A method and system for evaluating carbon reduction effects in the production process of healthy agricultural products

Abstract

The present application provides a method and system for evaluating the carbon reduction effect in the production process of healthy agricultural products. Among them, by tracking the carbon migration paths of fertilizer application, organic fertilizer degradation and soil respiration, combined with the matching analysis of chemical reaction chain data and agricultural carbon emission database, the carbon retention areas and methane generation distribution characteristics in the production process are accurately identified. The dynamic compensation engine is used to model the concentration of intermediate products, generate soil pore optimization schemes and organic fertilizer gradient feeding strategies, and accelerate the transformation of degradation residues into stable carbon forms by regulating the micro-oxygen environment. Finally, a carbon cycle balance map is constructed to achieve greenhouse gas emission reduction monitoring and quantitative evaluation of the effects throughout the production cycle, and simultaneously improve the soil carbon sequestration capacity. The technical solution provided by this application can optimize the carbon cycle efficiency of agricultural production.

Description

Technical Field

[0001] The present application relates to the technical field of ecological carbon cycle assessment technology, and in particular to a method and system for evaluating carbon reduction effects in the production process of healthy agricultural products. Background Art

[0002] The production of healthy agricultural products requires precise traceability and quantitative analysis of the carbon footprint of agricultural inputs such as fertilizers and pesticides throughout their entire lifecycle, encompassing production, transportation, application, and waste disposal. This allows for a scientific evaluation of the carbon reduction effects of different production management models. Technical requirements focus on the dynamic collection and cross-process correlation of data throughout the entire lifecycle, carbon emission modeling and accounting methods based on multi-source heterogeneous data, and decision support capabilities for optimizing carbon reduction pathways.

[0003] Currently, the mainstream solution is a digital carbon footprint traceability system based on IoT sensor networks and life cycle assessment models. This system deploys farmland environmental sensors, intelligent agricultural machinery terminals, and supply chain blockchain technology to automate the collection of data across the entire fertilizer and pesticide production, transportation, application, and waste disposal chain. A dynamic life cycle assessment model is embedded in a regionalized emission factor library and combined with machine learning algorithms to refine emission accounting results.

[0004] However, this approach still faces significant bottlenecks. Data coverage and cost are at odds with each other. The high cost of deploying high-precision sensors makes it unaffordable for small and medium-sized farms, and data collection at the edge, such as the recycling of pesticide packaging waste, suffers from blind spots. The model's dynamic adaptability is insufficient. The existing life cycle assessment parameter library relies heavily on static experimental data, making it difficult to respond to fluctuations in farmland carbon sinks. The decision-making feedback loop is missing, and the ability to interact with agricultural machinery control systems and variable-rate fertilization prescription maps is insufficient, making it impossible to form a monitoring, evaluation, and regulation closed loop. This disconnect between carbon reduction recommendations and actual farming operations is a result. Summary of the Invention

[0005] This application provides a method and system for evaluating the carbon reduction effects in the production process of healthy agricultural products, which is used to solve the problem in the existing technology that carbon reduction recommendations are out of touch with actual agricultural operations.

[0006] In a first aspect, the present application provides a method for evaluating the carbon reduction effect of the production process of healthy agricultural products, comprising:

[0007] Tracking the carbon migration pathways during fertilizer application, organic fertilizer degradation, and soil respiration, while collecting chemical reaction chain data at each stage of the production process;

[0008] Determine the carbon emission concentration area based on the carbon element migration path, combine the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database, and analyze the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers during the production process;

[0009] The carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data are input into a carbon emission dynamic compensation engine, and based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards, an optimization plan for soil pore structure and a compensation strategy for the organic fertilizer feeding gradient are generated;

[0010] Adjusting soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizing the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controlling the degradation rate of organic fertilizers through the methane product conversion scheme to convert residual intermediate products into stable carbon forms, thereby reducing greenhouse gas emissions;

[0011] The migration path corresponding to the stable carbon form is dynamically monitored, and a carbon cycle balance map for evaluating the carbon reduction effect is generated by combining the adjusted soil aeration parameters and chemical reaction chain data.

[0012] Optionally, the carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data are input into a carbon emission dynamic compensation engine, and based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards, a soil pore structure optimization plan and an organic fertilizer feeding gradient compensation strategy are generated, including:

[0013] The carbon retention amount, methane product distribution, and intermediate product concentration in the chemical reaction chain data caused by incomplete degradation of organic fertilizers during the production process are input into the carbon emission dynamic compensation engine, so that the carbon emission dynamic compensation engine can dynamically determine the optimization range of pore structure parameters based on the obtained soil type and carbon retention amount to generate a graded optimization interval for soil pore diameter;

[0014] Based on the soil pore diameter classification optimization interval and the distribution density of methane oxidizing bacteria in different soil tillage layers, an optimization scheme for soil pore structure is generated;

[0015] The carbon emission dynamic compensation engine analyzes the degradation chain break threshold of plant structural organic matter in organic fertilizers based on the distribution of methane products and the concentration of intermediate products. Combined with the range of ambient temperature fluctuations during the production process, it generates association rules between the staged feeding gradient and the regulation of degradation bacterial activity.

[0016] The soil pore diameter classification optimization interval and the association rule are integrated to generate a compensation strategy for the organic fertilizer feeding gradient.

[0017] Optionally, the soil pore diameter classification optimization interval is integrated with the association rule to generate an organic fertilizer feeding gradient compensation strategy including a micro-oxygen flux threshold, a feeding time window offset, and a degradation rate compensation factor, including:

[0018] According to the pore connectivity parameters of different soil types in the soil pore diameter classification optimization interval, the organic fertilizer degradation rate attenuation coefficient corresponding to the ambient temperature fluctuation in the association rule is matched to generate a dynamic constraint relationship table between soil pore diameter and degradation rate;

[0019] Based on the dynamic constraint relationship table, the maximum methane diffusion flux allowed within the soil pore diameter classification optimization interval is analyzed, and the response delay time of the degradation bacterial community activity and the feeding gradient in the association rule is combined to generate the micro-oxygen flux threshold interval and the feeding time window offset compensation range based on the soil tillage layer depth;

[0020] The soil pore diameter classification optimization interval is reversely constrained according to the micro-oxygen flux threshold interval, and a pore diameter distribution pattern that meets the metabolic energy level requirements of methanotrophic bacteria is screened. At the same time, the degradation bacteria activity compensation coefficient is dynamically weighted based on the feeding time window offset compensation range to generate an organic fertilizer feeding gradient compensation strategy that includes pore reconstruction priority, feeding interval hot zone division, and bacteria activation trigger conditions.

[0021] Optionally, adjusting soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizing the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controlling the degradation rate of organic fertilizers through the methane product conversion scheme, including:

[0022] Based on the compensation strategy, extracting the target value range of soil oxygen concentration in the microaerobic environment control instruction, and obtaining soil ventilation adjustment parameters according to the soil pore structure optimization scheme;

[0023] Calculating the deviation between the current soil oxygen concentration and the target value range according to the microaerobic environment control instruction, and dynamically adjusting the working mode of the soil ventilation device in combination with the soil ventilation adjustment parameter to generate optimized soil microaerobic environment data;

[0024] According to the methane product conversion scheme, the target rate range of organic fertilizer degradation is determined, and based on the soil microaerobic environment data, the feeding amount and distribution method of the organic fertilizer are dynamically adjusted to control the degradation rate of the organic fertilizer.

[0025] Optionally, based on the methane production distribution and intermediate product concentration, the degradation chain scission threshold of plant structural organic matter in the organic fertilizer is analyzed, and combined with the ambient temperature fluctuation range during the production process, an association rule between the staged feeding gradient and the regulation of the activity of the degradation bacterial community is generated, including:

[0026] Based on the methane generation distribution and the intermediate product concentration, the degradation chain scission threshold of plant structural organic matter in the organic fertilizer is analyzed, and according to the degradation chain scission threshold, the degradation process of the organic fertilizer is divided into multiple stages, and the feeding gradient range of each stage is determined;

[0027] Based on the ambient temperature fluctuation range during the production process, the influence of temperature changes on the degradation chain scission threshold is identified, and a mapping relationship between temperature and degradation chain scission threshold is established. Combined with this mapping relationship, the changing trend of the degradation bacterial community activity under different temperature conditions is analyzed to determine the influence of temperature on the degradation bacterial community activity;

[0028] Based on the degradation chain scission threshold and the feeding gradient range, and in combination with the influencing rules, an association rule between the staged feeding gradient and the regulation of the activity of the degradation bacterial community is generated.

[0029] Optionally, according to the methane product conversion scheme, a target rate range for organic fertilizer degradation is determined, and based on the soil microaerobic environment data, the feeding amount and distribution mode of the organic fertilizer are dynamically adjusted to control the degradation rate of the organic fertilizer, including:

[0030] Extracting a target rate range for organic fertilizer degradation based on the methane product conversion scheme, including an upper limit and a lower limit for the degradation rate, and analyzing the effect of the current soil oxygen concentration on the degradation rate based on the soil microaerobic environment data to determine applicable conditions for the target rate range;

[0031] According to the target rate range, calculating the deviation between the current organic fertilizer degradation rate and the target rate range, and determining the adjustment amount of the feeding amount based on the deviation;

[0032] Based on the applicable conditions of the target rate range, and according to the adjustment amount, dynamically adjusting the feeding amount and distribution mode of the organic fertilizer to ensure that the degradation rate is stable within the target rate range;

[0033] Optionally, dynamically monitoring the migration path of the stable carbon form and combining the adjusted chemical reaction chain data to generate a carbon cycle balance map for evaluating the carbon reduction effect include:

[0034] During the production process, monitoring points are set up along the geographical distribution of the field irrigation channels, and the stable carbon form retention and lateral diffusion rate at different soil tillage layer depths are collected. The collected retention and diffusion rate data are integrated to generate dynamic monitoring data corresponding to the migration path based on the field irrigation channels as the geographical reference;

[0035] Based on the dynamic monitoring data, the migration time delay of the stable carbon form under the soil pore connectivity gradient is analyzed, and combined with the concentration change rate of the organic fertilizer degradation intermediate product in the adjusted chemical reaction chain data, a dynamic mapping relationship table of the stable carbon form conversion efficiency and degradation rate is generated;

[0036] Based on the dynamic mapping relationship table, the constraints of the tillage layer depth and stable carbon form migration rate of different soil textures in open-air farmland are matched to generate a carbon cycle dynamic parameter set including the boundary of the humus retention hot zone, the lateral diffusion gradient of biochar, and the priority of methane oxidation inhibition;

[0037] The carbon cycle dynamic parameter set is spatially superimposed with the adjusted chemical reaction chain data to generate a carbon cycle balance map with the tillage layer depth as the vertical axis and the field irrigation channel as the horizontal axis.

[0038] In a second aspect, the present application provides a method for evaluating the carbon reduction effect of the production process of healthy agricultural products, comprising:

[0039] The collection module tracks the carbon migration path during fertilizer application, organic fertilizer degradation, and soil respiration, while also collecting chemical reaction chain data at each stage of the production process;

[0040] A matching module determines the carbon emission concentration area based on the carbon element migration path, combines the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database, and analyzes the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers during the production process;

[0041] A compensation module inputs the carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data into a carbon emission dynamic compensation engine, and generates an optimization plan for soil pore structure and a compensation strategy for the organic fertilizer feeding gradient based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards;

[0042] an adjustment module, adjusting soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizing the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controlling the degradation rate of organic fertilizers through the methane product conversion scheme to convert residual intermediate products into stable carbon forms, thereby reducing greenhouse gas emissions;

[0043] A generation module dynamically monitors the migration path corresponding to the stable carbon form, and generates a carbon cycle balance map for evaluating the carbon reduction effect by combining the adjusted soil aeration parameters and chemical reaction chain data.

[0044] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for evaluating carbon reduction effects in the production process of healthy agricultural products as described in the first aspect above.

[0045] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a method for evaluating carbon reduction effects in the production process of healthy agricultural products as described in the first aspect.

[0046] In the embodiment of the present application, the carbon migration path in the application of chemical fertilizers, degradation of organic fertilizers and soil respiration is tracked, and chemical reaction chain data at each stage of the production process are collected at the same time; the carbon emission concentration area is determined according to the carbon migration path, and the by-product generation amount in the chemical reaction chain data is matched with the pre-stored agricultural carbon emission path database to analyze the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers in the production process; the carbon retention amount, methane product distribution and the intermediate product concentration in the chemical reaction chain data are input into the carbon emission dynamic compensation engine, and the carbon emission concentration is calculated based on the production of healthy agricultural products. The carbon reduction effect evaluation indicators in the production standards are used to generate an optimization plan for the soil pore structure and a compensation strategy for the organic fertilizer feeding gradient; the soil ventilation parameters are adjusted according to the microaerobic environment control instructions in the compensation strategy, and the soil microaerobic environment is optimized in combination with the soil pore structure optimization plan. At the same time, the degradation rate of the organic fertilizer is controlled through the methane product conversion plan, so that the residual intermediate products are converted into stable carbon forms to reduce greenhouse gas emissions; the migration path corresponding to the stable carbon form is dynamically monitored, and a carbon cycle balance map for evaluating the carbon reduction effect is generated in combination with the adjusted soil ventilation parameters and chemical reaction chain data.

[0047] The technical solution of this application has the following beneficial effects:

[0048] This application achieves accurate monitoring and quantitative analysis of the carbon footprint in the agricultural production process by tracking the migration paths of carbon elements in fertilizer application, organic fertilizer degradation and soil respiration, and combining it with chemical reaction chain data collection; by matching the agricultural carbon emission path database, it accurately identifies areas of concentrated carbon emissions, and analyzes the carbon retention and methane product distribution caused by incomplete degradation of organic fertilizers, providing data support for carbon emission reduction; based on the dynamic compensation engine, it generates soil pore structure optimization schemes and organic fertilizer feeding gradient compensation strategies, effectively improving soil carbon sequestration capacity and optimizing fertilizer utilization efficiency; adjusts soil ventilation parameters through micro-aerobic environment control instructions, and combines methane product conversion schemes to promote the conversion of intermediate products to stable carbon forms, significantly reducing greenhouse gas emissions; finally, by dynamically monitoring the migration paths of stable carbon forms and combining adjusted chemical reaction chain data to generate a carbon cycle balance map, it provides a scientific evaluation basis for the carbon reduction effect of the healthy agricultural product production process, and realizes low-carbonization and sustainable optimization of the entire agricultural production cycle.

[0049] Furthermore, by inputting carbon retention, methane production distribution, and intermediate product concentrations into a dynamic carbon emission compensation engine, combined with the constraints of soil type-dynamically matched pore structure parameters, a hierarchical optimization range of soil pore diameters adapted to the organic matter decomposition rate was generated. Based on the distribution density of methane-oxidizing bacteria, a soil pore structure optimization scheme was constructed, constrained by pore connectivity and gas diffusion resistance. Finally, by integrating soil pore optimization with feeding rules, an organic fertilizer feeding gradient compensation strategy was generated, including a micro-oxygen flux threshold, a feeding time window offset, and a degradation rate compensation factor, to achieve synergistic optimization of soil structure and fertilizer degradation. A phased feeding gradient was generated based on the degradation chain scission threshold and temperature fluctuations to maximize fertilizer degradation efficiency and minimize carbon emissions, providing efficient and low-carbon soil management and fertilizer application solutions for the production of healthy agricultural products.

[0050] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1 A flow chart of a method for evaluating carbon reduction effects in the production process of healthy agricultural products provided by the present application is shown;

[0053] Figure 2A schematic diagram of the structure of a carbon reduction effect evaluation system for the production process of healthy agricultural products provided by the present application is shown;

[0054] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0056] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0057] This application aims to develop a carbon reduction effect evaluation and optimization system for the production process of healthy agricultural products. By tracking the migration path of carbon elements, collecting chemical reaction chain data, and matching the agricultural carbon emission database, the carbon retention and methane product distribution are accurately analyzed; a dynamic compensation engine is combined to generate a soil pore optimization plan and an organic fertilizer feeding gradient compensation strategy, and the micro-oxygen environment is regulated to promote the transformation of intermediate products into stable carbon forms; finally, through dynamic monitoring and the construction of a carbon cycle balance map, accurate monitoring of the carbon footprint of the entire agricultural production cycle, optimization of greenhouse gas emissions, and scientific evaluation of carbon reduction effects are achieved, providing low-carbon and sustainable technical support for the production of healthy agricultural products.

[0058] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0059] Figure 1 The present invention provides a flowchart of a method for evaluating the carbon reduction effect of a healthy agricultural product production process. Figure 1 As shown, the method includes:

[0060] 101. Track the carbon migration path during fertilizer application, organic fertilizer degradation, and soil respiration, while collecting chemical reaction chain data at each stage of the production process;

[0061] In this step, the carbon migration pathway refers to the dynamic flow trajectory of carbon during the application of chemical fertilizers, degradation of organic fertilizers and soil respiration, including the process of carbon from organic fertilizers to soil and then to the atmosphere.

[0062] Chemical reaction chain data refers to the chemical reaction data at each stage of the production process, including reactants, intermediates, by-products and their concentration changes.

[0063] In this application example, first, a sensor network is deployed to monitor the flow of carbon elements in the soil, and the carbon form changes during the degradation of organic fertilizers are analyzed using gas chromatography-mass spectrometry technology to determine the migration path of carbon elements. Next, a reactor experiment is used to simulate the chemical reactions in the production process, and chemical reaction chain data is collected, including changes in the concentrations of reactants, intermediates, and by-products. Finally, by integrating the carbon element migration path and chemical reaction chain data, a complete carbon flow model is formed to provide basic data support for subsequent analysis.

[0064] High-precision soil carbon sensors and gas collection equipment were deployed in a specific farmland to monitor carbon release during the degradation of organic fertilizer. Simultaneously, a laboratory reactor was constructed to simulate the production process of organic fertilizer and record the amount of byproducts generated at each stage of the reaction chain. Analysis using gas chromatography-mass spectrometry revealed that the degradation of organic fertilizer produces large amounts of carbon dioxide and small amounts of methane. After integrating this data, the migration pathways of carbon elements and key nodes in the reaction chain were preliminarily identified, revealing, for example, that carbon emissions in certain areas were significantly higher than in others.

[0065] 102. Determine the carbon emission concentration area based on the carbon element migration path, combine the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database, and analyze the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers during the production process;

[0066] In this step, the carbon emission concentrated area refers to the area where the carbon emission in the carbon element migration path is significantly higher than other areas.

[0067] Carbon retention refers to the amount of carbon retained in the soil due to incomplete degradation of organic fertilizers. Methane production distribution refers to the spatial distribution of methane produced during the degradation of organic fertilizers in the soil.

[0068] The agricultural carbon emission pathway database is a pre-stored data set used to match carbon emission pathways, and the random forest algorithm is a machine learning algorithm used to predict methane generation hotspots.

[0069] In the example of this application, we first determined the concentrated areas of carbon emissions based on the carbon migration path using spatial data analysis technology, and found that the carbon emissions from a certain piece of farmland were significantly higher than those in other areas. Next, we combined the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database to analyze the carbon retention amount and the distribution of methane products. Then, we used the random forest algorithm to predict the hotspot areas for methane generation. Finally, we found that the retained carbon was converted into methane under specific conditions, forming a hotspot for the distribution of methane products. This analysis result provides an important basis for subsequent carbon emission compensation.

[0070] Building on the previous process, spatial analysis of farmland carbon emission data using GIS tools revealed significantly higher carbon emissions in one region compared to others. Matching this data to a database of agricultural carbon emission pathways revealed incomplete organic fertilizer degradation in this region, resulting in significant carbon retention. Further analysis revealed that this retained carbon was converted to methane under anaerobic conditions, forming hotspots of methane production. A random forest algorithm predicted that methane production hotspots were primarily concentrated in the deep soil layer, providing target areas for subsequent methane conversion strategies.

[0071] 103. Input the carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data into a carbon emission dynamic compensation engine, and generate a soil pore structure optimization plan and an organic fertilizer feed gradient compensation strategy based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards;

[0072] In this step, the carbon emission dynamic compensation engine is a system based on an algorithm model that is used to dynamically adjust carbon retention and methane production distribution to reduce carbon emissions.

[0073] The soil pore structure optimization program refers to improving the migration and conversion efficiency of carbon elements by adjusting the soil pore structure.

[0074] The organic fertilizer feeding gradient compensation strategy refers to dynamically adjusting the feeding amount and frequency of organic fertilizer according to the carbon retention. Genetic algorithm is an algorithm used to generate optimization solutions.

[0075] The healthy agricultural product production standards are a standard system used to evaluate carbon reduction effects.

[0076] In this application example, the carbon retention, methane product distribution, and chemical reaction chain data are first input into the carbon emission dynamic compensation engine. Based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards, a genetic algorithm is then used to generate a soil pore structure optimization plan and an organic fertilizer feeding gradient compensation strategy. Then, by optimizing the soil pore structure, the oxygen supply is increased, promoting the stable conversion of carbon elements; finally, the organic fertilizer feeding amount is dynamically adjusted according to the carbon retention to reduce carbon emissions. This solution provides specific guidance for subsequent micro-aerobic environment control.

[0077] Building on the previous process, the data was fed into a dynamic carbon emissions compensation engine, which generated an optimization plan: by increasing soil aeration and adjusting the soil pore structure to increase oxygen supply, while also dynamically reducing the amount of organic fertilizer added based on carbon retention. This plan significantly reduced carbon emissions from farmland, improved carbon conversion efficiency in the soil, and reduced methane production.

[0078] 104. Adjust soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimize the soil microaerobic environment in combination with the soil pore structure optimization scheme, and control the degradation rate of organic fertilizer through the methane product conversion scheme to convert residual intermediate products into stable carbon forms, thereby reducing greenhouse gas emissions;

[0079] In this step, the microaerobic environment control instruction refers to creating a microaerobic environment suitable for the stable conversion of carbon elements by adjusting soil ventilation parameters.

[0080] The methane conversion program refers to converting methane into a stable carbon form by controlling the degradation rate of organic fertilizers.

[0081] The soil pore structure optimization program refers to a program that improves the efficiency of carbon migration and conversion by optimizing the soil pore structure (such as pore diameter, connectivity, etc.).

[0082] In the example of the present application, according to the micro-aerobic environment control instructions in the compensation strategy, the soil ventilation parameters are dynamically adjusted to ensure the stability of the soil micro-aerobic environment. At the same time, in combination with the soil pore structure optimization scheme, the soil micro-aerobic environment is further optimized by adjusting the soil pore diameter and connectivity, thereby improving the migration and conversion efficiency of carbon elements. In addition, the degradation rate of organic fertilizers is controlled by the methane product conversion scheme, and the degradation process is accelerated by bio-enzyme technology, and the residual intermediate products are converted into stable carbon forms, thereby reducing the release of greenhouse gases such as methane. Ultimately, by optimizing the soil micro-aerobic environment and controlling the degradation rate of organic fertilizers, efficient conversion of carbon elements and a significant reduction in greenhouse gas emissions are achieved.

[0083] Building on the previous process, soil aeration equipment was installed and aeration parameters were adjusted to create a micro-aerobic environment. Simultaneously, bio-enzyme preparations were sprayed to accelerate the degradation of organic fertilizers, converting methane into a stable carbon form. Monitoring revealed a significant reduction in greenhouse gas emissions, with soil carbon primarily converted into stable organic carbon, further reducing carbon emissions.

[0084] 105. Dynamically monitor the migration path corresponding to the stable carbon form, and generate a carbon cycle balance map for evaluating carbon reduction effects by combining the adjusted soil aeration parameters and chemical reaction chain data.

[0085] In this step, the stable carbon form migration path refers to the dynamic flow trajectory of carbon elements after they are converted into a stable form.

[0086] The carbon cycle balance map is a comprehensive map used to evaluate carbon reduction effects, including the input, output and retention of carbon elements.

[0087] Data visualization technology is a technique used to generate carbon cycle balance maps.

[0088] The adjusted soil ventilation parameters refer to the soil ventilation parameters that are optimized according to the microaerobic environment control instructions.

[0089] In this application example, a sensor network is first used to collect data on the migration paths of stable carbon forms and analyze their flow patterns in the soil. Then, combined with adjusted soil aeration parameters and chemical reaction chain data, spatial overlay technology is used to integrate the data and generate a carbon cycle balance map. Through map analysis, the carbon reduction effect is evaluated, and the soil microaerobic environment and organic fertilizer degradation rate are further optimized to ensure that carbon emissions in agricultural production processes continue to decrease.

[0090] Building on the previous process, we continued to monitor the migration paths of stable carbon forms and found that carbon was primarily retained deep in the soil. Combining this with adjusted chemical reaction chain data, we generated a carbon cycle balance map, revealing that the carbon cycle in farmland is approaching equilibrium, demonstrating significant carbon reduction results. Through further optimization of the plan, we further reduced carbon emissions from farmland, achieving the goal of low-carbon agricultural production.

[0091] In summary, steps 101 to 105 effectively reduce carbon emissions during agricultural production by tracking carbon migration pathways, analyzing concentrated carbon emission areas, dynamically compensating for carbon emissions, optimizing soil pore structure, and controlling the micro-oxygen environment. Furthermore, by monitoring the migration pathways of stable carbon forms and generating a carbon cycle balance map, this provides a scientific basis for the sustainable development of agricultural production and achieves the goal of low-carbon and efficient agricultural production.

[0092] The following is a carbon-reducing production process for healthy agricultural products:

[0093] First, we select high-quality crop varieties, particularly those with disease resistance, stress tolerance, and high carbon sequestration capacity. Combined with high-photosynthetic-efficiency varieties like legumes, we reduce pesticide use and fertilizer usage. We strictly adhere to non-GMO certification standards to avoid the risk of genetic contamination, ensure that varieties meet the requirements for healthy agricultural production, and reduce the use of chemical inputs at the source.

[0094] Secondly, we foster a healthy production environment. A dynamic compensation engine analyzes soil carbon retention and type, generating an optimized pore diameter grading scheme (e.g., 0.1-0.3 mm) to improve oxygen diffusion efficiency and inhibit methane production. Soil aeration equipment is deployed to regulate the micro-aerobic environment (target oxygen concentration 0.5-1.2 mg / L), accelerating the conversion of organic fertilizer into stable humus. Regular soil sampling monitors heavy metal levels, and soil remediation techniques are implemented based on actual conditions to provide a safe soil environment for crop growth. Furthermore, ambient air quality must comply with the secondary standard of GB 3095 and the requirements of GB 9137, and irrigation water quality must comply with the requirements of GB 5084.

[0095] Next, we invested in green and efficient production materials. Using meteorological sensors, GIS, and remote sensing technologies, we developed a phased organic fertilizer gradient feeding strategy (e.g., 3 days per application during high-temperature periods and 7 days per application during low-temperature periods) based on degradation chain break thresholds and ambient temperature fluctuations. Biochar addition was also implemented to improve soil pore connectivity and reduce lateral methane diffusion. Microbial agents were used to replace chemical pesticides to suppress pests. Low-energy agricultural machinery was encouraged to promote energy conservation. An integrated intelligent irrigation system, using integrated water and fertilizer technology, precisely controlled resource input, reduced nitrogen leaching and water waste, and achieved efficient utilization of production materials while minimizing carbon emissions.

[0096] Precision agriculture management is then implemented to monitor carbon migration pathways (such as CO2 and CH4 emissions) and identify carbon emission hotspots. Data such as carbon retention and methane distribution are fed into a compensation engine to generate instructions for optimizing soil aeration parameters (such as adjusting rotary tillage depth to 15-20cm) and dynamically adjust feed rates. By integrating data on stable carbon form migration pathways with chemical reaction chains, a carbon cycle balance map is constructed, with tillage layer depth as the vertical axis and field irrigation access as the horizontal axis. This quantifies carbon reduction effects and optimizes organic fertilizer distribution based on the boundaries of humus retention hotspots to maximize soil carbon sequestration efficiency.

[0097] Finally, the production and certification of healthy agricultural products involves testing for pesticide residues and heavy metal content to ensure compliance with GB2763 and GB2762 regulations. Fruit appearance, including size and color, is evaluated, along with nutritional indicators like vitamins and minerals and taste indicators like volatile flavor compounds, to assess the health of the produce. Carbon emission intensity per unit of output (kgCO₂e / kg) is calculated based on carbon cycle maps, and the "Low-Carbon Healthy Agricultural Products" certification mark is applied for. Blockchain technology is used to record full lifecycle data (such as fertilizer source and degradation efficiency) to establish a transparent traceability system for planting and enhance consumer trust. Ultimately, by synergizing environmental and social benefits, a "low-carbon, high-quality, and high-efficiency" agricultural production model is achieved.

[0098] In order to solve the problems of carbon retention, methane generation and greenhouse gas emissions caused by incomplete degradation of organic fertilizers in agricultural production, a soil pore structure and organic fertilizer feeding optimization system based on a carbon emission dynamic compensation engine has been developed, which achieves soil structure optimization, fertilizer degradation efficiency improvement and effective control of greenhouse gas emissions, and provides low-carbon and sustainable technical support for the production of healthy agricultural products. In some embodiments, the carbon retention amount, methane product distribution and intermediate product concentration in the chemical reaction chain data are input into the carbon emission dynamic compensation engine in step 103, and based on the carbon reduction effect evaluation index in the healthy agricultural product production standard, a soil pore structure optimization plan and an organic fertilizer feeding gradient compensation strategy are generated, including:

[0099] 201. Inputting the carbon retention amount, methane product distribution, and intermediate product concentration in the chemical reaction chain data caused by incomplete degradation of organic fertilizers during the production process into the carbon emission dynamic compensation engine, so that the carbon emission dynamic compensation engine dynamically determines the optimization range of pore structure parameters based on the obtained soil type and carbon retention amount to generate a graded optimization interval for soil pore diameter;

[0100] In step 201, carbon retention refers to the amount of carbon retained in the soil due to incomplete degradation of organic fertilizers. Methane product distribution refers to the spatial distribution of methane produced during organic fertilizer degradation in the soil, which is generally closely related to the soil's anaerobic environment and organic matter content. Intermediate product concentration refers to the concentration of intermediate products generated during the reaction process in the chemical reaction chain data. The carbon emission dynamic compensation engine is an algorithm-based system that dynamically adjusts carbon retention and methane product distribution to reduce carbon emissions. Its core function is to generate carbon reduction strategies through data analysis and optimization algorithms. Soil type refers to the classification of the physical and chemical properties of farmland soil, including sandy soil and clay soil. Different soil types have a significant impact on the migration and conversion efficiency of carbon. Pore structure parameters refer to parameters that affect soil pore structure, such as pore diameter and pore connectivity. The soil pore diameter classification optimization range refers to the optimal range of pore diameters dynamically determined based on carbon retention and soil type. Adjusting pore diameter can improve carbon conversion efficiency.

[0101] In the embodiment of the present application, the carbon retention, methane product distribution and intermediate product concentration data are first obtained through sensor network and laboratory analysis, and input into the carbon emission dynamic compensation engine. Then, the engine uses the optimization algorithm to dynamically determine the optimization range of the pore structure parameters according to the soil type and carbon retention, and generates the soil pore diameter classification optimization interval. Specifically, the engine determines the optimization range of the pore diameter by analyzing the impact of different soil types on carbon retention, combining the intermediate product concentration and methane product distribution. Finally, this interval provides a scientific basis for the subsequent soil pore structure optimization, ensuring that the migration and conversion efficiency of carbon elements is maximized and greenhouse gas release is reduced.

[0102] 202. Based on the soil pore diameter classification optimization interval and in combination with the distribution density of methanotrophic bacteria in different soil cultivation layers, generate an optimization scheme for soil pore structure;

[0103] In step 202, the soil pore diameter classification optimization interval refers to the pore diameter optimization range dynamically determined according to the carbon retention and soil type. The soil tillage layer refers to the thickness of the tillage layer of farmland soil, which is usually divided into the surface layer, the middle layer and the deep layer. Different tillage layers have a significant impact on the migration and conversion efficiency of carbon elements. The distribution density of methanotrophic bacteria refers to the distribution of methanotrophic bacteria in the soil. Methane oxidizing bacteria is a type of microorganism that can convert methane into carbon dioxide and water. Its distribution density directly affects the conversion efficiency of methane. The soil pore structure optimization program refers to a program to improve the migration and conversion efficiency of carbon elements by optimizing the soil pore structure, specifically including adjusting parameters such as pore diameter and pore connectivity.

[0104] In the examples of this application, the optimal range of pore diameter is first determined by optimizing the soil pore diameter classification interval. Next, spatial analysis techniques are used to generate a soil pore structure optimization scheme based on the distribution density of methanotrophic bacteria in different soil tillage layers. Specifically, the pore structure optimization scheme is determined by analyzing the distribution density of methanotrophic bacteria in different tillage layers and combining it with the pore diameter optimization range. Finally, by optimizing the soil pore structure, the activity of methanotrophic bacteria is increased, methane conversion is promoted, greenhouse gas release is reduced, and efficient carbon conversion is achieved.

[0105] 203. Analyze the degradation chain scission threshold of plant structural organic matter in organic fertilizers based on the distribution of methane products and the concentration of intermediate products through the dynamic carbon emission compensation engine. Combined with the range of ambient temperature fluctuations during the production process, generate association rules for staged feeding gradients and regulation of degradation bacterial activity.

[0106] In step 203, the methane product distribution refers to the spatial distribution of methane produced in the soil during the degradation process of organic fertilizer. The intermediate product concentration refers to the concentration value of the intermediate product generated during the reaction process in the chemical reaction chain data. The degradation chain breaking threshold of plant structural organic matter refers to the chain breaking conditions of plant structural organic matter in organic fertilizer during the degradation process, including factors such as temperature, humidity and microbial activity. The ambient temperature fluctuation range refers to the range of changes in ambient temperature during the production process. Temperature fluctuation has a significant impact on the degradation rate of organic fertilizer. The staged feeding gradient refers to the organic fertilizer feeding amount dynamically adjusted according to the degradation chain breaking threshold and the ambient temperature fluctuation range. The degradation efficiency of organic fertilizer can be improved by staged feeding. The association rule for regulating the activity of the degradation bacteria community refers to the rule for dynamically adjusting the feeding gradient according to the activity of the degradation bacteria community. The degradation efficiency of organic fertilizer can be improved by regulating the activity of the degradation bacteria community.

[0107] In the embodiment of the present application, the degradation chain scission threshold of plant structural organic matter is first analyzed by the distribution of methane products and the concentration of intermediate products to determine the key influencing factors in the degradation process. Then, combined with the range of ambient temperature fluctuations, data mining technology is used to generate association rules between the staged feeding gradient and the regulation of degradation bacterial activity. Specifically, by analyzing the impact of ambient temperature fluctuations on the degradation chain scission threshold, combined with the distribution of methane products and the concentration of intermediate products, the association rules between the staged feeding gradient and the regulation of degradation bacterial activity are generated. Finally, by dynamically adjusting the feeding gradient, the degradation efficiency of organic fertilizers is maximized, carbon retention and methane generation are reduced, and the low-carbon goal of agricultural production is achieved.

[0108] 204. Integrate the soil pore diameter classification optimization interval with the association rule to generate a compensation strategy for the organic fertilizer feeding gradient.

[0109] In step 204, the soil pore diameter classification optimization interval refers to the optimized pore diameter range dynamically determined based on carbon retention and soil type. The association rules refer to the rules governing the phased feeding gradient and the activity of degrading bacteria. The organic fertilizer feeding gradient compensation strategy refers to an organic fertilizer feeding strategy dynamically adjusted based on the soil pore diameter classification optimization interval and the association rules. By dynamically adjusting the feeding gradient, the degradation efficiency of organic fertilizer can be improved and carbon emissions reduced.

[0110] In the embodiment of the present application, the optimization range of the pore structure is first determined based on the optimized interval of the soil pore diameter classification. Then, in combination with the association rules, the optimization algorithm is used to generate a compensation strategy for the organic fertilizer feeding gradient. Specifically, by analyzing the impact of the pore structure optimization range on the degradation efficiency, combined with the association rules of the staged feeding gradient and the regulation of the activity of the degradation bacteria, a compensation strategy for the organic fertilizer feeding gradient is generated. Finally, by dynamically adjusting the organic fertilizer feeding gradient, the degradation efficiency of the organic fertilizer is maximized, carbon emissions are reduced, and sustainable development of agricultural production is achieved.

[0111] Here's a specific example:

[0112] In a practical application of carbon footprint traceability for the entire life cycle of agricultural inputs, using a large-scale corn cultivation base as an example, the engine first inputs carbon retention data, including methane production distribution and intermediate product concentrations from chemical reaction chain data, resulting from incomplete organic fertilizer degradation during production. Based on the acquired soil type (e.g., loam, clay) and carbon retention, the engine dynamically determines the optimal range for pore structure parameters and generates a hierarchical optimization interval for soil pore diameter (e.g., 0.1-0.3 mm). Based on this interval and the distribution density of methanotrophic bacteria in different soil tillage layers (e.g., 0-20 cm), it generates a soil pore structure optimization plan to ensure that pore connectivity and gas diffusion resistance meet the requirements for methane oxidation. Subsequently, the engine analyzes the degradation chain breakage thresholds for plant structural organic matter (e.g., cellulose and hemicellulose) in organic fertilizer based on the methane production distribution and intermediate product concentrations. Furthermore, combined with the ambient temperature fluctuation range during production (e.g., 15-35°C), it generates association rules for regulating the activity of degrading bacteria in staged feeding gradients. For example, when the temperature is above 25°C, the feeding interval is shortened to 3 days, and the compensation coefficient for degradation bacterial activity is increased to 1.5. Finally, by integrating the soil pore diameter classification optimization interval with association rules, a compensation strategy for the organic fertilizer feeding gradient is generated, including a micro-oxygen flux threshold (0.5-1.2 mg / L), a feeding time window offset (±3 days), and a degradation rate compensation factor (1.2-1.5). This achieves dynamic optimization and precise control of carbon emissions during corn cultivation.

[0113] In summary, through steps 201 to 204, a dynamic analysis based on soil type and carbon retention was achieved, the optimization range of pore structure parameters was accurately determined, and the optimized interval of soil pore diameter classification was generated, providing a scientific basis for the optimization of soil microecological environment; effectively improving the soil gas diffusion efficiency and methane oxidation capacity; further, by analyzing the degradation chain break threshold of plant structural organic matter in organic fertilizers, the efficiency and stability of the organic fertilizer degradation process were ensured; finally, a compensation strategy for the organic fertilizer feeding gradient was generated, which significantly improved the organic matter degradation efficiency and soil carbon sequestration capacity, and provided a systematic and precise technical solution for greenhouse gas emission reduction and green and low-carbon transformation in agricultural production.

[0114] In order to solve the problem of carbon retention and methane emissions caused by the mismatch between soil pore structure and organic fertilizer degradation efficiency in agricultural production, a system for generating a gradient compensation strategy for organic fertilizer feeding based on a dynamic constraint relationship has been developed. This system achieves the coordinated optimization of soil pore structure and fertilizer degradation efficiency, significantly improves the degradation efficiency of organic fertilizer and effectively reduces greenhouse gas emissions, and provides precise technical support for the low-carbonization of agricultural production. In some embodiments, the fusion of the soil pore diameter classification optimization interval and the association rule in step 204 generates an organic fertilizer feeding gradient compensation strategy including a micro-oxygen flux threshold, a feeding time window offset, and a degradation rate compensation factor, including:

[0115] 301. Based on the pore connectivity parameters of different soil types in the soil pore diameter classification optimization interval, matching the organic fertilizer degradation rate attenuation coefficient corresponding to the ambient temperature fluctuation in the association rule, generating a dynamic constraint relationship table between soil pore diameter and degradation rate;

[0116] In step 301, the soil pore diameter classification optimization interval refers to the pore diameter optimization range adapted according to the organic matter decomposition rate, the pore connectivity parameter refers to the degree of connectivity between soil pores, the ambient temperature fluctuation refers to the range of changes in ambient temperature during the production process, the organic fertilizer degradation rate attenuation coefficient refers to the influence coefficient of ambient temperature fluctuation on the organic fertilizer degradation rate, and the dynamic constraint relationship table refers to the dynamic relationship table between soil pore diameter and degradation rate, which is used to guide the optimization of pore structure and the feeding strategy of organic fertilizer.

[0117] In an embodiment of the present application, first, by analyzing the pore connectivity parameters of different soil types, their influence on the degradation rate of organic fertilizer is determined. According to the pore connectivity parameters of different soil types in the soil pore diameter classification optimization interval, the organic fertilizer degradation rate attenuation coefficient corresponding to the ambient temperature fluctuation in the matching association rule is matched. Then, in combination with the influence of ambient temperature fluctuation on the degradation rate, a dynamic constraint relationship table is generated using data matching technology. A dynamic constraint relationship table of soil pore diameter and degradation rate is generated. Finally, through the dynamic constraint relationship table, the relationship between soil pore diameter and degradation rate is clarified, providing a basis for the generation of subsequent micro-oxygen flux threshold interval and feeding time window offset compensation range.

[0118] 302. Based on the dynamic constraint relationship table, the maximum methane diffusion flux allowed within the soil pore diameter classification optimization interval is analyzed, and the response delay time of the degradation bacterial community activity and the feeding gradient in the association rule is combined to generate a micro-oxygen flux threshold interval based on the soil tillage layer depth and a feeding time window offset compensation range;

[0119] In step 302, the maximum methane diffusion flux refers to the maximum methane diffusion allowed in the soil, the response delay time of the degradation bacteria activity and the feeding gradient refers to the response time of the degradation bacteria activity to the change of the feeding gradient, the soil tillage layer depth refers to the thickness of the tillage layer of farmland soil, the micro-oxygen flux threshold range refers to the oxygen flux range under the micro-oxygen environment in the soil, and the feeding time window offset compensation range refers to the feeding time offset range dynamically adjusted according to the activity of the degradation bacteria.

[0120] In the embodiment of the present application, first, the maximum methane diffusion flux is determined by a dynamic constraint relationship table to ensure that the oxidation efficiency of methane is maximized. Based on the dynamic constraint relationship table, the maximum methane diffusion flux allowed within the soil pore diameter classification optimization interval is analyzed. Then, the micro-oxygen flux threshold interval and the feeding time window offset compensation range are generated by combining the activity of the degradation bacteria and the response delay time of the feeding gradient using an optimization algorithm. The micro-oxygen flux threshold interval and the feeding time window offset compensation range are generated based on the depth of the soil tillage layer. Finally, by taking the depth of the soil tillage layer as a benchmark, the applicability and effectiveness of the micro-oxygen flux threshold interval and the feeding time window offset compensation range are ensured.

[0121] 303. According to the micro-oxygen flux threshold interval, the soil pore diameter classification optimization interval is reversely constrained to screen the pore diameter distribution pattern that meets the metabolic energy level requirements of methane oxidizing bacteria. At the same time, the degradation bacteria activity compensation coefficient is dynamically weighted based on the feeding time window offset compensation range to generate an organic fertilizer feeding gradient compensation strategy that includes pore reconstruction priority, feeding interval hot zone division and bacteria activation trigger conditions.

[0122] In step 303, the metabolic energy level requirement of methanotrophic bacteria refers to the requirement of methanotrophic bacteria for pore diameter during the metabolic process, the pore diameter distribution mode refers to the pore diameter distribution mode that meets the metabolic energy level requirement of methanotrophic bacteria, the degradation bacteria activity compensation coefficient refers to the compensation coefficient dynamically adjusted according to the activity of the degradation bacteria, the pore reconstruction priority refers to the priority order of soil pore structure optimization, the feeding interval hot zone division refers to the feeding interval area divided according to the feeding time window offset compensation range, and the bacteria activation trigger condition refers to the trigger condition of the degradation bacteria activity compensation coefficient.

[0123] In an embodiment of the present application, first, the soil pore diameter classification optimization interval is reversely constrained by the micro-oxygen flux threshold interval, and the pore diameter distribution pattern that meets the metabolic energy level requirements of methanotrophic bacteria is screened out. The soil pore diameter classification optimization interval is reversely constrained according to the micro-oxygen flux threshold interval, and the pore diameter distribution pattern that meets the metabolic energy level requirements of methanotrophic bacteria is screened out. Then, based on the feeding time window offset compensation range, the degradation bacteria activity compensation coefficient is adjusted using a dynamic weighted algorithm to generate an organic fertilizer feeding gradient compensation strategy that includes pore reconstruction priority, feeding interval hot zone division, and bacteria activation trigger conditions. By dynamically adjusting these parameters, the degradation efficiency of organic fertilizers is maximized and carbon emissions are reduced.

[0124] Here's a specific example:

[0125] In the carbon footprint traceability of agricultural inputs throughout their lifecycle, using a tomato cultivation base as an example, the dynamic relationship between soil pore diameter and organic fertilizer degradation rate was analyzed. Combined with seasonal temperature fluctuations (15-25°C in spring, 25-35°C in summer), a constraint relationship table between soil pore diameter and degradation rate was generated. It was determined that the degradation rate peaked at 25°C for pore diameters of 0.2-0.3 mm. Based on this, the maximum allowable methane diffusion flux within the optimized soil pore diameter range was determined. Combined with the response delay time of the degrading bacterial community and the feeding gradient (2 days in summer, 4 days in spring), the micro-oxygen flux threshold range (0.8-1.2 mg / L in summer, 0.5-0.8 mg / L in spring) and the feeding time window offset (±2 days in summer, ±3 days in spring) were determined based on a tillage layer depth of 20 cm. Finally, the pore diameter distribution pattern that meets the metabolic energy level of methane-oxidizing bacteria was screened (0.25-0.3mm in summer, 0.2-0.25mm in spring), and the degradation bacterial activity compensation coefficient was dynamically weighted (1.5 in summer, 1.2 in spring) to generate an organic fertilizer feeding gradient compensation strategy that includes pore reconstruction priority, feeding interval thermal zone division, and bacterial activation trigger conditions, to achieve precise optimization of carbon emissions.

[0126] In summary, through steps 301 to 303, the dynamic matching of the soil pore diameter classification optimization interval and the organic fertilizer degradation rate attenuation coefficient was achieved, and a precise constraint relationship between soil pore diameter and degradation rate was constructed, thereby optimizing the gas diffusion and methane oxidation efficiency of the soil microecological environment; finally, through reverse constraint screening, a pore diameter distribution pattern that meets the metabolic energy level of methane oxidizing bacteria was selected, and combined with a dynamically weighted microbial activity compensation coefficient, an organic fertilizer feeding gradient compensation strategy was formed, which included pore reconstruction priority, feeding interval hot zone division, and microbial activation triggering conditions, significantly improving the organic matter degradation efficiency and soil carbon sequestration capacity, and providing scientific support for the green and low-carbon transformation of agricultural production.

[0127] In order to solve the greenhouse gas emission problem caused by the incoordination between soil aeration parameters and the degradation rate of organic fertilizers in agricultural production, a collaborative optimization system based on microaerobic environment control and methane product conversion has been developed. This system realizes the efficient conversion of residual intermediate products to stable carbon forms under preset boundary conditions, effectively controls the degradation rate of organic fertilizers and significantly reduces greenhouse gas emissions, providing precise technical support for the low-carbonization of agricultural production. In some embodiments, the soil aeration parameters are adjusted according to the microaerobic environment control instructions in the compensation strategy in step 104, the soil microaerobic environment is optimized in combination with the soil pore structure optimization scheme, and the degradation rate of organic fertilizers is controlled by the methane product conversion scheme, including:

[0128] 401. Based on the compensation strategy, extract the target value range of soil oxygen concentration in the microaerobic environment control instruction, and obtain soil ventilation adjustment parameters according to the soil pore structure optimization scheme;

[0129] In step 401, the compensation strategy refers to a strategy for reducing carbon emissions by dynamically adjusting soil ventilation parameters and the gradient of organic fertilizer feeding. The microaerobic environment control instruction refers to an instruction for achieving microaerobic environment control by adjusting soil ventilation parameters, aiming to create a microaerobic environment suitable for the stable conversion of carbon elements. The target value range of soil oxygen concentration refers to the ideal range of oxygen concentration in the soil under a microaerobic environment, which is usually determined by experimental data or model calculations. The soil pore structure optimization program refers to a program for improving the migration and conversion efficiency of carbon elements by optimizing the soil pore structure (such as pore diameter, connectivity, etc.). The soil ventilation adjustment parameters refer to the ventilation parameters determined according to the soil pore structure optimization program, including ventilation volume, ventilation frequency, etc.

[0130] In the embodiment of the present application, first, based on the compensation strategy, the target value range of soil oxygen concentration in the microaerobic environment control instruction is extracted to clarify the oxygen concentration requirement under the microaerobic environment. Then, according to the soil pore structure optimization scheme, the soil ventilation adjustment parameters are obtained, including ventilation volume and ventilation frequency, etc. Specifically, by analyzing the impact of soil pore structure on oxygen concentration and combining the microaerobic environment control instruction, the ventilation adjustment parameters are determined. Finally, by integrating the target value range and ventilation adjustment parameters, data support is provided for the subsequent optimization of the soil microaerobic environment to ensure that the migration and conversion efficiency of carbon elements are maximized.

[0131] 402. Calculate the deviation between the current soil oxygen concentration and the target value range according to the microaerobic environment control instruction, and dynamically adjust the working mode of the soil aeration device in combination with the soil ventilation adjustment parameter to generate optimized soil microaerobic environment data.

[0132] In step 402, the microaerobic environment control instruction refers to an instruction for achieving microaerobic environment control by adjusting soil ventilation parameters. The current soil oxygen concentration refers to the oxygen concentration value in the soil monitored by the sensor network. The target value range refers to the ideal range of soil oxygen concentration in the microaerobic environment. The soil ventilation adjustment parameter refers to the ventilation parameter determined according to the soil pore structure optimization plan. The soil ventilation equipment refers to equipment used to adjust soil ventilation parameters, such as ventilation pipes, fans, etc. The optimized soil microaerobic environment data refers to the soil microaerobic environment data generated by dynamically adjusting the operating mode of the soil ventilation equipment.

[0133] In the embodiment of the present application, first, according to the micro-aerobic environment control instruction, the deviation between the current soil oxygen concentration and the target value range is calculated to clarify the adjustment requirement of the oxygen concentration. Then, in combination with the soil ventilation adjustment parameters, the optimization algorithm is used to dynamically adjust the working mode of the soil ventilation equipment, including ventilation volume and ventilation frequency, etc. Specifically, the working mode of the ventilation equipment is determined by analyzing the relationship between the oxygen concentration deviation and the ventilation adjustment parameters. Finally, the optimized soil micro-aerobic environment data is generated to ensure that the oxygen concentration in the soil is stable within the target value range, providing a suitable micro-aerobic environment for the degradation of organic fertilizers.

[0134] 403. Determine a target rate range for organic fertilizer degradation based on the methane product conversion scheme, and dynamically adjust the amount and distribution of organic fertilizer based on the soil microaerobic environment data to control the degradation rate of the organic fertilizer.

[0135] In step 403, the methane product conversion scheme refers to a scheme for converting methane into a stable carbon form by controlling the degradation rate of organic fertilizer. The target rate range for organic fertilizer degradation refers to the ideal rate range for organic fertilizer degradation determined based on the methane product conversion scheme. The soil microaerobic environment data refers to the soil microaerobic environment data generated by dynamically adjusting the operating mode of the soil aeration equipment. The amount of organic fertilizer fed refers to the amount of organic fertilizer fed. The distribution method of organic fertilizer refers to the distribution form of the organic fertilizer in the soil, such as uniform distribution or layered distribution.

[0136] In the embodiment of the present application, first, according to the methane product conversion scheme, the target rate range of organic fertilizer degradation is determined, and the adjustment demand of degradation rate is clarified. Then, based on the soil microaerobic environment data, the feed amount and distribution mode of organic fertilizer are dynamically adjusted using an optimization algorithm to ensure that the degradation rate is stable within the target range. Specifically, by analyzing the impact of the soil microaerobic environment on the degradation rate, combined with the target rate range, the feed amount and distribution mode of organic fertilizer are determined. Finally, by controlling the degradation rate of organic fertilizer, the residual intermediate product is converted into a stable carbon form, reducing the release of greenhouse gases such as methane, and realizing efficient conversion of carbon element.

[0137] Here's a specific example:

[0138] In the practical application of carbon footprint traceability for the entire life cycle of agricultural inputs, taking a large-scale vegetable cultivation base as an example, the system first extracts the target range of soil oxygen concentration (0.5-1.2 mg / L) from the microaerobic environment control instructions based on a compensation strategy. Then, based on the soil pore structure optimization plan, it obtains soil aeration adjustment parameters (such as an optimized pore diameter range of 0.1-0.3 mm and a rotary tillage depth of 15-20 cm). Subsequently, based on the microaerobic environment control instructions, the system calculates the deviation between the current soil oxygen concentration and the target range. Combined with the soil aeration adjustment parameters, the operating mode of the soil aeration equipment is dynamically adjusted (for example, increasing the rotary tillage frequency or adjusting the blade spacing). This generates optimized soil microaerobic environment data, ensuring that the soil oxygen concentration remains stable within the target range. Next, based on the methane conversion plan, the target degradation rate range for organic fertilizer was determined (e.g., 0.1-0.15 kg / m² per day). Based on the optimized soil microaerobic environment data, the dosage and distribution of organic fertilizer were dynamically adjusted (e.g., reducing the dosage by 10% and adopting a uniform distribution method) to control the degradation rate of organic fertilizer to meet the target rate range. These steps achieved precise control of the soil microaerobic environment and optimized organic fertilizer degradation rate during vegetable cultivation, effectively reducing carbon emissions.

[0139] In summary, through steps 401 to 403, precise control of the soil microaerobic environment is achieved; optimized soil microaerobic environment data is generated, which significantly improves soil ventilation performance and gas diffusion efficiency; the target rate range of organic fertilizer degradation is determined according to the methane product conversion scheme, and based on the optimized soil microaerobic environment data, the feeding amount and distribution method of organic fertilizer are dynamically adjusted, thereby achieving precise control of the degradation rate of organic fertilizer, effectively reducing greenhouse gas emissions, and providing scientific and efficient technical support for carbon cycle optimization and green and low-carbon transformation in agricultural production.

[0140] In order to solve the problem of methane generation and greenhouse gas emissions caused by insufficient degradation of plant structural organic matter in organic fertilizers during agricultural production, a staged feeding gradient and degradation bacterial activity control system based on temperature fluctuation and degradation chain scission threshold has been developed, which achieves efficient degradation of plant structural organic matter and effective control of methane generation, significantly reduces greenhouse gas emissions, and provides precise technical support for low-carbon agricultural production. In some embodiments, the degradation chain scission threshold of plant structural organic matter in organic fertilizers is analyzed based on the distribution of methane products and the concentration of intermediate products in step 203. The degradation chain scission threshold is combined with the ambient temperature fluctuation range during the production process to generate association rules for staged feeding gradient and degradation bacterial activity control, including:

[0141] 501. Based on the methane generation distribution and intermediate product concentration, analyze the degradation chain scission threshold of plant structural organic matter in the organic fertilizer. According to the degradation chain scission threshold, divide the degradation process of the organic fertilizer into multiple stages and determine the feeding gradient range for each stage;

[0142] In step 501, the methane product distribution refers to the spatial distribution of methane generated during the degradation process of organic fertilizer in the soil. The intermediate product concentration refers to the concentration value of the intermediate product generated during the reaction process in the chemical reaction chain data. The degradation chain breaking threshold of plant structural organic matter refers to the chain breaking conditions of plant structural organic matter in organic fertilizer during the degradation process, including factors such as temperature, humidity and microbial activity. The degradation stage refers to the different stages of the degradation process of organic fertilizer divided according to the degradation chain breaking threshold. The feeding gradient range refers to the feeding amount range of organic fertilizer in each degradation stage.

[0143] In the embodiment of the present application, first, based on the distribution of methane products and the concentration of intermediate products, the degradation chain scission threshold value of plant structural organic matter in organic fertilizers is analyzed to clarify the key influencing factors in the degradation process. Then, according to the degradation chain scission threshold value, the degradation process of organic fertilizers is divided into multiple stages, and the feeding gradient range of each stage is determined. Specifically, by analyzing the influence of the distribution of methane products and the concentration of intermediate products on the degradation chain scission threshold value, combined with the degradation characteristics of organic fertilizers, the degradation process is divided into multiple stages, and the feeding gradient range is determined for each stage. Finally, through this division and range determination, data support is provided for the subsequent regulation of the activity of the degradation flora to ensure that the degradation efficiency of organic fertilizers is maximized.

[0144] 502. Based on the ambient temperature fluctuation range during the production process, identify the influence of temperature changes on the degradation chain scission threshold and establish a mapping relationship between temperature and degradation chain scission threshold. Combined with this mapping relationship, analyze the changing trend of degradation bacterial activity under different temperature conditions to determine the influence of temperature on the activity of degradation bacterial populations;

[0145] In step 502, the ambient temperature fluctuation range refers to the range of ambient temperature fluctuations during the production process. The influence of temperature changes on the degradation scission threshold refers to the trends and patterns of the impact of temperature changes on the degradation scission threshold. The mapping relationship between temperature and degradation scission threshold refers to the quantitative relationship between temperature and degradation scission threshold. Degradation bacterial activity refers to the activity level of degrading bacteria in the soil, which is typically affected by factors such as temperature, humidity, and nutrient availability. The influence of temperature on degrading bacterial activity refers to the trends and patterns of the impact of temperature changes on degrading bacterial activity.

[0146] In the embodiments of the present application, first, based on the range of ambient temperature fluctuations during the production process, the influence of temperature changes on the degradation chain scission threshold is identified, and the mechanism of action of temperature on the degradation chain scission threshold is clarified. Next, a mapping relationship between temperature and degradation chain scission threshold is established using data mining technology to quantify the influence of temperature on the degradation chain scission threshold. Then, combined with the mapping relationship, the changing trend of the activity of the degradation flora under different temperature conditions is analyzed to determine the influence of temperature on the activity of the degradation flora. Specifically, by analyzing the influence of temperature fluctuations on the activity of the degradation flora, combined with the changing trend of the degradation chain scission threshold, the mechanism of action of temperature on the activity of the degradation flora is clarified. Finally, through this regular determination, a scientific basis is provided for the subsequent generation of association rules to ensure the effective regulation of the activity of the degradation flora.

[0147] 503. Based on the degradation chain scission threshold and the feeding gradient range, and in combination with the influencing rules, an association rule between the staged feeding gradient and the regulation of the activity of the degradation bacteria is generated.

[0148] In step 503, the degradation chain scission threshold refers to the chain scission condition of plant structural organic matter in the organic fertilizer during the degradation process. The feeding gradient range refers to the feeding amount range of the organic fertilizer in each degradation stage. The influence rule refers to the influence of temperature on the activity of the degradation bacteria. The association rule for regulating the staged feeding gradient and the activity of the degradation bacteria refers to the association rule generated based on the degradation chain scission threshold and the feeding gradient range, combined with the influence of temperature on the activity of the degradation bacteria, and is used to dynamically adjust the feeding gradient of the organic fertilizer and the activity of the degradation bacteria.

[0149] In the embodiment of the present application, first, based on the degradation chain scission threshold and the feeding gradient range, the feeding requirements of each stage in the degradation process of organic fertilizer are clarified. Then, combined with the influence of temperature on the activity of degradation bacteria, an optimization algorithm is used to generate association rules for regulating the feeding gradient and the activity of degradation bacteria in stages. Specifically, by analyzing the influence of the degradation chain scission threshold and the feeding gradient range on the degradation efficiency, combined with the mechanism of action of temperature on the activity of degradation bacteria, association rules are generated. Finally, through this association rule, the feeding gradient and degradation bacteria activity of organic fertilizer are dynamically adjusted to ensure that the degradation efficiency of organic fertilizer is maximized and carbon retention and methane production are reduced.

[0150] Here's a specific example:

[0151] In the practical application of carbon footprint traceability for the entire life cycle of agricultural inputs, using a large-scale rice cultivation base as an example, the degradation chain-scission thresholds of plant structural organic matter (such as cellulose and hemicellulose) in organic fertilizers were first determined based on the distribution of methane production and the concentration of intermediate products. Based on the degradation chain-scission thresholds, the degradation process of organic fertilizers was divided into multiple stages (e.g., initial stage, rapid degradation stage, and stable stage), and the feeding gradient range for each stage was determined (e.g., feeding every 3 days in the initial stage and every 5 days in the rapid degradation stage). Subsequently, based on the ambient temperature fluctuation range during the production process (e.g., 15-35°C), the impact of temperature changes on the degradation chain-scission thresholds was identified, and a mapping relationship between temperature and degradation chain-scission thresholds was established (e.g., for every 5°C increase in temperature, the degradation chain-scission threshold decreased by 10%). Based on this mapping relationship, the changing trends in the activity of degrading bacteria under different temperature conditions were analyzed (e.g., bacterial activity significantly decreased when the temperature was below 20°C), confirming the influence of temperature on the activity of degrading bacteria. Finally, based on the degradation chain scission threshold and feeding gradient range, and combined with the influence of temperature on the activity of degrading bacteria, we generated rules linking the staged feeding gradient with the regulation of degrading bacterial activity (e.g., shortening the feeding interval to 3 days when the temperature is above 25°C and extending it to 7 days when the temperature is below 20°C, with the bacterial activity compensation coefficient adjusted to 1.2-1.5). Through these steps, we achieved precise control of the degradation rate of organic fertilizers during rice cultivation, effectively optimizing carbon emissions management.

[0152] In summary, steps 501 to 503 provide a scientific basis for the precise control of the organic fertilizer degradation process. At the same time, by identifying the influence of ambient temperature fluctuations on the degradation chain scission threshold, a mapping relationship between temperature and the degradation chain scission threshold is established, and combined with the changing trend of the degradation bacterial community activity under different temperature conditions, the influence of temperature on the degradation bacterial community activity is determined, and dynamic regulation of the degradation process is achieved. Finally, based on the degradation chain scission threshold and the feeding gradient range, combined with the influence of temperature on the degradation bacterial community activity, an association rule between the staged feeding gradient and the regulation of the degradation bacterial community activity is generated, which significantly improves the degradation efficiency and stability of organic fertilizers, and provides a systematic and precise technical solution for greenhouse gas emission reduction and efficient resource utilization in agricultural production.

[0153] In order to solve the problem of methane generation and greenhouse gas emissions caused by the lack of coordination between rotary tillage and microbial agent spraying in agricultural production, a collaborative control system based on the coupling of tillage depth gradient and microbial agent spraying path has been developed. This achieves efficient coordination between rotary tillage and microbial agent spraying, significantly reduces methane generation and greenhouse gas emissions, and provides precise technical support for low-carbon agricultural production. In some embodiments, the target rate range of organic fertilizer degradation is determined according to the methane product conversion scheme in step 403, and the feeding amount and distribution method of organic fertilizer are dynamically adjusted based on the soil microaerobic environment data to control the degradation rate of organic fertilizer, including:

[0154] 601. Extracting a target rate range for organic fertilizer degradation according to the methane product conversion scheme, including an upper limit and a lower limit of the degradation rate, and analyzing the effect of the current soil oxygen concentration on the degradation rate based on the soil microaerobic environment data to determine applicable conditions for the target rate range;

[0155] In step 601, the methane product conversion scheme refers to a scheme for converting methane into a stable carbon form by controlling the degradation rate of organic fertilizer. The target rate range for organic fertilizer degradation refers to the ideal rate range for organic fertilizer degradation determined according to the methane product conversion scheme, including the upper and lower limits of the degradation rate. Soil microaerobic environment data refers to soil microaerobic environment data generated by dynamically adjusting the working mode of the soil aeration equipment. The current soil oxygen concentration refers to the oxygen concentration value in the soil monitored by the sensor network. The applicable conditions of the target rate range refer to the applicable conditions of the target rate range in actual production, including factors such as soil oxygen concentration and temperature.

[0156] In the embodiment of the present application, first, according to the methane product conversion scheme, the target rate range of organic fertilizer degradation is extracted to clarify the ideal range of degradation rate. Then, based on the soil microaerobic environment data, the influence of the current soil oxygen concentration on the degradation rate is analyzed to determine the applicable conditions of the target rate range. Specifically, by analyzing the relationship between soil oxygen concentration and degradation rate, combined with the target rate range, its applicable conditions under the current soil microaerobic environment are determined. Finally, through this applicable condition, a scientific basis is provided for subsequent feed amount adjustment to ensure that the degradation rate is stable within the target range.

[0157] 602. Calculate the deviation between the current organic fertilizer degradation rate and the target rate range according to the target rate range, and determine the adjustment amount of the feed amount based on the deviation;

[0158] In step 602, the target rate range refers to the ideal rate range for organic fertilizer degradation. The current organic fertilizer degradation rate refers to the organic fertilizer degradation rate value monitored by the sensor network. The deviation value refers to the difference between the current degradation rate and the target rate range. The feed amount adjustment value refers to the adjustment value of the organic fertilizer feed amount determined based on the deviation value.

[0159] In the embodiment of the present application, first, the deviation between the current organic fertilizer degradation rate and the target rate range is calculated according to the target rate range, and the adjustment requirement of the degradation rate is clarified. Then, based on the deviation, the adjustment amount of the feed amount is determined using an optimization algorithm. Specifically, by analyzing the impact of the deviation on the degradation rate, the adjustment amount of the feed amount is determined in combination with the target rate range. Finally, through this adjustment amount, data support is provided for subsequent adjustments to the feed amount and distribution mode, ensuring that the degradation rate is stable within the target range.

[0160] 603. Based on the applicable conditions of the target rate range and according to the adjustment amount, dynamically adjust the feeding amount and distribution method of the organic fertilizer to ensure that the degradation rate is stable within the target rate range.

[0161] In step 603, the applicable conditions of the target rate range refer to the applicable conditions of the target rate range in actual production. The adjustment amount refers to the adjustment value of the organic fertilizer feeding amount determined based on the deviation amount. The feeding amount of organic fertilizer refers to the amount of organic fertilizer fed. The distribution mode of organic fertilizer refers to the distribution form of organic fertilizer in the soil, such as uniform distribution or layered distribution.

[0162] In the embodiment of the present application, first, based on the applicable conditions of the target rate range, the environmental restrictions for adjusting the degradation rate are clarified. Then, according to the adjustment amount, the feed amount and distribution mode of the organic fertilizer are dynamically adjusted using an optimization algorithm. Specifically, by analyzing the impact of the applicable conditions on the degradation rate, combined with the adjustment amount, an adjustment scheme for the feed amount and distribution mode is determined. Finally, by dynamically adjusting the feed amount and distribution mode of the organic fertilizer, it is ensured that the degradation rate is stable within the target rate range, carbon retention and methane generation are reduced, and efficient conversion of carbon elements is achieved.

[0163] Here's a specific example:

[0164] In the practical application of carbon footprint traceability for the entire life cycle of agricultural inputs, taking a large-scale wheat planting base as an example, the target rate range for organic fertilizer degradation is first extracted based on the methane product conversion plan, including the upper limit (e.g., 0.15kg / m² per day) and lower limit (e.g., 0.1kg / m² per day) of the degradation rate. Based on soil microaerobic environment data, the impact of the current soil oxygen concentration (e.g., 0.8mg / L) on the degradation rate is analyzed to determine the applicable conditions for the target rate range (e.g., the soil oxygen concentration needs to be maintained between 0.5-1.2mg / L). Subsequently, based on the target rate range, the deviation between the current organic fertilizer degradation rate (e.g., 0.12kg / m² per day) and the target rate range is calculated (e.g., below the lower limit by 0.02kg / m²), and the feed rate adjustment is determined based on this deviation (e.g., increasing the feed rate by 5%). Finally, based on the applicable conditions within the target rate range, the organic fertilizer dosage and distribution are dynamically adjusted according to the adjustment amount (for example, increasing the dosage from 500kg to 525kg per hectare and evenly distributing it) to ensure that the degradation rate remains within the target rate range (for example, 0.1-0.15kg / m² per day). Through these steps, the organic fertilizer degradation rate during wheat cultivation is precisely controlled, effectively optimizing carbon emissions management.

[0165] In summary, steps 601 to 603 are used to determine the applicable conditions of the target rate range, providing a scientific basis for the precise control of the degradation rate. At the same time, by calculating the deviation between the current organic fertilizer degradation rate and the target rate range and determining the adjustment amount of the feed amount based on the deviation, the dynamic optimization of the organic fertilizer degradation process is achieved. Finally, based on the applicable conditions of the target rate range, the feed amount and distribution method of the organic fertilizer are dynamically adjusted according to the adjustment amount to ensure that the degradation rate is stable within the target rate range, significantly improving the degradation efficiency and stability of the organic fertilizer, and providing a systematic and precise technical solution for greenhouse gas emission reduction and efficient resource utilization in agricultural production.

[0166] To address the incomplete carbon reduction effect evaluation caused by inaccurate monitoring of stable carbon form migration paths in agricultural production, a carbon cycle balance map generation system based on dynamic monitoring of carbon migration paths and fusion of chemical reaction chain data has been developed. This system achieves accurate monitoring of carbon migration paths and scientific evaluation of carbon reduction effects during agricultural production, providing comprehensive data support for low-carbon agriculture. In some embodiments, the dynamic monitoring of the stable carbon form migration path in step 105, combined with the adjusted chemical reaction chain data, generates a carbon cycle balance map for evaluating carbon reduction effects, including:

[0167] 701. During the production process, monitoring points are set up along the geographical distribution of the field irrigation channels, and the stable carbon form retention and lateral diffusion rate at different soil tillage layer depths are collected. The collected retention and diffusion rate data are integrated to generate dynamic monitoring data corresponding to the migration path based on the field irrigation channels as the geographical reference;

[0168] In step 701, the production process refers to the application and degradation process of organic fertilizers in the agricultural production process. The field irrigation channel refers to the irrigation channel in the farmland, which is usually used as a geographical benchmark for the spatial distribution of monitoring data. The monitoring point refers to the point set along the field irrigation channel for collecting data. The depth of the soil tillage layer refers to the thickness of the tillage layer of the farmland soil, which is usually divided into the surface layer, the middle layer and the deep layer. Different tillage layers have a significant impact on the migration and conversion efficiency of carbon elements. The stable carbon form retention refers to the retention of the stable carbon form in the soil, which is specifically manifested as the accumulation of incompletely decomposed organic carbon in the soil. The lateral diffusion rate refers to the lateral diffusion speed of the stable carbon form in the soil, which is usually affected by the soil pore structure and moisture content. Dynamic monitoring data refers to the stable carbon form retention and lateral diffusion rate data collected through the monitoring points, and the migration path dynamic monitoring data generated after integration with the field irrigation channel as the geographical benchmark.

[0169] In the embodiment of the present application, monitoring points are first set up along the geographical distribution of the field irrigation channel to ensure the spatial coverage and representativeness of the monitoring data. Then, the stable carbon form retention and lateral diffusion rate at different soil tillage layer depths are collected through the sensor network to obtain the migration and transformation data of the stable carbon form in the soil. Then, the collected retention and diffusion rate data are integrated, and spatial analysis technology is used to generate dynamic monitoring data of the migration path with the field irrigation channel as the geographical reference. Specifically, by analyzing the retention and diffusion rate of the stable carbon form in different tillage layers, combined with the geographical information of the field irrigation channel, dynamic monitoring data of the migration path is generated. Finally, this data provides a scientific basis for the subsequent optimization of carbon migration paths and carbon emission control, ensuring the efficient transformation of carbon elements and a significant reduction in greenhouse gas release. 702. According to the dynamic monitoring data, the migration time delay of the stable carbon form under the soil pore connectivity gradient is analyzed, and combined with the concentration change rate of the organic fertilizer degradation intermediate product in the adjusted chemical reaction chain data, a dynamic mapping relationship table of the stable carbon form conversion efficiency and degradation rate is generated;

[0170] In step 702, the dynamic monitoring data of the carbon migration path refers to the dynamic monitoring data of the carbon migration path based on the field irrigation channel as the geographical benchmark, the soil pore connectivity gradient refers to the change gradient of the soil pore connectivity, the migration time delay refers to the migration time delay value of the stable carbon form under the soil pore connectivity gradient, the chemical reaction chain data refers to the chemical reaction data of each stage in the production process, the concentration change rate of the intermediate product of organic fertilizer degradation refers to the changing rate of the intermediate product concentration during the degradation process of organic fertilizer, the stable carbon form conversion efficiency refers to the conversion efficiency of the stable carbon form, the degradation rate refers to the degradation rate of the organic fertilizer, and the dynamic mapping relationship table refers to the dynamic relationship table between the stable carbon form conversion efficiency and the degradation rate.

[0171] In the embodiment of the present application, first, the migration time delay of the stable carbon form under the soil pore connectivity gradient is determined by the dynamic monitoring data of the carbon migration path. According to the dynamic monitoring data of the carbon migration path, the migration time delay of the stable carbon form under the soil pore connectivity gradient is analyzed, and the concentration change rate of the organic fertilizer degradation intermediate product in the adjusted chemical reaction chain data is combined to generate a dynamic mapping relationship table of the stable carbon form conversion efficiency and the degradation rate. Then, combined with the concentration change rate of the organic fertilizer degradation intermediate product, a dynamic mapping relationship table is generated using data mining technology to clarify the relationship between the stable carbon form conversion efficiency and the degradation rate, providing a basis for the subsequent generation of the carbon cycle dynamic parameter set.

[0172] 703. Based on the dynamic mapping relationship table, matching the constraints of the tillage layer depth and stable carbon form migration rate of different soil textures in the open-air farmland, generating a carbon cycle dynamic parameter set including the humus retention hot zone boundary, biochar lateral diffusion gradient, and methane oxidation inhibition priority;

[0173] In step 703, open-air farmland refers to the open-air environment during the farmland production process, soil texture refers to the physical properties of farmland soil, such as sandy soil, clay soil, etc., tillage layer depth refers to the thickness of the tillage layer of farmland soil, stable carbon form migration rate refers to the migration speed of stable carbon form in the soil, humus retention hot zone boundary refers to the hot spot area boundary where humus is retained in the soil, biochar lateral diffusion gradient refers to the lateral diffusion gradient of biochar in the soil, methane oxidation inhibition priority refers to the priority order of controlling the activity of methane oxidizing bacteria, and carbon cycle dynamic parameter set refers to the carbon cycle dynamic parameter set including humus retention hot zone boundary, biochar lateral diffusion gradient and methane oxidation inhibition priority.

[0174] In the embodiment of the present application, first, the constraints of the stable carbon form migration rate and the tillage layer depth are determined through a dynamic mapping relationship table. Based on the dynamic mapping relationship table, the tillage layer depth of different soil textures in open-air farmland is matched with the constraints of the stable carbon form migration rate, and a carbon cycle dynamic parameter set including the humus retention hot zone boundary, biochar lateral diffusion gradient and methane oxidation inhibition priority is generated. Then, in combination with different soil textures, an optimization algorithm is used to generate a carbon cycle dynamic parameter set to ensure the applicability and effectiveness of the humus retention hot zone boundary, biochar lateral diffusion gradient and methane oxidation inhibition priority.

[0175] 704. Spatially superimpose the carbon cycle dynamic parameter set and the adjusted chemical reaction chain data to generate a carbon cycle balance map with the tillage layer depth as the vertical axis and the field irrigation channel as the horizontal axis.

[0176] In step 704, the carbon cycle dynamic parameter set refers to a set of carbon cycle dynamic parameters including the humus retention hot zone boundary, the biochar lateral diffusion gradient and the methane oxidation inhibition priority, the chemical reaction chain data refers to the chemical reaction data of each stage in the production process, the spatial superposition refers to the operation of spatially superimposing the carbon cycle dynamic parameter set and the chemical reaction chain data, the tillage layer depth refers to the thickness of the tillage layer of farmland soil, the field irrigation channel refers to the irrigation channel in the farmland, and the carbon cycle balance map refers to the carbon cycle balance map with the tillage layer depth as the vertical axis and the field irrigation channel as the horizontal axis.

[0177] In the examples of this application, a spatial overlay technique is first used to integrate the carbon cycle dynamic parameter set with chemical reaction chain data to determine the migration and transformation pathways of carbon in the soil. The carbon cycle dynamic parameter set and the adjusted chemical reaction chain data are spatially overlaid to generate a carbon cycle balance map with the tillage layer depth as the vertical axis and the field irrigation access as the horizontal axis. Then, combining the geographic information of the tillage layer depth and the field irrigation access, a carbon cycle balance map is generated, providing a scientific basis for carbon emission control during agricultural production.

[0178] Here's a specific example:

[0179] In the practical application of carbon footprint traceability for agricultural inputs, taking a large-scale rice cultivation base as an example, based on the chemical bond stability threshold of stable carbon forms, the stable carbon form retention and lateral diffusion rate at different soil tillage depths are collected simultaneously during the production process. This generates dynamic monitoring data on carbon migration pathways based on the field irrigation channels as a geographical reference. For example, within the tillage depth range of 10-20 cm, the stable carbon form retention is 0.8 kg per square meter and the lateral diffusion rate is 0.2 m per hour. Subsequently, based on this dynamic monitoring data, the migration time delay of stable carbon forms under the soil pore connectivity gradient is analyzed. Combined with the concentration change rate of organic fertilizer degradation intermediates in the adjusted chemical reaction chain data, a dynamic mapping relationship table is generated between the stable carbon form conversion efficiency and degradation rate. For example, at a pore connectivity of 0.5, the stable carbon form conversion efficiency reaches 85%, and the degradation rate is 0.15 kg / m² per hour. Next, based on a dynamic mapping table, the researchers matched the tillage depth of different soil textures in open-pit farmland with the constraints of stable carbon form migration rates. This generated a set of dynamic carbon cycle parameters, including the boundary of the humus retention hotspot (within 2 meters of the field ridge), the lateral diffusion gradient of biochar (0.5 kg of biochar per 10 meters), and the priority of methane oxidation inhibition. Finally, the dynamic carbon cycle parameter set was spatially overlaid with the adjusted chemical reaction chain data to generate a carbon cycle balance map with tillage depth as the vertical axis and field irrigation access as the horizontal axis. This provides a scientific basis and precise guidance for optimizing carbon emissions during rice cultivation.

[0180] In summary, through steps 701 to 704, the stable carbon form retention and lateral diffusion rate at different soil tillage layer depths were synchronously collected, and a dynamic mapping relationship table of stable carbon form conversion efficiency and degradation rate was constructed, providing a scientific basis for the optimization of the carbon cycle process; at the same time, a carbon cycle dynamic parameter set including the humus retention hot zone boundary, biochar lateral diffusion gradient and methane oxidation inhibition priority was generated, which significantly improved the soil carbon sequestration capacity and greenhouse gas emission reduction effect; finally, by spatially superimposing the carbon cycle dynamic parameter set with the chemical reaction chain data, a carbon cycle balance map was generated with the tillage layer depth as the vertical axis and the field irrigation channel as the horizontal axis, providing efficient and reliable technical support for the carbon cycle balance management and sustainable development of farmland ecosystems.

[0181] Figure 2 The present invention provides a structural diagram of a carbon reduction effect evaluation system for the production process of healthy agricultural products. Figure 2 As shown, the system includes:

[0182] Acquisition module 21 tracks the carbon migration path during fertilizer application, organic fertilizer degradation, and soil respiration, while collecting chemical reaction chain data at each stage of the production process;

[0183] Matching module 22 determines the carbon emission concentration area based on the carbon element migration path, combines the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database, and analyzes the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers during the production process;

[0184] The compensation module 23 inputs the carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data into a carbon emission dynamic compensation engine, and generates a soil pore structure optimization scheme and an organic fertilizer feed gradient compensation strategy based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards;

[0185] An adjustment module 24 adjusts soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizes the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controls the degradation rate of organic fertilizers through the methane product conversion scheme to convert residual intermediate products into stable carbon forms, thereby reducing greenhouse gas emissions;

[0186] The generation module 25 dynamically monitors the migration path corresponding to the stable carbon form, and generates a carbon cycle balance map for evaluating the carbon reduction effect by combining the adjusted soil aeration parameters and the chemical reaction chain data.

[0187] Figure 2 The carbon reduction effect evaluation system for the production process of healthy agricultural products can be implemented Figure 1The implementation principle and technical effects of the carbon reduction effect evaluation method for the production of healthy agricultural products described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the carbon reduction effect evaluation system for the production of healthy agricultural products in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0188] In one possible design, Figure 2 The carbon reduction effect evaluation system for the production process of healthy agricultural products of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0189] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0190] The processing component 32 is used for the above Figure 1 The embodiment provides a method for evaluating carbon reduction effects in the production process of healthy agricultural products.

[0191] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0192] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0193] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0194] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0195] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0196] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources obtained from the cloud computing platform.

[0197] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a method for evaluating carbon reduction effects in the production process of healthy agricultural products.

[0198] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0199] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0200] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating carbon reduction effects in the production process of healthy agricultural products, characterized in that: include: Tracking the carbon migration pathways during fertilizer application, organic fertilizer degradation, and soil respiration, while collecting chemical reaction chain data at each stage of the production process; Determine the carbon emission concentration area based on the carbon element migration path, combine the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database, and analyze the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers during the production process; The carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data are input into a carbon emission dynamic compensation engine, and based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards, an optimization plan for soil pore structure and a compensation strategy for the organic fertilizer feeding gradient are generated; Adjusting soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizing the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controlling the degradation rate of organic fertilizers through a methane product conversion scheme to convert residual intermediate products into stable carbon forms, thereby reducing greenhouse gas emissions; The migration path corresponding to the stable carbon form is dynamically monitored, and a carbon cycle balance map for evaluating the carbon reduction effect is generated by combining the adjusted soil aeration parameters and chemical reaction chain data.

2. The method according to claim 1, characterized in that The carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data are input into a carbon emission dynamic compensation engine. Based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards, a soil pore structure optimization plan and an organic fertilizer feeding gradient compensation strategy are generated, including: The carbon retention amount, methane product distribution, and intermediate product concentration in the chemical reaction chain data caused by incomplete degradation of organic fertilizers during the production process are input into the carbon emission dynamic compensation engine, so that the carbon emission dynamic compensation engine can dynamically determine the optimization range of pore structure parameters based on the obtained soil type and carbon retention amount to generate a graded optimization interval for soil pore diameter; Based on the soil pore diameter classification optimization interval and the distribution density of methane oxidizing bacteria in different soil tillage layers, an optimization scheme for soil pore structure is generated; The carbon emission dynamic compensation engine analyzes the degradation chain break threshold of plant structural organic matter in organic fertilizers based on the distribution of methane products and the concentration of intermediate products. Combined with the range of ambient temperature fluctuations during the production process, it generates association rules between the staged feeding gradient and the regulation of degradation bacterial activity. The soil pore diameter classification optimization interval and the association rule are integrated to generate a compensation strategy for the organic fertilizer feeding gradient.

3. The method according to claim 2, characterized in that The soil pore diameter classification optimization interval and the association rule are integrated to generate an organic fertilizer feeding gradient compensation strategy including a micro-oxygen flux threshold, a feeding time window offset, and a degradation rate compensation factor, including: According to the pore connectivity parameters of different soil types in the soil pore diameter classification optimization interval, the organic fertilizer degradation rate attenuation coefficient corresponding to the ambient temperature fluctuation in the association rule is matched to generate a dynamic constraint relationship table between soil pore diameter and degradation rate; Based on the dynamic constraint relationship table, the maximum methane diffusion flux allowed within the soil pore diameter classification optimization interval is analyzed, and the response delay time of the degradation bacterial community activity and the feeding gradient in the association rule is combined to generate the micro-oxygen flux threshold interval and the feeding time window offset compensation range based on the soil tillage layer depth; The soil pore diameter classification optimization interval is reversely constrained according to the micro-oxygen flux threshold interval, and a pore diameter distribution pattern that meets the metabolic energy level requirements of methanotrophic bacteria is screened. At the same time, the degradation bacteria activity compensation coefficient is dynamically weighted based on the feeding time window offset compensation range to generate an organic fertilizer feeding gradient compensation strategy that includes pore reconstruction priority, feeding interval hot zone division, and bacteria activation trigger conditions.

4. The method according to claim 1, wherein Adjusting soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizing the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controlling the degradation rate of organic fertilizers through a methane product conversion scheme, including: Based on the compensation strategy, extracting the target value range of soil oxygen concentration in the microaerobic environment control instruction, and obtaining soil ventilation adjustment parameters according to the soil pore structure optimization scheme; Calculating the deviation between the current soil oxygen concentration and the target value range according to the microaerobic environment control instruction, and dynamically adjusting the working mode of the soil ventilation equipment in combination with the soil ventilation adjustment parameter to generate optimized soil microaerobic environment data; According to the methane product conversion plan, the target rate range of organic fertilizer degradation is determined, and based on the soil microaerobic environment data, the feeding amount and distribution method of organic fertilizer are dynamically adjusted to control the degradation rate of organic fertilizer.

5. The method according to claim 2, characterized in that Based on the distribution of methane production and the concentration of intermediate products, the degradation chain scission threshold of plant structural organic matter in organic fertilizers is analyzed. Combined with the range of ambient temperature fluctuations during the production process, association rules between the staged feeding gradient and the regulation of degradation bacterial activity are generated, including: Based on the methane generation distribution and the intermediate product concentration, the degradation chain scission threshold of plant structural organic matter in the organic fertilizer is analyzed, and according to the degradation chain scission threshold, the degradation process of the organic fertilizer is divided into multiple stages, and the feeding gradient range of each stage is determined; Based on the ambient temperature fluctuation range during the production process, identify the influence of temperature changes on the degradation chain scission threshold and establish a mapping relationship between temperature and degradation chain scission threshold; Combined with the mapping relationship, the changing trend of the activity of the degrading bacteria under different temperature conditions is analyzed to determine the influence of temperature on the activity of the degrading bacteria; Based on the degradation chain scission threshold and the feeding gradient range, and in combination with the influencing rules, an association rule between the staged feeding gradient and the regulation of the activity of the degradation bacterial community is generated.

6. The method according to claim 4, characterized in that According to the methane product conversion plan, the target rate range of organic fertilizer degradation is determined, and based on the soil microaerobic environment data, the feeding amount and distribution method of organic fertilizer are dynamically adjusted to control the degradation rate of organic fertilizer, including: Extracting a target rate range for organic fertilizer degradation based on the methane product conversion scheme, including upper and lower limits of the degradation rate, and analyzing the effect of the current soil oxygen concentration on the degradation rate based on the soil microaerobic environment data to determine the applicable conditions of the target rate range; According to the target rate range, calculating the deviation between the current organic fertilizer degradation rate and the target rate range, and determining the adjustment amount of the feeding amount based on the deviation; Based on the applicable conditions of the target rate range, the feeding amount and distribution mode of the organic fertilizer are dynamically adjusted according to the adjustment amount to ensure that the degradation rate is stable within the target rate range.

7. The method according to claim 1, characterized in that Dynamically monitor the migration path of the stable carbon form and generate a carbon cycle balance map for evaluating the carbon reduction effect by combining the adjusted chemical reaction chain data, including: During the production process, monitoring points are set up along the geographical distribution of the field irrigation channels, and the stable carbon form retention and lateral diffusion rate at different soil tillage layer depths are collected. The collected retention and diffusion rate data are integrated to generate dynamic monitoring data corresponding to the migration path based on the field irrigation channels as the geographical reference; Based on the dynamic monitoring data, the migration time delay of the stable carbon form under the soil pore connectivity gradient is analyzed, and combined with the concentration change rate of the organic fertilizer degradation intermediate product in the adjusted chemical reaction chain data, a dynamic mapping relationship table of the stable carbon form conversion efficiency and degradation rate is generated; Based on the dynamic mapping relationship table, the constraints of the tillage layer depth and stable carbon form migration rate of different soil textures in open-air farmland are matched to generate a carbon cycle dynamic parameter set including the boundary of the humus retention hot zone, the lateral diffusion gradient of biochar, and the priority of methane oxidation inhibition; The carbon cycle dynamic parameter set is spatially superimposed with the adjusted chemical reaction chain data to generate a carbon cycle balance map with the tillage layer depth as the vertical axis and the field irrigation channel as the horizontal axis.

8. A carbon reduction effect evaluation system for the production process of healthy agricultural products, characterized in that: include: The collection module tracks the carbon migration path during fertilizer application, organic fertilizer degradation, and soil respiration, while also collecting chemical reaction chain data at each stage of the production process; A matching module determines the carbon emission concentration area based on the carbon element migration path, combines the by-product generation amount in the chemical reaction chain data with the pre-stored agricultural carbon emission path database, and analyzes the carbon retention amount and residual methane product distribution caused by incomplete degradation of organic fertilizers during the production process; A compensation module inputs the carbon retention, methane production distribution, and intermediate product concentrations in the chemical reaction chain data into a carbon emission dynamic compensation engine, and generates an optimization plan for soil pore structure and a compensation strategy for the organic fertilizer feeding gradient based on the carbon reduction effect evaluation indicators in the healthy agricultural product production standards; an adjustment module, adjusting soil aeration parameters according to the microaerobic environment control instructions in the compensation strategy, optimizing the soil microaerobic environment in combination with the soil pore structure optimization scheme, and controlling the degradation rate of organic fertilizers through a methane product conversion scheme, converting residual intermediate products into stable carbon forms to reduce greenhouse gas emissions; A generation module dynamically monitors the migration path corresponding to the stable carbon form, and generates a carbon cycle balance map for evaluating the carbon reduction effect by combining the adjusted soil aeration parameters and chemical reaction chain data.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a carbon reduction effect evaluation method for the production process of healthy agricultural products as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a carbon reduction effect evaluation method for a healthy agricultural product production process as described in any one of claims 1 to 7 is implemented.

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