Soil ecological environment monitoring management method and system based on big data

Abstract

The invention provides a soil ecological environment monitoring and management method based on big data, and relates to the technical field of soil ecological environment monitoring and management. According to the method, multi-source data including soil and pasture are collected and integrated to construct a central database through field stratified sampling, classified deployment of sensors and butt joint with a third-party mechanism; space-time pattern analysis, pollution source analysis and ecological risk evaluation are carried out based on the central database, and a pollution traceability report is generated; the method comprises the following steps: constructing a multi-dimensional risk evaluation system, dividing a region into four classes and implementing differentiation management, constructing a governance technology database, screening an optimal scheme based on multiple criteria, performing demonstration verification in a typical region, comparing monitoring data, judging a governance effect and generating a demonstration application summary report; the problems that the soil monitoring and treatment accuracy of the heavy metal mining area is insufficient and the scheme adaptability is poor are effectively solved, and the monitoring and management efficiency and scientificity are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of soil ecological environment monitoring and management technology, specifically to a soil ecological environment monitoring and management method and system based on big data. Background Technology

[0002] In the process of ecological environmental protection and sustainable agricultural development, soil ecological environment monitoring has gradually shifted from the traditional single-point sampling model to a networked and intelligent big data monitoring system. The widespread application of technologies such as satellite remote sensing, drones, and IoT sensors has led to an explosive growth in environmental data related to soil physicochemical properties, pollutant distribution, biological indicators, and hydrological and meteorological conditions. This data not only relates to regional ecological security assessments but also directly impacts agricultural production safety and national ecological environment governance decisions. Its scientific management has become a key requirement for the digital transformation of the environmental protection field, but many shortcomings still exist. On the one hand, heavy metal pollution in mining areas is characterized by strong spatial heterogeneity, complex pollution sources, and diverse migration and transformation mechanisms. However, existing monitoring systems mostly adopt static point layout and single-indicator evaluation models, lacking the ability to accurately capture the dynamic evolution of pollution. On the other hand, existing remediation schemes are mostly based on experience-based judgments or general technical templates, failing to be precisely designed according to the pollution characteristics, soil types, hydrogeological conditions, and other differentiated factors of specific mining areas. This results in insufficient accuracy of soil monitoring and remediation in heavy metal mining areas, poor scheme adaptability, low monitoring and management efficiency, and insufficient scientific rigor.

[0003] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] The purpose of this invention is to address the problems of insufficient accuracy and poor adaptability of soil monitoring and management in heavy metal mining areas, and to propose a soil ecological environment monitoring and management method and system based on big data.

[0005] The objective of this invention can be achieved through the following technical solutions: Big data-based methods for soil ecological environment monitoring and management include: S1. Construction of a multi-source data collection and integrated monitoring network: stratified sampling of soil, pasture, tailings, and wastewater in and around heavy metal mining areas; deployment of sensors for atmospheric deposition, surface runoff, soil moisture, and heavy metal monitoring, with real-time data transmission via 5G; connection with third-party institutions to supplement data; and integration of all data into a central database based on a unified structure to form six core datasets. S2. Big Data Analysis and Pollution Source Analysis: Based on the core dataset of the central database, conduct spatiotemporal pattern analysis, pollution source analysis and ecological risk assessment of multidimensional data, and generate risk assessment maps and pollution source tracing reports; S3. Risk Assessment and Intelligent Classification of Environmental Quality: Based on pollution source tracing reports and ecological risk assessment results, establish a multi-dimensional risk assessment system to classify and manage the environmental quality of the monitored area. S4. Screening and Demonstration Application of Intelligent Governance Solutions: Based on environmental quality classification results and pollution characteristic analysis, construct a governance technology database, formulate governance solutions, and conduct demonstration application verification.

[0006] As a further improvement of the present invention, the specific implementation process of the spatiotemporal pattern analysis is as follows: Based on a multi-parameter soil monitoring dataset, using the formula The spatial autocorrelation of heavy metal elements was quantified; among them... This indicates the intensity of the difference in heavy metal element measurements between all sampling point pairs. Indicates the spatial step size. Indicates the number of point pairs at the sampling points. Represents a set of point pairs, including all pairs whose distance is equal to or less than a given distance. sampling point pairs , These represent the sampling points. and Concentration values ​​of heavy metal elements collected at the site; Spatial autocorrelation function curves were generated based on the spatial autocorrelation quantification results of heavy metal elements to obtain the spatial aggregation characteristics of heavy metal pollution and construct a spatial distribution map of heavy metal elements; the formula was then used to... Obtain the net trend index ,in, This indicates the total number of time-series monitoring data points in the soil monitoring data. A counter representing the length of the time series, used to iterate through the time series. Indicates the first The concentration values ​​of heavy metal pollutants monitored at each time point. Indicates the first The concentration values ​​of heavy metal pollutants monitored at each time point. , indicating that it is used for comparison and Comparison functions; The net trend index is used to determine the trend of heavy metal pollutant concentration, including rising, falling and stable trends; the spatiotemporal dynamic map of heavy metal pollution is generated based on the results of spatiotemporal pattern analysis.

[0007] As a further improvement of the present invention, the specific implementation process of the pollution source analysis is as follows: Principal component analysis was used to construct a correlation coefficient matrix among heavy metal elements. Based on the principle that eigenvalues ​​are greater than 1, the number of major pollution sources was determined. The principal component loading matrix was obtained using the maximum variance rotation method to identify the main source types of each heavy metal. The formula was then used to... Get the first The first soil sample The actual measured concentration values ​​of the heavy metals, among which, Indicates the total number of pollution sources. Indicates the first The soil sample was taken at the... The contribution of each pollution source; Indicates the first Heavy metals in the first The contribution of each pollution source; Represents the residual, referring to the first The first soil sample The difference between the concentration values ​​of a certain heavy metal and the estimated concentration values ​​from all pollution sources; The artificial enrichment coefficient is obtained by comparing the ratio of heavy metal concentration in soil samples to the ratio of reference element concentration in soil samples with the ratio of heavy metal background value in background dataset to reference element background value in background dataset. The pollution accumulation index is obtained by comparing the measured concentration of heavy metals in soil samples with the background value and introducing a preset correction factor. Based on the anthropogenic enrichment coefficient and pollution accumulation index, the main pollution source types are identified, including mining sources, tailings dam discharge sources, atmospheric deposition sources, and surface runoff migration sources.

[0008] As a further improvement to the present invention, the specific operational steps of the ecological risk assessment are as follows: The bioaccumulation coefficient is obtained by comparing the measured concentrations of heavy metals in pasture samples with the measured concentrations of heavy metals in corresponding soil samples. The transfer coefficient is obtained by comparing the concentration values ​​of heavy metals in the above-ground parts of the pasture with the concentration values ​​of heavy metals in the roots of the pasture. Based on bioaccumulation coefficients and transfer coefficients, the migration and transformation characteristics of heavy metals in soil and pasture were obtained, and heavy metal elements and areas with high ecological risk were identified. Through formula A comprehensive potential ecological risk index was obtained. ,in, This indicates the quantity of different types of heavy metals actually detected in the soil sample. Indicates the first The toxicity response coefficients of various heavy metals Indicates the first in the soil sample The actual measured concentration values ​​of the heavy metals Indicates the first Soil background reference concentrations of several heavy metals; Based on the comparison between the comprehensive potential ecological risk index and the preset range, the ecological risk level is divided into three levels: low, medium and high. Based on the results of spatiotemporal pattern analysis, pollution source analysis, and ecological risk assessment, a pollution source tracing report is generated; a thematic map of pollution source contribution rate is generated using GIS technology, and a pollution source tracing database is established to record the spatiotemporal distribution information, emission characteristics, impact range, and control recommendations of each pollution source.

[0009] As a further improvement of the present invention, the specific operation process of step S3 is as follows: Regarding health risks, through formulas Get health risk index ,in, This indicates the daily amount of pasture consumed by livestock. represents the average weight of livestock, Indicates the first The reference dosages for these heavy metals are based on toxicity parameters for livestock from internationally recognized authoritative organizations. Indicates the first The pollution source affects the first The contribution rate of each heavy metal Indicates the first Risk weighting factors for each pollution source; The total livestock health risk index is obtained by statistical analysis based on the health risk index of different heavy metals. When the total livestock risk index is greater than the preset threshold, it is determined that there is a potential health risk. Regarding economic risks, through formulas Economic loss index ,in, Indicates the number of forage varieties, Indicates the first Actual yield of forage grass, Indicates the first The market price of forage grass, Indicates the first Discount factor for pasture planting due to excessive heavy metals. This indicates the yield of pasture under normal, uncontaminated conditions; The comprehensive risk index is obtained by combining the pollution cumulative index, the comprehensive potential ecological risk index, the health risk index, and the economic loss index with a weighted summation formula. Based on the comprehensive risk index and risk indicators of various dimensions, the region is divided into four categories: priority treatment area, key monitoring area, general monitoring area and safe area through preset classification rules, and an environmental quality classification report is generated; a classification management database is constructed to record the spatial distribution, area statistics, main pollution characteristics, management measures and expected effects of each category of area.

[0010] As a further improvement of the present invention, the specific operation process of step S4 is as follows: Based on the research progress and engineering practice experience of soil heavy metal pollution remediation technologies at home and abroad, a database containing four major categories of remediation technologies, including physical remediation, chemical remediation, biological remediation and combined remediation, is constructed. Establish a screening and evaluation system for governance solutions, including four primary indicators: technical feasibility, economic rationality, environmental friendliness, and social acceptance, as well as their corresponding secondary indicators; Through formula The comprehensive evaluation score is obtained through calculation, where, This indicates the total number of primary indicators. This indicates the total number of secondary indicators within each primary indicator. Indicates the first The first indicator The weights of each secondary indicator, Indicates the first The first indicator The scores of each secondary indicator are used; the scores are ranked based on the comprehensive evaluation scores, and the highest score is selected as the recommended governance solution. Within the priority treatment area and key monitoring area, typical plots are selected as demonstration projects, and the recommended treatment plan is applied for on-site treatment. At the same time, the changes in heavy metal speciation, pasture indicators and soil ecological function are continuously monitored through sensor networks during the treatment process. The monitoring data are compared with the expected effects of the treatment plan, and a comprehensive index of treatment effectiveness is obtained by combining the weighted formula. If the comprehensive governance effectiveness index is greater than the preset threshold, the governance effect is determined to meet the standard, and a demonstration application summary report is generated.

[0011] A second aspect of the present invention provides a soil ecological environment monitoring and management system based on big data, comprising: Multi-source data integration and monitoring network module: Sampling points are set up in and around grasslands in heavy metal mining areas to collect soil, pasture and tailings wastewater samples; atmospheric deposition, flow and water quality, soil moisture and heavy metal sensors are deployed to transmit data in real time via 5G; third-party data are integrated to form a central database including six core datasets; Big Data Analysis and Pollution Source Analysis Module: Conducts spatiotemporal pattern analysis, constructs spatial distribution maps, and calculates net trend indices; identifies pollution source types through multivariate statistical and geochemical indicators; determines pollution levels based on anthropogenic enrichment coefficients and pollution accumulation indices; assesses ecological risks and generates source tracing reports; Risk assessment and intelligent classification module: Construct an assessment system that includes pollution level, ecological risk, health risk and economic risk; calculate a comprehensive risk index and divide the region into four categories; formulate differentiated management strategies and establish a classification management database; The intelligent governance and demonstration application module includes: constructing a database containing physical, chemical, biological, and combined remediation technologies; selecting the optimal solution through a multi-criteria evaluation system; implementing and verifying the solution in typical sites; monitoring the implementation effect; and generating a demonstration application summary report.

[0012] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates multi-source data, including soil, pasture, atmospheric deposition, and hydrology, and constructs a central database. By combining spatiotemporal pattern analysis and quantitative analysis of pollution source contribution rates, it forms a multi-dimensional risk assessment system and an intelligent classification and management scheme for environmental quality. It generates differentiated governance measures and a dynamic monitoring mechanism for effectiveness, and optimizes the targeting of governance schemes based on multi-dimensional risk assessment results. It also optimizes the priority of resource input based on comprehensive evaluation scores. This effectively solves the problems of insufficient accuracy in monitoring heavy metal pollution in mining areas and poor adaptability of governance schemes, and significantly improves the efficiency and scientific nature of soil ecological environment monitoring and management. Attached Figure Description

[0013] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0016] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0017] like Figure 1 As shown, this invention is a soil ecological environment monitoring and management method based on big data, including multi-source data collection and integrated monitoring network construction, big data analysis and pollution source analysis, risk assessment and intelligent classification of environmental quality, and intelligent governance scheme screening and demonstration application.

[0018] S1. Construction of a multi-source data collection and integrated monitoring network: This involves acquiring soil, pasture, atmospheric deposition, and hydrological data through field sampling and IoT sensors, and building a central database for storing and integrating multi-source environmental data. The specific implementation process is as follows: The core sampling area was grassland in heavy metal mining areas, simultaneously covering the surrounding impact zone of tailings ponds, the downwind range of the prevailing atmospheric deposition winds, and the surface runoff catchment area. The sampling points were laid out according to the principle of combining uniform distribution with key dense distribution. The sampling objects included soil, different varieties of forage grass, tailings slag, and tailings wastewater. Soil samples were collected based on soil profile stratification, including the top 0-20cm, middle 20-40cm, and deep 40-60cm. Forage grass samples were mainly sheepgrass and needlegrass, and whole plant samples were collected, including roots, stems, and leaves. Tailings wastewater included tailings pond overflow water and leakage water from transportation pipelines. All sampled samples are identified by a unique code, which follows the rules of monitoring area number, object type code, sampling date, and location number. At the same time, various sensor devices are deployed according to monitoring needs; Atmospheric deposition sampling sensors were deployed in the atmospheric deposition impact area to collect heavy metal deposition flux and deposition amount; Flow sensors and water quality sensors are deployed at monitoring sections in the surface runoff catchment area to collect real-time data on runoff volume and heavy metal concentration in runoff. Soil moisture sensors and heavy metal online monitoring sensors were deployed in the core monitoring area of ​​pastureland and around the tailings dam to simultaneously acquire data on soil moisture content, pH value and key heavy metal content, including lead, cadmium and copper. All sensors upload real-time monitoring data to the central database via 5G wireless transmission, while local data caching is configured to prevent data loss due to network interruption. By connecting with third-party organizations to supplement key data, including obtaining the pollution source ledger of the mining area for the past 5 years from the environmental protection department, including the location of pollution sources, emission intensity and historical records of exceeding standards; We retrieved regional soil environmental background value reports, soil type distribution maps, and pasture vector boundary data from the natural resources department; we obtained meteorological data, including the annual average precipitation, prevailing wind direction, and wind speed of the monitoring area, from the meteorological department to correct for regional representativeness bias in atmospheric deposition data; and we collected production condition data, including mining scale, mineral processing reagent usage, and tailings discharge, from mining enterprises to form a multi-source data matrix of field, real-time, and third-party data. Based on a pre-defined unified data structure, the field sampling data, real-time sensor monitoring data, and third-party supplementary data are aligned and correlated based on time series and spatial coordinates. This includes removing outliers, filling missing values, adding spatiotemporal markers to all data (using UTC timestamps for time stamps and the National Geodetic Coordinate System for spatial markers), and storing the data in a central database to form six core datasets, including: Soil multi-parameter monitoring dataset: includes physicochemical indicators and heavy metal content of stratified soils; Forage bioaccumulation dataset: contains heavy metal content in various tissues and parts of different forage varieties; Atmospheric dry and wet deposition dataset: includes spatiotemporal distribution of heavy metal deposition flux; Hydrological migration monitoring dataset: includes runoff, water quality, and heavy metal concentration data; Background and basic geographic information dataset: includes soil background values, soil type, and grassland boundaries; Pollution source and operating condition dataset: includes historical pollution source ledgers, real-time production operating conditions, and meteorological data.

[0019] S2. Big Data Analysis and Pollution Source Identification: Based on the core dataset of the central database, spatiotemporal pattern analysis, pollution source identification, and ecological risk assessment are performed on multidimensional data to generate risk assessment maps and pollution source tracing reports. The specific implementation process is as follows: S201, Spatiotemporal Pattern Analysis: Based on soil multi-parameter monitoring datasets, using formulas... The spatial autocorrelation of heavy metal elements was quantified; among them... This indicates the intensity of the difference in heavy metal element measurements between all sampling point pairs. Indicates the spatial step size. Indicates the number of point pairs at the sampling points. Represents a set of point pairs, including all pairs whose distance is equal to or less than a given distance. sampling point pairs , These represent the sampling points. and Concentration values ​​of heavy metal elements collected at the site; Spatial autocorrelation function curves were generated based on the spatial autocorrelation quantification results of heavy metal elements to obtain the spatial clustering characteristics of heavy metal pollution, and a spatial distribution map of heavy metal elements was constructed using Kriging interpolation. Simultaneously, pollutant change trend curves were constructed using time series data from soil monitoring. The formula was then used to... The net trend index is calculated. ,in, This indicates the total number of time-series monitoring data points in the soil monitoring data. A counter representing the length of the time series, used to iterate through the time series. Indicates the first The concentration values ​​of heavy metal pollutants monitored at each time point. Indicates the first The concentration values ​​of heavy metal pollutants monitored at each time point. , indicating that it is used for comparison and Comparison functions; When the net trend index is positive, the concentration of heavy metal pollutants is determined to be in an upward trend. When the net trend index is negative, the concentration of heavy metal pollutants is determined to be in a downward trend. When the net trend index is zero, the concentration of heavy metal pollutants is determined to be in a stable trend. Based on the results of spatiotemporal pattern analysis, a spatiotemporal dynamic map of heavy metal pollution is generated; and a color gradient is used to represent the concentration of heavy metals. S202. Pollution Source Apportionment: Based on soil multi-parameter monitoring datasets, atmospheric dry and wet deposition datasets, hydrological migration monitoring datasets, and pollution source and operational condition datasets, a combination of multivariate statistical analysis and geochemical index methods is used to identify the contribution rate of each pollution source to soil heavy metals. Pollution source types were identified based on principal component analysis and cluster analysis, and a correlation coefficient matrix among different heavy metal elements was constructed. Based on the number of samples and heavy metal types of heavy metal concentration data from pasture soil samples, a correlation coefficient matrix was constructed. The correlation coefficients between different heavy metals were obtained by combining the Pearson coefficient. The eigenvalues ​​of the correlation coefficient matrix were calculated based on the principal component analysis method. The number of pollution sources extracted by the principal components was determined based on the principle that the eigenvalues ​​are greater than 1. The principal component loading matrix was obtained by the maximum variance rotation method. The main source types of each heavy metal element were identified based on the principal component loading coefficients. Through formula Calculation yields the first The first soil sample The actual measured concentration values ​​of the heavy metals, among which, Indicates the total number of pollution sources. Indicates the first The soil sample was taken at the... The contribution of each pollution source; Indicates the first Heavy metals in the first The contribution of each pollution source; Represents the residual, referring to the first The first soil sample The difference between the concentration value of a certain heavy metal and the estimated concentration values ​​from all pollution sources represents the concentration values ​​that cannot be detected by other sources. Part of the explanation of the pollution source; Based on the actual measured concentrations of heavy metals in soil samples, a contribution matrix of each pollution source to different heavy metals was obtained by combining multivariate statistical analysis. The anthropogenic enrichment coefficient is obtained by comparing the ratio of heavy metal concentration in soil samples to the ratio of reference element concentration in soil samples with the ratio of heavy metal background value in background dataset to reference element background value in background dataset. When the anthropogenic enrichment coefficient is greater than a preset threshold, it is determined that the heavy metal element has anthropogenic pollution contribution. The pollution accumulation index is obtained by comparing the measured concentration of heavy metals in soil samples with the background value and introducing a preset correction factor to correct for the natural fluctuations of the background value. The correction factor is set to 1.5 to correct for the natural fluctuations of the background value and measurement errors. When the pollution accumulation index is greater than a preset threshold, the current soil sample is determined to be polluted; the higher the pollution accumulation index, the higher the degree of pollution. Based on the calculation results of anthropogenic enrichment coefficient and pollution accumulation index, the main pollution source types are identified, including mining sources, tailings dam discharge sources, atmospheric deposition sources, and surface runoff migration sources. S203. Ecological risk assessment: Based on the forage bioaccumulation dataset, calculate the bioaccumulation coefficient and transfer coefficient of heavy metals in forage. Through formula The bioaccumulation coefficient was calculated, where, Indicates the first in the forage sample Measured concentration values ​​of several heavy metals Indicates the first soil sample. Measured concentration values ​​of several heavy metals; Through formula The transfer coefficients are calculated, where, Indicates the above-ground portion of the pasture. Measured concentration values ​​of several heavy metals Indicates the root of the pasture The concentration of certain heavy metals; Based on the calculation results of bioaccumulation coefficient and transfer coefficient, the migration and transformation characteristics of heavy metals in soil and pasture were obtained, and heavy metal elements and areas with high ecological risk were identified based on the migration and transformation characteristics. Through formula The comprehensive potential ecological risk index was calculated. ,in, This indicates the quantity of different types of heavy metals actually detected in the soil sample. Indicates the first The toxicity response coefficients of various heavy metals Indicates the first in the soil sample The actual measured concentration values ​​of the heavy metals Indicates the first Soil background reference concentrations of several heavy metals; Based on the comparison between the comprehensive potential ecological risk index and the preset range, the ecological risk level is divided as follows: when the comprehensive potential ecological risk index is less than the preset range, it is considered low ecological risk; when the comprehensive potential ecological risk index is within the preset range, it is considered medium ecological risk; and when the comprehensive potential ecological risk index is greater than the preset range, it is considered high ecological risk. S204. Pollution Source Tracing Report Generation: Based on the results of spatiotemporal pattern analysis, pollution source analysis, and ecological risk assessment, a pollution source tracing report is generated. The report content includes the spatial distribution characteristics and temporal variation trends of heavy metal pollution, the types of major pollution sources and their contribution rates, the distribution of ecological risk levels, and the identification of key control areas. A thematic map of pollution source contribution rate is generated using GIS technology. A pie chart is used to identify the pollution source contribution of each sampling point, and a color-coded classification is used to represent the spatial distribution of ecological risk levels. Establish a pollution source tracing database to record the spatiotemporal distribution information, emission characteristics, impact range, and control recommendations for each pollution source.

[0020] S3. Risk Assessment and Intelligent Classification of Environmental Quality: Based on pollution source tracing reports and ecological risk assessment results, a multi-dimensional risk assessment system is established to classify and manage the environmental quality of the monitored area. The specific implementation process is as follows: S301. Construction of a multi-dimensional risk assessment system: Based on the pollution source tracing report generated in the S2 stage, combined with the soil multi-parameter monitoring dataset, pasture bioaccumulation dataset, and background and basic geographic information dataset, a comprehensive risk assessment system including four dimensions: pollution level, ecological risk, health risk, and economic risk is constructed. For the degree of pollution, the pollution accumulation index calculated by step S203 is used directly. The higher the pollution accumulation index, the higher the degree of pollution; including light pollution, moderate pollution and heavy pollution. For ecological risks, the comprehensive potential ecological risk index calculated in stage S203 is directly adopted, and the risk level is divided based on the preset range, including low ecological risk, medium ecological risk and high ecological risk. Regarding health risks, through formulas The health risk index is calculated. ,in, This indicates the daily amount of pasture consumed by livestock. represents the average weight of livestock, Indicates the first The reference dosages for these heavy metals are based on toxicity parameters for livestock from internationally recognized authoritative organizations. Indicates the first The pollution source affects the first The contribution rate of each heavy metal Indicates the first Risk weighting factors for each pollution source; The total livestock health risk index is obtained by statistical analysis based on the health risk index of different heavy metals. When the total livestock health risk index is greater than the preset threshold, it is determined that there is a potential health risk. The higher the total livestock health risk index, the higher the health risk. Regarding economic risks, based on forage bioaccumulation datasets and third-party economic data, a formula is used... The economic loss index was calculated. ,in, Indicates the number of forage varieties, Indicates the first Actual yield of forage grass, Indicates the first The market price of forage grass, Indicates the first Discount factor for pasture planting due to excessive heavy metals. It represents the yield of pasture under normal, uncontaminated conditions; the higher the economic loss index, the higher the economic risk. The comprehensive risk index is obtained by combining the pollution cumulative index, the comprehensive potential ecological risk index, the health risk index, and the economic loss index with a weighted summation formula. Based on the comprehensive risk index and risk indicators of various dimensions, the region is divided into four categories: priority governance area, key monitoring area, general monitoring area and safe area through preset classification rules, and an environmental quality classification report is generated, including classification results, identification of risk hotspot areas and suggestions for priority governance measures.

[0021] Based on the environmental quality classification results, formulate differentiated management strategies and measures recommendations; For safe zones, implement preventive and protective measures, increase the frequency of daily monitoring, establish an early warning mechanism, and prevent the spread of pollution; For general monitoring areas, risk control measures should be implemented to limit the intensity of pasture use and increase investment in soil improvement. For key monitoring areas, implement remediation measures and use phytoremediation or chemical passivation techniques to reduce the activity of heavy metals; For priority remediation areas, strict control measures will be implemented, prohibiting the production and use of forage grass, and soil remediation projects will be initiated. Construct a categorized management database to record the spatial distribution, area statistics, main pollution characteristics, management measures, and expected effects of each category of area.

[0022] S4. Screening and Demonstration Application of Intelligent Governance Solutions: Based on environmental quality classification results and pollution characteristic analysis, a governance technology database is constructed, targeted governance solutions are formulated, and demonstration application verification is carried out. The specific implementation process is as follows: S401. Construction of a database of remediation technologies: Based on the research progress and engineering practice experience of soil heavy metal pollution remediation technologies at home and abroad, a database of four major categories of remediation technologies will be constructed, including physical remediation, chemical remediation, biological remediation and combined remediation. Physical remediation technologies include topsoil replacement, deep tillage and soil improvement, and electric remediation. The applicable conditions, remediation effects, cost input and technological maturity of each technology are recorded. Chemical remediation technologies include chemical rinsing, chemical solidification, and in-situ chemical oxidation. The types of reagents, reaction mechanisms, treatment efficiency, and environmental impacts of each technology are recorded. Bioremediation technologies include phytoremediation, microbial remediation, and animal remediation. The biological species, remediation mechanisms, treatment cycles, and ecological benefits of each technology are recorded. Combined remediation technologies include physical and chemical combinations, chemical and biological combinations, and physical and biological combinations. The combination methods, synergistic effects, and comprehensive advantages of each technology are recorded. A technical parameter file is established for each remediation technology, including technical principles, process flow, key parameters, applicable scope, technical advantages, limitations, cost analysis, and risk assessment. At the same time, relevant engineering cases are collected, and the project scale, remediation effect, operating cost, and long-term stability data are recorded. S402. Intelligent Screening of Remediation Solutions: Based on pollution characteristics, site conditions, economic costs, and remediation objectives, an evaluation system for screening remediation solutions is established, employing multi-criteria decision analysis methods for optimal solution selection; an evaluation index system for remediation solutions is established, including four primary indicators: technical feasibility, economic rationality, environmental friendliness, and social acceptance. Each primary indicator is followed by a corresponding secondary indicator: Technical feasibility includes governance efficiency, technological maturity, and implementation difficulty; Economic rationality includes initial investment, operating costs, and maintenance expenses; Environmental friendliness includes the risk of secondary pollution, ecological impact, and resource consumption; Social acceptance includes public recognition, policy support, and promotion and application. Through formula The comprehensive evaluation score is obtained through calculation, where, This indicates the total number of primary indicators. This indicates the total number of secondary indicators within each primary indicator. Indicates the first The first indicator The weights of each secondary indicator, Indicates the first The first indicator The scores of each secondary indicator; Based on the comprehensive evaluation score of each governance solution, they are sorted from high to low to form a list of preferred solutions; the solution with the highest score is selected as the recommended governance solution, and a governance solution recommendation report is generated, which includes the recommended technology, implementation parameters, expected effects, cost budget and risk control measures; S403. Demonstration Application Verification: Based on the environmental quality classification report and the recommended remediation plan report, typical demonstration sites are selected within the priority remediation area, and the recommended remediation plan is implemented for engineering verification. The specific implementation process is as follows: Within the priority treatment area and key monitoring area, typical plots were selected as demonstration projects, and the recommended treatment scheme with the highest comprehensive evaluation score was applied for on-site treatment. At the same time, based on the deployed monitoring sensor network, the soil, pasture and groundwater in the demonstration area were continuously tracked and monitored, and key performance data during the treatment process were collected, including changes in heavy metal speciation, pasture biomass and heavy metal content, and soil ecological function indicators. The monitoring data were compared with the expected effects of the treatment scheme, and a comprehensive treatment performance index was obtained by combining the weighted formula. If the comprehensive governance effectiveness index is greater than the preset threshold, the governance effect is determined to meet the standard, and a demonstration application summary report is generated, including governance effect, cost analysis, technology applicability evaluation and promotion suggestions.

[0023] This invention is a soil ecological environment monitoring and management system based on big data, comprising: Multi-source data integration and monitoring network module: Taking grassland in heavy metal mining areas as the core sampling area, it simultaneously covers the surrounding impact area of ​​tailings ponds. Sampling is conducted based on the principles of uniform distribution and key density. Soil is collected in layers, whole grass plants are collected, and tailings wastewater includes overflow and seepage water. All collected samples are assigned a unique code. At the same time, sensors are deployed in categories, including sampling sensors in atmospheric deposition areas, flow and water quality sensors in runoff areas, and moisture and heavy metal sensors in core areas. Data is uploaded via 5G and cached locally. Data is supplemented by third-party institutions, integrated and processed, and stored in a central database to form six core datasets. The Big Data Analysis and Pollution Source Identification Module: Based on the core dataset of the central database, it performs spatiotemporal pattern analysis on multi-dimensional data, generates spatial autocorrelation function curves to obtain clustering characteristics, constructs spatial distribution maps using Kriging interpolation, and calculates net trend indices based on time series data to generate spatiotemporal dynamic maps. It analyzes pollution sources using multivariate statistical and geochemical indicators, identifying the main source types of each heavy metal element; determines pollution source types based on anthropogenic enrichment coefficients and pollution accumulation indices; evaluates ecological risks based on pasture bioaccumulation datasets; and generates risk assessment maps and pollution source tracing reports. Risk assessment and intelligent classification module: Based on pollution source tracing reports and ecological risk assessment results, combined with soil multi-parameter monitoring datasets, pasture bioaccumulation datasets, and background and basic geographic information datasets, a multi-dimensional comprehensive risk assessment system is constructed, including pollution level, ecological risk, health risk, and economic risk. A comprehensive risk index is obtained by combining a weighted summation formula. Based on the comprehensive risk index and indicators of each dimension, the region is divided into priority treatment areas, key monitoring areas, general monitoring areas, and safe areas, and targeted management strategies are formulated. At the same time, a classified management database is constructed to record regional characteristics and measures. The intelligent governance and demonstration application module: Based on environmental quality classification results and pollution characteristic analysis, a governance technology database is constructed, integrating domestic and international research and practical experience in soil heavy metal pollution governance, including four major categories of technologies: physical, chemical, biological, and combined remediation. Parameters such as the applicable conditions, governance effects, and costs of each technology are recorded. Simultaneously, engineering case data is collected, and solutions are formulated through multi-criteria decision analysis. An evaluation system containing four primary indicators is constructed, and the optimal governance solution is selected based on the comprehensive evaluation score, generating a recommendation report with implementation parameters. Typical sites in priority governance areas and key monitoring areas are selected for treatment, and soil and pasture data are continuously monitored. If the expected results are met, a demonstration application summary report is generated.

[0024] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A soil ecological environment monitoring and management method based on big data, characterized in that, include: S1. Construction of a multi-source data collection and integrated monitoring network: stratified sampling of soil, pasture, tailings and wastewater in and around the grassland in the heavy metal mining area; Deploy sensors for monitoring atmospheric deposition, surface runoff, soil moisture, and heavy metals, and transmit data in real time via 5G; connect with third-party institutions to supplement data; integrate all data into a central database based on a unified structure to form six core datasets; S2. Big Data Analysis and Pollution Source Analysis: Based on the core dataset of the central database, conduct spatiotemporal pattern analysis, pollution source analysis and ecological risk assessment of multidimensional data, and generate risk assessment maps and pollution source tracing reports; S3. Risk Assessment and Intelligent Classification of Environmental Quality: Based on pollution source tracing reports and ecological risk assessment results, establish a multi-dimensional risk assessment system to classify and manage the environmental quality of the monitored area. S4. Screening and Demonstration Application of Intelligent Governance Solutions: Based on environmental quality classification results and pollution characteristic analysis, construct a governance technology database, formulate governance solutions, and conduct demonstration application verification.

2. The soil ecological environment monitoring and management method based on big data according to claim 1, characterized in that, The specific implementation process of the spatiotemporal pattern analysis is as follows: Based on a multi-parameter soil monitoring dataset, using the formula The spatial autocorrelation of heavy metal elements was quantified; among them, This indicates the intensity of the difference in heavy metal element measurements between all sampling point pairs. Indicates the spatial step size. Indicates the number of point pairs at the sampling points. Represents a set of point pairs, including all pairs whose distance is equal to or less than a given distance. sampling point pairs , These represent the sampling points. and Concentration values ​​of heavy metal elements collected at the site; Spatial autocorrelation function curves were generated based on the spatial autocorrelation quantification results of heavy metal elements to obtain the spatial aggregation characteristics of heavy metal pollution and construct a spatial distribution map of heavy metal elements; the formula was then used to... Obtain the net trend index ,in, This indicates the total number of time-series monitoring data points in the soil monitoring data. A counter representing the length of the time series, used to iterate through the time series. Indicates the first The concentration values ​​of heavy metal pollutants monitored at each time point. Indicates the first The concentration values ​​of heavy metal pollutants monitored at each time point. , indicating that it is used for comparison and Comparison functions; The net trend index is used to determine the trend of heavy metal pollutant concentration, including rising, falling and stable trends; the spatiotemporal dynamic map of heavy metal pollution is generated based on the results of spatiotemporal pattern analysis.

3. The soil ecological environment monitoring and management method based on big data according to claim 1, characterized in that, The specific implementation process of the pollution source analysis is as follows: Principal component analysis was used to construct a correlation coefficient matrix among heavy metal elements. Based on the principle that eigenvalues ​​are greater than 1, the number of major pollution sources was determined. The principal component loading matrix was obtained using the maximum variance rotation method to identify the main source types of each heavy metal. The formula was then used to... Get the first The first soil sample The actual measured concentration values ​​of the heavy metals, among which, Indicates the total number of pollution sources. Indicates the first The soil sample was taken at the... The contribution of each pollution source; Indicates the first Heavy metals in the first The contribution of each pollution source; Represents the residual, referring to the first The first soil sample The difference between the concentration values ​​of a certain heavy metal and the estimated concentration values ​​from all pollution sources; The artificial enrichment coefficient is obtained by comparing the ratio of heavy metal concentration in soil samples to the ratio of reference element concentration in soil samples with the ratio of heavy metal background value in background dataset to reference element background value in background dataset. The pollution accumulation index is obtained by comparing the measured concentration of heavy metals in soil samples with the background value and introducing a preset correction factor. Based on the anthropogenic enrichment coefficient and pollution accumulation index, the main pollution source types are identified, including mining sources, tailings dam discharge sources, atmospheric deposition sources, and surface runoff migration sources.

4. The soil ecological environment monitoring and management method based on big data according to claim 1, characterized in that, The specific operational steps for the ecological risk assessment are as follows: The bioaccumulation coefficient is obtained by comparing the measured concentrations of heavy metals in pasture samples with the measured concentrations of heavy metals in corresponding soil samples. The transfer coefficient is obtained by comparing the concentration values ​​of heavy metals in the above-ground parts of the pasture with the concentration values ​​of heavy metals in the roots of the pasture. Based on bioaccumulation coefficients and transfer coefficients, the migration and transformation characteristics of heavy metals in soil and pasture were obtained, and heavy metal elements and areas with high ecological risk were identified. Through formula A comprehensive potential ecological risk index was obtained. ,in, This indicates the quantity of different types of heavy metals actually detected in the soil sample. Indicates the first The toxicity response coefficients of various heavy metals Indicates the first in the soil sample The actual measured concentration values ​​of the heavy metals Indicates the first Soil background reference concentrations of several heavy metals; Based on the comparison between the comprehensive potential ecological risk index and the preset range, the ecological risk level is divided into three levels: low, medium and high. Based on the results of spatiotemporal pattern analysis, pollution source analysis, and ecological risk assessment, a pollution source tracing report is generated; a thematic map of pollution source contribution rate is generated using GIS technology, and a pollution source tracing database is established to record the spatiotemporal distribution information, emission characteristics, impact range, and control recommendations of each pollution source.

5. The soil ecological environment monitoring and management method based on big data according to claim 1, characterized in that, The specific operation process of step S3 is as follows: Regarding health risks, through formulas Get health risk index ,in, This indicates the daily amount of pasture consumed by livestock. represents the average weight of livestock, Indicates the first The reference dosages for these heavy metals are based on toxicity parameters for livestock from internationally recognized authoritative organizations. Indicates the first The pollution source affects the first The contribution rate of each heavy metal Indicates the first Risk weighting factors for each pollution source; The total livestock health risk index is obtained by statistical analysis based on the health risk index of different heavy metals. When the total livestock risk index is greater than the preset threshold, it is determined that there is a potential health risk. Regarding economic risks, through formulas Economic loss index ,in, Indicates the number of forage varieties, Indicates the first Actual yield of forage grass, Indicates the first The market price of forage grass, Indicates the first Discount factor for pasture planting due to excessive heavy metals. This indicates the yield of pasture under normal, uncontaminated conditions; The comprehensive risk index is obtained by combining the pollution cumulative index, the comprehensive potential ecological risk index, the health risk index, and the economic loss index with a weighted summation formula. Based on the comprehensive risk index and risk indicators of various dimensions, the region is divided into four categories: priority treatment area, key monitoring area, general monitoring area and safe area through preset classification rules, and an environmental quality classification report is generated; a classification management database is constructed to record the spatial distribution, area statistics, main pollution characteristics, management measures and expected effects of each category of area.

6. The soil ecological environment monitoring and management method based on big data according to claim 1, characterized in that, The specific operation process of step S4 is as follows: Based on the research progress and engineering practice experience of soil heavy metal pollution remediation technologies at home and abroad, a database containing four major categories of remediation technologies, including physical remediation, chemical remediation, biological remediation and combined remediation, is constructed. Establish a screening and evaluation system for governance solutions, including four primary indicators: technical feasibility, economic rationality, environmental friendliness, and social acceptance, as well as their corresponding secondary indicators; Through formula The comprehensive evaluation score is obtained through calculation, where, This indicates the total number of primary indicators. This indicates the total number of secondary indicators within each primary indicator. Indicates the first The first indicator The weights of each secondary indicator, Indicates the first The first indicator The scores of each secondary indicator are used; the scores are ranked based on the comprehensive evaluation scores, and the highest score is selected as the recommended governance solution. Within the priority treatment area and key monitoring area, typical plots are selected as demonstration projects, and the recommended treatment plan is applied for on-site treatment. At the same time, the changes in heavy metal speciation, pasture indicators and soil ecological function are continuously monitored through sensor networks during the treatment process. The monitoring data are compared with the expected effects of the treatment plan, and a comprehensive index of treatment effectiveness is obtained by combining the weighted formula. If the comprehensive governance effectiveness index is greater than the preset threshold, the governance effect is determined to meet the standard, and a demonstration application summary report is generated.

7. A system applied to the soil ecological environment monitoring and management method based on big data as described in any one of claims 1-6, comprising: Multi-source data integration and monitoring network module: Sampling points are set up in and around grasslands in heavy metal mining areas to collect soil, pasture and tailings wastewater samples; Deploy sensors for atmospheric deposition, flow and water quality, soil moisture and heavy metals, and transmit data in real time via 5G; integrate third-party data to form a central database including six core datasets; Big Data Analysis and Pollution Source Identification Module: Conducts spatiotemporal pattern analysis, constructs spatial distribution maps, and calculates net trend indices; identifies pollution source types through multivariate statistical and geochemical indicators; The degree of pollution is determined based on the anthropogenic enrichment coefficient and the pollution accumulation index; Assess ecological risks and generate source tracing reports; Risk assessment and intelligent classification module: Construct an assessment system that includes pollution level, ecological risk, health risk and economic risk; Calculate the comprehensive risk index and divide the region into four categories; formulate differentiated management strategies and establish a classification management database; The intelligent governance and demonstration application module includes: constructing a database containing physical, chemical, biological, and combined remediation technologies; selecting the optimal solution through a multi-criteria evaluation system; implementing and verifying the solution in typical sites; monitoring the implementation effect; and generating a demonstration application summary report.

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