Farmland tail water pollution load analysis and evaluation method

Abstract

The invention belongs to the technical field of farmland tail water pollution load analysis and evaluation, particularly relates to a farmland tail water pollution load analysis and evaluation method, and aims to overcome the obvious defects of an existing monitoring and evaluation method in the aspects of monitoring point layout, index coverage, data processing, analysis depth and the like. In order to solve the problems that farmland tail water pollution load analysis and evaluation cannot meet accurate and dynamic farmland tail water pollution load analysis and evaluation requirements and pollution prevention and control decisions are difficult to effectively support in the prior art, the invention provides the following scheme: the method comprises the following steps: S1, carrying out multi-source acquisition on farmland information data, and carrying out fusion processing; s2, setting a fixed sampling unit and a movable sampling unit according to farmland information setting; according to the method, multi-source data such as farmland basic information, agricultural production activity data, meteorological data and water quality monitoring data are comprehensively considered, and comprehensive and accurate quantification and evaluation of farmland tail water pollution load are realized through a data preprocessing and fusion technology.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis and evaluation of farmland tailwater pollution load, and in particular to a method for analyzing and evaluating farmland tailwater pollution load. Background Art

[0002] With the continuous development of agricultural production, the problem of farmland tailwater pollution has become increasingly prominent, causing serious impacts on the surrounding water environment. Most of the existing methods for evaluating farmland tailwater pollution load have certain limitations. For example, relying only on single water quality monitoring data cannot comprehensively and accurately reflect the sources, compositions, and variation laws of pollution loads; or insufficient consideration is given to the interaction between the farmland ecosystem and the water environment system, making it difficult to meet the needs of precise treatment of farmland tailwater pollution.

[0003] In the prior art, there are significant deficiencies in the monitoring point layout, index coverage, data processing, and analysis depth of the monitoring and evaluation methods, which cannot meet the requirements of precise and dynamic analysis and evaluation of farmland tailwater pollution load, and it is difficult to effectively support pollution prevention and control decisions. Therefore, we propose a method for analyzing and evaluating farmland tailwater pollution load to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve the deficiencies in the prior art that there are significant deficiencies in the monitoring point layout, index coverage, data processing, and analysis depth of the monitoring and evaluation methods, which cannot meet the requirements of precise and dynamic analysis and evaluation of farmland tailwater pollution load, and it is difficult to effectively support pollution prevention and control decisions, and to propose a method for analyzing and evaluating farmland tailwater pollution load.

[0005] A method for analyzing and evaluating farmland tailwater pollution load provided by this application adopts the following technical solutions: A method for analyzing and evaluating farmland tailwater pollution load includes the following steps: S1: Collect multi-source farmland information data and perform fusion processing; S2: Set up fixed sampling units and mobile sampling units according to farmland information; S3: Collect samples of farmland tailwater and perform multi-dimensional detection on the collected samples; S4: Process the detection data and perform multi-modal analysis on the detection results; S5: Construct an ecological risk assessment model according to the analysis results; S6: Develop precise tailwater pollution prevention and control strategies according to the analysis and evaluation results; S7: Construct a real-time monitoring system to track the prevention and control effects; S8: Comprehensively evaluate the implementation effects of the prevention and control measures, and form a continuous improvement mechanism through feedback optimization.

[0006] Further, in S1, the farmland information data includes farmland soil property data, topographic and geomorphic data, crop growth data, agricultural activity data, meteorological condition data, and surrounding hydrological environment data. The farmland soil property data includes soil texture, fertility index, pH value, and organic matter content; the topographic and geomorphic data includes farmland elevation, slope, aspect, and drainage direction; the crop growth data includes planting structure, growth period, irrigation method, and fertilization pattern; the agricultural activity data includes records of pesticide application history, tillage method, and agricultural film usage; the meteorological condition data includes precipitation, temperature, wind speed, wind direction, and evaporation; and the surrounding hydrological environment data includes the hydrological characteristics of the receiving water body, current water quality status, and water body functional zoning.

[0007] Further, in S2, using geographic information system and geostatistical methods, combined with farmland functional zoning to determine the basic sampling points, introducing a deep reinforcement learning algorithm, dynamically adjusting the sampling point density and location according to historical pollution data and real-time environmental changes, configuring an autonomous mobile robot equipped with multi-parameter water quality sensors, and performing supplementary sampling tasks according to preset rules. The fixed sampling unit is set at key drainage nodes, tail water collection areas, and the entrance of the receiving water body, equipped with an automatic water quality monitor and a flowmeter to achieve long-term continuous monitoring. The mobile sampling unit includes portable sampling equipment and an unmanned aerial vehicle sampling system, which is flexibly deployed according to real-time instructions to enhance the mobility and flexibility of sampling.

[0008] Further, in S3, the multi-dimensional detection combines on-site rapid detection and laboratory precision analysis. On-site, a hyperspectral imager, a heavy metal rapid detector, and a water quality multi-parameter analyzer are used to obtain preliminary data. In the laboratory, ICP-MS, GC-MS, and LC-MS technologies are used to accurately determine the content of heavy metals and organic pollutants. The biological toxicity test uses a microplate luminescent bacteria toxicity test, a daphnia acute toxicity test, and a fish embryo development toxicity test to evaluate the comprehensive toxicity effect of the samples.

[0009] Further, in S4, multivariate statistical analysis uses principal component analysis to extract the main pollution factors, cluster analysis to identify the types of pollution sources, factor analysis to determine the contribution rate of pollution sources, the machine learning algorithm uses random forest to screen key characteristic variables, establishes an XGBoost prediction model to simulate the temporal and spatial variation trend of pollution load. The hydrological model selects SWAT and MikeShe models, combines the farmland topography and soil properties to simulate the tail water flow and the migration and transformation process of pollutants, constructs a dynamic spatio-temporal database of farmland tail water pollution load based on the above analysis, stores multi-source heterogeneous data, and uses NoSQL database technology to achieve efficient storage and rapid query; XGBoost prediction model XGBoost constructs an integrated model through gradient boosting. Assume that the model of the t-th iteration is: ; The objective function is: ; Among them, the loss function is usually the mean square error or logarithmic loss, and the regularization term Ω(f t ) is defined as: ; Among them, γ is the tree complexity parameter, T is the number of leaf nodes, λ is the regularization parameter, and w is the weight of the leaf nodes; SWAT model The SWAT model is used to simulate the flow of agricultural tail water and the migration and transformation of pollutants. Its core equations include the water balance equation: ; Among them: ΔS: change in soil water storage; P: precipitation; Q: surface runoff; E: evaporation; T: transpiration; W gw : groundwater outflow; Pollutant load calculation formula: ; Among them: H: number of hydrological response units (HRUs); : amount of pollutants lost from the surface of the hth HRU; : amount of pollutants lost from the subsurface of the hth HRU; The purpose of calculating the pollutant load L: to quantify the pollution load and provide a basis for risk assessment and prevention and control; The pollutant load in each HRU is further calculated by the following formula: ; Among them: and : surface runoff and subsurface runoff of the hth HRU, respectively; : area of the hth HRU; and : surface and subsurface pollutant concentrations of the hth HRU, respectively; MikeShe model The MikeShe model simulates the flow of water and the transport of pollutants based on the finite difference method. Its basic equations are the Saint-Venant equations: ; ; Among them: A: cross-sectional area of flow; Q: flow rate; t: time; x: spatial coordinate; h: water depth; g: acceleration due to gravity; S f : friction slope; τ: shear stress; ρ: density of water; : inflow rate; : outflow rate; : Cross-sectional flow rate; The pollutant transport equation is: ; Where: C: Pollutant concentration; D: Diffusion coefficient; S: Source / sink term.

[0010] Furthermore, in S5, the ecological risk assessment model is based on the HRA framework, combines the toxicity unit method to quantify heavy metal risks, uses the ECOSAR model to predict organic pollutant risks, applies a comprehensive evaluation model based on the entropy weight method, integrates the risk identification index and the risk characterization value, generates an intuitive ecological risk distribution map, divides the risk levels into high, medium, and low levels, and identifies high-risk areas and pollution indicators.

[0011] Furthermore, in S6, the prevention and control strategies include an optimized fertilization plan and green pesticide substitution for source reduction, the construction of ecological ditches and the setting of buffer zones for process control, the construction of artificial wetland systems and the tail water recycling project for end treatment, strengthening measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas.

[0012] Furthermore, in S7, a water quality automatic monitoring station and an Internet of Things sensor network are built, a multi-source data fusion platform is constructed, edge computing technology is used to preprocess data, deep analysis on the cloud platform is carried out to achieve dynamic monitoring, a feedback optimization mechanism is established, the monitoring data and the model evaluation results are fed back in real time, and the model parameters and prevention and control strategies are dynamically adjusted to form a closed-loop management.

[0013] Furthermore, in S8, cost-benefit analysis is used to compare the economy of prevention and control measures for effect evaluation and continuous improvement, a comprehensive index system is adopted to evaluate the prevention and control effect, the model parameters are continuously optimized based on the feedback data, the evaluation model is upgraded, and a continuous improvement mechanism is formed to provide long-term support for agricultural environmental management.

[0014] Furthermore, the tail water pollution prevention and control strategy document formulated in step S6, the time series data of the prevention and control effect tracked and recorded in step S7, and the prevention and control measure implementation effect evaluation report generated in step S8 constitute the prevention and control records. The monitoring data, evaluation results, and prevention and control records are stored in a distributed database. Blockchain technology is used to ensure data security and traceability. A visual decision support system is developed to integrate the geographic information system to display pollution distribution, risk assessment, and prevention and control effect, providing an interactive analysis tool and an intelligent decision-making plan for managers.

[0015] In summary, the present application includes at least one of the following beneficial technical effects: 1. This solution can comprehensively understand the influencing factors of farmland tailwater pollution by collecting multi-dimensional data such as farmland soil, terrain, and crop growth from multiple sources and performing fusion processing, providing detailed data support for subsequent accurate assessment and prevention and control, and overcoming the limitation of single data in traditional methods. 2. This solution uses multivariate statistical analysis, machine learning algorithms, and hydrological models to deeply analyze pollution loads from multiple perspectives such as spatio-temporal distribution and source analysis, construct a dynamic spatio-temporal database, and achieve an accurate portrait of farmland tailwater pollution, providing a scientific basis for pollution prevention and control decision-making. 3. Based on the results of pollution analysis and ecological risk assessment, this solution formulates a full-process prevention and control strategy from source reduction to end treatment, classifies and implements policies for different risk-level regions, which can not only effectively prevent and control pollution but also rationally allocate prevention and control resources. The present invention comprehensively considers multi-source data such as basic farmland information, agricultural production activity data, meteorological data, and water quality monitoring data, and through data preprocessing and fusion technology, realizes the comprehensive and accurate quantification and evaluation of farmland tailwater pollution loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of a method for analyzing and evaluating farmland tailwater pollution loads proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0018] Embodiment 1 Referring to Figure 1 , a method for analyzing and evaluating farmland tailwater pollution loads includes the following steps: S1: Collect multi-source farmland information data and perform fusion processing; S2: Set up fixed sampling units and mobile sampling units according to farmland information; S3: Collect samples of farmland tailwater and perform multi-dimensional detection on the collected samples; S4: Process the detection data and perform multi-modal analysis on the detection results; S5: Construct an ecological risk assessment model according to the analysis results; S6: Develop accurate tailwater pollution prevention and control strategies based on the analysis and assessment results; S7: Construct a real-time monitoring system to track the prevention and control effects; S8: Conduct a comprehensive evaluation of the implementation effects of the prevention and control measures, and form a continuous improvement mechanism through feedback optimization.

[0019] In this embodiment, in S1, the farmland information data includes farmland soil property data, topographic and geomorphic data, crop growth data, agricultural activity data, meteorological condition data, and surrounding hydrological environment data. The farmland soil property data includes soil texture, fertility index, pH value, and organic matter content; the topographic and geomorphic data includes farmland elevation, slope, aspect, and drainage direction; the crop growth data includes planting structure, growth period, irrigation method, and fertilization pattern; the agricultural activity data includes records of pesticide application history, tillage method, and agricultural film usage; the meteorological condition data includes precipitation, temperature, wind speed, wind direction, and evaporation; and the surrounding hydrological environment data includes the hydrological characteristics of the receiving water body, current water quality status, and water body function zoning.

[0020] In this embodiment, in S2, using the geographic information system and geostatistical method, combined with the farmland function zoning to determine the basic sampling points, introducing the deep reinforcement learning algorithm, dynamically adjusting the sampling point density and location according to historical pollution data and real-time environmental changes, configuring an autonomous mobile robot equipped with multi-parameter water quality sensors, and performing supplementary sampling tasks according to preset rules. Fixed sampling units are set at key drainage nodes, tail water collection areas, and the entrance of the receiving water body, equipped with automatic water quality monitors and flow meters to achieve long-term continuous monitoring. The mobile sampling unit includes portable sampling equipment and an unmanned aerial vehicle sampling system, which are flexibly deployed according to real-time instructions to enhance the mobility and flexibility of sampling.

[0021] In this embodiment, in S3, the multi-dimensional detection combines on-site rapid detection and laboratory precision analysis. On-site, a hyperspectral imager, a heavy metal rapid detector, and a water quality multi-parameter analyzer are used to obtain preliminary data. In the laboratory, ICP-MS, GC-MS, and LC-MS technologies are used to accurately determine the contents of heavy metals and organic pollutants. The biological toxicity test uses microplate luminescent bacteria toxicity test, daphnia acute toxicity test, and fish embryo development toxicity test to evaluate the comprehensive toxicity effect of the samples.

[0022] In this embodiment, in S4, multivariate statistical analysis uses principal component analysis to extract the main pollution factors, cluster analysis to identify the types of pollution sources, factor analysis to determine the contribution rate of pollution sources, and the machine learning algorithm uses random forest to screen key characteristic variables, establishes an XGBoost prediction model, and simulates the spatio-temporal change trend of pollution load. The hydrological model selects the SWAT and MikeShe models, combines the farmland topography and soil properties to simulate the tail water flow and the migration and transformation process of pollutants, constructs a dynamic spatio-temporal database of farmland tail water pollution load based on the above analysis, stores multi-source heterogeneous data, and uses NoSQL database technology to achieve efficient storage and rapid query; XGBoost prediction model XGBoost constructs an integrated model through gradient boosting. Assume that the model of the t-th iteration is: ; The objective function is as follows: ; Among them, the loss function is usually the mean square error or logarithmic loss, and the regularization term Ω(f t ) is defined as: ; Among them, γ is the tree complexity parameter, T is the number of leaf nodes, λ is the regularization parameter, and w is the weight of the leaf nodes; SWAT model The SWAT model is used to simulate the process of agricultural tailwater flow and pollutant migration and transformation. Its core equations include the water balance equation: ; Among them: ΔS: change in soil water storage; P: precipitation; Q: surface runoff; E: evaporation; T: transpiration; W gw : groundwater outflow; Pollutant load calculation formula: ; Among them: H: number of hydrological response units (HRUs); : amount of pollutants lost from the surface of the hth HRU; : amount of pollutants lost from the subsurface of the hth HRU; The purpose of calculating the pollutant load L is to provide a basis for risk assessment and prevention and control by quantifying the pollution load; The pollutant load is further calculated in each HRU by the following formula: ; Among them: and : surface runoff and subsurface runoff of the hth HRU, respectively; : area of the hth HRU; and : surface and subsurface pollutant concentrations of the hth HRU, respectively; MikeShe model The MikeShe model simulates the processes of water flow and pollutant transport based on the finite difference method. Its basic equations are the Saint-Venant equations: ; ; Among them: A: cross-sectional area of flow; Q: flow rate; t: time; x: spatial coordinate; h: water depth; g: acceleration due to gravity; S f : friction slope; τ: shear stress; ρ: density of water; : inflow rate; : outflow rate; : Cross-sectional flow rate; The pollutant transport equation is: ; Where: C: Pollutant concentration; D: Diffusion coefficient; S: Source / sink term.

[0023] In this embodiment, in S5, the ecological risk assessment model is based on the HRA framework, combines the toxicity unit method to quantify heavy metal risks, uses the ECOSAR model to predict organic pollutant risks, applies a comprehensive evaluation model based on the entropy weight method, integrates the risk identification index and the risk characterization value, generates an intuitive ecological risk distribution map, divides the risk levels into high, medium, and low levels, and identifies high-risk areas and pollution indicators.

[0024] In this embodiment, in S6, the prevention and control strategies include an optimized fertilization plan and green pesticide substitution for source reduction, construction of ecological ditches and buffer zone setting for process control, construction of an artificial wetland system and tail water recycling project for end treatment, strengthening measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas.

[0025] In this embodiment, in S7, a water quality automatic monitoring station and an Internet of Things sensor network are built, a multi-source data fusion platform is constructed, edge computing technology is used to preprocess data, in-depth analysis on the cloud platform is carried out to achieve dynamic monitoring, a feedback optimization mechanism is established, the monitoring data and the model evaluation results are fed back in real time, and the model parameters and prevention and control strategies are adjusted dynamically to form a closed-loop management.

[0026] In this embodiment, in S8, for effect evaluation and continuous improvement, cost-benefit analysis is used to compare the economy of prevention and control measures, a comprehensive index system is adopted to evaluate the prevention and control effect, the model parameters are continuously optimized based on the feedback data, the evaluation model is upgraded, a continuous improvement mechanism is formed to provide long-term support for agricultural environmental management. The tail water pollution prevention and control strategy document formulated in step S6, the time-series data of the prevention and control effect tracked and recorded in step S7, and the evaluation report on the implementation effect of the prevention and control measures generated in step S8 constitute the prevention and control records. The monitoring data, evaluation results, and prevention and control records are stored in a distributed database, and blockchain technology is used to ensure data security and traceability. A visual decision support system is developed to integrate the geographic information system to display pollution distribution, risk assessment, and prevention and control effect, providing an interactive analysis tool and an intelligent decision-making scheme for managers.

[0027] Embodiment 2 The difference between this embodiment and Embodiment 1 is that a method for analyzing and evaluating the pollution load of farmland tail water includes the following steps: S1: Collect multi-source farmland information data, perform fusion processing, and at the same time conduct feature learning and preliminary screening on the multi-source data; S2: Set up fixed and mobile sampling units according to the farmland information; S3: Collect samples of farmland tail water and conduct multi-dimensional detection on the collected samples; S4: Process the detection data and conduct multi-modal analysis on the detection results; S5: Construct an ecological risk assessment model according to the analysis results; S6: Develop precise tail water pollution prevention and control strategies according to the analysis and assessment results; S7: Construct a real-time monitoring system to track the prevention and control effect; S8: Conduct a comprehensive assessment of the implementation effect of the prevention and control measures, and optimize and form a continuous improvement mechanism in combination with the feedback.

[0028] In this embodiment, in S1, in addition to the existing data types, the collection of long-term change data on the historical land use pattern of the farmland, soil erosion situation, and surrounding topography and geomorphology is newly added. In the fusion processing link, a deep learning algorithm is introduced to perform feature learning and preliminary screening on multi-source data, removing redundant and highly correlated data to streamline the data volume for subsequent analysis and improve the processing efficiency.

[0029] Embodiment III The difference between this embodiment and Embodiment I is that a method for analyzing and evaluating the pollution load of farmland tail water includes the following steps: S1: Conduct multi-source collection of farmland information data and perform fusion processing; S2: Set up fixed and mobile sampling units according to the farmland information to construct a two-level sampling network; S3: Collect samples of farmland tail water and conduct multi-dimensional detection on the collected samples; S4: Process the detection data and conduct multi-modal analysis on the detection results; S5: Construct an ecological risk assessment model according to the analysis results; S6: Develop precise tail water pollution prevention and control strategies according to the analysis and assessment results; S7: Construct a real-time monitoring system to track the prevention and control effect; S8: Conduct a comprehensive assessment of the implementation effect of the prevention and control measures, and optimize and form a continuous improvement mechanism in combination with the feedback.

[0030] In this embodiment, in S2, when constructing the two-level sampling network, the first level is fixed sampling points. Based on the farmland topography and water flow direction, key nodes are determined using topological principles. The second level is mobile sampling units, and their movement trajectories are dynamically planned according to the real-time monitored hydrological conditions. At the same time, real-time data transmission modules are equipped for the sampling units so that the sampling data can be immediately transmitted back to the data center, shortening the response time.

[0031] Embodiment IV The difference between this embodiment and the first embodiment lies in: A method for analyzing and evaluating the pollution load of farmland tail water, comprising the following steps: S1: Collect multi-source farmland information data and perform fusion processing; S2: Set up fixed and mobile sampling units according to farmland information; S3: Collect samples of farmland tail water, conduct multi-dimensional detection on the collected samples, and expand the multi-dimensional detection technology and index system; S4: Process the detection data and perform multi-modal analysis on the detection results; S5: Construct an ecological risk assessment model according to the analysis results; S6: Develop precise tail water pollution prevention and control strategies based on the analysis and evaluation results; S7: Construct a real-time monitoring system to track the prevention and control effects; S8: Conduct a comprehensive evaluation of the implementation effects of the prevention and control measures, and form a continuous improvement mechanism through feedback optimization.

[0032] In this embodiment, in S3, the on-site rapid detection technology is upgraded, a portable Raman spectrometer is introduced to achieve rapid qualitative and semi-quantitative analysis of organic pollutants. In terms of laboratory precision analysis, high-resolution mass spectrometry coupling technology is used to accurately identify the structures of new organic pollutants, expand the bio-toxicity test indicators, add algae growth inhibition tests and large aquatic organism behavior response tests to comprehensively evaluate the potential threats of tail water to the aquatic ecosystem.

[0033] Embodiment Five The difference between this embodiment and the first embodiment lies in: A method for analyzing and evaluating the pollution load of farmland tail water, comprising the following steps: S1: Collect multi-source farmland information data and perform fusion processing; S2: Set up fixed and mobile sampling units according to farmland information; S3: Collect samples of farmland tail water and conduct multi-dimensional detection on the collected samples; S4: Process the detection data and perform multi-modal analysis on the detection results; S5: Construct an ecological risk assessment model according to the analysis results; S6: Develop precise tail water pollution prevention and control strategies based on the analysis and evaluation results; S7: Construct a real-time monitoring system to track the prevention and control effects; S8: Conduct a comprehensive evaluation of the implementation effects of the prevention and control measures, form a continuous improvement mechanism through feedback optimization, and construct an intelligent prevention and control decision support system.

[0034] In this embodiment, in S8, an integrated intelligent prevention and control decision support system is developed. Using an evolutionary algorithm, according to the real-time analysis and evaluation data, the parameters of the tail water pollution prevention and control strategy are automatically optimized. In the dynamic monitoring module, computer vision technology is introduced to perform real-time image recognition and abnormal behavior warning on key areas. In terms of effect evaluation, a comprehensive index system is constructed, multi-dimensional data is integrated, and the fuzzy comprehensive evaluation method is used to comprehensively measure the prevention and control effect, ensuring the effectiveness and scientific nature of the continuous improvement mechanism.

[0035] Experimental example I. Experimental purpose A typical farmland area is selected. This farmland mainly grows wheat and corn, with small rivers and irrigation channels around it, and relatively complete meteorological data. The purpose of this experiment is to comprehensively understand the current situation of tail water pollution in this farmland by implementing the above-mentioned method for analyzing and evaluating the tail water pollution load of farmland, and to provide a basis for precise prevention and control; II. Experimental steps (I) Data collection and fusion processing Collect data on the soil texture, fertility index, pH value and organic matter content, topography, crop growth, agricultural activities, meteorological conditions and surrounding hydrological environment of this farmland. After data collection, through data fusion processing technology, data in different formats and from different sources are integrated to form a unified farmland information database; (II) Sampling unit setting Using geographic information system and geostatistical methods, combined with the farmland functional zoning to determine the basic sampling points, introducing a deep reinforcement learning algorithm, dynamically adjusting the sampling point density and position according to historical pollution data and real-time environmental changes, configuring an autonomous mobile robot equipped with multi-parameter water quality sensors, and performing supplementary sampling tasks according to preset rules; (III) Tail water sample collection and multi-dimensional detection Samples of farmland tail water are collected at different agricultural production stages and hydrological conditions. On-site, a hyperspectral imager, a heavy metal rapid detector and a water quality multi-parameter analyzer are used to obtain preliminary data, and ICP-MS, GC-MS, and LC-MS technologies are used in the laboratory to accurately determine the contents of heavy metals and organic pollutants; (IV) Data processing and multi-modal analysis The multi-source heterogeneous data collected is cleaned and standardized. Principal component analysis is used to extract the main pollution factors, cluster analysis is used to identify the types of pollution sources, factor analysis is used to determine the contribution rate of pollution sources, key characteristic variables are screened through random forest, an XGBoost prediction model is established to simulate the spatio-temporal change trend of pollution load, and SWAT and Mike She models are used to simulate the tail water flow and the migration and transformation process of pollutants in combination with the farmland topography and soil properties; (V) Construction of ecological risk assessment model Based on the HRA framework, combined with the toxicity unit method to quantify heavy metal risks, the ECOSAR model is used to estimate the risks of organic pollutants, and a comprehensive evaluation model based on the entropy weight method is employed to integrate the risk identification index and the risk characterization value to generate an intuitive ecological risk distribution map; (6) Formulation of Precise Prevention and Control Strategies According to the analysis and evaluation results, formulate precise prevention and control strategies for tail water pollution. Strengthen measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas; (7) Construction of Real-time Monitoring System Build automatic water quality monitoring stations and Internet of Things sensor networks, construct a multi-source data fusion platform, use edge computing technology to preprocess data, and conduct in-depth analysis on the cloud platform to achieve dynamic monitoring. Establish a feedback and optimization mechanism to provide real-time feedback of monitoring data and model evaluation results; (8) Effect Evaluation and Continuous Improvement Use cost-benefit analysis to compare the economy of prevention and control measures, adopt a comprehensive index system to evaluate the prevention and control effect, continuously optimize model parameters based on feedback data, upgrade the evaluation model, and form a continuous improvement mechanism; III. Experimental Data and Result Display Table 1 Sampling Point Layout and Sampling Time Arrangement

[0036] Table 2 Monitoring Data

[0037] Table 3 Results of Ecological Risk Assessment Model

[0038] IV. Experimental Conclusions Through the above experiments, the method for analyzing and evaluating the pollution load of farmland tail water can effectively analyze the sources, composition and variation laws of the pollution load of farmland tail water, construct a precise ecological risk assessment model and prevention and control strategies, realize the scientific treatment and effective prevention and control of farmland tail water pollution. This method provides a scientific basis and technical support for the treatment of farmland tail water pollution, helps to improve the farmland ecological environment, and ensures the water environment quality and agricultural sustainable development. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A method for analyzing and evaluating the pollution load of farmland tail water, characterized in that: It includes the following steps: S1: Collect multi-source farmland information data and perform fusion processing; S2: Set up fixed sampling units and mobile sampling units according to the farmland information; S3: Collect samples of farmland tailwater and conduct multi-dimensional detection on the collected samples; S4: Process the detection data and perform multi-modal analysis on the detection results; S5: Build an ecological risk assessment model based on the analysis results; S6: Develop precise tailwater pollution prevention and control strategies according to the analysis and assessment results; S7: Build a real-time monitoring system to track the prevention and control effects; S8: Conduct a comprehensive evaluation of the implementation effects of the prevention and control measures, and optimize and form a continuous improvement mechanism in combination with the feedback.

2. The method for analyzing and evaluating the pollution load of farmland tail water according to claim 1, wherein: In S1, the farmland information data includes farmland soil property data, topographic and geomorphic data, crop growth data, agricultural activity data, meteorological condition data, and surrounding hydrological environment data. The farmland soil property data includes soil texture, fertility index, pH value, and organic matter content; the topographic and geomorphic data includes farmland elevation, slope, aspect, and drainage direction; the crop growth data includes planting structure, growth period, irrigation method, and fertilization pattern; the agricultural activity data includes records of pesticide application history, tillage method, and agricultural film usage; the meteorological condition data includes precipitation, temperature, wind speed, wind direction, and evaporation; the surrounding hydrological environment data includes the hydrological characteristics of the receiving water body, water quality status, and water body function zoning.

3. The method for analyzing and evaluating the pollution load of farmland tail water according to claim 1, wherein: In S2, use geographic information systems and geostatistical methods, combine with farmland function zoning to determine the basic sampling points, introduce deep reinforcement learning algorithms, dynamically adjust the sampling point density and location according to historical pollution data and real-time environmental changes, configure autonomous mobile robots equipped with multi-parameter water quality sensors, and execute supplementary sampling tasks according to preset rules. The fixed sampling units are set at key drainage nodes, tailwater collection areas, and the entrances of receiving water bodies, equipped with automatic water quality monitors and flow meters to achieve long-term continuous monitoring. The mobile sampling units include portable sampling equipment and unmanned aerial vehicle sampling systems, which are flexibly deployed according to real-time instructions to enhance the mobility and flexibility of sampling.

4. The method for analyzing and evaluating the pollution load of farmland tail water according to claim 3, characterized in that: In S3, the multi-dimensional detection includes the combination of on-site rapid detection and laboratory precise analysis. On-site, hyperspectral imagers, heavy metal rapid detectors, and water quality multi-parameter analyzers are used to obtain preliminary data. In the laboratory, ICP-MS, GC-MS, and LC-MS technologies are used to accurately determine the contents of heavy metals and organic pollutants. Biological toxicity tests use microplate luminescent bacteria toxicity tests, water flea acute toxicity tests, and fish embryo development toxicity tests to evaluate the comprehensive toxicity effects of samples.

5. The method for analyzing and evaluating the pollution load of farmland tail water according to claim 4, characterized in that: In S4, multivariate statistical analysis uses principal component analysis to extract the main pollution factors, cluster analysis to identify the types of pollution sources, factor analysis to determine the contribution rate of pollution sources, and machine learning algorithms to screen key feature variables through random forests, establish an XGBoost prediction model, and simulate the spatio-temporal variation trend of pollution load. The hydrological models select the SWAT and MikeShe models, combine the farmland terrain and soil properties to simulate the tail water flow and the migration and transformation process of pollutants, build a dynamic spatio-temporal database of farmland tail water pollution load based on the above analysis, store multi-source heterogeneous data, and use NoSQL database technology to achieve efficient storage and fast query; XGBoost constructs an ensemble model through gradient boosting. Assume that the model at the t-th iteration is: ； The objective function is: ； Among them, the loss function is usually the mean squared error or the logarithmic loss, and the regularization term Ω(f t ) is defined as: ； Among them, γ is the tree complexity parameter, T is the number of leaf nodes, λ is the regularization parameter, and w is the weight of the leaf node; The SWAT model is used to simulate the tail water flow and the migration and transformation process of pollutants in farmland. Its core equations include the water balance equation: ； Where: ΔS: change in soil water storage; P: precipitation; Q: surface runoff; E: evaporation; T: transpiration; W gw : groundwater outflow; The pollutant load calculation formula: ； Where: H: the number of hydrological response units (HRUs); : the amount of pollutants lost from the surface of the h-th HRU; : the amount of pollutants lost in the subsurface flow of the h-th HRU; The purpose of calculating the pollutant load L is to quantify the pollution load and provide a basis for risk assessment and prevention and control; The pollutant load is further calculated by the following formula in each HRU: ； ； Wherein: and are the surface runoff and subsurface runoff of the h-th HRU, respectively; is the area of the h-th HRU; and are the surface and subsurface pollutant concentrations of the h-th HRU, respectively; The MikeShe model simulates the water flow and pollutant transport process based on the finite difference method. Its basic equation is the Saint-Venant equations: ； ； Where: A: cross-sectional area of flow; Q: discharge; t: time; x: spatial coordinate; h: water depth; g: acceleration due to gravity; S f : friction slope; τ: shear stress; ρ: density of water; : inflow discharge; : outflow discharge; : sectional discharge; The pollutant transport equation is: ； Among them: C: pollutant concentration; D: diffusion coefficient; S: source / sink term.

6. The method for analyzing and evaluating the pollution load of farmland tail water according to claim 5, wherein: In S5, the ecological risk assessment model is based on the HRA framework, combines the toxicity unit method to quantify the heavy metal risk, uses the ECOSAR model to estimate the risk of organic pollutants, and uses a comprehensive evaluation model based on the entropy weight method to fuse the risk identification index and the risk characterization value to generate an intuitive ecological risk distribution map, divides the risk level into high, medium, and low levels, and clarifies the high-risk areas and pollution indicators.

7. The method for analyzing and evaluating the pollution load of farmland tail water according to claim 6, characterized in that: In S6, the prevention and control strategies include an optimized fertilization plan and green pesticide substitution for source reduction, construction of ecological ditches and buffer zone settings for process control, construction of an artificial wetland system and a tail water recycling project for end treatment, strengthening measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas.

8. A method for analyzing and evaluating the pollution load of farmland tail water according to claim 7, characterized in that: In S7, build a water quality automatic monitoring station and an Internet of Things sensor network, construct a multi-source data fusion platform, use edge computing technology to preprocess data, perform in-depth analysis on the cloud platform to achieve dynamic monitoring, establish a feedback optimization mechanism, and real-time feedback the monitoring data and the model evaluation results to dynamically adjust the model parameters and prevention and control strategies to form a closed-loop management.

9. A method for analyzing and evaluating the pollution load of farmland tail water according to claim 8, characterized in that: In S8, the effect evaluation and continuous improvement use cost-benefit analysis to compare the economy of prevention and control measures, use a comprehensive index system to evaluate the prevention and control effect, continuously optimize the model parameters based on the feedback data, upgrade the evaluation model, form a continuous improvement mechanism, and provide long-term support for agricultural environmental management.

10. A method for analyzing and evaluating the pollution load of farmland tail water according to claim 9, characterized in that: The tail water pollution prevention and control strategy document formulated in step S6, the time-series data of the prevention and control effect tracked and recorded in step S7, and the evaluation report on the implementation effect of the prevention and control measures generated in step S8 constitute the prevention and control records. The monitoring data, evaluation results, and prevention and control records are stored in a distributed database, and blockchain technology is used to ensure data security and traceability. A visual decision support system is developed to integrate the geographic information system to display pollution distribution, risk assessment, and prevention and control effects, providing managers with interactive analysis tools and intelligent decision-making solutions.

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