A method and system for visualizing straw utilization characteristics

Abstract

The application discloses a kind of visual straw utilization characteristic analysis method and system, belong to soil carbon emission field, the method includes: collection soil physical and chemical property data and the characteristic data of different types of rotten straw, and the data collected are preprocessed, and integrated database is obtained by construction;The data in integrated database is processed by biological carbon utilization characteristic analysis model, simulates the dynamic process of soil carbon cycle after corresponding type of rotten straw is applied, obtains key variable data;According to the key variable data calculated, traverse the time series in integrated database, calculate carbon sequestration efficiency, microbial activity and physical protection intensity, obtain enhanced time series data, use enhanced time series data and key variable data as data source, and render to obtain visual diagram.The application improves the calculation accuracy and accuracy of soil carbon emission flux and accumulation.

Description

Technical Field

[0001] This invention relates to the field of soil carbon emissions, and in particular to a method and system for visualizing the characteristics of straw utilization. Background Technology

[0002] Returning straw to the field is an important agricultural ecological management measure widely used in crop rotation systems. Traditional straw return techniques mainly include direct return, crushing and returning, and straw mulching. These methods increase soil organic matter content, improve soil structure, and promote soil fertility. However, existing straw return technologies often lack a systematic carbon emission assessment mechanism, making it difficult to accurately reflect the comprehensive impact of straw return on soil carbon emissions. Furthermore, current farmland carbon emission monitoring mainly relies on static chamber methods and eddy covariance methods, which have limitations such as insufficient spatiotemporal resolution, high monitoring costs, and complex data interpretation. Moreover, due to crop type changes and seasonal variations leading to complex and variable soil carbon cycling processes, existing single-factor or linear models are insufficient to accurately reflect the comprehensive impact of straw return on soil carbon emissions.

[0003] Therefore, how to construct a system that comprehensively considers multi-dimensional factors such as microbial activity, environmental factors, and crop growth parameters to accurately quantify and predict soil carbon emissions under different straw return treatment methods has become a key technical problem that urgently needs to be solved in the field of farmland carbon management. At the same time, how to intuitively display the dynamic changes in the soil carbon cycle through visualization methods to provide more scientific decision support for agricultural production is also an urgent issue to be addressed. Summary of the Invention

[0004] One of the objectives of this invention is to provide a visual method for analyzing the characteristics of straw utilization, in order to solve the problem that existing technologies using single factors or linear models cannot accurately reflect the comprehensive impact of straw return to the field on soil carbon emissions.

[0005] This invention is achieved through the following technical solution: a method for visualizing the characteristics of straw utilization, comprising the following steps: S100, collecting soil physicochemical property data and characteristic data of different types of decomposed straw, and preprocessing the collected data to construct a comprehensive database; S200, processing the data in the comprehensive database through a biocarbon utilization characteristic analysis model, simulating the dynamic process of soil carbon cycle after applying corresponding types of decomposed straw, and obtaining key variable data. The biocarbon utilization characteristic analysis model uses the microbial activity field as the central coupling variable, and uses the straw residual carbon concentration, dissolved organic carbon concentration, microbial biomass carbon density, and stable organic carbon accumulation as key variables, and constructs coupled differential equations for these four key variables, thereby coupling the biological, chemical, and physical processes in the soil carbon cycle; S300, based on the calculated key variable data, traversing the time series in the comprehensive database, calculating carbon fixation efficiency, microbial activity, and physical protection intensity, and obtaining enhanced time series data; S400, using the enhanced time series data and key variable data as data sources, rendering a visualization graph.

[0006] Furthermore, the soil physicochemical properties data include: soil moisture content, soil pH value, dissolved organic carbon, microbial biomass carbon, soil organic carbon, soil stable organic carbon content, soil amino sugar content, lignin phenol content, and soil aggregates; the characteristic data of the decomposed straw include: straw carbon-nitrogen ratio, lignin content, cellulose content, amount added, decomposition index, and particle size distribution.

[0007] Furthermore, preprocessing includes: identifying and handling errors, missing values, and outliers in the data to ensure the accuracy and reliability of the data, and standardizing the data using the Z-score algorithm.

[0008] Furthermore, the coupled differential equations include: a straw decomposition kinetic equation describing the changes in straw residual carbon concentration, constructed by attributing the dynamic changes in straw residual carbon to two core processes: in-situ biochemical reactions and spatial physical transport; a dissolved organic carbon migration-transformation equation describing the concentration of dissolved organic carbon, constructed based on the source-sink-transport balance, treating dissolved organic carbon as an intermediate product, whose concentration change depends on the dynamic balance of the source-sink-transport process, including the source from straw decomposition, the sink consumed by microorganisms, and the transport through physical diffusion in the soil; a microbial growth equation describing microbial biomass carbon density, constructed by attributing the dynamic changes in the microbial community to three core life processes: growth using dissolved organic carbon resources, natural mortality and decay, and active spatial migration in search of a better living environment; and a stable organic carbon stabilization equation describing the accumulation of stable organic carbon, constructed by considering the main sources of stable organic carbon: biological and chemical pathways, and taking into account the slow decomposition process of this portion of carbon.

[0009] Furthermore, the straw decomposition kinetic equation is shown below:

[0010] ,in, The symbol is for partial differentials. The concentration of residual carbon in straw. For time, This indicates the rate of change in the residual carbon concentration in straw; Based on the basic decomposition rate constant, For temperature, This represents the fundamental decomposition rate constant affected by temperature. is the suppression function for the carbon-nitrogen ratio; It is an inhibitor of lignin content; For microbial active fields, The diffusion coefficient of straw particles is given. For soil depth, The second partial derivative of the straw carbon concentration at depth represents the intensity of diffusion.

[0011] Furthermore, the migration-conversion equation for dissolved organic carbon is shown below:

[0012] ,in, It is soluble organic carbon. This indicates the rate of change of dissolved organic carbon concentration over time. The conversion coefficient for the production of dissolved organic carbon from straw decomposition. This represents the maximum absorption rate of dissolved organic carbon by microorganisms. It is the Michaelis constant; The density of microbial biomass carbon; The diffusion coefficient of dissolved organic carbon in soil solution.

[0013] Furthermore, the microbial growth equation is shown below:

[0014] ,in, This represents the rate of change of microbial biomass carbon density over time. This is the microbial yield coefficient. The decay rate constant of the microorganism; For divergence operators; This refers to the directional migration speed of microorganisms.

[0015] Furthermore, the stabilization equation for stable organic carbon is shown below:

[0016] ,in, To stabilize the accumulation of organic carbon, This represents the rate of change of the amount of stable organic carbon accumulation over time. The efficiency of converting microbial residues into stable organic carbon. To stabilize the proportion of organic carbon converted into stable organic carbon through chemical complexation. To stabilize the slow decomposition weight of organic carbon This indicates the slow decomposition rate of stable organic carbon itself, which is affected by temperature.

[0017] Furthermore, the microbial activity field is a nonlinear multi-factor comprehensive evaluation function. It calculates a comprehensive activity index based on the current biological, environmental, and physical states, and uses the calculated value as a key coupling variable in the coupled differential equation.

[0018] Furthermore, the microbial active field is shown in the following equation:

[0019] ,in, This represents the microbial activity field at depth z and time t. It is a natural constant. This represents the maximum activity value. As a factor influencing microbial populations, As an environmental impact factor, Factors affecting soil structure.

[0020] Furthermore, the factors influencing microbial populations, through microbial entropy and biomass, jointly reflect the maturity and scale of the microbial community; the environmental factors represent the negative impacts on activity when temperature and moisture deviate from suitable environmental values; and the soil structure factors represent the influence of soil structure on microbial activity.

[0021] Furthermore, the influencing factors of microbial populations are calculated using the following formula:

[0022] ,

[0023] Environmental impact factors are calculated using the following formula:

[0024] ,

[0025] The factors influencing soil structure are calculated using the following formula:

[0026] ,in, The weight of microbial entropy, Microbial entropy; Weighted by temperature, Weight for soil moisture Soil moisture content, As the weight of soil structure, This is a soil structure index.

[0027] Another aspect of the present invention provides a visual straw utilization characteristic analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the visual straw utilization characteristic analysis method as described above.

[0028] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0029] 1. This invention establishes a comprehensive database by collecting relevant data on soil and decomposed straw, enabling comprehensive monitoring and dynamic analysis of the soil carbon cycle process, overcoming the problems of insufficient spatiotemporal resolution, high monitoring costs, and complex data interpretation in existing technologies.

[0030] 2. This invention simulates the process of carbon transformation, migration and stabilization in soil after straw is returned to the field by coupling multiple partial differential equations. The system considers multi-dimensional factors such as microbial activity, environmental factors, and crop growth parameters, which improves the accuracy and precision of calculating soil carbon emission flux and accumulation. It also establishes a systematic carbon emission assessment mechanism, realizing accurate quantification and dynamic prediction of soil carbon emissions under different straw treatment methods, and providing scientific decision support for agricultural production.

[0031] 3. This invention uses a visualization engine to render data into an interactive dashboard interface with multiple linked charts. The Sankey diagram visually shows the flow of organic carbon, effectively improving the visualization and information transmission capabilities of the data. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a flowchart of the method provided in Embodiment 1 of the present invention.

[0034] Figure 2 Algorithm flowchart provided in Embodiment 1 of the present invention

[0035] Figure 3 The timing diagram of the algorithm provided in Embodiment 2 of the present invention.

[0036] Figure 4 This is a Sankey diagram provided in Embodiment 2 of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0038] Example 1

[0039] In traditional agricultural practices, the addition of exogenous organic materials significantly improves soil quality and enhances soil carbon storage. Numerous studies have shown that it can increase soil organic carbon content, stimulate the decomposition of background organic matter, release nutrients, promote crop growth, and thus improve soil carbon pool levels and soil quality. Returning straw to the field is a common management practice in modern agriculture. my country is a major agricultural country with abundant straw resources, producing approximately one-fifth of the world's total, primarily corn and wheat straw. In recent years, increased food demand and agricultural technology development have led to a significant increase in crop straw production, resulting in a supply exceeding demand. Consequently, returning straw to the field has become a common management practice in modern agriculture. However, my country's straw utilization rate is low; resource reuse measures, represented by returning straw to the field, account for only half of the total straw production. This is one of the urgent problems to be solved in the utilization of straw resources in my country.

[0040] Straw is rich in C, N, P, K elements, as well as lignin, crude protein, and cellulose, providing carbon and nitrogen sources for microorganisms, promoting microbial activity, accelerating the decomposition of soil organic matter, and improving soil fertility. However, direct return of straw to the field increases the frequency of pests and diseases, and the organic acids produced during decomposition can harm seedling roots and disrupt the soil pH balance. Furthermore, crude fiber and other substances are difficult to decompose, consuming nitrogen and causing competition between straw and seedlings for nitrogen. In addition, the impact of different amounts of straw returned to the field on soil organic carbon varies. Studies have shown that with increasing straw return amounts, the total soil organic carbon content increases significantly. Based on this, some scholars have proposed humification straw return technology, where composted straw can significantly increase soil organic matter content, improve soil structure, reduce bulk density, promote microbial reproduction, stimulate the decomposition of background organic carbon, and release nutrients for crop absorption. However, research on composted straw return under different carbon-nitrogen ratios is limited, and a comprehensive and systematic research mechanism has not yet been established. Therefore, in this embodiment, a visual straw utilization characteristic analysis method is used to analyze the utilization characteristics during the humification straw return process. Figure 1 The flowchart of the feature analysis method in this embodiment is shown. As can be seen from the figure, this embodiment includes the following steps:

[0041] Step 1: Collect and measure soil physicochemical properties, soil enzyme activity, and climate data for the target field. Simultaneously, collect characteristic data for different types of decomposed straw to analyze the carbon-to-nitrogen ratio (C / N) of different types of decomposed straw and the conversion and accumulation characteristics of soil organic carbon based on the amount of straw returned to the field.

[0042] Specifically, soil physicochemical property data can include: soil moisture content, pH value, dissolved organic carbon (DOC), microbial biomass carbon (MBC), soil organic carbon (SOC), soil stable organic carbon (ROC) content, soil amino sugar content, lignin phenol content, and soil aggregates. The dynamic changes in soil SOC and easily oxidizable organic carbon (DOC, MBC, ROC) and soil SOC mineralization characteristics can be compared between different periods and the same treatment. The effect of adding organic materials with different carbon-to-nitrogen ratios (C / N) on soil aggregate particle size can be determined using the dry sieving method. The contents of soil MNC, lignin phenol, and GRSP at different particle sizes can be measured to explore the physical protection mechanism of different aggregate sizes for recalcitrant organic matter, thus obtaining soil physicochemical property data. In addition, the contents of humin, humic acid, and fulvic acid in the soil can be measured, and the humification ratio can be calculated to compare the soil humification process and soil fertility stability under different C / N organic material applications.

[0043] The characteristic data of decomposed straw can include: the C / N ratio of straw, lignin content, cellulose content, amount added, decomposition index, particle size distribution, etc.

[0044] Step 2: Preprocess the collected data to remove noise and integrate these heterogeneous data from different sources into a unified and well-organized comprehensive database.

[0045] Specifically, preprocessing the collected data includes identifying and handling errors, missing values, and outliers to ensure accuracy and reliability. Missing values ​​are addressed by checking for blank cells or specific marker values ​​in the data table. If most key data for a sample is missing, or if the missing rate for a particular indicator is too high, deletion may be considered. Finally, the data is standardized using the Z-score algorithm.

[0046] Finally, check if the data conforms to basic scientific logic. For example: Is the pH value within a reasonable range of 0-14? Is the microbial biomass carbon (MBC) less than the soil organic carbon (SOC)? Is the sum of the percentages of each particle size of soil aggregates approximately equal to 100%? Data that does not conform to logic needs to be returned to the original records for verification and correction to ensure the integrity and accuracy of the data.

[0047] Step 3: Then, the data in the comprehensive database is used as the raw data. The biocarbon utilization feature analysis model is used to process these raw datasets to accurately simulate the dynamic process of soil carbon cycle in the target field after the application of the corresponding type of decomposed straw, and obtain key variable data.

[0048] In this embodiment, the biocarbon utilization characteristic analysis model simulates the transformation, migration, and stabilization of carbon in the soil after straw is returned to the field by coupling multiple partial differential equations. It identifies the core participants and transformation pathways of biocarbon utilization, clarifies the objects of simulation, and defines the basic relationships between these objects. Furthermore, by introducing a "microbial active field" as a central hub, it closely links various biological, chemical, and physical processes.

[0049] Specifically, the core objective of this application is to characterize the process of straw being converted into organic carbon after being returned to the field. Therefore, it is necessary to identify the main forms of carbon in the soil and connect them into a logically clear transformation chain. This includes: straw residual carbon concentration, dissolved organic carbon concentration, microbial biomass carbon density, and stable organic carbon accumulation. Based on these four key variables (state variables), equations describing their dynamic changes are established. Figure 2 A flowchart of the biocarbon utilization characteristic analysis model in this embodiment is shown.

[0050] For example, the coupled equations of the biocarbon utilization feature analysis model in this embodiment may include the following:

[0051] By attributing the dynamic changes in residual carbon in straw to two core processes—in-situ biochemical reactions (decomposition by microorganisms) and spatial physical transport (diffusion)—the biodecomposition term is a complex, nonlinear process regulated by multiple factors (temperature, substrate mass, and microbial activity), while the physical transport term is simplified to a linear diffusion process. This leads to the construction of the straw decomposition kinetic equation.

[0052] For example, in this embodiment, the straw decomposition kinetic equation is shown as follows:

[0053]

[0054] in, The symbol is for partial differentials. The concentration of residual carbon in straw. For time, It indicates the rate of change of residual carbon concentration in straw; its positive or negative sign indicates whether the residual carbon concentration in straw is increasing or decreasing. Based on the basic decomposition rate constant, For temperature, This represents the fundamental decomposition rate constant affected by temperature. This is the suppression function for the carbon-to-nitrogen ratio (C / N); It is an inhibitor of lignin content; For microbial active fields, The diffusion coefficient of straw particles is given. For soil depth, The second partial derivative of the straw carbon concentration at depth represents the intensity of diffusion.

[0055] It should be noted that in the above formula, The term used to describe the biodegradation process of straw is a "sink" term, indicating that the reduction in residual carbon concentration in straw is determined by multiple factors: It is a basic reaction term, indicating that the decomposition rate is proportional to the current amount of straw, is affected by temperature, and is regulated by the Arrhenius equation; and These are two inhibitory factors, representing the influence of substrate quality. An excessively high C / N ratio or lignin content will reduce decomposition efficiency. This is the core coupling factor, representing the overall working efficiency of the microbial community; the higher the efficiency, the faster the decomposition rate. This describes the physical diffusion process of straw residues in the soil, simulating the spatial transfer of straw carbon in the soil profile caused by physical mixing due to tillage, animal activities, etc.

[0056] Based on the "source-sink-transport" balance, dissolved organic carbon is considered as an intermediate product, and its concentration change depends on the dynamic equilibrium of three processes: the "source" from straw decomposition, the "sink" from microbial consumption, and the "transport" from physical diffusion in the soil. This equation serves as a crucial bridge connecting solid straw carbon with microbial life activities.

[0057] For example, in this embodiment, the migration-conversion equation for dissolved organic carbon is shown below:

[0058] ,

[0059] in, It is soluble organic carbon. This indicates the rate of change of dissolved organic carbon (DOC) concentration over time. The conversion factor for DOC production from straw decomposition. The maximum absorption rate of DOC by microorganisms is affected by moisture (W); It is the Michaelis constant (half-saturation constant) and is affected by pH; The density of microbial biomass carbon (MBC); denoted as DOC diffusion coefficient in soil solution.

[0060] It should be noted that in the above formula, The "source" item describes the process of DOC generation, which is proportional to the rate of straw decomposition. This represents the proportion of carbon released from decomposed straw as DOC. This term, a "sink" term, describes the process of DOC absorption and consumption by microorganisms. It follows classic Michaelis-Menten kinetics, with the absorption rate proportional to the amount of microorganisms. The fractional term within this term describes the non-linear dependence of the absorption rate on the substrate (DOC) concentration. When the DOC concentration is very low, the absorption rate approximates... It is directly proportional; when the DOC concentration is very high, the absorption rate reaches saturation and approaches its maximum value. . This describes the process of DOC diffusing through the pores along with soil moisture, and also applies Fick's second law.

[0061] Based on the dynamic changes of microbial populations, a microbial growth equation is constructed by attributing these changes to three core life processes: growth utilizing dissolved organic carbon resources, natural mortality and decay, and active spatial migration in search of better living environments. For example, in this embodiment, the microbial growth equation is shown below:

[0062] ,

[0063] in, This represents the rate of change of microbial biomass carbon density over time. This is the microbial yield coefficient, which represents how much new microbial biomass can be generated from consuming one unit of DOC. It is affected by the C / N ratio. The decay rate constant of the microorganism; For divergence operators; This refers to the directional migration speed of microorganisms.

[0064] It should be noted that in the above formula, It is a "source" term that describes the growth and reproduction of microorganisms. It is directly related to the DOC consumption term in the dissolved organic carbon migration-transformation equation. The consumed DOC is converted into new microbial biomass through a yield coefficient. It is a "sink" term that describes the death and decay of microorganisms. It assumes that the death rate is proportional to the number of existing microorganisms and is a simple first-order decay process. This is an "advection" term, describing the directional migration of microorganisms. Unlike random diffusion, it indicates that microorganisms actively move in a specific direction. In this embodiment, Indicates microorganisms It will move towards the microbial activity field The gradient increases, indicating directional movement (chemotaxis), which means actively gathering in a more "comfortable" environment.

[0065] By identifying the two main sources of stable organic carbon (i.e., soil organic matter in the general sense): the biological pathway (transformation by dead microorganisms) and the chemical pathway (fixation of dissolved organic carbon), and taking into account the slow decomposition process of this carbon, a stable organic carbon stabilization equation is constructed. For example, in this embodiment, the stable organic carbon stabilization equation is shown below:

[0066] ,

[0067] in, To stabilize the accumulation of organic carbon (ROC), This represents the rate of change of ROC accumulation over time. The efficiency of converting microbial residues into stable organic carbon is affected by the protective effect of soil clay content; This refers to the proportion of DOC that is converted into stable organic carbon through pathways such as chemical complexation. To stabilize the slow decomposition weight of organic carbon This indicates the slow decomposition rate of the ROC itself, which is affected by temperature.

[0068] It should be noted that in the above formula, It is a "source" item, representing the stabilization of microbial remains; a portion (a proportion of) the dead microorganisms. It will be protected by soil minerals, thus forming stable organic carbon. This represents the chemical stabilization of DOC, which occurs when the DOC concentration is high (i.e., During the formation phase (when positive), some DOCs are fixed through abiotic processes such as complexation with soil minerals. It is a "sink" term that describes the extremely slow decomposition of stable organic carbon itself, a rate that is also temperature-regulated, but with a rate constant... Much smaller than the basic decomposition rate constant of straw .

[0069] For example, in this embodiment, the microbial active field is shown in the following formula:

[0070] ,

[0071] in, This represents the microbial activity field at depth z and time t. It is a natural constant. This represents the maximum activity value. As a factor influencing microbial populations, As an environmental impact factor, Factors affecting soil structure.

[0072] The three influencing factors in this formula serve as index terms, forming a comprehensive score that integrates the influence of three dimensions. Specifically, in this embodiment, the influencing factors of microbial populations can be calculated using the following formula:

[0073] This formula indicates that microbial entropy (MBC / SOC) and biomass together reflect the maturity and size of the microbial community.

[0074] Environmental impact factors can be calculated using the following formula:

[0075] This formula indicates that when the temperature and moisture deviate from their optimal values ​​(set to 20℃ and 30% humidity in this formula), it will have a negative impact on the activity, creating stress.

[0076] The factors influencing soil structure can be calculated using the following formula:

[0077] This formula indicates that a good soil structure can provide better protection and resource access channels for microorganisms, thereby enhancing their activity.

[0078] in, The weight of microbial entropy, Microbial entropy; Weighted by temperature, Weight for soil moisture Soil moisture content, As the weight of soil structure, This is a soil structure index.

[0079] It should be noted that the microbial activity field in this embodiment is essentially a nonlinear, multi-factor comprehensive evaluation function. It does not directly describe the temporal evolution of a process, but rather calculates a comprehensive activity index in real-time based on various states (biological, environmental, and physical) at the current moment. The calculated... As a key coupling variable, it in turn affects other processes (such as straw decomposition rate and microbial migration direction), thus forming the core feedback loop of the entire model system.

[0080] The biocarbon utilization characteristic analysis model disclosed in this embodiment abstracts complex soil ecological processes into a set of interconnected mathematical equations through four coupled differential equations. Through numerical solution, it achieves dynamic, quantitative, and visual simulation of soil carbon cycling, a core ecosystem function, serving as a powerful bridge connecting in-situ field monitoring data with the understanding of ecosystem processes. By inputting initial condition data (such as straw return amount and initial soil state) and boundary condition data (such as temperature and moisture data changing over time), it predicts the spatiotemporal distribution and evolution patterns of various soil carbon pools (straw, DOC, microorganisms, and stable organic matter) at different depths over a future period. This reveals internal mechanisms invisible to the naked eye, such as: visualizing carbon flux and identifying "hotspots" of carbon decomposition. It quantifies stabilization efficiency and evaluates the long-term effects of different management measures (such as different straw types) on soil carbon sequestration. It analyzes system response and, by constructing a microbial activity response surface, finds the combination of environmental conditions that maximizes beneficial biological processes (such as decomposition). This provides a scientific basis for formulating optimal farmland management strategies.

[0081] Figure 3 A time series diagram of the biocarbon utilization characteristic analysis model in this embodiment is disclosed.

[0082] Step 4: Based on the calculated key variable data, iterate through the time series of the target field data and calculate the following composite index for each day:

[0083] Carbon sequestration efficiency index (%) = (newly added stable carbon pool storage on the same day / total initial input of straw carbon) × 100;

[0084] Microbial activity index = (MBC reserves on the day / initial MBC reserves) × decomposition rate coefficient on the day;

[0085] Physical protection strength index = the amount of carbon added to the large aggregate on the same day / the total amount of carbon added on the same day.

[0086] This results in "enhanced time series data" that includes the aforementioned composite index series.

[0087] All the data obtained from the above processing (enhanced time series data and key variable data) are integrated into a final, well-structured JSON object to obtain visualized data.

[0088] Step 5: Activate the visualization engine, using the visualization data as the data source, and render it into an interactive dashboard interface with multiple linked charts, ultimately resulting in a dynamic Sankey diagram. This diagram is used to display the changes in total SOC, activated carbon, and stable carbon over time.

[0089] Example 2

[0090] In this embodiment, relevant data were collected from a field located in the northern part of the North China Plain. The experimental field was located at the Shunyi National Agricultural Environment Observation and Experiment Station (40°05'02"N, 116°55'19"E, altitude 30.0 m), which belongs to a typical warm temperate semi-humid continental monsoon climate, with hot and rainy summers and cold and dry winters. The average annual temperature is about 12.5℃, and the average annual precipitation is about 623.5 mm, with precipitation mainly concentrated in summer (July). The average annual sunshine is about 2684 h, the effective accumulated temperature ≥ 0℃ is about 4500℃, and the frost-free period is about 195 days. The soil is alluvial brown soil with a pH of 8.38, organic matter of 15.5 g·kg⁻¹, total nitrogen of 0.37 g·kg⁻¹, total phosphorus of 0.61 g·kg⁻¹, and total potassium of 20.4 g·kg⁻¹.

[0091] This experiment designed to apply different C / N ratios of decomposed straw to the soil at different application rates. The C / N ratios of the decomposed straw were 20, 30, and 40. The different application rates included full application and half application, for a total of 9 treatments. Each treatment was replicated 3 times.

[0092] Experimental treatment setup scheme

[0093] Processing Name Specific handling measures CK Apply compound fertilizer (15-15-15). <![CDATA[WS1]]> Compound fertilizer + half of the uncomposted straw returned to the field <![CDATA[WS2]]> Compound fertilizer + return all uncomposted straw to the field <![CDATA[F 20 S1]]> Compound fertilizer + half-volume straw returned to the field with a C / N ratio of 20 <![CDATA[F 20 S2]]> Compound fertilizer + full amount of straw returned to the field with a C / N ratio of 20 <![CDATA[F 30 S1]]> Compound fertilizer + half-volume straw returned to the field with a C / N ratio of 30 <![CDATA[F 30 S2]]> Compound fertilizer + full amount of straw returned to the field with a C / N ratio of 30 <![CDATA[F 40 S1]]> Compound fertilizer + half-volume straw returned to the field with a C / N ratio of 40 <![CDATA[F 40 S1]]> Compound fertilizer + full amount of straw returned to the field with a C / N ratio of 40

[0094] The experimental site was divided into 27 plots, each 6 m × 6 m in size. Sampling was conducted from June 2023 to December 2024 to track the winter wheat-summer maize rotation cycle. Field treatments were based on local fertilization practices: winter wheat received 40 kg of compound fertilizer per mu as basal fertilizer and 40 kg of urea per mu as topdressing during the greening stage; maize received 40 kg of compound fertilizer per mu. The compound fertilizer was evenly spread on the surface of each plot, and half / full amounts of well-rotted straw were evenly spread in specific plots. A 15 cm deep rotary tillage was then performed to bury the straw. For the wheat season, fertilization, rotary tillage, and sowing were carried out in mid-October each year, topdressing in mid-March of the following year, and harvesting in June. For the maize season, fertilization, rotary tillage, and sowing were carried out in late October each year, with appropriate irrigation based on market conditions, and harvesting in October of the same year.

[0095] On days 3, 9, 20, 40, 60, 90, 120, and 150 after the start of the experiment, soil samples were taken from the 0-20 cm topsoil layer in each experimental plot using the S-shaped sampling method. Visible stones, plant debris, roots, and other impurities were removed. After thorough mixing, 400 g of soil was collected using the quartering method and placed in a resealable bag. The bag was labeled with the sampling point number, time, and other information. After being brought back to the laboratory, the samples were packaged into four portions. One portion was stored at 4 ℃ for determining soil physicochemical properties (pH, moisture content, DOC, MBC, ROC, SOC); one portion was stored at -80 ℃ for determining soil microbial indicators; one portion was freeze-dried for determining lignin phenols, microbial residue carbon, and soil protein; and the remaining portion was used for determining soil aggregates.

[0096] Then, the collected data was analyzed and processed using a visualization method for straw utilization characteristics analysis as described in Example 1, and the final Sankey diagram is shown below. Figure 4 As shown.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of visualizing straw utilization characteristic analysis, characterized by, The method for analyzing the utilization characteristics of straw includes: S100. Collect soil physicochemical property data and characteristic data of different types of decomposed straw, and preprocess the collected data to construct a comprehensive database; S200: By processing data from the comprehensive database using a biocarbon utilization characteristic analysis model, the dynamic process of soil carbon cycling after applying corresponding types of decomposed straw is simulated to obtain key variable data. The biocarbon utilization characteristic analysis model uses the microbial active field as the central coupling variable, and takes the straw residual carbon concentration, dissolved organic carbon concentration, microbial biomass carbon density and stable organic carbon accumulation as key variables, and constructs coupled differential equations for these four key variables, thereby coupling the biological, chemical and physical processes in the soil carbon cycle. S300. Based on the calculated key variable data, traverse the time series data in the comprehensive database to calculate carbon fixation efficiency, microbial activity, and physical protection strength, thereby obtaining enhanced time series data. S400: Using enhanced time series data and key variable data as data sources, a visualization is rendered. The coupled differential equations include: The straw decomposition kinetic equation describing the change in straw residual carbon concentration is constructed by attributing the dynamic change of straw residual carbon to two core processes: in-situ biochemical reaction and spatial physical transport. The dissolved organic carbon migration-transformation equation describing the concentration of dissolved organic carbon is based on the source-sink-transport balance. Dissolved organic carbon is treated as an intermediate product, and its concentration change depends on the dynamic balance of the source-sink-transport process. The equation is constructed from the source of straw decomposition, the sink consumed by microorganisms, and the transport through physical diffusion in the soil. The microbial growth equation describing the carbon density of microbial biomass is constructed by attributing the dynamic changes of the microbial community to three core life processes: growth by utilizing dissolved organic carbon resources, natural mortality and decay, and active spatial migration in search of a better living environment. The stable organic carbon stabilization equation, describing the stable accumulation of organic carbon, is constructed by stabilizing the main sources of organic carbon: biological and chemical pathways, and taking into account the slow decomposition process of this portion of carbon. The straw decomposition kinetic equation is shown below: ， wherein, is the partial differential symbol, is the concentration of residual straw carbon, is time, denotes the rate of change of the concentration of residual straw carbon; is the base decomposition rate constant, is the temperature, denotes the base decomposition rate constant influenced by temperature; is the inhibition function of the carbon to nitrogen ratio; is the inhibition factor of the lignin content; is the field of microbial activity, is the diffusion coefficient of the straw particles, is the soil depth, denotes the second order partial derivative of the concentration of straw carbon over the depth, representing the intensity of the diffusion.

2. The method for visualizing straw utilization characteristics analysis according to claim 1, characterized in that, The soil physicochemical properties data include: soil moisture content, soil pH value, dissolved organic carbon, microbial biomass carbon, soil stable organic carbon content, soil amino sugar content, lignin phenol content, and soil aggregates. The characteristic data of the decomposed straw include: the carbon-to-nitrogen ratio, lignin content, cellulose content, amount added, decomposition index, and particle size distribution.

3. The method for visualizing straw utilization characteristics analysis according to claim 1, characterized in that, The preprocessing includes: identifying and handling errors, missing values, and outliers in the data to ensure the accuracy and reliability of the data. The data was then standardized using the Z-score algorithm.

4. The method for visualizing straw utilization characteristics analysis according to claim 1, characterized in that, The migration-conversion equation for the dissolved organic carbon is shown below: ， in, It is soluble organic carbon. This indicates the rate of change of dissolved organic carbon concentration over time. The conversion coefficient for the production of dissolved organic carbon from straw decomposition. This represents the maximum absorption rate of dissolved organic carbon by microorganisms. It is the Michaelis constant; The density of microbial biomass carbon; The diffusion coefficient of dissolved organic carbon in soil solution; The microbial growth equation is shown below: ， in, This represents the rate of change of microbial biomass carbon density over time. This is the microbial yield coefficient. The decay rate constant of the microorganism; For divergence operators; The directional migration rate of microorganisms; The stabilization equation for stable organic carbon is shown below: ， in, To stabilize the accumulation of organic carbon, This represents the rate of change of the amount of stable organic carbon accumulation over time. The efficiency of converting microbial residues into stable organic carbon. To stabilize the proportion of organic carbon converted into stable organic carbon through chemical complexation. To stabilize the slow decomposition weight of organic carbon This indicates the slow decomposition rate of stable organic carbon itself.

5. The method for visualizing straw utilization characteristics analysis according to claim 4, characterized in that, The microbial activity field is a nonlinear multi-factor comprehensive evaluation function. It calculates a comprehensive activity index based on the current biological, environmental, and physical states, and uses the calculated value as a key coupling variable in the coupled differential equation.

6. The method for visualizing straw utilization characteristics analysis according to claim 5, characterized in that, The microbial active field is shown in the following formula: ， in, This represents the microbial activity field at depth z and time t. It is a natural constant. This represents the maximum activity value. As a factor influencing microbial populations, As an environmental impact factor, Factors affecting soil structure.

7. The method for visualizing straw utilization characteristics analysis according to claim 6, characterized in that, The factors influencing microbial populations are reflected by microbial entropy and biomass, which together indicate the maturity and size of the microbial community. The environmental factors mentioned refer to the negative impacts on activity when temperature and moisture deviate from suitable environmental values; The factors influencing soil structure refer to the impact of soil structure on microbial activity.

8. The method for visualizing straw utilization characteristics analysis according to claim 7, characterized in that, The influencing factors of the microbial population are calculated using the following formula: ， Environmental impact factors are calculated using the following formula: ， The factors influencing soil structure are calculated using the following formula: ， in, The weight of microbial entropy, Microbial entropy; Weighted by temperature, Weight for soil moisture Soil moisture content, As the weight of soil structure, This is a soil structure index.

9. A visual straw utilization characteristic analysis system, characterized in that, The straw utilization characteristic analysis system includes: processor; The memory stores a computer program that, when executed by a processor, implements the method for visualizing straw utilization characteristics analysis as described in any one of claims 1 to 8.

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