A mine heavy metal pollutant risk early warning system and method

Abstract

The application discloses a mine heavy metal pollutant risk early warning system and method, relates to the mine pollution early warning technical field, and solves the technical problem of low efficiency of the existing heavy metal pollutant early warning system in analyzing mineral heavy metal pollutants; pollution data corresponding to each heavy metal pollutant of a mine and a plurality of geological characteristic data on each set diffusion path are acquired; a preliminary diffusion rate of the corresponding heavy metal is generated based on the pollution data and the geological characteristic data of the heavy metal pollutant; a plurality of historical environmental data of the mine within a set time are acquired, and the heavy metal retention of each region is generated based on the historical environmental data and the preliminary diffusion rate; an early warning report is generated based on the heavy metal retention; and the efficiency of heavy metal early warning of the mine is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mine pollution early warning, and particularly relates to a mine heavy metal pollutant risk early warning system and method. BACKGROUND

[0002] The mine environment problem refers to the pollution and damage of mining activities to the environment, such as the damage to the original topography, landform and geological structure, etc. The mine pollution not only causes damage to the ecological environment, but also seriously threatens the sustainable development of human health and social economy.

[0003] The existing mine heavy metal analysis early warning often sets a plurality of sampling points to analyze the soil of the sampling points, judges whether there is heavy metal pollutant, and performs early warning. This method needs to consume a large amount of manpower and material resources, and the effect finally achieved is difficult to comprehensively reflect the distribution of heavy metal pollutants around the mine area; thus, the efficiency of early warning of heavy metal pollutants in the mine area is low; therefore, a mine heavy metal pollutant risk early warning system and method are needed. SUMMARY

[0004] The application aims to at least solve one of the technical problems existing in the prior art; for this purpose, the application provides a mine heavy metal pollutant risk early warning system and method, which is used to solve the technical problem of low efficiency of the existing heavy metal pollutant early warning system in analyzing heavy metal pollutants.

[0005] To achieve the above-mentioned purpose, the first aspect of the application provides a mine heavy metal pollutant risk early warning method, which comprises:

[0006] Obtaining the pollution data corresponding to each heavy metal pollutant of the mine area, and a plurality of geological feature data on each set diffusion path;

[0007] Generating the preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological feature data of the heavy metal pollutant;

[0008] Obtaining a plurality of historical environmental data of the mine area within a set time, generating the heavy metal retention amount of each area based on the historical environmental data and the preliminary diffusion rate, and generating an early warning report based on the heavy metal retention amount.

[0009] The application obtains the pollution data of each heavy metal pollutant in the mining area, and the geological feature data of each set diffusion path; generates the preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological feature data of the heavy metal pollutants; obtains the historical environmental data of the mining area within a set time, generates the heavy metal retention amount of each region based on the historical environmental data and the preliminary diffusion rate; generates an early warning report based on the heavy metal retention amount; analyzes the characteristics of each heavy metal pollutant, analyzes the influence of the soil of each region on the diffusion of heavy metals, and further estimates the heavy metals in each region in combination with the diffusion environment and time, and generates a corresponding alarm signal according to the estimation result; realizes comprehensive analysis of the surrounding area of the mining area, and improves the efficiency of heavy metal early warning in the mining area.

[0010] Preferably, the preliminary diffusion rate of the corresponding heavy metal is generated based on the pollution data and the geological feature data of the heavy metal pollutants, including:

[0011] The pollution source concentration and the pollutant characteristic data corresponding to each pollutant type in the pollution data are extracted; the pollutant characteristic data is input into a stability evaluation model to obtain a stability score of the heavy metal pollutant, and a diffusion capacity coefficient for evaluating the diffusion capacity of the corresponding heavy metal is generated based on the pollution source concentration and the stability score corresponding to each pollutant type;

[0012] The distribution area, the content of the adhering substance in the soil, and the porosity in the geological feature data of each region are extracted, and a diffusion resistance coefficient is generated based on the distribution area, the content of the adhering substance, and the porosity of each region;

[0013] The difference between the diffusion capacity coefficient and the diffusion resistance coefficient is recorded as the diffusion rate coefficient of the corresponding region, and the product of the diffusion rate coefficient and the set unit diffusion rate is recorded as the preliminary diffusion rate of the heavy metal in the corresponding region.

[0014] Preferably, the stability evaluation model is obtained by training an artificial intelligence model, including:

[0015] Obtain a plurality of pollutant characteristic data and corresponding stability scores thereof through a database, the pollutant characteristic data being corresponding to the form of heavy metal pollutants, such as ionic state, molecular state, complex and precipitate, etc., boiling point and ion strength, etc., related parameters affecting the stability thereof; the stability score being a score given by an expert to the ability of the heavy metal pollutants to exist in nature according to the related parameters of the heavy metal pollutants in the pollutant characteristics, for example, the higher the boiling point, the higher the stability of the corresponding heavy metal pollutants, the higher the stability score set, the weaker the diffusion ability under the same environmental conditions; for another example, the compound form thereof is ionic state, which is easy to dissolve in water, the corresponding stability is lower, the stability score set is lower, and the diffusion ability is stronger under the same environmental conditions; integrate the plurality of pollutant characteristic data and stability scores into a plurality of training data and test data;

[0016] Train the artificial intelligence model using the training data, and test the trained artificial intelligence model using the test data; finally obtain a stability evaluation model with the input being the pollutant characteristic data and the output being the stability score; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0017] Preferably, the diffusion capacity coefficient of the corresponding heavy metal is generated based on the corresponding pollutant source concentration and stability score of each pollutant type, comprising:

[0018] Obtain the stability score and the pollutant source concentration of each heavy metal pollutant; substitute the stability score and the pollutant source concentration into the set diffusion capacity quantification function to obtain the diffusion capacity coefficient of the corresponding heavy metal pollutant;

[0019] Sum the diffusion capacity coefficients of each heavy metal pollutant to obtain the diffusion capacity coefficient of the corresponding heavy metal.

[0020] Preferably, the diffusion resistance coefficient is generated based on the distribution area, the content of adhering substances and the porosity of each region, comprising:

[0021] Substitute the porosity and the content of adhering substances of each region into the set quantification function to obtain the quantification value of the influence of the heavy metal pollutant diffusion thereof; the adhering substances include organic matter in soil, such as humus; clay minerals, such as montmorillonite, illite and kaolinite; and metal oxides or hydroxides in soil, such as iron, aluminum and manganese;

[0022] Generate the diffusion resistance coefficient for representing the influence of the region on the diffusion of heavy metal pollutants based on the product of the quantification value and the distribution area;

[0023] Obtain the diffusion resistance coefficients of each region on each set diffusion path in turn.

[0024] Preferably, the set diffusion path is a line segment with a set length in a direction set from the heavy metal pollution source; and the set diffusion path is equally divided into a plurality of regional segments based on a fan shape formed by the center of the heavy metal pollution source and the set diffusion path as a side.

[0025] Preferably, the heavy metal retention amount is generated based on the historical environmental data and the preliminary diffusion rate; and the heavy metal retention amount includes:

[0026] The temperature value and the humidity value corresponding to each time period in the historical data are extracted, and each preliminary diffusion rate is adjusted based on the temperature value and the humidity value to obtain a corrected diffusion rate under a corresponding environmental condition.

[0027] The time period heavy metal retention amount of the time period is generated based on the product of the corrected diffusion rate and the length of the corresponding time period; and the heavy metal retention amount of the corresponding region is obtained by summing the time period heavy metal retention amounts of each time period in the same region.

[0028] Preferably, the preliminary diffusion rate is adjusted based on the temperature value and the humidity value to obtain the corrected diffusion rate, and the adjusting includes:

[0029] The heavy metal pollutant concentration and the preliminary diffusion rate corresponding to each region, and the distance from the center of each region to the heavy metal pollution source are obtained; and the corresponding attenuation factor is obtained by searching the attenuation factor lookup standard based on the heavy metal pollutant concentration and the distance.

[0030] The theoretical diffusion rate affected by the distance from the heavy metal pollutant source is obtained by correcting the preliminary diffusion rate based on the attenuation factor.

[0031] The corrected diffusion rate of the corresponding region under the corresponding environmental condition is obtained by correcting the theoretical diffusion rate based on the temperature value and the humidity value.

[0032] Preferably, the time period heavy metal retention amount is generated based on the product of the corrected diffusion rate and the length of the corresponding time period, and the generating includes:

[0033] The corrected diffusion rate of each region and each time period on the set diffusion path is obtained; and the product of the corrected diffusion rate and the length of the corresponding time period is recorded as the heavy metal diffusion amount of the region in the time period.

[0034] The heavy metal diffusion amount of each region and each time period is obtained in sequence.

[0035] The heavy metal diffusion amount of the target region and the region above the target region is obtained, and the heavy metal diffusion amount of the region above the target region is marked as the heavy metal transfer amount; and the difference between the heavy metal transfer amount and the heavy metal diffusion amount is recorded as the time period heavy metal retention amount of the target region in the corresponding time period.

[0036] Preferably, the early warning report is generated based on the heavy metal retention amount, including:

[0037] The heavy metal retention amount of each area is obtained, and it is determined whether the heavy metal retention amount of each area is greater than a set heavy metal content threshold value; if yes, the area is marked as a heavy pollution area; if no, the area is marked as a light pollution area.

[0038] Another aspect of the present application provides a mine heavy metal pollutant risk early warning system, comprising a data acquisition module, a data analysis model, an interactive display module and a database.

[0039] The data acquisition module: obtains pollution data and geological characteristic data through the data acquisition device connected thereto; the pollution data includes a plurality of pollutant types, and corresponding pollution source concentration and pollutant characteristic data; the geological characteristic data includes distribution area, content of adhering material in soil and porosity;

[0040] The data analysis module: generates a preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological characteristic data of the heavy metal pollutant; obtains a plurality of historical environmental data of the mine within a set time through the database connected thereto, generates a heavy metal retention amount of each area based on the historical environmental data and the preliminary diffusion rate, and generates an early warning report based on the heavy metal retention amount;

[0041] The interactive display module: obtains and displays the early warning report.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] 1. The present application obtains the pollution data of each heavy metal pollutant in the mine area, and a plurality of geological characteristic data on each set diffusion path; generates a preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological characteristic data of the heavy metal pollutant; obtains a plurality of historical environmental data of the mine within a set time, generates a heavy metal retention amount of each area based on the historical environmental data and the preliminary diffusion rate, generates an early warning report based on the heavy metal retention amount; analyzes the characteristics of each heavy metal pollutant, analyzes the influence of the soil of each area on the diffusion of heavy metals, and estimates the heavy metals of each area in combination with the diffusion environment and time, generates a corresponding alarm signal according to the estimation result; realizes comprehensive analysis of the surrounding area of the mine, and improves the efficiency of heavy metal early warning of the mine.

[0044] 2. The present application analyzes each heavy metal pollutant, and then comprehensively analyzes the pollution diffusion of the heavy metal, so that the analysis result is more accurate.

[0045] 3. The application significantly improves the early warning efficiency through automatic and intelligent analysis means, and reduces the errors caused by manual intervention and subjective judgment.

[0046] 4. The application generates early warning reports and visual support, enabling environmental management departments to timely discover pollution risks, take targeted measures to prevent the spread and harm of heavy metal pollutants, and improve the overall level and effect of environmental management. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 The figure is a schematic diagram of the steps of the risk early warning method in the present application.

[0049] Figure 2 The figure is a schematic diagram of the module connection of the risk early warning system in the present application. DETAILED DESCRIPTION

[0050] The technical solutions of the present application will be described in detail below in conjunction with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] Please refer to Figure 1 The first aspect of the present application provides a mine heavy metal pollutant risk early warning method, comprising:

[0052] Obtain the pollution data corresponding to each heavy metal pollutant in the mining area. Different resources are mined in different mining areas, so that the corresponding heavy metal pollutants are also different. According to the type of resources mined in the mining area, the type of corresponding heavy metal pollutants can be obtained. And a plurality of geological feature data on each set diffusion path; The set diffusion path is a line segment with a set length in a set direction starting from the heavy metal pollution source; The center of the heavy metal pollution source is taken as one side to make a sector, and the set diffusion path is equally divided to obtain a plurality of regional segments, and the sector is divided based on the regional segment to obtain a region; The geological feature data is the relevant data of the soil in the corresponding region, including the distribution area, the content of the adhering substance in the soil and the porosity, etc.

[0053] generate a preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological characteristic data of the heavy metal pollutants; the preliminary diffusion rate is a theoretical diffusion rate obtained by theoretically analyzing the influence of the characteristics of the heavy metal pollutants and the geological characteristics of the diffusion area on the diffusion of the heavy metal pollutants;

[0054] obtain a plurality of historical environmental data of the mining area within a set time, and generate a heavy metal retention amount of each area based on the historical environmental data and the preliminary diffusion rate; the heavy metal retention amount is an estimated retention amount of the heavy metal in each area after being affected by the environment and diffusion; and generate a warning report based on the heavy metal retention amount.

[0055] In this embodiment, the pollution data corresponding to each heavy metal pollutant of the mining area and a plurality of geological characteristic data on each set diffusion path are obtained; a preliminary diffusion rate of the corresponding heavy metal is generated based on the pollution data and the geological characteristic data of the heavy metal pollutants; a plurality of historical environmental data of the mining area within a set time are obtained, and a heavy metal retention amount of each area is generated based on the historical environmental data and the preliminary diffusion rate; a warning report is generated based on the heavy metal retention amount; the characteristics of each heavy metal pollutant are analyzed, the influence of the soil of each area on the diffusion of the heavy metal is analyzed, and the heavy metal of each area is estimated in combination with the diffusion environment and time, and a corresponding warning signal is generated according to the estimation result; the surrounding area of the mining area is comprehensively analyzed, and the efficiency of the heavy metal warning of the mining area is improved.

[0056] generate a preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological characteristic data of the heavy metal pollutants, comprising:

[0057] extract each pollutant type in the pollution data; since one heavy metal corresponds to a plurality of heavy metal pollutants, the characteristics of each heavy metal pollutant are different, so different heavy metal pollutants belonging to the same heavy metal are analyzed differently in this embodiment to improve the accuracy of the final analysis; the pollution type is a certain pollution compound or ion corresponding to the heavy metal; the corresponding pollution source concentration and the pollutant characteristic data; the pollution source concentration can be understood as the pollutant concentration of the pollution source such as the pit mouth or waste accumulation area; input the pollutant characteristic data into a stability evaluation model to obtain a stability score of the heavy metal pollutant, generate a diffusion capacity coefficient for evaluating the diffusion capacity of the corresponding heavy metal based on the pollution source concentration and the stability score corresponding to each pollutant type; the diffusion capacity coefficient is the total diffusion capacity of each pollutant of the corresponding heavy metal; the larger the diffusion capacity coefficient is, the stronger the diffusion capacity of the heavy metal with each pollutant is;

[0058] The distribution area, the content of the adhering substance in the soil and the porosity in the geological characteristic data in each region are extracted, and the diffusion resistance coefficient is generated based on the distribution area, the content of the adhering substance and the porosity in each region; the diffusion resistance coefficient is the resistance of the soil to the diffusion of heavy metals, the greater the diffusion resistance coefficient, the greater the influence of the soil in the corresponding region on the diffusion of heavy metals, the slower the diffusion speed of heavy metals in the region, and the greater the heavy metal retention amount;

[0059] The difference between the diffusion capacity coefficient and the diffusion resistance coefficient is recorded as the diffusion rate coefficient of the corresponding region, and the product of the diffusion rate coefficient and the set unit diffusion rate is recorded as the preliminary diffusion rate of the heavy metals in the corresponding region; it can be understood that in the embodiment, by setting the values of the parameters in different quantitative functions, the diffusion capacity coefficient and the diffusion resistance coefficient are in the same range, and the quantitative calculation can be performed, and by setting the unit diffusion rate, the difference between the diffusion capacity coefficient and the diffusion resistance coefficient is converted into the preliminary diffusion rate.

[0060] The embodiment comprehensively analyzes each heavy metal by the characteristics of the heavy metal itself and the characteristics of its propagation medium, so that the propagation of the heavy metal can be accurately grasped.

[0061] The stability evaluation model is obtained by artificial intelligence model training, including: obtaining a plurality of pollutant characteristic data and corresponding stability scores from a database, the pollutant characteristic data is the form of the corresponding heavy metal pollutant, such as ion state, molecular state, complex and precipitate, etc., boiling point and ion strength, etc. related parameters affecting its stability; the stability score is a score given by experts according to the related parameters of the heavy metal pollutant in the pollutant characteristics on its ability to exist in nature, for example, the higher the boiling point, the higher the stability of the corresponding heavy metal pollutant, the higher the stability score, and under the same environmental conditions, the weaker the diffusion ability; for example, the compound form is ion state, which is easy to dissolve in water, and the corresponding stability is lower, and the stability score is set lower, and under the same environmental conditions, the stronger the diffusion ability; the plurality of pollutant characteristic data and stability scores are integrated into a plurality of training data and test data;

[0062] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data; specifically, the pollutant characteristic data in the test data is input into the trained artificial intelligence model to obtain an output stability score, and it is judged whether the difference between the stability score and the stability score recorded in the corresponding test data is less than a set threshold value. If yes, it means that the test data passes the test, and the next group of test data is tested. If no, the test data is retested. Finally, after a certain proportion of test data passes the test, a stability evaluation model with input of pollutant characteristic data and output of stability score is obtained. The artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0063] The diffusion capacity coefficient of the corresponding heavy metal is generated based on the corresponding pollutant source concentration and stability score of each pollutant type, including: obtaining the stability score and pollutant source concentration of each heavy metal pollutant; substituting the stability score and pollutant source concentration into a set diffusion capacity quantification function to obtain the diffusion capacity coefficient of the corresponding heavy metal pollutant; summing the diffusion capacity coefficients of each heavy metal pollutant to obtain the diffusion capacity coefficient of the corresponding heavy metal;

[0064] The diffusion capacity quantification function is:

[0065] KN = H(WN, WP)

[0066] Where KN is the diffusion capacity coefficient, H() is the set diffusion capacity quantification function, WN is the pollutant source concentration, and WP is the stability score. H() includes at least two functions, one function for quantifying the influence of the pollutant source on the diffusion capacity, which is an increasing function; the other function for quantifying the influence of the stability score on the diffusion capacity, which is a decreasing function. The diffusion capacity quantification function in this embodiment is:

[0067]

[0068] Where DWN is a set unit pollutant source concentration, used to remove the dimension of the pollutant source concentration, δ1 is a set adjustment coefficient one, the specific value of which is set according to expert experience, and can be used to limit the range of the influence value of the pollutant source concentration on the diffusion capacity coefficient in cooperation with the unit pollutant source concentration. DWP is a set unit stability score, and δ2 is a set adjustment coefficient two, the specific value of which is set according to expert experience, and can be used to limit the range of the influence value of the stability score on the diffusion capacity coefficient in cooperation with the unit stability score.

[0069] The embodiment quantifies the influence of the nature of the heavy metal pollutants on the diffusion of the pollutants by a set diffusion capacity quantification function; the greater the concentration of the pollution source, the greater the diffusion driving force of the pollutants, and the greater the corresponding diffusion capacity coefficient; the more unstable the nature of the pollutants, the stronger the diffusion capacity of the pollutants, such as pollutants that are easy to volatilize or easy to dissolve, which diffuse with air or water flow, and thus the greater the corresponding diffusion capacity coefficient; since the same heavy metal may have multiple heavy metal pollutants with different properties, the properties of different heavy metal pollutants are different; the embodiment analyzes each heavy metal pollutant, and then comprehensively analyzes the heavy metal pollution diffusion, so that the analysis result is more accurate.

[0070] The diffusion resistance coefficient is generated based on the distribution area, the content of the adhering substance and the porosity of each region, including: substituting the porosity and the content of the adhering substance of each region into a set quantification function to obtain a quantification value of the influence of the region on the diffusion of the heavy metal pollutants; the adhering substance includes the content of organic matter such as humus, clay minerals such as montmorillonite, illite and kaolinite, and metal oxides or hydroxides such as iron, aluminum and manganese in the soil;

[0071] The diffusion resistance coefficient for representing the influence of the region on the diffusion of the heavy metal pollutants is generated based on the product of the quantification value and the distribution area;

[0072] The diffusion resistance coefficients of the regions on each set diffusion path are sequentially obtained.

[0073] Specifically, the porosity and the content of the corresponding adhering substance of each region are marked as KXD and XFN respectively; the distribution area is marked as FM;

[0074] The corresponding diffusion resistance coefficient KS is calculated by the formula KS=FM×[L(KXD)+M(XFN)];

[0075] Wherein, L() is a quantification function corresponding to the porosity, and M() is a quantification function corresponding to the content of the adhering substance; the greater the content of the adhering substance in the soil, the greater the possibility of adsorption of the heavy metal pollutants in the soil, the greater the diffusion resistance, and the greater the corresponding quantification value; the quantification value is positively correlated with the content of the corresponding adhering substance, so the quantification function M() in the embodiment is an increasing function; the greater the porosity of the soil, the faster the speed of the soil in propagating the heavy metal pollutants, the smaller the corresponding diffusion resistance, and the smaller the quantification value; the quantification value is negatively correlated with the corresponding soil porosity, so the quantification function L() in the embodiment is a decreasing function; specifically, in one of the embodiments, the quantification function M(XFN)=ε1×e XFN; wherein, ε1 is an adjustment coefficient, the specific value is set according to the experience of experts, for adjusting the influence value of the content of the adhering substance on the diffusion resistance; L(KXD) = ε2 × e -KXD ; wherein, ε2 is an adjustment coefficient, the specific value is set according to the experience of experts, for adjusting the influence value of the soil porosity on the diffusion resistance; meanwhile, ε1 and ε2 also play the function of removing units.

[0076] Generate the heavy metal retention based on historical environmental data and preliminary diffusion rate; including:

[0077] Extract the temperature value and humidity value corresponding to each period in the historical data, and adjust each preliminary diffusion rate based on the temperature value and humidity value to obtain the corrected diffusion rate under the corresponding environmental state; specifically including: obtaining the concentration of heavy metal pollutants and the preliminary diffusion rate corresponding to each region, and the distance of each region center from the heavy metal pollution source; based on the concentration of heavy metal pollutants and the distance, the corresponding attenuation factor is found in the attenuation factor lookup standard; the attenuation factor lookup table is obtained by the following way: experts obtain the ratio of the diffusion speed of heavy metal pollutants source at different distances to the diffusion speed at the heavy metal pollutants source by constructing the diffusion model of each heavy metal pollutant under the set environment, and record it as the attenuation factor under the corresponding distance; the heavy metal pollutant source and the attenuation factor at different distances are integrated into the attenuation factor lookup table; the set environment is the same temperature, humidity and soil environment;

[0078] Correct the preliminary diffusion rate based on the attenuation factor to obtain the theoretical diffusion rate affected by the distance of the heavy metal pollutant source; correct the theoretical diffusion rate based on the temperature value and humidity value to obtain the corrected diffusion rate of the corresponding region under the corresponding environmental conditions; specifically, the corresponding corrected diffusion rate XS is calculated by the formula ; wherein, WD is the temperature value of the corresponding region, DWD is the set unit temperature; SD is the humidity value of the corresponding region, DSD is the set unit humidity; LS is the theoretical diffusion rate; ε and δ are respectively the adjustment factors of temperature influence and humidity influence on heavy metal diffusion rate, the specific value is set according to the experience of experts; when the temperature rises, the molecular motion will be active, and the corresponding diffusion speed will be improved; when the humidity rises, the water content in the soil increases, which is conducive to the diffusion of matter, at the same time, the pollutants in the air will have a certain probability to settle in the soil, so that the content of pollutants in the soil increases, which further improves the diffusion speed.

[0079] Generate the period heavy metal retention of the period based on the product of the corrected diffusion rate and the length of the corresponding period; including: obtaining the corrected diffusion rate of each region under each period on the set diffusion path; the product of the corrected diffusion rate and the length of the corresponding period is recorded as the heavy metal diffusion amount of the region in the period;

[0080] obtaining the heavy metal diffusion amount of each region at each time period;

[0081] obtaining the heavy metal diffusion amount of the target region and a previous region of the target region, marking the heavy metal diffusion amount of the previous region as a heavy metal transfer amount, and marking the difference between the heavy metal transfer amount and the heavy metal diffusion amount as a time period heavy metal retention amount of the target region at the corresponding time period;

[0082] summing the time period heavy metal retention amounts of each time period in the same region to obtain the heavy metal retention amount of the corresponding region.

[0083] generating a warning report based on the heavy metal retention amount, including: obtaining the heavy metal retention amount of each region, judging whether the heavy metal retention amount of each region is greater than a set heavy metal content threshold value; if yes, marking the region as a heavy pollution region; and if no, marking the region as a light pollution region.

[0084] Referring to Figure 2 Another aspect of the present application provides a heavy metal pollutant risk warning system for a mining area, including: a data acquisition module, a data analysis model, an interactive display module, and a database;

[0085] The data acquisition module: obtains pollution data and geological characteristic data through a data acquisition device connected thereto; the pollution data includes a plurality of pollutant types, and corresponding pollution source concentration and pollutant characteristic data; the geological characteristic data includes distribution area, content of adhesion material in soil, and porosity;

[0086] The data analysis module: generates a preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological characteristic data of the heavy metal pollutant; obtains a plurality of historical environmental data of the mining area within a set time through the database connected thereto, generates a heavy metal retention amount of each region based on the historical environmental data and the preliminary diffusion rate, and generates a warning report based on the heavy metal retention amount;

[0087] The interactive display module: obtains and displays the warning report.

[0088] Some data in the above formula are calculated by removing the dimension and taking the numerical value, the formula is obtained by software simulation of a large amount of collected data to obtain a formula closest to the actual situation; the preset parameters and the preset threshold value in the formula are set by a person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0089] Working principle of the present application:

[0090] The application obtains the pollution data of each heavy metal pollutant in the mining area, and a plurality of geological feature data on each set diffusion path, generates a preliminary diffusion rate of the corresponding heavy metal based on the pollution data and the geological feature data of the heavy metal pollutant, obtains a plurality of historical environmental data of the mining area within a set time, generates the heavy metal retention amount of each region based on the historical environmental data and the preliminary diffusion rate, generates a warning report based on the heavy metal retention amount, analyzes the characteristics of each heavy metal pollutant, analyzes the influence of the soil of each region on the diffusion of heavy metals, and then estimates the heavy metals of each region in combination with the diffusion environment and time, generates a corresponding alarm signal according to the estimation result, and improves the efficiency of heavy metal warning in the mining area.

[0091] The above examples are only used to illustrate the technical method of the application and are not limited. Although the application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the application.

Claims

1. A method for early warning of heavy metal contaminant risk in a mining area, characterized in that, include: Obtain pollution data corresponding to various heavy metal pollutants in the mining area; And several geological feature data along each set diffusion path; Preliminary diffusion rates of heavy metals are generated based on pollution data and geological characteristic data of heavy metal pollutants; including: Extract the pollution source concentration and pollutant characteristic data corresponding to each pollutant type from the pollution data; input the pollutant characteristic data into the stability evaluation model to obtain the stability score of the heavy metal pollutant; and generate a diffusion capacity coefficient to evaluate the diffusion capacity of the corresponding heavy metal based on the pollution source concentration and stability score corresponding to each pollutant type. Extract the distribution area, content of clay substances in the soil, and porosity from the geological characteristic data of each region, and generate the diffusion resistance coefficient based on the distribution area, content of clay substances, and porosity of each region. The difference between the diffusion capacity coefficient and the diffusion resistance coefficient is recorded as the diffusion rate coefficient of the corresponding region, and the product of the diffusion rate coefficient and the set unit diffusion rate is recorded as the initial diffusion rate of the heavy metal in the corresponding region. Acquire historical environmental data of the mining area within a set time period, generate heavy metal retention in each area based on the historical environmental data and preliminary diffusion rate, and generate early warning reports based on the heavy metal retention.

2. The method according to claim 1, wherein, The stability evaluation model is obtained through training an artificial intelligence model, including: Several pollutant characteristic data and their corresponding stability scores are obtained from the database, and the pollutant characteristic data and stability scores are integrated into several training data and test data. The artificial intelligence model is trained using training data and tested using validation data; the final result is a stability evaluation model with pollutant characteristic data as input and stability score as output; the artificial intelligence model includes a BP neural network model and an RBF neural network model.

3. The method of claim 1, wherein, The generation of diffusion capacity coefficients for corresponding heavy metals based on the pollution source concentration and stability score corresponding to each pollutant type includes: Obtain the stability score and pollution source concentration of each heavy metal pollutant; substitute the stability score and pollution source concentration into the set diffusion capacity quantification function to obtain the diffusion capacity coefficient of the corresponding heavy metal pollutant. The diffusion capacity coefficients of each heavy metal pollutant are summed to obtain the diffusion capacity coefficient of the corresponding heavy metal.

4. The method of claim 1, wherein, The diffusion resistance coefficient is generated based on the distribution area, the content of the adhesive material, and the porosity of each region, including: The porosity and content of adhering substances in each region are substituted into the set quantification function to obtain the quantified value of its impact on the diffusion of heavy metal pollutants; the diffusion resistance coefficient is generated based on the product of the quantified value and the distribution area to represent the impact of the region on the diffusion of heavy metal pollutants. The diffusion resistance coefficients of each region along each set diffusion path are obtained sequentially.

5. The method of claim 1, wherein, The heavy metal retention level is generated based on historical environmental data and preliminary diffusion rates; including: Extract the temperature and humidity values ​​corresponding to each time period from the historical data, and adjust each preliminary diffusion rate based on the temperature and humidity values ​​to obtain the corrected diffusion rate under the corresponding environmental conditions. The time-period heavy metal retention is generated by multiplying the modified diffusion rate by the duration of the corresponding time period; the time-period heavy metal retention of each time period in the same region is summed to obtain the heavy metal retention of the corresponding region.

6. The method for early warning of heavy metal pollutant risks in mining areas according to claim 5, characterized in that, The corrected diffusion rate is obtained by adjusting each initial diffusion rate based on temperature and humidity values, including: The concentrations of heavy metal pollutants and the preliminary diffusion rates for each region, as well as the distances from the center of each region to the heavy metal pollution source, are obtained. Based on the concentrations and distances of heavy metal pollutants, the corresponding attenuation factors are found using the attenuation factor lookup criteria. The theoretical diffusion rate, which is affected by the distance from the heavy metal pollutant source, is obtained by correcting the initial diffusion rate based on the attenuation factor. The theoretical diffusion rate is corrected based on temperature and humidity values ​​to obtain the corrected diffusion rate for the corresponding region under the corresponding environmental conditions.

7. The method for early warning of heavy metal pollutant risks in mining areas according to claim 5, characterized in that, The heavy metal retention amount for the specified time period is generated based on the product of the modified diffusion rate and the duration of the corresponding time period, including: Obtain the corrected diffusion rate for each region and time period along the set diffusion path; and record the product of the corrected diffusion rate and the duration of the corresponding time period as the amount of heavy metal diffusion in the region during that time period. The amount of heavy metal diffusion in each region at each time period was obtained sequentially; Obtain the heavy metal diffusion amount in the target area and the area above the target area, and mark the heavy metal diffusion amount in the area above the target area as the heavy metal transfer amount; record the difference between the heavy metal transfer amount and the heavy metal diffusion amount as the time period heavy metal retention amount in the target area during the corresponding time period.

8. The method for early warning of heavy metal pollutant risks in mining areas according to claim 1, characterized in that, The generation of early warning reports based on heavy metal retention includes: Obtain the heavy metal retention amount in each region and determine whether the heavy metal retention amount in each region is greater than the set heavy metal content threshold; if yes, mark the region as a heavily polluted region; if no, mark the region as a lightly polluted region.

9. A risk early warning system for heavy metal pollutants in mining areas, based on the application of a risk early warning method for heavy metal pollutants in mining areas as described in any one of claims 1 to 8, characterized in that, include: Data acquisition module, data analysis model, interactive display module, and database; The data acquisition module acquires pollution data and geological characteristic data through a connected data acquisition device. The pollution data includes several types of pollutants, as well as their corresponding pollution source concentrations and pollutant characteristic data. The geological characteristic data includes distribution area, content of clay substances in the soil, and porosity. The data analysis module generates the preliminary diffusion rate of the corresponding heavy metals based on pollution data and geological characteristic data of heavy metal pollutants; it obtains a number of historical environmental data of the mining area within a set time period through a database connected to it, and generates the heavy metal retention amount of each area based on the historical environmental data and the preliminary diffusion rate. Generate early warning reports based on heavy metal retention levels; Interactive display module: retrieves and displays early warning reports.

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