Online source analysis data fitting method and device and computer equipment

Abstract

The invention provides an online source analysis data fitting method and device and computer equipment, and the method comprises the steps: carrying out the source analysis detection through a central monitoring point, and obtaining a source analysis monitoring sample; acquiring environmental factor data of a target area where the central monitoring point is located in the process of performing the source analysis detection, and selecting a corresponding ratio coefficient matrix based on the environmental factor data; based on the ratio coefficient matrix and the source analysis monitoring sample, calculating a source analysis fitting result corresponding to each satellite monitoring point; calculating a fitting value of source analysis distribution of the target area based on the source analysis fitting result; by adopting the method provided by the invention, the fitting source analysis data of the plurality of satellite monitoring points can be calculated according to the measurement result of the single central monitoring point, so that the representativeness of the source analysis data of the single monitoring point is improved, and powerful support is provided for environmental governance of the whole target area.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric environment data processing, and more specifically, to an online source resolution data fitting method, device and computer equipment. Background Art

[0002] Monitoring points established for the purpose of monitoring the overall status and changing trends of air quality in urban built-up areas and participating in urban ambient air quality assessments are referred to as urban ambient air quality assessment points, hereinafter referred to as urban points. Urban points generally represent a range of 500 meters to 4 kilometers in radius, and the number of monitoring points established in each city is determined by the built-up area and population. For example, a city with a population greater than 3 million and a built-up area greater than 400 square kilometers should have no fewer than 10 monitoring points. When evaluating urban ambient air quality, the arithmetic mean of pollutant concentrations at all urban points represents the overall average pollutant concentration in the city's built-up area, and this value serves as the basis for conducting urban air quality assessments.

[0003] Online single-particle time-of-flight mass spectrometer is a popular technical means for online source analysis of fine particulate matter in recent years. It can detect and analyze the mass spectral composition and particle size of fine particulate matter in real time, and compare it with the pollution source spectral library data in real time to achieve online source analysis of fine particulate matter pollution sources.

[0004] Since single-particle mass spectrometer equipment is relatively expensive, in actual work, monitoring can only be carried out at a single city point, or patrol monitoring is carried out at various city points. This makes it difficult to cover the entire city area at the same time. The online source analysis results have certain regional limitations when reflecting the current status of the sources of fine particulate matter pollution in the city, and its monitoring results are difficult to represent the overall air quality of the city. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an online source analysis data fitting method, device and computer equipment to overcome the above shortcomings.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: an online source analysis data fitting method, comprising:

[0007] S1. Conduct source analysis testing at a central monitoring point to obtain a source analysis monitoring sample; the central monitoring point is located at the center of the target area, and the source analysis monitoring sample is specifically the actual contribution ratio of each pollution source to the air pollution at the location of the central monitoring point;

[0008] S2. Acquire the environmental factor data of the target area where the central monitoring point is located during the source analysis detection process, and select a corresponding ratio coefficient matrix based on the environmental factor data; the elements in the ratio coefficient matrix are specifically: under the environmental factor data conditions, the pollution source Y n For each satellite monitoring point A m Satellite station contribution ratio A m Y n The ratio of the central station contribution to the central monitoring point of the pollution source A0Y n Ratio A m Y n / A0Y n ;

[0009] Wherein, m is the index of satellite monitoring point A, m=1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n=1, 2, ..., N, N is the total number of pollution sources;

[0010] S3, based on the ratio coefficient matrix and the source analysis monitoring sample, calculate the source analysis fitting result corresponding to each satellite monitoring point, and the source analysis fitting result is specifically the satellite station contribution ratio A of each satellite monitoring point. m Y n The fitted value of

[0011] S4. Calculate the fitting value of the source apportionment distribution of the target area based on the source apportionment fitting result.

[0012] In one embodiment, the ratio coefficient matrix is ​​constructed using the following method:

[0013] A central monitoring point A0 and multiple satellite monitoring points A are set in the target area. m , where the central monitoring point A0 is located at the center of the target area, and the satellite monitoring point A m Dispersedly arranged around the central monitoring point;

[0014] In multiple unit cycles, the central monitoring point A0 and multiple satellite monitoring points A are used simultaneously. m Perform source analysis detection to obtain source analysis detection results corresponding to each unit period. The source analysis monitoring results are specifically: in the unit period, each pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n , and each pollution source Y n The satellite monitoring point A m Satellite station contribution ratio A m Y n ;

[0015] A ratio matrix is ​​constructed based on the source analysis monitoring results. The elements in the ratio matrix are: n For any satellite monitoring point A m Satellite station contribution ratio A m Y n With the pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n Ratio A m Y n / A0Y n ;

[0016] recording the environmental factor data of each unit period, and classifying the ratio matrix based on the environmental factor data;

[0017] The probability distribution function of each category is constructed using the classified ratio matrix; the probability distribution function is solved to determine the maximum probability ratio corresponding to each element in the ratio matrix; the probability distribution function is specifically: any pollution source Y n For any satellite monitoring point A m Satellite station contribution ratio A m Y n With the pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n Ratio A m Y n / A0Y n The probability distribution function of

[0018] A ratio coefficient matrix corresponding to each category is constructed based on each of the maximum probability ratios.

[0019] In one embodiment, the ratio matrix is ​​constructed based on the source apportionment monitoring results, specifically:

[0020] Construct a source apportionment result matrix Q based on the source apportionment monitoring results ω ;

[0021] The source analysis result matrix Q ω Specifically, the size is (M+1)×N matrix:

[0022]

[0023] The ratio matrix P ω Specifically, the size is M×N matrix:

[0024]

[0025] Among them, the source analysis result matrix Q ωThe matrix elements in represent the pollution source Y n The contribution ratio of each monitoring point to the area where it is located, M represents the number of satellite monitoring points, N represents the number of pollution sources, n represents the index of the pollution source, and m represents the index of the satellite monitoring point; ω is the number index of the source analysis result matrix Q, and ω is also the number index of the ratio matrix P.

[0026] In one embodiment, constructing the probability distribution function of each category using the classified ratio matrix specifically includes:

[0027] Define the number of ratio matrices P in any category as f; convert matrix elements at the same position in the ratio matrix into ratio sequences, the number of the ratio sequences is M×N, and the number of sequence elements in each ratio sequence is f;

[0028] The elements in each ratio series are sorted from small to large, and after removing abnormal data in the ratio series, a probability distribution function is constructed using each ratio series, including:

[0029] Based on the value range [d, e] of the ratio sequence, the sorted ratio sequence is divided into k subintervals; based on the element distribution of the ratio sequence, the probability of the elements of the ratio sequence falling into each subinterval is counted respectively to obtain the probability distribution (t c , h c ), where t c represents the representative value of the cth subinterval; h c represents the probability of the cth subinterval,

[0030] Based on the probability distribution, a k-2 function group corresponding to the ratio sequence is established, including:

[0031]

[0032] Among them, a k-2 、a k-3 ,...,a are coefficients, b is a constant;

[0033] The probability distribution of the ratio series (t c , h c ) is brought into the k-2 function group, and after calculating the coefficients and constants, a probability distribution function corresponding to the ratio series is constructed.

[0034] In one embodiment, removing abnormal data from the ratio series specifically includes:

[0035] Get the first quartile Q1 and the third quartile Q3 in the sorted ratio series;

[0036] Calculating the interquartile range based on the first quartile Q1 and the third quartile Q3 includes: IQR=Q3-Q1;

[0037] Determining the lower bound LB of abnormal data using the interquartile range IQR and the first quartile Q1 includes: LB=Q1-1.5×IQR;

[0038] Determining an upper bound UB of abnormal data using the interquartile range IQR and the third quartile Q3 includes: UB=Q3+1.5×IQR;

[0039] The elements in the ratio sequence that are smaller than the abnormal data lower bound LB or larger than the abnormal data upper bound UB are recorded as abnormal data and are removed from the ratio sequence.

[0040] In one embodiment, solving the probability distribution function to determine the maximum probability ratio corresponding to each element in the ratio matrix specifically includes:

[0041] Solve the maximum probability value h based on the probability distribution function max The corresponding maximum probability ratio t max , where t max ∈[d,e].

[0042] In one embodiment, the calculating of the source parsing fitting results corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source parsing monitoring samples specifically includes:

[0043] The actual contribution ratio corresponding to each pollution source in the source analysis monitoring sample is multiplied by the ratio coefficient element corresponding to the pollution source in the ratio coefficient matrix to obtain the fitted contribution ratio of each satellite monitoring point corresponding to the pollution source. The number of the fitted contribution ratios is the same as the number of satellite monitoring points.

[0044] In one embodiment, calculating the fitting value of the source apportionment distribution of the target area based on the source apportionment fitting result specifically includes:

[0045] Normalize the fitting contribution ratio of each satellite monitoring point separately;

[0046] Calculate the actual contribution ratio corresponding to each pollution source and the arithmetic mean of all fitted contribution ratios corresponding to the pollution source to obtain the fitted value of the source analysis distribution of each pollution source in the target area.

[0047] An online source apportionment data fitting device, comprising:

[0048] A monitoring unit is configured to perform source analysis detection using a central monitoring point to obtain a source analysis monitoring sample; the central monitoring point is located at the center of the target area, and the source analysis monitoring sample is specifically: the actual contribution ratio of each pollution source to the air pollution at the location of the central monitoring point;

[0049] The selection unit is used to obtain the environmental factor data of the target area where the central monitoring point is located during the source analysis detection process, and select the corresponding ratio coefficient matrix based on the environmental factor data; the elements in the ratio coefficient matrix are specifically: under the environmental factor data conditions, the pollution source Y n For each satellite monitoring point A m Satellite station contribution ratio A m Y n The ratio of the central station contribution to the central monitoring point of the pollution source A0Y n Ratio A m Y n / A0Y n ; Wherein, m is the index of satellite monitoring point A, m = 1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n = 1, 2, ..., N, N is the total number of pollution sources;

[0050] A calculation unit is used to calculate the source parsing fitting result corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source parsing monitoring sample. The source parsing fitting result is specifically the satellite station contribution ratio A of each satellite monitoring point. m Y n The fitted value of

[0051] A fitting unit is used to calculate a fitting value of the source parsing distribution of the target area based on the source parsing fitting result.

[0052] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0053] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0054] In summary, the present invention has the following beneficial effects: an online source parsing data fitting method, apparatus and computer equipment, wherein the method comprises: performing source parsing detection using a central monitoring point to obtain a source parsing monitoring sample; obtaining environmental factor data of a target area where the central monitoring point is located during the source parsing detection, and selecting a corresponding ratio coefficient matrix based on the environmental factor data; calculating the source parsing fitting results corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source parsing monitoring sample; calculating the fitting value of the source parsing distribution of the target area based on the source parsing fitting results; adopting the method of the present invention, the fitted source parsing data of multiple satellite monitoring points can be calculated through the measurement results of a single central monitoring point, thereby improving the representativeness of the source parsing data of a single monitoring point. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of an online source analysis data fitting method of the present invention;

[0056] Figure 2 This is a structural diagram of an online source analysis data fitting device of the present invention;

[0057] Figure 3 This is a diagram of the internal structure of a computer device according to an embodiment of the present invention;

[0058] Figure 4 Schematic diagram of the classification of the three-dimensional ratio matrix of the present invention;

[0059] Figure 5 Schematic diagram of the ratio series in the three-dimensional ratio matrix of the present invention.

[0060] In the figure: 1. Monitoring unit; 2. Selection unit; 3. Calculation unit; 4. Fitting unit. DETAILED DESCRIPTION

[0061] To make the objectives, features, and advantages of the present invention more readily apparent, the following detailed description of the present invention is provided with reference to the accompanying drawings. The accompanying drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein.

[0062] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0063] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0064] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0065] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0066] The above description is merely a specific embodiment of the present application. Any person skilled in the art may easily conceive of variations or substitutions within the technical scope disclosed in this application, and such variations or substitutions shall be within the scope of protection of this application. The scope of protection of this application shall be subject to the scope of protection of the claims.

[0067] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0068] In order to facilitate the understanding of this technical solution, the background technology is first stated: the city points for environmental air quality evaluation are set up for the purpose of monitoring the overall status and changing trends of the air quality in urban built-up areas. The monitoring points participating in the urban environmental air quality evaluation are hereinafter referred to as city points. The representative range of city points is generally 500 meters to 4 kilometers in radius. The number of monitoring points set up in each city is determined by the area of ​​the urban built-up area and the population. For example, for a city with a population of more than 3 million and a built-up area of ​​more than 400 square kilometers, there should be no less than 10 monitoring points. When evaluating the urban environmental air quality, the arithmetic mean of the pollutant concentrations of all city points represents the overall average value of the pollutant concentrations in the urban built-up area, and the urban air quality evaluation work is carried out based on this value.

[0069] Online single-particle time-of-flight mass spectrometer is a popular technical means for online source analysis of fine particulate matter in recent years. It can detect and analyze the mass spectral composition and particle size of fine particulate matter in real time, and compare it with the pollution source spectral library data in real time to achieve online source analysis of fine particulate matter pollution sources.

[0070] The function of online source analysis is to analyze the composition of particulate matter in the air and determine the source of the particulate matter in the air. For example, 30% of particulate matter comes from exhaust emissions and 25% comes from construction site dust. By judging the source of particulate matter, it is possible to determine which specific pollution source has caused serious air pollution, so that relevant management departments can carry out targeted treatment of the pollution source.

[0071] Since single-particle mass spectrometer equipment is relatively expensive, in actual work, monitoring can only be carried out at a single city point, or patrol monitoring is carried out at various city points. This makes it difficult to cover the entire city area at the same time. The online source analysis results have certain regional limitations when reflecting the current status of the sources of fine particulate matter pollution in the city, and its monitoring results are difficult to represent the overall air quality of the city.

[0072] In view of the above problems, the purpose of the present invention is to provide a data fitting method that is fast, efficient, easy to operate, and has low overall operating costs, and can improve the representativeness of the source analysis results of an online single particle time-of-flight mass spectrometer.

[0073] This method mainly includes two major steps:

[0074] The first step is to build a fitting database, that is, to establish the correlation between the central monitoring point and the satellite monitoring point. The specific steps are as follows: establish the central monitoring point and the satellite monitoring point, and conduct online source parsing monitoring synchronously within a certain period of time to obtain the source parsing data between each monitoring point, and analyze the correlation between the central monitoring point and the satellite monitoring point based on this source parsing data.

[0075] The second step is the fitting calculation process. After the correlation between the central monitoring point and the satellite monitoring points is established, only one central monitoring point is retained among the above monitoring points and its position is maintained unchanged. The remaining satellite monitoring points are removed and the air quality at the location of the retained central monitoring point is monitored to obtain the source parsed monitoring sample at the location of the central monitoring point. Using this source parsed monitoring sample and the correlation established previously, the source parsed data of the other satellite monitoring points can be fitted and generated. After the source parsed data of the other satellite monitoring points are fitted and generated, the arithmetic mean of the polluted areas in the city is calculated based on the above fitting data as the pollutant concentration in the urban built-up area of ​​the entire city to evaluate the city's air quality.

[0076] In the first step, the target area of ​​the city to be monitored must be determined. Based on the effective monitoring range of the single-particle online source apportionment device, the target area is divided into multiple sub-areas, and monitoring points are established in each sub-area. To improve the correlation between the monitoring points, the monitoring points can be divided into a central monitoring point and multiple satellite monitoring points. The central point is set at the center of the target area, and the satellite monitoring points need to be dispersed around the central monitoring point to ensure a certain spatial correlation between each satellite monitoring point and the central monitoring point. After the monitoring points are established, all monitoring points are turned on simultaneously to conduct online source apportionment of air pollutants in the target area.

[0077] The data obtained from online source analysis is the specific source of pollutants in the air, that is, the proportion of contribution of each pollution source near the monitoring point to the pollutants.

[0078] PM 2.5 For example, PM 2.5 PM refers to particles with an aerodynamic equivalent diameter of less than or equal to 2.5 microns in the ambient air, also known as fine particles or respirable particles. 2.5 The evaluation method is the weight of particulate matter contained in each cubic meter of air, such as PM 2.5 ≤15 micrograms / cubic meter is the first-level environmental assessment standard. 2.5 There are many sources of particulate matter, such as coal burning, fuel vehicle exhaust, industrial dust, construction dust, etc. 2.5When performing online source resolution, calculate the PM per hour. 2.5 The number of particles from different pollution sources is used to calculate the pollution source ratio. For example, in a certain hour, a total of 1000 PM 2.5 If 200 of these particles are identified as coming from dust sources, the output shows a 20% contribution from dust sources for that hour, and the same applies to the remaining pollution sources. The sum of the contributions of all pollution sources to particulate matter at any monitoring point is 100%. Based on the above, the following Table 1 is obtained.

[0079] Table 1: Source apportionment results for each monitoring point

[0080]

[0081] The elements in Table 1 represent the contribution ratio of the air pollution caused by the pollution source at a certain monitoring point, which is specifically a percentage value. For the convenience of subsequent explanation, the pollution source Y is used in this embodiment. n The contribution ratio to the central monitoring point A0 is recorded as the central station contribution ratio A0Y n , the pollution source is directed to the satellite monitoring point A m The contribution ratio is recorded as the satellite station contribution ratio A m Y n ; Wherein, m is the index of satellite monitoring point A, m=1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n=1, 2, ..., N, N is the total number of pollution sources.

[0082] For example, A0Y1 represents the central station contribution percentage of pollution source Y1 to the area of ​​central monitoring point A0, while A1Y1 represents the satellite station contribution percentage of pollution source Y1 to the area of ​​satellite monitoring point A1. As shown in Table 2, A2Y4 indicates that building source Y4 contributes 9.2% of the air pollutant particulate matter at satellite monitoring point A2. After each unit cycle, both the central and satellite monitoring points perform source analysis on the target area, resulting in a source analysis results table.

[0083] Table 2: Example of source apportionment results for each monitoring point

[0084]

[0085] Abstracting the above table 1 into a matrix, we can get a source analysis result matrix Q with a size of (M+1)×N ω :

[0086]

[0087] Among them, the source analysis result matrix Q ωThe matrix elements in represent the pollution source Y n The contribution ratio to the area where the monitoring point is located, M represents the number of satellite monitoring points, N represents the number of pollution sources, n represents the index of the pollution source, m represents the index of the satellite monitoring point; ω represents the number index of the source analysis result matrix Q.

[0088] In order to collect a sufficient amount of data, the data collection process needs to be limited. In this embodiment, the minimum unit period is hours, and the data collection continues for one year. That is, the number of tables collected per day is 24, and the total number of tables collected per year is 365×24=8760, that is, ω=1,2,3,...,8760. Those skilled in the art will appreciate that the minimum unit can be other time lengths, such as half an hour, fifteen minutes, ten minutes, etc., and a corresponding number of tables can be obtained. For example, if half an hour is the minimum unit, the number of tables collected is 365×24×2=17520, that is, ω=1,2,3,...,17520; if fifteen minutes is the minimum unit, the number of tables collected is 365×24×4=35040, that is, ω=1,2,3,...,35040. Similarly, the duration of data collection can also be other time lengths, such as two years, three years, etc., but the duration must cover at least one year to ensure that all types of climate in the target area are covered.

[0089] In one embodiment, the data collection process can also be achieved through a sampling process. For example, in each of the four seasons of the year, a representative month is selected to collect data to reduce the investment cost of the database. In my country, spring usually corresponds to March, April, and May; April can be taken as the representative month of spring; the remaining seasons can be selected according to the different adaptability of regions and climates.

[0090] After obtaining the above source analysis result table, it is necessary to establish the association between the central monitoring point and the satellite monitoring point. Specifically, based on each pollution source, calculate the ratio of the satellite station contribution ratio of each satellite monitoring point to the central station contribution ratio of the central monitoring point. For example, A1Y1 / A0Y1 represents the ratio of the satellite station contribution ratio of pollution source Y1 to the satellite monitoring point A1 to the central station contribution ratio of pollution source Y1 to the central monitoring point A0. Through the above steps, the ratio matrix P can be obtained. ω ,include:

[0091]

[0092] In the ratio matrix P ωIn the matrix, the number of columns is M, which is the same as the number of satellite monitoring points, and the number of rows is N, which is the same as the number of pollution sources. ω is represented as the number index of the ratio matrix P, which is the same as the number of source apportionment result matrix Q.

[0093] After obtaining the above ratio matrix, in order to improve the accuracy of the fitting value, it is also necessary to adjust the above ratio matrix P ω Because air pollution is greatly affected by season, wind direction and wind speed, in this application, it is necessary to classify the above ratio matrix based on environmental factor data. That is, in the process of source analysis, the corresponding environmental factor data is what, and the ratio matrix P is classified. ω The corresponding labels are marked, where the seasons include spring, summer, autumn, and winter. The wind direction is divided into 8 wind directions: north, northeast, east, southeast, south, southwest, west, and northwest. The wind speed is divided into low, medium, and high intervals according to 0m / s-2m / s, 2m / s-5m / s, and >5m / s. In summary, the ratio matrix P ω There are 3*4*8=96 categories in total. As those skilled in the art will know, there are many ways to classify environmental factors. The above classifications are only exemplary classifications. The classification standards and the number of classifications can be determined according to actual needs. Figure 4 、 Figure 5 As shown, the ratio matrix P is a two-dimensional matrix, and the size of each ratio matrix is ​​M×N. Therefore, the ratio matrices of different periods can be spliced ​​to form a three-dimensional matrix. After classifying all the ratio matrices, the matrix contained in each category is a three-dimensional matrix of M×N*f, where f represents the number of ratio matrices contained in the category, and each small square in the figure represents an element of a ratio matrix.

[0094] Through the above classification, we can get the ratio matrix under each category. In addition, due to the differences in the geographical location of each city, the number of ratio matrices under each category is not necessarily the same. For example, in the northern region (North China, Northeast China, Huanghuaihai Plain), the winter is relatively long, and the prevailing northwest wind (northwest, north wind) has a medium to high wind speed (above 5m / s); in the northern region, the summer is dominated by southeast wind (east, southeast wind) with a medium wind speed (2-5m / s). Therefore, after the comparison value matrix is ​​classified, the number of ratio matrices in each category is not necessarily equal, and there may even be a situation where the number difference is relatively large, such as Figure 5 As shown, the length of the three-dimensional matrix in the Z-axis direction is not exactly the same, that is, the value of f is not the same in each category and needs to be determined according to the actual classification standards and the actual climate environment.

[0095] In order to avoid this, it is also necessary to screen the data for each coefficient in each category.

[0096] The specific screening process is as follows:

[0097] First, select a category, such as Spring - Low Wind Speed ​​- North Wind. In this category, if the number of ratio coefficient matrices included is 650, that is, f = 650, then each ratio element corresponds to 650. Figure 4 As shown, the ratio coefficient matrix P ω It is a two-dimensional matrix distributed on the XY plane. After stacking 650 matrices in the Z-axis direction, a three-dimensional matrix can be obtained. In the Z-axis direction, the number of each element is 650, which forms a ratio series composed of ratio elements. There are a total of M×N such ratio series.

[0098] Take one of the ratio series, such as Figure 5 As shown by the shadow, the ratio represented by this series is A m For the ratio series Y1 / A0Y1, sort the ratio elements in this series from smallest to largest, then remove any data that exceeds 1.5 times the interquartile range. The remaining data is retained and re-formed into a ratio series at that position. To remove outliers, first sort the ratio elements and then take their median, which is recorded as the second quartile, Q2. When the ratio elements are even, the median is the average of the two middle numbers. Based on the second quartile Q2, the ratio series is divided into two parts, the median of the first part is re-determined and recorded as the first quartile Q1, and the median of the second part is re-determined and recorded as the third quartile Q3; the interquartile range is calculated based on the first quartile Q1 and the third quartile Q3, including: IQR = Q3-Q1; the lower bound LB of abnormal data is determined using the interquartile range IQR and the first quartile Q1, including: LB = Q1-1.5×IQR; the upper bound UB of abnormal data is determined using the interquartile range IQR and the third quartile Q3, including: UB = Q3+1.5×IQR; the data in the ratio series that exceeds the interquartile range, that is, the elements that are less than the lower bound LB of abnormal data or greater than the upper bound UB of abnormal data are recorded as abnormal data, and are eliminated from the ratio series. IQR is based on the median and quartiles, and does not rely on the mean and standard deviation of the data. Therefore, it is more robust to skewed distributions or asymmetric data with outliers, and can more accurately identify outliers in the sampled data within multiple unit periods in this method. mAfter sorting the Y1 / A0Y1 elements from smallest to largest, the data that exceeds 1.5 times the interquartile range is removed. After removing 80 data points, 570 data points remain. After removing the data that exceeds 1.5 times the interquartile range, the abnormal data can be removed to prevent the abnormal data from affecting the calculation results of the probability distribution. Based on the above example, since the element values ​​contained in each position are different, after removing the data, the number of data points contained in the element sequence corresponding to each position is not exactly the same.

[0099] After removing abnormal data, we need to establish a probability distribution function based on each series. In order to facilitate the description of each ratio series, we define the minimum value of each ratio series after removing data as d and the maximum value as e. In other words, the value range of each ratio series is [d, e]. m Take the Y1 / A0Y1 element as an example, its value range is recorded as First, it needs to be divided into k equal parts. The equal division formula is The value of k can be determined according to actual needs. Then, among the k intervals after division, the data range of the first interval is:

[0100]

[0101] The data range of the second interval is:

[0102]

[0103] Similarly, the data range of the kth interval is:

[0104]

[0105] The probability that the statistical ratio coefficient falls into each interval is expressed as (t c , h c ) indicates that:

[0106] [(t1, h1), (t2, h2), (t3, h3), ..., (t k , h k )];

[0107] Where c is the index value of the interval, c = 1, 2, 3, ..., k; t c It is expressed as a representative ratio in the cth interval. Since the elements in the matrix can only be a specific value and cannot be expressed using the interval of the ratio, it is necessary to select a representative value from the cth interval. Specifically, the minimum value or the maximum value of the interval can be selected. Preferably, the middle value of the interval is selected as t c .h cIt represents the probability that the ratio coefficient falls into this interval. For example, among the 570 elements mentioned above, 100 fall into the third interval, so the probability h3 corresponding to this interval is h3=100÷570≈0.18.

[0108] Based on the above statistical data, we construct a k-2 function group and get:

[0109]

[0110] Among them, a k-2 、a k-3 ,...,a are coefficients, b is a constant;

[0111] The probability distribution of the ratio series (t c , h c ) is brought into the k-2 function group, and after calculating the values ​​of each coefficient and constant, a probability distribution function corresponding to the ratio series is constructed, which is recorded as:

[0112]

[0113] The above probability distribution function shows that in the category of spring-low wind speed-north wind, the ratio element is A m The probability distribution function of the sequence corresponding to Y1 / A0Y1, represents the independent variable of the probability distribution function; Represents the dependent variable of the probability distribution function, that is, the probability.

[0114] The above probability distribution function only corresponds to a ratio coefficient in the environmental factor data. In the classification of spring-low wind speed-north wind, there are M×N probability distribution functions h c ,Based on the aforementioned classification standard, there are 96 categories of environmental factor data, ,so there are M×N×96 probability distribution functions.

[0115] After the probability distribution function is calculated, that is, the coefficient of the probability distribution function is determined, the probability distribution function is used to calculate the independent variable corresponding to the maximum probability value. That is to say, in the above probability distribution function, When taking any value, The value of is the largest. Since this probability distribution function is generated by fitting the discrete elements in the ratio series, its value is not completely equal to each sampling point. Therefore, it is necessary to use the continuous characteristics of the function to recalculate the maximum value. Corresponding because The distribution comes from the ratio series, so The value of needs to be within the range of the ratio series, that is, In the calculation Then, it means that under the category of environmental factor data of spring-low wind speed-north wind, the ratio of the contribution ratio A1Y1 of pollution source Y1 to the satellite station of satellite monitoring point A1 and the contribution ratio A0Y1 of pollution source Y1 to the central station of central monitoring point A0 is A1Y1 / A0Y1, and the maximum probability of its value is

[0116] Similarly, through the above process, we can also calculate the maximum probability values ​​of other ratio elements under this classification, and obtain the ratio coefficient matrix corresponding to the category of spring-low wind speed-north wind environmental factor data.

[0117] Based on the above steps, we can generalize and obtain the ratio coefficient matrix corresponding to the categories of all environmental factor data, thus completing the construction process of the fitting database.

[0118] In the second step, after obtaining the fitting database, the surrounding satellite monitoring points A can be m Remove it and only keep the central monitoring point A0.

[0119] like Figure 1 The method steps shown in the figure use the central monitoring point to start online source analysis of the air sample, and obtain the source analysis detection results of the area where the central monitoring point A0 is located, that is, the pollution sources Y in the city. n Contribution ratio A0Y to air pollutants at the central monitoring point A0 n The duration of online source analysis is the same as the duration of the previous correction coefficient matrix construction. For example, if the previous online source analysis lasted one hour, then the duration of this step will also be one hour. If the online source analysis lasted half an hour in the first step, then the duration in the second step will also be half an hour. After the measurement is completed, it is necessary to record the environmental factor data during this online source analysis process, namely the current season, wind direction, and wind speed. The specific environmental factor data needs to be determined according to the classification criteria in the first step and is not limited to these three types.

[0120] Based on the environmental factor data, the corresponding coefficient correction matrix is ​​selected. For example, during the online source analysis process, the environmental factor is spring-southwest wind-medium speed, so the coefficient correction matrix of spring-southwest wind-medium speed is selected accordingly. Each element in the coefficient correction matrix is ​​the ratio of the central monitoring point to each satellite monitoring point. Therefore, the source analysis monitoring results of the central monitoring point are multiplied by each ratio to obtain the source analysis results corresponding to each satellite monitoring point.

[0121] For example, the source analysis monitoring samples monitored by the central monitoring point should be as shown in Table 3 below, which is the source of each pollution source Y nThe contribution ratio of the central station to the central monitoring point A0.

[0122] Table 3: Source apportionment monitoring samples at central monitoring points

[0123] <![CDATA[Pollution source Y1]]> <![CDATA[Pollution source Y2]]> <![CDATA[Pollution source Y3]]> <![CDATA[Pollution source Y4]]> …… <![CDATA[Pollution source Y N > <![CDATA[Central monitoring point A0]]> <![CDATA[A0Y1]]> <![CDATA[A0Y2]]> <![CDATA[A0Y3]]> <![CDATA[A0Y4]]> …… <![CDATA[A0Y N ]]>

[0124] Based on the online source apportionment monitoring samples obtained in Table 3 and the corresponding correction coefficient matrix, the original source apportionment results of each satellite monitoring point can be fitted and estimated to generate fitting values ​​of the source apportionment results at each location. The fitting values ​​are based on the contribution ratio of the central station and the maximum probability ratio that needs to be adopted under the conditions of the environmental factor data to estimate the satellite station contribution ratio of each satellite monitoring point.

[0125] Since the actual sum of the data obtained by fitting estimation may not be 100%, in order to make the weight of each satellite monitoring point equal, the estimated values ​​of each satellite monitoring point need to be normalized so that the sum of the estimated values ​​of each satellite monitoring point remains at 100%.

[0126] Finally, the normalized estimated values ​​of each location are used to calculate the average contribution ratio of each pollution source in the target to determine the various sources of air pollution in the entire city, so as to facilitate targeted governance of air pollution.

[0127] Example 2

[0128] See also Figure 2 , an online source apportionment data fitting device, comprising:

[0129] Monitoring unit 1 is configured to perform source analysis detection using a central monitoring point to obtain a source analysis monitoring sample; the central monitoring point is located at the center of the target area, and the source analysis monitoring sample is specifically: the actual contribution ratio of each pollution source to the air pollution at the location of the central monitoring point;

[0130] The selection unit 2 is used to obtain the environmental factor data of the target area where the central monitoring point is located during the source analysis detection process, and select a corresponding ratio coefficient matrix based on the environmental factor data; the elements in the ratio coefficient matrix are specifically: under the environmental factor data conditions, the pollution source Y n For each satellite monitoring point A m Satellite station contribution ratio A m Y n The ratio of the central station contribution to the central monitoring point of the pollution source A0Y n Ratio A m Y n / A0Y n; Wherein, m is the index of satellite monitoring point A, m = 1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n = 1, 2, ..., N, N is the total number of pollution sources;

[0131] Calculation unit 3 is used to calculate the source parsing fitting result corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source parsing monitoring sample. The source parsing fitting result is specifically the satellite station contribution ratio A of each satellite monitoring point. m Y n The fitted value of

[0132] The fitting unit 4 is configured to calculate a fitting value of the source apportionment distribution of the target area based on the source apportionment fitting result.

[0133] The specific limitations of the online source analysis data fitting device can be found in the limitations of the online source analysis data fitting method above and will not be repeated here. The various modules in the above-mentioned online source analysis data fitting device can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0134] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation of the present application scheme. The specific online source analysis data fitting device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0135] Example 3

[0136] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the online source analysis data fitting method as described in Example 1 is implemented.

[0137] Example 4

[0138] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. When the computer program is executed by the processor, an online source resolution data fitting method is implemented.

[0139] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0140] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0141] S1. Conduct source analysis testing at a central monitoring point to obtain a source analysis monitoring sample; the central monitoring point is located at the center of the target area, and the source analysis monitoring sample is specifically the actual contribution ratio of each pollution source to the air pollution at the location of the central monitoring point;

[0142] S2. Acquire the environmental factor data of the target area where the central monitoring point is located during the source analysis detection process, and select a corresponding ratio coefficient matrix based on the environmental factor data; the elements in the ratio coefficient matrix are specifically: under the environmental factor data conditions, the pollution source Y n For each satellite monitoring point A m Satellite station contribution ratio A m Y n The ratio of the central station contribution to the central monitoring point of the pollution source A0Y n Ratio A m Y n / A0Y n ; Wherein, m is the index of satellite monitoring point A, m = 1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n = 1, 2, ..., N, N is the total number of pollution sources;

[0143] S3, based on the ratio coefficient matrix and the source analysis monitoring sample, calculate the source analysis fitting result corresponding to each satellite monitoring point, and the source analysis fitting result is specifically the satellite station contribution ratio A of each satellite monitoring point. m Yn The fitted value of

[0144] S4. Calculate the fitting value of the source apportionment distribution of the target area based on the source apportionment fitting result.

[0145] In one embodiment, the ratio coefficient matrix is ​​constructed using the following method:

[0146] A central monitoring point A0 and multiple satellite monitoring points A are set in the target area. m , where the central monitoring point A0 is located at the center of the target area, and the satellite monitoring point A m Dispersedly arranged around the central monitoring point;

[0147] In multiple unit cycles, the central monitoring point A0 and multiple satellite monitoring points A are used simultaneously. m Perform source analysis detection to obtain source analysis detection results corresponding to each unit period. The source analysis monitoring results are specifically: in the unit period, each pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n , and each pollution source Y n The satellite monitoring point A m Satellite station contribution ratio A m Y n ;

[0148] A ratio matrix is ​​constructed based on the source analysis monitoring results. The elements in the ratio matrix are: n For any satellite monitoring point A m Satellite station contribution ratio A m Y n With the pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n Ratio A m Y n / A0Y n ;

[0149] recording the environmental factor data of each unit period, and classifying the ratio matrix based on the environmental factor data;

[0150] The probability distribution function of each category is constructed using the classified ratio matrix; the probability distribution function is solved to determine the maximum probability ratio corresponding to each element in the ratio matrix; the probability distribution function is specifically: any pollution source Y n For any satellite monitoring point A m Satellite station contribution ratio A m Y n With the pollution source Yn The central station contribution ratio A0Y to the central monitoring point A0 n Ratio A m Y n / A0Y n The probability distribution function of

[0151] A ratio coefficient matrix corresponding to each category is constructed based on each of the maximum probability ratios.

[0152] In one embodiment, the ratio matrix is ​​constructed based on the source apportionment monitoring results, specifically:

[0153] Construct a source apportionment result matrix Q based on the source apportionment monitoring results ω ;

[0154] The source analysis result matrix Q ω Specifically, the size is (M+1)×N matrix:

[0155]

[0156] The ratio matrix P ω Specifically, the size is M×N matrix:

[0157]

[0158] Among them, the source analysis result matrix Q ω The matrix elements in represent the pollution source Y n The contribution ratio of each monitoring point to the area where it is located, M represents the number of satellite monitoring points, N represents the number of pollution sources, n represents the index of the pollution source, and m represents the index of the satellite monitoring point; ω is the number index of the source analysis result matrix Q, and ω is also the number index of the ratio matrix P.

[0159] In one embodiment, constructing the probability distribution function of each category using the classified ratio matrix specifically includes:

[0160] Define the number of ratio matrices P in any category as f; convert matrix elements at the same position in the ratio matrix into ratio sequences, the number of the ratio sequences is M×N, and the number of sequence elements in each ratio sequence is f;

[0161] Sort the elements in each ratio series from small to large. After removing the abnormal data in the ratio series, use each ratio series to construct a probability distribution function, including:

[0162] Based on the value range [d, e] of the ratio sequence, the sorted ratio sequence is divided into k subintervals;

[0163] Based on the distribution of sequence elements of the ratio sequence, the probability of the elements of the ratio sequence falling into each subinterval is counted respectively, and the probability distribution (t c , h c ), where t c represents the representative value of the cth subinterval; h c represents the probability of the cth subinterval,

[0164] Based on the probability distribution, a k-2 function group corresponding to the ratio series is established, including:

[0165]

[0166] Among them, a k-2 、a k-3 ,...,a are coefficients, b is a constant;

[0167] The probability distribution of the ratio series (t c , h c ) is brought into the k-2 function group, and after calculating the coefficients and constants, a probability distribution function corresponding to the ratio series is constructed.

[0168] In one embodiment, solving the probability distribution function to determine the maximum probability ratio corresponding to each element in the ratio matrix specifically includes:

[0169] Solve the maximum probability value h based on the probability distribution function max The corresponding maximum probability ratio t max , where t max ∈[d,e].

[0170] In one embodiment, based on the ratio coefficient matrix and the source analysis monitoring samples, the source analysis fitting results corresponding to each satellite monitoring point are calculated, specifically including:

[0171] Multiply the actual contribution ratio of each pollution source in the source apportionment monitoring sample by the ratio coefficient element corresponding to the pollution source in the ratio coefficient matrix to obtain the fitted contribution ratio of each satellite monitoring point corresponding to the pollution source. The number of fitted contribution ratios is the same as the number of satellite monitoring points.

[0172] In one embodiment, based on the source parsing fitting results, the fitting value of the source parsing distribution of the target area is calculated, specifically including: normalizing the fitting contribution ratio of each satellite monitoring point respectively; calculating the actual contribution ratio corresponding to each pollution source, and the arithmetic mean of all the fitting contribution ratios corresponding to the pollution source, to obtain the fitting value of the source parsing distribution of each pollution source in the target area.

[0173] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (RAmbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0174] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0175] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An online source apportionment data fitting method, characterized in that: include: S1. Conduct source analysis testing at a central monitoring point to obtain a source analysis monitoring sample; the central monitoring point is located at the center of the target area, and the source analysis monitoring sample is specifically the actual contribution ratio of each pollution source to the air pollution at the location of the central monitoring point; S2. Acquire the environmental factor data of the target area where the central monitoring point is located during the source analysis detection process, and select a corresponding ratio coefficient matrix based on the environmental factor data; the elements in the ratio coefficient matrix are specifically: under the environmental factor data conditions, the pollution source Y n For each satellite monitoring point A m Satellite station contribution ratio A m Y n The ratio of the central station contribution to the central monitoring point of the pollution source A0Y n Ratio A m Y n / A0Y n ; Where m is the index of satellite monitoring point A, m = 1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n = 1, 2, ..., N, N is the total number of pollution sources; S3, based on the ratio coefficient matrix and the source analysis monitoring sample, calculate the source analysis fitting result corresponding to each satellite monitoring point, and the source analysis fitting result is specifically the satellite station contribution ratio A of each satellite monitoring point. m Y n The fitted value of S4. Calculate the fitting value of the source apportionment distribution of the target area based on the source apportionment fitting result.

2. The online source analysis data fitting method according to claim 1, characterized in that: The ratio coefficient matrix is ​​constructed specifically using the following method: A central monitoring point A0 and multiple satellite monitoring points A are set in the target area. m , where the central monitoring point A0 is located at the center of the target area, and the satellite monitoring point A m Dispersedly arranged around the central monitoring point; In multiple unit cycles, the central monitoring point A0 and multiple satellite monitoring points A are used simultaneously. m Perform source analysis detection to obtain source analysis detection results corresponding to each unit period. The source analysis monitoring results are specifically: in the unit period, each pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n , and each pollution source Y n The satellite monitoring point A m Satellite station contribution ratio A m Y n ; A ratio matrix is ​​constructed based on the source analysis monitoring results. The elements in the ratio matrix are: n For any satellite monitoring point A m Satellite station contribution ratio A m Y n With the pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n Ratio A m Y n / A0Y n ; recording the environmental factor data of each unit period, and classifying the ratio matrix based on the environmental factor data; The probability distribution function of each category is constructed using the classified ratio matrix; the probability distribution function is solved to determine the maximum probability ratio corresponding to each element in the ratio matrix; the probability distribution function is specifically: any pollution source Y n For any satellite monitoring point A m Satellite station contribution ratio A m Y n With the pollution source Y n The central station contribution ratio A0Y to the central monitoring point A0 n Ratio A m Y n / A0Y n The probability distribution function of A ratio coefficient matrix corresponding to each category is constructed based on each of the maximum probability ratios.

3. The online source analysis data fitting method according to claim 2, characterized in that: The ratio matrix is ​​constructed based on the source apportionment monitoring results, specifically: Construct a source apportionment result matrix Q based on the source apportionment monitoring results ω ; The source analysis result matrix Q ω Specifically, the size is (M+1)×N matrix: The ratio matrix P ω Specifically, the size is M×N matrix: Among them, the source analysis result matrix Q ω The matrix elements in represent the pollution source Y n The contribution ratio of each monitoring point to the area where it is located, M represents the number of satellite monitoring points, N represents the number of pollution sources, n represents the index of the pollution source, and m represents the index of the satellite monitoring point; ω is the number index of the source analysis result matrix Q, and ω is also the number index of the ratio matrix P.

4. The online source analysis data fitting method according to claim 3, characterized in that: The method of constructing the probability distribution function of each category using the classified ratio matrix specifically includes: Define the number of ratio matrices P in any category as f; convert matrix elements at the same position in the ratio matrix into ratio sequences, the number of the ratio sequences is M×N, and the number of sequence elements in each ratio sequence is f; The elements in each ratio series are sorted from small to large, and after removing abnormal data in the ratio series, a probability distribution function is constructed using each ratio series, including: Based on the value range [d, e] of the ratio sequence, the sorted ratio sequence is divided into k subintervals; based on the distribution of the sequence elements of the ratio sequence, the probability of the elements of the ratio sequence falling into each subinterval is counted respectively to obtain the probability distribution (t c ,h c ), where t c represents the representative value of the cth subinterval; h c represents the probability of the c-th subinterval, Based on the probability distribution, a k-2 function group corresponding to the ratio sequence is established, including: Among them, a k-2 、a k-3 ,…,a are coefficients, b is a constant; The probability distribution of the ratio series (t c ,h c ) is brought into the k-2 function group, and after calculating the coefficients and constants, a probability distribution function corresponding to the ratio series is constructed.

5. The online source analysis data fitting method according to claim 4, characterized in that: The step of removing abnormal data from the ratio series specifically includes: Get the first quartile Q1 and the third quartile Q3 in the sorted ratio series; Calculating the interquartile range based on the first quartile Q1 and the third quartile Q3 includes: IQR=Q3-Q1; Determining the lower bound LB of abnormal data using the interquartile range IQR and the first quartile Q1 includes: LB=Q1-1.5×IQR; Determining an upper bound UB of abnormal data using the interquartile range IQR and the third quartile Q3 includes: UB=Q3+1.5×IQR; The elements in the ratio sequence that are smaller than the abnormal data lower bound LB or larger than the abnormal data upper bound UB are recorded as abnormal data and are removed from the ratio sequence.

6. The online source analysis data fitting method according to claim 5, characterized in that: Solving the probability distribution function to determine the maximum probability ratio corresponding to each element in the ratio matrix specifically includes: Solve the maximum probability value h based on the probability distribution function max The corresponding maximum probability ratio t max , where t max ∈[d,e].

7. The online source analysis data fitting method according to claim 6, characterized in that: The step of calculating the source analysis fitting results corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source analysis monitoring samples specifically includes: The actual contribution ratio corresponding to each pollution source in the source analysis monitoring sample is multiplied by the ratio coefficient element corresponding to the pollution source in the ratio coefficient matrix to obtain the fitted contribution ratio of each satellite monitoring point corresponding to the pollution source. The number of the fitted contribution ratios is the same as the number of satellite monitoring points.

8. The online source analysis data fitting method according to claim 7, characterized in that: The calculating, based on the source apportionment fitting result, a fitting value of the source apportionment distribution of the target area, specifically includes: Normalize the fitting contribution ratio of each satellite monitoring point separately; Calculate the actual contribution ratio corresponding to each pollution source and the arithmetic mean of all fitted contribution ratios corresponding to the pollution source to obtain the fitted value of the source analysis distribution of each pollution source in the target area.

9. An online source analysis data fitting device, characterized in that: The online source analysis data fitting device comprises: A monitoring unit is configured to perform source analysis detection using a central monitoring point to obtain a source analysis monitoring sample; the central monitoring point is located at the center of the target area, and the source analysis monitoring sample is specifically: the actual contribution ratio of each pollution source to the air pollution at the location of the central monitoring point; The selection unit is used to obtain the environmental factor data of the target area where the central monitoring point is located during the source analysis detection process, and select the corresponding ratio coefficient matrix based on the environmental factor data; the elements in the ratio coefficient matrix are specifically: under the environmental factor data conditions, the pollution source Y n For each satellite monitoring point A m Satellite station contribution ratio A m Y n The ratio of the central station contribution to the central monitoring point of the pollution source A0Y n Ratio A m Y n / A0Y n ; Where m is the index of satellite monitoring point A, m = 1, 2, ..., M, M is the total number of satellite monitoring points A; n is the index of pollution source Y, n = 1, 2, ..., N, N is the total number of pollution sources; A calculation unit is used to calculate the source parsing fitting result corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source parsing monitoring sample. The source parsing fitting result is specifically the satellite station contribution ratio A of each satellite monitoring point. m Y n The fitted value of A fitting unit is used to calculate a fitting value of the source parsing distribution of the target area based on the source parsing fitting result.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the online source analysis data fitting method according to any one of claims 1 to 8 is implemented.

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