A green management supervision system based on big data analysis technology

Abstract

The present invention is applicable to the technical field of farmland pollutant treatment, and provides a green management and supervision system based on big data analysis technology, including a benchmark data acquisition module for selecting a standard farmland area and performing a complete soil pollution test on it to obtain pollutant benchmark data; a pollutant retention coefficient calculation module for measuring the inflow and outflow of the standard farmland area and other non-standard farmland areas to be treated, and combining water quality testing to calculate the pollutant retention coefficient of the standard farmland area and the non-standard farmland area. The present invention obtains accurate pollutant benchmark data by selecting a standard farmland area for comprehensive soil pollution testing, and scientifically calculates the pollutant retention coefficient of the farmland by combining the inflow, outflow and water quality testing.

Description

Technical Field

[0001] The present invention belongs to the technical field of farmland pollutant treatment, and in particular relates to a green management and supervision system based on big data analysis technology. Background Art

[0002] In the technical field of farmland pollution control, different farmland areas in the same watershed have significant differences in the distribution and retention of pollutants due to differences in their location, soil type, crop type and irrigation method. During the irrigation process, the water flow will carry various pollutants, such as pesticide residues and fertilizers, and the retention effects of these pollutants in different farmland areas vary. Due to factors such as water flow velocity, soil permeability and topography in the watershed, pollutants may accumulate more in some areas and relatively less in other areas. Therefore, accurate monitoring of the accumulation of pollutants in each piece of farmland is of great significance for optimizing pesticide application and preventing environmental pollution, thereby achieving effective farmland pollution control.

[0003] Most current farmland pollution management technologies rely on local soil pollution detection of each farmland area in order to estimate its overall pollution situation. However, local detection often cannot reflect the true pollution level of the entire farmland area. Especially in large-scale farmland, local samples may deviate greatly from the overall situation, resulting in inaccurate monitoring data. In addition, in order to improve monitoring accuracy, some technologies propose comprehensive pollution detection of each farmland, but this method is not only time-consuming and labor-intensive, but also leads to high detection costs and low efficiency. Especially in large-scale farmland management, it is difficult to achieve rapid and efficient pollutant control. Summary of the invention

[0004] The purpose of the present invention is to provide a green management and supervision system based on big data analysis technology, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a green management and supervision system based on big data analysis technology, the system includes a benchmark data acquisition module, a pollutant retention coefficient calculation module, a first pollutant estimated data determination module, a second pollutant estimated data determination module and a pesticide application density corresponding module, wherein:

[0006] The benchmark data acquisition module is used to select standard farmland areas and conduct complete soil pollution detection on them to obtain pollutant benchmark data;

[0007] The pollutant retention coefficient calculation module is used to measure the inflow and outflow of standard farmland areas and other non-standard farmland areas to be treated, and calculate the pollutant retention coefficients of standard farmland areas and non-standard farmland areas in combination with water quality testing;

[0008] A first pollutant estimation data determination module is used to compare the pollutant retention coefficients of the non-standard farmland area and the standard farmland area, calculate the first deviation, and use the first deviation combined with the pollution baseline data of the standard farmland area to estimate the pollutant accumulation of each non-standard farmland area, that is, the first pollutant estimation data;

[0009] A second pollutant estimation data determination module is used to obtain soil property values ​​of standard farmland areas and non-standard farmland areas, calculate a second deviation between the soil property of each non-standard farmland area and the standard farmland area, and correct the first pollutant estimation data according to the second deviation to obtain the second pollutant estimation data of each non-standard farmland area;

[0010] The pesticide application density corresponding module is used to find the corresponding pesticide application density based on the pollutant baseline data of the standard farmland area and the second pollutant estimated data of each non-standard farmland area.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the standard farmland area refers to a farmland area that is representative and relatively balanced in terms of topography, soil type and water flow characteristics, and is used as a reference point for the pollution benchmark;

[0012] When selecting a standard farmland area, it is required that the terrain is flat, the soil characteristics are uniform, and the inflow and outflow are relatively balanced.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the pollutant retention coefficient calculation module specifically includes:

[0014] A water flow measurement unit is used to measure the water inflow of irrigation inlets and the water outflow of irrigation outlets in standard farmland areas and non-standard farmland areas;

[0015] A pollutant concentration detection unit is used to detect the water quality of the irrigation inlet and the irrigation outlet in the standard farmland area and the non-standard farmland area to obtain the pollutant concentration at the water inlet and the pollutant concentration at the water outlet;

[0016] The pollutant retention coefficient calculation unit is used to obtain the pollutant retention coefficient calculation formula, and calculate the pollutant retention coefficient of each farmland area according to the inlet flow rate, the outlet flow rate, the pollutant concentration at the inlet and the pollutant concentration at the outlet.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the pollutant retention coefficient calculation formula is: , where R is the pollutant retention coefficient, C in is the pollutant concentration at the water inlet, C out is the pollutant concentration at the outlet, Q in is the water inflow rate, Q outis the water outflow rate.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the accumulation of pollutants in each non-standard farmland area is estimated by the first pollutant estimation data calculation formula;

[0019] The calculation formula for the first pollutant estimation data is: , where P1 is the estimated data of the first pollutant, P s is the pollution baseline data of the standard farmland area, R n is the pollutant retention coefficient in non-standard farmland areas, R s is the pollutant retention coefficient of the standard farmland area, is the first deviation.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, a preset comparison table is set in the second pollutant estimation data determination module, and the preset comparison table records the soil characteristic values ​​of each farmland area and the standard pesticide application density values ​​corresponding to different pollutant data;

[0021] The first pollutant estimated data is corrected by the calculation formula of the second pollutant estimated data determination module to obtain the second pollutant estimated data of each non-standard farmland area;

[0022] The calculation formula of the second pollutant estimation data determination module is: , where P2 is the estimated data of the second pollutant, P1 is the estimated data of the first pollutant, T n is the soil property value of non-standard farmland area, T s is the soil property value of the standard farmland area, is the second deviation.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the pesticide application density corresponding module specifically includes:

[0024] A preset comparison table acquisition unit, used to acquire a preset comparison table;

[0025] The pesticide application density corresponding unit is used to search the pesticide application density corresponding to the pollutant baseline data of the standard farmland area and the pesticide application density corresponding to the second pollutant estimated data of each non-standard farmland area in a preset comparison table.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention selects standard farmland areas for comprehensive soil pollution detection, obtains accurate pollutant baseline data, and combines the inflow flow, outflow flow and water quality detection to scientifically calculate the pollutant retention coefficient of the farmland. This technical solution avoids the inaccuracy caused by traditional local soil pollution monitoring of each piece of farmland, especially because local measurements often cannot represent the pollution situation in the entire area, which can easily lead to excessive or insufficient application of pesticides. The present invention estimates the accumulation of pollutants in non-standard farmland by comparing the retention coefficient calculation with the standard farmland data, and further corrects it in combination with soil characteristics, so as to more accurately determine the estimated pollutant data for each piece of non-standard farmland.

[0028] This method based on multi-factor comparison and correction allows the accumulation of pollutants in each non-standard farmland to be effectively evaluated, and on this basis, the corresponding pesticide application density can be found. This method avoids the tedious and time-consuming comprehensive soil monitoring, achieves efficient pollutant control and accurate pesticide application, solves the measurement errors and resource waste problems in traditional methods, and improves the scientific nature of farmland management and environmental protection effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 An application architecture diagram of a system provided by an embodiment of the present invention;

[0030] Figure 2 A structural block diagram of a pollutant retention coefficient calculation module in a system provided by an embodiment of the present invention;

[0031] Figure 3 This is a structural block diagram of a module corresponding to pesticide application density in a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0034] Figure 1 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0035] Among them, in the preferred embodiment provided by the present invention, a green management and supervision system based on big data analysis technology includes:

[0036] The benchmark data acquisition module 100 is used to select a standard farmland area and perform a complete soil pollution test on it to obtain pollutant benchmark data.

[0037] A standard farmland area refers to a farmland area that is representative and relatively balanced in topography, soil type, and water flow characteristics, and is used as a reference point for pollution benchmarks.

[0038] When selecting a standard farmland area, it is required that the terrain is flat, the soil characteristics are uniform, and the inflow and outflow are relatively balanced.

[0039] In the embodiment of the present invention, in order to conduct a complete soil pollution detection and obtain pollutant baseline data, it is necessary to first select a standard farmland area. This process starts from the topography, soil type and water flow characteristics to select a representative and relatively balanced farmland area. The selection of flat terrain ensures the consistency of the hydrological process, while the uniformity of soil characteristics helps to reduce variables and improve the comparability and reliability of data.

[0040] In addition, the relative balance of inflow and outflow not only helps to accurately measure the input and output of pollutants, but also reflects the soil's ability to retain pollutants. Therefore, through these strict selection criteria, it can be ensured that the obtained pollutant baseline data has wide applicability and scientific reliability, providing a solid foundation for further pollution control and environmental protection work.

[0041] Furthermore, the green management supervision system based on big data analysis technology also includes:

[0042] The pollutant retention coefficient calculation module 200 is used to measure the inflow and outflow of the standard farmland area and other non-standard farmland areas to be treated, and calculate the pollutant retention coefficients of the standard farmland area and the non-standard farmland area in combination with water quality testing.

[0043] Specifically, Figure 2 The structure block diagram of the pollutant retention coefficient calculation module 200 in the system provided by the embodiment of the present invention is shown.

[0044] Among them, in the preferred embodiment provided by the present invention, the pollutant retention coefficient calculation module 200 specifically includes:

[0045] The water flow measurement unit 201 is used to measure the water inflow of the irrigation inlet and the water outflow of the irrigation outlet in the standard farmland area and the non-standard farmland area;

[0046] The pollutant concentration detection unit 202 is used to detect the water quality of the irrigation water inlet and the irrigation outlet in the standard farmland area and the non-standard farmland area to obtain the pollutant concentration at the water inlet and the pollutant concentration at the water outlet;

[0047] The pollutant retention coefficient calculation unit 203 is used to obtain the pollutant retention coefficient calculation formula, and calculate the pollutant retention coefficient of each farmland area according to the inlet flow rate, the outlet flow rate, the pollutant concentration at the inlet and the pollutant concentration at the outlet.

[0048] The pollutant retention coefficient calculation formula is: , where R is the pollutant retention coefficient, C in is the pollutant concentration at the water inlet, C out is the pollutant concentration at the outlet, Q in is the water inflow rate, Q out is the water outflow rate.

[0049] In the embodiment of the present invention, the standard farmland area and the non-standard farmland area are located in the same irrigation water flow. A portable device can be used to measure the water flow rate of their irrigation inlet and the water flow rate of the irrigation outlet. This method is more convenient and ensures the accuracy and real-time nature of the data.

[0050] For the irrigation inlets and outfalls in these areas, detailed water quality testing must be carried out to obtain pollutant concentration data at the inlet and outlet. The portable device detection method is easy to operate and can monitor in real time, which can quickly obtain the necessary water quality information.

[0051] Pollutant retention coefficients are a measure of the proportion of pollutants that are retained in agricultural soil. They are calculated based on the process by which pollutants are retained in the soil as the irrigation water enters and leaves the soil at different rates. Specifically, these coefficients are determined by comparing the changes in pollutant concentrations at the inlet and outlet of the irrigation water, taking into account the relationship between pollutant concentrations and flow rates at the inlet and outlet, as well as the soil's ability to adsorb and transform pollutants.

[0052] The calculation formula of the pollutant retention coefficient is established based on the results of multiple experiments and verifications to ensure its accuracy and reliability under different soil types and water flow conditions. These experiments not only consider the impact of soil characteristics on pollutants, but also verify the applicability of the calculation formula in actual farmland scenarios. Therefore, the accurate calculation of the pollutant retention coefficient is crucial for evaluating the accumulation of pollutants in farmland areas, and helps to formulate scientific and effective environmental protection and soil remediation strategies.

[0053] Furthermore, the green management supervision system based on big data analysis technology also includes:

[0054] The first pollutant estimated data determination module 300 is used to compare the pollutant retention coefficients of the non-standard farmland area and the standard farmland area, calculate the first deviation, and use the first deviation combined with the pollution baseline data of the standard farmland area to estimate the pollutant accumulation of each non-standard farmland area, that is, the first pollutant estimated data.

[0055] The pollutant accumulation in each non-standard farmland area is estimated by using the first pollutant estimation data calculation formula;

[0056] The calculation formula for the first pollutant estimation data is: , where P1 is the estimated data of the first pollutant, P s is the pollution baseline data of the standard farmland area, R n is the pollutant retention coefficient in non-standard farmland areas, R s is the pollutant retention coefficient of the standard farmland area, is the first deviation.

[0057] In the embodiment of the present invention, by comparing the pollutant retention coefficients of the standard farmland area and the non-standard farmland area, their different retention capacities for pollutants can be evaluated. The standard farmland area is usually selected to be representative and reference because they are relatively balanced in topography, soil type and water flow characteristics, and can provide typical conditions for pollutant retention.

[0058] The calculated first deviation reflects the difference in pollutant retention between non-standard farmland areas and standard farmland areas. Combined with the pollution baseline data of standard farmland areas, the accumulation of pollutants in each non-standard farmland area can be estimated, that is, the first pollutant estimation data. This scientific estimation method is based on actual data and calculation results, and can help decision makers accurately assess the degree of pollution in non-standard farmland areas.

[0059] The scientific nature of this technical solution is reflected in its ability to quantitatively evaluate the retention of pollutants in different farmland areas, providing a scientific basis for formulating effective soil remediation and environmental protection strategies.

[0060] Furthermore, the green management supervision system based on big data analysis technology also includes:

[0061] The second pollutant estimated data determination module 400 is used to obtain the soil characteristic values ​​of the standard farmland area and the non-standard farmland area, calculate the second deviation between the soil characteristics of each non-standard farmland area and the standard farmland area, and correct the first pollutant estimated data according to the second deviation to obtain the second pollutant estimated data for each non-standard farmland area.

[0062] The second pollutant estimation data determination module is provided with a preset comparison table, which records the soil characteristic values ​​of each farmland area and the standard pesticide application density values ​​corresponding to different pollutant data.

[0063] The first pollutant estimated data is corrected by the calculation formula of the second pollutant estimated data determination module to obtain the second pollutant estimated data for each non-standard farmland area.

[0064] The calculation formula of the second pollutant estimation data determination module is: , where P2 is the estimated data of the second pollutant, P1 is the estimated data of the first pollutant, T n is the soil property value of non-standard farmland area, T s is the soil property value of the standard farmland area, is the second deviation.

[0065] In the embodiment of the present invention, the preset comparison table is a tool obtained through comprehensive soil property detection and pollutant concentration analysis, which is intended to record the soil property values ​​of each farmland area and the standard pesticide application density values ​​under different pollutant data. Specifically, the soil property values ​​include but are not limited to soil type (such as sandy loam, loam, clay, etc.), soil texture (such as coarse grains, medium grains, fine grains), organic matter content, pH value, and key permeability values.

[0066] After comparing the soil property values ​​of the non-standard farmland area with the standard farmland area, the second deviation can be calculated, which reflects the degree of difference in soil properties between the non-standard farmland area and the standard farmland area. According to the second deviation, the first pollutant estimation data can be adjusted to obtain the second pollutant estimation data for each non-standard farmland area. This correction process is based on the data recorded in the preset comparison table, ensuring accurate pollutant estimation for different soil properties.

[0067] The second revised estimate of the second pollutant has several significant advantages:

[0068] First, this correction takes into account the difference in soil properties between non-standard farmland areas and standard farmland areas (i.e., the second degree of deviation). By comparing soil property values, the corrected estimated data more accurately reflects the adsorption and retention capacity of pollutants in different areas, thereby improving the accuracy and reliability of the prediction.

[0069] Secondly, the correction process is based on comprehensive data recorded in a preset comparison table, including pollutant retention capacity under different soil characteristics and standard pesticide application density values. This data-driven approach ensures the scientific and verifiable nature of the correction process, making the prediction results more credible.

[0070] Third, the estimated data after the second revision takes into account the combined effects of multiple soil characteristics, such as soil type, texture, organic matter content, etc., which have an important impact on the migration and accumulation of pollutants. Therefore, the revised data more comprehensively reflects the behavior patterns of pollutants in different soil environments, providing an important basis for formulating targeted environmental protection and remediation strategies.

[0071] Finally, the revised second pollutant estimate data can help decision makers better understand and evaluate the pollution status of non-standard farmland areas, and provide a scientific basis for formulating effective soil protection measures and environmental management policies. This refined prediction and correction process effectively improves the prediction ability and management effect of pollutant accumulation in farmland areas.

[0072] Furthermore, the green management supervision system based on big data analysis technology also includes:

[0073] The pesticide application density corresponding module 500 is used to find the corresponding pesticide application density according to the pollutant baseline data of the standard farmland area and the second pollutant estimated data of each non-standard farmland area.

[0074] Specifically, Figure 3 It shows a structural block diagram of the pesticide application density corresponding module 500 in the system provided by an embodiment of the present invention.

[0075] Among them, in the preferred embodiment provided by the present invention, the pesticide application density corresponding module 500 specifically includes:

[0076] A preset comparison table acquisition unit 501 is used to acquire a preset comparison table;

[0077] The pesticide application density corresponding unit 502 is used to search the preset comparison table for the pesticide application density corresponding to the pollutant baseline data of the standard farmland area and the pesticide application density corresponding to the second pollutant estimated data of each non-standard farmland area.

[0078] In the embodiment of the present invention, the corresponding pesticide application density is found using a preset comparison table based on the pollutant baseline data of the standard farmland area and the second pollutant estimated data of the non-standard farmland area. The preset comparison table records the standard pesticide application recommendations under different soil characteristics and pollutant concentration conditions, ensuring the scientificity and effectiveness of pesticide application.

[0079] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0080] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0081] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A green management and supervision system based on big data analysis technology, characterized in that: The system includes a reference data acquisition module, a pollutant retention coefficient calculation module, a first pollutant estimated data determination module, a second pollutant estimated data determination module and a pesticide application density corresponding module, wherein: The benchmark data acquisition module is used to select standard farmland areas and conduct complete soil pollution detection on them to obtain pollutant benchmark data; The pollutant retention coefficient calculation module is used to measure the inflow and outflow of standard farmland areas and other non-standard farmland areas to be treated, and calculate the pollutant retention coefficients of standard farmland areas and non-standard farmland areas in combination with water quality testing; A first pollutant estimation data determination module is used to compare the pollutant retention coefficients of the non-standard farmland area and the standard farmland area, calculate the first deviation, and use the first deviation combined with the pollution baseline data of the standard farmland area to estimate the pollutant accumulation of each non-standard farmland area, that is, the first pollutant estimation data; A second pollutant estimation data determination module is used to obtain soil property values ​​of standard farmland areas and non-standard farmland areas, calculate a second deviation between the soil property of each non-standard farmland area and the standard farmland area, and correct the first pollutant estimation data according to the second deviation to obtain the second pollutant estimation data of each non-standard farmland area; The second pollutant estimation data determination module is provided with a preset comparison table, which records the soil characteristic values ​​of each farmland area and the standard pesticide application density values ​​corresponding to different pollutant data; The first pollutant estimated data is corrected by the calculation formula of the second pollutant estimated data determination module to obtain the second pollutant estimated data of each non-standard farmland area; The calculation formula of the second pollutant estimation data determination module is: , where P2 is the estimated data of the second pollutant, P1 is the estimated data of the first pollutant, T n is the soil property value of non-standard farmland area, T s is the soil property value of the standard farmland area, is the second deviation; A pesticide application density corresponding module is used to find the corresponding pesticide application density according to the pollutant baseline data of the standard farmland area and the second pollutant estimated data of each non-standard farmland area; The pesticide application density corresponding module specifically includes: A preset comparison table acquisition unit, used to acquire a preset comparison table; The pesticide application density corresponding unit is used to search the pesticide application density corresponding to the pollutant baseline data of the standard farmland area and the pesticide application density corresponding to the second pollutant estimated data of each non-standard farmland area in a preset comparison table.

2. The green management and supervision system based on big data analysis technology according to claim 1 is characterized in that: The pollutant retention coefficient calculation module specifically includes: A water flow measurement unit is used to measure the water inflow of irrigation inlets and the water outflow of irrigation outlets in standard farmland areas and non-standard farmland areas; A pollutant concentration detection unit is used to detect the water quality of the irrigation inlet and the irrigation outlet in the standard farmland area and the non-standard farmland area to obtain the pollutant concentration at the water inlet and the pollutant concentration at the water outlet; The pollutant retention coefficient calculation unit is used to obtain the pollutant retention coefficient calculation formula, and calculate the pollutant retention coefficient of each farmland area according to the inlet flow rate, the outlet flow rate, the pollutant concentration at the inlet and the pollutant concentration at the outlet.

3. The green management and supervision system based on big data analysis technology according to claim 2 is characterized in that: The pollutant retention coefficient calculation formula is: , where R is the pollutant retention coefficient, C in is the pollutant concentration at the water inlet, C out is the pollutant concentration at the outlet, Q in is the water inflow rate, Q out is the water outflow rate.

4. The green management and supervision system based on big data analysis technology according to claim 1 is characterized in that: The pollutant accumulation in each non-standard farmland area is estimated by using the first pollutant estimation data calculation formula; The calculation formula for the first pollutant estimation data is: , where P1 is the estimated data of the first pollutant, P s is the pollution baseline data of the standard farmland area, R n is the pollutant retention coefficient in non-standard farmland areas, R s is the pollutant retention coefficient of the standard farmland area, is the first deviation.

5. The green management and supervision system based on big data analysis technology according to claim 1 is characterized in that: The soil property values ​​include organic matter content, pH value and permeability value.

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