An online source-resolving data fitting method, device and computer equipment

Abstract

The application provides an online source resolution data fitting method and device and computer equipment, and the method comprises the following steps: source resolution detection is performed by using a central monitoring point to obtain a source resolution monitoring sample; environmental factor data of a target area where the central monitoring point is located during the source resolution detection is obtained, and a corresponding ratio coefficient matrix is selected based on the environmental factor data; a source resolution fitting result corresponding to each satellite monitoring point is calculated based on the ratio coefficient matrix and the source resolution monitoring sample; and a fitting value of a source resolution distribution of the target area is calculated based on the source resolution fitting result. By using the method of the application, fitting source resolution data of multiple satellite monitoring points can be calculated based on the measurement result of a single central monitoring point, thereby improving the representativeness of the source resolution data of the single monitoring point and providing strong support for environmental management of the overall target area.

Description

Technical Field

[0001] This invention relates to the field of atmospheric environmental data processing technology, and more specifically, to an online source analysis data fitting method, apparatus, and computer equipment. Background Technology

[0002] Monitoring points established for the purpose of monitoring the overall air quality and trends in urban built-up areas are called urban air quality assessment points, hereinafter referred to as urban points. The area represented by an urban point is generally between 500 meters and 4 kilometers in radius. The number of urban points 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 greater than 3 million and an urban built-up area greater than 400 square kilometers, there should be no fewer than 10 monitoring points. When assessing urban ambient air quality, the arithmetic mean of pollutant concentrations at all urban points represents the overall average pollutant concentration in the urban built-up area of ​​that city. Urban air quality assessment work is based on this value.

[0003] Online single-particle time-of-flight mass spectrometry (OFMS) is a popular online source apportionment technique for fine particulate matter in recent years. It can detect and analyze the mass spectrometric composition and particle size of fine particulate matter in real time, and compare it with the pollution source spectral library data in real time, so as to realize online source apportionment of fine particulate matter pollution sources.

[0004] Because single-particle mass spectrometers are relatively expensive, in practice they can often only be used for monitoring in a single city or for patrol monitoring in various cities. This makes it difficult to cover the entire city area at the same time. Online source apportionment results have certain regional limitations in reflecting the current status of fine particulate matter pollution sources in cities, and their monitoring results are difficult to characterize the overall air quality of the city. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an online source analysis data fitting method, apparatus and computer equipment to overcome the above-mentioned disadvantages.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: an online source analysis data fitting method, comprising:

[0007] S1. Source apportionment detection is performed using a central monitoring point to obtain a source apportionment monitoring sample; the central monitoring point is set at the center of the target area, and the source apportionment monitoring sample specifically refers to the proportion of the actual contribution of each pollution source to the air pollution at the location of the central monitoring point.

[0008] S2. Obtain environmental factor data of the target area where the central monitoring point is located during the source apportionment 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, pollution sources For each satellite monitoring point Satellite station contribution ratio The proportion of pollution sources to the central monitoring stations of the central monitoring points ratio ;

[0009] in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , This represents the total number of pollution sources.

[0010] S3. Based on the ratio coefficient matrix and the source apportionment monitoring samples, calculate the source apportionment fitting result for each satellite monitoring point. Specifically, the source apportionment fitting result represents the satellite station contribution ratio for each satellite monitoring point. The fitted value;

[0011] S4. Based on the source resolution fitting results, calculate the fitting value of the source resolution distribution of the target region.

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

[0013] Set up a central monitoring point within the target area and multiple satellite monitoring points Among them, the central monitoring point Located in the center of the target area, satellite monitoring point The monitoring points are distributed around the central monitoring point;

[0014] Simultaneously utilizing the central monitoring point within multiple unit cycles. and multiple satellite monitoring points Source apportionment detection is performed to obtain source apportionment detection results corresponding to each unit period. Specifically, the source apportionment monitoring results are: within each unit period, the data for each pollution source... For the central monitoring point Central station contribution ratio and various pollution sources For the satellite monitoring points Satellite station contribution ratio ;

[0015] A ratio matrix is ​​constructed based on the source apportionment monitoring results. Each element in the ratio matrix represents: any pollution source... For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio ;

[0016] Record environmental factor data for each of the aforementioned unit periods, and classify the ratio matrix based on the environmental factor data;

[0017] The probability distribution function for 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: for any pollution source For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio The probability distribution function;

[0018] Construct a ratio coefficient matrix corresponding to each category based on each of the maximum probability ratios.

[0019] In one embodiment, constructing the ratio matrix based on the source analysis monitoring results specifically involves:

[0020] A source resolution result matrix is ​​constructed based on the source resolution monitoring results. ;

[0021] The source resolution result matrix Specifically, the dimensions are... The matrix:

[0022]

[0023] The ratio matrix Specifically, the dimensions are... The matrix:

[0024]

[0025] Among them, the source resolution result matrix The matrix elements represent pollution sources. The contribution ratio of each monitoring point to the area where it is located. Indicates the number of satellite monitoring points. Indicates the number of pollution sources. Index representing pollution sources, Indicates the index of the satellite monitoring point; Source resolution result matrix The number index, and It is also a ratio matrix The quantity index.

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

[0027] Define the ratio matrix for any category. The quantity is The matrix elements in the same position in the ratio matrix are transformed into a ratio sequence, the number of which is... The number of sequence elements in each of the aforementioned ratio sequences is 1. ;

[0028] The elements in each of the ratio sequences are sorted in ascending order. After removing outliers from the ratio sequences, a probability distribution function is constructed for each ratio sequence, including:

[0029] Based on the range of values ​​of the ratio sequence The sorted ratio sequence is divided into Each subinterval; based on the distribution of elements in the ratio sequence, the probability that an element of the ratio sequence falls into each subinterval is calculated to obtain the probability distribution. ,in, Indicates the first Each sub-interval represents a value; Indicates the first The probability of each sub-interval

[0030] Based on the probability distribution, establish a sequence corresponding to the ratio. The set of functions includes:

[0031]

[0032] in, , … All are coefficients. It is a constant;

[0033] The probability distribution of the ratio series Substitute into the After calculating the coefficients and constants of the set of functions, a probability distribution function corresponding to the ratio sequence is constructed.

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

[0035] Get the first quartile of the sorted ratio sequence and the third and fourth quartiles ;

[0036] Based on the first quartile and the third quartile Calculating the interquartile range includes: ;

[0037] Using the interquartile range and the first quartile Determine the lower bound of outlier data ,include: ;

[0038] Using the interquartile range and the third quartile Determine the upper bound of outlier data ,include: ;

[0039] The ratio series that is less than the lower bound of the abnormal data Or greater than the upper bound of the abnormal data. Elements that are not in the range are considered outliers and are removed from the ratio sequence.

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

[0041] Solve for the maximum probability value based on the probability distribution function. The corresponding maximum probability ratio ,in, .

[0042] In one embodiment, calculating the source apportionment fitting result for each satellite monitoring point based on the ratio coefficient matrix and the source apportionment monitoring samples specifically includes:

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

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

[0045] The fitting contribution ratio of each satellite monitoring point was normalized.

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

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

[0048] The monitoring unit is used to perform source apportionment detection using a central monitoring point to obtain source apportionment monitoring samples. The central monitoring point is located at the center of the target area, and the source apportionment monitoring samples are specifically the proportion of the actual contribution of each pollution source to the air pollution at the location of the central monitoring point.

[0049] The selection unit is used to acquire environmental factor data of the target area where the central monitoring point is located during the source apportionment 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, pollution sources For each satellite monitoring point Satellite station contribution ratio The proportion of pollution sources to the central monitoring stations of the central monitoring points ratio ;in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , This represents the total number of pollution sources.

[0050] The calculation unit is used to calculate the source apportionment fitting result for each satellite monitoring point based on the ratio coefficient matrix and the source apportionment monitoring samples. Specifically, the source apportionment fitting result is the satellite station contribution ratio of each satellite monitoring point. The fitted value;

[0051] The fitting unit is used to calculate the fitting value of the source resolution distribution of the target region based on the source resolution fitting result.

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

[0053] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0054] In summary, the present invention has the following beneficial effects: an online source apportionment data fitting method, apparatus, and computer equipment, wherein the method includes: performing source apportionment detection using a central monitoring point to obtain source apportionment monitoring samples; acquiring environmental factor data of the target area where the central monitoring point is located during the source apportionment detection process, and selecting a corresponding ratio coefficient matrix based on the environmental factor data; calculating the source apportionment fitting result corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source apportionment monitoring samples; and calculating the fitting value of the source apportionment distribution of the target area based on the source apportionment fitting result. Using the method of the present invention, fitted source apportionment data of multiple satellite monitoring points can be calculated from the measurement results of a single central monitoring point, thereby improving the representativeness of the source apportionment data of a single monitoring point. Attached Figure Description

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

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

[0057] Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram of the three-dimensional ratio matrix classification of the present invention;

[0059] Figure 5 This is a schematic diagram of the ratio sequence in the three-dimensional ratio matrix of the present invention.

[0060] In the diagram: 1. Monitoring unit; 2. Selection unit; 3. Calculation unit; 4. Fitting unit. Detailed Implementation

[0061] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0062] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related 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 represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0063] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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 understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0065] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

[0067] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0068] To facilitate understanding of this technical solution, the background technology is first described: Urban monitoring stations for ambient air quality assessment are established to monitor the overall air quality status and trends in urban built-up areas. These monitoring stations, hereinafter referred to as urban monitoring stations, participate in the assessment of urban ambient air quality. The area represented by an urban monitoring station is generally between 500 meters and 4 kilometers in radius. The number of monitoring stations 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 greater than 3 million and an urban built-up area greater than 400 square kilometers, there should be no fewer than 10 monitoring stations. When assessing urban ambient air quality, the arithmetic mean of pollutant concentrations at all urban monitoring stations represents the overall average pollutant concentration in the urban built-up area of ​​that city. Urban air quality assessment work is based on this value.

[0069] Online single-particle time-of-flight mass spectrometry (OFMS) is a popular online source apportionment technique for fine particulate matter in recent years. It can detect and analyze the mass spectrometric composition and particle size of fine particulate matter in real time, and compare it with the pollution source spectral library data in real time, so as to realize online source apportionment of fine particulate matter pollution sources.

[0070] Contribution ratio refers to the proportion of total pollutants from a particular pollution source in a specific area and at a specific time (period). Online source apportionment analyzes the composition of airborne particulate matter and identifies its sources, thereby determining the contribution of each pollution source to the total pollution in that area during a specific time period—that is, the contribution ratio. For example, if 30% of particulate matter comes from vehicle exhaust emissions and 25% from construction dust, identifying the source of particulate matter allows us to pinpoint which specific pollution source is causing severe air pollution, enabling relevant management departments to implement targeted control measures.

[0071] Because single-particle mass spectrometers are relatively expensive, in practice they can often only be used for monitoring in a single city or for patrol monitoring in various cities. This makes it difficult to cover the entire city area at the same time. Online source apportionment results have certain regional limitations in reflecting the current status of fine particulate matter pollution sources in cities, and their monitoring results are difficult to characterize the overall air quality of the city.

[0072] To address the aforementioned problems, the purpose of this invention is to provide a fast, efficient, easy-to-operate, and low-cost data fitting method that can improve the representativeness of source apportionment results from online single-particle time-of-flight mass spectrometry.

[0073] This method mainly includes two major steps:

[0074] The first step is to build a fitting database, which 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, conduct online source analysis monitoring synchronously within a certain period of time, obtain the source analysis data between each monitoring point, and analyze the correlation between the central monitoring point and the satellite monitoring point based on these source analysis data.

[0075] The second step is the fitting calculation process. After establishing the correlation between the central monitoring point and the satellite monitoring points, only one central monitoring point is retained and its position is kept unchanged. The remaining satellite monitoring points are removed. The retained central monitoring point is used to monitor the air quality at its location, obtaining the source apportionment monitoring sample for that location. Using this source apportionment monitoring sample and the previously established correlation, source apportionment data for all other satellite monitoring points can be fitted and generated. After generating the source apportionment data for all other satellite monitoring points, the arithmetic mean of the polluted areas in the city is calculated based on the fitted data. This arithmetic mean is used 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 described above, it is necessary to first determine the target area of ​​the city to be monitored. Based on the effective monitoring range of the single-particle online source apportionment device, the target area is divided into multiple sub-regions, and monitoring points are established in each sub-region. To improve the correlation between 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 distributed 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 activated simultaneously to perform online source apportionment of air pollutants in the target area.

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

[0078] PM 2.5 For example, PM 2.5 PM2.5 refers to particulate matter in ambient air with an aerodynamic equivalent diameter of less than or equal to 2.5 micrometers, also known as fine particulate matter or respirable particulate matter. 2.5 The evaluation method is based on the weight of particulate matter contained in each cubic meter of air, such as PM2.5. 2.5 ≤15 micrograms per cubic meter is the first-level environmental assessment standard. In cities, PM2.5... 2.5 Particulate matter can originate from many sources, such as coal combustion, exhaust from fuel-powered vehicles, industrial dust, and construction dust. When online source apportionment monitoring points detect PM2.5... 2.5When performing online source resolution, the PM per hour is calculated. 2.5 The proportion of pollution sources is calculated based on the number of particulate matter from different sources. For example, if a total of 1000 PM2.5 particles are monitored within a certain hour... 2.5 The particles are analyzed, and 200 of them are identified as originating from dust sources. This would output that the dust source's contribution rate for that hour is 20%, and so on for other pollution sources. The sum of the particulate matter contributions from all pollution sources at any monitoring point is 100%. Based on the above, Table 1 below can be obtained.

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

[0080]

[0081] The elements in Table 1 represent the percentage contribution of air pollution caused by pollution sources to a particular monitoring point. For ease of explanation later, this embodiment uses the pollution source... For the central monitoring point The contribution ratio made is recorded as the central station's contribution ratio. The pollution source will be monitored by satellite. The proportion of contribution made is recorded as the satellite station's contribution proportion. ;in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , This represents the total number of pollution sources.

[0082] For example Indicates pollution source For the central monitoring point The proportion of central station contributions made by the region. Indicates pollution source Satellite monitoring points The proportion of satellite station contributions made in the region. As shown in Table 2. That is, the source of the building At satellite monitoring points The location contributed 9.2% of the air pollutant particulate matter. After each unit cycle, the central monitoring point and satellite monitoring points simultaneously perform source apportionment of pollutants in the target area, resulting in a source apportionment result table.

[0083] Table 2: Example Table of Source Apportionment Results for Each Monitoring Point

[0084]

[0085] Abstracting Table 1 above into a matrix, we can obtain a matrix of size [missing information]. Source resolution result matrix :

[0086]

[0087] Among them, the source resolution result matrix The matrix elements represent pollution sources. The proportion of contribution to the area where the monitoring point is located, Indicates the number of satellite monitoring points. Indicates the number of pollution sources. Index representing pollution sources, Indicates the index of the satellite monitoring point; Represents the source resolution result matrix The quantity index.

[0088] To collect a sufficient amount of data, the data collection process needs to be defined. In this embodiment, the smallest unit of time is the hour, and data is collected continuously for one year. That is, 24 tables are collected each day, and a total of 24 tables are collected in one year. One, that is to say As those skilled in the art will know, the smallest unit can be any other time length, such as half an hour, fifteen minutes, or ten minutes, all of which will yield a corresponding number of tables. For example, using half an hour as the smallest unit, the number of tables collected would be... In other words, If we take fifteen minutes as the smallest unit, the number of forms collected is: In other words, Similarly, the data collection period can be other lengths, such as two or three years, but it must cover at least one year to ensure coverage of various climates in the target area.

[0089] In one embodiment, the data collection process can also be implemented through a sampling process. For example, in the four seasons of the year, a representative month is selected for each season to collect data in order to reduce the investment cost of the database. In my country, spring usually corresponds to the three months of March, April and May; April can be taken as the representative month of spring; the other seasons can be selected according to the different adaptability of the region and climate.

[0090] After obtaining the source apportionment results table above, it is necessary to establish the correlation between the central monitoring point and the satellite monitoring points. Specifically, based on each pollution source, the ratio of the satellite station contribution ratio of each satellite monitoring point to the central station contribution ratio of the central monitoring point is calculated. For example, This indicates the source of pollution. right The contribution ratio of satellite monitoring stations to pollution sources For the central monitoring point The ratio of the contribution proportion of the central station. The ratio matrix can be obtained through the above steps. ,include:

[0091]

[0092] In the ratio matrix In this matrix, the number of columns is the same as the number of satellite monitoring points. The number of rows in the matrix is ​​equal to the number of pollution sources. . Represented as a ratio matrix Quantity index, ratio matrix Source resolution result matrix The quantities are the same.

[0093] After obtaining the ratio matrix above, in order to improve the accuracy of the fitted values, it is also necessary to further refine the ratio matrix. Classification is necessary because air pollution is significantly affected by seasons, wind direction, and wind speed. Therefore, this application requires classifying the aforementioned ratio matrix based on environmental factor data. In other words, the ratio matrix is ​​classified according to the corresponding environmental factor data obtained during source analysis. The corresponding labels are added, with seasons categorized into four types: spring, summer, autumn, and winter. Wind direction is divided into eight directions: north, northeast, east, southeast, south, southwest, west, and northwest. Wind speed is divided into three intervals: low, medium, and high, based on 0 m / s-2 m / s, 2 m / s-5 m / s, and >5 m / s. Based on the above, the ratio matrix... Total There are several categories. As those skilled in the art will know, there are many ways to classify environmental factors; the above classification is merely exemplary, and the various classification criteria and the number of categories can be determined according to actual needs. For example... Figure 4 , Figure 5 As shown, the ratio matrix It is a two-dimensional matrix, and the dimensions of each ratio matrix are... Therefore, ratio matrices with different periods can be pieced together to form a three-dimensional matrix. After classifying all the ratio matrices, each category contains a matrix. A three-dimensional matrix, where, This indicates the number of ratio matrices contained in this category. Each small square in the diagram represents an element of a ratio matrix.

[0094] Through the above classification, we can obtain the ratio matrix for each category. However, due to differences in the geographical locations of various cities, the number of ratio matrices in each category may not be the same. For example, in northern regions (North China, Northeast China, and the Huang-Huai-Hai Plain), winters are relatively long, with prevailing northwesterly winds (northwesterly and northwesterly winds) and medium to high wind speeds (above 5 m / s); summers in northern regions are dominated by southeasterly winds (easterly and southeasterly winds) with medium wind speeds (2-5 m / s). Therefore, after classifying the ratio matrix, the number of ratio matrices in each category may not be equal, and there may even be significant differences in the number of matrices. Figure 5 As shown, the lengths of the three-dimensional matrices along the Z-axis are not all the same; that is, in each category, The values ​​are not the same and need to be determined based on the actual classification standards and the actual climate environment.

[0095] To avoid the need to filter 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. Within this category, if the number of ratio coefficient matrices is 650, that is... Therefore, each ratio element corresponds to 650. For example... Figure 4 As shown, the ratio coefficient matrix Given a two-dimensional matrix arranged in the XY plane, stacking 650 matrices along the Z-axis yields a three-dimensional matrix where each element has 650 elements along the Z-axis, forming a ratio sequence. There are a total of [number] such ratio sequences. indivual.

[0098] Take one of the ratio sequences, such as Figure 5 As shown in the shaded area, the ratio represented by this sequence is... Sort all ratio elements in this sequence from smallest to largest. Then, remove data exceeding 1.5 times the interquartile range, keeping the remaining data. Use the retained data as a new ratio sequence for that position. Removing outliers first requires sorting the ratio elements, then taking the median, denoted as the second quartile. When the ratio elements are even, the median is the average of the two middle numbers. (Based on the second quartile) Divide the ratio series into two parts, and redetermine the median of the first part, denoted as the first quartile. The second part redetermines the median and denots it as the third quartile. Based on the first quartile and the third and fourth quartiles Calculating the interquartile range includes: ; using the interquartile range and the first quartile Determine the lower bound of outlier data ,include: ; using the interquartile range and the third quartile Determine the upper bound of outlier data ,include: The data in the ratio series that exceed the interquartile range, i.e., those less than the lower bound of the outlier, are considered abnormal data. Or greater than the upper bound of the abnormal data. Elements that are not in the range are considered outliers and are removed from the ratio sequence. Based on the median and quartiles, and independent of the data mean and standard deviation, this method is more robust to skewed distributions or asymmetric data with outliers, and can more accurately identify outliers in sampled data across multiple unit periods. For example, it can analyze 650... After arranging the elements in ascending order, data exceeding 1.5 times the interquartile range are removed. After removing 80 data points, 570 data points remain. Removing data exceeding 1.5 times the interquartile range eliminates outliers, preventing them from affecting the probability distribution calculation results. Based on this example, it can be seen that because the element values ​​at each position are not identical, the number of data points in the sequence corresponding to each position will not be completely identical after data removal.

[0099] After removing outliers, it is necessary to establish probability distribution functions for each data series. To facilitate the description of each ratio series, the minimum value of each ratio series after data removal is defined as follows: The maximum value is In other words, the range of values ​​for each ratio sequence is... Of the 570 mentioned above Taking element as an example, its numerical range is denoted as First, it needs to be... Divide into equal parts, the formula for dividing into equal parts is: , The value can be determined according to actual needs, then the divided Of the intervals, the data range of the first interval is:

[0100]

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

[0102]

[0103] Similarly, we can obtain the first... The data range for each interval is:

[0104]

[0105] The probability of the statistical ratio coefficient falling into each interval is... If we express this, then we have:

[0106]

[0107] in Specifically, the index value of the interval has ; Represented as the first Since each element in a matrix can only be a specific numerical value, it cannot be represented using intervals of ratios. Therefore, it is necessary to start from the _th interval_. Choose a representative value from the given intervals. Specifically, you can choose the minimum value or the maximum value of the interval. Preferably, you can choose the median value of the interval. . This represents the probability that the ratio coefficient falls within this interval, for example, the above. Among the elements, there are The one that fell into the first In a given interval, what is the probability of that interval? that is .

[0108] Based on the above statistical data, construct From the system of quadratic functions, we can obtain:

[0109]

[0110] in, , … All are coefficients. It is a constant;

[0111] The probability distribution of the ratio series Substitute into the After calculating the values ​​of each coefficient and constant of the system of functions, a probability distribution function corresponding to the ratio sequence is constructed, denoted as:

[0112] .

[0113] The above probability distribution function indicates that, in the category of spring-low wind-northward wind, the ratio element is... The probability distribution function of the corresponding sequence, The independent variable represents the probability distribution function; The dependent variable represents the probability distribution function, which is the probability itself.

[0114] The probability distribution function described above corresponds only to a ratio coefficient in a single environmental factor data point. In the category of spring-low wind-northward wind, there are a total of... A probability distribution function Based on the aforementioned classification criteria, environmental factor data comprises 96 categories, therefore a total of A probability distribution function.

[0115] After calculating the probability distribution function, that is, after determining the coefficients of the probability distribution function, we then use this probability distribution function to calculate the independent variable corresponding to the maximum probability value. In other words, in the above probability distribution function, When to take which value? The value of is the largest. Since this probability distribution function is generated by fitting discrete elements in the ratio series, its value is not exactly equal to that of each sampling point. Therefore, it is necessary to utilize the continuous property of the function to recalculate the maximum value. Corresponding .because The distribution comes from the ratio series, therefore The value needs to be within the range of values ​​for that ratio sequence, that is... In the calculation Subsequently, its meaning indicates that under the environmental factor data category of spring-low wind speed-northward wind, the pollution source Satellite monitoring points Satellite station contribution ratio With pollution sources For the central monitoring point Central station contribution ratio ratio The maximum probability of its value is .

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

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

[0118] In the second step, after obtaining the fitted database, the surrounding satellite monitoring points can be... Remove it, only retain the central monitoring point. .

[0119] like Figure 1 The method steps shown involve using a central monitoring point to perform online source apportionment of air samples, thereby obtaining the central monitoring point. The source apportionment detection results for the area, that is, the various pollution sources in the city. For the central monitoring point The contribution ratio of air pollutants The online source apportionment time is the same as the time required to construct the correction coefficient matrix. For example, if each online source apportionment lasts for one hour, then the time in this step will also be one hour. If the online source apportionment in the first step lasts for half an hour, then the duration in the second step will also be half an hour. After the measurement is completed, it is also necessary to record the environmental factor data during this online source apportionment 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 environmental factor data, the corresponding coefficient correction matrix is ​​selected. For example, when the environmental factor is spring-southwest wind-medium speed during online source apportionment, the corresponding coefficient correction matrix for spring-southwest wind-medium speed is selected. Each element in the coefficient correction matrix is ​​the ratio of the central monitoring point to each satellite monitoring point. Therefore, by multiplying the source apportionment monitoring results of the central monitoring point with each ratio, the source apportionment results corresponding to each satellite monitoring point can be obtained.

[0121] For example, the source apportionment monitoring samples detected by the central monitoring point should be as shown in Table 3 below, representing various pollution sources. For the central monitoring point The contribution ratio of the central station.

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

[0123]

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

[0125] Because the actual sum of the data obtained from the fitting estimation may not be the same. To ensure equal weighting for all satellite monitoring points, the estimated values ​​for each point need to be normalized to maintain the sum of their estimated values ​​at a certain level. .

[0126] Finally, by using the normalized estimates of each location, the average contribution ratio of each pollution source in the target is calculated to determine the various sources of air pollution in the overall city, so as to facilitate targeted air pollution control.

[0127] Example 2

[0128] Please see Figure 2 An online source analysis data fitting device, comprising:

[0129] Monitoring unit 1 is used to perform source apportionment detection using a central monitoring point to obtain source apportionment monitoring samples; the central monitoring point is set at the center of the target area, and the source apportionment monitoring samples are specifically: the proportion of the actual contribution of each pollution source to the air pollution at the location of the central monitoring point;

[0130] Selection unit 2 is used to acquire environmental factor data of the target area where the central monitoring point is located during the source apportionment 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, pollution sources For each satellite monitoring point Satellite station contribution ratio The proportion of pollution sources to the central monitoring stations of the central monitoring points ratio ;in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , This represents the total number of pollution sources.

[0131] Calculation unit 3 is used to calculate the source apportionment fitting result corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source apportionment monitoring samples. Specifically, the source apportionment fitting result is the satellite station contribution ratio of each satellite monitoring point. The fitted value;

[0132] Fitting unit 4 is used to calculate the fitting value of the source resolution distribution of the target region based on the source resolution fitting result.

[0133] Specific limitations regarding the online source-analysis data fitting device can be found in the limitations of the online source-analysis data fitting method described above, and will not be repeated here. Each module in the aforementioned online source-analysis data fitting device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0134] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the present application. Specific online source analysis data fitting devices may include more or fewer components than those shown in the figures, or may combine certain components, or may have different component arrangements.

[0135] Example 3

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

[0137] Example 4

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

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

[0140] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: including:

[0141] S1. Source apportionment detection is performed using a central monitoring point to obtain a source apportionment monitoring sample; the central monitoring point is set at the center of the target area, and the source apportionment monitoring sample specifically refers to the proportion of the actual contribution of each pollution source to the air pollution at the location of the central monitoring point.

[0142] S2. Obtain environmental factor data of the target area where the central monitoring point is located during the source apportionment 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, pollution sources For each satellite monitoring point Satellite station contribution ratio The proportion of pollution sources to the central monitoring stations of the central monitoring points ratio ;in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , This represents the total number of pollution sources.

[0143] S3. Based on the ratio coefficient matrix and the source apportionment monitoring samples, calculate the source apportionment fitting result for each satellite monitoring point. Specifically, the source apportionment fitting result represents the satellite station contribution ratio for each satellite monitoring point. The fitted value;

[0144] S4. Based on the source resolution fitting results, calculate the fitting value of the source resolution distribution of the target region.

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

[0146] Set up a central monitoring point within the target area and multiple satellite monitoring points Among them, the central monitoring point Located in the center of the target area, satellite monitoring point The monitoring points are distributed around the central monitoring point;

[0147] Simultaneously utilizing the central monitoring point within multiple unit cycles. and multiple satellite monitoring points Source apportionment detection is performed to obtain source apportionment detection results corresponding to each unit period. Specifically, the source apportionment monitoring results are: within each unit period, the data for each pollution source... For the central monitoring point Central station contribution ratio and various pollution sources For the satellite monitoring points Satellite station contribution ratio ;

[0148] A ratio matrix is ​​constructed based on the source apportionment monitoring results. Each element in the ratio matrix represents: any pollution source... For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio ;

[0149] Record environmental factor data for each of the aforementioned unit periods, and classify the ratio matrix based on the environmental factor data;

[0150] The probability distribution function for 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: for any pollution source For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio The probability distribution function;

[0151] Construct a ratio coefficient matrix corresponding to each category based on each of the maximum probability ratios.

[0152] In one embodiment, constructing the ratio matrix based on the source analysis monitoring results specifically involves:

[0153] A source resolution result matrix is ​​constructed based on the source resolution monitoring results. ;

[0154] The source resolution result matrix Specifically, the dimensions are... The matrix:

[0155]

[0156] The ratio matrix Specifically, the dimensions are... The matrix:

[0157]

[0158] Among them, the source resolution result matrix The matrix elements represent pollution sources. The contribution ratio of each monitoring point to the area where it is located. Indicates the number of satellite monitoring points. Indicates the number of pollution sources. Index representing pollution sources, Indicates the index of the satellite monitoring point; Source resolution result matrix The number index, and It is also a ratio matrix The quantity index.

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

[0160] Define the ratio matrix for any category. The quantity is The matrix elements in the same position in the ratio matrix are transformed into a ratio sequence, the number of which is... The number of sequence elements in each of the aforementioned ratio sequences is 1. ;

[0161] Sort the elements in each ratio series in ascending order. After removing outliers from the ratio series, construct probability distribution functions for each ratio series, including:

[0162] Based on the range of values ​​of the ratio sequence Divide the sorted ratio sequence into Each subinterval; based on the distribution of elements in the ratio sequence, the probability that an element of the ratio sequence falls into each subinterval is calculated to obtain the probability distribution. ,in, Indicates the first Each sub-interval represents a value; Indicates the first The probability of each sub-interval

[0163] Based on probability distribution, establish a sequence corresponding to the ratio. The set of functions includes:

[0164]

[0165] in, , … All are coefficients. It is a constant;

[0166] The probability distribution of the ratio series Bring into After calculating the coefficients and constants of the set of functions, a probability distribution function corresponding to the ratio sequence is constructed.

[0167] In one embodiment, solving the probability distribution function to determine the maximum probability ratio corresponding to each element in the ratio matrix specifically includes:

[0168] Solving for the maximum probability value based on the probability distribution function The corresponding maximum probability ratio ,in, .

[0169] In one embodiment, based on the ratio coefficient matrix and the source apportionment monitoring samples, the source apportionment fitting result corresponding to each satellite monitoring point is calculated, specifically including:

[0170] The actual contribution ratio corresponding to each pollution source in the source apportionment monitoring sample is multiplied by the ratio coefficient element corresponding to that pollution source in the ratio coefficient matrix to obtain the fitted contribution ratio of each satellite monitoring point corresponding to that pollution source. The number of fitted contribution ratios is the same as the number of satellite monitoring points.

[0171] In one embodiment, based on the source apportionment fitting results, the fitted value of the source apportionment distribution in the target area is calculated, specifically including: normalizing the fitted contribution ratio of each satellite monitoring point; calculating the actual contribution ratio corresponding to each pollution source and the arithmetic mean of all fitted contribution ratios corresponding to that pollution source to obtain the fitted value of the source apportionment distribution of each pollution source in the target area.

[0172] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM). AM) or external cache memory. By way of illustration and not limitation, R A M can be obtained in various forms, such as static R. A M(SR) A M), Dynamic R A M(DR) A M), synchronous DR A M (SDR) A M), Dual Data Rate SDR A M (DDRSDR) A M), Enhanced SDR A M (ESDR) A M), Synchlink DR A M(SLDR) A M), Memory Bus (R) A mbus) direct R A M(RDR) A M), Direct Memory Bus Dynamic R A M(DRDR) A M), and memory bus dynamics R A M(RDR) A M), etc.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0174] 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 embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing 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 analysis data fitting method, characterized in that, include: S1. Source apportionment detection is performed using a central monitoring point to obtain a source apportionment monitoring sample; the central monitoring point is set at the center of the target area, and the source apportionment monitoring sample specifically refers to the proportion of the actual contribution of each pollution source to the air pollution at the location of the central monitoring point. S2. Obtain 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 ratio coefficient matrix is ​​constructed using the following method: Set up a central monitoring point within the target area and multiple satellite monitoring points Among them, the central monitoring point Located at the center of the target area, satellite monitoring point The monitoring points are distributed around the central monitoring point; Simultaneously utilizing the central monitoring point within multiple unit cycles. and multiple satellite monitoring points Source apportionment detection is performed to obtain source apportionment detection results corresponding to each unit period. Specifically, the source apportionment monitoring results are: within each unit period, the data for each pollution source... For the central monitoring point Central station contribution ratio and various pollution sources For the satellite monitoring points Satellite station contribution ratio ; A ratio matrix is ​​constructed based on the source apportionment monitoring results. Each element in the ratio matrix represents a specific pollution source. For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio ; Record environmental factor data for each of the aforementioned unit periods, and classify the ratio matrix based on the environmental factor data; The probability distribution function for 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: for any pollution source For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio The probability distribution function; Construct a ratio coefficient matrix corresponding to each category based on each of the maximum probability ratios; in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , This represents the total number of pollution sources. S3. Based on the ratio coefficient matrix and the source apportionment monitoring samples, calculate the source apportionment fitting result for each satellite monitoring point. Specifically, the source apportionment fitting result represents the satellite station contribution ratio for each satellite monitoring point. The fitting value; the calculation method of the source apportionment fitting result is as follows: multiply the actual contribution ratio corresponding to each pollution source in the source apportionment monitoring sample with the ratio coefficient element corresponding to the pollution source in the ratio coefficient matrix to obtain the fitting contribution ratio of each satellite monitoring point corresponding to the pollution source, and the number of fitting contribution ratios is the same as the number of satellite monitoring points; S4. Based on the source resolution fitting results, calculate the fitting value of the source resolution distribution of the target region.

2. The online source analysis data fitting method according to claim 1, characterized in that, The construction of the ratio matrix based on the source analysis monitoring results is specifically as follows: A source resolution result matrix is ​​constructed based on the source resolution monitoring results. ; The source resolution result matrix Specifically, the dimensions are... The matrix: The ratio matrix Specifically, the dimensions are... The matrix: Among them, the source resolution result matrix The matrix elements represent pollution sources. The contribution ratio of each monitoring point to the area where it is located Indicates the number of satellite monitoring points. Indicates the number of pollution sources. Index representing pollution sources, Indicates the index of the satellite monitoring point; Source resolution result matrix The quantity index, and It is also a ratio matrix The quantity index.

3. The online source analysis data fitting method according to claim 2, characterized in that, The construction of probability distribution functions for each category using the classified ratio matrix specifically includes: Define the ratio matrix for any category. The quantity is The matrix elements in the same position in the ratio matrix are transformed into a ratio sequence, the number of which is... The number of sequence elements in each of the aforementioned ratio sequences is 1. ; The elements in each of the ratio sequences are sorted in ascending order. After removing outliers from the ratio sequences, a probability distribution function is constructed for each ratio sequence, including: Based on the range of values ​​of the ratio sequence The sorted ratio sequence is divided into Each subinterval is calculated; based on the distribution of elements in the ratio sequence, the probability that an element of the ratio sequence falls into each subinterval is obtained, thus yielding the probability distribution. ,in, Indicates the first Each sub-interval represents a value; Indicates the first The probability of each sub-interval Based on the probability distribution, establish a sequence corresponding to the ratio. The set of functions includes: in, , … All are coefficients. It is a constant; The probability distribution of the ratio series Substitute into the After calculating the coefficients and constants of the set of functions, a probability distribution function corresponding to the ratio sequence is constructed.

4. The online source analysis data fitting method according to claim 3, characterized in that, The removal of outlier data from the ratio series specifically includes: Get the first quartile of the sorted ratio sequence and the third and fourth quartiles ; Based on the first quartile and the third quartile Calculating the interquartile range includes: ; Using the interquartile range and the first quartile Determine the lower bound of outlier data ,include: ; Using the interquartile range and the third quartile Determine the upper bound of outlier data ,include: ; The ratio series that is less than the lower bound of the abnormal data Or greater than the upper bound of the abnormal data. Elements that are not in the range are considered outliers and are removed from the ratio sequence.

5. The online source analysis data fitting method according to claim 4, characterized in that, The process of solving the probability distribution function to determine the maximum probability ratio corresponding to each element in the ratio matrix specifically includes: Solve for the maximum probability value based on the probability distribution function. The corresponding maximum probability ratio ,in, .

6. The online source analysis data fitting method according to claim 5, characterized in that, The step of calculating the fitted value of the source resolution distribution of the target region based on the source resolution fitting result specifically includes: The fitting contribution ratio of each satellite monitoring point was normalized. Calculate the actual contribution ratio corresponding to each pollution source and the arithmetic mean of all fitted contribution ratios corresponding to that pollution source to obtain the fitted value of the source apportionment distribution of each pollution source in the target area.

7. An online source analysis data fitting device, characterized in that, The online source analysis data fitting device includes: The monitoring unit is used to perform source apportionment detection using a central monitoring point to obtain source apportionment monitoring samples. The central monitoring point is located at the center of the target area, and the source apportionment monitoring samples are specifically the proportion of the actual contribution of each pollution source to the air pollution at the location of the central monitoring point. The selection unit is used to acquire environmental factor data of the target area where the central monitoring point is located during the source apportionment 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, pollution sources For each satellite monitoring point Satellite station contribution ratio The proportion of pollution sources to the central monitoring stations ratio ;in, For satellite monitoring points index, , For satellite monitoring points The total quantity; As a source of pollution index, , The total number of pollution sources; the ratio coefficient matrix is ​​constructed using the following method: Set up a central monitoring point within the target area and multiple satellite monitoring points Among them, the central monitoring point Located at the center of the target area, satellite monitoring point The monitoring points are distributed around the central monitoring point; Simultaneously utilizing the central monitoring point within multiple unit cycles. and multiple satellite monitoring points Source apportionment detection is performed to obtain source apportionment detection results corresponding to each unit period. Specifically, the source apportionment monitoring results are: within each unit period, the data for each pollution source... For the central monitoring point Central station contribution ratio and various pollution sources For the satellite monitoring points Satellite station contribution ratio ; A ratio matrix is ​​constructed based on the source apportionment monitoring results. Each element in the ratio matrix represents a specific pollution source. For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio ; Record environmental factor data for each of the aforementioned unit periods, and classify the ratio matrix based on the environmental factor data; The probability distribution function for 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: for any pollution source For any satellite monitoring point Satellite station contribution ratio With the source of pollution For the central monitoring point Central station contribution ratio ratio The probability distribution function; Construct a ratio coefficient matrix corresponding to each category based on each of the maximum probability ratios; The calculation unit is used to calculate the source apportionment fitting result corresponding to each satellite monitoring point based on the ratio coefficient matrix and the source apportionment monitoring samples. The calculation method of the source apportionment fitting result is as follows: multiply the actual contribution ratio corresponding to each pollution source in the source apportionment monitoring samples by the ratio coefficient element corresponding to the pollution source in the ratio coefficient matrix to obtain the fitting contribution ratio of each satellite monitoring point corresponding to the pollution source. The number of fitting contribution ratios is the same as the number of satellite monitoring points. Specifically, the source apportionment fitting result is the satellite station contribution ratio of each satellite monitoring point. The fitted value; The fitting unit is used to calculate the fitting value of the source resolution distribution of the target region based on the source resolution fitting result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the online source parsing data fitting method as described in any one of claims 1-6.

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