Farmland non-point source pollution monitoring and evaluating method and system

Abstract

The invention belongs to the technical field of farmland non-point source pollution monitoring and evaluation, particularly relates to a farmland non-point source pollution monitoring and evaluation method and system, and aims at solving the problems that in the existing farmland non-point source pollution monitoring and evaluation process, monitoring indexes and sampling modes are single, farmland information cannot be fully collected, and the accuracy of monitoring and evaluation is low. According to the scheme, the method comprises the following steps that S1, farmland information data are collected, processed and analyzed; s2, arranging sampling points on the farmland key runoff path and the soil area; s3, farmland soil and runoff are sampled and detected; s4, processing the collected data, and carrying out deep mining and analysis on the monitoring data; s5, constructing an evaluation model, and evaluating the farmland pollution condition; the fixed sampling points are combined with the movable sampling device, farmland information is fully collected, and therefore the accuracy of monitoring and evaluation is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of farmland non-point source pollution monitoring and evaluation, and particularly relates to a farmland non-point source pollution monitoring and evaluation method and system. BACKGROUND

[0002] With the continuous expansion of agricultural production scale and the continuous use of agricultural inputs, farmland non-point source pollution has become an important factor threatening the ecological environment and the quality and safety of agricultural products.

[0003] In the prior art, the monitoring index and sampling method in the farmland non-point source pollution monitoring and evaluation process are relatively single, and farmland information cannot be fully collected, resulting in low accuracy of monitoring and evaluation. SUMMARY

[0004] The present application aims to solve the problem in the prior art that the monitoring index and sampling method in the farmland non-point source pollution monitoring and evaluation process are relatively single, and farmland information cannot be fully collected, resulting in low accuracy of monitoring and evaluation.

[0005] The farmland non-point source pollution monitoring and evaluation method and system provided by the present application adopts the following technical solution:

[0006] A farmland non-point source pollution monitoring and evaluation method, comprising the following steps:

[0007] S1: Collecting and processing and analyzing farmland information data;

[0008] S2: Arranging sampling points on the key runoff path and soil area of the farmland;

[0009] S3: Sampling and detecting the farmland soil and runoff;

[0010] S4: Processing the collected data and deeply mining and analyzing the monitoring data;

[0011] S5: Constructing an evaluation model to evaluate the pollution status of the farmland;

[0012] S6: Formulating pollution prevention and control strategies and continuously monitoring the changes of farmland non-point source pollution indexes;

[0013] S7: Performing chain operation on the monitoring data and automatically executing and supervising the pollution prevention and control measures.

[0014] Further, in the S1, the farmland information data includes topographic feature data of the farmland, soil type distribution data of the farmland, crop planting mode of the farmland, water system distribution around the farmland, meteorological data around the farmland, past agricultural production records of the farmland, and historical data of environmental quality monitoring around the farmland.

[0015] The topographic feature data of the farmland includes detailed data of terrain undulation and slope change, and a topographic map of the farmland is drawn by using high-precision global positioning system and geographic information system technology to accurately locate key points of terrain.

[0016] The crop planting mode of the farmland includes crop type, planting area, planting density, and crop rotation system information.

[0017] The water system distribution around the farmland includes position, flow direction, and flow rate information of rivers, lakes, reservoirs, and ditches.

[0018] The meteorological data around the farmland includes multi-year average rainfall, rainfall seasonal distribution, rainstorm frequency, air temperature change, and wind direction and speed meteorological element data.

[0019] The past agricultural production records of the farmland include fertilizer application records, pesticide use records, and irrigation records.

[0020] Further, in the S2, according to the information of farmland topography, soil type, crop planting distribution, and surrounding water system distribution collected in the early stage, different sampling units are divided by using geographic information system technology and geostatistical methods, fixed sampling points are set on the key runoff paths of the farmland, and automatic sampling devices are equipped to automatically collect farmland runoff samples according to a preset program. Meanwhile, mobile sampling devices are arranged to move along a preset track or flexibly move sampling according to actual runoff direction to capture the migration and diffusion path and rule of farmland non-point source pollution in space. Based on the agricultural activity cycle of the farmland, rainfall rule, and seasonal change factors, an intelligent time sequence sampling model is constructed to increase the sampling frequency in the key periods of the fertilizer application period, pesticide spraying period, and before and after rainfall. The sampling model can be dynamically adjusted according to real-time meteorological data and farmland soil humidity and other information.

[0021] Further, in the S3, on the basis of monitoring of conventional pollutants such as ammonia nitrogen, nitrate nitrogen, total phosphorus and chemical oxygen demand in farmland runoff and soil, monitoring of total nitrogen, total phosphorus, suspended solids and biochemical oxygen demand is increased, and portable heavy metal rapid testers and high-precision instruments such as laboratory atomic absorption spectrometers are used to monitor mercury, cadmium, lead, arsenic, chromium, copper and zinc in farmland soil and runoff, and advanced on-site rapid detection and laboratory analysis methods such as immune analysis technology, gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry are used to detect various types and contents of pesticide residues in farmland runoff and soil, and biological toxicity testing methods such as luminous bacteria toxicity testing, Daphnia magna acute toxicity testing and fish embryo toxicity testing are used to periodically evaluate the ecological toxicity of farmland runoff samples.

[0022] Further, in the S4, a large amount of data obtained by multi-dimensional monitoring indicators is preprocessed, statistical methods and data verification rules are used to identify and eliminate obviously incorrect or abnormal data points, data of different dimensions and different orders of magnitude are normalized, standardization methods or range normalization methods are used to convert monitoring data into dimensionless numerical ranges, advanced data fusion technologies such as Kalman filter data fusion algorithm and neural network data fusion algorithm are used to fuse spatial sampling data and time series sampling data, construct a spatio-temporal data matrix of farmland non-point source pollution, restore the real distribution status of farmland non-point source pollution in three-dimensional space and time dimension, and establish a pollutant migration and transformation model.

[0023] Kalman filter data fusion algorithm

[0024] The Kalman filter assumes that the system state and observation data satisfy the following linear Gaussian model:

[0025] x k =F k x k-1 +B k u k +w k

[0026] z k =H k x k +v k

[0027] Wherein: x k is the system state vector at time k; F k is the state transition matrix at time k. B k is the control input matrix at time k; u k is the control input vector at time k; w kLet Q be the process noise vector at time k, assumed to be Gaussian white noise with zero mean, and its covariance matrix be Q. k ;z k H is the observation vector at time k; k It is the observation matrix at time k; v k Let R be the observation noise vector at time k, assumed to be Gaussian white noise with zero mean, and its covariance matrix be R. k ;

[0028] Kalman filter algorithm steps

[0029] State prediction:

[0030]

[0031] Covariance prediction:

[0032]

[0033] Kalman gain calculation:

[0034]

[0035] Status Update:

[0036]

[0037] Covariance update:

[0038] P k|k =(IK k H k )P k|k-1

[0039] in: It is the state estimation vector predicted at time k based on information from time k-1; It is the state estimation vector updated based on the current observation data at time k; P k∣k-1 P is the covariance matrix of the predicted state at time k; k∣k It is the covariance matrix of the state after the update at time k; K k I is the Kalman gain matrix at time k; I is the identity matrix.

[0040] Furthermore, in S5, the analytic hierarchy process (AHP) combined with fuzzy comprehensive evaluation is used to comprehensively consider the degree of harm and pollution contribution rate of each monitoring indicator to the farmland ecosystem, agricultural product quality and safety, and surrounding water environment. The weights of different indicators in the evaluation model are determined, and the weights are periodically re-evaluated and adjusted. Based on the determined weights, a multi-level comprehensive evaluation model for farmland non-point source pollution is constructed. The evaluation model can dynamically output evaluation results. With the real-time update of monitoring data, it can promptly reflect the changing trend of farmland non-point source pollution and generate corresponding early warning information, providing a scientific basis for agricultural management departments to formulate precise pollution prevention and control strategies.

[0041] Furthermore, in S6 and S7, based on the comprehensive assessment results, personalized non-point source pollution control measures are formulated for farmland areas with different pollution levels and types. A dynamic monitoring system for the control effect is established. After the implementation of control measures, changes in farmland non-point source pollution indicators are continuously monitored according to a certain monitoring frequency and monitoring point layout. The monitoring data before and after the implementation of control measures are compared to evaluate the actual effect of the control measures. All data in the farmland non-point source pollution monitoring process, including the collected raw data, data processing results, and the construction process and results of the assessment model, are encrypted, stored, and uploaded to the blockchain using blockchain technology. Utilizing the distributed ledger characteristics of blockchain, the authenticity and integrity of data are ensured during transmission, storage, and sharing, preventing data from being tampered with or forged. By leveraging the traceability capabilities of blockchain, the source, collection time, and location of data can be tracked, providing a reliable traceability path for pollution monitoring data and enhancing its credibility and public trust. Based on blockchain smart contract technology, pre-set pollution prevention and control strategies and measures can be encoded and deployed in the form of smart contracts. When farmland non-point source pollution indicators reach or exceed the warning threshold, the smart contract can automatically trigger and execute corresponding prevention and control measures, such as automatically adjusting irrigation water volume and fertilizer application, or sending instructions to relevant agricultural machinery and equipment to initiate physical prevention and control measures. At the same time, the smart contract can also automatically record the execution status of prevention and control measures and store them on the blockchain, realizing transparent management and supervision of the prevention and control process, ensuring the effective implementation of prevention and control measures, and improving prevention and control efficiency and effectiveness.

[0042] This invention also proposes a farmland non-point source pollution monitoring and assessment system for implementing the farmland non-point source pollution monitoring and assessment method described above, comprising:

[0043] The monitoring module includes a water quality sensor unit and a soil sensor unit. The water quality sensor unit includes sensors for monitoring conventional pollutants such as ammonia nitrogen, nitrate nitrogen, total phosphorus, chemical oxygen demand, total nitrogen, total phosphorus, and suspended solids, as well as on-site rapid detection sensors for heavy metals and pesticide residues. These sensors are distributed at various sampling points in the farmland and transmit monitoring data to the data processing center in real time via wireless communication technology. The soil sensor unit is used to monitor soil moisture, temperature, pH, heavy metal content, and pesticide residue parameters to assist in the analysis of the impact of non-point source pollution on the farmland soil environment and to combine with soil and water loss monitoring. The soil sensor unit adopts a layered arrangement and is installed in the soil at different depths to obtain detailed information on the soil profile.

[0044] The sampling module includes fixed sampling units and mobile sampling units. The fixed sampling units are installed at fixed locations along key runoff paths and have the function of timed and quantitative sampling. They automatically collect farmland runoff samples according to a preset time sequence sampling strategy and transport them to the on-site sample storage device through pipelines. They are equipped with an automatic water quality monitoring pretreatment system to perform preliminary filtration and constant temperature treatment on the collected water samples to ensure that the water sample quality meets the monitoring requirements. The mobile sampling units are equipped with flexible moving tracks or wheeled drive devices, which can move flexibly in the farmland according to preset routes or according to remote commands. They are equipped with a variety of sampling tools to achieve dynamic sampling at different locations in the farmland. The mobile sampling vehicle also has real-time data transmission and positioning functions, which can transmit sampling location and sampling time information back to the data processing center in real time, so that monitoring personnel can keep track of the sampling progress and sampling status.

[0045] The data processing module is used to receive and store large amounts of monitoring data from the monitoring module and the sampling module. The data storage adopts a redundant backup mechanism to ensure the security and reliability of the data.

[0046] The data transmission module is used to transmit the processed data;

[0047] The terminal module is used to view monitoring data, assessment results, pollution distribution maps and early warning information of farmland non-point source pollution in real time, and has data query, analysis chart generation and report export functions.

[0048] The early warning module sets early warning thresholds based on comprehensive assessment results. When the non-point source pollution index in farmland exceeds the corresponding threshold, it automatically sends early warning information to relevant personnel to remind them to take appropriate measures.

[0049] The data upload module is used to encrypt and store monitoring data and upload it to the blockchain using blockchain technology.

[0050] The data traceability module is used to track the source, collection time, and collection location of data, providing a reliable traceability path for pollution monitoring data.

[0051] The driver-controlled prevention and control module is used to automatically trigger and execute corresponding prevention and control measures.

[0052] The present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for monitoring and assessing non-point source pollution in farmland.

[0053] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for monitoring and assessing non-point source pollution in farmland.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. This scheme collects comprehensive farmland information data and rationally arranges sampling points. Combined with high-precision sensors, it can accurately monitor multiple pollutants in farmland soil and runoff. At the same time, by using advanced data processing and fusion technology, it preprocesses, normalizes and fuses multi-dimensional monitoring data to restore the true distribution of farmland non-point source pollution and establishes a pollutant migration and transformation model, providing a scientific basis for subsequent pollution assessment and prevention.

[0056] 2. This scheme uses the analytic hierarchy process (AHP) combined with the fuzzy comprehensive evaluation method to construct an assessment model. It comprehensively considers the degree of harm and pollution contribution rate of each pollution indicator, determines the weights, and adjusts them regularly to achieve a dynamic and accurate assessment of farmland pollution.

[0057] This invention combines fixed sampling points with mobile sampling devices to fully collect farmland information, thereby effectively improving the accuracy of monitoring and assessment. Attached Figure Description

[0058] Figure 1 This is a flowchart of a method for monitoring and assessing non-point source pollution in farmland proposed in this invention;

[0059] Figure 2 This is a flowchart of step S1 of a method for monitoring and assessing non-point source pollution in farmland proposed in this invention.

[0060] Figure 3 This is a flowchart of step S2 of the method for monitoring and assessing non-point source pollution in farmland proposed in this invention;

[0061] Figure 4 This is a flowchart of step S3 of the method for monitoring and assessing non-point source pollution in farmland proposed in this invention;

[0062] Figure 5 This is a flowchart of step S4 in the method for monitoring and assessing non-point source pollution in farmland proposed in this invention;

[0063] Figure 6 This is a flowchart of step S5 of a method for monitoring and assessing non-point source pollution in farmland proposed in this invention.

[0064] Figure 7 This is a flowchart of step S6 of the method for monitoring and assessing non-point source pollution in farmland proposed in this invention;

[0065] Figure 8 This is a structural block diagram of a farmland non-point source pollution monitoring and assessment system proposed in this invention;

[0066] Figure 9 This is a structural block diagram of the monitoring module of a farmland non-point source pollution monitoring and assessment system proposed in this invention;

[0067] Figure 10 This is a structural block diagram of the sampling module of a farmland non-point source pollution monitoring and assessment system proposed in this invention. Detailed Implementation

[0068] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0069] Example 1

[0070] Reference Figures 1-10 A method for monitoring and assessing non-point source pollution in farmland, comprising the following steps:

[0071] S1: Collect, process, and analyze farmland information data;

[0072] S2: Sampling points are set up along key runoff paths in farmland and in soil areas;

[0073] S3: Sample and test farmland soil and runoff;

[0074] S4: Process the collected data and perform in-depth mining and analysis of the monitoring data;

[0075] S5: Construct an assessment model to evaluate the pollution status of farmland;

[0076] S6: Develop pollution prevention and control strategies and continuously monitor changes in farmland non-point source pollution indicators;

[0077] S7: Upload monitoring data to the blockchain and automatically execute and monitor pollution prevention and control measures.

[0078] In this embodiment, in S1, the farmland information data includes farmland topographic and geomorphological feature data, farmland soil type distribution data, farmland crop planting patterns, farmland water system distribution, farmland meteorological data, farmland past agricultural production records, and farmland surrounding environmental quality monitoring historical data.

[0079] The topographic and geomorphological features of farmland include detailed data on terrain undulation and slope changes. High-precision global positioning system and geographic information system technology are used to draw topographic maps of farmland and accurately locate key terrain points.

[0080] Farmland crop planting patterns include information on crop types, planting area, planting density, and crop rotation systems;

[0081] The distribution of water systems around farmland includes the location, flow direction, and flow rate information of water bodies such as rivers, lakes, reservoirs, and ditches;

[0082] Meteorological data around farmland includes multi-year average rainfall, seasonal distribution of rainfall, frequency of rainstorms, temperature changes, and meteorological element data such as wind direction and wind speed.

[0083] Past agricultural production records for farmland include fertilization records, pesticide use records, and irrigation records.

[0084] In this embodiment, in S2, based on the previously collected information such as farmland topography, soil type, crop planting distribution, and surrounding water system distribution, geographic information system technology and geostatistical methods are used to divide different sampling units. Fixed sampling points are set up on key runoff paths in the farmland and equipped with automatic sampling devices that can automatically collect farmland runoff samples according to a preset program. At the same time, mobile sampling devices are deployed that can move flexibly along preset tracks or according to the actual runoff direction to capture the spatial migration and diffusion paths and patterns of farmland non-point source pollution. Based on the agricultural activity cycle, rainfall patterns, and seasonal variation factors of the farmland, an intelligent time-series sampling model is constructed. The sampling frequency is increased during the fertilization period, pesticide spraying period, and key periods before and after rainfall. The sampling model can be dynamically adjusted according to real-time meteorological data and farmland soil moisture and other information.

[0085] In this embodiment, S3 employs a combination of high-precision water quality sensors and laboratory analysis methods. In addition to monitoring conventional pollutants such as ammonia nitrogen, nitrate nitrogen, total phosphorus, and chemical oxygen demand in farmland runoff and soil, monitoring of indicators such as total nitrogen, total phosphorus, suspended solids, and biochemical oxygen demand is added. High-precision instruments such as portable heavy metal rapid analyzers and laboratory atomic absorption spectrometers are used to monitor multiple heavy metals, including mercury, cadmium, lead, arsenic, chromium, copper, and zinc, in farmland soil and runoff. Advanced on-site rapid detection and laboratory analysis methods, including immunoassay, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry, are used to detect the types and contents of various pesticide residues in farmland runoff and soil. Biotoxicity testing methods, such as luminescent bacteria toxicity testing, Daphnia magna acute toxicity testing, and fish embryo toxicity testing, are employed to periodically conduct ecotoxicity assessments on farmland runoff samples.

[0086] In this embodiment, in S4, a large amount of data obtained from multi-dimensional monitoring indicators is preprocessed. Statistical methods and data verification rules are used to identify and remove obviously erroneous or abnormal data points. Data of different dimensions and magnitudes are normalized. Standardization or range normalization methods are used to convert the monitoring data into a dimensionless numerical range. Advanced data fusion technologies such as Kalman filter data fusion algorithm and neural network data fusion algorithm are used to fuse spatial sampling data and time series sampling data to construct a spatiotemporal data matrix of farmland non-point source pollution, restore the true distribution of farmland non-point source pollution in three-dimensional space and time dimensions, and establish a pollutant migration and transformation model.

[0087] Kalman filter data fusion algorithm

[0088] Kalman filtering assumes that the system state and the observed data satisfy the following linear Gaussian model:

[0089] x k =F k x k-1 +B k u k +w k

[0090] z k =H k x k +v k

[0091] Where: x k F is the system state vector at time k; k It is the state transition matrix at time k. B k It is the control input matrix at time k; u k It is the control input vector at time k; w kLet Q be the process noise vector at time k, assumed to be Gaussian white noise with zero mean, and its covariance matrix be Q. k ;z k H is the observation vector at time k; k It is the observation matrix at time k; v k Let R be the observation noise vector at time k, assumed to be Gaussian white noise with zero mean, and its covariance matrix be R. k ;

[0092] Kalman filter algorithm steps

[0093] State prediction:

[0094]

[0095] Covariance prediction:

[0096]

[0097] Kalman gain calculation:

[0098]

[0099] Status Update:

[0100]

[0101] Covariance update:

[0102] P k|k =(IK k H k )P k|k-1

[0103] in: It is the state estimation vector predicted at time k based on information from time k-1; ∣ k is the state estimation vector updated based on the current observation data at time k; P k∣k-1 P is the covariance matrix of the predicted state at time k; k∣k It is the covariance matrix of the state after the update at time k; K k I is the Kalman gain matrix at time k; I is the identity matrix.

[0104] In this embodiment, in step S5, the analytic hierarchy process (AHP) combined with fuzzy comprehensive evaluation is used to comprehensively consider the degree of harm and pollution contribution rate of each monitoring indicator to the farmland ecosystem, agricultural product quality and safety, and surrounding water environment. The weights of different indicators in the evaluation model are determined, and the weights are periodically re-evaluated and adjusted. Based on the determined weights, a multi-level comprehensive evaluation model for farmland non-point source pollution is constructed. The evaluation model can dynamically output evaluation results. With the real-time update of monitoring data, it can promptly reflect the changing trend of farmland non-point source pollution and generate corresponding early warning information, providing a scientific basis for agricultural management departments to formulate precise pollution prevention and control strategies.

[0105] In this embodiment, in steps S6 and S7, based on the comprehensive evaluation results, personalized non-point source pollution control measures are formulated for farmland areas with different pollution levels and types. A dynamic monitoring system for the control effect is established. After the implementation of control measures, changes in farmland non-point source pollution indicators are continuously monitored according to a certain monitoring frequency and monitoring point layout. The monitoring data before and after the implementation of control measures are compared to evaluate the actual effect of the control measures. All data in the farmland non-point source pollution monitoring process, including the collected raw data, data processing results, and the construction process and results of the evaluation model, are encrypted, stored, and uploaded to the blockchain using blockchain technology. Utilizing the distributed ledger characteristics of blockchain, the authenticity and integrity of data are ensured during transmission, storage, and sharing, preventing data from being tampered with or forged. By leveraging the traceability capabilities of blockchain, the source, collection time, and location of data can be tracked, providing a reliable traceability path for pollution monitoring data and enhancing its credibility and public trust. Based on blockchain smart contract technology, pre-defined pollution prevention and control strategies and measures can be encoded and deployed in the form of smart contracts. When farmland non-point source pollution indicators reach or exceed warning thresholds, smart contracts can automatically trigger and execute corresponding prevention and control measures, such as automatically adjusting irrigation water and fertilizer application, or sending instructions to relevant agricultural machinery and equipment to initiate physical prevention and control measures. At the same time, smart contracts can also automatically record the execution status of prevention and control measures and store them on the blockchain, realizing transparent management and supervision of the prevention and control process, ensuring the effective implementation of prevention and control measures, and improving prevention and control efficiency and effectiveness.

[0106] This embodiment also proposes a farmland non-point source pollution monitoring and assessment system for implementing the farmland non-point source pollution monitoring and assessment method described above, including:

[0107] The monitoring module includes a water quality sensor unit and a soil sensor unit. The water quality sensor unit includes sensors for monitoring conventional pollutants such as ammonia nitrogen, nitrate nitrogen, total phosphorus, chemical oxygen demand, total nitrogen, total phosphorus, and suspended solids, as well as on-site rapid detection sensors for heavy metals and pesticide residues. These sensors are distributed at various sampling points in the farmland and transmit monitoring data to the data processing center in real time via wireless communication technology. The soil sensor unit is used to monitor soil moisture, temperature, pH, heavy metal content, and pesticide residue parameters to assist in the analysis of the impact of non-point source pollution on the farmland soil environment and to combine with soil and water loss monitoring. The soil sensor unit adopts a layered arrangement and is installed in the soil at different depths to obtain detailed information on the soil profile.

[0108] The sampling module includes fixed sampling units and mobile sampling units. The fixed sampling units are installed at fixed locations along key runoff paths and have the function of timed and quantitative sampling. They automatically collect farmland runoff samples according to a preset time sequence sampling strategy and transport them to the on-site sample storage device through pipelines. They are equipped with an automatic water quality monitoring pretreatment system to perform preliminary filtration and constant temperature treatment on the collected water samples to ensure that the water sample quality meets the monitoring requirements. The mobile sampling units are equipped with flexible moving tracks or wheeled drive devices, which can move flexibly in the farmland according to preset routes or according to remote commands. They are equipped with a variety of sampling tools to achieve dynamic sampling at different locations in the farmland. The mobile sampling vehicle also has real-time data transmission and positioning functions, which can transmit sampling location and sampling time information back to the data processing center in real time, so that monitoring personnel can keep track of the sampling progress and sampling status.

[0109] The data processing module is used to receive and store large amounts of monitoring data from the monitoring module and the sampling module. The data storage adopts a redundant backup mechanism to ensure the security and reliability of the data.

[0110] The data transmission module is used to transmit the processed data;

[0111] The terminal module is used to view monitoring data, assessment results, pollution distribution maps and early warning information of farmland non-point source pollution in real time, and has data query, analysis chart generation and report export functions.

[0112] The early warning module sets early warning thresholds based on comprehensive assessment results. When the non-point source pollution index in farmland exceeds the corresponding threshold, it automatically sends early warning information to relevant personnel to remind them to take appropriate measures.

[0113] The data upload module is used to encrypt and store monitoring data and upload it to the blockchain using blockchain technology.

[0114] The data traceability module is used to track the source, collection time, and collection location of data, providing a reliable traceability path for pollution monitoring data.

[0115] The driver-controlled prevention and control module is used to automatically trigger and execute corresponding prevention and control measures.

[0116] This embodiment also proposes a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for monitoring and assessing farmland non-point source pollution.

[0117] This embodiment also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for monitoring and assessing non-point source pollution in farmland.

[0118] Example 2

[0119] The difference between this embodiment and Embodiment 1 is that the monitoring module and the sampling module are connected to a resource scheduling module. The resource scheduling module is used to schedule sampling equipment, monitoring resources and prevention and control materials, adjust priorities according to pollution dynamics, and rationally allocate limited resources to ensure that the monitoring and prevention and control needs of key areas and high-risk periods are met first.

[0120] Example 3

[0121] The difference between this embodiment and Embodiment 1 is that the early warning module is connected to the prevention and control strategy module. The prevention and control strategy module dynamically generates personalized pollution prevention and control plans based on the evaluation model and big data analysis, such as optimizing fertilization and adjusting planting patterns. According to the pollution level and type, it formulates precise and flexible prevention and control strategies to improve resource utilization efficiency.

[0122] Example 4

[0123] The difference between this embodiment and Embodiment 1 is that the terminal module is connected to a pollution source tracking module. The pollution source tracking module uses Geographic Information System (GIS) and geostatistical methods, combined with spatiotemporal data analysis, to accurately locate the pollution source and diffusion path, providing targeted targets for subsequent governance and avoiding blind prevention and control.

[0124] Example 5

[0125] The difference between this embodiment and Embodiment 1 is that the early warning module is connected to the supervision and execution module. The supervision and execution module is used to track and evaluate the implementation of pollution prevention and control strategies in real time, including the progress of measure implementation, resource utilization efficiency and short-term results, to ensure that prevention and control measures are implemented as planned, to promptly detect and correct implementation deviations, to ensure the effectiveness of prevention and control strategies and the rational use of resources, and to improve the overall prevention and control effectiveness.

[0126] Experimental Example

[0127] I. Experimental Objective

[0128] A typical farmland area was selected, primarily planted with wheat and corn, surrounded by small rivers and irrigation canals, with relatively complete meteorological data. This experiment aims to comprehensively understand the current status of farmland non-point source pollution by implementing the aforementioned farmland non-point source pollution monitoring and assessment methods, providing a basis for precise prevention and control.

[0129] II. Experimental Procedure

[0130] (I) Data Collection and Preprocessing

[0131] Data on the topographic features of the farmland (terrain undulation, slope changes, topographic maps drawn using high-precision GPS and GIS), soil type distribution (mainly clay, loam, and sandy soil), crop planting patterns (wheat planting area of ​​30 mu, density of 300 plants / square meter; corn planting area of ​​25 mu, density of 280 plants / square meter), distribution of surrounding water systems (one small river and three irrigation canals nearby, recording their location, flow direction, and flow rate), surrounding meteorological data (annual average rainfall of 650 mm, frequency of rainstorms 3 times / year, etc.), past agricultural production records (fertilization records show annual fertilizer application of 120 kg / mu nitrogen fertilizer and 80 kg / mu phosphorus fertilizer, etc.), and historical data on surrounding environmental quality monitoring were collected.

[0132] (II) Sampling Point Layout

[0133] Based on the collected data, sampling units were divided using GIS technology and geostatistical methods. Five fixed sampling points were set up along key runoff paths, equipped with automatic sampling devices; simultaneously, two mobile sampling devices were deployed to flexibly sample along preset tracks and according to runoff direction. An intelligent time-series sampling model was constructed to increase sampling frequency during key periods such as fertilization and before and after rainfall.

[0134] (III) Sampling and Testing

[0135] A combination of high-precision water quality sensors and laboratory analysis is employed. Conventional pollutant indicators such as ammonia nitrogen, nitrate nitrogen, total phosphorus, and chemical oxygen demand (COD) are monitored in farmland runoff and soil, while additional monitoring of total nitrogen, total phosphorus, suspended solids, and biochemical oxygen demand is added. Portable heavy metal rapid analyzers and laboratory atomic absorption spectrometers are used to monitor heavy metals such as mercury, cadmium, lead, arsenic, chromium, copper, and zinc. Immunoassay, gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS) techniques are used to detect the types and contents of various pesticide residues, and biotoxicity testing methods are employed to regularly assess ecotoxicity.

[0136] (iv) Data Processing and Analysis

[0137] The collected, multi-dimensional monitoring data were preprocessed to remove outlier data points and normalize the data. Kalman filter and neural network data fusion algorithms were used to fuse spatial and temporal sampled data, constructing a spatiotemporal data matrix of farmland non-point source pollution. This reconstructed the distribution of pollution in three-dimensional space and time, and established a pollutant migration and transformation model.

[0138] (V) Evaluation Model Construction

[0139] By combining the analytic hierarchy process (AHP) with the fuzzy comprehensive evaluation method, and taking into account the degree of harm and pollution contribution rate of each monitoring indicator to the farmland ecosystem, agricultural product quality and safety and the surrounding water environment, the weight of different indicators in the evaluation model is determined, and a multi-level comprehensive evaluation model for farmland non-point source pollution is constructed.

[0140] (vi) Formulation and monitoring of prevention and control strategies

[0141] Based on the comprehensive assessment results, personalized non-point source pollution control measures were formulated for farmland areas with different pollution levels and types. A dynamic monitoring system for the control effectiveness was established to continuously monitor changes in farmland non-point source pollution indicators, compare monitoring data before and after the implementation of control measures, and evaluate the control effectiveness.

[0142] III. Experimental Data and Results Presentation

[0143] (I) Sampling point layout and sampling time arrangement

[0144]

[0145]

[0146] (II) Monitoring Data

[0147]

[0148] (III) Evaluation Model Results

[0149]

[0150] This experiment provides a more comprehensive understanding of the status of non-point source pollution in farmland, offering a scientific basis for developing precise prevention and control strategies.

[0151] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for monitoring and assessing non-point source pollution in farmland, characterized in that: Includes the following steps: S1: Collect, process, and analyze farmland information data; S2: Sampling points are set up along key runoff paths in farmland and in soil areas; S3: Sample and test farmland soil and runoff; S4: Process the collected data and perform in-depth mining and analysis of the monitoring data; S5: Construct an assessment model to evaluate the pollution status of farmland; S6: Develop pollution prevention and control strategies and continuously monitor changes in farmland non-point source pollution indicators; S7: Upload monitoring data to the blockchain and automatically execute and monitor pollution prevention and control measures.

2. The method for monitoring and assessing non-point source pollution in farmland according to claim 1, characterized in that: In S1, the farmland information data includes farmland topographic and geomorphological features, farmland soil type distribution data, farmland crop planting patterns, farmland water system distribution, farmland meteorological data, farmland past agricultural production records, and farmland surrounding environmental quality monitoring historical data. The topographic and geomorphological features of the farmland include detailed data on terrain undulation and slope changes. A topographic map of the farmland is drawn using high-precision global positioning system and geographic information system technology to accurately locate key terrain points. The crop planting patterns of the farmland include information on crop types, planting area, planting density, and crop rotation system; The distribution of water systems around farmland includes the location, flow direction, and flow rate information of rivers, lakes, reservoirs, and ditches; Meteorological data around farmland includes multi-year average rainfall, seasonal distribution of rainfall, frequency of rainstorms, temperature changes, and meteorological element data such as wind direction and wind speed. Past agricultural production records for farmland include fertilization records, pesticide use records, and irrigation records.

3. The method for monitoring and assessing non-point source pollution in farmland according to claim 2, characterized in that: In step S2, based on previously collected information on farmland topography, soil type, crop planting distribution, and surrounding water system distribution, geographic information system technology and geostatistical methods are used to divide different sampling units. Fixed sampling points are set up along key runoff paths in the farmland, equipped with automatic sampling devices, and farmland runoff samples are automatically collected according to a preset program. At the same time, mobile sampling devices are deployed to flexibly move and sample along preset tracks or according to the actual runoff direction to capture the spatial migration and diffusion paths and patterns of farmland non-point source pollution. Based on the agricultural activity cycle, rainfall patterns, and seasonal variation factors of the farmland, an intelligent time-series sampling model is constructed. The sampling frequency is increased during the fertilization period, pesticide spraying period, and key periods before and after rainfall. The sampling model is dynamically adjusted according to real-time meteorological data and farmland soil moisture information.

4. The method for monitoring and assessing farmland non-point source pollution according to claim 3, characterized in that: In S3, a combination of high-precision water quality sensors and laboratory analysis methods is used to monitor conventional pollutants such as ammonia nitrogen, nitrate nitrogen, total phosphorus, and chemical oxygen demand in farmland runoff and soil. This is supplemented by monitoring total nitrogen, total phosphorus, suspended solids, and biochemical oxygen demand. High-precision instruments, including portable heavy metal rapid analyzers and laboratory atomic absorption spectrometers, are used to monitor multiple heavy metals such as mercury, cadmium, lead, arsenic, chromium, copper, and zinc in farmland soil and runoff. Advanced on-site rapid detection and laboratory analysis methods, including immunoassay, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry, are employed to detect the types and contents of various pesticide residues in farmland runoff and soil. Biotoxicity testing methods are used to periodically conduct ecotoxicity assessments on farmland runoff samples.

5. The method for monitoring and assessing non-point source pollution in farmland according to claim 4, characterized in that: In step S4, a large amount of data obtained from multi-dimensional monitoring indicators is preprocessed. Statistical methods and data verification rules are used to identify and remove obviously erroneous or abnormal data points. Data of different dimensions and magnitudes are normalized. Standardization or range normalization methods are used to convert the monitoring data into a dimensionless numerical range. Advanced data fusion technologies, such as Kalman filter data fusion algorithm and neural network data fusion algorithm, are used to fuse spatial sampling data and time series sampling data to construct a spatiotemporal data matrix of farmland non-point source pollution. This restores the true distribution of farmland non-point source pollution in three-dimensional space and time dimensions and establishes a pollutant migration and transformation model. Kalman filter data fusion algorithm Kalman filtering assumes that the system state and the observed data satisfy the following linear Gaussian model: x k =F k x k-1 +B k u k +w k z k =H k x k +v k Where: x k F is the system state vector at time k; k B is the state transition matrix at time k; k It is the control input matrix at time k; u k It is the control input vector at time k; w k Let Q be the process noise vector at time k, assumed to be Gaussian white noise with zero mean, and its covariance matrix be Q. k ;z k H is the observation vector at time k; k It is the observation matrix at time k; v k Let R be the observation noise vector at time k, assumed to be Gaussian white noise with zero mean, and its covariance matrix be R. k ; Kalman filter algorithm steps State prediction: Covariance prediction: Kalman gain calculation: Status Update: Covariance update: P k∣k =(I-K k H k )P k∣k-1 in: It is the state estimation vector predicted at time k based on information from time k-1; It is the state estimation vector updated based on the current observation data at time k; P k∣k-1 P is the covariance matrix of the predicted state at time k; k∣k It is the covariance matrix of the state after the update at time k; K k I is the Kalman gain matrix at time k; I is the identity matrix.

6. The method for monitoring and assessing non-point source pollution in farmland according to claim 5, characterized in that: In S5, the analytic hierarchy process (AHP) combined with fuzzy comprehensive evaluation is used to comprehensively consider the degree of harm and pollution contribution rate of each monitoring indicator to the farmland ecosystem, agricultural product quality and safety, and surrounding water environment. The weights of different indicators in the evaluation model are determined, and the weights are periodically re-evaluated and adjusted. Based on the determined weights, a multi-level comprehensive evaluation model for farmland non-point source pollution is constructed. The evaluation model can dynamically output evaluation results. With the real-time update of monitoring data, it can promptly reflect the changing trend of farmland non-point source pollution and generate corresponding early warning information, providing a scientific basis for agricultural management departments to formulate precise pollution prevention and control strategies.

7. The method for monitoring and assessing non-point source pollution in farmland according to claim 6, characterized in that: In S6 and S7, based on the comprehensive assessment results, personalized non-point source pollution control measures are formulated for farmland areas with different pollution levels and types. A dynamic monitoring system for the control effect is established. After the implementation of control measures, the changes in farmland non-point source pollution indicators are continuously monitored according to a certain monitoring frequency and monitoring point layout. The monitoring data before and after the implementation of control measures are compared to evaluate the actual effect of the control measures. All data in the farmland non-point source pollution monitoring process, including the collected raw data, data processing results, and the construction process and results of the assessment model, are encrypted, stored, and uploaded to the chain using blockchain technology. The distributed ledger characteristics of blockchain ensure the authenticity and integrity of data during transmission, storage, and sharing, preventing data from being tampered with or forged. At the same time, the traceability function of blockchain is used to track the source, collection time, and collection location information of the data. Based on blockchain smart contract technology, the pre-set pollution control strategies and measures are encoded and deployed in the form of smart contracts. When farmland non-point source pollution indicators reach or exceed the warning threshold, the smart contract can automatically trigger and execute the corresponding control measures.

8. A farmland non-point source pollution monitoring and assessment system, used to implement the farmland non-point source pollution monitoring and assessment method as described in any one of claims 1 to 7, characterized in that, include: The monitoring module includes a water quality sensor unit and a soil sensor unit. The water quality sensor unit includes sensors for monitoring conventional pollutants such as ammonia nitrogen, nitrate nitrogen, total phosphorus, chemical oxygen demand, total nitrogen, total phosphorus, and suspended solids, as well as on-site rapid detection sensors for heavy metals and pesticide residues. These sensors are distributed at various sampling points in the farmland and transmit monitoring data to the data processing center in real time via wireless communication technology. The soil sensor unit is used to monitor soil moisture, temperature, pH, heavy metal content, and pesticide residue parameters. It assists in analyzing the impact of non-point source pollution on the farmland soil environment and is combined with soil and water loss monitoring. The soil sensor unit is arranged in layers and installed in the soil at different depths to obtain detailed information about the soil profile. The sampling module includes fixed sampling units and mobile sampling units. The fixed sampling units are installed at fixed locations along key runoff paths and have the function of timed and quantitative sampling. They automatically collect farmland runoff samples according to a preset time sequence sampling strategy and transport them to the on-site sample storage device through pipelines. They are equipped with an automatic water quality monitoring pretreatment system to perform preliminary filtration and constant temperature treatment on the collected water samples to ensure that the water sample quality meets the monitoring requirements. The mobile sampling units are equipped with flexible moving tracks or wheeled drive devices, which can move flexibly in the farmland according to preset routes or according to remote commands. They are equipped with a variety of sampling tools to achieve dynamic sampling at different locations in the farmland. The mobile sampling vehicle also has real-time data transmission and positioning functions, which can transmit sampling location and sampling time information back to the data processing center in real time, so that monitoring personnel can keep track of the sampling progress and sampling status. The data processing module is used to receive and store large amounts of monitoring data from the monitoring module and the sampling module. The data storage adopts a redundant backup mechanism to ensure the security and reliability of the data. The data transmission module is used to transmit the processed data; The terminal module is used to view monitoring data, assessment results, pollution distribution maps and early warning information of farmland non-point source pollution in real time, and has data query, analysis chart generation and report export functions. The early warning module sets early warning thresholds based on comprehensive assessment results. When the non-point source pollution index in farmland exceeds the corresponding threshold, it automatically sends early warning information to relevant personnel to remind them to take appropriate measures. The data upload module is used to encrypt and store monitoring data and upload it to the blockchain using blockchain technology. The data traceability module is used to track the source, collection time, and collection location of data, providing a reliable traceability path for pollution monitoring data. The driver-controlled prevention and control module is used to automatically trigger and execute corresponding prevention and control measures.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the farmland non-point source pollution monitoring and assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the farmland non-point source pollution monitoring and assessment method according to any one of claims 1 to 7.

Images