A method for analyzing and evaluating farmland tailwater pollution load

Abstract

The present invention belongs to the technical field of analysis and evaluation of farmland tail water pollution load, and in particular to a method for analysis and evaluation of farmland tail water pollution load. In view of the fact that existing monitoring and evaluation methods have significant deficiencies in monitoring point layout, indicator coverage, data processing and analysis depth, and cannot meet the needs of accurate and dynamic analysis and evaluation of farmland tail water pollution load, and are difficult to effectively support pollution prevention and control decision-making, the following scheme is proposed, which includes the following steps: S1: multi-source collection of farmland information data and fusion processing; S2: establishment of fixed and mobile sampling units according to farmland information settings; S3: sample collection of farmland tail water. 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 realizes comprehensive and accurate quantification and evaluation of farmland tail water pollution load through data preprocessing and fusion technology.

Description

Technical Field

[0001] The present invention relates to the technical field of farmland tail water pollution load analysis and evaluation, and in particular to a method for analyzing and evaluating farmland tail water 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 solely on single water quality monitoring data cannot fully and accurately reflect the source, composition and change pattern of the pollution load; or insufficient consideration is given to the interaction between the farmland ecosystem and the water environment system, etc., which makes it difficult to meet the needs of precise control of farmland tailwater pollution.

[0003] In the existing technology, monitoring and evaluation methods have significant deficiencies in monitoring point layout, indicator coverage, data processing and analysis depth. They cannot meet the needs of accurate and dynamic analysis and evaluation of farmland tailwater pollution loads, and it is difficult to effectively support pollution prevention and control decisions. To this end, we proposed a method for analyzing and evaluating farmland tailwater pollution loads to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to solve the shortcomings of the existing monitoring and evaluation methods in the field, such as the significant deficiencies in monitoring point layout, indicator coverage, data processing and analysis depth, which cannot meet the needs of accurate and dynamic analysis and evaluation of farmland tailwater pollution load and are difficult to effectively support pollution prevention and control decisions. A method for analyzing and evaluating farmland tailwater pollution load is proposed.

[0005] This application provides a method for analyzing and evaluating the pollution load of farmland tailwater using the following technical solutions:

[0006] A method for analyzing and evaluating farmland tailwater pollution load includes the following steps:

[0007] S1: Collect farmland information data from multiple sources and perform fusion processing;

[0008] S2: Set up fixed and mobile sampling units based on farmland information;

[0009] S3: Collect samples of farmland tailwater and conduct multi-dimensional testing on the collected samples;

[0010] S4: Process the test data and perform multimodal analysis on the test results;

[0011] S5: Based on the analysis results, construct an ecological risk assessment model;

[0012] S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results;

[0013] S7: Build a real-time monitoring system to track the effectiveness of prevention and control;

[0014] S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, and combine feedback optimization to form a continuous improvement mechanism.

[0015] Furthermore, in S1, farmland soil attribute data include soil texture, fertility index, pH and organic matter content; topographic data cover farmland elevation, slope, slope direction and drainage direction; crop growth data involve planting structure, growth period, irrigation method and fertilization pattern; agricultural activity data include records of pesticide application history, tillage methods and agricultural film use; meteorological condition data include precipitation, temperature, wind speed, wind direction and evaporation; surrounding hydrological environment data describe the hydrological characteristics, water quality status and water body functional zoning of the receiving water body.

[0016] Furthermore, in S2, geographic information systems and geostatistical methods are used, combined with farmland functional zoning to determine basic sampling points, and deep reinforcement learning algorithms are introduced to dynamically adjust the density and location of sampling points based on historical pollution data and real-time environmental changes. Autonomous mobile robots are equipped with multi-parameter water quality sensors to perform supplementary sampling tasks according to preset rules. The fixed sampling units are set at key drainage nodes, tailwater collection areas and receiving water body entrances, and are equipped with automatic water quality monitors and flow meters to achieve long-term continuous monitoring. The mobile sampling units include portable sampling equipment and drone sampling systems, which are flexibly deployed according to real-time instructions to enhance sampling maneuverability and flexibility.

[0017] Furthermore, in the S3, the multi-dimensional detection includes the combination of on-site rapid detection and laboratory precision analysis. Hyperspectral imagers, heavy metal rapid testers and water quality multi-parameter analyzers are used on-site to obtain preliminary data. The laboratory uses ICP-MS, GC-MS, and LC-MS technologies to accurately determine the content 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 toxic effects of the samples.

[0018] Furthermore, in S4, multivariate statistical analysis uses principal component analysis to extract major pollution factors, cluster analysis to identify pollution source types, factor analysis to determine pollution source contribution rates, and machine learning algorithms use random forests to screen key feature variables, establish an XGBoost prediction model, and simulate the spatiotemporal variation trend of pollution loads. The hydrological model uses SWAT and MikeShe models, combining farmland topography and soil properties to simulate tailwater flow and pollutant migration and transformation processes. Based on the above analysis, a dynamic spatiotemporal database of farmland tailwater pollution loads is constructed to store multi-source heterogeneous data, and NoSQL database technology is used to achieve efficient storage and fast query.

[0019] XGBoost prediction model

[0020] XGBoost builds an integrated model by gradient boosting. Assume that the model of the tth iteration is:

[0021]

[0022] The objective function is:

[0023]

[0024] in, : The model of the tth iteration, representing the tree model added at the tth iteration, used to fit and correct the residuals of the previous round of model; : The predicted value of the model for the input sample x at the tth iteration, that is, the tree model Output result when input x; : Represents the predicted value of the entire integrated model at the tth iteration, which is determined by the previous round model And the tree model added this time Together they constitute : Loss function, which measures the difference between the model prediction value and the true value the differences between; represents the objective function at the tth iteration; the regularization term Ω( ) is defined as:

[0025]

[0026] Among them, γ is the complexity parameter of the tree, T is the number of leaf nodes, λ is the regularization parameter, and w is the weight of the leaf node;

[0027] SWAT Model

[0028] The SWAT model is used to simulate the flow of farmland tailwater and the migration and transformation of pollutants. Its core equations include the water balance equation:

[0029]

[0030] Where: ΔS: change in soil water storage; P: precipitation; Q: surface runoff; E: evaporation; T: transpiration; W gw : Groundwater outflow;

[0031] Pollutant load calculation formula:

[0032]

[0033] Where: H: the number of hydrological response units HRU; : Pollutant loss from the hth HRU surface : the amount of pollutants lost in the soil of the hth HRU;

[0034] The pollutant load in each HRU is further calculated by the following formula:

[0035]

[0036]

[0037] in: and : are the surface runoff and soil runoff of the hth HRU respectively; : the area of ​​the hth HRU; and : are the surface and soil pollutant concentrations of the hth HRU respectively;

[0038] MikeShe Model

[0039] The MikeShe model simulates water flow and pollutant transport processes based on the finite difference method. Its basic equations are the Saint-Venant equations:

[0040]

[0041]

[0042] Where: A: cross-sectional area of ​​water flow; Q: flow rate; t: time; x: spatial coordinate; h: water depth; g: gravitational acceleration; S f : friction slope; τ: shear stress; ρ: water density;

[0043] The pollutant transport equation is:

[0044]

[0045] Where: C: pollutant concentration; D: diffusion coefficient; S: source / sink term.

[0046] Furthermore, in S5, the ecological risk assessment model is based on the HRA framework, combined with the toxicity unit method to quantify heavy metal risks, the ECOSAR model is used to estimate organic pollutant risks, and a comprehensive evaluation model based on the entropy weight method is used to integrate the risk identification index and risk characterization value to generate an intuitive ecological risk distribution map, divide the risk levels into high, medium and low, and clarify high-risk areas and pollution indicators.

[0047] Furthermore, in S6, the prevention and control strategies include optimized fertilization programs and green pesticide substitution for source reduction, ecological ditch construction and buffer zone setting for process control, artificial wetland system construction and tailwater recycling projects for end-of-pipe treatment, enhanced measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas.

[0048] Furthermore, in the S7, an automatic water quality 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 pre-process data, and the cloud platform conducts in-depth analysis to achieve dynamic monitoring. A feedback optimization mechanism is established to provide real-time feedback on monitoring data and model evaluation results, dynamically adjust model parameters and prevention and control strategies, and form a closed-loop management.

[0049] Furthermore, in S8, the effect evaluation and continuous improvement use cost-benefit analysis to compare the economic efficiency of prevention and control measures, adopt a comprehensive indicator system to evaluate the prevention and control effects, continuously optimize model parameters based on feedback data, upgrade the evaluation model, and form a continuous improvement mechanism to provide long-term support for agricultural environmental management.

[0050] Furthermore, the monitoring data, assessment 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, and geographic information systems are integrated to display pollution distribution, risk assessment and prevention and control effects, providing managers with interactive analysis tools and intelligent decision-making solutions.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] 1. This solution collects multi-dimensional data on farmland soil, topography, crop growth, and other aspects from multiple sources and integrates them to comprehensively understand the factors affecting farmland tailwater pollution. This provides detailed data support for subsequent precise assessment and prevention and control, overcoming the limitations of traditional methods that rely on single data.

[0053] 2. This plan uses multivariate statistical analysis, machine learning algorithms, and hydrological models to deeply analyze pollution loads from multiple perspectives, including temporal and spatial distribution and source apportionment. This builds a dynamic spatiotemporal database, provides an accurate picture of farmland tailwater pollution, and provides a scientific basis for pollution prevention and control decisions.

[0054] 3. Based on the results of pollution analysis and ecological risk assessment, this plan formulates a full-process prevention and control strategy from source reduction to end-of-pipe treatment, and classifies and implements policies for areas with different risk levels, which can not only effectively prevent and control pollution, but also rationally allocate prevention and control resources.

[0055] The present invention comprehensively considers multi-source data such as basic farmland information, agricultural production activity data, meteorological data, and water quality monitoring data. Through data preprocessing and fusion technology, it achieves comprehensive and accurate quantification and evaluation of farmland tailwater pollution load. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of a method for analyzing and evaluating farmland tailwater pollution load proposed in the present invention. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0058] Example 1

[0059] Reference Figure 1 A method for analyzing and evaluating farmland tailwater pollution load comprises the following steps:

[0060] S1: Collect farmland information data from multiple sources and perform fusion processing;

[0061] S2: Set up fixed and mobile sampling units based on farmland information;

[0062] S3: Collect samples of farmland tailwater and conduct multi-dimensional testing on the collected samples;

[0063] S4: Process the test data and perform multimodal analysis on the test results;

[0064] S5: Based on the analysis results, construct an ecological risk assessment model;

[0065] S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results;

[0066] S7: Build a real-time monitoring system to track the effectiveness of prevention and control;

[0067] S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, and combine feedback optimization to form a continuous improvement mechanism.

[0068] In this embodiment, in S1, farmland soil attribute data include soil texture, fertility index, pH and organic matter content; topographic data covers farmland elevation, slope, slope direction and drainage direction; crop growth data involves planting structure, growth period, irrigation method and fertilization pattern; agricultural activity data includes records of pesticide application history, tillage methods and agricultural film use; meteorological condition data includes precipitation, temperature, wind speed, wind direction and evaporation; surrounding hydrological environment data describes the hydrological characteristics of the receiving water body, water quality status and water body functional zoning.

[0069] In this embodiment, in S2, geographic information systems and geostatistical methods are used, combined with farmland functional zoning to determine basic sampling points, and a deep reinforcement learning algorithm is introduced. The density and location of sampling points are dynamically adjusted based on historical pollution data and real-time environmental changes. An autonomous mobile robot is equipped with a multi-parameter water quality sensor to perform supplementary sampling tasks according to preset rules. Fixed sampling units are set at key drainage nodes, tailwater collection areas, and receiving water body entrances, and are equipped with automatic water quality monitors and flow meters to achieve long-term continuous monitoring. Mobile sampling units include portable sampling equipment and drone sampling systems, which are flexibly deployed according to real-time instructions to enhance sampling maneuverability and flexibility.

[0070] In this embodiment, in S3, multi-dimensional detection includes a combination of on-site rapid detection and laboratory precision analysis. Preliminary data are obtained on-site using a hyperspectral imager, a heavy metal rapid tester, and a water quality multi-parameter analyzer. The laboratory uses ICP-MS, GC-MS, and LC-MS technologies to accurately determine the content of heavy metals and organic pollutants. The biological toxicity test uses a microplate luminescent bacterial toxicity test, a Daphnia acute toxicity test, and a fish embryo development toxicity test to evaluate the comprehensive toxic effects of the samples.

[0071] In this embodiment, in S4, multivariate statistical analysis uses principal component analysis to extract major pollution factors, cluster analysis to identify pollution source types, factor analysis to determine pollution source contribution rates, and machine learning algorithms use random forests to screen key feature variables, establish an XGBoost prediction model, and simulate the spatiotemporal variation trend of pollution loads. The hydrological model uses SWAT and MikeShe models, combining farmland topography and soil properties to simulate tailwater flow and pollutant migration and transformation processes. Based on the above analysis, a dynamic spatiotemporal database of farmland tailwater pollution loads is constructed to store multi-source heterogeneous data, and NoSQL database technology is used to achieve efficient storage and fast query.

[0072] XGBoost prediction model

[0073] XGBoost builds an integrated model by gradient boosting. Assume that the model of the tth iteration is:

[0074]

[0075] The objective function is:

[0076]

[0077] in, : The model of the tth iteration, representing the tree model added at the tth iteration, used to fit and correct the residuals of the previous round of model; : The predicted value of the model for the input sample x at the tth iteration, that is, the tree model Output result when input x; : Represents the predicted value of the entire integrated model at the tth iteration, which is determined by the previous round model And the tree model added this time Together they constitute : Loss function, which measures the difference between the model prediction value and the true value the differences between; represents the objective function at the tth iteration; the regularization term Ω( ) is defined as:

[0078]

[0079] Among them, γ is the complexity parameter of the tree, T is the number of leaf nodes, λ is the regularization parameter, and w is the weight of the leaf node;

[0080] SWAT Model

[0081] The SWAT model is used to simulate the flow of farmland tailwater and the migration and transformation of pollutants. Its core equations include the water balance equation:

[0082]

[0083] Where: ΔS: change in soil water storage; P: precipitation; Q: surface runoff; E: evaporation; T: transpiration; W gw : Groundwater outflow;

[0084] Pollutant load calculation formula:

[0085]

[0086] Where: H: the number of hydrological response units HRU; : Pollutant loss from the hth HRU surface : the amount of pollutants lost in the soil of the hth HRU;

[0087] The pollutant load in each HRU is further calculated by the following formula:

[0088]

[0089]

[0090] in: and : are the surface runoff and soil runoff of the hth HRU respectively; : the area of ​​the hth HRU; and : are the surface and soil pollutant concentrations of the hth HRU respectively;

[0091] MikeShe Model

[0092] The MikeShe model simulates water flow and pollutant transport processes based on the finite difference method. Its basic equations are the Saint-Venant equations:

[0093]

[0094]

[0095] Where: A: cross-sectional area of ​​water flow; Q: flow rate; t: time; x: spatial coordinate; h: water depth; g: gravitational acceleration; S f : friction slope; τ: shear stress; ρ: water density;

[0096] The pollutant transport equation is:

[0097]

[0098] Where: C: pollutant concentration; D: diffusion coefficient; S: source / sink term.

[0099] In this embodiment, in S5, the ecological risk assessment model is based on the HRA framework, combined with the toxicity unit method to quantify heavy metal risks, the ECOSAR model is used to estimate organic pollutant risks, and a comprehensive evaluation model based on the entropy weight method is used to integrate the risk identification index and the risk characterization value to generate an intuitive ecological risk distribution map, divide the risk levels into high, medium and low, and clarify high-risk areas and pollution indicators.

[0100] In this embodiment, in S6, the prevention and control strategies include optimized fertilization plans and green pesticide substitution for source reduction, ecological ditch construction and buffer zone setting for process control, artificial wetland system construction and tailwater recycling projects for end-of-pipe treatment, enhanced measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas.

[0101] In this embodiment, in S7, an automatic water quality monitoring station and an IoT sensor network are built, a multi-source data fusion platform is constructed, edge computing technology is used to pre-process data, and the cloud platform performs in-depth analysis to achieve dynamic monitoring. A feedback optimization mechanism is established to provide real-time feedback on monitoring data and model evaluation results, dynamically adjust model parameters and prevention and control strategies, and form a closed-loop management.

[0102] In this embodiment, in S8, effect evaluation and continuous improvement use cost-benefit analysis to compare the economic efficiency of prevention and control measures, adopt a comprehensive indicator system to evaluate the prevention and control effect, continuously optimize model parameters based on feedback data, upgrade the evaluation model, form a continuous improvement mechanism, and provide long-term support for agricultural environmental management. 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, develop a visual decision support system, integrate geographic information systems to display pollution distribution, risk assessment and prevention and control effects, and provide managers with interactive analysis tools and intelligent decision-making solutions.

[0103] Example 2

[0104] The difference between this embodiment and the first embodiment is that: a method for analyzing and evaluating the pollution load of farmland tail water includes the following steps:

[0105] S1: Collect farmland information data from multiple sources, perform fusion processing, and perform feature learning and preliminary screening on multi-source data;

[0106] S2: Set up fixed and mobile sampling units based on farmland information;

[0107] S3: Collect samples of farmland tailwater and conduct multi-dimensional testing on the collected samples;

[0108] S4: Process the test data and perform multimodal analysis on the test results;

[0109] S5: Based on the analysis results, construct an ecological risk assessment model;

[0110] S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results;

[0111] S7: Build a real-time monitoring system to track the effectiveness of prevention and control;

[0112] S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, and combine feedback optimization to form a continuous improvement mechanism.

[0113] In this embodiment, in S1, in addition to the existing data types, new data on the long-term changes in farmland historical land use, soil erosion conditions, and surrounding topography are collected. In the fusion processing link, a deep learning algorithm is introduced to perform feature learning and preliminary screening of multi-source data, remove redundant and highly correlated data, and streamline the amount of data for subsequent analysis to improve processing efficiency.

[0114] Example 3

[0115] The difference between this embodiment and the first embodiment is that: a method for analyzing and evaluating the pollution load of farmland tail water includes the following steps:

[0116] S1: Collect farmland information data from multiple sources and perform fusion processing;

[0117] S2: Set up fixed and mobile sampling units based on farmland information to build a two-level sampling network;

[0118] S3: Collect samples of farmland tailwater and conduct multi-dimensional testing on the collected samples;

[0119] S4: Process the test data and perform multimodal analysis on the test results;

[0120] S5: Based on the analysis results, construct an ecological risk assessment model;

[0121] S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results;

[0122] S7: Build a real-time monitoring system to track the effectiveness of prevention and control;

[0123] S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, and combine feedback optimization to form a continuous improvement mechanism.

[0124] In this embodiment, in S2, a two-level sampling network is constructed. The first level is fixed sampling points, and the key nodes are determined by topological principles based on the farmland terrain and water flow direction; the second level is mobile sampling units, whose movement trajectories are dynamically planned based on the real-time monitored hydrological conditions. At the same time, the sampling units are equipped with real-time data transmission modules so that the sampling data can be immediately transmitted back to the data center, shortening the response time.

[0125] Example 4

[0126] The difference between this embodiment and the first embodiment is that: a method for analyzing and evaluating the pollution load of farmland tail water includes the following steps:

[0127] S1: Collect farmland information data from multiple sources and perform fusion processing;

[0128] S2: Set up fixed and mobile sampling units based on farmland information;

[0129] S3: Collect samples of farmland tailwater, conduct multi-dimensional testing on the collected samples, and expand the multi-dimensional testing technology and indicator system;

[0130] S4: Process the test data and perform multimodal analysis on the test results;

[0131] S5: Based on the analysis results, construct an ecological risk assessment model;

[0132] S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results;

[0133] S7: Build a real-time monitoring system to track the effectiveness of prevention and control;

[0134] S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, and combine feedback optimization to form a continuous improvement mechanism.

[0135] In this embodiment, in S3, the on-site rapid detection technology is upgraded and a portable Raman spectrometer is introduced to realize rapid qualitative and semi-quantitative analysis of organic pollutants. In terms of laboratory precision analysis, high-resolution mass spectrometry technology is used to accurately identify the structure of new organic pollutants, expand biological toxicity test indicators, add algae growth inhibition tests and large aquatic organism behavioral response tests, and comprehensively evaluate the potential threat of tail water to aquatic ecosystems.

[0136] Example 5

[0137] The difference between this embodiment and the first embodiment is that: a method for analyzing and evaluating the pollution load of farmland tail water includes the following steps:

[0138] S1: Collect farmland information data from multiple sources and perform fusion processing;

[0139] S2: Set up fixed and mobile sampling units based on farmland information;

[0140] S3: Collect samples of farmland tailwater and conduct multi-dimensional testing on the collected samples;

[0141] S4: Process the test data and perform multimodal analysis on the test results;

[0142] S5: Based on the analysis results, construct an ecological risk assessment model;

[0143] S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results;

[0144] S7: Build a real-time monitoring system to track the effectiveness of prevention and control;

[0145] S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, combine feedback optimization to form a continuous improvement mechanism, and build an intelligent prevention and control decision support system.

[0146] In this embodiment, in S8, an integrated intelligent prevention and control decision support system is developed, and evolutionary algorithms are used to automatically optimize the tailwater pollution prevention and control strategy parameters based on real-time analysis and evaluation data. In the dynamic monitoring module, computer vision technology is introduced to perform real-time image recognition and abnormal behavior warnings in key areas. In terms of effect evaluation, a comprehensive indicator system is constructed, multi-dimensional data is integrated, and fuzzy comprehensive evaluation methods are used to comprehensively measure the prevention and control effects to ensure the effectiveness and scientific nature of the continuous improvement mechanism.

[0147] Experimental example

[0148] 1. Experimental Purpose

[0149] A typical farmland area was selected. This area is mainly used for growing wheat and corn. There are small rivers and irrigation channels around it. The meteorological data is relatively complete. This experiment aims to fully understand the current status of farmland tailwater pollution by implementing the above-mentioned farmland tailwater pollution load analysis and evaluation method, and provide a basis for precise prevention and control.

[0150] 2. Experimental steps

[0151] (1) Data collection and fusion processing

[0152] Collect data on the farmland's soil texture, fertility indicators, pH, organic matter content, topography, crop growth, agricultural activities, meteorological conditions, and surrounding hydrological environment. After data collection, use data fusion processing technology to integrate data in different formats and sources to form a unified farmland information database;

[0153] (2) Sampling unit settings

[0154] Using geographic information systems and geostatistical methods, combined with farmland functional zoning, basic sampling points are determined. A deep reinforcement learning algorithm is introduced to dynamically adjust the density and location of sampling points based on historical pollution data and real-time environmental changes. Autonomous mobile robots are equipped with multi-parameter water quality sensors to perform supplementary sampling tasks according to preset rules.

[0155] (3) Tailwater sample collection and multi-dimensional testing

[0156] Farmland tailwater samples were collected at different agricultural production stages and under different hydrological conditions. Preliminary data were obtained on-site using a hyperspectral imager, a heavy metal rapid detector, and a multi-parameter water quality analyzer. In the laboratory, ICP-MS, GC-MS, and LC-MS techniques were used to accurately determine the levels of heavy metals and organic pollutants.

[0157] (IV) Data processing and multimodal analysis

[0158] The collected multi-source heterogeneous data was cleaned and standardized. Principal component analysis was used to extract the main pollution factors, cluster analysis was used to identify pollution source types, and factor analysis was used to determine the contribution rate of pollution sources. Key characteristic variables were screened using random forests, and an XGBoost prediction model was established to simulate the spatiotemporal trends of pollution loads. The SWAT and Mike She models were used to simulate tailwater flow and pollutant migration and transformation processes, combining farmland topography and soil properties.

[0159] (V) Construction of ecological risk assessment model

[0160] Based on the HRA framework, the toxicity unit method is combined to quantify heavy metal risks, the ECOSAR model is used to estimate organic pollutant risks, and a comprehensive evaluation model based on the entropy weight method is used to integrate the risk identification index and risk characterization value to generate an intuitive ecological risk distribution map;

[0161] (VI) Formulation of precise prevention and control strategies

[0162] Based on the analysis and assessment results, formulate precise tailwater pollution prevention and control strategies, including strengthening measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas;

[0163] (VII) Construction of real-time monitoring system

[0164] Build automatic water quality monitoring stations and IoT sensor networks, construct a multi-source data fusion platform, use edge computing technology to pre-process data, conduct in-depth analysis on the cloud platform to achieve dynamic monitoring, and establish a feedback optimization mechanism to provide real-time feedback on monitoring data and model evaluation results;

[0165] (8) Effect evaluation and continuous improvement

[0166] Use cost-benefit analysis to compare the economic efficiency of prevention and control measures, adopt a comprehensive indicator system to evaluate the effectiveness of prevention and control, continuously optimize model parameters based on feedback data, upgrade the evaluation model, and form a continuous improvement mechanism;

[0167] 3. Experimental Data and Results

[0168] (1) Sampling point layout and sampling time arrangement

[0169] time Sampling point 1 Sampling point 2 Sampling point 3 Sampling point 4 Sampling point 5 Move sampling point 1 position Move sampling point 2 position Fertilization period √ √ √ √ √ Northeast corner southwest corner Before the rain √ √ √ √ √ East side northwest corner After the rain √ √ √ √ √ West side southeast corner Crop growing season √ √ √ √ √ South side North side

[0170] Monitoring data

[0171] Monitoring indicators Sampling point 1 Sampling point 2 Sampling point 3 Sampling point 4 Sampling point 5 Chemical oxygen demand (COD, mg / L) 125 105 132 118 102 <![CDATA[Ammonia nitrogen (NH3-N, mg / L)]]> 6.2 5.3 6.8 5.9 4.8 Total phosphorus (TP, mg / L) 1.25 1.08 1.35 1.17 0.98 Total nitrogen (TN, mg / L) 28.5 24.3 30.1 26.7 22.8 Mercury (Hg, mg / L) 0.003 0.002 0.004 0.0025 0.0015 Cadmium (Cd, mg / L) 0.012 0.010 0.015 0.011 0.008 Lead (Pb, mg / L) 0.15 0.12 0.18 0.14 0.10 Toxicity of luminescent bacteria (inhibition rate, %) 28 22 31 25 20

[0172] Results of the ecological risk assessment model

[0173] Risk Level Risk value range Proportion of risk areas Summary of Prevention and Control Strategies High risk Risk value > 0.8 15% Strengthening measures: optimizing fertilization, green pesticide substitution, ecological ditch construction, etc. Medium risk 0.5<Risk Value≤0.8 30% Supplementary measures: moderate optimization of fertilization, construction of buffer zones, etc. Low risk Risk value ≤ 0.5 55% Preventive measures: promote green fertilization, regularly monitor water quality, etc.

[0174] 4. Experimental Conclusion

[0175] Through the above experiments, the farmland tailwater pollution load analysis and evaluation method can effectively analyze the source, composition and change law of farmland tailwater pollution load, construct an accurate ecological risk assessment model and prevention and control strategy, and realize the scientific management and effective prevention and control of farmland tailwater pollution. This method provides a scientific basis and technical support for farmland tailwater pollution control, which helps to improve the farmland ecological environment, ensure water environment quality and sustainable agricultural development.

[0176] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for analyzing and evaluating farmland tailwater pollution load, characterized by: The following steps are involved: S1: Collect farmland information data from multiple sources 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 conduct multi-dimensional testing on the collected samples; S4: Process the test data and conduct multimodal analysis on the test results. Multivariate statistical analysis uses principal component analysis to extract the main pollution factors, cluster analysis to identify the type of pollution source, and factor analysis to determine the contribution rate of the pollution source. The machine learning algorithm uses random forest to screen key characteristic variables, establishes an XGBoost prediction model, and simulates the spatiotemporal variation trend of pollution load. The hydrological model uses SWAT and MikeShe models, combining farmland topography and soil properties to simulate the tailwater flow and pollutant migration and transformation process. Based on the above analysis, a dynamic spatiotemporal database of farmland tailwater pollution load is constructed to store multi-source heterogeneous data and use NoSQL database technology to achieve efficient storage and fast query; XGBoost builds an integrated model by gradient boosting. Assume that the model of the tth iteration is: The objective function is: in, : The model of the tth iteration, representing the tree model added at the tth iteration, used to fit and correct the residuals of the previous round of model; : The predicted value of the model for the input sample x at the tth iteration, that is, the tree model Output result when input x; : Represents the predicted value of the entire integrated model at the tth iteration, which is determined by the previous round model And the tree model added this time Together they constitute : Loss function, which measures the difference between the model prediction value and the true value the differences between; represents the objective function at the tth iteration; the regularization term Ω( ) is defined as: Among them, γ is the complexity parameter of the tree, 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 flow of farmland tailwater and the migration and transformation of pollutants. 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; Pollutant load calculation formula: Where: H: the number of hydrological response units HRU; : the amount of pollutants lost to the surface of the hth HRU; : The amount of pollutant lost in the soil 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 in each HRU is further calculated by the following formula: in: and : are the surface runoff and soil runoff of the hth HRU respectively; : the area of ​​the hth HRU; and : are the surface and soil pollutant concentrations of the hth HRU respectively; The MikeShe model simulates water flow and pollutant transport processes based on the finite difference method. Its basic equations are the Saint-Venant equations: Where: A: cross-sectional area of ​​water flow; Q: flow rate; t: time; x: spatial coordinate; h: water depth; g: gravitational acceleration; S f : friction slope; τ: shear stress; ρ: water density; : Inflow flow; : outflow traffic; : cross-sectional flow rate; The pollutant transport equation is: Where: C: pollutant concentration; D: diffusion coefficient; S: source / sink term; S5: Based on the analysis results, construct an ecological risk assessment model; S6: Develop precise tailwater pollution prevention and control strategies based on the analysis and assessment results; S7: Build a real-time monitoring system to track the effectiveness of prevention and control; S8: Conduct a comprehensive assessment of the effectiveness of prevention and control measures, and combine feedback optimization to form a continuous improvement mechanism.

2. The method for analyzing and evaluating the pollution load of farmland tailwater according to claim 1, characterized in that: In S1, the farmland information data includes farmland soil attribute data, topography data, crop growth data, agricultural activity data, meteorological condition data and surrounding hydrological environment data. The farmland soil attribute data includes soil texture, fertility index, pH value and organic matter content; the topography data includes farmland elevation, slope, slope direction 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 methods and agricultural film use; 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, the current water quality and the water body functional zoning.

3. The method for analyzing and evaluating farmland tailwater pollution load according to claim 1, characterized in that: In S2, geographic information systems and geostatistical methods are used in combination with farmland functional zoning to determine basic sampling points, and a deep reinforcement learning algorithm is introduced to dynamically adjust the density and location of sampling points based on historical pollution data and real-time environmental changes. An autonomous mobile robot is equipped with a multi-parameter water quality sensor to perform supplementary sampling tasks according to preset rules. The fixed sampling units are set at key drainage nodes, tailwater collection areas and receiving water body entrances, and are equipped with automatic water quality monitors and flow meters to achieve long-term continuous monitoring. The mobile sampling units include portable sampling equipment and drone sampling systems, which are flexibly deployed according to real-time instructions to enhance sampling maneuverability and flexibility.

4. The method for analyzing and evaluating farmland tailwater pollution load according to claim 3, characterized in that: In the S3, multi-dimensional detection includes the combination of on-site rapid detection and laboratory precision analysis. Hyperspectral imagers, heavy metal rapid testers and water quality multi-parameter analyzers are used on-site to obtain preliminary data. The laboratory uses ICP-MS, GC-MS, and LC-MS technologies to accurately determine the content 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 toxic effects of the samples.

5. The method for analyzing and evaluating farmland tailwater pollution load according to claim 4, characterized in that: In S5, the ecological risk assessment model is based on the HRA framework, combined with the toxicity unit method to quantify heavy metal risks, the ECOSAR model is used to estimate organic pollutant risks, and a comprehensive evaluation model based on the entropy weight method is used to integrate the risk identification index and risk characterization value to generate an intuitive ecological risk distribution map, divide the risk levels into high, medium and low, and clarify high-risk areas and pollution indicators.

6. The method for analyzing and evaluating farmland tailwater pollution load according to claim 5, characterized in that: In S6, the prevention and control strategies include optimized fertilization programs and green pesticide substitution for source reduction, ecological ditch construction and buffer zone setting for process control, artificial wetland system construction and tailwater recycling projects for end-of-pipe treatment, enhanced measures for high-risk areas, supplementary measures for medium-risk areas, and preventive measures for low-risk areas.

7. The method for analyzing and evaluating farmland tailwater pollution load according to claim 6, characterized in that: In the above-mentioned S7, automatic water quality monitoring stations and IoT sensor networks are built, a multi-source data fusion platform is constructed, edge computing technology is used to pre-process data, and in-depth analysis on the cloud platform is used to achieve dynamic monitoring. A feedback optimization mechanism is established to provide real-time feedback on monitoring data and model evaluation results, dynamically adjust model parameters and prevention and control strategies, and form a closed-loop management.

8. The method for analyzing and evaluating farmland tailwater pollution load according to claim 7, characterized in that: In S8, the effectiveness evaluation and continuous improvement use cost-benefit analysis to compare the economic efficiency of prevention and control measures, adopt a comprehensive indicator system to evaluate the prevention and control effects, continuously optimize model parameters based on feedback data, upgrade the evaluation model, and form a continuous improvement mechanism to provide long-term support for agricultural environmental management.

9. The method for analyzing and evaluating farmland tailwater pollution load according to claim 8, characterized in that: The tailwater pollution prevention and control strategy document formulated in step S6, the prevention and control effect time series data tracked and recorded in step S7, and the prevention and control measures implementation effect evaluation report generated in step S8 constitute the prevention and control record. 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, develop a visual decision support system, and 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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