Method and device for analyzing soil source-sink relationship between historical left mine and surrounding agricultural land

Abstract

The invention relates to the field of heavy metal pollution cause analysis, and discloses a method and a device for analyzing a soil source-sink relationship between a historical remaining mine and surrounding agricultural land. The analysis method comprises the steps that heavy metal pollution data are collected, and the heavy metal pollution data comprise mine production related data and historical pollution condition information data; samples are collected and analyzed, sample analysis data are obtained, and the samples comprise a mine solid waste sample, an acid wastewater sample, an irrigation water sample, a bottom mud sample and an agricultural land soil sample; collecting and summarizing the heavy metal pollution data and the sample analysis data to form an achievement database; extracting sample input data from the result database; and inputting the sample input data into the geographically weighted regression model to obtain a correlation coefficient of the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste sample. According to the method, correlation coefficient calculation can be carried out through the geographic weighted regression model obtained by weighting the geographic position on the basis of the local regression analysis model, so that quantitative and accurate source analysis of the heavy metals in the soil around the mine is realized.

Description

Technical Field

[0001] The present application relates to the field of heavy metal pollution cause analysis, and more specifically, to an analysis method and device for the soil source-sink relationship between historical mines and surrounding agricultural lands. Background Art

[0002] In soil, the distribution of heavy metals is heterogeneous, hidden, cumulative, regional and complex. In recent years, in order to more accurately describe the sources of heavy metals in soil and the source-sink relationship with surrounding pollution, the analysis methods include emission inventory method, chemical mass balance model (element / isotope ratio), and gradually developed to multivariate statistical models (factor analysis, cluster analysis, positive matrix factorization (PMF), UNMIX model, etc.), advanced statistical algorithms (conditional inference tree, random forest, etc.) and spatial analysis methods (geographic detectors, etc.).

[0003] However, traditional multivariate statistical methods, such as Factor Analysis (FA), Principal Component Analysis (PCA) and Cluster Analysis (CA), although effective in source identification, usually can only provide qualitative results and are difficult to perform quantitative source analysis. This means that these methods can identify possible sources of pollution, but cannot accurately assess the specific contribution rate of each source to soil heavy metal pollution. Secondly, the Chemical Mass Balance (CMB) model and the isotope ratio law require frequent monitoring of sources and receptor samples in the study area, listing emission inventories, and continuously updating the emission source composition spectrum of the study area. Although this method can quantitatively evaluate the contribution rate of each pollution source, there may be errors in the processing process.

[0004] In particular, the sources of pollution in agricultural land around mines are complex, often superimposed with high geological background. And due to the influence of multiple factors such as mining scale, mining method, geological conditions, and climatic conditions, the degree of pollution in agricultural land around mines often varies greatly. Therefore, how to quantitatively and accurately analyze the source-sink relationship of heavy metals in the soil of mines and surrounding agricultural land is an urgent problem to be solved. Summary of the invention

[0005] In view of this, the embodiments of the present application are committed to providing a method and device for analyzing the source-sink relationship between historical mines and surrounding agricultural land soils, so as to solve the problem that it is difficult to perform quantitative and accurate source analysis of heavy metals in the soil around mines.

[0006] In the first aspect, this specification provides a method for analyzing the soil source-sink relationship between historical mines and surrounding agricultural land, including:

[0007] Collect heavy metal pollution data, including mine production-related data and historical pollution status information;

[0008] Collect samples for analysis and obtain sample analysis data, where the samples include mine solid waste samples, acid wastewater samples, irrigation water samples, sediment samples and agricultural soil samples;

[0009] Collect and summarize heavy metal pollution data and sample analysis data to form a results database;

[0010] Extract sample input data from the achievement database;

[0011] The sample input data were input into the geographically weighted regression model to obtain the correlation coefficients between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste samples;

[0012] Among them, the geographically weighted regression model is constructed by weighting the geographical location on the basis of the local regression analysis model. The independent variable of the local regression analysis model is the heavy metal content in the mine solid waste samples, and the dependent variable is the heavy metal content in the agricultural land soil samples. The geographical location characterizes the geographical relationship between the agricultural land soil samples and the mine solid waste samples. The rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater samples, irrigation water samples and sediment samples.

[0013] According to the first aspect, in a possible implementation, the parameters of the local regression analysis model are determined according to the ordinary least squares method, and the bandwidth of the geographically weighted regression model is determined according to the Akaike information criterion.

[0014] According to the first aspect, in a possible implementation manner, after collecting the heavy metal pollution data, the analysis method further includes: verifying and supplementing the heavy metal pollution data.

[0015] According to the first aspect, in a possible implementation, after inputting the sample input data into the geographically weighted regression model to obtain the correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the mine, the analysis method further includes:

[0016] Based on geographical conditions, meteorological conditions and historical mining activities, the correlation coefficient was tested;

[0017] Based on the mine pollution source-sink relationship evaluation system, the mine pollution is analyzed and evaluated. The mine pollution source-sink relationship evaluation system is constructed based on geographical conditions, meteorological conditions, mineral characteristics, mining technology and mining years. The correlation coefficient is significantly correlated with the mineral characteristics and mining years.

[0018] According to the first aspect, in a possible implementation, before collecting heavy metal pollution data, the analysis method also includes: determining the priority of the mine based on geographic information spatial overlay analysis and research literature, wherein the geographic information spatial overlay analysis includes spatial analysis and layer overlay, and the priority represents the degree of attention paid by the academic community or social opinion to the mine.

[0019] According to the first aspect, in a possible implementation, before collecting samples for analysis, the analysis method further includes: determining sample layout locations based on mine pollution diffusion conditions, pollution correlation relationships, and empirical analysis methods.

[0020] According to the first aspect, in a possible implementation manner, collecting a sample for analysis includes:

[0021] Determine single factor analysis indicators and test methods;

[0022] The samples are analyzed according to the single factor analysis indicators and test methods.

[0023] According to the first aspect, in one possible implementation, the mine pollution diffusion conditions include meteorological conditions, topographical features, physical and chemical properties of pollutants, and industrial and agricultural activities.

[0024] According to the first aspect, in a possible implementation, heavy metal pollution data and sample analysis data are collected and summarized, including: using a data management system to collect and summarize the heavy metal pollution data and sample analysis data.

[0025] In a second aspect, this specification provides an analysis device for the relationship between the source and sink of soil in a historical mine and surrounding agricultural land, including:

[0026] A pollution data collection unit is used to collect heavy metal pollution data, wherein the heavy metal pollution data includes mine production related data and historical pollution status information data;

[0027] Achievement database creation unit, used to obtain sample analysis data, collect and summarize heavy metal pollution data and sample analysis data to form an achievement database;

[0028] A sample extraction unit, used for extracting sample input data from the achievement database;

[0029] A correlation coefficient calculation unit is used to input the sample input data into the geographically weighted regression model to obtain the correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste sample;

[0030] Among them, the geographically weighted regression model is constructed by weighting the geographical location on the basis of the local regression analysis model. The independent variable of the local regression analysis model is the heavy metal content in the mine solid waste samples, and the dependent variable is the heavy metal content in the agricultural land soil samples. The geographical location characterizes the geographical relationship between the agricultural land soil samples and the mine solid waste samples. The rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater samples, irrigation water samples and sediment samples.

[0031] According to the second aspect, in a possible implementation, the parameters of the local regression analysis model are determined according to the ordinary least squares method, and the bandwidth of the geographically weighted regression model is determined according to the Akaike Information Criterion.

[0032] According to the second aspect, in a possible implementation, the pollution data collection unit further includes a verification and supplementation module for verifying and supplementing the heavy metal pollution data.

[0033] According to the second aspect, in a possible implementation manner, the correlation coefficient calculation unit further includes:

[0034] A verification module is used to verify the correlation coefficient based on geographical conditions, meteorological conditions and historical mining activities;

[0035] The analysis and evaluation module is used to analyze and evaluate the pollution in mines based on the mine pollution source-sink relationship evaluation system. The mine pollution source-sink relationship evaluation system is constructed based on geographical conditions, meteorological conditions, mineral characteristics, mining technology and mining years, and the correlation coefficient is significantly correlated with the mineral characteristics and mining years.

[0036] According to the second aspect, in a possible implementation, the pollution data collection unit also includes a priority determination module for determining the priority of the mine based on geographic information spatial overlay analysis and research literature, wherein the geographic information spatial overlay analysis includes spatial analysis and layer overlay, and the priority represents the degree of attention paid by the academic community or social opinion to the mine.

[0037] According to the second aspect, in a possible implementation, the achievement database creation unit further includes a point layout module for determining sample layout points based on mine pollution diffusion conditions, pollution correlation relationships and empirical analysis methods.

[0038] According to the second aspect, in one possible implementation, mine pollution diffusion conditions include meteorological conditions, topographical features, physical and chemical properties of pollutants, and industrial and agricultural activities.

[0039] According to the second aspect, in a possible implementation, the achievement database creation unit obtains sample analysis data using a data management system, and collects and summarizes the heavy metal pollution data and the sample analysis data.

[0040] In a third aspect, the present specification provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform each step in the analysis method as described above.

[0041] In a fourth aspect, the present specification provides a computer-readable storage medium storing a computer program, and the computer program is used to execute the steps in the analysis method described above.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Different from the prior art, the present invention provides an analysis method for the source-sink relationship between historical mines and surrounding agricultural land soils, which collects heavy metal pollution data, wherein the heavy metal pollution data includes mine production-related information and historical pollution status information; collects samples for analysis to obtain sample analysis data, wherein the samples include mine solid waste samples, acidic wastewater samples, irrigation water samples, sediment samples and agricultural land soil samples; collects and summarizes the heavy metal pollution data and sample analysis data to form a results database; extracts sample input data from the results database; inputs the sample input data into a geographically weighted regression model to obtain a correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste samples; wherein the geographically weighted regression model is constructed by weighting the geographical location on the basis of a local regression analysis model, wherein the independent variable of the local regression analysis model is the heavy metal content in the mine solid waste sample, and the dependent variable is the heavy metal content in the agricultural land soil sample, and the geographical location characterizes the geographical location relationship between the agricultural land soil sample and the mine solid waste sample, and the rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater sample, the irrigation water sample and the sediment sample. This scheme uses a geographically weighted regression model obtained by weighting the geographical location on the basis of a local regression analysis model to calculate the correlation coefficient. Since this model can reflect the spatial heterogeneity characteristics of the samples, it solves the problems of data discontinuity and low accuracy caused by spatial heterogeneity on the basis of quantitative analysis, and realizes quantitative and accurate source analysis of heavy metals in the soil around mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0045] Figure 1 A schematic flow chart of a method for analyzing the soil source-sink relationship between a historical mine and surrounding agricultural land provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a device for analyzing the relationship between soil sources and sinks of historical mines and surrounding agricultural lands provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the common meanings understood by persons with ordinary skills in the field to which this specification belongs. The words "first", "second" and similar words used in the embodiments of this specification do not indicate any order, quantity or importance, but are only used to avoid confusion of constituent elements.

[0049] Unless the context requires otherwise, throughout the specification, "plurality" means "at least two", and "including" is interpreted as an open, inclusive meaning, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" are intended to indicate that a particular feature, structure, material or characteristic associated with the embodiment or example is included in at least one embodiment or example of the specification. The schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0050] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0051] like Figure 1 As shown, it is a schematic flow chart of a method for analyzing the soil source-sink relationship between a historical mine and surrounding agricultural land provided by an embodiment of the present invention. The analysis method may specifically include the following steps.

[0052] S110: Collect heavy metal pollution data, including mine production related data and historical pollution status information;

[0053] S120: Collect samples for analysis to obtain sample analysis data, where the samples include mine solid waste samples, acid wastewater samples, irrigation water samples, sediment samples and agricultural soil samples;

[0054] S130: Collect and summarize the heavy metal pollution data and sample analysis data to form a results database;

[0055] S140: extracting sample input data from the achievement database;

[0056] S150: Input the sample input data into the geographically weighted regression model to obtain the correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste sample;

[0057] Among them, the geographically weighted regression model is constructed by weighting the geographical location on the basis of the local regression analysis model. The independent variable of the local regression analysis model is the heavy metal content in the mine solid waste samples, and the dependent variable is the heavy metal content in the agricultural land soil samples. The geographical location characterizes the geographical relationship between the agricultural land soil samples and the mine solid waste samples. The rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater samples, irrigation water samples and sediment samples.

[0058] In the above method, the mine includes non-ferrous and ferrous metal mines, as well as typical non-metallic mines such as pyrite, stone coal mine, and phosphate mine. The mine production-related information may include the type of ore, mining history, mining method, mineral processing technology, on-site mineral processing, solid waste properties and storage, distance from surrounding agricultural land, pollution prevention and control measures, surrounding agricultural land irrigation methods and irrigation water sources, etc. The historical pollution status information may include heavy metal pollution data, documents, journal reports, etc. related to the mine to be analyzed. When collecting samples, solid waste samples of mines can be collected from historical mines, and acidic wastewater samples, irrigation water samples, sediment samples, and agricultural soil samples can be collected from the surrounding areas of the mines for analysis to obtain comprehensive data. Acidic wastewater with low pH and containing heavy metals will be generated during the mining, mineral processing, and smelting of metal mines. These wastewaters will pollute the surrounding environment. Among them, irrigation water samples and sediment samples are used to analyze the pollution of water sources around mines, and agricultural soil samples are used to analyze the pollution of soil around mines. Information technology means such as data management systems can be used to collect and summarize heavy metal pollution data and sample analysis data to form a results database. The extracted sample input data can be sample analysis data after analyzing mine solid waste samples and agricultural soil samples. Sample analysis data for acidic wastewater samples, irrigation water samples, and sediment samples can be analyzed as controls instead of directly participating in the calculation to determine the rationality and accuracy of the data. Finally, the input sample input data is calculated using a geographically weighted regression model to obtain the correlation coefficient between the heavy metal content of the surrounding agricultural soil and the mine, where the geographical location can characterize the distance and orientation of the agricultural soil sample from the mine. The local regression analysis model can be geographically weighted, and the regression coefficient and predicted value (intercept) of the regression equation corresponding to the local regression analysis model can be weighted according to the geographical location of the sample. The correlation coefficient is a measure of the degree of linear correlation between the research variables, generally represented by the letter r. The correlation coefficient is a value between -1 and 1, and the larger its absolute value, the greater the degree of correlation. In addition, the obtained correlation coefficient can be added to the results database to form an updated results database for subsequent analysis and use.

[0059] This embodiment calculates the correlation coefficient by using a geographically weighted regression model obtained by weighting the geographical location on the basis of a local regression analysis model. Since this model can reflect the spatial heterogeneity characteristics of the samples, it solves the problems of data discontinuity and low accuracy caused by spatial heterogeneity on the basis of quantitative analysis, and realizes quantitative and accurate source analysis of heavy metals in the soil around mines.

[0060] In at least one embodiment of the present application, in order to utilize mature regression analysis and determine a reasonable analysis range, the present application determines a local regression analysis model according to the ordinary least squares method, and determines the bandwidth of the geographically weighted regression model according to the Akaike information criterion. The ordinary least squares method is a mature regression analysis method in regression analysis, which can ensure the reliability of regression analysis. In the regression model, bandwidth is an important parameter, which directly affects the estimation precision and accuracy of the model. The selection of bandwidth involves how to balance the smoothness and fitting degree of the data. Too small bandwidth may lead to overfitting, while too large bandwidth may lead to underfitting. After determining the bandwidth, the location of the mine to be analyzed can be used as a regression point to determine the farthest sample point. Methods for determining the bandwidth of the geographically weighted regression model include cross validation (CV), Akaike information criterion (or minimum information criterion, i.e., Akaike information criterion, referred to as AIC), generalized cross-validation criterion (GVC), etc. Simulation experiments and experience show that the CV and GCV criteria generally tend to determine a slightly smaller bandwidth, and a smaller bandwidth will reduce the deviation of the estimated value of the regression function. AIC requires fewer model parameters and takes into account both simplicity and accuracy. Therefore, this solution uses AIC to determine the bandwidth.

[0061] For example, the heavy metal content in the mine solid waste samples and the heavy metal content in the agricultural land soil samples were used to construct a regression equation using the ordinary least squares method, as follows.

[0062] y i = β 0 + β 1 x i +ε i (1)

[0063] In the formula, y i is the value of the dependent variable corresponding to the i-th sampling position, x i is the value of the independent variable at the i-th sampling position, β 0 is the intercept on the y-axis, β 1 is the estimated regression coefficient for the independent variable at the ith position, ε i is the error term. x i There can be N, where N is a positive integer.

[0064] The geographically weighted regression model provides local coefficient estimates by adding geographic location to the function, as shown below.

[0065] y i = β 0 (u i ,vi ) + β 1 (u i ,v i )x i +ε i (2)

[0066] In the formula, u i and v i is the coordinate of the i-th sampling position, β 0 (u i , v i ) is the intercept of the i-th sampling position, β 1 (u i 、v i ) is the regression coefficient of the independent variable at position i. It can be seen that by geographically weighting the intercept and regression coefficient of the regression equation of the local regression analysis model, the construction of the regression equation of the geographically weighted regression model can be achieved.

[0067] The geographically weighted regression model uses a weighted regression function to estimate the regression coefficients. Based on formula (2), it can be transformed into the following formula (3).

[0068] β (u i ,v i ) = (X T W (u i ,v i ) X) -1 X T W (u i ,v i ) Y (3)

[0069] In the formula, the matrix X corresponds to the independent variable x i , the matrix Y corresponds to the dependent variable y i , W(ui,vi) is a weight matrix chosen to ensure that closer observations have a greater influence on the result.

[0070] Based on the above formula, combined with the Pearson correlation coefficient calculation formula, the calculation formula (4) of the correlation coefficient (r) can be obtained.

[0071]

[0072] In the formula, R 2 i Determination coefficient R 2 , which indicates the degree to which the heavy metal content in solid waste explains the heavy metal content in soil or sediment in the geographically weighted regression model, β 1 (u i , v i ) is the same as β(u i , vi ) have the same regression coefficient. The correlation coefficient r can be calculated i To determine whether the dependent variable and the independent variable have a strong or weak correlation. A higher correlation coefficient value indicates that the heavy metal content in the soil or sediment is more affected by the heavy metal content in the solid waste.

[0073] In at least one embodiment of the present application, in order to ensure the accuracy and comprehensiveness of the heavy metal pollution data, the analysis method also includes verifying and supplementing the heavy metal pollution data. The verification and supplementation means include on-site investigation, personnel interviews, comparison of water resource exploitation and utilization, etc. By verifying and supplementing the heavy metal pollution data, the accuracy and comprehensiveness of the heavy metal pollution data can be ensured.

[0074] In at least one embodiment of the present application, in order to test the correlation coefficient of heavy metal pollution and obtain reliable analysis results, the analysis method also includes: testing the correlation coefficient based on geographical conditions, meteorological conditions and historical mining activities; analyzing and evaluating the pollution of the mine based on the mine pollution source-sink relationship evaluation system, wherein the mine pollution source-sink relationship evaluation system is constructed based on geographical conditions, meteorological conditions, mineral characteristics, mining technology and mining years, and the correlation coefficient is significantly correlated with the mineral characteristics and mining years (i.e., a positive correlation that has passed a significance test).

[0075] The calculation of the correlation coefficient sometimes leads to large deviations in the value due to factors such as the randomness of the sampling points and the number of samples. At this time, the correlation coefficient can be tested in combination with the geographical conditions, meteorological conditions and historical mining activities of the sampling points, so as to make appropriate adjustments, such as changing the sampling points and adjusting the number of samples. After obtaining an accurate correlation coefficient, the pollution of the mine can be analyzed and evaluated based on the constructed mine pollution source-sink relationship evaluation system. Specific evaluation indicators can be set as needed, such as the severity of pollution, the scope of pollution, the urgency of governance, the difficulty of governance, and the rate of return on governance. Different types of minerals and different mining years are reflected in the different source-sink relationships shown by the correlation coefficient. For example, the source-sink relationship between lead-zinc mines and surrounding farmland soils is stronger than that of sand and gravel mines. The reflection of the source-sink relationship of heavy metals is that the correlation coefficient calculated by lead-zinc mines will be higher than that of sand and gravel mines. The same is true for the mining years, that is, the correlation coefficient calculated for mines with a longer mining age will be higher than that for mines with a shorter mining age.

[0076] In at least one embodiment of the present application, in order to distinguish the importance of pollution sources, the analysis method also includes: determining the priority of the mine based on geographic information spatial overlay analysis and research literature, wherein the geographic information spatial overlay analysis includes spatial analysis and layer overlay, and the priority represents the degree of attention paid by the academic community or social opinion to the mine.

[0077] Spatial analysis is a technique in geographic information systems that uses geographic data to analyze spatial relationships, patterns, and trends; spatial analysis can reveal hidden patterns in the data, predict future developments, or evaluate certain characteristics of geospatial data. Layer overlay is a commonly used technique in geographic information systems that involves overlaying multiple geographic data layers according to certain rules to generate new information; each layer can contain different types of geographic data, such as topography, land use, and transportation networks. Spatial analysis and layer overlay can be combined with information such as water sources and farmland for analysis. For example, mines around water source protection areas and mines around cultivated land can be included in the survey first. Mines with high priority can be configured with higher sampling density, so as to more accurately and detailedly reflect the source-sink relationship between the mine and the surrounding soil heavy metals.

[0078] In at least one embodiment of the present application, in order to obtain reasonable sampling points, the analysis method further includes: determining the sample layout points based on mine pollution diffusion conditions, pollution associations and empirical analysis methods. Mine pollution diffusion conditions may include meteorological conditions, terrain features, physical and chemical properties of pollutants, and industrial and agricultural activities. For example, if a region has a large amount of precipitation, the pollution diffusion conditions are better. Pollution associations may include geographical locations, industrial and agricultural activities, etc. For example, the soil heavy metal content in area A is high, and there is a mine B nearby. Although mine B is not included in the source-sink relationship, mine B can be included in the analysis because it is geographically close to area A. Empirical analysis is a scientific analysis method that mainly relies on empirical knowledge to analyze and understand things. This method is more intuitive and closer to reality when compared with theoretical analysis methods. The basic idea of ​​the empirical analysis method is to conduct in-depth discussions and analyses on the research object through the experience, wisdom and collective wisdom of the analysts, so as to draw reasonable conclusions or select the best solution.

[0079] In at least one embodiment of the present application, in order to analyze the collected samples, the analysis method also includes: determining single-factor analysis indicators and test methods; analyzing the samples according to the single-factor analysis indicators and test methods. Single-factor analysis indicators refer to the evaluation criteria for solid waste, and the test indicators therein include more than 8 heavy metals, which can be expanded if necessary. Single-factor analysis indicators can be determined based on task requirements, mining conditions in the area to be analyzed, heavy metal pollution in the area to be analyzed, etc., and the test method and the best method can be determined by comprehensively considering factors such as detection cost, sample content, and detection limit.

[0080] In at least one embodiment of the present application, in order to collect and summarize the heavy metal pollution data and sample analysis data, information technology means can be used for processing, for example, the heavy metal pollution data and sample analysis data can be collected and summarized by using a data management system. In addition, software and hardware products related to fixed equipment and mobile equipment can be developed for different scenarios to conduct traceability analysis and management of heavy metal pollution.

[0081] In at least one embodiment of the present application, the principle of layout points and single factor analysis indicators can be determined according to national standards and industry standards. Relevant national standards and industry standards include HJ / T 20-1998, HJ 91.1-2019, DZ / T0295-2016, DZ / T 0289-2015, etc.

[0082] The present application also provides an analysis device for the soil source-sink relationship between historical mines and surrounding agricultural lands corresponding to the above-mentioned analysis method.

[0083] like Figure 2 As shown, an analysis device 200 for analyzing the relationship between the source and sink of soil in a historical mine and surrounding agricultural land is provided in an embodiment of the present invention. The analysis device comprises:

[0084] The pollution data collection unit 210 is used to collect heavy metal pollution data, wherein the heavy metal pollution data includes mine production related data and historical pollution status information data;

[0085] Achievement database creation unit 220, used to obtain sample analysis data, collect and summarize heavy metal pollution data and sample analysis data to form an achievement database;

[0086] The sample extraction unit 230 is used to extract sample input data from the achievement database;

[0087] A correlation coefficient calculation unit 240 is used to input the sample input data into a geographically weighted regression model to obtain a correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste sample;

[0088] Among them, the geographically weighted regression model is constructed by weighting the geographical location on the basis of the local regression analysis model. The independent variable of the local regression analysis model is the heavy metal content in the mine solid waste samples, and the dependent variable is the heavy metal content in the agricultural land soil samples. The geographical location characterizes the geographical relationship between the agricultural land soil samples and the mine solid waste samples. The rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater samples, irrigation water samples and sediment samples.

[0089] In the above-mentioned device, the mine includes non-ferrous and ferrous metal mines, as well as typical non-metallic mines such as pyrite, stone coal mine, and phosphate mine. The mine production-related information may include the type of ore, mining history, mining method, mineral processing technology, on-site smelting, solid waste properties and storage, distance from surrounding agricultural land, pollution prevention and control measures, surrounding agricultural land irrigation methods and irrigation water sources, etc. The historical pollution status information may include heavy metal pollution data, documents, journal reports, etc. related to the mine to be analyzed. When collecting samples, solid waste samples of mines can be collected from historical mines, and acidic wastewater samples, irrigation water samples, sediment samples, and agricultural soil samples can be collected from the surrounding areas of the mines for analysis to obtain comprehensive data. Acidic wastewater with low pH and containing heavy metals will be generated during the mining, mineral processing, and smelting of metal mines. These wastewaters will pollute the surrounding environment. Among them, irrigation water samples and sediment samples are used to analyze the pollution of water sources around mines, and agricultural soil samples are used to analyze the pollution of soil around mines. Information technology means such as data management systems can be used to collect and summarize heavy metal pollution data and sample analysis data to form a results database. The extracted sample input data can be sample analysis data after analyzing mine solid waste samples and agricultural soil samples. Sample analysis data for acidic wastewater samples, irrigation water samples, and sediment samples can be analyzed as controls instead of directly participating in the calculation to determine the rationality and accuracy of the data. Finally, the input sample input data is calculated using a geographically weighted regression model to obtain the correlation coefficient between the heavy metal content of the surrounding agricultural soil and the mine, where the geographical location can characterize the distance and orientation of the agricultural soil sample from the mine. The local regression analysis model can be geographically weighted, and the regression coefficient and predicted value (intercept) of the regression equation corresponding to the local regression analysis model can be weighted according to the geographical location of the sample. The correlation coefficient is a measure of the degree of linear correlation between the research variables, generally represented by the letter r. The correlation coefficient is a value between -1 and 1, and the larger its absolute value, the greater the degree of correlation. In addition, the obtained correlation coefficient can be added to the results database to form an updated results database for subsequent analysis and use.

[0090] This embodiment calculates the correlation coefficient by using a geographically weighted regression model obtained by weighting the geographical location on the basis of a local regression analysis model. Since this model can reflect the spatial heterogeneity characteristics of the samples, it solves the problems of data discontinuity and low accuracy caused by spatial heterogeneity on the basis of quantitative analysis, and realizes quantitative and accurate source analysis of heavy metals in the soil around mines.

[0091] In at least one embodiment of the present application, in order to utilize mature regression analysis and determine a reasonable analysis range, the present application determines a local regression analysis model according to the ordinary least squares method, and determines the bandwidth of the geographically weighted regression model according to the Akaike information criterion. The ordinary least squares method is a mature regression analysis method in regression analysis, which can ensure the reliability of regression analysis. In the regression model, bandwidth is an important parameter, which directly affects the estimation precision and accuracy of the model. The selection of bandwidth involves how to balance the smoothness and fitting degree of the data. Too small bandwidth may lead to overfitting, while too large bandwidth may lead to underfitting. After determining the bandwidth, the location of the mine to be analyzed can be used as a regression point to determine the farthest sample point. Methods for determining the bandwidth of the geographically weighted regression model include cross validation, Akaike information criterion (or minimum information criterion, i.e. Akaike information criterion, referred to as AIC), generalized cross-validation criterion (Generalized cross-validation criterion, GCV), etc. Simulation experiments and experience show that the CV and GCV criteria generally tend to determine a slightly smaller bandwidth, and a smaller bandwidth will reduce the deviation of the estimated value of the regression function. AIC requires fewer model parameters and takes into account both simplicity and accuracy. Therefore, this solution uses AIC to determine the bandwidth.

[0092] In at least one embodiment of the present application, in order to ensure the accuracy and comprehensiveness of the heavy metal pollution data, the pollution data collection unit also includes a verification and supplementation module for verifying and supplementing the heavy metal pollution data. By verifying and supplementing the heavy metal pollution data, the accuracy and comprehensiveness of the heavy metal pollution data can be ensured.

[0093] In at least one embodiment of the present application, in order to verify the correlation coefficient of heavy metal pollution and obtain reliable analysis results, the correlation coefficient calculation unit also includes: a verification module, which is used to verify the correlation coefficient based on geographical conditions, meteorological conditions and historical mining activities; an analysis and evaluation module, which is used to analyze and evaluate the pollution of the mine based on the mine pollution source-sink relationship evaluation system, wherein the mine pollution source-sink relationship evaluation system is constructed based on geographical conditions, meteorological conditions, mineral characteristics, mining technology and mining years, and the correlation coefficient is significantly correlated with the mineral characteristics and mining years.

[0094] The calculation of the correlation coefficient sometimes leads to large deviations in the value due to factors such as the randomness of the sampling points and the number of samples. At this time, the correlation coefficient can be tested in combination with the geographical conditions, meteorological conditions and historical mining activities of the sampling points, so as to make appropriate adjustments, such as changing the sampling points and adjusting the number of samples. After obtaining an accurate correlation coefficient, the pollution of the mine can be analyzed and evaluated based on the constructed mine pollution source-sink relationship evaluation system. Specific evaluation indicators can be set as needed, such as the severity of pollution, the scope of pollution, the urgency of governance, the difficulty of governance, and the rate of return on governance. Different types of minerals and different mining years are reflected in the different source-sink relationships shown by the correlation coefficient. For example, the source-sink relationship between lead-zinc mines and surrounding farmland soils is stronger than that of sand and gravel mines. The reflection of the source-sink relationship of heavy metals is that the correlation coefficient calculated by lead-zinc mines will be higher than that of sand and gravel mines. The same is true for the mining years, that is, the correlation coefficient calculated for mines with a longer mining age will be higher than that for mines with a shorter mining age.

[0095] In at least one embodiment of the present application, in order to distinguish the importance of pollution sources, the pollution data collection unit also includes a priority determination module, which is used to determine the priority of the mine based on geographic information spatial overlay analysis and research literature, wherein the geographic information spatial overlay analysis includes spatial analysis and layer overlay, and the priority represents the degree of attention paid by the academic community or social opinion to the mine.

[0096] Spatial analysis is a technique in geographic information systems that uses geographic data to analyze spatial relationships, patterns, and trends; spatial analysis can reveal hidden patterns in the data, predict future developments, or evaluate certain characteristics of geospatial data. Layer overlay is a commonly used technique in geographic information systems that involves overlaying multiple geographic data layers according to certain rules to generate new information; each layer can contain different types of geographic data, such as topography, land use, and transportation networks. Spatial analysis and layer overlay can be combined with information such as water sources and farmland for analysis. For example, mines around water source protection areas and mines around cultivated land can be included in the survey first. Mines with high priority can be configured with higher sampling density, so as to more accurately and detailedly reflect the source-sink relationship between the mine and the surrounding soil heavy metals.

[0097] In at least one embodiment of the present application, in order to obtain reasonable sampling points, the achievement database creation unit also includes a point layout module for determining the sample layout points based on mine pollution diffusion conditions, pollution associations and empirical analysis methods. Mine pollution diffusion conditions may include meteorological conditions, terrain features, physical and chemical properties of pollutants, and industrial and agricultural activities. For example, if a region has a large amount of precipitation, the pollution diffusion conditions are better. Pollution associations may include geographical location, industrial and agricultural activities, etc. For example, the soil heavy metal content in area A is high, and there is a mine B nearby. Although mine B is not included in the source-sink relationship, mine B can be included in the analysis due to its close geographical location. The empirical analysis method is a scientific analysis method that mainly relies on empirical knowledge to analyze and understand things. This method is more intuitive and closer to reality when compared with the theoretical analysis method. The basic idea of ​​the empirical analysis method is to conduct in-depth discussion and analysis of the research object through the experience, wisdom and collective wisdom of the analysts, so as to draw reasonable conclusions or select the best solution.

[0098] In at least one embodiment of the present application, in order to collect and summarize the heavy metal pollution data and sample analysis data, information technology means can be used for processing. For example, the achievement database creation unit obtains the sample analysis data using the data management system, and collects and summarizes the heavy metal pollution data and sample analysis data. In addition, software and hardware products related to fixed equipment and mobile equipment can also be developed for different scenarios to conduct traceability analysis and management of heavy metal pollution.

[0099] like Figure 3 , which is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. As shown in the figure, the electronic device 30 includes one or more processors 31 and a memory 32.

[0100] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 30 to perform desired functions.

[0101] The memory 32 may include one or more computer program products, and the memory may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may run the program instructions to implement the analysis method of the aforementioned various embodiments of the present application described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0102] In one example, the electronic device 30 may further include: an input device 33 and an output device 34, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0103] Of course, to simplify, Figure 3 Only some of the components related to the present application in the electronic device 30 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 30 may also include any other appropriate components.

[0104] Based on the embodiments of the present application, the present application also provides a computer-readable storage medium, wherein computer instructions are used to enable a computer to execute each step in the analysis method of each of the aforementioned embodiments.

[0105] Based on the embodiments of the present application, the present application further provides a processor, and the processor is used to run a program, wherein the program executes each step in the analysis method of each of the aforementioned embodiments when running.

[0106] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0107] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), 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 disk 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 and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0108] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the described embodiment, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0109] For the above description of the disclosed embodiments, the features recorded in each embodiment in this specification can be replaced or combined with each other, so that professionals in the field can implement or use the present application. The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as above with a preferred embodiment, it is not used to limit the present invention. Any technician familiar with the field can make many possible changes and modifications to the technical solution of the present invention using the methods and technical contents disclosed above without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of protection of the technical solution of the present invention.

Claims

1. A method for analyzing the soil source-sink relationship between historical mines and surrounding agricultural land, characterized in that: include: Collecting heavy metal pollution data, wherein the heavy metal pollution data includes mine production related data and historical pollution status information; Collecting samples for analysis to obtain sample analysis data, wherein the samples include mine solid waste samples, acid wastewater samples, irrigation water samples, bottom sediment samples and agricultural soil samples; Collecting and summarizing the heavy metal pollution data and the sample analysis data to form a results database; Extracting sample input data from the achievement database; Inputting the sample input data into a geographically weighted regression model to obtain a correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste sample; Among them, the geographically weighted regression model is constructed by weighting the geographical location on the basis of the local regression analysis model. The independent variable of the local regression analysis model is the heavy metal content in the mine solid waste sample, and the dependent variable is the heavy metal content in the agricultural land soil sample. The geographical location characterizes the geographical location relationship between the agricultural land soil sample and the mine solid waste sample. The rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater sample, the irrigation water sample and the sediment sample.

2. The analysis method according to claim 1, characterized in that The parameters of the local regression analysis model are determined according to the ordinary least squares method, and the bandwidth of the geographically weighted regression model is determined according to the Akaike information criterion.

3. The analysis method according to claim 1 or 2, characterized in that After the heavy metal pollution data is collected, the method further includes: verifying and supplementing the heavy metal pollution data.

4. The analysis method according to claim 1 or 2, characterized in that After inputting the sample input data into the geographically weighted regression model to obtain the correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the mine, the method further includes: Testing the correlation coefficient based on geographical conditions, meteorological conditions and historical mining activities; Based on the mine pollution source-sink relationship evaluation system, the pollution of the mine is analyzed and evaluated, wherein the mine pollution source-sink relationship evaluation system is constructed based on geographical conditions, meteorological conditions, mineral characteristics, mining technology and mining years, and the correlation coefficient is significantly correlated with the mineral characteristics and mining years.

5. The analysis method according to claim 1 or 2, characterized in that Before collecting the heavy metal pollution data, it also includes: determining the priority of the mine based on the spatial overlay analysis of geographic information and research literature, wherein the spatial overlay analysis of geographic information includes spatial analysis and layer overlay, and the priority represents the degree of attention paid by the academic community or social opinion to the mine.

6. The analysis method according to claim 1 or 2, characterized in that Before collecting samples for analysis, the method also includes: determining the sample layout locations based on mine pollution diffusion conditions, pollution correlation relationships and empirical analysis methods.

7. The analysis method according to claim 6, characterized in that The collecting of samples for analysis includes: Determine single factor analysis indicators and test methods; The sample is analyzed according to the single factor analysis index and the test method.

8. The analysis method according to claim 6, characterized in that The conditions for the diffusion of mine pollution include meteorological conditions, topographical features, the physical and chemical properties of pollutants, and industrial and agricultural activities.

9. The analysis method according to claim 1, characterized in that The collecting and summarizing the heavy metal pollution data and the sample analysis data includes: using a data management system to collect and summarize the heavy metal pollution data and the sample analysis data.

10. An analysis device for the relationship between the source and sink of soil in historical mines and surrounding agricultural lands, characterized in that: include: A pollution data collection unit, used to collect heavy metal pollution data, wherein the heavy metal pollution data includes mine production related data and historical pollution status information data; A result database creation unit is used to obtain sample analysis data, collect and summarize the heavy metal pollution data and the sample analysis data to form a result database; A sample extraction unit, used for extracting sample input data from the achievement database; A correlation coefficient calculation unit, used for inputting the sample input data into a geographically weighted regression model to obtain a correlation coefficient between the heavy metal content of the surrounding agricultural land soil and the heavy metal content of the mine solid waste sample; Among them, the geographically weighted regression model is constructed by weighting the geographical location on the basis of the local regression analysis model. The independent variable of the local regression analysis model is the heavy metal content in the mine solid waste samples, and the dependent variable is the heavy metal content in the agricultural land soil samples. The geographical location characterizes the geographical location relationship between the agricultural land soil samples and the mine solid waste samples. The rationality and accuracy of the correlation coefficient are determined according to the heavy metal content in the acidic wastewater samples, irrigation water samples and sediment samples.

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