Greenhouse gas intelligent algorithm system and method for biochar agricultural emission reduction

Abstract

The invention relates to the technical field of environment monitoring, in particular to a greenhouse gas intelligent algorithm system and method for biochar agricultural emission reduction, and the system comprises a data collection module, a data preprocessing module, a greenhouse gas emission calculation module, a biochar emission reduction analysis module and an intelligent decision output module. Wherein the data acquisition module is used for collecting agricultural environment, biochar attributes and greenhouse gas source data; the data preprocessing module is used for performing cleaning and normalization processing and outputting standardized data; the greenhouse gas emission calculation module is used for calculating the greenhouse gas emission amount of the current agricultural system; the biochar emission reduction analysis module is used for analyzing the emission reduction potential of biochar application; the decision output module is used for generating an optimized biochar application scheme; according to the invention, through multi-source data fusion and intelligent algorithm calculation, accurate evaluation of agricultural greenhouse gas emission and optimization decision of a charcoal application scheme are realized, so that scientificity and implementability of emission reduction management are improved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a smart algorithm system and method for greenhouse gas emission reduction in biochar agriculture. Background Technology

[0002] With the development of large-scale and intensive agricultural production activities, greenhouse gas emissions have become increasingly prominent. The agricultural system has become one of the major non-energy emission sources such as carbon dioxide, methane, and nitrous oxide. Under traditional agricultural management methods, factors such as high fertilization intensity, improper straw disposal, and poor soil aeration have led to a significant increase in the release of greenhouse gases during soil carbon and nitrogen cycles.

[0003] In recent years, biochar has been regarded as an important agricultural emission reduction material due to its high carbon content, good stability, and soil improvement effects. Its application in soil can effectively enhance carbon sequestration capacity, suppress greenhouse gas emissions, and improve the soil environment. However, existing emission reduction assessments mostly rely on static experiments or empirical models, lacking dynamic data fusion and algorithmic computational support for complex agricultural environments. This makes it difficult to achieve quantitative analysis and optimization decisions regarding biochar's emission reduction potential. Therefore, there is an urgent need for intelligent algorithm systems and methods for greenhouse gas emission reduction in agriculture using biochar, in order to achieve dynamic monitoring and scientific decision support for agricultural greenhouse gas emissions. Summary of the Invention

[0004] To achieve the above objectives, this invention provides an intelligent algorithm system and method for greenhouse gas emission reduction in biochar agriculture.

[0005] The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture includes a data acquisition module, a data preprocessing module, a greenhouse gas emission calculation module, a biochar emission reduction analysis module, and an intelligent decision output module; among which: Data acquisition module: used to collect agricultural environmental data, biochar property data and greenhouse gas source data, and output integrated raw dataset; Data preprocessing module: used to clean and normalize the integrated raw dataset, outputting standardized data; Greenhouse gas emission calculation module: Based on standardized data, calculates the greenhouse gas emissions of the current agricultural system and outputs the emission results; Biochar Emission Reduction Analysis Module: This module analyzes the emission reduction potential of biochar applications based on emission results and biochar property data, and outputs the emission reduction potential assessment results. Decision output module: Based on the emission reduction potential assessment results, it generates optimized biochar application schemes and outputs the final emission reduction strategy.

[0006] Optionally, the data acquisition module includes an agricultural environment acquisition unit, a biochar property acquisition unit, a greenhouse gas source acquisition unit, and a data fusion unit; wherein: Agricultural environment data acquisition unit: used to collect data on air temperature, humidity, soil moisture content and crop planting density through deployed meteorological sensors and soil monitoring nodes, forming an agricultural environment dataset; Biochar property acquisition unit: used to record biochar raw material type, carbon content, specific surface area, application rate and particle size parameters, and generate biochar property dataset; Greenhouse gas source acquisition unit: used to collect emission concentration and time series information of CO2, CH4 and N2O in agricultural systems through gas sensors and sampling devices, and generate greenhouse gas source datasets; Data fusion unit: It receives agricultural environment datasets, biochar attribute datasets, and greenhouse gas source datasets, and aligns them based on a unified timestamp to form an integrated original dataset with multidimensional feature fields.

[0007] Optionally, the data preprocessing module includes a data cleaning unit and a data normalization unit; wherein: Data cleaning unit: Used to receive and integrate the original dataset, detect and remove outliers, duplicates and missing items, correct incomplete records through interpolation and mean completion, and output a complete data sequence; Data normalization unit: used to convert data of different dimensions into a unified dimension interval. For continuous variables, min-max standardization is used, and for discrete variables, one-hot encoding is used to convert them to obtain a dimensionless dataset for comparison.

[0008] Optionally, the greenhouse gas emission calculation module includes an emission factor matching unit, an emission calculation unit, and a result aggregation unit; wherein: Emission factor matching unit: It receives standardized data output from the data preprocessing module, and retrieves the corresponding emission coefficient parameters from the preset emission factor database according to gas type, soil conditions, crop type and fertilization method, so as to establish the corresponding calculation basis for different greenhouse gases. Emissions calculation unit: Used to calculate the emission flux of various greenhouse gases based on the matched emission factors, including carbon dioxide emissions, methane emissions and nitrous oxide emissions, and generate the corresponding emission dataset; Results aggregation unit: Used to integrate emission data of various greenhouse gases, calculate the total emission index of agricultural systems, and output it in time series form.

[0009] Optionally, the emission calculation unit includes: Emission parameter parsing subunit: Used to receive emission factor parameters and standardized data output by emission factor matching unit, parse crop type, soil temperature, fertilizer application rate and irrigation frequency variables, and construct corresponding emission calculation parameter sets; Flux Calculation Subunit: Based on emission factors and the analyzed parameter set, it calculates the emissions of different greenhouse gases separately, obtaining the carbon dioxide emissions. methane emissions and ammonia oxide emissions ; Dataset Generation Subunit: This subunit is used to structure and organize the calculated greenhouse gas emission results of the three categories according to time series and geographic location codes, generating a dataset containing... , , A dataset of emissions across three dimensions.

[0010] Optionally, the result summarization unit includes: Equivalent conversion subunit: used to convert the emission results of different gases into carbon dioxide equivalents and generate the corresponding conversion results; The index calculation subunit is used to integrate the equivalent emission results of various gases and calculate the total emission index of the agricultural system at a predetermined time. and cumulative emissions within the time window ; Sequence output subunit: used to process the calculated results and The results are arranged in timestamp order and spatial location information is added to form a time series emissions dataset and a total emissions report.

[0011] Optionally, the biochar emission reduction analysis module includes a baseline emission construction unit, a biochar effect identification unit, an emission reduction potential calculation unit, and an evaluation result output unit; wherein: Baseline emission construction unit: Based on the total emission data output by the emission calculation module, historical emission data sequences under the scenario of no biochar application are extracted to construct a baseline emission curve under the same time window, crop type and environmental conditions as a reference. Biochar effect identification unit: used to identify time segments in the current data sequence where biochar intervention exists based on biochar attribute data in the data acquisition module, and mark the differences between them and the baseline segment; Emission reduction potential calculation unit: Used to assess the emission reduction of marked sections, calculate the emission difference between the applied biochar and baseline conditions, and normalize the output by unit area or unit application amount to generate emission reduction potential indicators. ; The assessment result output unit is used to summarize the emission reduction potential indicators of each assessment section according to the time dimension and spatial coordinates, forming a structured emission reduction potential assessment result dataset.

[0012] Optionally, the biochar effect recognition unit includes: The attribute parsing subunit is used to receive biochar attribute data output by the data acquisition module, extract carbon content, application rate, application cycle, specific surface area and particle size parameters, form an attribute feature vector set, and match it with the corresponding plot number and time tag to establish an attribute index table for biochar application events. Segment identification subunit: Based on the attribute index table, time series emission data is matched and retrieved. When the timestamp overlaps with the application event time window, the corresponding time period is defined as the biochar intervention segment, and the non-intervention segment is used as a control to form a segment mapping matrix. Difference marker subunit: Used to compare the differences in greenhouse gas emissions between the intervention section and the baseline section, calculate the emission change ratio, and record the difference value along with spatial location and application parameters to generate an intervention effect marker table.

[0013] Optionally, the decision output module includes a scheme generation unit, a parameter optimization unit, and a strategy output unit; wherein: Scheme generation unit: Used to extract emission reduction potential indicators for different regions, crop types, and application conditions based on the emission reduction potential assessment results. In conjunction with application frequency and crop growth cycle information, an initial biochar application scheme set is generated, which includes application timing, application amount and crop type. Parameter optimization unit: Based on the initial scheme set, an objective function is constructed, with the dual objective constraints of maximizing total emission reduction and minimizing application cost. The optimal combination of application parameters for each region is calculated through an iterative optimization algorithm. Strategy output unit: Used to generate the final emission reduction strategy table from the optimization results in the form of regional coordinates, application amount, application cycle and expected emission reduction rate, and output the results to the management terminal.

[0014] The intelligent algorithm method for greenhouse gas emission reduction in biochar agriculture, implemented by the aforementioned intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture, includes the following steps: S1: Collect agricultural environmental data, biochar property data, and greenhouse gas source data to generate an integrated raw dataset; S2: Clean and normalize the integrated original dataset to output standardized data; S3: Based on standardized data, match the corresponding coefficients in the preset emission factor database, calculate the emissions of CO2, CH4 and N2O respectively, convert them into CO2 equivalents and sum them up to obtain the total emission sequence of the agricultural system at different time points; S4: Based on the emission results of S3 and the biochar attribute data in S1, construct a biochar-free baseline emission reference, identify the biochar intervention time period and compare it with the baseline, calculate the emission reduction potential value, and output the emission reduction potential assessment result. S5: Based on the emission reduction potential assessment results of S4, generate an optimized biochar application scheme and output the final emission reduction strategy.

[0015] The beneficial effects of this invention are: This invention constructs an intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture, which achieves multi-source fusion of agricultural environmental data, biochar attribute data, and greenhouse gas source data. It can dynamically calculate the emissions of different gases and assess emission reduction potential by combining biochar parameters, forming quantitative emission reduction results. This provides efficient data support and decision-making basis for agricultural carbon emission reduction and ecological environment management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the intelligent greenhouse gas algorithm system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the intelligent algorithm method for greenhouse gases according to an embodiment of the present invention. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0019] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0020] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0021] like Figure 1 As shown, the intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture includes a data acquisition module, a data preprocessing module, a greenhouse gas emission calculation module, a biochar emission reduction analysis module, and an intelligent decision output module; wherein: Data acquisition module: used to collect agricultural environmental data, biochar property data and greenhouse gas source data, and output integrated raw dataset; Data preprocessing module: used to clean and normalize the integrated raw dataset, outputting standardized data; Greenhouse gas emission calculation module: Based on standardized data, calculates the greenhouse gas emissions of the current agricultural system and outputs the emission results; Biochar Emission Reduction Analysis Module: This module analyzes the emission reduction potential of biochar applications based on emission results and biochar property data, and outputs the emission reduction potential assessment results. Decision output module: Based on the emission reduction potential assessment results, it generates optimized biochar application schemes and outputs the final emission reduction strategy.

[0022] The data acquisition module includes an agricultural environment acquisition unit, a biochar property acquisition unit, a greenhouse gas source acquisition unit, and a data fusion unit; among which: Agricultural environment data acquisition unit: used to collect data on air temperature, humidity, soil moisture content and crop planting density through deployed meteorological sensors and soil monitoring nodes, forming an agricultural environment dataset; Biochar property acquisition unit: used to record biochar raw material type, carbon content, specific surface area, application rate and particle size parameters, and generate biochar property dataset; Greenhouse gas source acquisition unit: used to collect emission concentration and time series information of CO2, CH4 and N2O in agricultural systems through gas sensors and sampling devices, and generate greenhouse gas source datasets; The data fusion unit receives agricultural environmental datasets, biochar attribute datasets, and greenhouse gas source datasets, aligns them based on a unified timestamp, and forms an integrated original dataset with multi-dimensional feature fields for subsequent modules to use. Through the collaborative design of multiple units in this data acquisition module, the heterogeneous agricultural data is classified, collected, and fused in a unified manner, ensuring that the original input data has a standard structure, clear source, and strong traceability, thereby improving the accuracy and stability of greenhouse gas emission analysis and emission reduction strategy deduction.

[0023] The data preprocessing module includes a data cleaning unit and a data normalization unit; wherein: Data cleaning unit: Used to receive and integrate the original dataset, detect and remove outliers, duplicates and missing items, correct incomplete records through interpolation and mean completion, and output a complete data sequence; Data normalization unit: Used to convert data of different dimensions into a unified dimension interval. For continuous variables, it uses min-max standardization; for discrete variables, it uses one-hot encoding to convert them, resulting in a dimensionless dataset for comparison. Its standardization calculation expression is: ,in, The original data values, The minimum value in the dataset. The maximum value in the dataset. The result is normalized. Through the two-level processing structure of this data preprocessing module, the integrity of the original data is restored and the standardization conversion is achieved, ensuring the consistency and comparability of the input data in subsequent greenhouse gas emission calculations, and improving the overall data reliability and analysis accuracy of the system.

[0024] The greenhouse gas emissions calculation module includes an emission factor matching unit, an emission calculation unit, and a results aggregation unit; among which: Emission factor matching unit: It receives standardized data output from the data preprocessing module, and retrieves the corresponding emission coefficient parameters from the preset emission factor database according to gas type, soil conditions, crop type and fertilization method, so as to establish the corresponding calculation basis for different greenhouse gases. Table 1 Emission Factor Database In Table 1 above, crop type refers to the crop planted for the emission activity, used to match the crop classification in the standardized feature data; soil type reflects the impact of soil texture on the emission process, such as clay, loam, sandy loam, etc.; management methods include management parameters such as fertilization strategies and irrigation methods, which determine the dynamic changes in emissions; greenhouse gas type is used to identify the type of greenhouse gas corresponding to the current emission factor; emission factor is used to estimate the emission intensity per unit area per unit time under the given conditions; and condition description is used to record the boundary conditions applicable to the emission factor, enhancing the accuracy and traceability of data use.

[0025] Emissions calculation unit: Used to calculate the emission flux of various greenhouse gases based on the matched emission factors, including carbon dioxide emissions, methane emissions and nitrous oxide emissions, and generate the corresponding emission dataset; The results aggregation unit integrates emission data of various greenhouse gases, calculates the total emission index of the agricultural system, and outputs it in time series form as input for the subsequent biochar emission reduction analysis module. Through the multi-level structural design of this greenhouse gas emission calculation module, parameterized modeling and unified calculation of different gas emission sources are realized, ensuring that the emission results have clear sub-items, logical integrity, and data traceability, providing a quantitative basis for the analysis of biochar emission reduction potential.

[0026] The emission calculation unit includes: Emission parameter parsing subunit: Used to receive emission factor parameters and standardized data output by emission factor matching unit, parse crop type, soil temperature, fertilizer application rate and irrigation frequency variables, and construct corresponding emission calculation parameter sets; Flux Calculation Subunit: Based on emission factors and the analyzed parameter set, it calculates the emissions of different greenhouse gases separately, obtaining the carbon dioxide emissions. methane emissions and ammonia oxide emissions The calculation formula is as follows: ,in, Represents gas Emissions, For the matched emission factors, For the corresponding planting area, gas The correction factor is used to reflect the differences between climate and management conditions; Dataset Generation Subunit: This subunit is used to structure and organize the calculated greenhouse gas emission results of the three categories according to time series and geographic location codes, generating a dataset containing... , , The emission datasets are generated across three dimensions and output to the results aggregation unit. Through the hierarchical structure design of this emission calculation unit, parameter-driven multi-gas emission sub-item calculation and structured data generation are realized, ensuring the scientific rigor, hierarchy, and scalability of the emission results, and providing accurate basic data support for subsequent biochar emission reduction analysis.

[0027] The following is an example of calculating greenhouse gas emissions from agricultural systems: Background conditions: Crop type: Rice Soil type: clay loam Management method: Continuous flooding and fertilization Planting area A: 12 hectares Climate condition correction factor: carbon dioxide: Methane: Nitrous oxide: Emission factors (matched by searching the emission factor database): Carbon dioxide emission factors: Methane emission factor: 3 Ammonia oxide emission factor: According to the emission calculation formula: ; Carbon dioxide emissions calculation: ; Methane emissions calculation: ; Calculation of nitrous oxide emissions: .

[0028] The results summary unit includes: Equivalent conversion subunit: Used to convert the emission results of different gases into carbon dioxide equivalents, generating the corresponding conversion results; its calculation expression is: ,in, gas At any moment Emissions, gas The conversion factor for global warming potential. gas of Equivalent emissions; The index calculation subunit is used to integrate the equivalent emission results of various gases and calculate the total emission index of the agricultural system at a predetermined time. and cumulative emissions within the time window Its calculation expression is: ; ;in, For a moment Total Equivalent emissions For the statistical time window, For time step, Cumulative emissions; Sequence output subunit: used to process the calculated results and The results are arranged in time-stamp order and spatial location information is added to form a time-series emission dataset and a total emission report for subsequent emission reduction analysis modules to use. Through the layered design of equivalent conversion, index calculation and sequence output, the unified quantification, cumulative summary and time-series expression of multi-source gas emission data are realized, ensuring that the total emission index has consistency, continuity and traceability, and providing an accurate data foundation for the assessment of biochar emission reduction potential.

[0029] The biochar emission reduction analysis module includes a baseline emission construction unit, a biochar effect identification unit, an emission reduction potential calculation unit, and an assessment result output unit; among which: Baseline emission construction unit: Based on the total emission data output by the emission calculation module, historical emission data sequences under the scenario of no biochar application are extracted to construct a baseline emission curve under the same time window, crop type and environmental conditions as a reference. Biochar effect identification unit: used to identify time segments in the current data sequence where biochar intervention exists based on biochar attribute data in the data acquisition module, and mark the differences between them and the baseline segment; Emission reduction potential calculation unit: Used to assess the emission reduction of marked sections, calculate the emission difference between the applied biochar and baseline conditions, and normalize the output by unit area or unit application amount to generate emission reduction potential indicators. Its calculation expression is: ,in, As a baseline emission level, This represents the emissions after the application of biochar. The area to be applied; The assessment result output unit is used to summarize the emission reduction potential indicators of each assessment section according to the time dimension and spatial coordinates, forming a structured emission reduction potential assessment result dataset for the intelligent decision-making output module to call. By constructing a reference benchmark, identifying intervention sections, quantifying differential emissions and normalizing the output, the biochar emission reduction analysis module realizes the objective assessment and quantitative expression of the biochar application effect, providing data support for the precise formulation of regional biochar application strategies.

[0030] The biochar effect recognition unit includes: The attribute parsing subunit is used to receive biochar attribute data output by the data acquisition module, extract carbon content, application rate, application cycle, specific surface area and particle size parameters, form an attribute feature vector set, and match it with the corresponding plot number and time tag to establish an attribute index table for biochar application events. Segment identification subunit: Based on the attribute index table, time series emission data is matched and retrieved. When the timestamp overlaps with the application event time window, the corresponding time period is defined as the biochar intervention segment, and the non-intervention segment is used as a control to form a segment mapping matrix. The differential labeling subunit is used to compare the differences in greenhouse gas emissions between the intervention section and the baseline section, calculate the emission change ratio, and record the difference value along with spatial location and application parameters to generate an intervention effect labeling table. Through the hierarchical processing of biochar attribute analysis, section identification, and differential labeling, the precise location of biochar application periods and the quantification of emission changes in time series data are achieved, providing high-precision intervention data input for the emission reduction potential calculation unit and enhancing the accuracy and timeliness of the system's identification of biochar effects.

[0031] The decision output module includes a scheme generation unit, a parameter optimization unit, and a strategy output unit; wherein: Scheme generation unit: Used to extract emission reduction potential indicators for different regions, crop types, and application conditions based on the emission reduction potential assessment results. In conjunction with application frequency and crop growth cycle information, an initial biochar application scheme set is generated, which includes application timing, application amount and crop type. Parameter optimization unit: Based on the initial scheme set, an objective function is constructed, with the dual constraints of maximizing total emission reduction and minimizing application cost. The optimal combination of application parameters for each region is calculated using an iterative optimization algorithm. Its objective function expression is: ,in, For the region The potential for emission reduction per unit area This represents the area of ​​the corresponding region. For the region The cost of biochar application, This is the cost weighting coefficient. To comprehensively optimize the target value; The strategy output unit is used to generate the final emission reduction strategy table in the form of regional coordinates, application rate, application cycle and expected emission reduction rate, and output the results to the management terminal to guide agricultural production implementation and decision support. The above unit realizes an intelligent decision-making process based on quantitative evaluation results through the hierarchical design of scheme generation, parameter optimization and strategy output. It can automatically generate the optimal application scheme according to regional differences, ensure the balance between emission reduction effect and economic input, and provide a scientific basis for the precise implementation of biochar agricultural emission reduction.

[0032] like Figure 2 As shown, the intelligent algorithm method for greenhouse gas emission reduction in biochar agriculture, implemented by the aforementioned intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture, includes the following steps: S1: Collect agricultural environmental data, biochar property data, and greenhouse gas source data to generate an integrated raw dataset; S2: Clean and normalize the integrated original dataset to output standardized data; S3: Based on standardized data, match the corresponding coefficients in the preset emission factor database, calculate the emissions of CO2, CH4 and N2O respectively, convert them into CO2 equivalents and sum them up to obtain the total emission sequence of the agricultural system at different time points; S4: Based on the emission results of S3 and the biochar attribute data in S1, construct a biochar-free baseline emission reference, identify the biochar intervention time period and compare it with the baseline, calculate the emission reduction potential value, and output the emission reduction potential assessment result. S5: Based on the emission reduction potential assessment results of S4, generate an optimized biochar application scheme and output the final emission reduction strategy.

[0033] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0034] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart algorithm system for greenhouse gas emission reduction in biochar agriculture, characterized in that, It includes a data acquisition module, a data preprocessing module, a greenhouse gas emission calculation module, a biochar emission reduction analysis module, and an intelligent decision output module; among which: Data acquisition module: used to collect agricultural environmental data, biochar property data and greenhouse gas source data, and output integrated raw dataset; Data preprocessing module: used to clean and normalize the integrated raw dataset, outputting standardized data; Greenhouse gas emission calculation module: Based on standardized data, calculates the greenhouse gas emissions of the current agricultural system and outputs the emission results; Biochar Emission Reduction Analysis Module: This module analyzes the emission reduction potential of biochar applications based on emission results and biochar property data, and outputs the emission reduction potential assessment results. Decision output module: Based on the emission reduction potential assessment results, it generates optimized biochar application schemes and outputs the final emission reduction strategy.

2. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 1, characterized in that, The data acquisition module includes an agricultural environment acquisition unit, a biochar property acquisition unit, a greenhouse gas source acquisition unit, and a data fusion unit; wherein: Agricultural environment data acquisition unit: used to collect data on air temperature, humidity, soil moisture content and crop planting density through deployed meteorological sensors and soil monitoring nodes, forming an agricultural environment dataset; Biochar property acquisition unit: used to record biochar raw material type, carbon content, specific surface area, application rate and particle size parameters, and generate biochar property dataset; Greenhouse gas source acquisition unit: used to collect emission concentration and time series information of CO2, CH4 and N2O in agricultural systems through gas sensors and sampling devices, and generate greenhouse gas source datasets; Data fusion unit: It receives agricultural environment datasets, biochar attribute datasets, and greenhouse gas source datasets, and aligns them based on a unified timestamp to form an integrated original dataset with multidimensional feature fields.

3. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 1, characterized in that, The data preprocessing module includes a data cleaning unit and a data normalization unit; wherein: Data cleaning unit: Used to receive and integrate the original dataset, detect and remove outliers, duplicates and missing items, correct incomplete records through interpolation and mean completion, and output a complete data sequence; Data normalization unit: used to convert data of different dimensions into a unified dimension interval. For continuous variables, min-max standardization is used, and for discrete variables, one-hot encoding is used to convert them to obtain a dimensionless dataset for comparison.

4. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 1, characterized in that, The greenhouse gas emission calculation module includes an emission factor matching unit, an emission calculation unit, and a result aggregation unit; wherein: Emission factor matching unit: It receives standardized data output from the data preprocessing module, and retrieves the corresponding emission coefficient parameters from the preset emission factor database according to gas type, soil conditions, crop type and fertilization method, so as to establish the corresponding calculation basis for different greenhouse gases. Emissions calculation unit: Used to calculate the emission flux of various greenhouse gases based on the matched emission factors, including carbon dioxide emissions, methane emissions and nitrous oxide emissions, and generate the corresponding emission dataset; Results aggregation unit: Used to integrate emission data of various greenhouse gases, calculate the total emission index of agricultural systems, and output it in time series form.

5. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 4, characterized in that, The emission calculation unit includes: Emission parameter parsing subunit: Used to receive emission factor parameters and standardized data output by emission factor matching unit, parse crop type, soil temperature, fertilizer application rate and irrigation frequency variables, and construct corresponding emission calculation parameter sets; Flux Calculation Subunit: Based on emission factors and the analyzed parameter set, it calculates the emissions of different greenhouse gases separately, obtaining the carbon dioxide emissions. methane emissions and ammonia oxide emissions ; Dataset Generation Subunit: This subunit is used to structure and organize the calculated greenhouse gas emission results of the three categories according to time series and geographic location codes, generating a dataset containing... , , A dataset of emissions across three dimensions.

6. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 5, characterized in that, The result aggregation unit includes: Equivalent conversion subunit: used to convert the emission results of different gases into carbon dioxide equivalents and generate the corresponding conversion results; The index calculation subunit is used to integrate the equivalent emission results of various gases and calculate the total emission index of the agricultural system at a predetermined time. and cumulative emissions within the time window ; Sequence output subunit: used to process the calculated results and The results are arranged in timestamp order and spatial location information is added to form a time series emissions dataset and a total emissions report.

7. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 1, characterized in that, The biochar emission reduction analysis module includes a baseline emission construction unit, a biochar effect identification unit, an emission reduction potential calculation unit, and an evaluation result output unit; wherein: Baseline emission construction unit: Based on the total emission data output by the emission calculation module, historical emission data sequences under the scenario of no biochar application are extracted to construct a baseline emission curve under the same time window, crop type and environmental conditions as a reference. Biochar effect identification unit: used to identify time segments in the current data sequence where biochar intervention exists based on biochar attribute data in the data acquisition module, and mark the differences between them and the baseline segment; Emission reduction potential calculation unit: Used to assess the emission reduction of marked sections, calculate the emission difference between the applied biochar and baseline conditions, and normalize the output by unit area or unit application amount to generate emission reduction potential indicators. ; The assessment result output unit is used to summarize the emission reduction potential indicators of each assessment section according to the time dimension and spatial coordinates, forming a structured emission reduction potential assessment result dataset.

8. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 7, characterized in that, The biochar effect recognition unit includes: The attribute parsing subunit is used to receive biochar attribute data output by the data acquisition module, extract carbon content, application rate, application cycle, specific surface area and particle size parameters, form an attribute feature vector set, and match it with the corresponding plot number and time tag to establish an attribute index table for biochar application events. Segment identification subunit: Based on the attribute index table, time series emission data is matched and retrieved. When the timestamp overlaps with the application event time window, the corresponding time period is defined as the biochar intervention segment, and the non-intervention segment is used as a control to form a segment mapping matrix. Difference marker subunit: Used to compare the differences in greenhouse gas emissions between the intervention section and the baseline section, calculate the emission change ratio, and record the difference value along with spatial location and application parameters to generate an intervention effect marker table.

9. The intelligent algorithm system for greenhouse gas emission reduction in biochar agriculture according to claim 1, characterized in that, The decision output module includes a scheme generation unit, a parameter optimization unit, and a strategy output unit; wherein: Scheme generation unit: Used to extract emission reduction potential indicators for different regions, crop types, and application conditions based on the emission reduction potential assessment results. In conjunction with application frequency and crop growth cycle information, an initial biochar application scheme set is generated, which includes application timing, application amount and crop type. Parameter optimization unit: Based on the initial scheme set, an objective function is constructed, with the dual objective constraints of maximizing total emission reduction and minimizing application cost. The optimal combination of application parameters for each region is calculated through an iterative optimization algorithm. Strategy output unit: Used to generate the final emission reduction strategy table from the optimization results in the form of regional coordinates, application amount, application cycle and expected emission reduction rate, and output the results to the management terminal.

10. A smart algorithm method for greenhouse gas emission reduction in biochar agriculture, implemented by the smart algorithm system for greenhouse gas emission reduction in biochar agriculture as described in any one of claims 1-9, characterized in that, Includes the following steps: S1: Collect agricultural environmental data, biochar property data, and greenhouse gas source data to generate an integrated raw dataset; S2: Clean and normalize the integrated original dataset to output standardized data; S3: Based on standardized data, match the corresponding coefficients in the preset emission factor database, calculate the emissions of CO2, CH4 and N2O respectively, convert them into CO2 equivalents and sum them up to obtain the total emission sequence of the agricultural system at different time points; S4: Based on the emission results of S3 and the biochar attribute data in S1, construct a biochar-free baseline emission reference, identify the biochar intervention time period and compare it with the baseline, calculate the emission reduction potential value, and output the emission reduction potential assessment result. S5: Based on the emission reduction potential assessment results of S4, generate an optimized biochar application scheme and output the final emission reduction strategy.

Images