: After segmenting the background atmospheric particulate matter concentration fluctuation characteristics into a plurality of pollution process segments, the method further comprises performing minimum value normalization processing on the atmospheric particulate matter concentration in each pollution process segment.

9. The method for analyzing urban near-surface air pollution data according to claim 8, characterized in that: The pollution process fragments are subjected to time series clustering to obtain different pollution process patterns, including: Calculate the time series difference D(s′1||s′2) between any two pollution process segments, and its expression is as follows: D(s′1||s′2)=D DTW (s′1||s′2)×D shape (s′1||s′2)×D stretch (s′1||s′2) Among them, s′1 and s′2 are any two pollution process fragments on the same time scale after minimum value normalization, D DTW (s′1||s′2) and D shape (s′1||s′2) are the warping distance and shape distance after dynamic time warping, respectively. stretch (s′1||s′2) is the extension ratio of the original sequence on the time axis during dynamic time warping; According to the time series differences, a distance matrix between two pollution process segments is constructed; According to the distance matrix between the two pollution process fragments, the two pollution process fragments are subjected to agglomerative hierarchical clustering to obtain cluster fragments; The above steps are repeated until the cluster segments reach the optimal number of classifications, and each cluster segment represents a pollution process pattern.

10. The method for analyzing urban near-surface air pollution data according to claim 9, characterized in that: The method further includes calculating the maximum value of the silhouette coefficient SC of the agglomerative hierarchical clustering as the optimal number of classifications, and its expression is as follows: in, The profile value s(i) is the mean of the profile values ​​for agglomerative hierarchical clustering of all pollution process fragments. The profile value s(i) is defined as: in, is the pollution process segment i and cluster segment c i The average distance of other contaminated process fragments in the cluster indicates that the contaminated process fragment i is assigned to the cluster fragment c. i How good or bad is it? is the pollution process segment i and the cluster removal segment c i Other cluster fragments c k The average distance of the contaminated process fragments in , and the average dissimilarity between fragments in different clusters.

Images