Receptor model evaluation method and device based on road sediment synthetic data set

Abstract

The invention discloses a road sediment synthetic data set-based receptor model evaluation method and device. The method comprises the steps of obtaining a known pollution source component spectrum; obtaining an unknown pollution source component spectrum according to the known pollution source component spectrum; randomly generating pollution source contribution mass concentrations of a plurality of road sediments in a logarithmic normal manner; generating a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration; inputting the synthetic data set into a receptor model to obtain a traceability result of the receptor model and a pollution source contribution rate in the synthetic data set; and obtaining the accuracy of the receptor model according to the traceability result and the pollution source contribution rate in the synthetic data set. The embodiment of the invention can improve the accuracy of receptor model traceability result evaluation, and can be widely applied to the technical field of sediment traceability.

Description

Technical Field

[0001] The present invention relates to the technical field of sediment tracing, and in particular to a receptor model evaluation method and device based on a synthetic data set of road sediments. Background Art

[0002] Urban road pollution has received increasing attention. In the control and management of road sediment pollution, the identification and analysis of pollution sources are the key to taking effective control measures. This process includes tracing and analyzing pollution sources based on their quantitative contributions to the urban road environmental medium. Among them, the receptor model is a widely used tracing method, which has many applications in the fields of atmospheric particulate matter and soil. However, traditional receptor models were developed in the study of atmospheric pollution. The existing methods are also to verify the accuracy of receptor models in tracing atmospheric particulate matter, and the number of receptor models compared is often only 2 models. There is no accuracy of the mass contribution rate of each pollution source obtained by source analysis when the receptor model is used to trace the source of road sediment. In addition, the application of receptor models in road sediments is relatively limited, which makes it difficult to quantify and evaluate the accuracy of the results of receptor models applied to road sediment tracing. Summary of the invention

[0003] In view of this, the main purpose of the embodiments of the present invention is to provide a receptor model evaluation method and device based on a synthetic data set of road sediments, so as to improve the accuracy of the evaluation of the receptor model tracing results.

[0004] To achieve the above object, an embodiment of the present invention provides a receptor model evaluation method based on a synthetic data set of road sediments, the method comprising the following steps:

[0005] Obtain the composition spectrum of known pollution sources;

[0006] Obtaining a component spectrum of an unknown pollution source according to the known pollution source component spectrum;

[0007] Random log-normal distribution was used to generate the mass concentrations of pollution sources contributing to several road sediments;

[0008] Generate a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration;

[0009] Inputting the synthetic data set into a receptor model to obtain a source tracing result of the receptor model and a contribution rate of pollution sources in the synthetic data set;

[0010] The accuracy of the receptor model is obtained according to the tracing results and the contribution rate of the pollution sources in the synthetic data set.

[0011] In some embodiments, the step of obtaining a known pollution source component spectrum comprises the following steps:

[0012] Obtaining component spectrum data of actual pollution sources of road sediments;

[0013] The component spectrum data is normalized to obtain the component spectrum of the known pollution source.

[0014] In some embodiments, the normalization process of the component spectrum data is performed to obtain the calculation formula of the known pollution source component spectrum:

[0015]

[0016] Where D ik Represents the spectrum of known pollution sources; x 1k ,x 2k ,…,x ik They respectively represent the kth known pollution source data of the 1st element in the known pollution source component spectrum, the kth known pollution source data of the 2nd element, and so on, the kth known pollution source data of the ith element.

[0017] In some embodiments, the step of obtaining the unknown pollution source component spectrum according to the known pollution source component spectrum comprises the following steps:

[0018] According to the known pollution source component spectrum, obtaining the average concentration of all known pollution sources;

[0019] Multiply the average concentration by a random number to obtain concentration data of an unknown pollution source;

[0020] The concentration data of the unknown pollution source is normalized to obtain a component spectrum of the unknown pollution source.

[0021] In some embodiments, the concentration data of the unknown pollution source is normalized to obtain a calculation formula for the unknown pollution source component spectrum:

[0022]

[0023] In the formula, E iuns Represents the spectrum of unknown pollution sources; x 1uns ,x 2uns ,…,x iuns They respectively represent the concentration data of the unknown pollution source of the first element in the unknown pollution source component spectrum, the concentration data of the unknown pollution source of the second element, ..., the concentration data of the unknown pollution source of the i-th element.

[0024] In some embodiments, generating a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration includes the following steps:

[0025] Generate a metal element pollution source component spectrum in the road sediment according to the known pollution source component spectrum and the unknown pollution source component spectrum;

[0026] According to the composition spectrum of the metal element pollution source, obtaining the mass fractions of several metals;

[0027] Generate metal element concentration according to the mass fraction of the metal and the mass concentration contributed by the pollution source;

[0028] A set of metal element concentrations of a plurality of road sediments is obtained to obtain a synthetic data set.

[0029] To achieve the above object, another aspect of an embodiment of the present invention provides a receptor model evaluation device based on a synthetic data set of road sediments, the device comprising:

[0030] The first module is used to obtain the composition spectrum of known pollution sources;

[0031] The second module is used to obtain the unknown pollution source component spectrum according to the known pollution source component spectrum;

[0032] The third module is used to generate the mass concentration of pollution sources contributing to several road sediments using random log-normal method;

[0033] A fourth module is used to generate a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration;

[0034] The fifth module is used to input the synthetic data set into the receptor model to obtain the source tracing result of the receptor model and the contribution rate of the pollution source in the synthetic data set;

[0035] The sixth module is used to obtain the accuracy of the receptor model based on the tracing results and the contribution rate of the pollution sources in the synthetic data set.

[0036] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the receptor model evaluation method based on a synthetic data set of road sediments described above is implemented.

[0037] To achieve the above objectives, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned receptor model evaluation method based on a synthetic data set of road sediments is implemented.

[0038] To achieve the above object, another aspect of an embodiment of the present invention provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the aforementioned receptor model evaluation method based on a synthetic data set of road sediments.

[0039] The embodiments of the present invention include at least the following beneficial effects: the present invention provides a receptor model evaluation method and device based on a synthetic data set of road sediments, the scheme obtains a known pollution source component spectrum; obtains an unknown pollution source component spectrum according to the known pollution source component spectrum; generates a number of pollution source contribution mass concentrations of road sediments by random log-normal; generates a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration; inputs the synthetic data set into the receptor model to obtain the source tracing result of the receptor model and the pollution source contribution rate in the synthetic data set; obtains the accuracy of the receptor model according to the source tracing result and the pollution source contribution rate in the synthetic data set, and can quantify and evaluate the accuracy of the receptor model in the source tracing result of road sediments. The present invention improves the accuracy of the evaluation of the receptor model source tracing result. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 is a flow chart of a receptor model evaluation method based on a synthetic data set of road sediments provided by an embodiment of the present invention;

[0042] Figure 2 is a schematic diagram of normalized component spectra of known pollution sources and unknown pollution sources provided by an embodiment of the present invention;

[0043] Figure 3It is a schematic diagram of the pollution source contribution results obtained by inputting the generated 6 synthetic data sets into the PMF and Unmix receptor models respectively and running them according to an embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of the pollution source contribution results obtained by inputting the generated 6 synthetic data sets into the CMB and SCMD receptor models respectively and running them according to an embodiment of the present invention;

[0045] Figure 5 It is a schematic diagram of the overall steps of a receptor model evaluation method based on a synthetic data set of road sediments provided by an embodiment of the present invention;

[0046] Figure 6 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention, and they are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0048] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the flow chart. The terms "first / S100" and "second / S200" in the specification and claims and the above-mentioned drawings may be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiment of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0049] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0051] The receptor model is a widely used method for tracing the origin of road sediments, and has many applications in the fields of atmospheric particulate matter and soil. However, the accuracy of the results of the current receptor model applied to tracing the origin of road sediments is difficult to quantify and evaluate.

[0052] In view of this, if Figure 1 As shown, the embodiment of the present invention provides a receptor model evaluation method based on a synthetic data set of road sediments, the method including but not limited to steps S100 to S600:

[0053] Step S100, obtaining a known pollution source component spectrum;

[0054] Step S200, obtaining an unknown pollution source component spectrum according to the known pollution source component spectrum;

[0055] Step S300, generating a number of pollution source contribution mass concentrations of road sediments by random log-normal distribution;

[0056] Step S400, generating a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration;

[0057] Step S500, inputting the synthetic data set into a receptor model to obtain a source tracing result of the receptor model and a contribution rate of pollution sources in the synthetic data set;

[0058] Step S600, obtaining the accuracy of the receptor model according to the tracing result and the contribution rate of the pollution source in the synthetic data set.

[0059] In some embodiments, step S100 may include but is not limited to steps S110 to S120:

[0060] Step S110, obtaining component spectrum data of actual pollution sources of road sediments;

[0061] Step S120, normalizing the component spectrum data to obtain the component spectrum of the known pollution source.

[0062] In steps S110 to S120 of some embodiments, the component spectrum data x of the actual pollution source of road sediment can be obtained based on previous research data. ik Since the concentrations of different metal pollutants emitted by road sediment pollution sources vary greatly, the order of magnitude of low-concentration elements is 10-2 , the order of magnitude of high concentration elements can reach 10 5 , so for the component spectrum data x ik Perform mass normalization to obtain D ik The ratio of the concentration of each metal element to the total concentration of the metal element in the component spectrum data of the actual pollution source is defined as the normalized concentration of the element (dimensionless unit), which reduces the difference caused by the difference in the concentration of pollutants emitted by different pollution sources, thereby highlighting the profile characteristics of metal element pollution and reducing the standard deviation of the input data, which helps to improve the fitting effect of the subsequent synthetic data set in the receptor model. The calculation formula for normalization processing is as follows:

[0063]

[0064] Where D ik Represents the known pollution source component spectrum, dimensionless unit; x 1k ,x 2k ,…,x ik They represent the kth known pollution source data of the first element in the known pollution source component spectrum, the kth known pollution source data of the second element, and so on, the kth known pollution source data of the ith element, all in mg / kg. Calculate the normalized data of the ith element and the kth pollution source, that is, D ik , together they constitute the normalized total known pollution source component spectrum D.

[0065] In some embodiments, step S200 may include but is not limited to steps S210 to S230:

[0066] Step S210, obtaining the average concentration of all known pollution sources according to the known pollution source component spectrum;

[0067] Step S220, multiplying the average concentration value by a random number to obtain concentration data of an unknown pollution source;

[0068] Step 8230, normalize the concentration data of the unknown pollution source to obtain a component spectrum of the unknown pollution source.

[0069] In steps S210 to S230 of some embodiments, a component spectrum of an unknown pollution source is generated to simulate an unknown pollution source in the real world. The generation of an unknown source is based on the following criteria: 1) The source component spectrum of the unknown source is not similar to the source component spectrum of the real source; 2) The characteristic elements of the known source are not obvious in the unknown source; 3) The average contribution of the unknown source should account for about 15% of the total mass percentage. Exemplarily, in order to meet the above conditions and conform to the situation of unknown sources in reality, the average metal element concentration of all known pollution sources is obtained, multiplied by a randomly generated random number of 0.2-0.8, and then normalized. Then the expression for the average concentration of all known pollution sources is:

[0070]

[0071] In the formula, x iave is the average concentration of all known pollution sources of the i-th element; p is the number of known pollution sources; x i1 , x i2 , ..., x ik They are respectively the first known pollution source data of the ith element in the known pollution source component spectrum, the second known pollution source data of the ith element, ..., the kth known pollution source data of the ith element, and the unit is mg / kg.

[0072] The expression for the concentration data of unknown pollution sources is:

[0073] x iuns =x iave ×ran

[0074] In the formula, x iuns is the concentration data of the unknown pollution source of the i-th element; ran is a random number, optionally, ran is a random number between 0.2 and 0.8.

[0075] The expression of the unknown pollution source component spectrum is:

[0076]

[0077] In the formula, E iuns Represents the unknown pollution source component spectrum, dimensionless unit; x 1uns , x 2uns , ..., x iuns They represent the concentration data of the unknown pollution source of the first element in the unknown pollution source component spectrum, the concentration data of the unknown pollution source of the second element, and so on, and the concentration data of the unknown pollution source of the i-th element, and the unit is mg / kg. iuns Together they constitute the normalized total unknown source component spectrum E.

[0078] In step S300 of some embodiments, the mass and standard deviation of metal elements contained in the road sediments in the real road sediment samples can be obtained by sampling, and the total mass concentration of metal elements contained in j synthetic road sediments can be generated by random lognormal. The contribution rate of the real pollution source and the contribution rate of the unknown pollution source are set, and the total mass concentration of metal elements contained in the j synthetic road sediments generated by the random lognormal is multiplied by the set contribution rate of the real pollution source and the contribution rate of the unknown pollution source to obtain the contribution mass of several pollution sources with the same contribution rate. Then, the average value and standard deviation of the logarithm of the contribution mass of the several pollution sources with the same contribution rate are obtained. According to the average value and standard deviation, the pollution source contribution mass concentration (i.e., the total mass concentration of metal elements) B of j synthetic road sediments with different pollution source contribution rates are generated by random lognormal in Matlab. kj , then the set of pollution source contribution mass concentrations of synthetic road sediments with different contribution rates of j pollution sources is recorded as B.

[0079] In some embodiments, step S400 may include but is not limited to steps S410 to S440:

[0080] Step S410, generating a metal element pollution source component spectrum in the road sediment according to the known pollution source component spectrum and the unknown pollution source component spectrum;

[0081] Step S420, obtaining mass fractions of several metals according to the metal element pollution source component spectrum;

[0082] Step S430, generating a metal element concentration according to the mass fraction of the metal and the mass concentration contributed by the pollution source;

[0083] Step S440, obtaining a set of metal element concentrations of a plurality of road sediments to obtain a synthetic data set.

[0084] In step S410 of some embodiments, the normalized data of the i elements and k pollution sources respectively calculated in the above steps, namely D ik , together constitute the normalized total known pollution source component spectrum D; each unknown pollution source component spectrum E iuns Together they form a normalized total unknown source component spectrum E; the total known pollution source component spectrum D and the total unknown source component spectrum E together form a metal element pollution source component spectrum A in the environmental medium road sediment.

[0085] In steps S420 to S430 of some embodiments, the mass fraction A of the i-th metal in the k-th source of the j-th sample is obtained according to the metal element pollution source component spectrum A. ik According to the mass fraction A of the ith metal in the kth source of the jth sampleik As well as the mass concentration of pollution sources, the concentration C of the i-th metal element in the j-th sample can be generated. ij , then we have the following formula:

[0086]

[0087] In the formula, C ij is the concentration of the ith metal element in the jth sample; p is the number of pollution sources; A ik is the mass fraction of the ith metal in the kth source of the jth sample (dimensionless units); B kj is the total mass concentration of the metal element of the kth source of the jth sample (mg / m 2 ).

[0088] In step S440 of some embodiments, a set of metal element concentrations of j synthetic road sediments is obtained and the set is used as the synthetic data set C.

[0089] In steps S500 to S600 of some embodiments, the synthetic data set is incorporated into the source tracing analysis of each receptor model. Based on the source tracing results obtained by running the receptor model and the actual contribution rate of the pollution source in the synthetic data set, the accuracy of each receptor model can be compared, and the accuracy of the source tracing results of the receptor model for road sediments can be quantified and evaluated.

[0090] For example, as shown in Table 1, based on the existing research, the judgment and selection of urban road sediment pollution sources, brake wear (BW), automobile exhaust (VE), tire wear (TW) and road surrounding soil (RS) are selected as the main pollution sources. The metal element concentration data of the source component spectra of these four pollution sources in the existing research are averaged and multiplied by a randomly generated random number between 0.2 and 0.8 to obtain the source component spectrum of the unknown source (UnS) before normalization. The source component spectra of four real known pollution sources and one unknown pollution source are mass normalized, and the source component spectrum that can be obtained is shown in Table 2 below, including Pb, Zn, Cr, Cu, Ni, Fe, Mn, Ba, Na, K, Mg, Ca, Al, Cd, Sb, Co, Sn, Mo and Sr, a total of 19 metal elements, and 5×19 data are obtained, recorded as A. The source component spectra of known pollution sources and unknown pollution sources before normalization are shown in Table 1 below:

[0091] Table 1

[0092] Before normalization BW VE TW RS UnS Pb 152.2309 499.2755 17.6959 37.7865 121.7495 Zn 6289.8393 9387.8817 2050.1772 65.6244 3307.2605 Cr 856.1989 22029.4453 1.7103 14.1412 1581.3030 Cu 28941.4582 3009.5111 7.4758 8.7061 5978.0612 Ni 139.8922 10242.5283 2.4438 11.9021 1506.0120 Fe 254618.8051 139842.9427 169.2343 22453.0923 26956.5861 Mn 2605.6781 13282.8676 4.0369 430.4262 1498.0398 Ba 6512.8363 1248.0949 5.0180 84.9609 1036.5732 Na 1337.3546 4480.7808 471.7219 330.9283 1281.9564 K 4253.7440 6642.3245 189.9953 4121.6820 2961.4542 Mg 5132.0784 4230.1195 88.0469 1620.5969 815.2785 Ca 18847.1646 36749.9050 477.3804 4320.7732 11812.6363 Al 9897.5888 43604.3538 343.2844 68752.2676 23731.8152 Cd 6.4998 0.3804 1.8735 0.0565 1.0819 Sb 195.0762 513.3319 0.2655 5.7377 121.4800 Co 37.2553 268.3863 1.0424 7.2677 22.3794 Sn 873.2989 74.7717 1.0187 2.5071 107.7818 Mo 1472.9887 474.1755 0.7834 1.1617 365.1858 Sr 610.8831 175.4944 1.3736 21.2053 136.5770

[0093] refer to Figure 2 , the normalized source component spectra of known pollution sources and unknown pollution sources are as follows: Figure 2As shown, correspondingly, the normalized source component spectra of known pollution sources and unknown pollution sources are shown in Table 2 below:

[0094] Table 2

[0095]

[0096]

[0097] In some embodiments, 100 synthetic road sediments are generated. According to the actual road sediment sampling situation, it is assumed that every 1m 2The mass of the collected road sediment sample is 20g, and 935.90mg of metal elements are contained in 20g of road sediment, and the standard deviation is 270.55mg. The two data of 935.90 and 270.55 are used to generate 100 data of the total mass concentration of metal elements contained in the synthetic road sediment in the random log-normal of Matlab. To simulate the real world situation, the present embodiment establishes two different urban road traffic scenes, namely high traffic flow scene and low traffic flow scene. Assuming that the influence of brake wear (BW), vehicle exhaust (VE) and tire wear (TW) is more obvious on high traffic flow roads, and the contribution of road surrounding soil (RS) is relatively low, therefore, the contribution of BW, VE, TW and RS is set to about 20%, 25%, 35% and 20%, respectively. On the contrary, on roads with low traffic volume, the contribution of brake wear, automobile exhaust emissions and tire wear is reduced, and the contribution of soil around the road becomes more prominent. Therefore, the contribution rates of BW, VE, TW and RS are set to 10%, 20%, 30% and 40%, respectively. The contribution rate of unknown pollution sources is set to 10%, 15% and 20%. As the unknown pollution sources increase, the contribution rates of the four real pollution sources decrease proportionally. The total mass concentration data of metal elements of 100 synthetic road sediments generated by random lognormal are multiplied by the contribution rates of the five pollution sources to obtain 5×100 metal element data (i.e., contribution mass) of pollution sources with the same contribution rate. The mean value and standard deviation are calculated for these data, wherein the contribution rates of the five pollution sources refer to the contribution rates of BW, VE, TW and RS and the contribution rate of one of the set unknown pollution sources. According to the mean value and standard deviation, the random log-normal method was used in Matlab to generate the pollution source contribution mass concentration of 100 synthetic road sediments with different pollution source contribution rates, and finally the total mass concentration data of pollution source metal elements with 100×5 random contribution rates were obtained, which was recorded as B. The concentration of metal elements in synthetic road sediments C = A×B. Because there are two urban road traffic scenarios and three unknown pollution source contribution rates, a total of 6 100×19 synthetic data sets were generated, numbered SD1-SD6. SD1 is a high traffic flow scenario with 10% unknown source contribution, SD2 is a low traffic flow scenario with 10% unknown source contribution, SD3 is a high traffic flow scenario with 15% unknown source contribution, SD4 is a low traffic flow scenario with 15% unknown source contribution, SD5 is a high traffic flow scenario with 20% unknown source contribution, and SD6 is a low traffic flow scenario with 20% unknown source contribution. Table 3 below shows the contribution rates of each pollution source of the 6 synthetic data sets generated.

[0098] Table 3

[0099]

[0100]

[0101] The generated six synthetic data sets were input into the four receptor models of PMF, Unmix, CMB and SCMD respectively, and the pollution source contribution results obtained by running are as follows: Figure 3 , Figure 4 shown.

[0102] In order to quantify the errors of the four models, this embodiment uses relative error (RE), relative prediction error (RPE) and symmetric mean absolute percentage error (SMAPE) for evaluation. The calculation formulas of the three error indicators are as follows:

[0103]

[0104]

[0105]

[0106] Where p is the number of pollution sources; Y calculated is the model calculation result; Y SD is the data of the synthetic data set. When the measured value and the calculated value fit perfectly, RE = 0, RPE = 0, SMAPE = 0. The error table of the running results of the four models is shown in Table 4 below.

[0107] Table 4

[0108]

[0109] The errors of the unknown source models PMF and Unmix are significantly higher than those of the known source models CMB and SCMD. Except for the RE and SMAPE values ​​of SD1, the error values ​​of the SCMD tracing results are better than those of CMB. With the increase of unknown source contribution, the RE, RPE, and SMAPE of SCMD and CMB all increase, but the stability and accuracy of the SCMD results are significantly higher than those of CMB. Therefore, it is believed that SCMD is the more suitable receptor model for tracing the source of urban road sediments among the four models, and its tracing results are also more accurate.

[0110] The embodiment of the present invention further provides a receptor model evaluation device based on a synthetic data set of road sediments, which can implement the above-mentioned receptor model evaluation method based on a synthetic data set of road sediments. The device includes:

[0111] The first module is used to obtain the composition spectrum of known pollution sources;

[0112] The second module is used to obtain the unknown pollution source component spectrum according to the known pollution source component spectrum;

[0113] The third module is used to generate the mass concentration of pollution sources contributing to several road sediments using random log-normal method;

[0114] A fourth module is used to generate a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration;

[0115] The fifth module is used to input the synthetic data set into the receptor model to obtain the source tracing result of the receptor model and the contribution rate of the pollution source in the synthetic data set;

[0116] The sixth module is used to obtain the accuracy of the receptor model based on the tracing results and the contribution rate of the pollution sources in the synthetic data set.

[0117] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0118] The embodiment of the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the above-mentioned receptor model evaluation method based on a synthetic data set of road sediments is implemented. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0119] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0120] refer to Figure 6 , Figure 6 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0121] The processor 701 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0122] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 702, and the processor 701 calls and executes a receptor model evaluation method based on a road sediment synthetic data set according to an embodiment of the present invention;

[0123] Input / output interface 703, used to implement information input and output;

[0124] Communication interface 704, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0125] A bus 705 that transmits information between the various components of the device (e.g., the processor 701, the memory 702, the input / output interface 703, and the communication interface 704);

[0126] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0127] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for evaluating a receptor model based on a synthetic data set of road sediments is implemented.

[0128] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0129] The embodiment of the present invention also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the aforementioned receptor model evaluation method based on a synthetic data set of road sediments.

[0130] In summary, the receptor model evaluation method and device based on the road sediment synthetic data set according to the embodiment of the present invention have the following advantages:

[0131] The receptor model is rarely used in the field of urban road sediment tracing, and the accuracy of the tracing results of these receptor models is not clear. Figure 5 As shown, the embodiment of the present invention first obtains the component spectrum of the real pollution source, randomly generates the component spectrum of the unknown pollution source, and performs mass normalization processing on both, then generates the contribution value of the pollution source by random logarithm normal, and finally calculates to obtain a synthetic data set, and inputs the synthetic data set into the receptor model, so as to compare the accuracy of each model, quantify and evaluate the model. The synthetic data set generated by the method of this embodiment can more intuitively obtain the accuracy of the receptor model tracing results, and provide guidance for the application of the receptor model in the field of urban road sediment tracing.

[0132] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0133] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0134] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0138] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0139] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0140] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A receptor model evaluation method based on a synthetic road sediment dataset, characterized in that: The following steps are involved: Obtain the composition spectrum of known pollution sources; Obtaining a component spectrum of an unknown pollution source according to the known pollution source component spectrum; Random log-normal distribution was used to generate the mass concentrations of pollution sources contributing to several road sediments; Generate a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration; Inputting the synthetic data set into a receptor model to obtain a source tracing result of the receptor model and a contribution rate of pollution sources in the synthetic data set; The accuracy of the receptor model is obtained according to the tracing results and the contribution rate of the pollution sources in the synthetic data set.

2. A receptor model evaluation method based on a synthetic road sediment dataset according to claim 1, characterized in that: The step of obtaining a known pollution source component spectrum comprises the following steps: Obtaining component spectrum data of actual pollution sources of road sediments; The component spectrum data is normalized to obtain the component spectrum of the known pollution source.

3. A receptor model evaluation method based on a synthetic road sediment dataset according to claim 2, characterized in that: The calculation formula for normalizing the component spectrum data to obtain the known pollution source component spectrum is: Where D ik Represents the spectrum of known pollution sources; x 1k , x 2k , ..., x ik They respectively represent the kth known pollution source data of the 1st element in the known pollution source component spectrum, the kth known pollution source data of the 2nd element, and so on, the kth known pollution source data of the ith element.

4. The receptor model evaluation method based on a synthetic road sediment dataset according to claim 1, characterized in that: The step of obtaining the unknown pollution source component spectrum according to the known pollution source component spectrum comprises the following steps: According to the known pollution source component spectrum, obtaining the average concentration of all known pollution sources; Multiply the average concentration by a random number to obtain concentration data of an unknown pollution source; The concentration data of the unknown pollution source is normalized to obtain a component spectrum of the unknown pollution source.

5. A receptor model evaluation method based on a synthetic road sediment dataset according to claim 4, characterized in that: The concentration data of the unknown pollution source is normalized to obtain the calculation formula of the unknown pollution source component spectrum: In the formula, E iuns Represents the spectrum of unknown pollution sources; x 1uns , x 2uns , ..., x iuns They respectively represent the concentration data of the unknown pollution source of the first element in the unknown pollution source component spectrum, the concentration data of the unknown pollution source of the second element, ..., the concentration data of the unknown pollution source of the i-th element.

6. The receptor model evaluation method based on a synthetic road sediment dataset according to claim 1, characterized in that: The step of generating a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration comprises the following steps: Generate a metal element pollution source component spectrum in the road sediment according to the known pollution source component spectrum and the unknown pollution source component spectrum; According to the composition spectrum of the metal element pollution source, obtaining the mass fractions of several metals; Generate metal element concentration according to the mass fraction of the metal and the mass concentration contributed by the pollution source; A set of metal element concentrations of a plurality of road sediments is obtained to obtain a synthetic data set.

7. A receptor model evaluation device based on a synthetic data set of road sediments, characterized in that: include: The first module is used to obtain the composition spectrum of known pollution sources; The second module is used to obtain the unknown pollution source component spectrum according to the known pollution source component spectrum; The third module is used to generate the mass concentration of pollution sources contributing to several road sediments using random log-normal method; A fourth module is used to generate a synthetic data set according to the known pollution source component spectrum, the unknown pollution source component spectrum and the pollution source contribution mass concentration; The fifth module is used to input the synthetic data set into the receptor model to obtain the source tracing result of the receptor model and the contribution rate of the pollution source in the synthetic data set; The sixth module is used to obtain the accuracy of the receptor model based on the tracing results and the contribution rate of the pollution sources in the synthetic data set.

8. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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