Ecological environment big data processing method based on modeling of pollutant migration behavior in mining area

Abstract

The application discloses an ecological environment big data processing method based on modeling of pollutant migration behavior of a mining area, and relates to the technical field of big data processing. The method comprises the following steps: obtaining geodetic coordinate values of protection points and the mining area within a target range, wherein the protection points comprise residential areas, ecological protection zones and water source protection zones, and the geodetic coordinate values are used to represent longitude and latitude coordinates and an altitude; determining a maximum diffusion range interval of pollutants in the mining area according to the geodetic coordinate values of a first group and the geodetic coordinate values of the mining area, wherein the geodetic coordinate values of the first group are specifically geodetic coordinate values taken by any one protection point type. The three-dimensional geodetic coordinates of the protection points and the mining area are obtained, the scientific definition of the diffusion range of the pollutants is completed, the spatial coverage of the pollution monitoring is ensured to be comprehensive and effective, the potential threat of the pollution of the mining area to the surrounding residential areas, ecological protection zones and water source protection zones can be accurately identified, and the accuracy and pertinence of the environmental risk assessment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data processing, in particular to an ecological environment big data processing method based on a mine area pollutant migration behavior modeling. BACKGROUND

[0002] A large amount of pollutants will be generated in the process of resource exploitation in a mine area, including heavy metals, acid water, dust, and harmful gases. These pollutants often migrate and spread in the soil, groundwater, and atmosphere, seriously affecting the surrounding ecological system and the health of residents. In order to predict the spatiotemporal distribution of pollutants in complex geological environments and develop accurate management solutions, a mine area pollutant migration behavior model for processing ecological environment big data is needed.

[0003] According to the search, the Chinese invention patent application with the publication number "CN110245029 A" discloses a "data processing method, device, storage medium and server". The method receives an interface call task sent by a client, parses the interface call task to obtain the data to be processed in the interface call task and the data processing logic corresponding to the configuration parameters, then decomposes the interface call task into multiple data processing steps according to the data processing logic, determines the order of each data processing step, determines the data processing logic implementation class corresponding to each data processing step, and generates a processing chain according to the data processing logic implementation class and the order of the data processing steps. The method performs data processing on the data to be processed in the interface call task based on the processing chain. This method can realize the unified interface calling of subsystems, reduce the development difficulty, make the system respond to the call in time, and improve the efficiency of interface calling.

[0004] In addition, the Chinese invention patent application with the publication number "CN119961707 A" discloses an "abnormal data processing method in environmental air monitoring process". The method can quickly locate and identify the causes of abnormal data caused by equipment failure, ensure the normal operation of the monitoring equipment and the reliability of the monitoring data, and further realize the intelligentization and systematization of equipment maintenance by uploading the equipment failure data to the environmental air monitoring management cloud platform, and improve the overall efficiency of the monitoring system.

[0005] However, in actual use, the above-mentioned disclosed method and the existing technology disclosed scheme do not fully consider the weakening effect of natural neutralizing factors in the ecological system on pollutant migration, resulting in inaccurate prediction of pollution diffusion range and migration path. In addition, environmental fluctuations and seasonal changes have a great interference on pollution trend identification, which can easily cause misjudgment or omission of pollution trend, affecting the reliability of environmental risk assessment and early warning. SUMMARY

[0006] The present application aims to provide an ecological environment big data processing method based on modeling of pollutant migration behavior in a mining area to solve the problems presented in the background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solution: an ecological environment big data processing method based on modeling of pollutant migration behavior in a mining area, comprising:

[0008] Obtaining geodetic coordinate values of protection points and the mining area within a target range, the protection points including: a residential area, an ecological protection area, and a water source protection area, and the geodetic coordinate values being used to represent longitude and latitude coordinates and an altitude;

[0009] Determining a maximum diffusion range interval of the pollutants in the mining area according to the geodetic coordinate values of the first group and the geodetic coordinate values of the mining area, the geodetic coordinate values of the first group being specifically the geodetic coordinate values taken by any one protection point type;

[0010] Obtaining a pollutant concentration gradient in the maximum diffusion range area;

[0011] Generating a migration behavior model of the pollutants in the mining area based on the concentration distribution results of the spatial detection area.

[0012] As a further preferred embodiment of the present technical solution, the corresponding neutralizing term is retrieved from the ecological geographical environment parameters of the target mining area type around the mining area, and a neutralization efficiency index is established, which is used to update the migration behavior model.

[0013] As a further preferred embodiment of the present technical solution, the setting method of the neutralization efficiency index comprises:

[0014] Extracting the corresponding neutralizing factor of the pollutant in the ecological geographical environment parameters, and establishing a reaction control map;

[0015] Identifying the reaction relationship between the pollutant and the neutralizing term in the reaction control map;

[0016] Setting the neutralization efficiency index according to the reaction relationship between the pollutant and the neutralizing term, the neutralization efficiency index being used to evaluate the natural weakening effect of the neutralizing term on the migration behavior of the pollutant, and the neutralization efficiency index being determined by establishing a spatial distribution map of the neutralizing term and evaluating the percentage of the intersection area of the neutralizing term and the migration path of the pollutant in the total area.

[0017] As a further preferred embodiment of the present technical solution, the reaction control map is obtained by extracting environmental factors with pollution neutralization capability from the ecological geographical parameters obtained around the mining area, the environmental factors corresponding to the pollutants in the mining area, and the environmental factors being able to generate harmless substances through chemical reactions to reduce the migration and biological effectiveness in the environment.

[0018] As a further preferred embodiment of the present technical solution, the migration frequency of the same pollution factor as the ecological geographical environment parameter and the target mining area type is obtained, and a correction index is set based on the migration frequency. The correction index is used to update the migration behavior model, so that the migration behavior model reduces the interference of environmental fluctuations on pollution trend identification during actual operation.

[0019] As a further preferred embodiment of the present technical solution, the setting method of the correction index comprises:

[0020] The concentration time series data of the target pollution factor in the historical period is collected through the distributed environmental sensor network arranged in the periphery of the mining area.

[0021] The concentration sequence is decomposed into a trend item, a seasonal item and a residual item, and a discrimination threshold is set. According to the comparison result between the trend item, the seasonal item, the residual item and the discrimination threshold, the pollution accumulation trend is discriminated.

[0022] A dynamic correction coefficient is constructed according to the deviation degree of the residual item from the discrimination threshold.

[0023] As a further preferred embodiment of the present technical solution, the discrimination condition of the pollution accumulation trend is:

[0024] When the linear regression slope of the trend item in the concentration curve exceeds the discrimination threshold α, the amplitude standard deviation of the seasonal item is greater than the discrimination threshold β, and there is a continuous 6-period deviation in the same direction in the residual item, it is determined that there is an upward trend of non-human pollution.

[0025] The discrimination threshold α is obtained by using the monthly data of the pollutant concentration in the historical time sequence of the concentration curve, and performing linear regression on the monthly average concentration sequence of the pollutant to obtain the slope of the trend item. The mean value of the slope distribution at 95% confidence level + 2 times the standard deviation is taken as the discrimination threshold α, which is used to establish a dynamic reference value with statistical significance, effectively distinguishing natural fluctuations from abnormal accumulation phenomena.

[0026] For the determination of the discrimination threshold β, the absolute value of the standard deviation of the seasonal item amplitude data between the historical same period in the concentration curve and the real-time seasonal item amplitude data is doubled.

[0027] As a further preferred embodiment of the present technical solution, the migration behavior model is updated based on the neutralization efficiency index and the correction index, and a visual report is output.

[0028] The updating method of the migration behavior model comprises:

[0029] The neutralization efficiency index and the correction index are integrated into the input parameters of the migration behavior model;

[0030] The concentration distribution of the pollutant in the spatial and temporal dimensions is recalculated based on the updated input parameters.

[0031] generating a visual report according to the updated migration behavior model.

[0032] Compared with the prior art, the present application has the following beneficial effects:

[0033] The ecological environment big data processing method based on the migration behavior modeling of mine area pollutants, through obtaining the three-dimensional geodetic coordinates of the protection points and the mine area, completes the scientific definition of the pollutant diffusion range, ensures the comprehensive and effective spatial coverage of pollution monitoring, helps to accurately identify the potential threat of mine area pollution to the surrounding residential areas, ecological protection areas and water source protection areas, improves the accuracy and pertinence of environmental risk assessment, through obtaining the pollutant concentration gradient in the maximum diffusion range, a migration behavior model based on spatial concentration distribution is constructed, which provides a scientific quantitative basis for the dynamic evolution and migration path of mine area pollutants, realizes fine simulation and prediction of the pollution diffusion process, and enhances the early warning ability and decision support effect of environmental management;

[0034] In addition, by introducing the neutralization efficiency index based on the ecological geographical environment parameters around the mine area, the weakening effect of natural neutralization factors in the ecological system on the migration of pollutants is evaluated, the environmental self-purification ability is effectively reflected, and the ecological authenticity and reliability of the model are improved, it is necessary to supplement that by establishing the pollutant-neutralization reaction control chart and spatial distribution chart, the neutralization efficiency can be dynamically quantified, and the influence of the neutralization process on the spatial change of pollutant concentration can be accurately described, the adaptability and interpretability of the pollution migration behavior model are enhanced, finally, combined with the migration frequency data of the pollution factors in the mine area and the surrounding area, the non-human pollution rising trend identification and false alarm correction index is set, which effectively filters the interference of environmental fluctuations, improves the accuracy of pollution trend identification, reduces the risk of misjudgment, and enhances the scientific nature of pollution monitoring and early warning;

[0035] It should be noted that by using a distributed environmental sensor network, the present application realizes real-time collection of time series of pollutant concentration outside the mine area and multi-dimensional data decomposition, and adjusts the model parameters through dynamic correction coefficients, ensuring that the model has strong adaptability and stability in long-term operation, and can sensitively reflect the dynamic changes of pollution. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The step flow chart of the disclosed method of the present application;

[0037] Figure 2 The three-dimensional structure diagram of the pollution migration behavior model of the present application;

[0038] Figure 3 The reaction control chart disclosed by the present application;

[0039] Figure 4A concentration curve diagram disclosed by the present application;

[0040] Figure 5 A revised exponential dynamic change curve diagram disclosed by the present application;

[0041] Figure 6 A three-dimensional structure diagram of an updated pollution migration behavior model of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] Before understanding the technical solutions proposed in the present application, it is necessary to first clarify the meaning of "mining area pollutant behavior migration". It should be added that the mining area pollutant behavior migration specifically refers to the diffusion, transformation and fate process of the mining area pollutant in various environmental media, mainly including: seepage migration of heavy metal ions in hydrogeological units, chemical migration of acid wastewater in the process of surface runoff, and turbulent diffusion of suspended particulate matter in the atmospheric boundary layer. In order to scientifically predict the environmental impact degree of the mining area pollutant in the migration process, the present application proposes an ecological environment big data processing method based on mining area pollutant migration behavior modeling.

[0044] Specifically, referring to Figure 1 It can be seen that the ecological environment big data processing method based on mining area pollutant migration behavior modeling includes steps S100-S400.

[0045] Step S100: According to the mining area parameter data in the target range and the ecological geographical environment parameters around the mining area, a migration behavior model of the mining area pollutant is constructed.

[0046] It is worth noting that in step S100, the mining area parameter data includes: metal mining area pollutant concentration, coal mining area pollutant concentration, salt mining area pollutant concentration and rare earth mining area pollutant concentration, and the ecological geographical environment parameters include: water source characteristic parameter data, terrain characteristic parameter data, climate and weather parameter data, soil type parameter data and location parameter data, wherein the water source characteristic parameter data is obtained by the water source monitoring equipment in the prior art, including the pH value, dissolved oxygen content, temperature and flow rate of the water body, the terrain characteristic parameter data and the location parameter data are determined by the existing satellite stereo image to determine the elevation, slope, land cover type and latitude and longitude coordinates of the target mining area around, and the climate and weather parameter data is determined by the historical records and real-time observation from the local weather station to determine the precipitation, wind speed, humidity and air temperature, and finally the soil type parameter data is determined by the soil sampling analysis in the prior art to determine the pH value, organic matter content, cation exchange capacity and heavy metal background value of the soil.

[0047] Specifically, the method for constructing the migration behavior model of the mining area pollutant includes steps S101-S104.

[0048] Step S101: Obtain the geodetic coordinate values of the protection points and the mining area within the target range.

[0049] It should be clear that in step S101, the protection points include: residential areas, ecological protection areas and water source protection areas, it is worth noting that the influence degree of the pollutants on these protection point areas is an important basis for evaluating the migration behavior of the mining area pollutants, therefore, obtaining the geodetic coordinate values of the protection points is to clarify the spatial distribution of these areas, thereby providing spatial reference data for the subsequent model construction, in addition, it should be noted that in step S101, the geodetic coordinate values refer to latitude and longitude coordinates and altitude, and the geodetic coordinate values of the mining area, residential area, ecological protection area and water source protection area are the latitude and longitude coordinates and altitude of the central axis position in the area.

[0050] Step S102: Determine the maximum diffusion range interval of the mining area pollutant according to the first set of geodetic coordinate values and the geodetic coordinate values of the mining area.

[0051] It should be noted that in step S102, the first set of geodetic coordinate values specifically refers to the geodetic coordinate values taken by any one protection point type.

[0052] Step S103: Obtain the pollutant concentration gradient in the maximum diffusion range area.

[0053] It should be noted that in step S103, the method for obtaining the concentration gradient of the pollutant is to set the maximum diffusion range area as a plurality of spatial detection areas with the same volume, and use the concentration monitoring equipment in the prior art to measure and record the concentration of the pollutant in each spatial detection area.

[0054] Step S104: Based on the concentration distribution result of the spatial detection area, a migration behavior model of the mine area pollutant is generated.

[0055] It should be noted that in step S104, in order to enhance the dynamic prediction ability of the migration behavior model, the spatial distribution of the pollutant and the concentration change relationship of the pollutant with time are fused to construct the migration behavior model of the mine area pollutant. Specifically, based on the spatial concentration gradient information of the pollutant in the maximum diffusion range area obtained in step S103, and combined with the physical and chemical properties of the mine area pollutant, a migration behavior model of the mine area pollutant with time-varying concentration is constructed to reflect the scenario of the pollutant in three-dimensional space due to diffusion and natural attenuation.

[0056] It is worth noting that in step S104, the function expression form of the migration behavior model of the mine area pollutant is as follows:

[0057] ;

[0058] Wherein represents the concentration of the pollutant at time , , represents the initial concentration of the pollution source, represents the central position of the pollution source in the three-dimensional space coordinate system, represents the natural attenuation coefficient of the pollutant in the target environmental medium, and the unit is , The numerical value is determined by inputting the mine area parameter data and the ecological and geographical environment parameters around the mine area through internet technology. It can be regarded as a constant, represents the diffusion coefficient of the pollutant in the medium, and the unit is , which is obtained through field monitoring or literature data, is a time variable, and the unit is hour or day.

[0059] It is worth noting that the migration behavior model of the mine area pollutant is used to depict the concentration attenuation and spatial dilution characteristics of the pollutant on the diffusion path due to time lapse, and can reflect the behavior process of the pollutant migration and evolution in the environmental medium with time. It is a time dimension supplement to the static migration behavior model.

[0060] As a preferred embodiment, the present embodiment is mainly used to improve the practical application function of the mine area pollutant migration behavior model. Specifically, when a heavy metal leakage event occurs in the mine area stacking area (taking copper ions Cu² + as a typical pollutant), the mine area pollutant migration behavior model quantitatively describes the three-dimensional diffusion behavior of the pollutant along x (horizontal direction), y (vertical surface direction) and z (penetration depth direction) by simulating the three-dimensional space migration process of the pollutant in the soil-groundwater system. The core application target is to build the pollution diffusion degree within the 12-hour emergency response period.

[0061] It should be noted that in the present example, the pollution source is located at the coordinate origin =(0,0,0), the initial concentration is set to =500 μg / L, according to the actual parameters of the underground seepage medium, the diffusion coefficient =1.5 , the pollutant natural attenuation coefficient is taken as =0.015 , and the time point =12h is selected as the model running time. At this time, it can be known from the reference Figure 2 that the darker the gray area in the figure, the higher the concentration, and the concentration in the area near the pollution source is significantly higher than that away from the source point. Through the figure, the predicted concentration value of any spatial coordinate point at a specific time can be extracted. Taking the target monitoring point (x=5, y=5, z=2) as an example, the migration function model proposed in the present application is combined for calculation.

[0062] At this time ;

[0063] ;

[0064] ; ;

[0065] .

[0066] Therefore, 12 hours after the pollution source leakage occurs, the concentration of copper ions is as high as 197.24 μg / L, which is significantly higher than the limit value of the groundwater environmental quality standard (such as the “Groundwater Quality Standard” GB / T 14848).

[0067] Step S200: According to the type of the target mine area, the corresponding neutralizing term is searched in the ecological geographical environment parameters around the mine area, and a neutralization efficiency index is established.

[0068] It should be noted that the neutralization efficiency index in step S200 is used to evaluate the natural weakening effect of the neutralization term on the migration behavior of the pollutant.

[0069] As a preferred embodiment, the present embodiment should supplement the natural weakening effect of the neutralization term on the migration behavior of the pollutant in step S200.

[0070] Specifically, it includes steps S201-S203.

[0071] Step S201: Extract the corresponding neutralizing factor in the ecological and geographical environment parameters and the pollutant, and establish a reaction control diagram.

[0072] It should be noted that step S201 is to extract the environmental factors with pollution neutralization ability from the ecological and geographical parameters obtained around the mining area, such as high alkaline soil layer (such as carbonate or calcareous rock layer), biological community (such as metal-enriched plants), high-pH water source, etc., and establish a reaction control diagram with the type of pollutant (such as acid, metal ion). Figure 3 The reaction control diagram is expressed.

[0073] It is worth noting that the reaction control diagram takes acid solution (H + , H2SO4) and metal ions (Pb 2+ , Cd 2+ ) as representatives of pollutants, which correspond to different neutralizing factors. Acidic pollutants generate harmless substances (H2o, CO2, and Ca 2+ ) through chemical reactions in carbonate rock layers, and the specific reaction formula is CaCO3 + 2H + → Ca 2+ + CO2+ H2O, at the same time, high-pH water sources generate water by combining OH - ions with H + , further neutralizing the acidic environment (OH - + H + → H2O), on the other hand, heavy metal ions are fixed by the root system of metal-enriched plants, reducing their migration and bioavailability in the environment.

[0074] Step S202: Identify the reaction relationship between the pollutant and the neutralization term in the reaction control diagram.

[0075] Step S203: Set the neutralization efficiency index according to the reaction relationship between the pollutant and the neutralization term, and the neutralization efficiency index is used to evaluate the natural weakening effect of the neutralization term on the migration behavior of the pollutant.

[0076] It should be noted that the neutralization efficiency index in step S203 is determined by establishing a spatial distribution diagram of the neutralization term and evaluating the percentage of the intersection area of the total area of the pollutant migration path.

[0077] Step S300: Obtain the migration frequency of the same pollution factor as the ecological geographical environment parameter and the target mining area type, and set the correction index based on the migration frequency.

[0078] It should be noted that in step S300, the setting of the correction index is mainly used to reduce the interference of environmental fluctuations on pollution trend identification during the actual operation of the migration behavior model. The pollution factor is a pollution element that is contained in the ecological geographical environment parameter and the same as the pollutant in the target mining area.

[0079] As a preferred embodiment, the present embodiment is mainly used to supplement the setting of the correction index in step S300, specifically including steps S301-S303.

[0080] Step S301: Collect the concentration time series data of the target pollution factor in the historical period (at least 3 years) through the distributed environmental sensor network deployed on the periphery of the mining area.

[0081] It should be noted that for metal mining areas, the monthly concentration fluctuations of Cu 2+ , Pb 2+ , and Cd 2+ ion state pollutants need to be monitored, and for coal mining areas, the seasonal variation rules of benzene series and polycyclic aromatic hydrocarbon organic pollutants need to be focused on. The concentration time series data obtained is a concentration curve chart, which is Figure 4 in the past three years (only 34 months are shown in the figure).

[0082] It should be supplemented that in Figure 4 , the Cu²+ concentration shows a slight upward trend as a whole, accompanied by relatively obvious annual periodic fluctuations, with a peak generally occurring in summer each year. This feature may be related to water evaporation concentration caused by high temperature, reflecting the seasonal accumulation tendency of copper ions in nature. The Pb²+ concentration shows a slow downward trend, with a semi-annual periodic fluctuation feature, which may be related to changes in wind direction, rainfall frequency, or regional deposition process, indicating that this factor has significant short-period disturbance. The Cd²+ concentration is relatively stable as a whole, with small fluctuations and weak periodicity. In addition, Figure 4 , three pollution factor full-cycle average reference lines are also set in the content, corresponding to the average concentration levels of Cu²+, Pb²+, and Cd²+. These reference lines are used to judge the deviation of pollution concentration and help identify whether there is an abnormal rising trend.

[0083] Step S302: Decompose the concentration sequence into trend items, seasonal items, and residual items, and set a discrimination threshold. According to the comparison results between the trend items, seasonal items, and residual items and the discrimination threshold, the pollution accumulation trend is determined.

[0084] Specifically, the discrimination condition of pollution accumulation trend is that when the linear regression slope of the trend item in the concentration curve exceeds the discrimination threshold a (such as a = 0.15 μg / (L·month) in the metal mining area), the amplitude standard deviation of the seasonal item is greater than the discrimination threshold β (2 times the standard deviation of the historical same period), and the same deviation exists in the residual item for 6 consecutive monitoring periods, it is determined that there is a non-human pollution rising trend, and it should be noted that the non-human pollution is natural disaster pollution.

[0085] It should be noted that the discrimination threshold a is obtained by using the monthly data of the pollutant concentration in the concentration curve for 3 years (or longer) and performing linear regression on the monthly average concentration sequence of the pollutant to obtain the slope of the trend item, and taking the mean + 2 times the standard deviation of the slope distribution at the 95% confidence level as the a threshold. The significance of this setting is to establish a dynamic benchmark with statistical significance, effectively distinguishing between natural fluctuations and abnormal accumulation phenomena. For the determination of the discrimination threshold β, the absolute value of the standard deviation between the amplitude data of the seasonal item in the concentration curve and the real-time seasonal item amplitude data is doubled.

[0086] Step S303: Constructing a dynamic correction coefficient according to the deviation of the residual item from the discrimination threshold.

[0087] Specifically, the calculation function of the correction index is: wherein is used to represent the correction coefficient, is used to represent the concentration residual value of the i th monitoring period, is used to represent the tolerance threshold of the residual item, which is set according to the historical residual standard deviation, is a time weighting factor, which is determined according to the ratio of the current detection sequence to the total number of detection sequences, is used to represent the total number of detection periods.

[0088] As a preferred embodiment, the embodiment is used to supplement the correction index in actual use. Specifically, referring to Figure 5 It can be seen that by decomposing the monthly copper ion concentration data collected by the sensors outside the mining area into trend items, seasonal items, and residuals, and based on the ratio of the residual to the set tolerance threshold combined with the time weighting factor, the correction index is obtained, which changes dynamically with the monitoring period (month) and is used to dynamically adjust the false positive risk in the discrimination of non-human pollution rising trend, wherein Figure 5 The blue smooth curve in the figure represents the trend of the correction index over time, and the superimposed dots show the actual calculation value of each month. When the correction index takes a value between 0 and 1, the value closer to 1 indicates that the residual fluctuation is smaller, the false positive probability of pollution trend identification is lower, and the discrimination result is more reliable. The value closer to 0 indicates that the residual is larger, the false positive risk is higher, and the correction needs to be strengthened.

[0089] Step S400: The migration behavior model is updated based on the neutralization efficiency index and the correction index, and a visualization report is output.

[0090] It should be noted that the method for updating the migration behavior model in step S400 includes steps S401-S403.

[0091] Step S401: Integrate the neutralization efficiency index and the correction index into the input parameters of the migration behavior model.

[0092] It should be noted that in step S401, the neutralization efficiency index reflects the ability of the natural environment surrounding the mining area to weaken the migration of pollutants, while the correction index is used to dynamically adjust the false alarm risk in pollution trend identification. By using both as supplementary input parameters, the prediction accuracy and adaptability of the model can be enhanced.

[0093] Specifically, the updated migration behavior model, generated by combining the neutralization efficiency index and the correction index, is as follows:

[0094] ;

[0095] in For the updated migration behavior model, Used to represent the neutralization efficiency index, where let

[0096] and The addition is due to the presence of natural neutralizing agents in the surrounding environment of the mining area, such as highly alkaline soil and metal-accumulating plants. These factors accelerate the decay of pollutants or reduce their effective concentration. This process is similar to enhanced decay, involving not only the natural decay of the pollutants themselves but also the reduction of pollutants by the environment. Therefore, the neutralization efficiency index... For the original attenuation coefficient In addition, As a factor of the whole multiplication, because As a multiplication factor for concentration prediction results, it can smoothly adjust the model output. This correction is a global scaling of the model output, rather than a local adjustment.

[0097] Step S402: Recalculate the concentration distribution of pollutants in the spatial and temporal dimensions based on the updated input parameters.

[0098] Specifically, in step S402, by introducing a neutralization efficiency index and a correction index, the model can more accurately simulate the migration paths and attenuation processes of pollutants in the real environment, where the reference... Figure 6 It can be seen that, in the content displayed by the updated migration behavior model, compared toFigure 2 The migration behavior model shows that the pollution diffusion range is significantly reduced, mainly reflected in the significant reduction of peak concentration, which is due to the enhancement of neutralization efficiency index, which accelerates the chemical neutralization process of Cu²+ in high alkaline soil layer. At the same time, the correction index smooths the influence of seasonal fluctuations, making the model's prediction in the penetration depth direction (z-axis) closer to the actual environmental monitoring values, avoiding misjudgment caused by natural background changes.

[0099] Step S403: According to the updated migration behavior model and generate a visual report.

[0100] It should be noted that the generation of the visual report fully considers the actual needs of the ecological environment management of the mining area, all charts are output in standardized format, with detailed data explanation and technical notes, ensuring the accuracy and readability of information transmission, this design makes the report not only suitable for in-depth analysis of professional technicians, but also provides intuitive reference for non-technical decision makers.

[0101] In order to further improve the practical value of the report, the visual content also supports interactive operation, users can dynamically view the simulation results of pollutant diffusion by adjusting the time range, spatial coordinates or specific parameter values. This design not only enhances the flexibility of data analysis, but also provides more accurate decision support for mine environmental management, at the same time, the report embedded key indicators of statistical summary, including the maximum concentration value, average concentration value and its corresponding time and space position, help users quickly grasp the core characteristics of pollution diffusion.

[0102] It should be noted that the generation of the visual report fully considers the actual needs of the ecological environment management of the mining area, all charts are output in standardized format, with detailed data explanation and technical notes, ensuring the accuracy and readability of information transmission, this design makes the report not only suitable for in-depth analysis of professional technicians, but also provides intuitive reference for non-technical decision makers.

[0103] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An ecological environment big data processing method based on modeling of migration behavior of mine area pollutants, characterized in that, The method comprises the following steps: Obtain the geodetic coordinate values of the protection points and the mining area within the target range, the protection points including: the residential area, the ecological protection area, and the water source protection area, and the geodetic coordinate values being used to represent the longitude and latitude coordinates and the altitude; Determine the maximum diffusion range interval of the pollutants in the mining area according to the geodetic coordinate values of the first group and the geodetic coordinate values of the mining area, the geodetic coordinate values of the first group being specifically the geodetic coordinate values taken by any one protection point type; Obtain the concentration gradient of the pollutants in the maximum diffusion range area; Generate a migration behavior model of the pollutants in the mining area based on the concentration distribution result of the space detection area; Search for the corresponding neutralizing agent in the ecological geographical environment parameters of the target mining area type around the mining area, establish a neutralization efficiency index, and update the migration behavior model by using the neutralization efficiency index; The setting method of the neutralization efficiency index comprises the following steps: Extract the corresponding neutralizing factor of the pollutants in the ecological geographical environment parameters, and establish a reaction control diagram; Identify the reaction relationship between the pollutants and the neutralizing agent in the reaction control diagram; Set the neutralization efficiency index according to the reaction relationship between the pollutants and the neutralizing agent, the neutralization efficiency index being used to evaluate the natural weakening effect of the neutralizing agent on the migration behavior of the pollutants, and the neutralization efficiency index being determined by establishing the spatial distribution diagram of the neutralizing agent and evaluating the percentage of the intersection area of the neutralizing agent and the migration path of the pollutants in the total area; The reaction control diagram is obtained by extracting the environmental factors with pollution neutralization ability from the ecological geographical parameters around the mining area, the environmental factors corresponding to the pollutants in the mining area, and the environmental factors being able to generate harmless substances through chemical reaction to reduce the migration and biological availability in the environment; Obtain the migration frequency of the same pollution factor in the ecological geographical environment parameters around the mining area and the target mining area type, set a correction index for the identification and false alarm of the non-human pollution rising trend based on the migration frequency, and update the migration behavior model by using the correction index, so that the migration behavior model can reduce the interference of environmental fluctuations on the pollution trend identification during actual operation.

2. The ecological environment big data processing method based on modeling of ore field pollutant migration behavior according to claim 1, characterized in that: The setting method of the correction index comprises the following steps: Collect the concentration time series data of the target pollution factor in the historical period by using the distributed environmental sensor network arranged around the mining area; Dissociate the concentration sequence into a trend item, a seasonal item, and a residual item, set a discrimination threshold, and discriminate the pollution accumulation trend according to the comparison result between the trend item, the seasonal item, the residual item, and the discrimination threshold; Construct a dynamic correction coefficient according to the deviation degree of the residual item from the discrimination threshold.

3. The ecological environment big data processing method based on modeling of mining area pollutant migration behavior according to claim 2, characterized in that: The discrimination condition of the pollution accumulation trend is as follows: When the linear regression slope of the trend item in the concentration curve exceeds the discrimination threshold α, the amplitude standard deviation of the seasonal item is greater than the discrimination threshold β, and the same deviation exists in the residual item in the continuous 6 monitoring periods, it is determined that there is a non-human pollution rising trend; The discrimination threshold α is obtained by using the monthly data of the pollutant concentration in the historical time sequence of the concentration curve, performing linear regression on the monthly average concentration sequence of the pollutants, obtaining the slope of the trend item, and setting the mean value + 2 times the standard deviation of the slope distribution at the 95% confidence level as the discrimination threshold α, which is used to establish a dynamic benchmark with statistical significance, and effectively distinguish between natural fluctuations and abnormal accumulation phenomena. For the determination of the discrimination threshold β, the twice of the standard deviation absolute value between the historical seasonal term amplitude data and the real-time seasonal term amplitude data in the concentration curve chart is determined.

4. The ecological environment big data processing method based on modeling of mining area pollutant migration behavior according to claim 1, characterized in that: The migration behavior model is simultaneously updated according to the neutralization efficiency index and the correction index, and a visual report is outputted; The updating method of the migration behavior model comprises: The neutralization efficiency index and the correction index are integrated into the input parameters of the migration behavior model; The concentration distribution of the pollutants in the spatial and temporal dimensions is recalculated based on the updated input parameters; The updated migration behavior model is used to generate a visual report.

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