A pollutant tracing method, system, device and medium

Abstract

The present invention discloses a pollutant tracing method, system, device and medium. The tracing method obtains and detects a first water sample at a river section, constructs a water quality fingerprint spectrum at the river section based on the detection result of the first water sample, wherein the water quality fingerprint spectrum includes pollutant information at the river section; determines the suspected pollution source at the river section based on industrial data information within the river basin; obtains and detects a second water sample of the suspected pollution source, obtains characteristic pollutant information of the suspected pollution source based on the detection result of the second water sample; analyzes the water quality fingerprint spectrum and characteristic pollutant information through a pollution source identification model to determine the tracing result. The tracing method can realize the identification and tracing of multi-component complex pollutants, and the types of pollutants identified and traced are many and accurate, and also improves the pertinence of pollutant tracing and the accuracy of tracing results. The present invention can be widely used in the field of water environment technology.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment, and in particular to a method, system, device and medium for tracing the source of pollutants. Background Art

[0002] In recent years, the amount of wastewater discharged into rivers by key polluting units such as papermaking, chemical industry, and printing and dyeing has increased year by year, causing certain pollution to the water environment in the river basin. Among them, the pollution problem of organic pollutants in industrial wastewater is particularly prominent, posing a major threat to the water environment in the basin.

[0003] At present, water quality testing in river basins is still mainly based on routine testing items such as COD, ammonia nitrogen, and heavy metals. However, these testing items can only determine whether the water environment is polluted by wastewater. They lack organic pollutant detection indicators and cannot accurately determine the pollution status of water quality, nor can they effectively identify and trace the sources of organic pollution in water quality. In addition, in water quality testing, although there are existing detection technologies for organic pollutants, due to the complex composition of organic pollutants and large differences in concentration during testing, existing detection technologies can only provide a few major categories of emission sources with large receptor contributions. It cannot identify and trace sources outside of these major categories of emission sources, nor can it identify and trace specific emission sources. Its pertinence is not strong and its accuracy is not high.

[0004] Therefore, the problems existing in the existing technology still need to be solved and optimized. Summary of the Invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, one purpose of an embodiment of the present invention is to provide a pollutant tracing method, which can not only identify and trace multi-component complex pollutants, identify and trace a wide variety of pollutants with precise types, but also improve the targeted tracing of pollutants and the accuracy of tracing results.

[0007] Another purpose of the embodiments of the present application is to provide a pollutant tracing system.

[0008] In order to achieve the above technical objectives, the technical solutions adopted in the embodiments of the present application include:

[0009] In a first aspect, embodiments of the present application provide a method for tracing the source of pollutants, comprising:

[0010] Acquiring and detecting a first water sample at a river section, and constructing a water quality fingerprint spectrum at the river section based on the detection result of the first water sample, wherein the water quality fingerprint spectrum includes pollutant information at the river section;

[0011] Determining suspected pollution sources at the river section based on industrial data information within the river basin;

[0012] Acquiring and testing a second water sample from the suspected pollution source, and obtaining characteristic pollutant information of the suspected pollution source based on the test result of the second water sample;

[0013] The water quality fingerprint spectrum and the characteristic pollutant information are analyzed by a pollution source identification model to determine a source tracing result, wherein the source tracing result includes the actual pollution source at the river section.

[0014] In addition, the traceability method according to the above embodiment of the present application may also have the following additional technical features:

[0015] Furthermore, in one embodiment of the present application, the traceability method further comprises: verifying the traceability result;

[0016] The verifying the traceability result includes:

[0017] Analyzing the characteristic pollutant information by a quantitative analysis method to obtain analysis results of each characteristic pollutant in the characteristic pollutant information;

[0018] Performing pollutant concentration inversion and pollutant type inversion on the pollutants in the first water sample to obtain analysis results of the inverted pollutants;

[0019] The tracing result is verified based on the analysis results of the inverted pollutants and the analysis results of each of the characteristic pollutants.

[0020] Furthermore, in one embodiment of the present application, the obtaining and detecting a first water sample at a river section and constructing a water quality fingerprint spectrum at the river section include:

[0021] Obtaining a first water sample at the river section, and sequentially performing solid phase extraction and concentration treatment on the first water sample;

[0022] Detecting the first water sample using a non-targeted semi-quantitative analysis technique to obtain pollutant information at the river section, wherein the pollutant information includes pollutant name, peak area response value, matching factor, chemical formula, and retention time;

[0023] The water quality fingerprint spectrum is constructed according to the pollutant information.

[0024] Furthermore, in one embodiment of the present application, the detecting of the first water sample by a non-targeted semi-quantitative analysis technique to obtain pollutant information at the river section includes:

[0025] Detecting the first water sample using a non-targeted semi-quantitative analysis technique to obtain first pollutant information, wherein the first pollutant information is used to characterize information of all pollutants in the first water sample;

[0026] The first pollutant information is processed according to the retention conditions pre-set by the user to obtain the pollutant information, wherein the retention conditions are at least one of the matching factor being greater than a first threshold, the peak area response value being greater than a second threshold, or the peak area response value being greater than or equal to a third threshold, and the third threshold is 3 times the peak area response value of blank data.

[0027] Furthermore, in one embodiment of the present application, determining the suspected pollution source at the river section based on the industrial data information within the river basin includes:

[0028] Obtaining industrial data information of enterprises within the river basin, the industrial data information including industrial sector, geographic location, wastewater discharge volume, chemical oxygen demand, whether wastewater is discharged into a decentralized sewage treatment plant, whether wastewater is discharged into a centralized industrial wastewater treatment plant, production process, raw and auxiliary materials, final products, and intermediate products;

[0029] Statistical analysis is performed based on the industrial data information to identify enterprises that are listed as suspected pollution sources at the river section.

[0030] Furthermore, in one embodiment of the present application, the acquiring and detecting the second water sample of the suspected pollution source to obtain characteristic pollutant information of the suspected pollution source includes:

[0031] Obtaining a second water sample from the suspected pollution source, and sequentially performing solid phase extraction and concentration treatment on the second water sample;

[0032] Detecting the second water sample using a non-targeted semi-quantitative analysis technique to obtain second pollutant information of the suspected pollution source, wherein the second pollutant information is used to characterize information of all pollutants in the second water sample;

[0033] Analyzing and processing the second pollutant information based on the industrial data information and the water quality fingerprint spectrum to obtain third pollutant information, wherein the third pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the industrial data information and the water quality fingerprint spectrum;

[0034] performing feature calculation on the second pollutant information through feature engineering of machine learning to obtain fourth pollutant information, wherein the fourth pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the feature engineering;

[0035] The third pollutant information and the fourth pollutant information are combined to obtain characteristic pollutant information of the suspected pollution source.

[0036] Furthermore, in one embodiment of the present application, the feature calculation of the second pollutant information by feature engineering based on machine learning to obtain the fourth pollutant information includes:

[0037] Classifying and normalizing each second pollutant in the second pollutant information, wherein the second pollutant is one of the pollutants represented by the second pollutant information;

[0038] Calculating feature importance of each of the second pollutants after classification and normalization using a machine learning model, wherein the feature importance is used to characterize the degree of influence of the second pollutant on the target variable;

[0039] A second pollutant with the highest characteristic importance score is determined as the fourth pollutant information, or a plurality of second pollutants with relatively high characteristic importance scores are determined as the fourth pollutant information.

[0040] In a second aspect, an embodiment of the present application provides a pollutant tracing system, including:

[0041] a first acquisition module, configured to acquire and detect a first water sample at a river section, and construct a water quality fingerprint spectrum at the river section, wherein the water quality fingerprint spectrum includes pollutant information at the river section;

[0042] A first determination module is used to determine a suspected pollution source at a river section based on industrial data information within the river basin;

[0043] a second acquisition module, configured to acquire and detect a second water sample from the suspected pollution source to obtain characteristic pollutant information of the suspected pollution source;

[0044] The second determination module analyzes the water quality fingerprint spectrum and the characteristic pollutant information of the suspected pollution source through a pollution source identification model to determine the source tracing result, wherein the source tracing result includes the actual pollution source at the river section.

[0045] In a third aspect, an embodiment of the present application further provides a pollutant tracing device, comprising:

[0046] at least one processor;

[0047] at least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the pollutant tracing method of the first aspect mentioned above.

[0049] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing a program executable by a processor, wherein the program executable by the processor is used to implement a pollutant tracing method according to the first aspect above when executed by the processor.

[0050] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0051] The embodiments of the present application disclose a pollutant source tracing method, system, device, and medium. The method involves obtaining and testing a first water sample at a river section, constructing a water quality fingerprint spectrum at the river section based on the test results of the first water sample; determining the suspected pollution source at the river section based on industrial data information within the river basin; obtaining and testing a second water sample from the suspected pollution source, and obtaining characteristic pollutant information of the suspected pollution source based on the test results of the second water sample; and analyzing the water quality fingerprint spectrum and the characteristic pollutant information through a pollution source identification model to determine the tracing result. This tracing method can identify and trace the source of multi-component complex pollutants, identifying and tracing a wide range of pollutants with precise types, and improving the targeted nature of pollutant tracing and the accuracy of tracing results. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 A schematic diagram of a process for tracing the source of pollutants provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of a pollutant tracing system provided in an embodiment of the present application;

[0055] Figure 3 A schematic structural diagram of a pollutant tracing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0058] Before further describing the embodiments of the present application in detail, some of the nouns and terms designed in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0059] Water quality fingerprint spectrum: The traditional water quality fingerprint spectrum refers to the water quality fingerprint spectrum composed of the fluorescence spectra of multiple organic substances with different components in the water body, which are emitted by different organic substances in the water body according to their different compositions; in this application, the water quality fingerprint spectrum refers to the spectrum composed of pollutant types, peak area response values, industrial types, industrial use classifications, etc.

[0060] Chemical oxygen demand (COD): refers to the amount of oxidant consumed when a water sample is treated with a certain strong oxidant under certain conditions.

[0061] At present, water quality testing in river basins is still mainly based on routine testing items such as COD, ammonia nitrogen, and heavy metals. However, these testing items can only determine whether the water environment is polluted by wastewater. They lack organic pollutant detection indicators and cannot accurately determine the pollution status of water quality, nor can they effectively identify and trace the sources of organic pollution in water quality. In addition, in water quality testing, although there are existing detection technologies for organic pollutants, due to the complex composition of organic pollutants and large differences in concentration during testing, existing detection technologies can only provide a few major categories of emission sources with large receptor contributions. It cannot identify and trace sources outside of these major categories of emission sources, nor can it identify and trace specific emission sources. Its pertinence is not strong and its accuracy is not high.

[0062] In view of this, an embodiment of the present invention provides a pollutant tracing method, which can not only realize the identification and tracing of multi-component complex pollutants, and identify and trace the types of pollutants with multiple and accurate types, but also improve the targeted tracing of pollutants and the accuracy of tracing results.

[0063] Reference Figure 1 In an embodiment of the present application, a method for tracing the source of pollutants includes:

[0064] Step 110: Acquire and detect a first water sample at a river section, and construct a water quality fingerprint spectrum at the river section based on the detection result of the first water sample, wherein the water quality fingerprint spectrum includes pollutant information at the river section;

[0065] In this step, the river cross section refers to the section of the river cut perpendicular to the ground. This cross section must fully consider factors such as the river intake, stagnant and backwater areas, and river topography. The first water sample can be sampled using either instantaneous or mixed sampling methods. Mixed sampling can be categorized as time-integrated, depth-integrated, or area-integrated. Furthermore, prior to testing, the first water sample must be filtered to remove suspended matter, sediment, algae, and other microorganisms.

[0066] It is understandable that the step 110 of obtaining and detecting a first water sample at a river section and constructing a water quality fingerprint spectrum at the river section based on the detection result of the first water sample includes the following steps:

[0067] Step 111: obtaining a first water sample at the river section, and sequentially performing solid phase extraction and concentration treatment on the first water sample;

[0068] Step 112: Detecting the first water sample using a non-targeted semi-quantitative analysis technique to obtain pollutant information at the river section, wherein the pollutant information includes pollutant name, peak area response value, matching factor, chemical formula, and retention time;

[0069] Step 113: Construct the water quality fingerprint spectrum according to the pollutant information.

[0070] It can be understood that after the first water sample is filtered, the solid phase extraction concentration treatment includes liquid phase extraction and liquid chromatography hormones. By adopting selective adsorption and selective elution to achieve separation, purification and enrichment of the first water sample, the influence of other impurities in the first water sample can be reduced and the detection sensitivity of organic pollutants can be improved.

[0071] Specifically, after the first water sample is filtered and concentrated by solid-phase extraction, the complex pollutant information in the first water sample can be quickly, comprehensively and conveniently detected through non-targeted semi-quantitative analysis technology. The non-targeted quantitative analysis technology has no detection bias. From a qualitative perspective, it can achieve zero-difference identification of organic pollutants in water bodies, and thus obtain the peak table information of each pollutant in the first water sample.

[0072] It is understood that in step 113, the step of constructing a water quality fingerprint spectrum based on the pollutant information, a search can be performed in the Chemical Book database and the Pubchem database to obtain classification information and degradation information corresponding to each organic pollutant in the pollutant information. Specific classifications can be classified by industry, by specific use, by source, etc. For example, by source classification, it can be divided into urban sources, agricultural sources, industrial sources, etc.; by industry classification, it can be divided into the food industry, printing and dyeing industry, daily chemical industry, pharmaceutical industry, etc. In the construction of the water quality fingerprint spectrum, the water quality fingerprint spectrum includes the pollutant information and the specific classification information of each organic pollutant in the pollutant information, which can provide a key theoretical and data basis for subsequent pollutant source tracing.

[0073] It is understandable that the step 112 of detecting the first water sample using a non-targeted semi-quantitative analysis technique to obtain pollutant information at the river section includes the following steps:

[0074] Step 1121: Detect the first water sample using a non-targeted semi-quantitative analysis technique to obtain first pollutant information, wherein the first pollutant information is used to characterize information about all pollutants in the first water sample;

[0075] Step 1122: Process the first pollutant information according to the retention conditions pre-set by the user to obtain the pollutant information, wherein the retention conditions are at least one of the matching factor being greater than a first threshold, the peak area response value being greater than a second threshold, or the peak area response value being greater than or equal to a third threshold, and the third threshold is 3 times the peak area response value of the blank data.

[0076] It can be understood that the first pollutant information includes the peak table information of all pollutants in the first water sample. After filtration and solid phase extraction and concentration treatment, the first water sample still has certain interfering impurities. According to the retention conditions, the influence of these interfering impurities can be effectively eliminated for the first pollutant information, and the information of the complex and numerous organic pollutants in the first water sample can be retained.

[0077] Specifically, in an embodiment of the present application, the first water sample can be detected and analyzed by applying an ultra-high resolution quadrupole combination electrostatic field orbital trap liquid chromatography-mass spectrometry instrument, and the organic pollutant information in the first pollutant information with a matching factor greater than 60, a peak area response value greater than 10,000, and a peak area response value greater than or equal to 3 times the peak area response value of the blank data is retained. After eliminating the organic pollutant information that does not meet the retention conditions, the pollutant information containing all the organic pollutant information in the first water sample can be obtained. It can also be understood that the peak area response value of the blank data refers to the peak area response value of the blank sample that does not contain organic pollutants. The specific settings of the first threshold, the second threshold, and the third threshold can be set according to actual needs, and the retention conditions can also be set according to actual needs. The examples in this application are for illustration only and do not impose any limitations on this application.

[0078] Step 120: Determine a suspected pollution source at the river section based on industrial data information within the river basin;

[0079] It is understandable that the step 120 of determining the suspected pollution source at the river section based on the industrial data information within the river basin includes the following steps:

[0080] Step 121: Obtain industrial data information of enterprises within the river basin, the industrial data information including industrial sector, geographical location, wastewater discharge volume, chemical oxygen demand, whether wastewater is discharged to a decentralized sewage treatment plant, whether wastewater is discharged to a centralized industrial wastewater treatment plant, production process, raw and auxiliary materials, final products, and intermediate products;

[0081] Step 122: Perform statistical analysis based on the industrial data information to obtain enterprises listed as suspected pollution sources at the river section.

[0082] It can be understood that in step 120, the suspected pollution sources at the river section can be preliminarily determined based on the industrial data information and water quality fingerprint spectrum in the river basin. Specifically, the suspected pollution sources corresponding to various organic pollutants in the pollutant information can be preliminarily determined based on the organic pollutants in the water quality fingerprint spectrum and their corresponding classifications. The suspected pollution sources can be one or more specific enterprises.

[0083] Specifically, in an embodiment of the present application, the various organic substances in the water quality fingerprint spectrum can be preliminarily screened based on the industrial field, geographical location, whether the wastewater is discharged into a non-centralized sewage treatment plant, and whether the wastewater is discharged into a centralized industrial wastewater treatment plant in the industrial data information. Secondly, the wastewater discharge and chemical oxygen demand of the enterprises preliminarily screened are counted, and enterprises with higher wastewater discharge and / or chemical oxygen demand are preferentially listed as preliminary suspicious pollution sources. Then, the production process, raw and auxiliary materials, final products and intermediate products of the enterprises listed as preliminary suspicious pollution sources are screened and analyzed to see whether there are corresponding organic pollutants in the water quality fingerprint spectrum. If there are corresponding organic pollutants in the water quality fingerprint spectrum, it can be determined that the enterprise identified as the preliminary suspicious pollution source is indeed a suspicious pollution source. It can also be understood that after the statistical analysis in step 122, each organic pollutant in the water quality fingerprint spectrum will have a corresponding enterprise listed as a suspicious pollution source.

[0084] Step 130: Acquire and test a second water sample of the suspected pollution source, and obtain characteristic pollutant information of the suspected pollution source based on the test result of the second water sample;

[0085] It is understandable that step 130, obtaining and testing a second water sample of the suspected pollution source, and obtaining characteristic pollutant information of the suspected pollution source based on the test result of the second water sample, includes the following steps:

[0086] Step 131: obtaining a second water sample from the suspected pollution source, and sequentially performing solid phase extraction and concentration treatment on the second water sample;

[0087] Step 132: Detect the second water sample using a non-targeted semi-quantitative analysis technique to obtain second pollutant information of the suspected pollution source, wherein the second pollutant information is used to characterize information of all pollutants in the second water sample;

[0088] Step 133: Analyze and process the second pollutant information based on the industrial data information and the water quality fingerprint spectrum to obtain third pollutant information, wherein the third pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the industrial data information and the water quality fingerprint spectrum;

[0089] Step 134: Perform feature calculation on the second pollutant information through feature engineering based on machine learning to obtain the fourth pollutant information, wherein the fourth pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the feature engineering;

[0090] Step 135: Combine the third pollutant information and the fourth pollutant information to obtain characteristic pollutant information of the suspected pollution source.

[0091] It is understandable that the second water sample can be obtained from the sewage outlet of the suspected pollution source. The second water sample also needs to be filtered, concentrated by solid phase extraction, and tested by non-targeted semi-quantitative analysis technology. These contents are similar to the aforementioned contents related to the first water sample, and this application will not go into details here.

[0092] It can be understood that since the constructed water quality fingerprint spectrum includes pollutant types, peak area response values, industry types, industrial use classifications, etc., the water quality fingerprint spectrum can indicate that certain specific organic pollutants only potentially exist in a specific industry, or that certain specific organic pollutants mainly exist in the industry, then it can be determined that certain specific organic pollutants are identified as characteristic pollutants in the industry; in step 132, the second pollutant information is the organic pollutant information actually discharged by the suspected pollution source, and in step 133, the third pollutant information is the characteristic pollutant discharged by the suspected pollution source after analyzing the second pollutant information through the water quality fingerprint spectrum at the river section and the industrial data information.

[0093] It is understandable that due to the complex reasons that there are often multiple suspicious pollution sources in a river basin, and the distances between these suspicious pollution sources and the river sections are not the same, the components of organic pollutants discharged by the same suspicious pollution source are not the same, the concentrations of organic pollutants in sewage discharged by different suspicious pollution sources are different, and various organic pollutants discharged by multiple pollution sources may undergo certain chemical reactions or degradation reactions in the river basin, directly using the second pollutant information as the input of the final tracing results of the suspicious pollution source at the river section is not very targeted and accurate. Therefore, it is necessary to perform feature calculations on the second pollutant information to improve the accuracy and targeting of the tracing results at the river section.

[0094] It can be understood that after merging the third pollutant information and the fourth pollutant information, the characteristic pollutant information of the suspicious pollution source obtained is diverse and accurate in type, which can improve the pertinence of pollutant tracing and the accuracy of tracing results.

[0095] It is understandable that step 134, performing feature calculation on the second pollutant information through feature engineering based on machine learning to obtain the fourth pollutant information, includes the following steps:

[0096] Step 1341: Classify and normalize each second pollutant in the second pollutant information, wherein the second pollutant is one of the pollutants represented by the second pollutant information;

[0097] Step 1342: Calculate feature importance of each of the second pollutants after classification and normalization using a machine learning model, wherein feature importance is used to characterize the degree of influence of the second pollutant on the target variable;

[0098] Step 1343: Determine the second pollutant with the highest characteristic importance score as the fourth pollutant information, or determine multiple second pollutants with high characteristic importance scores as the fourth pollutant information.

[0099] It is understood that in the embodiment of the present application, the classification of the second pollutant information can be based on the aforementioned industrial data information classification. Specifically, the distribution of each second pollutant in the second pollutant information can be classified according to the industrial type and normalized so that each second pollutant has the same measurement scale. Then the distribution inputs each third pollutant after classification and normalization into the machine learning model, and the characteristic importance score of the second pollutant is obtained according to the change of the performance index (such as accuracy, F1 value, etc.) in the machine learning model. Then, the characteristic importance score of all second pollutants is sorted, and the second pollutant with the highest characteristic importance score is determined to be the fourth pollutant information of the suspected pollution source or the second pollutant with multiple higher characteristic importance scores is determined to be the fourth pollutant information of the suspected pollution source. It is also understandable that the target variable can be set according to actual needs. Specifically, the target variable can be the industrial type of the second pollutant, or it can be a performance index in the machine learning model. Specifically, it can be at least one of the accuracy, recall, and precision in the performance index. This application example is only for illustration and does not impose any restrictions on this application. It can meet actual needs.

[0100] Step 140: Analyze the water quality fingerprint spectrum and the characteristic pollutant information through a pollution source identification model to determine a source tracing result, wherein the source tracing result includes the actual pollution source at the river section.

[0101] It's understandable that characteristic pollutants and their feature importance scores can help train pollution source identification models to avoid overfitting. Furthermore, feature importance can enhance the interpretability of pollution source identification models during their analysis of water quality fingerprints and characteristic pollutant information.

[0102] It is understandable that the pollution source identification model determines the source tracing results by applying the angle cosine formula to measure the difference in the water quality fingerprint spectrum at the river section and the characteristic pollutant information of the suspected pollution source, and visualizes the tracing results through a clustering algorithm. Specifically, the characteristic pollutant information of different suspected pollution sources can form different profiles on the mass spectrum. Although the pollutants discharged by the suspected pollution source will undergo degradation and dilution during the migration of the water body, the mass spectrum profile of the characteristic pollutant information at the river section and the mass spectrum profile at the suspected pollution source are still similar.

[0103] The following is a formula for the cosine of an angle provided in an embodiment of the present application:

[0104]

[0105] Among them, θ refers to the angle between the response line segment of the mass spectrum profile of the characteristic pollutant information at the suspected pollution source and the response line segment of the mass spectrum profile at the river section; k refers to the dimension in the characteristic pollution source information, which is used to characterize the types of organic pollutants contained in the characteristic pollution source information; n refers to the maximum dimension of the characteristic pollution source information; X 1k A characteristic pollutant used to characterize the characteristic pollutant information of a suspected pollution source; X 2k It is used to characterize a characteristic pollutant corresponding to the river section where the characteristic pollutant information of a suspected pollution source flows.

[0106] It can be understood that, using the value range of the cosine of the angle cos(θ) being -1 to 1, the larger the value of cos(θ), the more similar the mass spectrum profile of the characteristic pollutant information at the river section is to the mass spectrum profile at the suspected pollution source, and the smaller the value of cos(θ), the less similar the mass spectrum profile of the characteristic pollutant information at the river section is to the mass spectrum profile at the suspected pollution source. When cos(θ) = 1, it indicates that the mass spectrum profile of the characteristic pollutant information at the river section is completely similar to the mass spectrum profile at the suspected pollution source, and when cos(θ) = -1, it indicates that the mass spectrum profile of the characteristic pollutant information at the river section is completely different from the mass spectrum profile at the suspected pollution source.

[0107] It can be understood that the cosine of the angle cos(θ) can make a directional judgment on the overall profile of the characteristic pollutant information, which can better realize the identification and tracing of multi-component complex pollutants, and avoid the tracing interference caused by the degradation and dilution of pollutants discharged from suspicious pollution sources during the migration of water bodies. The tracing is highly targeted and accurate.

[0108] In some embodiments, the traceability method further includes: step 150, verifying the traceability result;

[0109] The step 150 of verifying the traceability result includes:

[0110] Step 151: Analyze the characteristic pollutant information by a quantitative analysis method to obtain analysis results of each characteristic pollutant in the characteristic pollutant information;

[0111] Step 152: performing pollutant concentration inversion and pollutant type inversion on the pollutants in the first water sample to obtain analysis results of the inverted pollutants;

[0112] Step 153: Verify the tracing result based on the analysis result of the inverted pollutants and the analysis results of each of the characteristic pollutants.

[0113] It can be understood that the tracing results include the actual pollution sources at the river section. In the embodiment of the present application, the ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry can be used to perform quantitative analysis on each characteristic pollutant to obtain the analysis results of each characteristic pollutant, and then the concentration inversion and type inversion of the pollutants in the first water sample are performed to obtain the analysis results of the inverted pollutants, and the analysis results of the inverted pollutants are compared with the analysis results of each characteristic pollutant. If the comparison results are equal or similar, the tracing results can be considered correct.

[0114] Specifically, the concentration inversion formula can be:

[0115] c=c0 -kt

[0116] Among them, c is the inverted concentration of a characteristic pollutant at the actual pollution source, c0 is the concentration of the corresponding characteristic pollutant at the river section, k is the reaction constant, and t is the distance between the actual pollution source and the river section or the time required for the characteristic pollutant emitted by the actual pollution source to reach the river section.

[0117] It is understood that the inverted pollutant concentration analysis results can be obtained based on the concentration inversion formula. Pollutant type inversion can be achieved by inferring the pre-degradation pollutants corresponding to the pollutants at the river section based on the pollutants at the river section and their degradation during migration through the water body, thereby obtaining the inverted pollutant type analysis results. The concentration analysis results and type analysis results can then be combined and compared with the analysis results of each characteristic pollutant in the actual pollution source to verify the traceability results.

[0118] A pollutant tracing system proposed according to an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0119] Reference Figure 2 The pollutant tracing system proposed in the embodiments of the present application includes:

[0120] A first acquisition module 101 is configured to acquire and detect a first water sample at a river section, and construct a water quality fingerprint spectrum at the river section, wherein the water quality fingerprint spectrum includes pollutant information at the river section;

[0121] A first determining module 102 is configured to determine a suspected pollution source at a river section based on industrial data information within the river basin;

[0122] The second acquisition module 103 is configured to acquire and detect a second water sample from the suspected pollution source to obtain characteristic pollutant information of the suspected pollution source;

[0123] The second determination module 104 is configured to analyze the water quality fingerprint and characteristic pollutant information of the suspected pollution source through a pollution source identification model to determine a source tracing result, wherein the source tracing result includes the actual pollution source at the river section.

[0124] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system 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.

[0125] Reference Figure 3 , the embodiment of the present application further provides a pollutant tracing device, comprising:

[0126] at least one processor 201;

[0127] At least one memory 202, configured to store at least one program;

[0128] When the at least one program is executed by the at least one processor 201, the at least one processor 201 implements the above-mentioned embodiment of the method for tracing the source of pollutants.

[0129] Similarly, it can be understood that the contents of the above method embodiments are 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.

[0130] An embodiment of the present application also provides a computer-readable storage medium, which stores a program executable by the processor 201. The program executable by the processor 201 is used to implement the above-mentioned embodiment of the pollutant tracing method when executed by the processor 201.

[0131] Similarly, the contents of the above method embodiments are applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable 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.

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

[0133] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features 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 not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0134] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

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

[0136] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be 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 processing in another suitable manner as necessary, and then stored in a computer memory.

[0137] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific 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 above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / 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 application. 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 can be combined in any appropriate manner in any one or more embodiments or examples.

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

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

Claims

1. A method for tracing the source of pollutants, characterized in that: include: Acquiring and detecting a first water sample at a river section, and constructing a water quality fingerprint spectrum at the river section based on the detection result of the first water sample, wherein the water quality fingerprint spectrum includes pollutant information at the river section; Determining suspected pollution sources at the river section based on industrial data information within the river basin; Acquiring and testing a second water sample from the suspected pollution source, and obtaining characteristic pollutant information of the suspected pollution source based on the test result of the second water sample; Analyzing the water quality fingerprint spectrum and the characteristic pollutant information through a pollution source identification model, measuring the degree of difference between the mass spectrum profile of the water quality fingerprint spectrum and the mass spectrum profile of the characteristic pollutant information through the pollution source identification model, and determining a source tracing result, wherein the source tracing result includes the actual pollution source at the river section; The step of obtaining and detecting a second water sample from the suspected pollution source to obtain characteristic pollutant information of the suspected pollution source includes: Obtaining a second water sample from the suspected pollution source, and sequentially performing solid phase extraction and concentration treatment on the second water sample; Detecting the second water sample using a non-targeted semi-quantitative analysis technique to obtain second pollutant information of the suspected pollution source, wherein the second pollutant information is used to characterize information of all pollutants in the second water sample; Analyzing and processing the second pollutant information based on the industrial data information and the water quality fingerprint spectrum to obtain third pollutant information, wherein the third pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the industrial data information and the water quality fingerprint spectrum; performing feature calculation on the second pollutant information through feature engineering of machine learning to obtain fourth pollutant information, wherein the fourth pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the feature engineering; The third pollutant information and the fourth pollutant information are combined to obtain characteristic pollutant information of the suspected pollution source.

2. The method for tracing the source of pollutants according to claim 1, characterized in that: The traceability method further comprises: verifying the traceability result; The verifying the traceability result includes: Analyzing the characteristic pollutant information by a quantitative analysis method to obtain analysis results of each characteristic pollutant in the characteristic pollutant information; Performing pollutant concentration inversion and pollutant type inversion on the pollutants in the first water sample to obtain analysis results of the inverted pollutants; The tracing result is verified based on the analysis results of the inverted pollutants and the analysis results of each of the characteristic pollutants.

3. The method for tracing the source of pollutants according to claim 1, characterized in that: The step of obtaining and detecting a first water sample at a river section and constructing a water quality fingerprint spectrum at the river section includes: Obtaining a first water sample at the river section, and sequentially performing solid phase extraction and concentration treatment on the first water sample; Detecting the first water sample using a non-targeted semi-quantitative analysis technique to obtain pollutant information at the river section, wherein the pollutant information includes pollutant name, peak area response value, matching factor, chemical formula, and retention time; The water quality fingerprint spectrum is constructed according to the pollutant information.

4. The method for tracing the source of pollutants according to claim 3, characterized in that: The detecting the first water sample by using a non-targeted semi-quantitative analysis technique to obtain pollutant information at the river section includes: Detecting the first water sample using a non-targeted semi-quantitative analysis technique to obtain first pollutant information, wherein the first pollutant information is used to characterize information of all pollutants in the first water sample; The first pollutant information is processed according to the retention conditions pre-set by the user to obtain the pollutant information, wherein the retention conditions are at least one of the matching factor being greater than a first threshold, the peak area response value being greater than a second threshold, or the peak area response value being greater than or equal to a third threshold, and the third threshold is 3 times the peak area response value of blank data.

5. The method for tracing the source of pollutants according to claim 1, characterized in that: Determining the suspected pollution source at the river section based on the industrial data information within the river basin includes: Obtaining industrial data information of enterprises within the river basin, the industrial data information including industrial sector, geographic location, wastewater discharge volume, chemical oxygen demand, whether wastewater is discharged into a decentralized sewage treatment plant, whether wastewater is discharged into a centralized industrial wastewater treatment plant, production process, raw and auxiliary materials, final products, and intermediate products; Statistical analysis is performed based on the industrial data information to identify the enterprises that are listed as suspected pollution sources at the river section.

6. The method for tracing the source of pollutants according to claim 5, characterized in that: The performing feature calculation on the second pollutant information through feature engineering of machine learning to obtain fourth pollutant information includes: Classifying and normalizing each second pollutant in the second pollutant information, wherein the second pollutant is one of the pollutants represented by the second pollutant information; Calculating feature importance of each of the second pollutants after classification and normalization using a machine learning model, wherein the feature importance is used to characterize the degree of influence of the second pollutant on the target variable; A second pollutant with the highest characteristic importance score is determined as the fourth pollutant information, or a plurality of second pollutants with relatively high characteristic importance scores are determined as the fourth pollutant information.

7. A pollutant tracing system, characterized in that: include: a first acquisition module, configured to acquire and detect a first water sample at a river section, and construct a water quality fingerprint spectrum at the river section, wherein the water quality fingerprint spectrum includes pollutant information at the river section; A first determination module is used to determine a suspected pollution source at a river section based on industrial data information within the river basin; a second acquisition module, configured to acquire and detect a second water sample from the suspected pollution source to obtain characteristic pollutant information of the suspected pollution source; a second determination module, configured to analyze the water quality fingerprint spectrum and the characteristic pollutant information of the suspected pollution source through a pollution source identification model, measure the degree of difference between the mass spectrum profile of the water quality fingerprint spectrum and the mass spectrum profile of the characteristic pollutant information through the pollution source identification model, and determine a source tracing result, wherein the source tracing result includes the actual pollution source at the river section; The step of obtaining and detecting a second water sample from the suspected pollution source to obtain characteristic pollutant information of the suspected pollution source includes: Obtaining a second water sample from the suspected pollution source, and sequentially performing solid phase extraction and concentration treatment on the second water sample; Detecting the second water sample using a non-targeted semi-quantitative analysis technique to obtain second pollutant information of the suspected pollution source, wherein the second pollutant information is used to characterize information of all pollutants in the second water sample; Analyzing and processing the second pollutant information based on the industrial data information and the water quality fingerprint spectrum to obtain third pollutant information, wherein the third pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the industrial data information and the water quality fingerprint spectrum; performing feature calculation on the second pollutant information through feature engineering of machine learning to obtain fourth pollutant information, wherein the fourth pollutant information is used to characterize all characteristic pollutants emitted by the suspected pollution source determined by the feature engineering; The third pollutant information and the fourth pollutant information are combined to obtain characteristic pollutant information of the suspected pollution source.

8. A pollutant tracing device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the pollutant tracing method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the pollutant tracing method according to any one of claims 1 to 6 when executed by the processor.

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