Method, device and equipment for evaluating agricultural non-point source pollution output and storage medium

Abstract

The invention discloses an agricultural non-point source pollution output assessment method, device and equipment and a storage medium, relates to the technical field of pollution monitoring, and provides a non-point source pollution output assessment method aiming at the defects of complicated agricultural land condition, high data collection difficulty, tedious pollution load calculation and the like at present. The non-point source pollution output of the agricultural land is obtained through calculation of rainfall data, a calculation region area, agricultural land farming data, an overflow pollutant concentration detection value and the like, and meanwhile the linear regression relation between the farming area and the farming consumable consumption is analyzed through linear regression, so that the prediction function of the non-point source pollution output is achieved. Meanwhile, the output exceeding the effective load is calculated by obtaining the rainfall effective load of the agricultural land, and the exceeding amount is defined as the output of the agricultural non-point source pollution, so that the output of the agricultural non-point source pollution is quantified, and the output of the agricultural non-point source pollution can be obtained through the planned cultivation area and the planned material.

Description

Technical Field

[0001] The present application relates to the field of pollution monitoring technology, and in particular to a method, device, equipment and storage medium for evaluating the amount of agricultural non-point source pollution. Background Art

[0002] Agricultural non-point source pollution refers to the environmental pollution caused by nutrients such as nitrogen and phosphorus, pesticides, and other organic or inorganic pollutants in agricultural production activities, which are formed through surface runoff and farmland leakage during precipitation or irrigation. It mainly includes fertilizer pollution, pesticide pollution, livestock and poultry manure pollution, etc. The migration process of non-point source pollutants requires water as a carrier, so agricultural non-point source pollution is greatly affected by rainfall. At present, agricultural non-point source pollution is one of the important causes of eutrophication of water bodies. Compared with agricultural point source pollution, agricultural non-point source pollution has the characteristics of instability in location, path and quantity, greater randomness, wider distribution range, and greater difficulty in governance.

[0003] In actual production, agricultural producers often blindly increase the amount of fertilizer and pesticide spraying in pursuit of high output and high returns, among which the amount of nitrogen application is usually several times higher than that of conventional field crops. This makes it easy for the above-mentioned consumables to flow into rivers and other functional pipe networks (municipal pipe networks, etc.) after precipitation, causing water pollution. Although most farmland has been transformed by drainage diversion of rainwater and sewage, some consumables that have infiltrated the soil will still cause a certain degree of non-point source pollution. Therefore, it is necessary to conduct non-point source pollution assessment on the above-mentioned farmland to provide a quantitative data basis for subsequent restrictions on agricultural materials and pollution control. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, equipment and storage medium for evaluating the amount of agricultural non-point source pollution, so as to solve the problem in the prior art that there is no quantitative data basis for evaluating farmland non-point source pollution.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] A method for assessing the amount of agricultural non-point source pollution, wherein the amount of agricultural non-point source pollution is based on agricultural land in a preset area, and the method comprises:

[0007] Step S1, obtaining a plurality of historical agricultural area data of the agricultural land based on a preset time period;

[0008] Step S2, respectively obtaining the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical agricultural area data;

[0009] Step S3, calculating the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting;

[0010] Step S4, obtaining the future pollutant concentration of the agricultural land based on a preset number of steps in the future according to the linear regression relationship;

[0011] Step S5, acquiring a plurality of rainfall data of the preset area based on the preset time period;

[0012] Step S6, based on all rainfall data, using preset software to simulate precipitation data for the preset area to obtain the effective load of the preset area;

[0013] Step S7, assigning a pollution emission coefficient to the effective load according to a preset strategy;

[0014] Step S8, obtaining the comprehensive runoff coefficient of the preset area;

[0015] Step S9, based on the future pollutant concentration of agricultural land, the pollution emission coefficient, and the comprehensive runoff coefficient, a preset algorithm is used to calculate the amount of agricultural non-point source pollution generated based on the preset number of steps in the future.

[0016] As a further improvement of the present application, step S3, by multivariate linear fitting, calculates the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application, including:

[0017] Step S31, defining the historical agricultural land pollutant concentrations in the same preset time period as a dependent variable;

[0018] Step S32, defining the historical agricultural area data of the same preset time period as the first independent variable;

[0019] Step S33, defining the historical agricultural land water consumption in the same preset time period as a second independent variable;

[0020] Step S34, defining the historical agricultural land pesticide usage in the same preset time period as a third independent variable;

[0021] Step S35, defining the historical agricultural land fertilizer application amount in the same preset time period as a fourth independent variable;

[0022] Step S36, defining the linear regression relationship between the dependent variable and the first independent variable, the second independent variable, the third independent variable, and the fourth independent variable according to formula (1):

[0023]

[0024] Among them, y i is the dependent variable of the i-th preset time period, n is the number of all preset time periods, β0 is the intercept of the linear regression relationship, β1 is the linear regression coefficient of the first independent variable, β2 is the linear regression coefficient of the second independent variable, β3 is the linear regression coefficient of the third independent variable, β4 is the linear regression coefficient of the fourth independent variable, x 1,i is the first independent variable of the i-th preset time period, x 2,i is the second independent variable of the i-th preset time period, x 3,i is the third independent variable of the i-th preset time period, x 4,i is the fourth independent variable of the i-th preset time period, and e is the random error of the linear regression relationship.

[0025] As a further improvement of the present application, step S4, obtaining the future agricultural land pollutant concentration of the agricultural land based on a preset number of future steps according to the linear regression relationship, includes:

[0026] Step S41, calculate the values ​​of β1, β2, β3, and β4 by formula (2):

[0027]

[0028] in, β j The estimated value of , j = 1, 2, 3, 4, X is the matrix of all independent variables, X T is the transposed matrix of matrix X;

[0029] Step S42, substituting the solved β1, β2, β3, and β4 into the multivariate linear regression model to obtain a prediction model;

[0030] Step S43, substituting the future cultivated area data of the same future preset number of steps into the prediction model as the first independent variable;

[0031] Step S44, substituting the future agricultural land water consumption at the same future preset number of steps into the prediction model as a second independent variable;

[0032] Step S45, substituting the future agricultural land pesticide application amount of the same future preset number of steps into the prediction model as a third independent variable;

[0033] Step S46, substituting the future agricultural land fertilizer application amount of the same future preset number of steps into the prediction model as a fourth independent variable;

[0034] Step S47, solving the prediction model to obtain the future agricultural land pollutant concentration at the same future preset number of steps.

[0035] As a further improvement of the present application, step S6, based on all rainfall data, a precipitation data simulation is performed on the preset area by a preset software to obtain the effective load of the preset area, including:

[0036] Step S61, constructing a finite element model of the preset area based on the point cloud data of the preset area;

[0037] Step S62, assigning a preset unit precipitation amount to the finite element model, and obtaining the total precipitation amount when the finite element model starts to drain water;

[0038] Step S63, obtaining the remainder of the total precipitation at preset intervals;

[0039] Step S64, determining whether the remaining amount is less than or equal to a preset threshold, if the remaining amount is less than or equal to the preset threshold, executing step S65;

[0040] Step S65, obtaining a difference between the total amount of precipitation and the surplus, and defining the difference as the effective load.

[0041] As a further improvement of the present application, step S7, assigning a pollution emission coefficient to the effective load according to a preset strategy, includes:

[0042] Step S71, obtaining the drainage system assembly of the finite element model;

[0043] Step S72, obtaining the drainage network layout of the drainage system assembly;

[0044] Step S73, obtaining the confluence length and diversion length of the drainage network layout;

[0045] Step S74: defining the ratio of the diverging length to the merging length as the pollution emission coefficient.

[0046] As a further improvement of the present application, in step S74, the ratio of the diverging length to the converging length is defined as the pollution emission coefficient, including:

[0047] Step S741, when the ratio value is 0, assigning a first pollution emission coefficient;

[0048] Step S742, when the ratio value is between (0%, 20%], assigning a second pollution emission coefficient;

[0049] Step S743: when the ratio value is between (20%, 100%], assign a third pollution emission coefficient.

[0050] As a further improvement of the present application, step S9, based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient, calculates the amount of agricultural non-point source pollution based on the future preset number of steps through a preset algorithm, including:

[0051] The amount of agricultural non-point source pollution is calculated according to formula (3):

[0052] fh=H·ΔF·a·q·c·10 -9 (3);

[0053] Among them, fh is the amount of agricultural non-point source pollution generated, H is the average annual precipitation in the preset area, ΔF is the difference, a is the comprehensive runoff coefficient, q is the pollution emission coefficient, and c is the pollutant concentration.

[0054] In order to achieve the above objectives, this application also provides the following technical solutions:

[0055] An assessment device for the amount of agricultural non-point source pollution, the assessment device is applied to the above-mentioned assessment method, and the assessment device comprises:

[0056] A historical agricultural area data acquisition module is used to acquire a number of historical agricultural area data of the agricultural land based on a preset time period;

[0057] The farming consumables data acquisition module is used to respectively obtain the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical farming area data;

[0058] A linear regression module for agricultural consumables data is used to calculate the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting;

[0059] A future agricultural land pollutant concentration acquisition module is used to acquire the future agricultural land pollutant concentration of the agricultural land based on a preset number of steps in the future according to the linear regression relationship;

[0060] A rainfall data acquisition module, used for acquiring a plurality of rainfall data of the preset area based on the preset time period;

[0061] A payload acquisition module, used to simulate precipitation data for the preset area through preset software based on all rainfall data to obtain the payload of the preset area;

[0062] A pollution emission coefficient assigning module, used for assigning a pollution emission coefficient to the effective load according to a preset strategy;

[0063] A comprehensive runoff coefficient acquisition module, used to acquire the comprehensive runoff coefficient of the preset area;

[0064] The agricultural non-point source pollution generation amount calculation module is used to calculate the agricultural non-point source pollution generation amount based on the future preset number of steps through a preset algorithm based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient.

[0065] In order to achieve the above objectives, this application also provides the following technical solutions:

[0066] An electronic device comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the above-mentioned evaluation method is implemented.

[0067] In order to achieve the above objectives, this application also provides the following technical solutions:

[0068] A storage medium stores program instructions, which can implement the above-mentioned evaluation method when executed by a processor.

[0069] The present application obtains several historical agricultural land area data based on a preset time period for agricultural land; obtains the historical agricultural land water consumption, historical agricultural land drug consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical agricultural land data; calculates the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural land area data, historical agricultural land water consumption, historical agricultural land drug consumption, and historical agricultural land fertilizer consumption through multivariate linear fitting; obtains the future agricultural land pollutant concentration based on a preset number of steps in the future according to the linear regression relationship; obtains several rainfall data for a preset area based on a preset time period; simulates the precipitation data for the preset area through a preset software based on all rainfall data to obtain the effective load of the preset area; assigns a pollution emission coefficient to the effective load according to a preset strategy; obtains the comprehensive runoff coefficient of the preset area; calculates the amount of agricultural non-point source pollution based on a preset number of steps in the future through a preset algorithm based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient. In view of the current drawbacks of complex agricultural land conditions, difficulty in data collection, and cumbersome calculation of pollution loads, this application proposes an assessment method for the amount of non-point source pollution generated. The amount of non-point source pollution generated in agricultural land is calculated by precipitation data, calculation area, agricultural land farming data, overflow pollutant concentration detection values, etc. At the same time, this application also realizes the prediction function of the amount of non-point source pollution generated by linear regression analysis of the linear regression relationship between the cultivated area and the amount of agricultural consumables used. At the same time, by obtaining the effective load of precipitation on agricultural land, the amount exceeding the effective load is calculated, and the excess amount is defined as the amount of agricultural non-point source pollution generated. Therefore, the amount of agricultural non-point source pollution generated in this application is not only quantified, but also the predicted amount of agricultural non-point source pollution can be obtained through the planned cultivated area and planned materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of the process steps of an embodiment of the method for evaluating the amount of agricultural non-point source pollution generated by the present application;

[0071] Figure 2 This is a functional module diagram of an embodiment of a device for evaluating the amount of agricultural non-point source pollution generated by the present application;

[0072] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0073] Figure 4 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0076] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0077] like Figure 1 As shown, this embodiment provides an embodiment of a method for evaluating the amount of agricultural non-point source pollution. In this embodiment, the amount of agricultural non-point source pollution is based on agricultural land in a preset area.

[0078] Preferably, this embodiment is more suitable for modern farmland that has been transformed to separate rainwater and sewage, and has more quantitative significance than traditional farmland.

[0079] Specifically, the evaluation method includes the following steps:

[0080] Step S1, obtaining a number of historical agricultural area data of agricultural land based on a preset time period.

[0081] Preferably, the preset time period can be set to a natural time unit such as a natural day, a natural week, a natural month, etc.

[0082] Step S2, respectively obtain the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical cultivated area data.

[0083] Preferably, the amount of consumables used in step S2 can be obtained by direct statistics.

[0084] Step S3, calculating the linear regression relationship between the historical agricultural land pollutant concentration and the historical cultivated area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting.

[0085] Preferably, the multivariate linear fitting of this embodiment can adopt LASSO linear regression.

[0086] Step S4, obtaining the future pollutant concentration of the agricultural land based on a preset number of steps in the future according to the linear regression relationship.

[0087] Preferably, in order to prevent the multivariate linear regression model from overfitting and causing the model's objective function to be inaccurate, the following method can be used to avoid this:

[0088] ① Increase the number of sample points in the training set.

[0089] ②Reduce the complexity of the model, that is, the model order.

[0090] ③Regularization.

[0091] ④ Shorten the number of training rounds or lower the accuracy limit, and stop training the model before the model overfits.

[0092] ⑤Data enhancement and noise addition.

[0093] ⑥Deep learning: Dropout and Dropconnect.

[0094] Step S5, obtaining a plurality of rainfall data of a preset area based on a preset time period.

[0095] Preferably, rainfall data can be directly obtained through the local meteorological bureau, which is easy to obtain.

[0096] Step S6, based on all rainfall data, a precipitation data simulation is performed on a preset area through a preset software to obtain a valid load of the preset area.

[0097] Preferably, the preset software can be set to one of StormDesk, InfoWorks I CM, SWMM, XP-SWMM, and XP2D.

[0098] Among them, StormDesk is a drainage system model analysis software that focuses on the research and solutions to urban waterlogging and black and smelly water problems, and provides functions such as urban waterlogging simulation, rainwater and sewage mixing analysis, and rapid pipe network design.

[0099] I nfoWorks I CM is a comprehensive watershed drainage model analysis software from the UK. It integrates the drainage network system with the river model, can simulate the rainwater circulation system, and is suitable for the current status assessment, renovation planning, and design and planning of new drainage systems. The software combines a one-dimensional hydraulic model and a two-dimensional flood inundation model in an independent simulation engine.

[0100] SWMM is a dynamic precipitation-runoff simulation model analysis software, which is mainly used to simulate a single precipitation event or long-term water quantity and quality. The software can handle complex precipitation-runoff problems and is suitable for various types of drainage systems, including combined and separate systems.

[0101] XP-SWMM is a dynamic simulation software for stormwater, sewage and river systems. It is suitable for various complex precipitation events and water quality simulations. The software provides detailed simulation and analysis capabilities and is suitable for the design and optimization of drainage systems.

[0102] XP2D is an integrated 1D and 2D dynamic hydraulic simulation software that can analyze inundation conditions more accurately. The software is suitable for a variety of complex flood simulation scenarios and provides detailed simulation and analysis capabilities.

[0103] Preferably, the above software can perform precipitation simulation and obtain the effective load of the preset area. It is only necessary to input the finite element model below into the software and then set the precipitation amount.

[0104] Step S7: assigning a pollution emission coefficient to the effective load according to a preset strategy.

[0105] Step S8, obtaining the comprehensive runoff coefficient of the preset area.

[0106] Preferably, under the same rainfall conditions, the runoff volume of farmland increases with the increase of rainfall intensity. Specifically, under the three rainfall intensities of 10mm / h, 15mm / h, and 25mm / h, the total runoff volume generated is 197.07m 3 / hm 2 、381.92m 3 / hm 2 、649.45m 3 / hm 2 , and the corresponding runoff coefficients are 0.20, 0.38, and 0.65 respectively.

[0107] Preferably, the runoff coefficient of the farmland can be directly obtained by querying existing technologies, such as the Journal of Agricultural Resources and Environment.

[0108] Step S9, based on the future pollutant concentration of agricultural land, the pollution emission coefficient, and the comprehensive runoff coefficient, a preset algorithm is used to calculate the amount of agricultural non-point source pollution generated based on a preset number of steps in the future.

[0109] Furthermore, step S3 calculates the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting, including:

[0110] Step S31, defining the historical agricultural land pollutant concentrations in the same preset time period as the dependent variable.

[0111] Step S32, defining the historical cultivated area data of the same preset time period as the first independent variable.

[0112] Step S33, defining the historical agricultural land water consumption in the same preset time period as the second independent variable.

[0113] Step S34, defining the historical agricultural land pesticide usage in the same preset time period as the third independent variable.

[0114] Step S35, defining the historical agricultural land fertilizer application amount in the same preset time period as the fourth independent variable.

[0115] Step S36, defining the linear regression relationship between the dependent variable and the first independent variable, the second independent variable, the third independent variable, and the fourth independent variable according to formula (1):

[0116]

[0117] Among them, y i is the dependent variable of the i-th preset time period, n is the number of all preset time periods, β0 is the intercept of the linear regression relationship, β1 is the linear regression coefficient of the first independent variable, β2 is the linear regression coefficient of the second independent variable, β3 is the linear regression coefficient of the third independent variable, β4 is the linear regression coefficient of the fourth independent variable, x 1,i is the first independent variable of the i-th preset time period, x 2,i is the second independent variable of the i-th preset time period, x 3,i is the third independent variable of the i-th preset time period, x 4,i is the fourth independent variable of the i-th preset time period, and e is the random error of the linear regression relationship.

[0118] Further, step S4, according to the linear regression relationship, the future agricultural land pollutant concentration calculation based on the future preset number of steps is obtained.

[0119] Step S41, calculate the values ​​of β1, β2, β3, and β4 by formula (2):

[0120]

[0121] in, β j The estimated value of , j = 1, 2, 3, 4, X is the matrix of all independent variables, X T is the transposed matrix of matrix X.

[0122] Step S42, substituting the solved β1, β2, β3, and β4 into the multivariate linear regression model to obtain a prediction model.

[0123] Step S43, substituting the future cultivated area data of the same future preset number of steps into the prediction model as the first independent variable.

[0124] Step S44, substituting the future agricultural land water consumption at the same future preset number of steps into the prediction model as the second independent variable.

[0125] Step S45, substituting the future agricultural land pesticide application amount at the same future preset number of steps into the prediction model as the third independent variable.

[0126] Step S46, substituting the future agricultural land fertilizer application amount at the same future preset number of steps into the prediction model as the fourth independent variable.

[0127] Step S47, solving the prediction model to obtain the future agricultural land pollutant concentration at the same future preset number of steps.

[0128] Preferably, the square of the residual e 2 Test the significance of the prediction model. If e 2 >8, it means the prediction model is more significant; if 6 <e 2 ≤8, it means that the significance of the prediction model is average; if e 2 If ≤6, it means that the significance of the prediction model is poor, and the significance of the prediction model is linearly positively correlated with the square of the residual.

[0129] Further, step S6, based on all rainfall data, a precipitation data simulation is performed on a preset area through a preset software to obtain a valid load of the preset area, including:

[0130] Step S61: constructing a finite element model of the preset area based on the point cloud data of the preset area.

[0131] Preferably, the digital model can be directly acquired through one or more combinations of digital model acquisition strategies such as remote sensing interpretation, on-site mapping and measurement, drone photogrammetry, and three-dimensional laser scanning.

[0132] Step S62, assigning a preset unit precipitation amount to the finite element model, and obtaining the total precipitation amount when the finite element model starts to drain water.

[0133] Step S63, obtaining the remainder of the total precipitation at preset time intervals.

[0134] Step S64, determining whether the remaining amount is less than or equal to a preset threshold, if the remaining amount is less than or equal to the preset threshold, executing step S65.

[0135] Step S65, obtaining the difference between the total precipitation amount and the surplus, and defining the difference as the effective load.

[0136] Furthermore, in step S7, a pollution emission coefficient is assigned to the effective load according to a preset strategy, including:

[0137] Step S71, obtaining the drainage system assembly of the finite element model.

[0138] Step S72, obtaining the drainage network layout of the drainage system assembly.

[0139] Step S73, obtaining the confluence length and diversion length of the drainage network layout.

[0140] Step S74, defining the ratio of the diverging length to the merging length as the pollution emission coefficient.

[0141] Preferably, the pollution emission coefficient can be divided into 3 to 5 levels according to the rainwater and sewage diversion conditions in the preset area, and the pollution emission coefficient is assigned based on each level. Please refer to Table 1 below (Pollution Emission Coefficient Comparison Table). The higher the coefficient, the greater the comparison before and after the transformation, and the greater the benefit of reducing the new pollution load generated after the transformation.

[0142] The ratio of the diverging length to the converging length Pollution emission coefficient 0 (with fully separated sewer and sewage pipes) 0.19 (0％,20％] 0.32 (20％,100％] 1

[0143] Table 1: Pollution emission coefficient comparison table

[0144] Further, in step S74, the ratio of the diverging length to the converging length is defined as the pollution emission coefficient, including:

[0145] Step S741: when the proportional value is 0, a first pollution emission coefficient of 0.19 is assigned.

[0146] Step S742: when the ratio value is between (0%, 20%], a second pollution emission coefficient of 0.32 is assigned.

[0147] Step S743: when the ratio value is between (20%, 100%], assign the third pollution emission coefficient 1.

[0148] Further, step S9, based on the future agricultural land pollutant concentration, pollution emission coefficient, and comprehensive runoff coefficient, a preset algorithm is used to calculate the amount of agricultural non-point source pollution based on a preset number of steps in the future, including:

[0149] The amount of agricultural non-point source pollution is calculated according to formula (3):

[0150] fh=H·ΔF·a·q·c·10 -9 (3).

[0151] Among them, fh is the amount of agricultural non-point source pollution, H is the average annual precipitation in the preset area, ΔF is the difference, a is the comprehensive runoff coefficient, q is the pollution emission coefficient, and c is the pollutant concentration.

[0152] This embodiment obtains several historical cultivated area data of agricultural land based on a preset time period; obtains the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilization, and historical agricultural land pollutant concentration corresponding to each historical agricultural land data; calculates the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer consumption through multivariate linear fitting; obtains the future agricultural land pollutant concentration based on a preset number of steps in the future according to the linear regression relationship; obtains several rainfall data of a preset area based on a preset time period; simulates the precipitation data of the preset area through a preset software based on all rainfall data to obtain the effective load of the preset area; assigns a pollution emission coefficient to the effective load according to a preset strategy; obtains the comprehensive runoff coefficient of the preset area; calculates the amount of agricultural non-point source pollution based on a preset number of steps in the future through a preset algorithm based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient. In view of the current drawbacks of complex agricultural land conditions, difficulty in data collection, and cumbersome calculation of pollution loads, this embodiment proposes an assessment method for the amount of non-point source pollution generated. The amount of non-point source pollution generated in agricultural land is calculated by precipitation data, calculation area, agricultural land farming data, overflow pollutant concentration detection values, etc. At the same time, this embodiment also realizes the prediction function of the amount of non-point source pollution generated by linear regression analysis of the linear regression relationship between the farming area and the amount of farming consumables used. At the same time, by obtaining the precipitation effective load of agricultural land, the amount exceeding the effective load is calculated, and the excess amount is defined as the amount of agricultural non-point source pollution generated. Therefore, the amount of agricultural non-point source pollution generated in this embodiment is not only quantified, but also the predicted amount of agricultural non-point source pollution can be obtained through the planned cultivated area and planned materials.

[0153] like Figure 2 As shown, this embodiment provides an embodiment of a device for evaluating the amount of agricultural non-point source pollution. In this embodiment, the evaluation device is applied to the evaluation method in the above embodiment.

[0154] Specifically, the evaluation device includes a historical farming area data acquisition module 1, a farming consumables data acquisition module 2, a farming consumables data linear regression module 3, a future agricultural land pollutant concentration acquisition module 4, a rainfall data acquisition module 5, a valid load acquisition module 6, a pollution emission coefficient assignment module 7, a comprehensive runoff coefficient acquisition module 8, and an agricultural non-point source pollution generation amount calculation module 9, which are electrically connected in sequence.

[0155] Among them, the historical agricultural area data acquisition module 1 is used to obtain several historical agricultural area data of agricultural land based on a preset time period; the agricultural consumables data acquisition module 2 is used to respectively obtain the historical agricultural land water consumption, historical agricultural land drug consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical agricultural area data; the agricultural consumables data linear regression module 3 is used to calculate the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, historical agricultural land water consumption, historical agricultural land drug consumption, and historical agricultural land fertilizer consumption through multivariate linear fitting; the future agricultural land pollutant concentration acquisition module 4 is used to obtain the agricultural land based on the future according to the linear regression relationship. to obtain the future pollutant concentration of agricultural land with a preset number of steps; the rainfall data acquisition module 5 is used to obtain a number of rainfall data of a preset area based on a preset time period; the effective load acquisition module 6 is used to simulate the precipitation data of the preset area through a preset software based on all rainfall data, and obtain the effective load of the preset area; the pollution emission coefficient assignment module 7 is used to assign the pollution emission coefficient to the effective load according to the preset strategy; the comprehensive runoff coefficient acquisition module 8 is used to obtain the comprehensive runoff coefficient of the preset area; the agricultural non-point source pollution generation calculation module 9 is used to calculate the agricultural non-point source pollution generation based on the future preset number of steps based on the future agricultural land pollutant concentration, the pollution emission coefficient and the comprehensive runoff coefficient through a preset algorithm.

[0156] Furthermore, the agricultural consumables data linear regression module 3 specifically includes a first agricultural consumables data linear regression sub-module, a second agricultural consumables data linear regression sub-module, a third agricultural consumables data linear regression sub-module, a fourth agricultural consumables data linear regression sub-module, a fifth agricultural consumables data linear regression sub-module, and a sixth agricultural consumables data linear regression sub-module, which are electrically connected in sequence; the first agricultural consumables data linear regression sub-module is electrically connected to the agricultural consumables data acquisition module 2, and the sixth agricultural consumables data linear regression sub-module is electrically connected to the future agricultural land pollutant concentration acquisition module 4.

[0157] Among them, the first farming consumables data linear regression submodule is used to define the historical agricultural land pollutant concentration in the same preset time period as the dependent variable; the second farming consumables data linear regression submodule is used to define the historical agricultural area data in the same preset time period as the first independent variable; the third farming consumables data linear regression submodule is used to define the historical agricultural land water consumption in the same preset time period as the second independent variable; the fourth farming consumables data linear regression submodule is used to define the historical agricultural land pesticide consumption in the same preset time period as the third independent variable; the fifth farming consumables data linear regression submodule is used to define the historical agricultural land fertilizer application in the same preset time period as the fourth independent variable; the sixth farming consumables data linear regression submodule is used to define the linear regression relationship between the dependent variable and the first independent variable, the second independent variable, the third independent variable, and the fourth independent variable according to formula (1):

[0158]

[0159] Among them, y i is the dependent variable of the i-th preset time period, n is the number of all preset time periods, β0 is the intercept of the linear regression relationship, β1 is the linear regression coefficient of the first independent variable, β2 is the linear regression coefficient of the second independent variable, β3 is the linear regression coefficient of the third independent variable, β4 is the linear regression coefficient of the fourth independent variable, x 1,i is the first independent variable of the i-th preset time period, x 2,i is the second independent variable of the i-th preset time period, x 3,i is the third independent variable of the i-th preset time period, x 4,i is the fourth independent variable of the i-th preset time period, and e is the random error of the linear regression relationship.

[0160] Furthermore, the future agricultural land pollutant concentration acquisition module 4 includes a first future agricultural land pollutant concentration acquisition submodule, a second future agricultural land pollutant concentration acquisition submodule, a third future agricultural land pollutant concentration acquisition submodule, a fourth future agricultural land pollutant concentration acquisition submodule, a fifth future agricultural land pollutant concentration acquisition submodule, a sixth future agricultural land pollutant concentration acquisition submodule, and a seventh future agricultural land pollutant concentration acquisition submodule, which are electrically connected in sequence; the first future agricultural land pollutant concentration acquisition submodule is electrically connected to the sixth farming consumables data linear regression submodule, and the seventh future agricultural land pollutant concentration acquisition submodule is electrically connected to the rainfall data acquisition module 5.

[0161] Among them, the first future agricultural land pollutant concentration acquisition submodule is used to calculate the values ​​of β1, β2, β3, and β4 through formula (2):

[0162]

[0163] in, β j The estimated value of , j = 1, 2, 3, 4, X is the matrix of all independent variables, X T is the transposed matrix of matrix X.

[0164] The second submodule for obtaining the pollutant concentration of future agricultural land is used to substitute the solved β1, β2, β3, and β4 into the multivariate linear regression model to obtain a prediction model.

[0165] The third future agricultural land pollutant concentration acquisition submodule is used to substitute the future agricultural area data of the same future preset step number as the first independent variable into the prediction model.

[0166] The fourth future agricultural land pollutant concentration acquisition submodule is used to substitute the future agricultural land water consumption of the same future preset step number into the prediction model as the second independent variable.

[0167] The fifth future agricultural land pollutant concentration acquisition submodule is used to substitute the future agricultural land pesticide dosage of the same future preset step number as the third independent variable into the prediction model.

[0168] The sixth future agricultural land pollutant concentration acquisition submodule is used to substitute the future agricultural land fertilizer application amount of the same future preset step number as the fourth independent variable into the prediction model.

[0169] The seventh submodule for obtaining the pollutant concentration of future agricultural land is used to solve the prediction model to obtain the pollutant concentration of future agricultural land at the same future preset number of steps.

[0170] Furthermore, the payload acquisition module 6 specifically includes a first payload acquisition submodule, a second payload acquisition submodule, a third payload acquisition submodule, a fourth payload acquisition submodule, and a fifth payload acquisition submodule, which are electrically connected in sequence; the first payload acquisition submodule is electrically connected to the rainfall data acquisition module 5, and the fifth payload acquisition submodule is electrically connected to the pollution emission coefficient assignment module 7.

[0171] Among them, the first effective load acquisition submodule is used to construct a finite element model of a preset area based on the point cloud data of the preset area; the second effective load acquisition submodule is used to assign a preset unit precipitation to the finite element model and obtain the total precipitation when the finite element model starts to drain; the third effective load acquisition submodule is used to obtain the remainder of the total precipitation at a preset time interval; the fourth effective load acquisition submodule is used to determine whether the remainder is less than or equal to a preset threshold; the fifth effective load acquisition submodule is used to obtain the difference between the total precipitation and the remainder if the remainder is less than or equal to the preset threshold, and define the difference as the effective load.

[0172] Furthermore, the pollution emission coefficient assigning module 7 specifically includes a first pollution emission coefficient assigning submodule, a second pollution emission coefficient assigning submodule, a third pollution emission coefficient assigning submodule, and a fourth pollution emission coefficient assigning submodule, which are electrically connected in sequence; the first pollution emission coefficient assigning submodule is electrically connected to the fifth effective load acquisition submodule, and the fourth pollution emission coefficient assigning submodule is electrically connected to the comprehensive runoff coefficient acquisition module 8.

[0173] Among them, the first pollution emission coefficient assignment submodule is used to obtain the drainage system assembly of the finite element model; the second pollution emission coefficient assignment submodule is used to obtain the drainage network layout of the drainage system assembly; the third pollution emission coefficient assignment submodule is used to obtain the confluence length and diversion length of the drainage network layout; the fourth pollution emission coefficient assignment submodule is used to define the ratio of the diversion length to the confluence length as the pollution emission coefficient.

[0174] Furthermore, the fourth pollution emission coefficient assigning submodule specifically includes a first pollution emission coefficient assigning unit, a second pollution emission coefficient assigning unit, and a third pollution emission coefficient assigning unit which are electrically connected in sequence; the first pollution emission coefficient assigning unit is electrically connected to the third pollution emission coefficient assigning submodule, and the third pollution emission coefficient assigning unit is electrically connected to the comprehensive runoff coefficient acquisition module 8.

[0175] The first pollution emission coefficient assigning unit is used to assign the first pollution emission coefficient when the proportion value is 0; the second pollution emission coefficient assigning unit is used to assign the second pollution emission coefficient when the proportion value is between (0%, 20%]; the third pollution emission coefficient assigning unit is used to assign the third pollution emission coefficient when the proportion value is between (20%, 100%]).

[0176] Furthermore, the agricultural non-point source pollution generation amount calculation module 9 is specifically used to calculate the agricultural non-point source pollution generation amount according to formula (3):

[0177] fh=H·ΔF·a·q·c·10 -9 (3).

[0178] Among them, fh is the amount of agricultural non-point source pollution, H is the average annual precipitation in the preset area, ΔF is the difference, a is the comprehensive runoff coefficient, q is the pollution emission coefficient, and c is the pollutant concentration.

[0179] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. The optimization, expansion, limitation, example, and principle description of this embodiment can be referred to the above embodiment, and this embodiment will not be repeated.

[0180] This embodiment obtains several historical cultivated area data of agricultural land based on a preset time period; obtains the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilization, and historical agricultural land pollutant concentration corresponding to each historical agricultural land data; calculates the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer consumption through multivariate linear fitting; obtains the future agricultural land pollutant concentration based on a preset number of steps in the future according to the linear regression relationship; obtains several rainfall data of a preset area based on a preset time period; simulates the precipitation data of the preset area through a preset software based on all rainfall data to obtain the effective load of the preset area; assigns a pollution emission coefficient to the effective load according to a preset strategy; obtains the comprehensive runoff coefficient of the preset area; calculates the amount of agricultural non-point source pollution based on a preset number of steps in the future through a preset algorithm based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient. In view of the current drawbacks of complex agricultural land conditions, difficulty in data collection, and cumbersome calculation of pollution loads, this embodiment proposes an assessment method for the amount of non-point source pollution generated. The amount of non-point source pollution generated in agricultural land is calculated by precipitation data, calculation area, agricultural land farming data, overflow pollutant concentration detection values, etc. At the same time, this embodiment also realizes the prediction function of the amount of non-point source pollution generated by linear regression analysis of the linear regression relationship between the farming area and the amount of farming consumables used. At the same time, by obtaining the precipitation effective load of agricultural land, the amount exceeding the effective load is calculated, and the excess amount is defined as the amount of agricultural non-point source pollution generated. Therefore, the amount of agricultural non-point source pollution generated in this embodiment is not only quantified, but also the predicted amount of agricultural non-point source pollution can be obtained through the planned cultivated area and planned materials.

[0181] Figure 3 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 3 As shown, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101 .

[0182] The memory 102 stores program instructions for implementing a fault detection method for an oil-immersed transformer according to any one of the above embodiments.

[0183] The processor 101 is used to execute program instructions stored in the memory 102 to perform fault detection on the oil-immersed transformer.

[0184] The processor 101 may also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip having a signal processing capability. The processor 101 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0185] Further, Figure 4 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present application. Figure 4 The storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all the above methods, wherein the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes, or terminal devices such as computers, servers, mobile phones, tablets, etc.

[0186] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0187] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for evaluating the amount of agricultural non-point source pollution, wherein the amount of agricultural non-point source pollution is based on agricultural land in a preset area, characterized in that: The evaluation methods include: Step S1, obtaining a plurality of historical agricultural area data of the agricultural land based on a preset time period; Step S2, respectively obtaining the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical agricultural area data; Step S3, calculating the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting; Step S4, obtaining the future pollutant concentration of the agricultural land based on a preset number of steps in the future according to the linear regression relationship; Step S5, acquiring a plurality of rainfall data of the preset area based on the preset time period; Step S6, based on all rainfall data, using preset software to simulate precipitation data for the preset area to obtain the effective load of the preset area; Step S7, assigning a pollution emission coefficient to the effective load according to a preset strategy; Step S8, obtaining the comprehensive runoff coefficient of the preset area; Step S9, based on the future pollutant concentration of agricultural land, the pollution emission coefficient, and the comprehensive runoff coefficient, a preset algorithm is used to calculate the amount of agricultural non-point source pollution generated based on the preset number of steps in the future.

2. The evaluation method according to claim 1, characterized in that: Step S3, calculating the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting, including: Step S31, defining the historical agricultural land pollutant concentrations in the same preset time period as a dependent variable; Step S32, defining the historical agricultural area data of the same preset time period as the first independent variable; Step S33, defining the historical agricultural land water consumption in the same preset time period as a second independent variable; Step S34, defining the historical agricultural land pesticide usage in the same preset time period as a third independent variable; Step S35, defining the historical agricultural land fertilizer application amount in the same preset time period as a fourth independent variable; Step S36, defining the linear regression relationship between the dependent variable and the first independent variable, the second independent variable, the third independent variable, and the fourth independent variable according to formula (1): Among them, y i is the dependent variable of the i-th preset time period, n is the number of all preset time periods, β0 is the intercept of the linear regression relationship, β1 is the linear regression coefficient of the first independent variable, β2 is the linear regression coefficient of the second independent variable, β3 is the linear regression coefficient of the third independent variable, β4 is the linear regression coefficient of the fourth independent variable, x 1,i is the first independent variable of the i-th preset time period, x 2,i is the second independent variable of the i-th preset time period, x 3,i is the third independent variable of the i-th preset time period, x 4,i is the fourth independent variable of the i-th preset time period, and e is the random error of the linear regression relationship.

3. The evaluation method according to claim 2, characterized in that: Step S4, obtaining the future agricultural land pollutant concentration of the agricultural land based on a preset number of future steps according to the linear regression relationship, including: Step S41, calculate the values ​​of β1, β2, β3, and β4 by formula (2): in, β j The estimated value of , j = 1, 2, 3, 4, X is the matrix of all independent variables, X T is the transposed matrix of matrix X; Step S42, substituting the solved β1, β2, β3, and β4 into the multivariate linear regression model to obtain a prediction model; Step S43, substituting the future cultivated area data of the same future preset number of steps into the prediction model as the first independent variable; Step S44, substituting the future agricultural land water consumption at the same future preset number of steps into the prediction model as a second independent variable; Step S45, substituting the future agricultural land pesticide application amount of the same future preset number of steps into the prediction model as a third independent variable; Step S46, substituting the future agricultural land fertilizer application amount of the same future preset number of steps into the prediction model as a fourth independent variable; Step S47, solving the prediction model to obtain the future agricultural land pollutant concentration at the same future preset number of steps.

4. The evaluation method according to claim 1, characterized in that: Step S6, based on all rainfall data, using preset software to simulate precipitation data for the preset area to obtain the effective load of the preset area, including: Step S61, constructing a finite element model of the preset area based on the point cloud data of the preset area; Step S62, assigning a preset unit precipitation amount to the finite element model, and obtaining the total precipitation amount when the finite element model starts to drain water; Step S63, obtaining the remainder of the total precipitation at preset intervals; Step S64, determining whether the remaining amount is less than or equal to a preset threshold, if the remaining amount is less than or equal to the preset threshold, executing step S65; Step S65, obtaining a difference between the total amount of precipitation and the surplus, and defining the difference as the effective load.

5. The evaluation method according to claim 4, characterized in that: Step S7, assigning a pollution emission coefficient to the effective load according to a preset strategy, including: Step S71, obtaining the drainage system assembly of the finite element model; Step S72, obtaining the drainage network layout of the drainage system assembly; Step S73, obtaining the confluence length and diversion length of the drainage network layout; Step S74: defining the ratio of the diverging length to the merging length as the pollution emission coefficient.

6. The evaluation method according to claim 5, characterized in that: Step S74, defining the ratio of the diverging length to the converging length as the pollution emission coefficient, includes: Step S741, when the ratio value is 0, assigning a first pollution emission coefficient; Step S742, when the ratio value is between (0%, 20%], assigning a second pollution emission coefficient; Step S743: when the ratio value is between (20%, 100%], assign a third pollution emission coefficient.

7. The evaluation method according to claim 4, characterized in that: Step S9, based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient, a preset algorithm is used to calculate the amount of agricultural non-point source pollution generated based on the preset number of steps in the future, including: The amount of agricultural non-point source pollution is calculated according to formula (3): fh=H·ΔF·a·q·c·10 -9 (3); Among them, fh is the amount of agricultural non-point source pollution generated, H is the average annual precipitation in the preset area, ΔF is the difference, a is the comprehensive runoff coefficient, q is the pollution emission coefficient, and c is the pollutant concentration.

8. An evaluation device for the amount of agricultural non-point source pollution, the evaluation device being applied to the evaluation method according to any one of claims 1 to 7, characterized in that: The evaluation device comprises: A historical agricultural area data acquisition module is used to acquire a number of historical agricultural area data of the agricultural land based on a preset time period; The farming consumables data acquisition module is used to respectively obtain the historical agricultural land water consumption, historical agricultural land pesticide consumption, historical agricultural land fertilizer consumption, and historical agricultural land pollutant concentration corresponding to each historical farming area data; A linear regression module for agricultural consumables data is used to calculate the linear regression relationship between the historical agricultural land pollutant concentration and the historical agricultural area data, the historical agricultural land water consumption, the historical agricultural land pesticide consumption, and the historical agricultural land fertilizer application through multivariate linear fitting; A future agricultural land pollutant concentration acquisition module is used to acquire the future agricultural land pollutant concentration of the agricultural land based on a preset number of steps in the future according to the linear regression relationship; A rainfall data acquisition module, used for acquiring a plurality of rainfall data of the preset area based on the preset time period; A payload acquisition module, used to simulate precipitation data for the preset area through preset software based on all rainfall data to obtain the payload of the preset area; A pollution emission coefficient assigning module, used for assigning a pollution emission coefficient to the effective load according to a preset strategy; A comprehensive runoff coefficient acquisition module, used to acquire the comprehensive runoff coefficient of the preset area; The agricultural non-point source pollution generation amount calculation module is used to calculate the agricultural non-point source pollution generation amount based on the future preset number of steps through a preset algorithm based on the future agricultural land pollutant concentration, the pollution emission coefficient, and the comprehensive runoff coefficient.

9. An electronic device, characterized in that: It comprises a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the evaluation method according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the evaluation method according to any one of claims 1 to 7 can be implemented.

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