Ion adsorption type rare earth mining area nitrogen pollution source analysis method and device and storage medium

Abstract

The invention discloses an ion adsorption type rare earth mining area nitrogen pollution source analysis method and device and a storage medium, and can be applied to the cross technical field of environmental geology and mine pollution abatement. After different water bodies are adopted, topographic parameters, physicochemical indexes, nitrogen pollution indexes and isotope data of samples are obtained, principal component score calculation is performed on the to-be-analyzed samples based on a principal component analysis method to obtain principal component scores, and then a first mean vector and a first covariance matrix of the principal component scores are calculated; performing hierarchical clustering analysis on the to-be-analyzed sample according to the principal component score to obtain a clustering sample, and then performing nitrogen pollution source marking on the clustering sample according to the principal component score, the first mean vector and the first covariance matrix, thereby effectively identifying a pollution source and delineating a pollution point; meanwhile, the nitrate nitrogen source in the to-be-analyzed sample is quantified based on the Bayesian isotope mixed model, so that the contribution rate of the nitrogen source can be effectively quantified.

Description

Technical Field

[0001] The present application relates to the interdisciplinary technical field of environmental geology and mine pollution control, and in particular to an ion adsorption-type rare earth mining area nitrogen pollution source analysis method, device and storage medium. Background Art

[0002] In the relevant technologies, the mining process of ion-adsorption rare earth deposits has undergone a transformation from pool leaching technology, heap leaching technology to the current in-situ leaching technology. In the in-situ leaching process, ammonium sulfate is commonly used as the leaching solution in the industry. It is injected from a high-level water pool into a sealed injection well. The leaching solution penetrates into the ore body along the pores and cracks of the ore body; then the more active ammonium ions (NH4+) in the leaching solution exchange and desorb the rare earth ions adsorbed on the surface of the clay minerals, while a large amount of NH4+ remains in the tailings. These pollutants, during the later artificial water flushing stage and the leaching of rainwater after mine closure, continue to move with the pore water and crack water in the ore body, enter the surface water and groundwater around the mining area, and cause serious nitrogen pollution to the water environment of the mining area.

[0003] In addition to traditional natural sources (such as atmospheric deposition and soil nitrogen) and human activities (nitrogen fertilizer application, fossil fuel combustion, and feces / sewage discharge), ammonium sulfate as a new nitrogen source in ion-adsorption rare earth mining areas has seriously changed the existing nitrogen cycle, accelerating the deterioration of groundwater quality and the degradation of aquatic ecosystems. The biogeochemical evolution of nitrogen is often closely related to major issues such as human health, the natural environment, and climate. For example, excessive nitrogen can lead to eutrophication of water bodies and the proliferation of harmful algae, a decrease in the number of aquatic animal species such as fish, poisoning, and even death, resulting in the loss of biological habitats and a reduction in diversity in aquatic systems. In addition, accidentally drinking high-nitrate drinking water will damage human health, such as "blue baby syndrome", cancer, and methemoglobinemia.

[0004] Therefore, accurately identifying and delineating point sources of nitrogen pollution and quantifying their contribution are crucial for estimating nitrogen budgets and developing effective strategies to prevent, control, and remediate nitrogen pollution in groundwater and surface water in mining areas. However, existing methods, due to insufficient spatial resolution, are unable to effectively identify pollution sources and delineate contamination points in complex mixed-source scenarios. Furthermore, existing methods are unable to accurately determine the contribution of different nitrogen sources.

[0005] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0006] The main purpose of the embodiments of the present application is to propose an ion adsorption type rare earth mining area nitrogen pollution source analysis method, device and storage medium, which can effectively identify pollution sources and delineate pollution points, and effectively quantify the contribution rate of nitrogen sources.

[0007] To achieve the above objectives, one aspect of the embodiments of the present application provides an ion adsorption-based rare earth mining area nitrogen pollution source analysis method, the method comprising the following steps:

[0008] Sampling different water bodies to obtain samples to be analyzed corresponding to different water bodies;

[0009] Obtaining terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data corresponding to the sample to be analyzed;

[0010] Calculating a principal component score for the sample to be analyzed based on the topographic parameters, the physical and chemical indicators, the nitrogen pollution indicators, and the isotope data based on a principal component analysis method to obtain a principal component score;

[0011] Calculating a first mean vector and a first covariance matrix of the principal component scores;

[0012] Performing hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples;

[0013] Marking the cluster samples as nitrogen pollution sources according to the principal component scores, the first mean vector, and the first covariance matrix;

[0014] The sources of nitrate nitrogen in the sample to be analyzed are quantified based on a Bayesian isotope mixing model.

[0015] In some embodiments, the calculating of the principal component scores of the sample to be analyzed based on the topographic parameters, the physical and chemical indicators, the nitrogen pollution indicators, and the isotope data based on the principal component analysis method to obtain the principal component scores includes:

[0016] forming an original observation matrix according to the terrain parameters, the physical and chemical indicators, the nitrogen pollution indicators and the isotope data;

[0017] Normalizing the original measurement matrix to obtain a standardized matrix;

[0018] constructing a covariance matrix based on the standardized matrix;

[0019] Performing KMO test and Bartlett's sphericity test on the data in the original observation matrix;

[0020] After confirming that both the KMO test and the Bartlett sphericity test are passed, the eigenvalues ​​and eigenvectors are calculated according to the covariance matrix;

[0021] constructing a factor loading matrix according to the eigenvalues ​​and the eigenvectors;

[0022] Optimizing the factor loading matrix to obtain a rotated loading matrix and a rotated eigenvector;

[0023] The principal component score is calculated based on the rotated eigenvector and the normalized matrix.

[0024] In some embodiments, performing a KMO test and a Bartlett sphericity test on the data in the original observation matrix includes:

[0025] Calculating the correlation coefficient and partial correlation coefficient between the data in the original observation matrix in each of the samples to be analyzed;

[0026] Calculate the KMO value of each sample to be analyzed according to the correlation coefficient and the partial correlation coefficient;

[0027] Performing a KMO test on the sample to be analyzed according to the KMO value and a first preset value;

[0028] determining the total number of all samples to be analyzed;

[0029] A Bartlett's sphericity test is performed on the samples to be analyzed according to the total number of samples to be analyzed and a second preset value.

[0030] In some embodiments, calculating the first mean vector and the first covariance matrix of the principal component scores includes:

[0031] Obtaining the first mean vector according to the principal component scores;

[0032] The first covariance matrix is ​​calculated based on the first mean vector and the principal component scores.

[0033] In some embodiments, performing hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples includes:

[0034] Calculating the Euclidean distances between all samples to be analyzed based on the principal component scores;

[0035] Determining the similarity of the pollution characteristics of the sample to be analyzed according to the Euclidean distance;

[0036] Calculating the merging cost of the samples to be analyzed according to the principal component scores;

[0037] Performing hierarchical clustering analysis on the samples to be analyzed according to the Euclidean distance and the merging cost to obtain cluster samples.

[0038] In some embodiments, labeling the cluster samples as nitrogen pollution sources according to the principal component scores, the first mean vector, and the first covariance matrix includes:

[0039] Calculating an abnormality index corresponding to each sample to be analyzed according to the principal component score, the first mean vector, and the first covariance matrix corresponding to each sample to be analyzed;

[0040] determining nitrogen pollution point sources from all of the samples to be analyzed based on the anomaly index;

[0041] The samples to be analyzed corresponding to the nitrogen pollution point sources are taken as the pollution type clusters to be identified;

[0042] Correlating the rotated loading matrix with the factor loading matrix to obtain a correlation equation;

[0043] Nitrogen pollution type identification is performed on each of the pollution type clusters to be identified according to the association equation.

[0044] In some embodiments, the calculation formula of the abnormality index is as follows:

[0045]

[0046] In the formula, PC i represents the principal component score vector of the i-th sample to be analyzed, μ PC is the mean vector of the principal component scores corresponding to all samples to be analyzed; S PC Represents the covariance matrix of the principal component scores.

[0047] To achieve the above objectives, another aspect of the present invention provides an ion adsorption type rare earth mining area nitrogen pollution source analysis device, the device comprising:

[0048] The first module is used to sample different water bodies and obtain samples to be analyzed corresponding to different water bodies;

[0049] The second module is used to obtain terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data corresponding to the sample to be analyzed;

[0050] A third module is configured to calculate a principal component score for the sample to be analyzed based on the terrain parameters, the physical and chemical indicators, the nitrogen pollution indicators, and the isotope data using a principal component analysis method to obtain a principal component score;

[0051] A fourth module is used to calculate a first mean vector and a first covariance matrix of the principal component scores;

[0052] A fifth module is used to perform hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples;

[0053] A sixth module is configured to label the nitrogen pollution sources of the cluster samples according to the principal component scores, the first mean vector, and the first covariance matrix;

[0054] The seventh module is used to quantify the source of nitrate nitrogen in the sample to be analyzed based on the Bayesian isotope mixing model.

[0055] To achieve the above objectives, another aspect of the present application provides a computer device, including:

[0056] at least one processor;

[0057] at least one memory for storing at least one program;

[0058] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0059] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above-mentioned method when executed by a processor.

[0060] The embodiments of the present application include at least the following beneficial effects: The present application provides an ion adsorption type rare earth mining area nitrogen pollution source analysis method, device and storage medium. The scheme obtains the terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data of the samples to be analyzed corresponding to different water bodies after adopting different water bodies, and calculates the principal component scores of the samples to be analyzed based on the principal component analysis method according to the terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data to obtain the principal component scores, and then calculates the first mean vector and the first covariance matrix of the principal component scores, and performs hierarchical clustering analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples, and then marks the nitrogen pollution sources of the cluster samples according to the principal component scores, so as to effectively identify the pollution sources and delineate the pollution points; at the same time, this embodiment will also quantify the sources of nitrate nitrogen in the samples to be analyzed based on the Bayesian isotope mixing model, so as to effectively quantify the contribution rate of the nitrogen source. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of the ion adsorption type rare earth mining area nitrogen pollution source analysis method provided in an embodiment of the present application;

[0062] Figure 2 This is a complete flow chart of the ion adsorption type rare earth mining area nitrogen pollution source analysis method provided in the embodiment of the present application;

[0063] Figure 3 This is a flowchart of the principal component analysis provided by the embodiment of the present application;

[0064] Figure 4 Schematic diagram of the analysis results of the main components of groundwater and surface water provided in the embodiment of the present application;

[0065] Figure 5 Schematic diagram of the contribution of each variable to the first principal component (PC1) provided in the embodiment of the present application;

[0066] Figure 6 Schematic diagram of the contribution of each variable to the second principal component (PC2) provided in the embodiment of the present application;

[0067] Figure 7 This is a schematic diagram of the results of the principal component analysis-cluster analysis coupling of surface water and groundwater provided in an embodiment of the present application;

[0068] Figure 8 This is the δ provided in the embodiment of the present application. 18 O-NO3 - -δ 15 N-NO3 - -ln(NO3 - )Identify the schematic diagram of denitrification;

[0069] Figure 9 1 is a table schematic diagram of the contribution ratios of different nitrogen sources obtained based on the method of the embodiment of the present application provided in the embodiment of the present application;

[0070] Figure 10 is a tabular diagram of the uncertainty analysis results provided in the embodiments of the present application;

[0071] Figure 11 It is a structural schematic diagram of the ion adsorption type rare earth mining area nitrogen pollution source analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0073] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, 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 embodiments of the present application, 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 determining".

[0074] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0075] 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.

[0076] In related technologies, rare earths are called the "vitamins" of modern industry and play an irreplaceable role in high-tech industries and new energy fields. Ion-adsorption rare earth deposits, also known as weathering crust elution rare earth deposits, are the main suppliers of rare earth resources in the world and are mainly formed in granite weathering crusts. In ion-adsorption rare earth deposits, 70%-90% of rare earth elements (17 elements, including lanthanides, scandium, and yttrium) are adsorbed on the surface of weathering crust minerals such as clay minerals in the form of hydrated ions or hydroxyl hydrated cations, and are easily reacted with active cations (NH4 + , Na + Mg 2+ The mining process of ion adsorption type rare earth deposits has undergone a transformation from pool leaching technology, heap leaching technology to the current in-situ leaching technology. In the in-situ leaching process, ammonium sulfate is commonly used as the leaching solution in the industry. It is injected from a high-level water pool into a sealed injection well. The leaching solution penetrates into the ore body along the pores and cracks of the ore body; then the more active ammonium ions (NH 4+ ) exchange and desorb the rare earth ions adsorbed on the surface of clay minerals, and a large amount of NH 4+ These pollutants, during the subsequent artificial water flushing phase and the leaching of rainwater after mine closure, continue to migrate with the pore water and fissure water in the ore body and enter the surface water and groundwater around the mining area, causing serious nitrogen pollution to the mining area's water environment.

[0077] In addition to traditional natural sources (such as atmospheric deposition and soil nitrogen) and human activities (nitrogen fertilizer application, fossil fuel combustion, and feces / sewage discharge), ammonium sulfate as a new nitrogen source in ion-adsorption rare earth mining areas has seriously changed the existing nitrogen cycle, accelerating the deterioration of groundwater quality and the degradation of aquatic ecosystems. The biogeochemical evolution of nitrogen is often closely related to major issues such as human health, the natural environment, and climate. For example, excessive nitrogen can lead to eutrophication of water bodies and the proliferation of harmful algae, a decrease in the number of aquatic animal species such as fish, poisoning, and even death, resulting in the loss of biological habitats and a reduction in diversity in aquatic systems. In addition, accidentally drinking high-nitrate drinking water will damage human health, such as "blue baby syndrome", cancer, and methemoglobinemia.

[0078] Therefore, accurately identifying and delineating point sources of nitrogen pollution and quantifying the contribution of nitrogen sources are crucial for estimating nitrogen budgets and formulating effective governance strategies to prevent, control, and remediate nitrogen pollution in groundwater and surface water in mining areas. However, (1) existing methods are currently unable to effectively identify pollution sources and delineate pollution points in complex mixed pollution source scenarios, but their spatial resolution is insufficient; (2) due to the increasing number of pollution sources in rare earth mining areas, source components often vary or even overlap; and different nitrogen species undergo different forms of transformation during migration, resulting in isotopic fractionation; traditional hydrochemical or isotope mixing models do not consider the uncertainty of pollution source end members and do not correct for isotope fractionation, and therefore cannot accurately obtain the contribution of different nitrogen sources.

[0079] In view of this, the embodiments of the present application provide an ion adsorption type rare earth mining area nitrogen pollution source analysis method, device and storage medium, which can effectively identify pollution sources and delineate pollution points, and effectively quantify the contribution rate of nitrogen sources.

[0080] The ion adsorption type rare earth mining area nitrogen pollution source analysis method provided in the embodiment of the present application relates to the cross-technical field of environmental geology and mine pollution control. The ion adsorption type rare earth mining area nitrogen pollution source analysis method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the ion adsorption type rare earth mining area nitrogen pollution source analysis method, etc., but is not limited to the above forms.

[0081] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0082] The following is a detailed description of the embodiments of the present application with reference to the accompanying drawings:

[0083] Figure 1 This is an optional flow chart of the ion adsorption type rare earth mining area nitrogen pollution source analysis method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S170:

[0084] Step S110: sampling different water bodies to obtain samples to be analyzed corresponding to the different water bodies;

[0085] Step S120: obtaining terrain parameters, physical and chemical indicators, nitrogen pollution indicators, and isotope data corresponding to the sample to be analyzed;

[0086] Step S130: Calculate the principal component score of the sample to be analyzed based on the topographic parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data using the principal component analysis method to obtain the principal component score;

[0087] Step S140, calculating a first mean vector and a first covariance matrix of the principal component scores;

[0088] Step S150: Perform hierarchical cluster analysis on the samples to be analyzed based on the principal component scores to obtain cluster samples;

[0089] Step S160: labeling the cluster samples as nitrogen pollution sources according to the principal component scores, the first mean vector, and the first covariance matrix;

[0090] Step S170: quantify the source of nitrate nitrogen in the sample to be analyzed based on the Bayesian isotope mixing model.

[0091] It is understandable that the water bodies in this embodiment include but are not limited to river water, groundwater, mine drainage, etc. This embodiment collects part of the water samples from these water bodies as the samples to be analyzed corresponding to the water body. Specifically, this embodiment can collect samples from the ion adsorption type rare earth mining area and the surrounding river water, groundwater and mine drainage in a certain area. For groundwater, before collecting water samples, start the pump to pump water continuously for at least 20 minutes, and start collecting and testing after the water chemical indicators such as conductivity, pH, and temperature are stable. A water level meter is used on site to measure the water level depth (accuracy 0.01m), and a bailer tube is used to collect groundwater samples in the borehole. A multi-parameter water quality analyzer is used on site to measure the total dissolved solids (TDS), pH, conductivity (EC), and oxidation-reduction potential (ORP); a digital titrator titration (Model 16900, Hatch) is used to measure the alkalinity (HCO3- and CO3 2- ); determination of Fe by portable spectrophotometer 2+ NH3 + -N and NO2 - -N. All other water samples used for cation, anion and isotope testing and analysis were collected in high-density polyethylene plastic bottles (HDPE) and tested and analyzed in the laboratory; cations (Na + , K + Mg 2+ and Ca 2+ ) and anions (F - 、Cl - 、SO4 2- and NO3 - ) was tested and analyzed in the laboratory using an ion chromatography system. 2 H and δ 18 O) was tested and analyzed using a liquid isotope laser tester (Picarro L1102-I); nitrate (δ 15 N-NO3 - , δ 18 O-NO3 - ) and nitrite (δ 15 N-NO2 - , δ 18 O-NO2 - ) were determined and analyzed by isotope ratio mass spectrometry (Delta V Plus); ammonium salt (δ 15 N-NH4 + ) isotopes were analyzed using an elemental analyzer-stable isotope ratio mass spectrometer system. Dissolved inorganic nitrogen (DIN = NH4 + –N+NO2 - –N+NO3 - –N)) is NO3 - -N, NO3- -N and NH4 + -N. Then, the hydrochemical information and isotope characteristic data of different water bodies can be obtained.

[0092] like Figure 2 As shown, after obtaining the physical and chemical indicators, nitrogen pollution indicators and isotope characteristic data of the samples to be analyzed corresponding to different water bodies, this embodiment performs principal component analysis on the samples to be analyzed through the pollution source feature extraction module based on principal component analysis. Specifically, Figure 3 As shown, the process of performing principal component analysis on the sample to be analyzed includes but is not limited to the following steps:

[0093] The original observation matrix is ​​formed based on terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data;

[0094] Normalize the original observation matrix to obtain a standardized matrix;

[0095] Construct the covariance matrix based on the standardized matrix;

[0096] Perform KMO test and Bartlett's sphericity test on the data in the original observation matrix;

[0097] After confirming that both the KMO test and Bartlett's sphericity test are passed, the eigenvalues ​​and eigenvectors are calculated based on the covariance matrix;

[0098] Construct a factor loading matrix based on eigenvalues ​​and eigenvectors;

[0099] Optimize the factor loading matrix to obtain the rotated loading matrix and rotated eigenvector;

[0100] The principal component scores are calculated based on the rotated eigenvectors and the normalized matrix.

[0101] It is understandable that in the process of standardization, the input variables include topographic parameters (altitude, distance from the leaching area), physical and chemical indicators (pH, TDS, Fe 2+ , Ca 2+ Mg 2+ 、Na + , K + 、HCO3 - 、SO4 2- 、Cl - 、F - ), nitrogen pollution index (NH4 + -N, NO2 - -N, NO3 - -N, DIN) and stable isotope data (δ 18O-H2O, δD-H2O, etc.; the data of all input variables after Z-score standardization in formula 1 are not affected by the differences in dimension and order of magnitude.

[0102]

[0103] In the formula, X std is the standardized matrix, X is the original observation matrix, μ is the variable mean vector, σ is the variable standard deviation vector, X std is the data matrix after Z-score standardization.

[0104] The covariance matrix is ​​shown in Formula 2:

[0105]

[0106] In the formula, C is the covariance matrix; n is the number of samples; For X std The transpose of .

[0107] Specifically, the covariance matrix is ​​used to measure the correlation between different variables and can provide a basis for the subsequent calculation of eigenvalues ​​and eigenvectors.

[0108] In this embodiment of the present application, the process of performing the KMO test and the Bartlett sphericity test on the data in the original observation matrix includes but is not limited to the following steps:

[0109] Calculate the correlation coefficient and partial correlation coefficient between the data in the original observation matrix in each sample to be analyzed;

[0110] Calculate the KMO value of each sample to be analyzed based on the correlation coefficient and partial correlation coefficient;

[0111] Performing a KMO test on the sample to be analyzed according to the KMO value and the first preset value;

[0112] Determine the total number of samples to be analyzed;

[0113] The Bartlett's sphericity test is performed on the samples to be analyzed according to the total number of samples to be analyzed and a second preset value.

[0114] It can be understood that in this embodiment, when the Kaiser-Meyer-Olkin (KMO) test and the Bartlett sphericity test are performed for verification, the KMO value> the first preset value and the Bartlett test significance p< the second preset value. For example, the KMO value> 0.7 and the Bartlett test significance p< 0.05 can be required. Specifically, the KMO test ensures that the common variance between variables is sufficient (KMO> 0.7), which is suitable for dimensionality reduction. Failure to perform the KMO test may result in invalid PCA (principal component analysis). The Bartlett test confirms that the variable correlation is significant (p< 0.05) (if the Bartlett test is not performed, PCA may be misused for independent variables), and subsequent steps are performed after verification; if it fails, it is necessary to increase the sample size and adjust the variables. The calculation process of the KMO value is shown in Formula 3:

[0115]

[0116] In the formula, r ij is the Pearson correlation coefficient between variables i and j, q ij is the partial correlation coefficient between variables i and j, and KMO is the sampling appropriateness measure.

[0117] Among them, the KMO test is used to test the sample suitability, and the Bartlett test of sphericity is used to confirm the existence of significant correlations between variables. Both ensure the applicability and effectiveness of PCA. For example, if the KMO test or Bartlett's test of sphericity fails, it is necessary to delete independent variables with KMO < 0.5 or increase the sample size to n > 10p, where p is the total number of variables.

[0118] It can be understood that, in this embodiment, when calculating the eigenvalues ​​and eigenvectors according to the covariance matrix, the eigenvalues ​​are decomposed by the covariance matrix, as shown in Formula 4:

[0119] C=VΛV T Formula 4;

[0120] In the formula, C is the covariance matrix; Λ is the eigenvalue diagonal matrix (λ1,λ2,…,λp, representing the variance contribution of the principal components); V is the eigenvector, indicating the direction of the principal component.

[0121] This embodiment uses the standardized covariance matrix C obtained by inputting formula 2 to output the eigenvalue diagonal matrix Λ and the eigenvector V. The specific calculation steps are:

[0122] Orthogonal diagonalization of C is performed by the Jacobi iterative numerical algorithm:

[0123] a. Iteration termination condition: max i≠j |C ij |<10-6 ;

[0124] c. Output sorting: eigenvalue λ1≥λ2≥…≥λp)(according to λ1≥λ2≥…≥λ p Sorting), the corresponding eigenvectors are arranged in columns.

[0125] In the embodiment of the present application, the factor loading matrix is ​​shown in Formula 5:

[0126]

[0127] In the formula, L is the factor loading matrix; p is the total number of variables; k is the number of principal components (according to the cumulative variance ≥ 80% or λ k >1 OK).

[0128] Among them, the factor loading matrix describes the relationship between each principal component and the original variable, which facilitates the interpretation of the pollution source characteristics of the principal component.

[0129] It is understandable that after obtaining the factor loading matrix, this embodiment optimizes the factor loading matrix. This embodiment optimizes the factor loading matrix by using the Varimax rotation method. The optimized factor loading matrix allows each principal component to only load a small number of variables, thereby improving interpretability. Among them, the rotation convergence condition in this embodiment is that the number of iterations is ≥50 or the load change rate is <0.001. Specifically, the objective function of this embodiment in the optimization process is shown in Formula 6:

[0130]

[0131] In the formula, L * is the factor loading matrix after L rotation; Q is the Varimax objective function value (measures the degree of simplification of factor loading).

[0132] The Varimax rotation is implemented by maximizing the loading matrix L:

[0133] ① Calculate the objective function Q;

[0134] ② Iteratively adjust the orthogonal rotation matrix T (implicitly generated and the calculation medium of L*) to maximize Q;

[0135] ③ Output the load matrix L after rotation * =LT and the rotated eigenvector V * =VT.

[0136] After completing the optimization of the factor loading matrix, the principal component score of each sample to be analyzed is calculated using Formula 7:

[0137] PC=X std V * Formula 7;

[0138] In the formula, V * is the rotated eigenvector matrix, and PC is the score matrix of the principal component.

[0139] In an embodiment of the present application, after calculating the principal component scores, the first mean vector is calculated based on the principal component scores, and the first covariance matrix is ​​calculated based on the first mean vector and the principal component scores. Specifically, the first mean vector of the principal component scores is calculated by formula 8, and the first covariance matrix of the principal component scores is calculated by formula 9:

[0140]

[0141] In the formula, μ PC is the first mean vector, S PC is the first covariance matrix, PC i Represents a row vector in the principal component score matrix.

[0142] It is understood that after obtaining the principal component scores, this embodiment uses the Ward minimum variance method based on Euclidean distance to perform hierarchical cluster analysis (HCA) based on the principal component score matrix after dimensionality reduction to identify water sample groups with similar pollution characteristics. Specifically, the identification process includes but is not limited to the following steps:

[0143] Calculate the Euclidean distance between all samples to be analyzed based on the principal component scores;

[0144] Determining the similarity of the contamination characteristics of the sample to be analyzed based on the Euclidean distance;

[0145] Calculate the merge cost of the samples to be analyzed based on the principal component scores;

[0146] Hierarchical cluster analysis is performed on the samples to be analyzed based on Euclidean distance and merging cost to obtain cluster samples.

[0147] Among them, each Euclidean distance in the Euclidean distance matrix is ​​calculated by formula 10:

[0148]

[0149] In the formula, PC im is the principal component score after PCA dimensionality reduction, and ||·|| represents the Euclidean distance.

[0150] This embodiment can further generate a symmetric distance matrix D based on the Euclidean distance matrix. n×n , where D ij Indicates the distance between samples i and j in the dimensionality reduction space. ij The smaller the value, the more similar the contamination characteristics of samples i and j are.

[0151] This embodiment determines the similarity between different samples to be analyzed based on the Euclidean distance matrix and then merges the samples (clusters) based on the Ward minimum variance method. The merging criteria are as follows:

[0152] ① Initialization: each water sample is treated as an independent cluster;

[0153] ②Iterative merging:

[0154] a. Calculate the merging cost of any two clusters A and B based on the objective function of formula 11:

[0155]

[0156] in and is the mean vector of the principal component scores of all samples in cluster A and cluster B. ||·|| represents the Euclidean distance (Formula 10);

[0157] b. Prioritize merging cluster pairs that minimize Δ(A, B), that is, cluster pairs with close distances between cluster centers and balanced sample sizes; stop merging when the preset number of clusters (such as the number of contamination types) is reached.

[0158] It is understood that after completing the clustering of the samples to be analyzed, this embodiment labels the clustered samples for nitrogen pollution sources. The labeling process includes but is not limited to the following steps:

[0159] Calculate the abnormality index corresponding to the sample to be analyzed according to the principal component score, the first mean vector and the first covariance matrix corresponding to each sample to be analyzed;

[0160] Determine nitrogen pollution point sources from all samples to be analyzed based on anomaly indices;

[0161] The samples to be analyzed corresponding to the nitrogen pollution point sources are taken as the pollution type clusters to be identified;

[0162] Correlate the rotated loading matrix with the factor loading matrix to obtain the correlation equation;

[0163] Nitrogen pollution type identification is performed for each pollution type cluster to be identified according to the association equation.

[0164] Specifically, the calculation formula of the abnormality index in this embodiment is shown in Formula 12:

[0165]

[0166] In the formula, PC i represents the principal component score vector of the i-th sample to be analyzed, μ PC is the mean vector of the principal component scores corresponding to all samples to be analyzed; S PCRepresents the covariance matrix of the principal component scores.

[0167] In this embodiment, when the abnormality index is greater than 3, the corresponding sample to be analyzed is a pollution point source (99.7% confidence interval). The pollution point source is regarded as a cluster to be identified, and the nitrogen pollution type is identified for the cluster to be identified.

[0168] Specifically, the correlation equation of this embodiment is shown in Formula 13:

[0169]

[0170] In the formula, L* is the load matrix after rotation.

[0171] Then for each cluster to be identified, find the principal component score PC within the cluster to be identified i The dimension j with the largest absolute value; and extract L * The original variables with loadings > 0.7 in the jth column explain the pollution source types through variable combinations.

[0172] It is understood that when this embodiment identifies nitrogen pollution point sources and pollution types, it also quantifies the source of nitrate nitrogen in the sample to be analyzed based on the Bayesian isotope mixing model. Specifically, this embodiment uses the Bayesian isotope mixing model to identify and quantify the nitrate nitrogen (NO3 - The contributions of different sources of isotope mixtures (N) can effectively integrate uncertainties such as the variability of source isotope composition, isotope fractionation effects, and measurement errors of measured isotope characteristics. The core equation of the Bayesian isotope mixing model is shown in Equation 14:

[0173]

[0174] In the formula, X ij is the measured value of isotope j in mixture i; S jk is the characteristic value of isotope j in pollution source k; C jk is the fractionation coefficient of isotope j on source k; P k is the contribution ratio of pollution source k; ε ij is the residual error.

[0175] Specifically, when quantifying the sources of nitrate nitrogen in the samples to be analyzed, the input files of the Bayesian isotope mixing model include the δ 15 N-NO3 - and δ 18 O-NO3 -It is understandable that the potential nitrogen sources in the mining area and surrounding areas include atmospheric nitrogen deposition, nitrogen fertilizer, soil nitrogen, feces and sewage, and drainage from rare earth mines; among them, the four end members of atmospheric nitrogen deposition, nitrogen fertilizer, soil nitrogen, and feces and sewage have all been studied, and the literature has clearly stated the δ 15 N-NO3 - and δ 18 O-NO3 - The drainage of rare earth mining area is a unique end member of rare earth mining area. 15 N-NO3 - and δ 18 O-NO3 - The values ​​are based on on-site sampling test analysis. The determination of nitrogen sources for specific cases should be combined with the pollution sources identified in the study area based on principal component analysis and cluster analysis, and different nitrogen source end members should be selected. The pollution end metadata in this embodiment fully considers the uncertainty of the pollution source end members and covers the characteristic range of the end member isotope values ​​(mean ± standard deviation). When analyzing the uncertainty in the stable isotope mixing model, the Markov Chain Monte Carlo (MCMC) sampling method is adopted.

[0176] Table 1

[0177]

[0178] It is understandable that in this embodiment, during the nitrogen cycle, the isotope fractionation effect will significantly affect the δ 15 N-NO3 - and δ 18 O-NO3 - Initial composition, which leads to the deviation of the traditional pollution source analysis results that do not consider isotope fractionation. The denitrification fractionation characteristics are mainly expressed by δ 15 N-NO3 - and δ 18 O-NO3 - The positive linear correlation between 15 N-NO3 - With ln[NO3 - If the denitrification process is significant, that is, if there is an isotope fractionation effect, it is necessary to obtain the fractionation coefficient using the Rayleigh fractionation equation (equation) shown in formula 15:

[0179]

[0180] In the formula, δ is the δ of residual nitrate 15 N-NO3 - and δ 18 O-NO3 - value, δ0 is the initial isotope value before denitrification, ε is the denitrification enrichment factor, and are the residual and initial nitrate concentrations, respectively.

[0181] During the operation of the Bayesian isotope mixing model, due to the uncertainty of different nitrogen sources and isotope fractionation coefficients, the Bayesian isotope mixing model in this embodiment is based on the MixSIAR software package in the R environment, and uses the Markov Chain Monte Carlo (MCMC) sampling method to quantify the source of nitrate nitrogen.

[0182] Specifically, in this embodiment, when analyzing the uncertainty of pollution sources in the sample to be analyzed, the uncertainty of nitrate source analysis results exists due to the spatiotemporal variation of the pollution source isotope fingerprint and the isotope fractionation effect. Although the sum of the estimated values ​​of the contribution ratio of each source should be equal to 1, the contribution ratio of each source is a random statistical variable, and its probability distribution must fluctuate within a specific range. The uncertainty of this contribution ratio can be characterized by the cumulative probability distribution curve. Therefore, this embodiment adopts the uncertainty index (UI 90 ) quantifies the uncertainty of the MixSIAR model results, and the calculation formula is shown in Equation 16:

[0183] UI 90 =(C 95 -C5) / 0.9 Formula 16;

[0184] In the formula, C 95 C and C5 represent the maximum and minimum values ​​of the rapid rising section (90% cumulative probability interval) of the posterior distribution curve, respectively. 90 Characterizing the uncertainty intensity under high probability conditions (90%) can effectively eliminate the impact of low probability (10%) extreme values.

[0185] The following is an analysis of the effects of this application based on actual cases:

[0186] Taking a regional production site for ion-adsorption rare earth minerals as an example, six major mining areas have been discovered and mined since early planning for rare earth mining. Rare earth mining methods shifted from early barrel leaching, pond leaching, and heap leaching to in-situ leaching in September 2009, using ammonium sulfate ((NH₄)₂SO₄), the leaching agent currently used. The mining method involves drilling holes at the top of the mountain with a diameter of 0.10 m to a total depth of approximately 3.60 to 5.00 m, with a spacing of 3 m x 3 m. Next, a prepared leaching solution is added to the longitudinal infusion orifices at the top of the mountain for irrigation. Finally, a drainage ditch is dug at the foot of the mountain, and the leachate flows back to the collection pool at the foot of the mountain for recovery. Sampling in this example focused on the mining area and its surroundings. The groundwater in the study area primarily consists of pore water in unconsolidated rocks, water from weathered fissures in igneous clastic rocks, and water from tectonic fissures.

[0187] First, samples were collected from the ion adsorption rare earth mining area and surrounding river water, groundwater, and mining drainage. The samples were numbered in order from upstream to downstream of the Renju River. A total of 13 river water samples, 14 groundwater samples, and 5 mining drainage samples were collected. The altitude, distance from the leaching area, pH, TDS, Fe2+, and Ca 2+ ,Mg 2+ ,Na + ,K + ,HCO3 - ,SO4 2- ,Cl - ,F - ,NH4 + -N,NO2 - -N,NO3 - -N,DIN,δ 18 O-H2O,δD-H2O、δ 15 N-NO3 - , δ 18 O-NO3 - , δ 15 N-NO2 - , δ 18 O-NO2 - and δ 15 N-NH4 + etc. Water chemistry and isotope data.

[0188] Then from Figure 4 It can be seen from the relationship between each water quality index and the principal component that the greater the absolute value of the load coefficient between the index and a principal component, the closer the relationship between the principal component. Principal component 1 and principal component 2 can explain 56.03% and 11.48% of the variables respectively. Figure 5 It can be seen that TDS, DIN, NH4 + -N,Cl - and SO4 2- The loads of these indicators on the first principal component are high, and these indicators are closely related to mine drainage; Figure 6 It can be seen that HCO3 - ,pH,Mg 2+ ,Ca 2+ and Na+ have higher loadings on the second principal component, and these indicators reflect water-rock interaction.

[0189] Based on principal component analysis, this example uses Ward method and Euclidean distance matrix to perform hierarchical cluster analysis (HCA) to identify four groups of samples with similar water chemistry and isotope characteristics. Figure 7 It can be seen that the groundwater GD7 anomaly point is a nitrogen pollution emission point.

[0190] Groundwater anomalies and water chemical indicators DIN, NH4 + -N,NO3 - -N,NO2 - -N,SO4 2- ,Cl - ,Fe 2+ It is positively correlated with TDS and negatively correlated with distance from the mining area, elevation and pH, indicating that mining drainage is an important source of nitrogen pollution; while groundwater nitrogen pollution has a limited impact on groundwater around the mining area, mainly concentrated in groundwater within <1 km of the nitrogen discharge point (GD5), which is controlled by the heterogeneity of the groundwater aquifer; considering that groundwater is an important source of river water recharge, and the principal component analysis and cluster analysis of groundwater and rivers show that the river anomalies belong to the same category as groundwater GD5, indicating that the impact of mining drainage on river water is caused by groundwater nitrogen pollution discharge points; and mining wastewater is discharged into the river through groundwater, and the resulting nitrogen pollution has a far-reaching impact on the river, at least covering the area <2 km upstream of the mining area (RD3, RD5, RD6 and RD7) and the entire downstream area (>10 km, including RD8-RD14).

[0191] After identifying the nitrogen pollution sources and pollution types, this example quantifies the contribution of different nitrate nitrogen sources based on the Bayesian isotope mixing model. First, the input data of the model include the δ 15 N-NO3 - The range of values ​​was (-2.65)-6.34‰, with an average of -0.76±2.57‰; δ 18 O-NO3 - The value range is 5.23-8.22‰, with an average value of 6.88±1.04‰; the δ 15 N-NO3 - The value range is (-3.77)-11.66‰, with an average value of 4.82±4.61‰; δ 18 O-NO3 - The range of values ​​is 1.89-10.15‰, with an average value of 5.56±2.76‰. Then, the metadata and uncertainty of the pollution source are analyzed, and it is known that the δ 15 N-NO3 - The range of values ​​was (-8.97)-0.65‰, with an average of -6.31±3.66‰, and δ 18 O-NO3 - The range of values ​​was 5.92-14.26‰, with an average of 8.42±2.99‰. The end members of atmospheric nitrogen deposition, feces and domestic sewage, nitrogen fertilizer, and soil nitrogen are shown in Table 1.

[0192] After isotope fractionation correction, Figure 8(a) It can be seen that river water δ 15 N-NO3 - Value and δ 18 O-NO3 - value; from Figure 8 (b) It can be seen that δ 15 N-NO3 - Value and ln(NO3 - );from Figure 8 (c) It can be seen that δ 18 O-NO3 - Value and ln(NO3 - ) did not show a significant negative correlation; while the δ 15 N-NO3 - Value and δ 18 O-NO3 - The values ​​did not show a significant positive correlation, indicating that river water and groundwater were not significantly denitrified and no significant isotope fractionation occurred; however, mining drainage, as a nitrogen source, showed a δ 15 N-NO3 - Value and δ 18 O-NO3 - The value is positively correlated, δ 15 N-NO3 - Value and ln(NO3 - ), δ 18 O-NO3 - Value and ln(NO3 - ) showed a significant negative correlation, indicating significant denitrification. Based on equation (11), we can obtain δ 15 The N fractionation coefficient is -2.26±0.19‰, and the δ18O fractionation coefficient is -1.95±0.19‰.

[0193] In the examples of this application, the Bayesian isotope mixing model is based on the MixSIAR software package in the R environment, using the Markov chain Monte Carlo (MCMC) sampling method, with a chain length of 300,000, an annealing time of 200,000, a dilution interval of 100, and 3 chains. Figure 9 The primary source of nitrate in Groups 1 and 2 (GD7) was rare earth mining drainage, with nitrogen contributions of 40%-53.9% and 53.4%, respectively, followed by manure and sewage. Nitrate in Groups 3 and 4 primarily came from manure and sewage, with contributions of 51.1%-78.9% and 34.7%-71.1%, respectively, followed by rare earth mining drainage. This indicates that nitrogen pollution from mining drainage decreases with distance from point sources, with its impact on river water being significantly greater than that on groundwater. Atmospheric nitrogen deposition, nitrogen fertilizer, and soil nitrogen contributed less.

[0194] At the same time, when this embodiment performs uncertainty analysis on the pollution in the area, such as Figure 10 As shown, uncertainty analysis shows that feces and sewage (UI 90 =0.23-0.64) and drainage from rare earth mining areas (UI 90 =0.15-0.38) has a high uncertainty in the contribution ratio.

[0195] In summary, the method of the embodiment of the present application determines the water chemistry and isotope characteristic value intervals of different nitrogen sources in rare earth mining areas by systematically monitoring the water chemistry and isotope indicators of surface water and groundwater; combines principal component analysis and cluster analysis to determine the pollution source and delineate the nitrogen pollution emission points; and uses the Bayesian isotope mixing model to consider the uncertainty of source end members and the isotope fractionation process, and quantify the contribution rate of different nitrate sources.

[0196] Reference Figure 11 The embodiment of the present application provides an ion adsorption type rare earth mining area nitrogen pollution source analysis device, the device comprising:

[0197] The first module 1110 is used to sample different water bodies and obtain samples to be analyzed corresponding to the different water bodies;

[0198] The second module 1120 is used to obtain terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data corresponding to the sample to be analyzed;

[0199] The third module 1130 is configured to calculate a principal component score for the sample to be analyzed based on the terrain parameters, physical and chemical indicators, nitrogen pollution indicators, and isotope data using a principal component analysis method to obtain a principal component score;

[0200] A fourth module 1140 is configured to calculate a first mean vector and a first covariance matrix of the principal component scores;

[0201] The fifth module 1150 is configured to perform a hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples;

[0202] The sixth module 1160 is used to label the nitrogen pollution sources of the cluster samples according to the principal component scores, the first mean vector and the first covariance matrix;

[0203] The seventh module 1170 is used to quantify the source of nitrate nitrogen in the sample to be analyzed based on the Bayesian isotope mixing model.

[0204] 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.

[0205] The present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program. The computer device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0206] 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.

[0207] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program implements the above method when executed by a processor.

[0208] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment 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.

[0209] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0210] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0211] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0212] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0213] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0214] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0215] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0216] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0217] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0218] If the integrated unit 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 all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple 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 methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, 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.

[0219] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. An ion adsorption type rare earth mining area nitrogen pollution source analysis method, characterized in that: The method comprises the following steps: Sampling different water bodies to obtain samples to be analyzed corresponding to different water bodies; Obtaining terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data corresponding to the sample to be analyzed; Calculating a principal component score for the sample to be analyzed based on the topographic parameters, the physical and chemical indicators, the nitrogen pollution indicators, and the isotope data based on a principal component analysis method to obtain a principal component score; Calculating a first mean vector and a first covariance matrix of the principal component scores; Performing hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples; Marking the cluster samples as nitrogen pollution sources according to the principal component scores, the first mean vector, and the first covariance matrix; The sources of nitrate nitrogen in the sample to be analyzed are quantified based on a Bayesian isotope mixing model.

2. The method according to claim 1, characterized in that The step of calculating a principal component score of the sample to be analyzed based on the terrain parameters, the physical and chemical indicators, the nitrogen pollution indicators, and the isotope data using a principal component analysis method to obtain the principal component score includes: forming an original observation matrix according to the terrain parameters, the physical and chemical indicators, the nitrogen pollution indicators and the isotope data; Normalizing the original measurement matrix to obtain a standardized matrix; constructing a covariance matrix based on the standardized matrix; Performing KMO test and Bartlett's sphericity test on the data in the original observation matrix; After confirming that both the KMO test and the Bartlett sphericity test are passed, the eigenvalues ​​and eigenvectors are calculated according to the covariance matrix; constructing a factor loading matrix according to the eigenvalues ​​and the eigenvectors; Optimizing the factor loading matrix to obtain a rotated loading matrix and a rotated eigenvector; The principal component score is calculated based on the rotated eigenvector and the normalized matrix.

3. The method according to claim 2, characterized in that The performing of KMO test and Bartlett's sphericity test on the data in the original observation matrix includes: Calculating the correlation coefficient and partial correlation coefficient between the data in the original observation matrix in each of the samples to be analyzed; Calculate the KMO value of each sample to be analyzed according to the correlation coefficient and the partial correlation coefficient; Performing a KMO test on the sample to be analyzed according to the KMO value and a first preset value; determining the total number of all samples to be analyzed; A Bartlett's sphericity test is performed on the samples to be analyzed according to the total number of samples to be analyzed and a second preset value.

4. The method according to claim 2, characterized in that The calculating the first mean vector and the first covariance matrix of the principal component scores includes: Obtaining the first mean vector according to the principal component scores; The first covariance matrix is ​​calculated based on the first mean vector and the principal component scores.

5. The method according to claim 1, characterized in that The step of performing hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples includes: Calculating the Euclidean distances between all samples to be analyzed based on the principal component scores; Determining the similarity of the pollution characteristics of the sample to be analyzed according to the Euclidean distance; Calculating the merging cost of the samples to be analyzed according to the principal component scores; Performing hierarchical clustering analysis on the samples to be analyzed according to the Euclidean distance and the merging cost to obtain cluster samples.

6. The method according to claim 2, characterized in that The step of labeling the nitrogen pollution sources of the cluster samples according to the principal component scores, the first mean vector, and the first covariance matrix includes: Calculating an abnormality index corresponding to each sample to be analyzed according to the principal component score, the first mean vector, and the first covariance matrix corresponding to each sample to be analyzed; determining nitrogen pollution point sources from all of the samples to be analyzed based on the anomaly index; The samples to be analyzed corresponding to the nitrogen pollution point sources are taken as the pollution type clusters to be identified; Correlating the rotated loading matrix with the factor loading matrix to obtain a correlation equation; Nitrogen pollution type identification is performed on each of the pollution type clusters to be identified according to the association equation.

7. The method according to claim 6, characterized in that The calculation formula of the abnormality index is as follows: In the formula, PC i represents the principal component score vector of the i-th sample to be analyzed, μ PC is the mean vector of the principal component scores corresponding to all samples to be analyzed; S PC Represents the covariance matrix of the principal component scores.

8. An ion adsorption type rare earth mining area nitrogen pollution source analysis device, characterized in that: The device comprises: The first module is used to sample different water bodies and obtain samples to be analyzed corresponding to different water bodies; The second module is used to obtain terrain parameters, physical and chemical indicators, nitrogen pollution indicators and isotope data corresponding to the sample to be analyzed; A third module is configured to calculate a principal component score for the sample to be analyzed based on the terrain parameters, the physical and chemical indicators, the nitrogen pollution indicators, and the isotope data using a principal component analysis method to obtain a principal component score; A fourth module is used to calculate a first mean vector and a first covariance matrix of the principal component scores; A fifth module is used to perform hierarchical cluster analysis on the samples to be analyzed according to the principal component scores to obtain cluster samples; A sixth module is configured to label the nitrogen pollution sources of the cluster samples according to the principal component scores, the first mean vector, and the first covariance matrix; The seventh module is used to quantify the source of nitrate nitrogen in the sample to be analyzed based on the Bayesian isotope mixing model.

9. A computer 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 method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing 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 7 is implemented.

Images