Automobile pollutant emission evaluation method based on analytic hierarchy process and basic data

Abstract

The invention discloses an automobile pollutant emission evaluation method based on analytic hierarchy process and basic data. The method comprises the following steps: (1) constructing an evaluation model; (2) managing the evaluation basic data according to time; (3) converting the basic data into an EAPC index; (4) determining a free weight coefficient in each evaluation level; (5) calculating a pollutant comprehensive AHP index; (6) carrying out AHP index positive value; (7) constructing an AHP judgment matrix by using the AHP index; (8) calculating an evaluation weight coefficient according to the AHP judgment matrix; (9) checking the consistency of the AHP judgment matrix; (10) carrying out normalization processing on the pollutant index values; and (11) summing the evaluation weight coefficient of each pollutant index and the normalized index product to obtain the pollutant comprehensive evaluation index of the evaluation subject. According to the invention, an objective, fair and comprehensive automobile pollutant comprehensive evaluation result is provided based on multi-standard decision analysis and public data.

Description

Technical Field

[0001] The present invention belongs to the field of automobile pollutant emission evaluation, and specifically relates to a comprehensive evaluation method for automobile CO, NOx, HC and particulate matter (PM) pollutants based on AHP hierarchical analysis method using atmospheric data and resident health data. Background Art

[0002] At present, the limits of automobile exhaust pollutants are mainly controlled according to the provisions of GB18352.6-2016 "Light-duty vehicle pollutant emission limits and measurement methods (China Phase VI)" and GB 17691-2018 "Heavy-duty diesel vehicle pollutant emission limits and measurement methods (China Phase VI)". In the above standards, the limit data are mainly determined according to the requirements of relevant Western, especially European standards, which are difficult to reflect the needs of specific regions or cities for automobile exhaust pollutant control, and it is also difficult to reflect the changes in the needs for automobile pollutant emission control caused by the impact of pollutants.

[0003] Generally speaking, automobile pollutants are believed to include CO, NOx, HC and particulate matter (PM). The main impacts of these pollutants are mainly in two aspects: one is the impact on the atmosphere and the overall environment, and the other is the impact on the health of residents within the pollution range.

[0004] At present, there are few methods that combine the data of various pollutants and comprehensively evaluate automobile exhaust pollutants by considering the impact on the environment and residents' health. Evaluating according to standards, only considering the size of a single pollutant, or evaluating the pollutant level only for a certain goal (such as environmental impact) obviously lacks fairness and comprehensiveness. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating automobile pollutant emissions based on hierarchical analysis and basic data, combining atmospheric data with resident health data (hereinafter also referred to as "evaluation basic data" or "basic data"), calculating the EAPC (annual average percentage change) index and multi-level weight coefficients to determine the importance of pollutant indicators corresponding to these basic data in weight calculation, using the AHP method to quantify these importances into evaluation weight coefficients, and normalizing automobile pollutant indicators to indicators of the same order of magnitude, multiplying each indicator with the corresponding evaluation weight coefficient and then adding them up, and giving an evaluation based on the result to obtain a fair and comprehensive automobile pollutant comprehensive evaluation result.

[0006] The present invention is achieved through the following technical solutions:

[0007] The automobile pollutant emission evaluation method based on hierarchical analysis and basic data includes the following steps:

[0008] (1) Construct an evaluation model, including evaluation levels and basic evaluation data;

[0009] (2) Manage the basic evaluation data at a uniform time interval;

[0010] (3) Convert the evaluation basic data into EAPC index;

[0011] (4) Determine the free weight coefficient of indicators at each evaluation level;

[0012] (5) Determine the comprehensive AHP importance index of pollutant indicators;

[0013] (6) convert negative values ​​in the comprehensive AHP importance index to positive values;

[0014] (7) Use the comprehensive AHP importance index to construct the AHP judgment matrix;

[0015] (8) Calculate the evaluation weight coefficient using the square root method based on the AHP judgment matrix;

[0016] (9) Perform consistency check on the AHP judgment matrix;

[0017] (10) According to the range of each pollutant index value involved in the evaluation, the pollutant index value is normalized to obtain the normalized index of each pollutant index;

[0018] (11) Calculate the product of the evaluation weight coefficient and the normalized index of each pollutant indicator, and then add them together to obtain the comprehensive pollutant evaluation index of each evaluation subject.

[0019] Furthermore, the evaluation level is divided into three levels:

[0020] The first layer is the category layer, which is the basic classification of evaluation content;

[0021] The second layer is the phenomenon layer, which is a description of the phenomenon that affects the category layer;

[0022] The third layer is the indicator layer, which is the description of the indicators that affect the phenomenon layer;

[0023] Each category has its own corresponding phenomena and indicators.

[0024] Furthermore, in step (3), the method for converting the evaluation basic data into the EAPC index is:

[0025] Sort the basic evaluation data by time;

[0026] The data y at each time point t Logarithmic transformation is performed, and the result is ln(y t );

[0027] After the transformation, ln(y t ) is subjected to linear regression analysis:

[0028] ln(y t )=α+βt+∈ t

[0029] Among them, α: intercept, β: slope, ∈ t : residual;

[0030] Calculate the EAPC data within the specified time range using the results of the linear regression analysis:

[0031]

[0032] Furthermore, in step (4), the free weight coefficient of the category layer, the free weight coefficient of the phenomenon layer, and the free weight coefficient of the indicator layer are set respectively;

[0033] In step (5), the method for determining the comprehensive AHP importance index of pollutant indicators is:

[0034] The EAPC index of a certain pollutant indicator in a specified time period is multiplied by the free weight coefficient it occupies in the corresponding evaluation level to obtain the importance index at the evaluation level, and then the importance indexes at each evaluation level are added together to finally obtain the comprehensive AHP importance index a of the pollutant indicator. c ;

[0035] In step (6), the method of converting the negative values ​​in the comprehensive AHP importance index into positive values ​​is:

[0036] If all pollutant indicators are combined with the AHP importance index a c If any of the items has a negative value, first determine the minimum value a of all pollutant importance indexes min , and then transform all importance indices according to the following formula:

[0037] a′ c =a c +|a min |×1.1

[0038] If all pollutant indicators are combined with the AHP importance index a c are all positive, then a′ c =a c ;

[0039] All subsequent calculations will use a′ c .

[0040] Furthermore, in step (7), the method of constructing the AHP judgment matrix is:

[0041] The indicator layer is used as the criterion layer of the AHP judgment matrix, and the matrix A is constructed for the criterion layer:

[0042]

[0043] For the pollutant type m, i=j=m in the matrix, and element k ij It is the comprehensive AHP importance index a′ after all pollutant indicators are converted to positive values c After arranging one horizontal row and one vertical row in the same order, the i-th comprehensive AHP importance index a′ in the vertical row ci and the jth comprehensive AHP importance index a′ in the row cj Divide them and we get

[0044] Furthermore, in step (8), according to the AHP judgment matrix A, the method of using the square root method to calculate the evaluation weight coefficient is:

[0045] Calculate the value of the i-th row vector in matrix A:

[0046]

[0047] Then, normalize the resulting vector values:

[0048]

[0049] The obtained w′ i is the evaluation weight coefficient corresponding to pollutant index i.

[0050] Furthermore, in step (9), the consistency check method for the AHP judgment matrix is:

[0051] First, calculate the maximum eigenvalue λ of matrix A max ;

[0052] Use (A×W′) i Represents the i-th row of data in the vector after the product of matrix A and vector W′, then (A×W′) i And the corresponding weight coefficient w′ i Divide them and get the maximum characteristic root λ max have:

[0053]

[0054] Then calculate the consistency index CI:

[0055]

[0056] Then determine the RI value by looking up the table according to the matrix order, and then calculate the consistency ratio CR using the following formula:

[0057]

[0058] Final judgment: If the CR result is less than 0.1, the consistency of the judgment matrix is ​​considered acceptable.

[0059] Furthermore, in step (10), the method for obtaining the normalized index of each pollutant index is:

[0060] First, determine the overall minimum value M of the c-th pollutant index among all the participating individuals. c_min and M c_max ,

[0061] Then, for the individual numbered p, the cth pollutant index M c_p Normalize and get the index:

[0062]

[0063] Furthermore, in step (11), the evaluation weight coefficient and normalized index of the pollutant index are combined and summed to obtain the comprehensive evaluation index of each individual participating in the evaluation:

[0064] S p =M′ 1_p ×w′ 1 +…+M′ c_p ×w′ c ;

[0065] M′ 1_p is the normalized index of the first pollutant index in the individual numbered p, w′ 1 is the evaluation weight coefficient of the first pollutant index, M′ c_p is the normalized index of the cth pollutant index of the participant numbered p, w′ c is the evaluation weight coefficient of the cth pollutant index;

[0066] For all participating individuals, S p The lower the comprehensive evaluation index is, the better the emission level of the individual is.

[0067] In order to solve the above problem, the present invention further provides a computing device for implementing the above method, including:

[0068] A computer-readable storage medium, wherein the computer-readable storage medium is a memory for storing a computer program;

[0069] A processor, configured to implement required data calculation using a computer program stored in a computer-readable storage medium;

[0070] When the computer program is executed by a processor, the computing functions in the method are realized.

[0071] The beneficial technical effects embodied by the present invention are:

[0072] The present invention provides a comprehensive, fair and objective comprehensive evaluation method for automobile emission pollutants. The method of the present invention is based on multi-standard decision analysis and atmospheric and resident health data, objectively assigns weights to each evaluation index, and realizes comprehensive evaluation of automobile emission pollutants through the AHP method and the normalized index. The method of the present invention is a multi-attribute evaluation method that can consider different atmospheric and resident health data in many aspects to calculate the evaluation weight coefficient of each pollutant index.

[0073] In order to increase the flexibility of this method, this method sets corresponding levels of free weight coefficients for different atmospheric and resident health data, as well as the classification of "atmosphere" and "resident health" itself, so as to adjust the importance of corresponding data in weight calculation according to actual needs during evaluation.

[0074] As a commonly used method in multi-criteria decision analysis, the AHP method has a simple, clear and visual weight calculation process. Furthermore, this method also uses the EAPC index calculated based on atmospheric and resident health data instead of the highly subjective "importance" value commonly used in the classical AHP method, which ensures the objectivity and accuracy of this method. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A flow chart of the automobile pollutant emission evaluation method based on hierarchical analysis and basic data of the present invention;

[0076] Figure 2 The diagram is a diagram of an evaluation model architecture in one embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] The present invention provides a method for evaluating automobile pollutant emissions based on hierarchical analysis and basic data, such as Figure 1 As shown, the method comprises the following steps:

[0079] (1) Construct an evaluation model, which includes evaluation levels and basic evaluation data contained in each level;

[0080] (2) Manage the basic evaluation data at a uniform time interval;

[0081] (3) Convert the evaluation basic data into EAPC index;

[0082] (4) Determine the free weight coefficient of indicators at each evaluation level;

[0083] (5) Multiply the EAPC index of a certain pollutant index in a specified time period by its free weight coefficient in the corresponding evaluation level to obtain the importance index at each evaluation level, and then add the importance index at each evaluation level to obtain the comprehensive AHP importance index of the pollutant;

[0084] (6) convert negative values ​​in the comprehensive AHP importance index to positive values;

[0085] (7) Use the comprehensive AHP importance index to construct the AHP judgment matrix;

[0086] (8) Calculate the evaluation weight coefficient using the square root method based on the AHP judgment matrix;

[0087] (9) Perform consistency check on the AHP judgment matrix;

[0088] (10) Based on the range of each pollutant index value involved in the evaluation, the pollutant index value is normalized using the minimum-maximum method to obtain the normalized index of each pollutant index;

[0089] (11) The product of the evaluation weight coefficient of each pollutant indicator and the normalized index is combined and then summed to obtain the comprehensive evaluation index of each pollutant for each evaluation subject.

[0090] The details are as follows:

[0091] The evaluation object in the present invention is the exhaust pollutants of automobiles. It is assumed that there are n automobiles participating in the evaluation of exhaust pollutants, and four evaluation indicators are set, namely, four exhaust pollutants: CO, NOx, PM, and HC.

[0092] In step (1), the evaluation levels and the basic evaluation data contained in each level are determined, and a three-level evaluation model is constructed.

[0093] Implementation example Figure 2 As shown, it is assumed that there are three categories and nine items in total of the basic data types for evaluation that affect the weights of various parameters of automobile emission pollutants.

[0094] The three major categories are the basic classification of evaluation contents, such as environmental impact and health impact, including: 1) the health impact of each pollutant on residents, 2) the historical concentration of each pollutant, and 3) the environmental impact of each pollutant.

[0095] The 9 items are the development of specific phenomena in the 3 major categories of content, including:

[0096] The health impacts of pollutants in the first category include: 11) the proportion of deaths from chronic diseases caused by PM in the total population, and 12) the proportion of deaths from chronic diseases caused by ozone (photochemical pollutants) in the total population.

[0097] The historical concentrations of the pollutants described in the 2nd category include: 21) historical concentrations of CO pollutants, 22) historical concentrations of NOx pollutants, 23) historical concentrations of PM pollutants, and 24) historical concentrations of HC pollutants.

[0098] The environmental impacts of each pollutant described in the 3rd category include: 31) surface ozone generation index, 32) rainwater pH value, and 33) visibility index.

[0099] The three major categories are regarded as the first layer - the category layer, and the nine minor items are regarded as the second layer - the phenomenon layer.

[0100] The pollutant indicators that cause each phenomenon in the phenomenon layer are used as the third layer - the indicator layer. The pollutant indicators corresponding to the 9 sub-items in the phenomenon layer are as follows, classified by number:

[0101] 11)PM, 12)CO, NOx, HC, 21)CO, 22)NOx, 23)HC, 24)PM, 31)CO, NOx, HC, 32)NOx, 33)PM.

[0102] In step (2), the basic evaluation data is managed at a unified time interval to obtain unified data at annual / monthly or other time intervals. For example, some data are counted in years and some in months. If they are unified as annual data, the average / median method can be used to reasonably convert the data into data at years.

[0103] In step (3), the evaluation basic data is converted into the EAPC index by:

[0104] Sort the evaluation basic data by time, and calculate the data y at each time point in each evaluation basic data. t Logarithmic transformation is performed, and the result is ln(y t ).

[0105] After the transformation, ln(y t ) Using the least squares method, or weighted least squares method, for linear regression analysis, but not limited thereto, the results are:

[0106] ln(y t )=α+βt+∈ t

[0107] Among them, α: intercept, β: slope, ∈ t : residual.

[0108] Calculate the EAPC data within the specified time range using the results of linear regression analysis:

[0109]

[0110] In step (4), the free weight coefficient of each evaluation level indicator is determined, which means that in order to evaluate the importance of each level of data based on possible subjective judgment (such as joint judgment of experts), it is necessary to introduce the free weight coefficient of each level.

[0111] Set the category layer free weight coefficient to w f1_i , because there are three categories in the category layer, let i = 1, 2, 3.

[0112] Set the phenomenon layer free weight coefficient to w f2_j ,Since there are 9 small items in the phenomenon layer, we can set j = 11, 12, 21, 22, 23, 24, 31, 32, 33, corresponding to the phenomenon numbers respectively.

[0113] Set the index layer free weight coefficient to w f3_k , because different pollutants have different degrees of influence on different phenomena, for example, PM affects the 11th sub-item phenomenon, CO, NOx, and HC affect the 12th sub-item phenomenon, CO affects the 21st sub-item phenomenon, NOx affects the 22nd sub-item phenomenon, HC affects the 23rd sub-item phenomenon, PM affects the 24th sub-item phenomenon, CO, NOx, and HC affect the 31st sub-item phenomenon, NOx affects the 32nd sub-item phenomenon, and PM affects the 33rd sub-item phenomenon. Therefore, k can be used to represent different pollutant indicators, as mentioned above: k = 11PM, 12CO, 12NOx, 12HC, 21CO, 22NOx, 23HC, 24PM, 31CO, 31NOx, 31HC, 32NOx, 33PM. It should be explained that the weight of each pollutant indicator is involved only if there are multiple pollutant indicators that affect a certain phenomenon. Otherwise, if there is only one pollutant indicator, the weight is naturally 100%.

[0114] In step (5), the method for determining the comprehensive AHP importance index of pollutants is:

[0115] The EAPC index of a pollutant indicator in a specified time period is multiplied by the free weight coefficient it occupies in the corresponding category, phenomenon, and indicator group to obtain the importance index at the category level, and then the importance index at each level is added to obtain the comprehensive AHP importance index of the pollutant. c .

[0116] The specific calculation example is as follows:

[0117] Assume that for pollutant index c, within a specified time period, the EAPC data related to the pollutant index are divided into 3 groups according to the relevant phenomena under each major category, and the corresponding weight coefficients are as follows:

[0118] 1) Large category data set {ω 12}, the free weight coefficient of this category is w f1_1 , where ω 12 The corresponding phenomenon layer free weight coefficient is w f2_12 , the corresponding index layer free weight coefficient is w f3_12c ;

[0119] 2) Large category data group {ω 22}, the free weight coefficient of this category is w f1_2 , where ω 22 The corresponding phenomenon layer free weight coefficient is w f2_22 , the corresponding index layer free weight coefficient is w f3_22c ;

[0120] 3) Large category data group {ω 32 ,ω 33}, the free weight coefficient of this category is w f1_3 , where ω 32 The corresponding phenomenon layer free weight coefficient is w f2_32 , the corresponding index layer free weight coefficient is w f3_32c ;ω 33 The corresponding phenomenon layer free weight coefficient is w f2_33 , the corresponding index layer free weight coefficient is w f3_33c .

[0121] The comprehensive AHP importance index of pollutant c is:

[0122] a c =w f1_1 × f2_12 × f3_12c ×ω 12 +w f1_2 × f2_22 × f3_22c ×ω 22 +w f1_3 ×(w f2_32 × f3_32c ×ω 32 +w f2_33 × f3_33c ×ω 33 )

[0123] In step (6), if the comprehensive AHP importance index is negative, it is necessary to use the minimum translation constant to convert it to a positive value. The method is:

[0124] If a of all pollutants c There are negative values ​​in the value:

[0125] First, determine the minimum value a of all pollutant importance indexes min , transform all importance indices according to the following formula:

[0126] a′ c =a c +|a min |×1.1

[0127] If a c are all positive, then a′ c =a c . In the following calculations, a′ is used c .

[0128] In step (7), the AHP judgment matrix is ​​constructed using the comprehensive AHP importance index, and the method is:

[0129] The index layer is used as the criterion layer of the AHP judgment matrix. The matrix A is constructed for the criterion layer. In this example, there are 4 types of pollutants, the matrix order is 4, and i=j=4:

[0130]

[0131] For element k in the matrix ij The numerical values ​​are listed below. The column pollutants and row pollutants in the table should be arranged in the same order. Each value in the table is the corresponding a′ of the column pollutant and the row pollutant. c Ratio of:

[0132]

[0133]

[0134] Expressed in matrix form, that is:

[0135]

[0136] In step (8), the evaluation weight coefficient is calculated using the square root method according to the AHP judgment matrix:

[0137] First, for the matrix A constructed in step (7), calculate the fourth square root of the product of the data in each row vector (corresponding to the amount of vector data, which is also the number of pollutant types), and obtain the corresponding vector value w i as follows:

[0138]

[0139]

[0140]

[0141]

[0142] Then, for the obtained w i Standardization is performed separately, including:

[0143]

[0144]

[0145]

[0146]

[0147] w′ i That is, the evaluation weight coefficient corresponding to the pollutant index i;

[0148] Then, we can get w′ i Form a 4-dimensional column vector is the weight vector.

[0149] In step (9), the method for consistency check of AHP judgment matrix is:

[0150] First, calculate the maximum eigenvalue λ of matrix A max ;

[0151] Use (A×W′) i Represents the i-th row of data in the vector after the product of matrix A and vector W′, then (A×W′) i And the corresponding weight coefficient w′ i Divide and calculate the maximum characteristic root λ of matrix A max have:

[0152]

[0153] For the four pollutants CO, NOx, HC, and PM in this embodiment, there are:

[0154]

[0155]

[0156] Further calculate the consistency index CI:

[0157]

[0158] The RI value is further determined by looking up the table according to the matrix order. For this example (4th order), RI = 0.9. The consistency ratio CR is then calculated using the following formula:

[0159]

[0160] Final judgment: If the CR result is less than 0.1, the consistency of the judgment matrix is ​​considered acceptable.

[0161] In step (10), the pollutant index values ​​are subjected to minimum-maximum normalization processing according to the range of each pollutant index value involved in the evaluation to obtain the normalized index of each pollutant index value, and the method is:

[0162] It is clear that the overall minimum and maximum values ​​of each pollutant indicator among all participating vehicles are:

[0163] M CO_min 、M CO_max 、M NOx_min 、M NOx_max 、M HC_min 、M HC_max 、M PM_min 、M PM_max ;

[0164] For each pollutant index value M of each vehicle c_p (p is the vehicle number), normalized to get:

[0165]

[0166]

[0167]

[0168]

[0169] In step (11), the product of the evaluation weight coefficient of each pollutant and the normalized data is combined and then summed to obtain the comprehensive evaluation index of the emission pollutants of each participating individual (each vehicle):

[0170] S p =M′ CO_p ×w′ CO +M′ NOx_p ×w′ NOx +M′ HC_p ×w′ HC +M′ PM_p ×w′ PM

[0171] The number of pollutant indicators involved in the evaluation here is 4. Therefore, the AHP method is used to obtain the comprehensive evaluation index of pollutant emissions from each vehicle, which is used as the basis for ranking the pollutant emission levels among vehicles. The lower the comprehensive evaluation index, the better the vehicle's emission level is, and the more it meets the needs of environmental protection and residents' health.

[0172] In order to solve the above technical problems, the present application also provides a computing device for implementing the required data calculation process (such as the EAPC index), which includes: a memory for storing a computer program for implementing the above calculation; and a processor for using the computer program stored in the memory to implement the required data calculation.

[0173] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, including: a computer program is stored on the computer-readable storage medium, and the computer program implements the computing function in the above method when executed by a processor.

[0174] The following is a specific example:

[0175] The example shows the mass emissions of three different models of cars that have been subjected to the Type I test described in the GB 18352.6-2016 standard and have measured CO, NOx, HC and PM (particulate matter). The results are:

[0176]

[0177] There are four pollutant evaluation indicators, namely CO, NOx, PM and HC exhaust pollutants.

[0178] In this embodiment, a three-level evaluation system is constructed, and it is considered that the evaluation basic data types that affect the weights of various parameters of automobile emission pollutants have three categories and nine sub-items:

[0179] The three major categories at the category level are: 1) the impact of each pollutant on residents’ health, 2) the historical concentration of each pollutant, and 3) the environmental impact of each pollutant.

[0180] The 9 items of the phenomenal layer are:

[0181] The health impacts of various pollutants in the first category include: 11) the proportion of deaths from chronic diseases caused by PM in the total population, 12) the proportion of deaths from chronic diseases caused by ozone (photochemical pollutants) in the total population;

[0182] The historical concentrations of pollutants in the second category include: 21) historical concentrations of CO pollutants, 22) historical concentrations of NOx pollutants, 23) historical concentrations of PM pollutants, and 24) historical concentrations of HC pollutants;

[0183] The environmental impacts of each pollutant described in the 3rd category include: 31) surface ozone generation index, 32) rainwater pH value, and 33) visibility index.

[0184] The three major categories are regarded as the first layer: the category layer, and the nine small items are regarded as the second layer: the phenomenon layer.

[0185] The pollutant indicators that cause each phenomenon in the phenomenon layer are used as the third layer: the indicator layer. The relationship between the phenomenon layer number and the pollutant is as follows:

[0186] 11)PM, 12)CO, NOx, HC, 21)CO, 22)NOx, 23)HC, 24)PM, 31)CO, NOx, PM, 32)NOx, 33)PM.

[0187] The unit time for collecting basic evaluation data is year, and the interval is 5 years (2015-2019). After processing the collected basic evaluation data, the corresponding basic data of a certain region are obtained as follows:

[0188] 1) Impact of various pollutants on residents’ health:

[0189]

[0190] 2) Historical concentration of each pollutant:

[0191] There is a lack of data on HC pollutants. Combined with the evaluation of industrial pollutants and mobile pollutants, HC pollutant emissions are usually greater than NOx pollutant emissions. Combined with the experience in automobile emission tests, the historical concentration index of HC pollutants is set to 1.2 to 2.0 times the historical concentration index of NOx pollutants, and the data for each year is randomized.

[0192]

[0193] 3) Environmental impact of each pollutant:

[0194] Among them, the higher the pH value of rainwater, the lower the acidity, so the "rainwater pH value" item should be converted from positive to negative.

[0195]

[0196] Determine the free weight coefficient of each level. In this embodiment, the free weight coefficient is defined by the experimenter. The customized free weight coefficient is as follows:

[0197] 1. Category layer:

[0198] 1) Impact of various pollutants on residents’ health f1_1 =1.3

[0199] 2) Historical concentration of each pollutant w f1_2=1.0

[0200] 3) Environmental impact of each pollutant f1_3 =1.1

[0201] 2. Phenomenon layer:

[0202] 11) The proportion of deaths from chronic diseases caused by PM to the total population f2_11 =1.2

[0203] 12) Ozone (photochemical pollutants) causes chronic disease deaths as a percentage of the total population f2_12 =1.0

[0204] 21) Historical concentration of CO pollutants w f2_21 =1.0

[0205] 22) Historical concentration of NOx pollutants w f2_22 =1.0

[0206] 23) Historical concentration of HC pollutants w f2_23 =1.0

[0207] 24) Historical concentration of PM pollutants w f2_24 =1.0

[0208] 31) Surface ozone generation index w f2_31 =1.0

[0209] 32) Rainwater pH value w f2_32 =0.9

[0210] 33) Visibility index w f2_33 =0.8

[0211] 3. Indicator layer

[0212] During the generation of ozone (photochemical pollutants), the CO, NOx, and HC pollutants involved in the generation have different effects. To reflect this, the free weight coefficients of the indicator layers CO, NOx, and HC under the 12) and 31) sub-items of the phenomenon layer are defined as follows:

[0213] 12) and 31) reflect the role of CO in the ozone generation process. f3_12CO =w f3_31CO =0.5

[0214] 12) and 31) reflect the role of NOx in the ozone generation process. f3_12NOx =wf3_31MOx=1.0

[0215] 12) and 31) reflect the role of HC in the ozone generation process. f3_12HC =w f3_31HC=0.7

[0216] Calculate the comprehensive AHP importance index of each pollutant:

[0217] a CO =w f1_1 × f2_12 × f3_12CO ×ω 12 +w f1_2 × f2_21 ×ω 21 +w f1_3 × f2_31 × f3_31CO ×ω 31

[0218] =1.3×1×0.5×(-4.55)+1×1×3.13+1.1×1×0.5×15.34=8.6095

[0219] a NOx =w f1_1 × f2_12 × f3_12NOx ×ω 12 +w f1_2 × f2_22 ×ω 22 +w f1_3 × f2_31 × f3_31NOx ×ω 31 +w f1_3 × f2_32 ×ω 32

[0220] =1.3×1×1×(-4.55)+1×1×(-2.03)+1.1×1×1×15.34+1.1×0.9×(-0.87)=8.0677

[0221] a HC =w f1_1 × f2_12 × f3_12HC ×ω 12 +w f1_2 × f2_23 ×ω 23 +w f1_3 × f2_31 × f3_31HC ×ω 31

[0222] =1.3×1×0.7×(-4.55)+1×1×(-4.05)+1.1×1×0.7×15.34=3.62

[0223] a PM =wf1_1 × f2_11 ×ω 11 +w f1_2 × f2_24 ×ω 24 +w f1_3 × f2_33 ×ω 33

[0224] =1.3×1.2×(-0.95)+1×1×(-9.52)+1.1×0.8×15.1

[0225] =2.29

[0226] Since all importance indices are > 0, there is no need to minimize the translation constant.

[0227] Use the comprehensive AHP importance index to construct the AHP judgment matrix:

[0228] The indicator layer is used as the criterion layer of the AHP judgment matrix, and an evaluation matrix is ​​constructed for the criterion layer, with a matrix order of 4.

[0229] The matrix value table is as follows:

[0230]

[0231] Expressed in matrix form, that is:

[0232]

[0233] According to the AHP judgment matrix, the evaluation weight coefficient is calculated using the square root method. For the above four pollutants, there are:

[0234]

[0235]

[0236]

[0237]

[0238] Standardization is performed separately, including:

[0239]

[0240]

[0241]

[0242]

[0243] Perform consistency check on the AHP judgment matrix:

[0244] According to the calculation above, the weight vector

[0245] The product of matrix A and weight vector W′:

[0246]

[0247] Then the largest characteristic root is:

[0248]

[0249] Calculate the consistency index CI:

[0250]

[0251] The RI value is determined by looking up the table according to the matrix order. For this example (4th order), RI = 0.9. Then the consistency ratio CR is calculated using the following formula:

[0252]

[0253] The degree of consistency meets the requirements (CR<0.1).

[0254] According to the range of values ​​of each pollutant index involved in the evaluation, the pollutant index is normalized from the minimum value to the maximum value.

[0255] According to the Type I emission test results measured at the beginning of this embodiment, we obtain (all in mg / km):

[0256] M CO_min =121.0,M CO_max =371.1

[0257] M NOx_min =16.5,M NOx_max =21.7

[0258] M HC_min =18.4,M HC_max =34.9

[0259] M PM_min =1.70,M PM_max =2.59

[0260] Perform normalization from the minimum value to the maximum value to obtain the normalized index:

[0261]

[0262]

[0263]

[0264]

[0265] The results are as follows:

[0266]

[0267] Combining the weight coefficient and normalized data, the exhaust pollutant comprehensive evaluation index of each vehicle is obtained by adding up:

[0268] S 1 =M′ CO_1 ×w′ CO +M′ NOx_1 ×w′ NOx +M′ HC_1 ×w′ HC +M′ PM_1 ×w′ PM

[0269] =0.762×0.3917+0.759×0.3458+0.840×0.1675+0.735×0.0952=0.7716

[0270] S 2 =M′ CO_2 ×w′ CO +M′ NOx_2 ×w′ NOx +M′ HC_2 ×w′ HC +M′ PM_2 ×w′ PM

[0271] =0.876×0.3917+0.183×0.3458+0.359×0.1675+0.129×0.0952=0.4788

[0272] S 3 =M′ CO_3 ×w′ CO +M′ NOx_3 ×w′ NOx +M′ HC_3 ×w′ HC +M′ PM_3 ×w′ PM

[0273] =0.0404×0.3917+0.394×0.3458+0.0843×0.1675+0.804×0.0952=0.2427

[0274] By ranking the emission levels of the three vehicles according to the comprehensive evaluation index, it can be concluded that from the perspective of curbing the deterioration of the atmosphere and residents' health, vehicle No. 3 has the best emission level and vehicle No. 1 has the worst emission level.

[0275] It is hoped that those skilled in the art can implement or use the above embodiments. However, it should be noted that the present invention is not limited to the embodiments shown, and equivalent modifications or replacements of these embodiments will be obvious to those skilled in the art. Other embodiments should also be protected without departing from the spirit of the present invention.

Claims

1. The automobile pollutant emission evaluation method based on hierarchical analysis and basic data is characterized by: The steps include: (1) Construct an evaluation model, including evaluation levels and basic evaluation data; (2) Manage the basic evaluation data at a uniform time interval; (3) Convert the evaluation basic data into EAPC index; (4) Determine the free weight coefficient of indicators at each evaluation level; (5) Determine the comprehensive AHP importance index of pollutant indicators; (6) convert negative values ​​in the comprehensive AHP importance index to positive values; (7) Use the comprehensive AHP importance index to construct the AHP judgment matrix; (8) Calculate the evaluation weight coefficient using the square root method based on the AHP judgment matrix; (9) Perform consistency check on the AHP judgment matrix; (10) According to the range of each pollutant index value involved in the evaluation, the pollutant index value is normalized to obtain the normalized index of each pollutant index; (11) Calculate the product of the evaluation weight coefficient and the normalized index of each pollutant indicator, and then add them together to obtain the comprehensive pollutant evaluation index of each evaluation subject.

2. The evaluation method according to claim 1, characterized in that: The evaluation level is divided into three levels: The first layer is the category layer, which is the basic classification of evaluation content; The second layer is the phenomenon layer, which is a description of the phenomenon that affects the category layer; The third layer is the indicator layer, which is the description of the indicators that affect the phenomenon layer; Each category has its own corresponding phenomena and indicators.

3. The evaluation method according to claim 1, characterized in that: In step (3), the method for converting the evaluation basic data into the EAPC index is: Sort the basic evaluation data by time; The data y at each time point t Logarithmic transformation is performed, and the result is ln(y t ); Linear regression analysis of the transformed ln(yt) yields: ln(y t )=α+βt+∈ t Among them, α: intercept, β: slope, ∈ t : residual; Calculate the EAPC data within the specified time range using the results of the linear regression analysis:

4. The evaluation method according to claim 1, characterized in that: In step (4), the free weight coefficient of the category layer, the free weight coefficient of the phenomenon layer, and the free weight coefficient of the indicator layer are set respectively; In step (5), the method for determining the comprehensive AHP importance index of pollutant indicators is: The EAPC index of a certain pollutant indicator in a specified time period is multiplied by the free weight coefficient it occupies in the corresponding evaluation level to obtain the importance index at the evaluation level, and then the importance indexes at each evaluation level are added together to finally obtain the comprehensive AHP importance index a of the pollutant indicator. c ; In step (6), the method of converting the negative values ​​in the comprehensive AHP importance index into positive values ​​is: If all pollutant indicators are combined with the AHP importance index a c If any of the items has a negative value, first determine the minimum value a of all pollutant importance indexes min , and then transform all importance indices according to the following formula: a′ c =a c +|a min |×1.1 If all pollutant indicators are combined with the AHP importance index a c are all positive, then a′ c =a c ; All subsequent calculations will use a′ c .

5. The evaluation method according to claim 1 or 4, characterized in that: In step (7), the method of constructing the AHP judgment matrix is: The indicator layer is used as the criterion layer of the AHP judgment matrix, and the matrix A is constructed for the criterion layer: For the pollutant type m, i=j=m in the matrix, and element k ij It is the comprehensive AHP importance index a′ after all pollutant indicators are converted to positive values c After arranging one horizontal row and one vertical row in the same order, the i-th comprehensive AHP importance index a′ in the vertical row ci and the jth comprehensive AHP importance index a′ in the row cj Divide them and we get 6. The evaluation method according to claim 5, characterized in that: In step (8), according to the AHP judgment matrix A, the method of calculating the evaluation weight coefficient using the square root method is: Calculate the value of the i-th row vector in matrix A: Then, normalize the resulting vector values: The obtained w′ i is the evaluation weight coefficient corresponding to pollutant index i.

7. The evaluation method according to claim 6, characterized in that: In step (9), the consistency test method for the AHP judgment matrix is: First, calculate the maximum eigenvalue λ of matrix A max ; Use (A×W′) i Represents the i-th row of data in the vector after the product of matrix A and vector W′, then (A×W′) i And the corresponding weight coefficient w′ i Divide them and get the maximum characteristic root λ max have: Then calculate the consistency index CI: Then determine the RI value by looking up the table according to the matrix order, and then calculate the consistency ratio CR using the following formula: Final judgment: If the CR result is less than 0.1, the consistency of the judgment matrix is ​​considered acceptable.

8. The evaluation method according to claim 1, characterized in that: In step (10), the method for obtaining the normalized index of each pollutant index is: First, determine the overall minimum value M of the c-th pollutant index among all participating individuals. c_min and M c_max , Then, for the individual numbered p, the cth pollutant index M c_p Normalize and get the index:

9. The evaluation method according to claim 1, 6 or 8, characterized in that: In step (11), the comprehensive evaluation index of each individual participating in the evaluation is obtained by combining the evaluation weight coefficient and the normalized index of the pollutant index and adding them together: S p =M′ 1_p ×w′1+…+M′ c_p ×w′ c ; M′ 1_p is the normalized index of the first pollutant index in the individual numbered p, w′1 is the evaluation weight coefficient of the first pollutant index, M′ c_p is the normalized index of the cth pollutant index of the participant numbered p, w′ c is the evaluation weight coefficient of the cth pollutant index; For all participating individuals, S p The lower the comprehensive evaluation index is, the better the emission level of the individual is.

10. A computing device for implementing the method according to any one of claims 1 to 9, characterized in that: include: A computer-readable storage medium, wherein the computer-readable storage medium is a memory for storing a computer program; A processor, configured to implement required data calculation using a computer program stored in a computer-readable storage medium; When the computer program is executed by a processor, the computing functions in the method are realized.

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