Method and system for optimizing the synergy of stubble strips and mulch cover for erosion control

Abstract

The application relates to the technical field of anti-erosion optimization data processing, and discloses an anti-erosion optimization method and system of stubble strips and film mulching cooperation, wherein a film mulching temperature increasing effect model and a stubble anti-erosion effect model are constructed, the temperature increasing and soil conservation function and the soil fixation and anti-erosion function are alternately and cooperatively arranged, the film mulching amount and pollution risk are reduced, and the low temperature, the soil erosion rate and the carbon fixation and emission reduction are improved; based on an initial strip layout scheme set, a multi-objective optimization algorithm is used for iterative optimization, the temperature increasing effect value, the anti-erosion effect value and the carbon sink income per unit area are used as multi-objective functions, the optimal strip layout parameter combination can be dynamically output, the stubble anti-erosion effect model is updated, and then the meteorological conditions, the soil characteristics and the crop demand of different regions are adaptively matched, so that the effect of the anti-erosion optimization of the stubble strips and the film mulching cooperation is improved.

Description

Technical Field

[0001] This application relates to the field of corrosion protection optimization data processing technology, and in particular to a corrosion protection optimization method and system that combines stubble strips and film covering. Background Technology

[0002] Currently, stubble strips and mulching are usually applied as independent tillage measures, lacking a dynamic optimization method to combine them and optimize the erosion prevention effect of stubble strips and mulching. The strip layout parameters mostly rely on empirical settings, making it difficult to adaptively achieve the synergistic effect of temperature increase and erosion prevention according to different regional conditions. Summary of the Invention

[0003] This application provides a method and system for optimizing corrosion prevention by combining stubble strips and film covering, the main purpose of which is to solve the problem of low efficiency in optimizing corrosion prevention by combining stubble strips and film covering.

[0004] To achieve the above objectives, the erosion prevention optimization method for synergistic use of stubble strips and mulching provided in this application includes: S1, obtaining multivariate basic data of the target cultivated area.

[0005] S2, based on multivariate basic data, constructs a film-coating temperature-increasing effect model and a stubble-retention corrosion prevention effect model, and forms a film-stubble coupling effect evaluation model.

[0006] S3, based on the set combination of several candidate strip layout parameters and the evaluation model of the coating-stubble coupling effect, generates an initial strip layout scheme set through evaluation and screening.

[0007] S4. Based on the initial strip layout scheme set, a multi-objective optimization algorithm is used to iteratively find the optimal combination of strip layout parameters.

[0008] S5 generates collaborative anti-erosion tillage instructions based on the optimal strip layout parameter combination.

[0009] S6, after the collaborative anti-erosion tillage instruction is completed, field measurement data is collected, and the parameters of the mulching-stubble coupling effect evaluation model are updated.

[0010] In a preferred embodiment, the construction of a film-based warming effect model and a stubble-based corrosion prevention effect model based on multivariate basic data, and the formation of a film-stubble coupling effect evaluation model, includes: S301 extracts meteorological, soil, and stubble topographic fields from multivariate basic data to construct a model of the warming effect of mulching and outputs the increase in surface temperature; at the same time, it constructs a model of the erosion prevention effect of stubble retention and outputs the soil erosion rate under different stubble retention parameters.

[0011] S302, based on the crop demand field and meteorological field in the multivariate basic data, completes the construction of temperature-erosion synergistic constraints. Then, the surface temperature rise and soil erosion rate generated in S031 are substituted into the temperature-erosion synergistic constraints for verification. Only then can the verified model be coupled to generate a mulching-stubble retention coupling effect evaluation model.

[0012] In a preferred embodiment, the step of constructing a model of the warming effect of land cover and outputting the increase in surface temperature includes: The core calculation formula for the film-coated warming effect model is as follows: ; in This indicates the amount by which the mulch strip increases the surface temperature. This represents the solar radiation absorption and conversion coefficient of the plastic film. Indicates the intensity of solar radiation. Indicates the width of the laminating strip. Indicates the total width of the stripe unit. Indicates the coverage rate of the film. The coefficient representing the attenuation of the warming effect of the plastic film. Indicates the surface stubble coverage rate. Represents the latitude correction function. The latitude of the plot.

[0013] In a preferred embodiment, the simultaneous construction of a stubble retention erosion prevention effect model and the output of soil erosion rates under different stubble retention parameter conditions includes: the core calculation formula of the stubble retention erosion prevention effect model is as follows: ; in This indicates the soil erosion rate in the stubble strip area. This indicates the soil erosion rate in the control area without stubble cover. Indicates the maximum effective stubble height. This indicates the relative effective value of the stubble height. Indicates the width of the stubble strip. This indicates the width proportion of the stubble strip within the strip unit. A nonlinear exponent representing the corrosion protection effect. Indicates the soil's soil-fixing capacity factor. As a soil erodibility factor, For reference soil erodibility factors, For soil organic carbon content in the soil field of multivariate basic data, For reference, soil organic carbon content.

[0014] In a preferred embodiment, the initial strip layout scheme set is generated through evaluation and screening based on a set of candidate strip layout parameter combinations and a film-stubble coupling effect evaluation model, including: S601, using film strip width, stubble strip width, film coverage rate and stubble height as design variables, generating a set of candidate strip layout parameter combinations covering the entire value space; and calculating the temperature increase effect value and corrosion prevention effect value corresponding to each set of candidate strip layout parameter combinations respectively; S602, compare the anti-corrosion effect value of each group of candidate strip layout parameter combinations with the preset anti-corrosion effect threshold, retain the candidate strip layout parameter combinations whose heating effect value is greater than or equal to the heating effect threshold and whose anti-corrosion effect value is less than or equal to the anti-corrosion effect threshold, and then generate the initial strip layout scheme set.

[0015] In a preferred embodiment, the step of using a multi-objective optimization algorithm to iteratively optimize based on the initial strip layout scheme set to obtain the optimal strip layout parameter combination includes: S701, constructing a comprehensive benefit evaluation function with the objective functions of maximizing the temperature increase effect, maximizing the corrosion prevention effect, and maximizing the carbon sink benefit per unit area.

[0016] S702 uses the combination of candidate strip layout parameters from the initial strip layout scheme set as the initial population individuals for the genetic algorithm, and sets the population size, crossover probability, mutation probability, and maximum number of iterations.

[0017] S703: In each iteration, the comprehensive benefit evaluation value of each initial population individual in the current population is calculated based on the comprehensive benefit evaluation function to generate a new generation population.

[0018] S704: Determine whether the current iteration count has reached the maximum iteration count or whether the comprehensive benefit evaluation value of the best individual in the current population has remained unchanged for the preset number of generations. If the termination condition is not met, return to S703 to continue iterating. If the termination condition is met, stop iterating and output the optimal strip layout parameter combination corresponding to the individual with the highest comprehensive benefit evaluation value.

[0019] In a preferred embodiment, the step of generating a collaborative anti-erosion tillage instruction based on the optimal strip layout parameter combination includes: S801, calculating the alternating spatial layout parameters of the strips based on the width of the mulching strip and the width of the stubble strip in the optimal strip layout parameter combination.

[0020] S802, based on the film coverage rate in the optimal strip layout parameter combination, combined with the film strip width, the film covering operation parameters are analyzed and obtained.

[0021] S803, based on the stubble height in the optimal strip layout parameter combination, analyzes and derives the stubble operation parameters.

[0022] S804 integrates the parameters of alternating strip spatial layout, mulching operation, and stubble retention operation into a unified standardized tillage instruction. The standardized tillage instruction includes plot identification, strip sequence, mulching or stubble retention operation parameters for each strip, and rotary tillage and sowing parameters.

[0023] In a preferred embodiment, after the collaborative erosion prevention tillage instruction is executed, field measured data is collected, and the parameters of the mulching-stubble coupling effect evaluation model are updated, including: S901, after executing the collaborative erosion prevention tillage instruction, field measured data is collected at preset time nodes during the crop growth cycle. The field measured data includes the measured values ​​of the surface temperature of the mulched strip, the measured values ​​of the soil erosion rate of the stubble strip, and the measured values ​​of the soil organic carbon content.

[0024] S902, the prediction deviations for warming and corrosion prevention were calculated, and the corrected solar radiation absorption conversion coefficient of the mulch film, the corrected nonlinear index of corrosion prevention effect, and the corrected reference soil organic carbon content were obtained by Bayesian parameter correction method.

[0025] S903 updates the modified solar radiation absorption and conversion coefficient of the mulch film to the mulch film warming effect model, updates the modified nonlinear index of the erosion prevention effect and the modified reference soil organic carbon content to the stubble retention erosion prevention effect model, and recouples the updated mulch film warming effect model and stubble retention erosion prevention effect model into the mulch film-stubble retention coupling effect evaluation model for the next cropping cycle or strip layout optimization of adjacent plots.

[0026] This application also provides a system for optimizing corrosion prevention through the synergistic use of stubble strips and film covering, including: The multivariate basic data acquisition module is used to acquire multivariate basic data of the target cultivated area.

[0027] The modeling module for the film-stubble coupling effect constructs a film-heating effect model and a stubble-corrosion prevention effect model based on multivariate basic data, and forms an evaluation model for the film-stubble coupling effect.

[0028] The initial strip layout scheme generation module generates an initial strip layout scheme set based on several sets of candidate strip layout parameter combinations and the film-stubble coupling effect evaluation model.

[0029] The multi-objective iterative optimization module uses a multi-objective optimization algorithm to iteratively find the optimal combination of strip layout parameters based on the initial set of strip layout schemes.

[0030] The collaborative erosion prevention tillage instruction generation module generates collaborative erosion prevention tillage instructions based on the optimal strip layout parameter combination.

[0031] The field measurement feedback and model update module collects field measurement data and updates the parameters of the mulching-stubble coupling effect evaluation model after the collaborative anti-erosion tillage command is executed.

[0032] Compared with the prior art, this application has the following beneficial effects: 1. The erosion prevention optimization method and system for stubble strip and mulching provided in this application constructs a mulching warming effect model and a stubble erosion prevention effect model, and alternately and synergistically configures the warming and moisture retention functions with the soil stabilization and erosion prevention functions. While reducing the amount of mulch film used and the risk of pollution, it improves low temperature, reduces soil erosion rate and carbon sequestration and emission reduction. Based on the initial strip layout scheme set, a multi-objective optimization algorithm is used for iterative optimization. With the warming effect value, erosion prevention effect value and carbon sink benefit per unit area as multi-objective functions, it can dynamically output the optimal strip layout parameter combination, update the stubble erosion prevention effect model, and then adaptively match the meteorological conditions, soil characteristics and crop needs of different regions, thereby improving the erosion prevention optimization effect of stubble strip and mulching.

[0033] 2. After the execution of the synergistic anti-erosion tillage instruction, this application collects field measurement data, calculates the warming prediction deviation and anti-erosion prediction deviation, and obtains the corrected mulch film solar radiation absorption conversion coefficient, the corrected anti-erosion effect nonlinear index, and the corrected reference soil organic carbon content through the Bayesian parameter correction method. The corrected mulch film solar radiation absorption conversion coefficient is then updated into the mulch film warming effect model, and the corrected anti-erosion effect nonlinear index and the corrected reference soil organic carbon content are updated into the stubble retention anti-erosion effect model. The updated mulch film warming effect model and stubble retention anti-erosion effect model are then recoupled into the mulch film-stubble retention coupling effect evaluation model for the next tillage cycle or the strip layout optimization of adjacent plots, effectively improving the anti-erosion optimization effect of the stubble retention strip and mulch film coverage. Attached Figure Description

[0034] Figure 1 A schematic flowchart illustrating a corrosion-prevention optimization method that combines stubble strips and film covering in an embodiment of this application; Figure 2 A functional block diagram of a system for a corrosion-prevention optimization method that combines stubble strips and film covering, as provided in an embodiment of this application; Figure 3 This is a simulation diagram of the optimized anti-corrosion layout of the stubble strip and film covering in this application. The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0035] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0036] This application provides a corrosion prevention optimization method that combines stubble stripe retention with film covering. The execution entity of this corrosion prevention optimization method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the corrosion prevention optimization method that combines stubble stripe retention with film covering can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0037] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing erosion control through the synergy of stubble strips and mulching, according to an embodiment of this application. In this embodiment, the method for optimizing erosion control through the synergy of stubble strips and mulching includes: S1, acquiring multivariate basic data of the target cultivated area.

[0038] In this embodiment of the application, the acquisition of multivariate basic data of the target cultivated area includes: acquiring the original meteorological monitoring data, original soil sampling data, original crop rotation survey data and original crop demand data of the target cultivated area, preprocessing them respectively, and then generating a standardized multivariate basic dataset of the target cultivated area; wherein the standardized multivariate basic data includes meteorological fields, soil fields, crop rotation topography fields and crop demand fields.

[0039] It should be noted that the original meteorological monitoring data, original soil sampling data, original crop rotation survey data, and original crop demand data for the target cultivated area were extracted from the meteorological monitoring network, soil survey database, field survey records, and crop planting archives. The original meteorological monitoring data includes hourly wind speed, wind direction and frequency, daily rainfall intensity, sunshine duration, plot latitude, and daily accumulated temperature records. The original soil sampling data includes laboratory test results of soil bulk density, soil organic carbon content, soil aggregate particle size distribution, and soil erodibility factor K value collected based on the field grid sampling method. The original crop rotation survey data includes the type of previous crop, measured stubble height, stubble strip width, visual assessment of surface stubble coverage, and plot slope measurement. The original crop demand data includes the variety name of the planted crop, the material of the mulch film used, the degree of aging of the mulch film, the width of the mulch strip, the optimal temperature range for each growth stage, and the accumulated temperature requirement for the entire growth period.

[0040] Furthermore, preprocessing includes data cleaning, missing value imputation, outlier detection and correction, and data format standardization.

[0041] It should be noted that the unified spatial coordinate system is a local Cartesian coordinate system established with the center point of the target cultivated area as the origin. Field alignment is to match meteorological data, soil data, crop rotation topography data and crop demand data with the same spatial location label one by one according to spatial coordinates. Data fusion is to merge the aligned data according to field names to form a unified data table containing all data fields.

[0042] Furthermore, in the standardized multivariate basic dataset, each data record corresponds to the spatial location of a grid cell, with each grid cell measuring 10 meters × 10 meters. Each grid cell contains four types of data fields: meteorological field, soil field, crop rotation topography field, and crop requirement field.

[0043] S2, based on multivariate basic data, constructs a film-coating temperature-increasing effect model and a stubble-retention corrosion prevention effect model, and forms a film-stubble coupling effect evaluation model.

[0044] In this embodiment of the application, the construction of a film-mulching warming effect model and a stubble retention anti-erosion effect model based on multivariate basic data, and the formation of a film-mulching-stubble retention coupling effect evaluation model, includes: S301, extracting meteorological fields, soil fields and stubble topography fields based on multivariate basic data, constructing a film-mulching warming effect model, and outputting the increase in surface temperature; at the same time, constructing a stubble retention anti-erosion effect model, and outputting the soil erosion rate under different stubble retention parameter conditions.

[0045] S302, based on the crop demand field and meteorological field in the multivariate basic data, completes the construction of temperature-erosion synergistic constraints. Then, the surface temperature rise and soil erosion rate generated in S031 are substituted into the temperature-erosion synergistic constraints for verification. Only then can the verified model be coupled to generate a mulching-stubble retention coupling effect evaluation model.

[0046] It should be noted that the temperature-corrosion protection synergistic constraint conditions are as follows: ; in This represents the increase in surface temperature output by the model of the warming effect of mulch film. Indicates the optimal temperature for crop growth. This represents the base ground temperature of the strip area under uncovered conditions. This indicates the minimum warming requirement needed for crop growth. This represents the soil erosion rate output by the stubble retention erosion prevention effect model. Indicates the target allowable soil erosion rate. Indicates the width of the laminating strip. Indicates the width of the stubble strip. Indicates the total width of the stripe unit. This indicates that the sum of the width of the film-coated strip and the width of the stubble strip equals the total width of the strip unit.

[0047] Furthermore, the temperature-erosion synergistic constraint condition is used to ensure that the configuration of the mulch strips and stubble strips can simultaneously meet the warming requirements for crop growth and the erosion prevention requirements for soil protection; the unit of surface temperature increase is degrees Celsius; the unit of optimal growth temperature is degrees Celsius, obtained from the crop requirement field in the multivariate basic data; the unit of basic soil temperature is degrees Celsius, calculated from the accumulated temperature data of the meteorological field in the multivariate basic data; the unit of minimum warming requirement is degrees Celsius; the unit of target allowable soil erosion rate is tons per hectare per year, determined according to the local soil and water conservation plan; the unit of mulch strip width is meters; the unit of stubble strip width is meters; the unit of total strip unit width is meters.

[0048] Furthermore, the validated models are coupled together, and the film-covered warming effect model and the stubble-retained erosion prevention effect model, which simultaneously satisfy the warming constraint, erosion prevention constraint, and spatial integrity constraint, are jointly invoked. The film-covered strip width, stubble strip width, film coverage, and stubble height are used as common input variables, and the surface temperature increase and the soil erosion rate are used as joint output variables to form a film-covered stubble coupling effect evaluation model. The film-covered stubble coupling effect evaluation model is used to simultaneously output the corresponding warming effect value and erosion prevention effect value for any set of strip layout parameter combinations.

[0049] In this embodiment of the application, a model for the warming effect of mulch film is constructed to output the increase in land surface temperature. The core calculation formula of the model for the warming effect of mulch film is as follows: ; in This represents the increase in surface temperature caused by the film-covered strips. This represents the solar radiation absorption and conversion coefficient of the plastic film. Indicates the intensity of solar radiation. For the width of the laminating strip, Indicates the total width of the stripe unit. Indicates the coverage rate of the film. The attenuation coefficient of the temperature increase caused by the plastic film is... Indicates the surface stubble coverage rate. Represents the latitude correction function. The latitude of the plot.

[0050] It should be noted that the solar radiation absorption conversion coefficient is a dimensionless value, ranging from 0.15 to 0.35, with a default value of 0.25. Its value is affected by the material and light transmittance of the mulch film; a larger value is used for transparent mulch film, and a smaller value for black mulch film. Solar radiation intensity is obtained from the sunshine duration and plot latitude in the meteorological field of the multivariate basic data. The unit for the width of the mulch strip is meters, and it is a design variable. The unit for the total width of the strip unit is meters, which is the sum of the width of the mulch strip and the width of the stubble strip. The mulch coverage rate is zero. The dimensionless value reflects the area proportion of the mulch strip in the planting area; the attenuation coefficient of the mulch film's warming effect is a dimensionless value, ranging from 0.5 to 2.0, with a default value of 1.2. Its value is related to the degree of mulch film aging, with smaller values ​​for new film and larger values ​​for old film; the percentage of surface stubble coverage, ranging from 0 to 100, is obtained from the stubble topography field in the multivariate basic data. The higher the stubble coverage, the greater the attenuation of the mulch film's warming effect due to stubble shading; the latitude correction function is a dimensionless value, and its calculation formula is... ,in The latitude of the plot is expressed in degrees and is obtained from the cropping terrain field in the multivariate basic data.

[0051] In this embodiment of the application, the simultaneous construction of the stubble retention erosion prevention effect model and the output of soil erosion rates under different stubble retention parameter conditions includes: the core calculation formula of the stubble retention erosion prevention effect model is as follows: ; in This indicates the soil erosion rate in the stubble strip area. This indicates the soil erosion rate in the control area without stubble cover. Indicates the maximum effective stubble height. This indicates the relative effective value of the stubble height. Indicates the width of the stubble strip. This indicates the width proportion of the stubble strip within the strip unit. A nonlinear exponent representing the corrosion protection effect. Indicates the soil's soil-fixing capacity factor. As a soil erodibility factor, For reference soil erodibility factors, For soil organic carbon content in the soil field of multivariate basic data, For reference, soil organic carbon content.

[0052] It should be noted that the soil erosion rate in the stubble strip area is measured in tons per hectare per year. The soil erosion rate in the control area without stubble cover is expressed in tons per hectare per year. It is obtained by weighted fusion of the precipitation wind speed and daily rainfall intensity from the meteorological field and the soil erodibility factor K value from the soil field in the multivariate basic data. This represents the measured stubble height in centimeters, and is a design variable. The maximum effective stubble height is expressed in centimeters and is set to 30 centimeters. This means that when the stubble height exceeds 30 centimeters, the anti-corrosion effect no longer increases significantly. The relative effective value of the stubble height is dimensionless and reflects the anti-corrosion contribution rate of the stubble height. The width of the stubble strip is expressed in meters and is a design variable. The width ratio of the stubble strip within the strip unit is a dimensionless value; the nonlinear index of the erosion prevention effect is a dimensionless value, ranging from 0.5 to 1.5, with a default value of 0.8. Its value depends on the soil texture type, with smaller values ​​for clay and larger values ​​for sandy soil. The soil soil-holding capacity factor is a dimensionless value that reflects the correction effect of the soil's own erosion resistance on the erosion prevention effect. Its calculation formula is: ,in For the soil erodibility factor in the soil field of multivariate basic data, For reference, the soil erodibility factor is set to 0.032. This refers to the soil organic carbon content in the soil field of the multivariate basic data, expressed in grams per kilogram. For reference purposes, the soil organic carbon content is set at 10 grams per kilogram.

[0053] S3, based on the set combination of several candidate strip layout parameters and the evaluation model of the coating-stubble coupling effect, generates an initial strip layout scheme set through evaluation and screening.

[0054] In this embodiment of the application, the initial strip layout scheme set is generated by evaluating and screening several sets of candidate strip layout parameter combinations based on the set film-stubble coupling effect evaluation model, including: S601, using the film strip width, stubble strip width, film coverage rate and stubble height as design variables, generating several sets of candidate strip layout parameter combinations covering the entire value space; and calculating the temperature increase effect value and corrosion prevention effect value corresponding to each set of candidate strip layout parameter combinations respectively.

[0055] S602, compare the anti-corrosion effect value of each group of candidate strip layout parameter combinations with the preset anti-corrosion effect threshold, retain the candidate strip layout parameter combinations whose heating effect value is greater than or equal to the heating effect threshold and whose anti-corrosion effect value is less than or equal to the anti-corrosion effect threshold, and then generate the initial strip layout scheme set.

[0056] It should be noted that, using the width of the mulching strip, the width of the stubble strip, the mulching coverage rate, and the stubble height as design variables, and based on the preset lower limit, upper limit, and step size of the design variables, a full factorial combination method is used to generate several sets of candidate strip layout parameter combinations covering the entire value space. This is based on the preset lower limit, upper limit, and step size of the design variables, generating several discrete value points within the value range of each design variable according to the step size, and then combining the Cartesian product of all the discrete value points of the design variables to generate several sets of candidate strip layout parameter combinations covering the entire value space.

[0057] Furthermore, the preset lower limit, upper limit, and step size of the design variables are set as follows:

[0058] It should be noted that inputting the candidate strip layout parameter combinations of each group into the film-stubble coupling effect evaluation model simultaneously means: treating each group of candidate strip layout parameter combinations as an evaluation unit, substituting the film strip width, stubble strip width, and film coverage rate in this unit into the film-heating effect model to calculate the heating effect value, and simultaneously substituting the stubble height, stubble strip width, and total width of the strip unit in this unit into the stubble corrosion prevention effect model to calculate the corrosion prevention effect value.

[0059] It should be noted that the preset warming effect threshold is determined based on the difference between the optimal crop growth temperature and the baseline soil temperature, and its calculation formula is as follows: ; in This indicates the preset temperature increase threshold, in degrees Celsius. This indicates the optimal growing temperature for crops, expressed in degrees Celsius. This represents the base ground temperature of the strip area under uncovered conditions, expressed in degrees Celsius.

[0060] It should be noted that the preset erosion prevention effect threshold is equal to the target allowable soil erosion rate specified in the local soil and water conservation plan. The target allowable soil erosion rate is determined according to the classification standards for the Northeast Black Soil Region or the Northwest Loess Region in the national soil and water conservation standards. For the Northeast Black Soil Region, the value is generally 2 tons per hectare per year, and for the Northwest Loess Region, the value is generally 5 tons per hectare per year. The corresponding preset erosion prevention effect threshold is 2 tons per hectare per year or 5 tons per hectare per year, that is, only candidate schemes with stubble erosion prevention effect values ​​less than or equal to this threshold are retained.

[0061] It should be noted that the initial strip layout scheme set is a collection of candidate strip layout parameter combinations that simultaneously satisfy the temperature increase constraint and the corrosion prevention constraint. Each element in the initial strip layout scheme set includes the specific values ​​of four design variables: the width of the coated strip, the width of the stubble strip, the coating coverage rate, and the stubble height, as well as the temperature increase effect value and corrosion prevention effect value corresponding to the parameter combination.

[0062] Furthermore, the number of schemes in the initial strip layout scheme set is set according to the strictness of the preset temperature increase effect threshold and corrosion prevention effect threshold. The stricter the threshold, the fewer the number of schemes that meet the conditions, and the smaller the size of the initial strip layout scheme set.

[0063] S4. Based on the initial strip layout scheme set, a multi-objective optimization algorithm is used to iteratively find the optimal combination of strip layout parameters.

[0064] In this embodiment of the application, the step of using a multi-objective optimization algorithm to iteratively optimize based on the initial strip layout scheme set to obtain the optimal strip layout parameter combination includes: S701, constructing a comprehensive benefit evaluation function with the objective functions of maximizing the temperature increase effect, maximizing the corrosion prevention effect, and maximizing the carbon sink benefit per unit area.

[0065] S702 uses the combination of candidate strip layout parameters from the initial strip layout scheme set as the initial population individuals for the genetic algorithm, and sets the population size, crossover probability, mutation probability, and maximum number of iterations.

[0066] S703: In each iteration, the comprehensive benefit evaluation value of each initial population individual in the current population is calculated based on the comprehensive benefit evaluation function to generate a new generation population.

[0067] S704: Determine whether the current iteration count has reached the maximum iteration count or whether the comprehensive benefit evaluation value of the best individual in the current population has remained unchanged for the preset number of generations. If the termination condition is not met, return to S703 to continue iterating. If the termination condition is met, stop iterating and output the optimal strip layout parameter combination corresponding to the individual with the highest comprehensive benefit evaluation value.

[0068] It should be noted that the optimal combination of strip layout parameters is the width of the mulch strip, the width of the stubble strip, the mulch coverage rate, and the stubble height corresponding to the individual with the highest comprehensive benefit evaluation value.

[0069] It should be noted that the comprehensive benefit evaluation function is constructed by using the weighted ideal point method to normalize and integrate the three objective functions of warming effect, corrosion prevention effect, and carbon sequestration benefit per unit area. Its mathematical expression is as follows: ; in This represents the comprehensive benefit evaluation value, which is a dimensionless value and ranges from 0 to 1. , and Let these represent the weighting coefficients for the warming effect, the corrosion prevention effect, and the carbon sequestration benefit, respectively, satisfying... ; This indicates the current warming effect value for an individual, expressed in degrees Celsius. This represents the minimum warming effect value in the current population, expressed in degrees Celsius. This represents the maximum value of the warming effect in the current population, expressed in degrees Celsius. The soil erosion rate in the control area without stubble cover is expressed in tons per hectare per year. This represents the current individual erosion control effect value, i.e., the soil erosion rate of the stubble strip area under the current scheme, in tons per hectare per year; This indicates the target allowable rate of soil erosion, expressed in tons per hectare per year. This represents the carbon sequestration benefit per unit area for an individual, expressed in tons of carbon per hectare per year. This represents the minimum carbon sequestration benefit per unit area in the current population, expressed in tons of carbon per hectare per year. This represents the maximum carbon sequestration benefit per unit area in the current population, expressed in tons of carbon per hectare per year.

[0070] Furthermore, the formula for calculating carbon sequestration revenue per unit area is as follows: ; in This indicates carbon sequestration revenue per unit area, expressed in tons of carbon per hectare per year. This represents the carbon conversion coefficient of straw returned to the field. It is a dimensionless value with a default value of 0.4, which is the proportion of each unit of dry straw converted into soil organic carbon. Indicates the stubble height, in centimeters; The soil bulk density is expressed in tons per cubic meter and is obtained from the soil field in the aforementioned multivariate basic data. This indicates the soil organic carbon content, expressed in grams per kilogram. This represents the proportion of the width of the stubble strip to the total width of the strip unit, and is a dimensionless value.

[0071] It should be noted that the comprehensive benefit evaluation function is constructed as follows:

[0072] Furthermore, the sum of the weighting coefficients for the warming effect, the corrosion prevention effect, and the carbon sink benefit is 1.

[0073] It should be noted that normalization maps the values ​​of the three objective functions to the interval between 0 and 1, eliminating differences between different dimensions and magnitudes, and enabling the three objective functions to be weighted and integrated on the same scale. For the warming effect and carbon sink benefit, the larger the value, the stronger the benefit, and positive normalization is used. For the erosion prevention effect, a positive benefit index is constructed by the difference between the soil erosion rate in the control area without stubble cover and the target allowable soil erosion rate, and the larger the value, the stronger the erosion prevention benefit.

[0074] It should be noted that the population size is set to 50% of the number of schemes in the initial stripe layout scheme set. When the number of schemes in the initial stripe layout scheme set exceeds 100, the population size is fixed at 100. The crossover probability is set to 0.85; the mutation probability is set to 0.05; and the maximum number of iterations is set to 200 generations. In each iteration, the comprehensive benefit evaluation value of each individual in the current population is calculated based on the comprehensive benefit evaluation function. Individuals are ranked according to their comprehensive benefit evaluation values, and dominant individuals are retained through selection operations. Crossover and mutation operations are then performed on the retained dominant individuals to generate a new generation of population.

[0075] Furthermore, the selection operation employs a roulette wheel selection method, where the probability of each individual being selected is proportional to its comprehensive benefit evaluation value; individuals with higher comprehensive benefit evaluation values ​​are more likely to be selected and retained for the next generation. The crossover operation uses a simulated binary crossover operator to combine the design variables of two parent individuals to generate two offspring individuals. The mutation operation uses a polynomial mutation operator to randomly perturb the design variables of individuals within their value range, thereby increasing population diversity and preventing the algorithm from getting trapped in local optima. "No change for a preset number of generations" means that the comprehensive benefit evaluation value of the best individual in the current population changes less than a preset convergence threshold within 20 consecutive generations; the preset convergence threshold is 0.001.

[0076] Furthermore, the optimal strip layout parameter combination is the globally optimal solution or near-global optimal solution obtained through iterative optimization using a multi-objective optimization algorithm. It includes the specific values ​​of four design variables: the width of the film-covered strip, the width of the stubble strip, the film coverage rate, and the stubble height, as well as the comprehensive benefit evaluation value, the warming effect value, the corrosion prevention effect value, and the carbon sequestration benefit per unit area corresponding to this parameter combination.

[0077] S5 generates collaborative anti-erosion tillage instructions based on the optimal strip layout parameter combination.

[0078] In this embodiment of the application, the step of generating a collaborative anti-erosion tillage instruction based on the optimal strip layout parameter combination includes: S801, calculating the alternating spatial layout parameters of the strips based on the width of the mulching strip and the width of the stubble strip in the optimal strip layout parameter combination.

[0079] S802, based on the film coverage rate in the optimal strip layout parameter combination, combined with the film strip width, the film covering operation parameters are analyzed and obtained.

[0080] S803, based on the stubble height in the optimal strip layout parameter combination, analyzes and derives the stubble operation parameters.

[0081] S804 integrates the parameters of alternating strip spatial layout, mulching operation, and stubble retention operation into a unified standardized tillage instruction. The standardized tillage instruction includes plot identification, strip sequence, mulching or stubble retention operation parameters for each strip, and rotary tillage and sowing parameters.

[0082] It should be noted that, based on the width of the mulched strip and the width of the stubble strip in the optimal strip layout parameter combination, the alternating sequence of mulched strips and stubble strips in the planting area is calculated, generating the spatial coordinates and boundary lines of each strip, thus obtaining the alternating spatial layout parameters of the strips; wherein the alternating strip arrangement sequence starts from the boundary of the planting area and is arranged in an alternating manner with mulched strips and stubble strips until the boundary of the planting area ends; when the total number of strips is odd, the starting strip and the ending strip are both mulched strips or both stubble strips, specifically determined by the mulching coverage rate in the optimal strip layout parameter combination. When the mulching coverage rate is greater than 50%, the starting strip and the ending strip are both mulched strips; when the mulching coverage rate is less than or equal to 50%, the starting strip and the ending strip are both stubble strips.

[0083] Furthermore, the calculation process for the spatial coordinates of each strip is as follows: For the k-th strip, the formula for calculating its starting boundary position coordinates is: ; in This represents the coordinates of the starting boundary position of the k-th strip. Indicates the coordinates of the starting boundary of the planting area. This represents the stripe number, which is an integer value ranging from 1 to N, where N is the total number of stripes. This represents the width of the coating strip when the p-th strip is a coating strip; when the p-th strip is a stubble strip, this value is zero. This represents the width of the stubble strip when the p-th strip is a stubble strip; when the p-th strip is a mulch strip, this value is zero.

[0084] Furthermore, the units for the starting boundary position coordinates, the width of the mulching strip, and the width of the stubble strip are meters. The ending boundary position coordinates of the kth strip are equal to the starting boundary position coordinates of the kth strip plus the width of the kth strip; when the kth strip is a mulching strip, its width is the mulching strip width; when the kth strip is a stubble strip, its width is the stubble strip width.

[0085] It should be noted that the actual mulch width and mulch area boundary within each mulch strip are calculated to generate mulch operation parameters. The mulch operation parameters include the actual mulch width and mulch area boundary of each mulch strip. The actual mulch width refers to the actual width of the mulch film laid within the mulch strip, and the actual mulch width is equal to the mulch strip width multiplied by the mulch coverage rate.

[0086] Furthermore, the boundaries of the mulching area are determined by extending half the actual mulching width to both sides of the starting and ending boundaries of each mulching strip, with the center line of the strip as the axis of symmetry; the mulch film is laid continuously along the length of the planting area, and the edges of the mulch film are fixed by pressing soil.

[0087] It should be noted that, based on the stubble height in the optimal strip layout parameter combination, the stubble retention height and stubble treatment method within each stubble strip are determined, generating stubble operation parameters. These parameters include the stubble retention height and stubble treatment method for each stubble strip. The stubble retention height is directly adopted from the stubble height in the optimal strip layout parameter combination. The stubble treatment method is determined based on the stubble height. When the stubble height is greater than or equal to 15 cm, a high stubble treatment method is adopted, which means retaining all upright stubble and only lightly crushing the top of the stubble. When the stubble height is less than 15 cm, a low stubble treatment method is adopted, which means retaining the stubble roots and evenly spreading stubble on the ground surface.

[0088] Furthermore, in the high stubble retention method, the proportion of upright stubble is greater than 80%, the top of the stubble is crushed to a length of less than 10 cm, and the crushed stubble debris falls naturally to the surface of the stubble strip; in the low stubble retention method, the height of the stubble root is the stubble height, and the upper part of the stubble stem is crushed and evenly spread on the surface of the stubble strip with a thickness of 2 to 3 cm.

[0089] It should be noted that the rotary tillage and sowing parameters include rotary tillage depth and sowing row spacing. The rotary tillage depth is set to 12 to 15 centimeters, and the sowing row spacing is determined according to the variety of crop. For example, when the crop is spring corn, the sowing row spacing is set to 60 centimeters. Rotary tillage and sowing operations are only carried out in the mulched strips, and no rotary tillage or sowing operations are carried out in the stubble strips.

[0090] Furthermore, the standardized tillage instructions are in the format of structured data instructions, containing the following fields: plot identifier field; strip sequence field used to record the type (mulched strip or stubble strip), starting boundary coordinates, ending boundary coordinates, and width of each strip in the order of arrangement; mulching operation parameter field used to record the actual mulching width, mulch film type, mulch film thickness, and soil compaction method of each mulched strip; stubble operation parameter field used to record the stubble height, stubble treatment method, and crushing length of each stubble strip; rotary tillage and sowing parameter field used to record the rotary tillage depth, sowing row spacing, and sowing depth.

[0091] Specific examples of standardized farming instructions are as follows:

[0092] The mulching operation parameters include: number of mulching strips: 25; actual mulching width: 0.40m; mulch film type: transparent PE mulch film, thickness 0.008mm; mulching location: extending 0.20m to each side from the center line of the strip; soil compaction method: soil compaction width of 5cm at both edges of the mulch film, with soil compaction blocks placed every 2m; stubble retention operation parameters include: number of stubble strips: 25; stubble height: 20cm; stubble treatment method: high stubble treatment (retaining ≥80% upright stubble, lightly crushing the top of the stubble, crushing length ≤10cm), with the stubble covering material naturally falling to the surface of the stubble strips; rotary tillage and sowing parameters include: rotary tillage depth: 14cm; sowing row spacing: 60cm; sowing depth: 5cm; sown crop: spring corn (Zhengdan 958); operation scope: limited to the mulched strips, no rotary tillage or sowing within the stubble strips.

[0093] It should be noted that standardized tillage instructions can be directly imported into intelligent agricultural machinery terminals equipped with the Beidou navigation system. The intelligent agricultural machinery automatically generates a travel path based on the strip boundary coordinates in the strip sequence field, performs combined mulching, rotary tillage and sowing operations in the mulching strip area, and performs stubble treatment and stubble covering operations in the stubble strip area, realizing differentiated and precise operations between mulching strips and stubble strips.

[0094] S6, after the collaborative anti-erosion tillage instruction is completed, field measurement data is collected, and the parameters of the mulching-stubble coupling effect evaluation model are updated.

[0095] In this embodiment of the application, after the collaborative anti-erosion tillage instruction is executed, field measured data is collected, and the parameters of the mulching-stubble coupling effect evaluation model are updated, including: S901, after executing the collaborative anti-erosion tillage instruction, field measured data is collected at preset time nodes during the crop growth cycle. The field measured data includes the measured values ​​of the surface temperature of the mulched strip, the measured values ​​of the soil erosion rate of the stubble strip, and the measured values ​​of the soil organic carbon content.

[0096] S902, the prediction deviations for warming and corrosion prevention were calculated, and the corrected solar radiation absorption conversion coefficient of the mulch film, the corrected nonlinear index of corrosion prevention effect, and the corrected reference soil organic carbon content were obtained by Bayesian parameter correction method.

[0097] S904 updates the mulch film warming effect model by using the corrected solar radiation absorption and conversion coefficient; updates the stubble retention erosion effect model by using the corrected nonlinear index of the erosion prevention effect and the corrected reference soil organic carbon content; and updates the mulch film-stubble retention coupling effect evaluation model.

[0098] It should be noted that the preset time nodes include five time nodes: the 7th, 14th, 30th, 60th, and 90th day after mulching, and an additional temporary measurement node within 24 hours after the end of each rainfall or strong wind event. The measured surface temperature of the mulched strips is obtained by burying ground temperature sensors at the center of each mulched strip. Three ground temperature sensors are deployed in each mulched strip, located at one-quarter, one-half, and three-quarters of the strip length, respectively. The arithmetic mean of the three sensor readings is taken as the value of that mulched strip. The measured values ​​of surface temperature in the membrane strips; the measured values ​​of soil erosion rate in the stubble strips were obtained by setting up erosion needle measurement sections in each stubble strip. Three measurement sections were set up in each stubble strip, located at one-quarter, one-half, and three-quarters of the strip length, respectively. The arithmetic mean of the measurements from the three sections was taken as the measured value of soil erosion rate for that stubble strip; the measured values ​​of soil organic carbon content were obtained by collecting surface soil samples at the center of each stubble strip and conducting laboratory tests at a sampling depth of 0 to 20 cm.

[0099] Furthermore, the formula for calculating the temperature rise prediction deviation is as follows: ; in This represents the temperature rise prediction bias, expressed in degrees Celsius. A positive value indicates that the model prediction is too low, while a negative value indicates that the model prediction is too high. This represents the measured surface temperature of the covered strip, in degrees Celsius. This represents the base ground temperature of the strip area under uncovered conditions, in degrees Celsius. This represents the predicted temperature increase effect output by the film-coated temperature increase model, expressed in degrees Celsius.

[0100] Furthermore, the formula for calculating the corrosion prediction deviation is as follows: ; in This indicates the deviation in corrosion prediction, expressed in tons per hectare per year. A positive value indicates that the model prediction is too low, while a negative value indicates that the model prediction is too high. This represents the measured soil erosion rate of the stubble strip, expressed in tons per hectare per year. This represents the predicted corrosion prevention effect output by the stubble retention corrosion prevention effect model, in tons per hectare per year.

[0101] It should be noted that the Bayesian parameter correction method is used to correct the solar radiation absorption and conversion coefficient of the mulch film in the mulch film warming effect model, the nonlinear exponent of the erosion prevention effect in the stubble retention erosion prevention effect model, and the reference soil organic carbon content in the soil soil consolidation capacity factor. The Bayesian parameter correction method uses the key parameters in the mulch film warming effect model and the stubble retention erosion prevention effect model as the variables to be corrected, and field measured data as the observed data. It calculates the posterior probability distribution of the key parameters through Bayesian inference, and uses the expected value of the posterior probability distribution as the corrected parameter value. The core calculation formula is as follows: ; in This represents the vector of key parameters to be corrected, including the solar radiation absorption conversion coefficient of the mulch film. Nonlinear exponent of corrosion protection effect and the reference soil organic carbon content in the soil soil consolidation capacity factor Three parameters; This represents a field measurement dataset, which includes a sequence of measured surface temperature values ​​for mulched strips and a sequence of measured soil erosion rates for stubble strips. This represents the posterior probability distribution of key parameters given actual measured data. It represents the likelihood function of the measured data under given key parameters, reflecting the degree of agreement between the model prediction and the measured value; The prior probability distribution of the key parameters is represented by the default value set in S2 as the mean and a normal distribution constructed with the preset variance as the prior. This represents the marginal probability of the measured data and serves as a normalization constant.

[0102] Furthermore, the likelihood function The calculation formula is: ; in This represents the total number of measured data points, and is an integer value. The index represents the measured data point, which is an integer value ranging from 1 to n; Indicates the first The measured values ​​of each data point are expressed in degrees Celsius when the data point corresponds to the surface temperature of the mulched strip, and in tons per hectare per year when the data point corresponds to the soil erosion rate of the stubble strip. Indicates key parameters The model under the given value is for the first The predicted value for each measured data point, when that data point corresponds to the surface temperature of the covered strip. This is a predicted value from the mulch film warming effect model. When this data point corresponds to the soil erosion rate of the stubble strip, These are the predicted values ​​from the stubble retention corrosion prevention effect model; The standard deviation of the measurement error is taken as 0.5 degrees Celsius for the surface temperature of the mulched strip and 0.2 tons per hectare per year for the soil erosion rate of the stubble strip. This represents the natural exponential function.

[0103] Furthermore, the prior probability distribution The data was constructed using a multivariate normal distribution, with its mean vector representing the initial default values ​​of the key parameters. Specifically, the initial default value for the solar radiation absorption and conversion coefficient of the mulch film was 0.25, the initial default value for the nonlinear index of the anti-corrosion effect was 0.8, and the initial default value for the reference soil organic carbon content was 10 grams per kilogram. The diagonal elements of the covariance matrix of the prior probability distribution were set to 10% of the initial default values ​​of each parameter as the standard deviation, while the off-diagonal elements were set to zero.

[0104] It should be noted that the corrected solar radiation absorption and conversion coefficient of the mulch film, the corrected nonlinear index of the anti-corrosion effect, and the corrected reference soil organic carbon content are calculated using the Bayesian parameter correction method to obtain the expected value of the posterior probability distribution, and then the default values ​​of the corresponding parameters in S2 are replaced and updated in the mulch film warming effect model and the stubble erosion prevention effect model.

[0105] Furthermore, the updated mulching warming effect model and stubble retention erosion prevention effect model are recoupled according to the temperature-erosion synergistic constraint conditions in S2 to form an updated mulching-stubble retention coupling effect evaluation model. The updated mulching-stubble retention coupling effect evaluation model is used for strip layout optimization in the next tillage cycle or strip layout optimization of adjacent plots, so that the prediction accuracy of the model is continuously improved with the accumulation of measured data, forming a complete closed-loop optimization mechanism from data acquisition to model optimization, then to instruction generation, and finally to measured feedback correction.

[0106] Furthermore, the measured data table for the corrected data in the specific embodiment is as follows:

[0107] After the implementation of the coordinated anti-erosion tillage instruction, this application collects field measurement data, calculates the warming prediction deviation and anti-erosion prediction deviation, and obtains the corrected mulch film solar radiation absorption conversion coefficient, the corrected anti-erosion effect nonlinear index, and the corrected reference soil organic carbon content through the Bayesian parameter correction method. The corrected mulch film solar radiation absorption conversion coefficient is then updated into the mulch film warming effect model, and the corrected anti-erosion effect nonlinear index and the corrected reference soil organic carbon content are updated into the stubble retention anti-erosion effect model. Finally, the updated mulch film warming effect model and stubble retention anti-erosion effect model are recoupled into the mulch film-stubble retention coupling effect evaluation model for the next tillage cycle or the strip layout optimization of adjacent plots, effectively improving the anti-erosion optimization effect of the stubble retention strip and mulch film coverage.

[0108] like Figure 2 The diagram shown is a functional block diagram of a corrosion prevention optimization method that combines stubble strips and film covering according to an embodiment of this application.

[0109] The system 100 of the stubble strip and mulching synergistic erosion control optimization method described in this application can be installed in an electronic device. Depending on the functions implemented, the system 100 may include a multi-element basic data acquisition module 101, a mulching-stubble coupling effect modeling module 102, an initial strip layout scheme generation module 103, a multi-objective iterative optimization module 104, a synergistic erosion control tillage instruction generation module 105, and a field measurement feedback and model update module 106. The module described in this application can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0110] In this embodiment, the functions of each module / unit are as follows: The multivariate basic data acquisition module is mainly responsible for collecting various standardized data and transmitting these data to the film-stubble coupling effect modeling module; it also generates a coupling evaluation model based on the coordination conditions between temperature and corrosion prevention, and transmits the results to the initial strip layout scheme generation module.

[0111] The initial strip layout scheme generation module generates a set of possible parameters through a full factor combination method. These parameters are then input into the coupled model to calculate the values ​​of the heating effect and corrosion prevention effect, as well as the carbon sequestration benefit per unit area. Based on the set threshold, some preliminary layout schemes are selected from these parameters and passed to the multi-objective iterative optimization module.

[0112] The optimization module treats the selected initial schemes as a "population," aiming to maximize warming, corrosion prevention, and carbon sequestration benefits. It uses a weighted ideal point method and a genetic algorithm to find the optimal parameter combination. The optimized parameters are then passed to the collaborative corrosion prevention tillage instruction generation module. This module generates specific strip arrangement sequences and operational instructions based on the parameters, and then issues these standardized instructions to the equipment in the field.

[0113] The field measurement feedback and model update module collects actual data such as surface temperature, soil erosion rate, and organic carbon content throughout the crop's growth cycle. It compares these data with the model's predictions, adjusts the key parameters in the model using correction methods, and then sends the updated parameters back to the modeling module, forming a closed loop to continuously optimize the model's performance.

[0114] The multivariate basic data acquisition module is used to acquire multivariate basic data of the target cultivated area.

[0115] The modeling module for the film-stubble coupling effect constructs a film-heating effect model and a stubble-corrosion prevention effect model based on multivariate basic data, and forms an evaluation model for the film-stubble coupling effect.

[0116] The initial strip layout scheme generation module generates an initial strip layout scheme set based on several sets of candidate strip layout parameter combinations and the film-stubble coupling effect evaluation model.

[0117] The multi-objective iterative optimization module uses a multi-objective optimization algorithm to iteratively find the optimal combination of strip layout parameters based on the initial strip layout scheme set.

[0118] The collaborative erosion prevention tillage instruction generation module generates collaborative erosion prevention tillage instructions based on the optimal strip layout parameter combination.

[0119] The field measurement feedback and model update module collects field measurement data and updates the parameters of the mulching-stubble coupling effect evaluation model after the collaborative anti-erosion tillage command is executed.

[0120] The erosion control optimization method and system for stubble strip and mulching provided in this application constructs a mulching warming effect model and a stubble erosion control effect model, and alternately and synergistically configures the warming and moisture retention functions with the soil stabilization and erosion control functions. This reduces the amount of mulch film used and the risk of pollution while improving low temperature, reducing soil erosion rate, and carbon sequestration and emission reduction. Based on an initial strip layout scheme set, a multi-objective optimization algorithm is used for iterative optimization. With warming effect value, erosion control effect value, and carbon sequestration benefit per unit area as multi-objective functions, it can dynamically output the optimal strip layout parameter combination, update the stubble erosion control effect model, and then adaptively match the meteorological conditions, soil characteristics, and crop requirements of different regions, thereby improving the erosion control optimization effect of stubble strip and mulching.

[0121] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0122] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional modules in the various embodiments of this 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 integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0124] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application.

[0125] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A corrosion prevention optimization method that combines stubble strips and film covering, characterized in that, include: S1, Obtain multivariate basic data for the target cultivated area; S2, based on multivariate basic data, construct a film-coating warming effect model and a stubble-retention corrosion prevention effect model, and form a film-stubble coupling effect evaluation model; S3, based on the set combination of several candidate strip layout parameters and the evaluation model of the coating-stubble coupling effect, an initial strip layout scheme set is generated through evaluation and screening; S4. Based on the initial strip layout scheme set, a multi-objective optimization algorithm is used to iteratively find the optimal combination of strip layout parameters. S5 generates collaborative anti-erosion tillage instructions based on the optimal strip layout parameter combination; S6, after the collaborative anti-erosion tillage instruction is completed, field measurement data is collected, and the parameters of the mulching-stubble coupling effect evaluation model are updated.

2. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 1, characterized in that, The acquisition of multivariate basic data for the target cultivated area includes: The original meteorological monitoring data, original soil sampling data, original crop rotation survey data, and original crop demand data of the target cultivated area are obtained, preprocessed, and then a standardized multivariate basic dataset of the target cultivated area is generated. The standardized multivariate basic data includes meteorological fields, soil fields, crop rotation topography fields, and crop demand fields.

3. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 1, characterized in that, Based on multivariate fundamental data, a film-coating warming effect model and a stubble-retention corrosion prevention effect model are constructed, forming a film-coating-stubble-retention coupling effect evaluation model, including: S301, based on multivariate basic data, extracts meteorological, soil and stubble topographic fields, constructs a mulch film warming effect model, and outputs the surface temperature increase; at the same time, it constructs a stubble retention erosion prevention effect model and outputs the soil erosion rate under different stubble retention parameter conditions. S302, based on the crop demand field and meteorological field in the multivariate basic data, completes the construction of temperature-erosion synergistic constraints. Then, the surface temperature rise and soil erosion rate generated in S031 are substituted into the temperature-erosion synergistic constraints for verification. Only then can the verified model be coupled to generate a mulching-stubble retention coupling effect evaluation model.

4. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 3, characterized in that, The construction of the land cover warming effect model and the output of the surface temperature increase include: The core calculation formula for the film-coated warming effect model is as follows: ； in This indicates the amount by which the mulch strip increases the surface temperature. This represents the solar radiation absorption and conversion coefficient of the plastic film. Indicates the intensity of solar radiation. Indicates the width of the laminating strip. Indicates the total width of the stripe unit. Indicates the coverage rate of the film. This represents the attenuation coefficient of the temperature rise caused by the plastic film. Indicates the surface stubble coverage rate. Represents the latitude correction function. The latitude of the plot.

5. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 3, characterized in that, The simultaneous construction of a stubble retention erosion prevention effect model and output of soil erosion rates under different stubble retention parameter conditions includes: The core calculation formula of the stubble-leaving corrosion prevention effect model is as follows: ； in This indicates the soil erosion rate in the stubble strip area. This indicates the soil erosion rate in the control area without stubble cover. Indicates the maximum effective stubble height. This indicates the relative effective value of the stubble height. Indicates the width of the stubble strip. This indicates the width proportion of the stubble strip within the strip unit. A nonlinear exponent representing the corrosion protection effect. Indicates the soil's soil-fixing capacity factor. As a soil erodibility factor, For reference soil erodibility factors, For soil organic carbon content in the soil field of multivariate basic data, For reference, soil organic carbon content.

6. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 1, characterized in that, The evaluation model based on several sets of candidate strip layout parameter combinations and the coating-stubble coupling effect generates an initial strip layout scheme set through evaluation and screening, including: S601 uses the width of the film-coated strip, the width of the stubble strip, the film coverage rate, and the stubble height as design variables to generate several sets of candidate strip layout parameter combinations covering the entire value space; and calculates the heating effect value and corrosion prevention effect value corresponding to each set of candidate strip layout parameter combinations respectively. S602, compare the anti-corrosion effect value of each group of candidate strip layout parameter combinations with the preset anti-corrosion effect threshold, retain the candidate strip layout parameter combinations whose heating effect value is greater than or equal to the heating effect threshold and whose anti-corrosion effect value is less than or equal to the anti-corrosion effect threshold, and then generate the initial strip layout scheme set.

7. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 1, characterized in that, The process involves iteratively optimizing the initial stripe layout scheme set using a multi-objective optimization algorithm to obtain the optimal combination of stripe layout parameters, including: S701, with the objective functions of maximizing the warming effect, maximizing the corrosion prevention effect, and maximizing the carbon sequestration benefit per unit area, constructs a comprehensive benefit evaluation function; S702 uses the combination of candidate strip layout parameters in the initial strip layout scheme set as the initial population individuals of the genetic algorithm, and sets the population size, crossover probability, mutation probability and maximum number of iterations. S703, in each iteration, calculates the comprehensive benefit evaluation value of each initial population individual in the current population based on the comprehensive benefit evaluation function, and generates a new generation population; S704: Determine whether the current iteration count has reached the maximum iteration count or whether the comprehensive benefit evaluation value of the best individual in the current population has remained unchanged for the preset number of generations. If the termination condition is not met, return to S703 to continue iterating. If the termination condition is met, stop iterating and output the optimal strip layout parameter combination corresponding to the individual with the highest comprehensive benefit evaluation value.

8. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 1, characterized in that, The generation of collaborative erosion-resistant tillage instructions based on the optimal strip layout parameter combination includes: S801, based on the width of the film-coated strip and the width of the stubble strip in the optimal strip layout parameter combination, the strip alternation spatial layout parameters are calculated; S802, based on the film coverage rate in the optimal strip layout parameter combination, combined with the film strip width, the film covering operation parameters are analyzed and obtained; S803, based on the stubble height in the optimal strip layout parameter combination, the stubble operation parameters are analyzed and derived; S804 integrates the parameters of alternating strip spatial layout, mulching operation, and stubble retention operation into a unified standardized tillage instruction. The standardized tillage instruction includes plot identification, strip sequence, mulching or stubble retention operation parameters for each strip, and rotary tillage and sowing parameters.

9. The corrosion prevention optimization method of synergistic use of stubble strips and film covering as described in claim 1, characterized in that, After the collaborative erosion prevention tillage instruction is executed, field measurement data is collected, and the parameters of the mulching-stubble coupling effect evaluation model are updated, including: S901, after executing the collaborative anti-erosion tillage instruction, collects field measured data at preset time nodes during the crop growth cycle. The field measured data includes the measured values ​​of surface temperature of the mulched strip, soil erosion rate of the stubble strip, and soil organic carbon content. S902, the warming prediction deviation and corrosion prevention prediction deviation were calculated, and the corrected solar radiation absorption conversion coefficient of the mulch film, the corrected nonlinear index of corrosion prevention effect and the corrected reference soil organic carbon content were obtained by Bayes parameter correction method. S904 updates the mulch film warming effect model by using the corrected solar radiation absorption and conversion coefficient; updates the stubble retention erosion effect model by using the corrected nonlinear index of the erosion prevention effect and the corrected reference soil organic carbon content; and updates the mulch film-stubble retention coupling effect evaluation model.

10. A system for implementing the corrosion-prevention optimization method of synergistic stubble strip and film covering as described in any one of claims 1-9, characterized in that, include: The multi-source basic data acquisition module is used to acquire multi-source basic data of the target cultivated area; The modeling module for the film-stubble coupling effect constructs a film-heating effect model and a stubble-corrosion prevention effect model based on multivariate basic data, and forms an evaluation model for the film-stubble coupling effect. The initial strip layout scheme generation module generates an initial strip layout scheme set based on several sets of candidate strip layout parameter combinations and the evaluation model of film-stubble coupling effect through evaluation and screening. The multi-objective iterative optimization module, based on the initial strip layout scheme set, uses a multi-objective optimization algorithm to iteratively find the optimal combination of strip layout parameters; The collaborative erosion prevention tillage instruction generation module generates collaborative erosion prevention tillage instructions based on the optimal strip layout parameter combination. The field measurement feedback and model update module collects field measurement data and updates the parameters of the mulching-stubble coupling effect evaluation model after the collaborative anti-erosion tillage command is executed.

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