Determination method of organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph

Abstract

The invention relates to the technical field of organic carbon content determination, and specifically discloses a method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph. The method comprises: sampling soil aggregates in an artificial forest area of ​​the coastal saline-alkali land to be detected, separating and grading the sample soil aggregates, storing the sample soil aggregate grading results and forest age as designated labels, obtaining and importing soil microbial characteristic values ​​of soil aggregates and characteristic values ​​of organic carbon content of soil aggregates into a soil aggregate soil carbon content assessment model, comparing to obtain the soil carbon content of soil aggregates, and integrating the soil carbon content of soil aggregates corresponding to each designated label into a knowledge graph. The invention solves the problem that the traditional determination of organic carbon content of soil aggregates in coastal saline-alkali land is not comprehensive enough, is limited to directly determining the organic carbon content, and ignores the problem of dynamic changes in soil organic carbon, and is helpful to evaluate soil aggregates and their organic carbon contents.

Description

Technical Field

[0001] The present invention relates to the technical field of organic carbon content determination, and specifically to a method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph. Background Art

[0002] Coastal saline-alkali land is an important reserve land resource in my country. Its surface carbon density is only about 20% of the carbon density of the surface soil of grassland or forest. Therefore, the development and utilization of my country's coastal saline-alkali land resources and the improvement of its carbon sequestration and sink potential are of great practical significance for mitigating climate change. The establishment of artificial forests is an important bioremediation measure to increase the input of organic carbon to coastal saline-alkali land and improve soil structure. Domestic and foreign studies have found that the organic carbon content in the soil of artificial forests may increase or increase first and then decrease, or decrease first and then increase with the increase of forest age. However, how the soil organic carbon of artificial forests (such as Robinia pseudoacacia forests) in the special habitat of coastal saline-alkali land changes with forest age, and whether there is a forest age threshold for organic carbon storage capacity is still unclear, which is not conducive to the maintenance and improvement of soil carbon sink function. Aggregates are the basic units for maintaining the stability of soil structure and the main carrier of organic carbon sequestration. Organic carbon is an important cementing material for the formation of aggregates. The organic carbon in aggregates is difficult to be contacted by microorganisms, thereby promoting the accumulation of soil carbon pools. It is of great significance to explore the accumulation of organic carbon in aggregates.

[0003] At present, there are still some shortcomings in the research on the determination method of organic carbon content in coastal saline-alkali soil aggregates based on knowledge graphs. Specifically, the traditional determination of organic carbon content in coastal saline-alkali soil aggregates is not comprehensive enough, limited to direct determination of organic carbon content, and ignores the dynamic changes of soil organic carbon, which may lead to one-sided results. The importance of dynamic change monitoring lies in tracking the accumulation and loss process of organic carbon and providing an assessment of the effectiveness of soil management and restoration measures. The lack of long-term monitoring may lead to the failure to timely identify changes in soil health status. Measuring only organic carbon content may not be able to assess the long-term impact of these measures on soil health, thereby affecting the formulation of ecological restoration and sustainable management strategies. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph, comprising the following steps: obtaining a basic information data set of a coastal saline-alkali land planted forest area to be detected, sampling soil aggregates in the coastal saline-alkali land planted forest area to be detected based on the obtained basic information data set of the coastal saline-alkali land planted forest area to be detected, separating and grading the sample soil aggregates; storing the grading results of the sample soil aggregates in the coastal saline-alkali land planted forest area to be detected and the forest age of the coastal saline-alkali land planted forest area to be detected as specified labels; obtaining the soil aggregates corresponding to the specified labels. The soil microorganism dataset is obtained, and the soil microorganism characteristic value of soil aggregate is obtained by comprehensive analysis based on the soil microorganism dataset corresponding to the specified label. The soil aggregate organic carbon content dataset corresponding to the specified label is obtained, and the characteristic value of soil aggregate organic carbon content is obtained by comprehensive analysis based on the soil aggregate organic carbon content dataset corresponding to the specified label. The soil microorganism characteristic value of soil aggregate and the characteristic value of soil aggregate organic carbon content are introduced into the soil aggregate soil carbon content assessment model, and the soil carbon content of soil aggregate corresponding to the specified label is obtained by comparison, and the soil carbon content of soil aggregate corresponding to each specified label is integrated into a knowledge graph.

[0006] As a further method, a basic information data set of the coastal saline-alkali land planted forest area to be detected specifically includes the forest age of the coastal saline-alkali land planted forest area to be detected, the area of ​​the coastal saline-alkali land planted forest area to be detected, and the average height of vegetation in the coastal saline-alkali land planted forest area to be detected.

[0007] As a further method, based on the basic information data set of the coastal saline-alkali land planted forest to be detected, soil aggregates in the coastal saline-alkali land planted forest to be detected are sampled, and the sample soil aggregates are separated and graded. The specific analysis process is: based on the basic information data set of the coastal saline-alkali land planted forest to be detected, a comprehensive analysis is performed to obtain the basic information characteristic values ​​of the coastal saline-alkali land planted forest to be detected, and the basic information characteristic values ​​of the coastal saline-alkali land planted forest to be detected are used as the analysis basis for comparing the sampling plan of the soil aggregates in the coastal saline-alkali land planted forest to be detected; the basic information characteristic values ​​of the coastal saline-alkali land planted forest to be detected are compared with the soil aggregates in the coastal saline-alkali land planted forest to be detected corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest to be detected stored in the database. The sampling schemes are compared to obtain the soil aggregate sampling scheme of the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected; based on the soil aggregate sampling scheme of the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected, the soil aggregates of the coastal saline-alkali land planted forest area to be detected are sampled; the sample soil aggregates are separated based on particle size based on the wet sieving method; the sample soil aggregates with a particle size less than 250 microns after separation are marked as primary soil aggregates; the sample soil aggregates with a particle size greater than or equal to 250 microns and less than or equal to 2000 microns after separation are marked as secondary soil aggregates; the sample soil aggregates with a particle size greater than 2000 microns after separation are marked as tertiary soil aggregates.

[0008] As a further method, the basic information characteristic values ​​of the coastal saline-alkali land plantation area to be detected, the specific analysis process is as follows:

[0009]

[0010] Where ω is the basic information characteristic value of the coastal saline-alkali land planted forest area to be detected, nl is the forest age of the coastal saline-alkali land planted forest area to be detected, ms is the area of ​​the coastal saline-alkali land planted forest area to be detected, gd is the average height of vegetation in the coastal saline-alkali land planted forest area to be detected, μ 1 is the compensation factor of the forest age of the coastal saline-alkali land artificial forest area to be tested, μ 2 is the compensation factor for the area of ​​coastal saline-alkali land plantations to be tested, μ 3 It is the compensation factor for the average height of vegetation in the coastal saline-alkali land artificial forest area to be tested.

[0011] As a further method, the results of soil aggregate classification of samples in the coastal saline-alkali land planted forest area to be tested and the forest age of the coastal saline-alkali land planted forest area to be tested are stored as designated labels. The specific analysis process is: obtaining the soil aggregate grade of samples in the coastal saline-alkali land planted forest area to be tested after separation and the forest age of the coastal saline-alkali land planted forest area to be tested corresponding to the soil aggregate grade of samples in the coastal saline-alkali land planted forest area to be tested after separation; storing the soil aggregate grade of samples in the coastal saline-alkali land planted forest area to be tested and the forest age of the coastal saline-alkali land planted forest area to be tested as designated labels.

[0012] As a further method, based on the obtained soil microbial dataset of soil aggregates corresponding to the designated labels, a comprehensive analysis is performed to obtain the soil microbial characteristic values ​​of soil aggregates. The specific analysis process is: obtaining the soil microbial dataset of soil aggregates corresponding to the designated labels, the soil microbial dataset of soil aggregates corresponding to the designated labels specifically includes the total amount of soil microorganisms of soil aggregates corresponding to the designated labels, the Shannon diversity index of soil microorganisms of soil aggregates corresponding to the designated labels, and the respiration rate of soil microorganisms of soil aggregates corresponding to the designated labels; based on the obtained soil microbial dataset of soil aggregates corresponding to the designated labels, a comprehensive analysis is performed to obtain the soil microbial characteristic values ​​of soil aggregates, and the soil microbial characteristic values ​​of soil aggregates are used as the analysis basis for comparing the soil carbon content of soil aggregates.

[0013] As a further method, soil aggregate soil microbial characteristic values, the specific analysis process is:

[0014]

[0015] Where α is the soil microbial characteristic value of soil aggregates, zn is the total amount of soil microorganisms in the soil aggregates corresponding to the specified label, xn is the Shannon diversity index of soil microorganisms in the soil aggregates corresponding to the specified label, hv is the respiration rate of soil microorganisms in the soil aggregates corresponding to the specified label, σ 1 is the compensation factor for the total amount of soil microorganisms in the soil aggregate corresponding to the specified label, σ 2 is the compensation factor of the Shannon diversity index of soil microorganisms in the soil aggregate corresponding to the specified label, σ 3 The compensation factor for the respiration rate of soil microorganisms in the soil aggregate corresponding to the specified label.

[0016] As a further method, based on the obtained data set of soil aggregate organic carbon content corresponding to the designated label, a comprehensive analysis is performed to obtain the characteristic value of soil aggregate organic carbon content. The specific analysis process is: obtaining the data set of soil aggregate organic carbon content corresponding to the designated label, the data set of soil aggregate organic carbon content corresponding to the designated label specifically includes the total amount of plant residues in soil aggregates corresponding to the designated label, the total amount of dissolved organic carbon in soil aggregates corresponding to the designated label, and the total amount of humus in soil aggregates corresponding to the designated label; based on the obtained data set of soil aggregate organic carbon content corresponding to the designated label, a comprehensive analysis is performed to obtain the characteristic value of soil aggregate organic carbon content, and the characteristic value of soil aggregate organic carbon content is used as the analysis basis for comparing the soil carbon content of soil aggregates.

[0017] As a further method, the characteristic value of soil aggregate organic carbon content, the specific analysis process is:

[0018]

[0019] Where β is the characteristic value of organic carbon content in soil aggregates, ct is the total amount of plant residues in soil aggregates corresponding to the specified label, rj is the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, fz is the total amount of humus in soil aggregates corresponding to the specified label, τ 1 is the compensation factor for the total amount of plant residues in soil aggregates corresponding to the specified label, τ 2 is the compensation factor for the total dissolved organic carbon in soil aggregates corresponding to the specified label, τ 3 is the compensation factor for the total amount of humus in soil aggregates corresponding to the specified label, and e is a natural constant.

[0020] As a further method, the soil carbon content of the soil aggregate corresponding to the designated label is obtained by comparison, and the soil carbon content of the soil aggregate corresponding to each designated label is integrated into a knowledge graph. The specific analysis process is: the soil microbial characteristic value of the soil aggregate and the characteristic value of the organic carbon content of the soil aggregate are introduced into the soil aggregate soil carbon content assessment model, and the soil aggregate soil carbon content assessment value is obtained by comprehensive analysis. The soil aggregate soil carbon content assessment value is used as the analysis basis for comparing the soil aggregate soil carbon content; the soil aggregate soil carbon content assessment value is compared with the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value stored in the database to obtain the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value, and the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value is marked as the soil aggregate soil carbon content corresponding to the designated label corresponding to the soil aggregate soil carbon content assessment value; the soil aggregate soil carbon content corresponding to each designated label is integrated into a knowledge graph.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0022] (1) The present invention provides a method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph. Through data-driven analysis, it can provide a scientific basis for soil management and ecological restoration of coastal saline-alkali land. The evaluation of soil aggregates and their organic carbon content can identify and promote the best practices for soil carbon storage. The analysis of the microbial characteristic values ​​and organic carbon content of soil aggregates can provide a deep understanding of the soil condition. Healthy soil aggregates can enhance the soil's water retention capacity, nutrient supply capacity and biodiversity, thereby improving the service functions of the entire ecosystem, such as water conservation, soil protection and ecological balance.

[0023] (2) The present invention obtains a basic information data set of the coastal saline-alkali land planted forest area to be tested, samples the soil aggregates in the coastal saline-alkali land planted forest area to be tested, separates and grades the sample soil aggregates, and clarifies the characteristics of aggregates of different grades, which can provide guidance for the rational use and management of land. The analysis of soil aggregates at different levels can provide a scientific basis for ecological restoration projects and help formulate more sophisticated restoration strategies. Different grades of soil aggregates have different storage capacities for organic carbon. By analyzing the carbon content of aggregates of each grade, effective measures to enhance soil carbon storage can be formulated.

[0024] (3) The present invention imports soil aggregate soil microbial characteristic values ​​and soil aggregate organic carbon content characteristic values ​​into a soil aggregate soil carbon content assessment model, compares and obtains the soil carbon content of the soil aggregate corresponding to the specified label, and integrates the soil carbon content of the soil aggregate corresponding to each specified label into a knowledge graph. The knowledge graph can construct a systematic knowledge framework by integrating soil aggregate-related characteristics and assessment results, which is convenient for researchers to quickly understand relevant information and relationships. The knowledge graph provides a visual method to make complex relationships and data easier to understand and analyze, which helps to identify the relationship between soil microbial characteristics, aggregate structure and carbon storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0026] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Reference Figure 1 As shown, the present invention provides a method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph, including: obtaining a basic information data set of a coastal saline-alkali land planted forest area to be detected, sampling soil aggregates in the coastal saline-alkali land planted forest area to be detected based on the obtained basic information data set of the coastal saline-alkali land planted forest area to be detected, and separating and grading the sample soil aggregates.

[0029] Specifically, the basic information dataset of the coastal saline-alkali land planted forest area to be tested includes the forest age of the coastal saline-alkali land planted forest area to be tested, the area of ​​the coastal saline-alkali land planted forest area to be tested, and the average height of vegetation in the coastal saline-alkali land planted forest area to be tested. The forest age usually refers to the time interval from the planting of the planted forest to the present, that is, the age of the trees. Understanding the forest age is helpful to evaluate the growth status, biodiversity and carbon storage capacity of the forest. Electromagnetic wave or ultrasonic instruments (such as tree ring meters) are used to measure the age of trees without damaging the trees. The area of ​​the area to be tested refers to the total coverage area of ​​the coastal saline-alkali land planted forest, usually in hectares or square meters. The definition of area can help understand the distribution of vegetation and its impact on the environment. Satellite images or drones are used for remote sensing monitoring, and the area of ​​the planted forest area is calculated through image processing and analysis software (such as GIS software). The average height of vegetation refers to the average height of trees or other plants in a specific area. The use of lidar technology can quickly and accurately obtain vegetation height information, which is suitable for a large range of forest areas.

[0030] It needs to be explained that the average height of the above-mentioned trees is positively correlated with the age of the forest. As the growth years of trees increase, the height of trees usually increases. Trees grow faster in the young stage, but the growth rate gradually slows down with age. In the early stages of artificial forest development, as the age of the forest increases, more trees usually survive and expand, thereby increasing the coverage area, indicating the growth and reproduction capacity of trees. The relationship between area and the average height of vegetation is more complicated. Within a certain range, a larger area can accommodate more trees, thereby increasing the biomass of the overall forest, which may lead to an increase in the average height. However, if the trees in the area are too dense to compete for resources, it may inhibit the growth of height.

[0031] Furthermore, based on the obtained basic information data set of the coastal saline-alkali land planted forest area to be tested, soil aggregates in the coastal saline-alkali land planted forest area to be tested are sampled, and the sample soil aggregates are separated and graded. The specific analysis process is: based on the obtained basic information data set of the coastal saline-alkali land planted forest area to be tested, a comprehensive analysis is performed to obtain the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be tested, and the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be tested are used as the analysis basis for comparing the soil aggregate sampling plan for the coastal saline-alkali land planted forest area to be tested; the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be tested are compared with the soil aggregate sampling plan of the coastal saline-alkali land planted forest area to be tested corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be tested stored in the database. The schemes are compared to obtain the sampling scheme of soil aggregates in the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected; based on the sampling scheme of soil aggregates in the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected, soil aggregates in the coastal saline-alkali land planted forest area to be detected are sampled; the sample soil aggregates are separated based on particle size based on the wet sieving method; the sample soil aggregates with a particle size less than 250 microns after separation are marked as primary soil aggregates; the sample soil aggregates with a particle size greater than or equal to 250 microns and less than or equal to 2000 microns after separation are marked as secondary soil aggregates; the sample soil aggregates with a particle size greater than 2000 microns after separation are marked as tertiary soil aggregates.

[0032] In a specific embodiment, the particle size of soil aggregates directly affects the physical, chemical and biological properties of the soil. Aggregates with smaller particle sizes (such as primary soil aggregates) generally have higher biological activity, which may help promote the growth of soil microorganisms and soil fertility. Larger aggregates (such as tertiary soil aggregates) help maintain the air permeability and drainage capacity of the soil and are suitable for root growth. The selection of the two particle size limits of 250 microns and 2000 microns not only takes into account the impact of soil aggregates on ecological functions, but also provides important data support and guidance for subsequent soil research and restoration. Sampling and grading of soil aggregates can obtain quantitative data on soil structure and quality, thereby providing a basis for the assessment of soil health, helping to identify soil problems and providing a basis for decision-making. Recognizing the characteristics of soil aggregates of different particle sizes can help land managers and farmers formulate more targeted management measures. Smaller soil aggregates generally have better water retention capacity, while larger aggregates can provide better air permeability and root growth space. A healthy soil aggregate structure can enhance the physical, chemical and biological properties of the soil, thereby improving the ecosystem service function of the soil.

[0033] It needs to be explained that the above-mentioned method obtains the basic information data set of the coastal saline-alkali land planted forest area to be tested, samples the soil aggregates in the coastal saline-alkali land planted forest area to be tested, separates and grades the sample soil aggregates, and clarifies the characteristics of aggregates of different grades. This can provide guidance for the rational use and management of land. The analysis of soil aggregates at different levels can provide a scientific basis for ecological restoration projects and help formulate more refined restoration strategies. Different grades of soil aggregates have different storage capacities for organic carbon. By analyzing the carbon content of aggregates of each grade, effective measures to enhance soil carbon storage can be formulated.

[0034] Specifically, the basic information characteristic values ​​of the coastal saline-alkali land plantation area to be detected, the specific analysis process is:

[0035]

[0036] Where ω is the basic information characteristic value of the coastal saline-alkali land planted forest area to be detected, nl is the forest age of the coastal saline-alkali land planted forest area to be detected, ms is the area of ​​the coastal saline-alkali land planted forest area to be detected, gd is the average height of vegetation in the coastal saline-alkali land planted forest area to be detected, μ 1 is the compensation factor of the forest age of the coastal saline-alkali land artificial forest area to be tested, μ 2 is the compensation factor for the area of ​​coastal saline-alkali land plantations to be tested, μ 3 It is the compensation factor for the average height of vegetation in the coastal saline-alkali land artificial forest area to be tested.

[0037] It should be explained that the basic information characteristic values ​​of the above-mentioned coastal saline-alkali land artificial forest areas to be tested are calculated through the forest age of the coastal saline-alkali land artificial forest areas to be tested, the area of ​​the coastal saline-alkali land artificial forest areas to be tested, and the average height of the vegetation in the coastal saline-alkali land artificial forest areas to be tested. The forest age of the coastal saline-alkali land artificial forest areas to be tested, the area of ​​the coastal saline-alkali land artificial forest areas to be tested, and the average height of the vegetation in the coastal saline-alkali land artificial forest areas to be tested are normalized. The forest age, area and vegetation height can provide key information on the health status and development stage of the forest ecosystem, and help evaluate the ecological stability and diversity of the region. By monitoring these characteristic values, changes in the ecological environment can be discovered in a timely manner, and the possible ecological restoration process can be evaluated. Understanding characteristic values ​​such as forest age and vegetation height can help forestry managers formulate reasonable afforestation, tending and felling plans, optimize resource allocation and compare sampling plans. The forest age, area and vegetation height directly affect the carbon storage capacity of the forest. Evaluating these characteristic values ​​is helpful to estimate the carbon sink in the region. Potential, thereby providing data support for global climate change response measures. The compensation factors for the age of coastal saline-alkali land artificial forests to be detected, the area of ​​coastal saline-alkali land artificial forests to be detected, and the average height of vegetation in coastal saline-alkali land artificial forests to be detected are obtained from the database. According to historical data, a mapping set of compensation factors for the historically measured age of coastal saline-alkali land artificial forests to be detected, the area of ​​coastal saline-alkali land artificial forests to be detected, and the average height of vegetation in coastal saline-alkali land artificial forests to be detected and the age of coastal saline-alkali land artificial forests to be detected, the area of ​​coastal saline-alkali land artificial forests to be detected, and the average height of vegetation in coastal saline-alkali land artificial forests to be detected is established to obtain the compensation factors for the age of coastal saline-alkali land artificial forests to be detected, the area of ​​coastal saline-alkali land artificial forests to be detected, and the average height of vegetation in coastal saline-alkali land artificial forests to be detected corresponding to the current age of coastal saline-alkali land artificial forests to be detected, the area of ​​coastal saline-alkali land artificial forests to be detected, and the average height of vegetation in coastal saline-alkali land artificial forests to be detected.

[0038] The soil aggregate classification results of the samples in the coastal saline-alkali land planted forest area to be detected and the forest age of the coastal saline-alkali land planted forest area to be detected are stored as designated labels.

[0039] Specifically, the results of soil aggregate grade classification of samples of coastal saline-alkali land planted forests to be tested and the forest age of coastal saline-alkali land planted forests to be tested are stored as designated labels. The specific analysis process is: obtaining the soil aggregate grade of samples of coastal saline-alkali land planted forests to be tested after separation and the forest age of coastal saline-alkali land planted forests to be tested corresponding to the soil aggregate grade of samples of coastal saline-alkali land planted forests to be tested after separation; storing the soil aggregate grade of samples of coastal saline-alkali land planted forests to be tested and the forest age of coastal saline-alkali land planted forests to be tested as designated labels.

[0040] It should be explained that the use of designated tags to store soil aggregate grade and forest age helps to achieve systematic management, making data more orderly and easy to retrieve. It also improves the traceability of data, allowing researchers to quickly find the information they need. Associating soil aggregate grade with forest age can more deeply analyze the relationship between the two, help identify the impact of forest age on soil aggregate characteristics, and further understand the dynamic changes of ecosystems more comprehensively. Storing soil aggregate grade and forest age data lays the foundation for subsequent longitudinal research, allowing researchers to track soil changes in specific areas and evaluate the long-term impact of artificial forest growth and management on soil health. By storing soil aggregate grade and forest age data at the same time, data analysis can be performed more accurately and potential patterns or trends can be extracted, which can provide objective data support for researchers in related fields such as ecology, soil science, and forestry.

[0041] A soil microorganism dataset corresponding to a soil aggregate with a specified label is obtained. Based on the obtained soil microorganism dataset corresponding to a soil aggregate with a specified label, a comprehensive analysis is performed to obtain soil microorganism characteristic values ​​of the soil aggregate.

[0042] Specifically, based on the soil microorganism dataset corresponding to the soil aggregates with the specified labels, the soil microorganism characteristic values ​​of the soil aggregates are obtained through comprehensive analysis. The specific analysis process is: obtaining the soil microorganism dataset corresponding to the soil aggregates with the specified labels, the soil microorganism dataset corresponding to the soil aggregates with the specified labels specifically includes the total amount of soil microorganisms corresponding to the soil aggregates with the specified labels, the Shannon diversity index of soil microorganisms corresponding to the soil aggregates with the specified labels, and the respiration rate of soil microorganisms corresponding to the soil aggregates with the specified labels. The total amount of soil microorganisms refers to the number of microorganisms per unit soil volume or mass, usually including bacteria, fungi, actinomycetes, etc., which can reflect the biological activity and quality of the soil, and is cultivated by homogenizing soil samples, dilution culture methods (such as plate method), or using molecular biology techniques (such as PCR method) is used to estimate the number of microorganisms. The Shannon diversity index is an important indicator for measuring species diversity in ecosystems. In soil microbial communities, the Shannon diversity index reflects the richness and uniformity of microbial species. The higher the index, the more diverse the soil microbial community. It is calculated by classifying and counting soil microorganisms. The soil microbial respiration rate refers to the rate at which microorganisms in the soil produce carbon dioxide (CO2) through respiration. The respiration rate is usually measured using a respiration chamber or a soil respiration measurement system. Based on the soil microbial dataset corresponding to the specified label obtained, the soil microbial characteristic values ​​of soil aggregates are obtained through comprehensive analysis. The soil microbial characteristic values ​​of soil aggregates are used as the analytical basis for comparing the soil carbon content of soil aggregates.

[0043] It needs to be explained that the higher total amount of microorganisms mentioned above means that there are more microorganisms in the soil participating in the decomposition of organic matter and biological metabolism, resulting in a higher microbial respiration rate. Microorganisms convert organic matter into carbon dioxide (CO2) through respiration. Therefore, when the number of microorganisms increases, the respiration rate will usually increase accordingly. The diversity of microorganisms (measured by the Shannon diversity index, for example) can affect the respiration rate. In an ecosystem, a highly diverse microbial community can promote the synergy and interaction of different metabolic processes, thereby improving the overall function and stability of the soil. Communities with higher diversity tend to decompose organic matter in the soil more effectively, and therefore may also exhibit a higher respiration rate. The relationship between the total amount of microorganisms and diversity is more complex. An increase in the total amount of microorganisms may lead to an increase in diversity, especially when the soil environment is relatively good. When the number of microorganisms is too high and the species is single, it may lead to a decrease in diversity because the dominant population may occupy most of the ecological niches. Extreme environmental conditions (such as soil pollution, barrenness, etc.) may lead to an increase in the number of microbial communities but a decrease in diversity.

[0044] It needs to be explained that the above-mentioned soil microorganisms are the key drivers of soil organic carbon cycle. By analyzing the characteristic values ​​of microorganisms, a correlation model with organic carbon content can be established to help more accurately predict the soil organic carbon storage and changing trends in coastal saline-alkali areas. Microbial activity is closely related to the decomposition and transformation of organic carbon. Analysis of indicators such as microbial diversity and respiration rate can provide an in-depth understanding of the carbon storage mechanism of saline-alkali soils and reveal how microorganisms affect the input, output and storage process of carbon. Healthy soil microbial communities are usually associated with rich organic carbon content and good soil structure. Analysis of microbial characteristic values ​​can evaluate the health of coastal saline-alkali soils. The health status of saline-alkali soil can be determined, and then the soil fertility and production potential can be judged, providing a basis for farmland management and improvement measures. In saline-alkali soil, the characteristic values ​​of soil microorganisms can help evaluate the effectiveness of fertilization, amendments and other management measures. Combining microbial data with organic carbon content analysis can provide scientific guidance for soil improvement and restoration, and help select appropriate management measures to increase soil organic carbon content. Changes in microbial characteristic values ​​can serve as an early warning mechanism to help identify signs of degradation of saline-alkali soil. Through the relationship analysis with organic carbon content, changes in soil environment can be tracked, the effectiveness of restoration measures can be evaluated, and changes in soil health and organic carbon dynamics during the restoration process can be monitored.

[0045] Furthermore, the specific analysis process of soil microbial characteristic values ​​of soil aggregates is as follows:

[0046]

[0047] Where α is the soil microbial characteristic value of soil aggregates, zn is the total amount of soil microorganisms in the soil aggregates corresponding to the specified label, xn is the Shannon diversity index of soil microorganisms in the soil aggregates corresponding to the specified label, hv is the respiration rate of soil microorganisms in the soil aggregates corresponding to the specified label, σ 1 is the compensation factor for the total amount of soil microorganisms in the soil aggregate corresponding to the specified label, σ 2 is the compensation factor of the Shannon diversity index of soil microorganisms in the soil aggregate corresponding to the specified label, σ 3 The compensation factor for the respiration rate of soil microorganisms in the soil aggregate corresponding to the specified label.

[0048] It needs to be explained that the above soil aggregate soil microbial characteristic values ​​are calculated by specifying the total amount of soil microorganisms in soil aggregates corresponding to the specified label, the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified label, and the respiration rate of soil microorganisms in soil aggregates corresponding to the specified label. Normalization of the total amount of soil microorganisms in soil aggregates corresponding to the specified label, the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified label, and the respiration rate of soil microorganisms in soil aggregates corresponding to the specified label can provide a scientific basis for decisions such as soil improvement, fertilization management, and organic agriculture to enhance soil microbial activity and biodiversity. Understanding the characteristics of soil aggregates can help optimize soil structure under different management conditions, improve water retention capacity and air permeability. The activity of microorganisms directly affects the decomposition and storage of soil organic carbon. The carbon storage capacity and carbon fixation potential of the soil can be evaluated through characteristics such as respiration rate and microbial abundance, providing support for responding to climate change. Monitoring microbial characteristic values ​​can help promote the development of sustainable agricultural practices, enhance soil resilience, and improve agricultural production. Understanding microbial characteristics can help formulate reasonable rotation and Management measures such as mixed cropping are adopted to promote soil health. The compensation factors of the total amount of soil microorganisms in soil aggregates corresponding to the specified labels, the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified labels, and the respiration rate of soil microorganisms in soil aggregates corresponding to the specified labels are obtained from the database. According to the historical data, a mapping set of the historically measured total amount of soil microorganisms in soil aggregates corresponding to the specified labels, the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified labels, the respiration rate of soil microorganisms in soil aggregates corresponding to the specified labels and the compensation factors of the total amount of soil microorganisms in soil aggregates corresponding to the specified labels, the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified labels, and the respiration rate of soil microorganisms in soil aggregates corresponding to the specified labels is established to obtain the compensation factors of the total amount of soil microorganisms in soil aggregates corresponding to the specified labels, the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified labels, and the respiration rate of soil microorganisms in soil aggregates corresponding to the specified labels.

[0049] A data set of organic carbon content in soil aggregates corresponding to a specified label is obtained. Based on the obtained data set of organic carbon content in soil aggregates corresponding to a specified label, a comprehensive analysis is performed to obtain the characteristic value of organic carbon content in soil aggregates.

[0050] Specifically, based on the obtained soil aggregate organic carbon content dataset corresponding to the specified label, a comprehensive analysis is performed to obtain the characteristic value of soil aggregate organic carbon content. The specific analysis process is: obtain the soil aggregate organic carbon content dataset corresponding to the specified label. The soil aggregate organic carbon content dataset corresponding to the specified label specifically includes the total amount of plant residues in the soil aggregate corresponding to the specified label, the total amount of dissolved organic carbon in the soil aggregate corresponding to the specified label, and the total amount of humus in the soil aggregate corresponding to the specified label. The total amount of plant residues in soil aggregates refers to the total weight or total amount of plant residues (such as leaves, stems, roots, etc.) in the soil aggregates. After decomposing in the soil, plant residues provide nutrition for microorganisms and also affect the physical structure and fertility of the soil. The total amount of dissolved organic carbon in soil aggregates refers to the organic carbon dissolved in water or other solvents. The total amount of dissolved organic carbon is an important active organic component in the soil, which has an important influence on microbial activity and nutrient cycling. The total amount of humus in soil aggregates refers to the total amount of humus in the soil (mainly derived from the decomposition of plant and animal residues). Humus is an important component of soil organic matter and has a significant impact on the water retention capacity, fertility and structure of the soil. A spectrophotometer or other chemical analysis methods are used to determine the total amount of plant residues in soil aggregates corresponding to the specified label, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, and the total amount of humus in soil aggregates corresponding to the specified label; based on the obtained data set of soil aggregate organic carbon content corresponding to the specified label, a comprehensive analysis is performed to obtain the characteristic value of soil aggregate organic carbon content, and the characteristic value of soil aggregate organic carbon content is used as the analysis basis for comparing the soil carbon content of soil aggregates.

[0051] It needs to be explained that the plant residues (such as roots, stems, leaves, etc.) in the above-mentioned soil aggregates become part of organic matter during the decomposition process in the soil. Plant residues are an important source of soil organic carbon. Plant residues will decompose under the action of microorganisms and release soluble organic carbon. The amount of plant residues is usually positively correlated with the concentration of dissolved organic carbon in the soil. Through further chemical decomposition and transformation, plant residues and their decomposition products can eventually be converted into humus, forming an important component of soil organic matter. The total amount of humus is also closely related to the amount of plant residues and microbial decomposition activities. Plant residues and dissolved organic carbon are important energy sources for microbial growth and reproduction, and the metabolic activities of microorganisms contribute to the further decomposition of plant residues to form humus. Humus provides a habitat and nutrients for microorganisms. Through the decomposition of plant residues, the release of dissolved organic carbon and the formation of humus, the soil ecosystem realizes the recycling of nutrients and promotes plant growth.

[0052] It needs to be explained that the above comprehensive analysis of plant residues, dissolved organic carbon and humus in soil aggregates can help to clarify the formation, transformation and accumulation mechanism of soil carbon, enhance the understanding of soil carbon dynamics, and understand the characteristics and content of organic carbon in soil aggregates, so as to formulate more scientific fertilization and soil improvement programs. The characteristic value of organic carbon content in soil aggregates can be used as an important indicator of soil health, helping to monitor and evaluate soil quality and its changes. The increase in soil organic carbon can help reduce the concentration of carbon dioxide in the atmosphere and mitigate climate change. Through effective soil management, it can support global efforts to combat climate change. Subsequent academic research provides a data basis, enabling researchers to conduct in-depth analysis related to soil organic carbon and promote academic development in this field.

[0053] Furthermore, the characteristic value of soil aggregate organic carbon content, the specific analysis process is as follows:

[0054]

[0055] Where β is the characteristic value of organic carbon content in soil aggregates, ct is the total amount of plant residues in soil aggregates corresponding to the specified label, rj is the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, fz is the total amount of humus in soil aggregates corresponding to the specified label, τ 1 is the compensation factor for the total amount of plant residues in soil aggregates corresponding to the specified label, τ 2 is the compensation factor for the total dissolved organic carbon in soil aggregates corresponding to the specified label, τ 3 is the compensation factor for the total amount of humus in soil aggregates corresponding to the specified label, and e is a natural constant.

[0056] It should be explained that the above soil aggregate organic carbon content characteristic values ​​are calculated by specifying the total amount of plant residues in soil aggregates corresponding to the specified label, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, and the total amount of humus in soil aggregates corresponding to the specified label. The total amount of plant residues in soil aggregates corresponding to the specified label, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, and the total amount of humus in soil aggregates corresponding to the specified label are normalized. Integrating organic carbon from different sources can more comprehensively reflect the organic matter level of soil and its impact on soil health, and help evaluate the biological, chemical and physical properties of soil. Plant residues, dissolved organic carbon and humus represent different sources of organic carbon, respectively. A comprehensive understanding of their relative contents and distribution will help us understand the circulation process of nutrients in soil, thereby optimizing fertilization and management strategies. Combining different forms of organic carbon can better evaluate the carbon storage capacity of soil and its carbon fixation potential. Different forms of organic carbon have an impact on the activity, structure and diversity of soil microbial communities. Through the analysis of characteristic values, It can reveal the impact of organic carbon on microorganisms, and then understand the function and dynamics of soil ecosystems. The compensation factors for the total amount of plant residues in soil aggregates corresponding to the specified labels, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified labels, and the total amount of humus in soil aggregates corresponding to the specified labels are obtained from the database. According to historical data, a mapping set of compensation factors for the total amount of plant residues in soil aggregates corresponding to the specified labels, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified labels, and the total amount of humus in soil aggregates corresponding to the specified labels and the total amount of plant residues in soil aggregates corresponding to the specified labels, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified labels, and the total amount of humus in soil aggregates corresponding to the specified labels is established to obtain the compensation factors for the total amount of plant residues in soil aggregates corresponding to the specified labels, the total amount of dissolved organic carbon in soil aggregates corresponding to the specified labels, and the total amount of humus in soil aggregates corresponding to the specified labels.

[0057] The soil microbial characteristic values ​​of soil aggregates and the characteristic values ​​of organic carbon content of soil aggregates are imported into the soil aggregate soil carbon content assessment model, and the soil carbon content of the soil aggregates corresponding to the specified label is obtained by comparison, and the soil carbon content of the soil aggregates corresponding to each specified label is integrated into a knowledge graph.

[0058] Specifically, the soil carbon content of the soil aggregate corresponding to the designated label is obtained by comparison, and the soil carbon content of the soil aggregate corresponding to each designated label is integrated into a knowledge graph. The specific analysis process is: the soil microbial characteristic value of the soil aggregate and the characteristic value of the organic carbon content of the soil aggregate are introduced into the soil aggregate soil carbon content assessment model, and the soil aggregate soil carbon content assessment value is obtained by comprehensive analysis. The soil aggregate soil carbon content assessment value is used as the analysis basis for comparing the soil aggregate soil carbon content; the soil aggregate soil carbon content assessment value is compared with the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value stored in the database to obtain the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value, and the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value is marked as the soil aggregate soil carbon content corresponding to the designated label corresponding to the soil aggregate soil carbon content assessment value; the soil aggregate soil carbon content corresponding to each designated label is integrated into a knowledge graph.

[0059] In a specific embodiment, the knowledge graph can present complex soil aggregate soil carbon content data in a structured form for easy management and retrieval. Through the form of a graph, the relationship between the characteristics of different soil aggregates and their carbon content can be more intuitively displayed, helping researchers to quickly obtain information. By integrating data from different sources, a more comprehensive and in-depth analysis can be conducted to explore the relationship between soil aggregate characteristics and soil carbon content. The knowledge graph's assessment of soil carbon content can provide a scientific basis for land management and help decision makers formulate precise soil management strategies. Based on the information in the knowledge graph, it can be identified which management measures can effectively increase the soil organic carbon content of saline-alkali land, thereby improving soil quality. The information in the knowledge graph can provide basic data for future soil carbon research and promote scientific progress in related fields. Healthy soil can provide better ecological services. By measuring and managing soil carbon content, the stability and service functions of the ecosystem can be enhanced.

[0060] In a specific embodiment, the soil aggregate soil carbon content assessment model, the specific analysis process is:

[0061]

[0062] Where ω is the assessed value of soil carbon content in soil aggregates, α is the characteristic value of soil microorganisms in soil aggregates, β is the characteristic value of organic carbon content in soil aggregates, and e is a natural constant.

[0063] It needs to be explained that the above-mentioned import of soil microbial characteristic values ​​of soil aggregates and characteristic values ​​of organic carbon content of soil aggregates into the soil carbon content assessment model of soil aggregates, and comprehensive analysis to obtain the soil carbon content assessment value of soil aggregates, combining microbial characteristics with organic carbon content can more comprehensively reflect soil carbon reserves and increase the accuracy of the assessment model. The activity and species of microorganisms are important indicators of soil health, and their changes can affect the circulation and storage of organic carbon. By analyzing the relationship between microbial characteristics and soil organic carbon, we can better understand the formation and transformation process of soil carbon. Combining multiple factors can improve the model's ability to predict soil carbon content under different soil types and management practices, making it more practical. Through accurate soil carbon content assessment values, it can provide a scientific basis for land management agencies and help formulate reasonable management strategies.

[0064] It needs to be explained that the above-mentioned method introduces the soil microbial characteristic values ​​of soil aggregates and the characteristic values ​​of organic carbon content of soil aggregates into the soil aggregate soil carbon content assessment model, and compares the soil carbon content of soil aggregates corresponding to the specified label, and integrates the soil carbon content of soil aggregates corresponding to each specified label into a knowledge graph. The knowledge graph can construct a systematic knowledge framework by integrating soil aggregate-related characteristics and evaluation results, which is convenient for researchers to quickly understand relevant information and relationships. The knowledge graph provides a visual way to make complex relationships and data easier to understand and analyze, which helps to identify the relationship between soil microbial characteristics, aggregate structure and carbon storage.

[0065] It needs to be explained that the above-mentioned method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on a knowledge graph can provide a scientific basis for soil management and ecological restoration of coastal saline-alkali land through data-driven analysis. The evaluation of soil aggregates and their organic carbon content can identify and promote best practices for soil carbon storage. Analysis of the microbial characteristic values ​​and organic carbon content of soil aggregates can provide an in-depth understanding of the condition of the soil. Healthy soil aggregates can enhance the soil's water retention capacity, nutrient supply capacity and biodiversity, thereby improving the service functions of the entire ecosystem, such as water conservation, soil protection and ecological balance.

[0066] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for determining the organic carbon content of soil aggregates in coastal saline-alkali land based on knowledge graph, characterized in that: The following steps are involved: The basic information dataset of the coastal saline-alkali land planted forest area to be detected is obtained. Based on the obtained basic information dataset of the coastal saline-alkali land planted forest area to be detected, soil aggregates in the coastal saline-alkali land planted forest area to be detected are sampled, and the sample soil aggregates are separated and graded. The specific analysis process is as follows: Based on the basic information data set of the coastal saline-alkali land planted forest area to be detected, the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected are obtained through comprehensive analysis. The basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected are used as the analysis basis for comparing and obtaining the soil aggregate sampling plan of the coastal saline-alkali land planted forest area to be detected. Compare the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected with the soil aggregate sampling schemes of the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic values ​​of each coastal saline-alkali land planted forest area to be detected stored in the database, and obtain the soil aggregate sampling scheme of the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic values ​​of the coastal saline-alkali land planted forest area to be detected; Based on the soil aggregate sampling plan of the coastal saline-alkali land planted forest area to be detected corresponding to the basic information characteristic value of the coastal saline-alkali land planted forest area to be detected, the soil aggregates of the coastal saline-alkali land planted forest area to be detected are sampled; The sample soil aggregates were separated based on particle size based on wet sieving method; The sample soil aggregates with a particle size less than 250 μm after separation were marked as primary soil aggregates; The sample soil aggregates with a particle size greater than or equal to 250 μm and less than or equal to 2000 μm after separation were marked as secondary soil aggregates; The sample soil aggregates with a particle size larger than 2000 μm after separation were marked as tertiary soil aggregates; The soil aggregate classification results of the samples in the coastal saline-alkali land planted forest area to be tested and the forest age of the coastal saline-alkali land planted forest area to be tested are stored as designated tags; A soil microorganism dataset of soil aggregates corresponding to a specified label is obtained, and based on the obtained soil microorganism dataset of soil aggregates corresponding to a specified label, a comprehensive analysis is performed to obtain soil microorganism characteristic values ​​of soil aggregates; Obtain a data set of organic carbon content of soil aggregates corresponding to a specified label, and obtain a characteristic value of organic carbon content of soil aggregates through comprehensive analysis based on the obtained data set of organic carbon content of soil aggregates corresponding to a specified label; The soil microbial characteristic values ​​of soil aggregates and the characteristic values ​​of organic carbon content of soil aggregates are imported into the soil aggregate soil carbon content assessment model, and the soil carbon content of the soil aggregates corresponding to the specified label is obtained by comparison, and the soil carbon content of the soil aggregates corresponding to each specified label is integrated into a knowledge graph.

2. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 1, characterized in that: The basic information data set of the coastal saline-alkali land planted forest area to be detected specifically includes the forest age of the coastal saline-alkali land planted forest area to be detected, the area of ​​the coastal saline-alkali land planted forest area to be detected, and the average height of vegetation in the coastal saline-alkali land planted forest area to be detected.

3. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 2, characterized in that: The specific analysis process of the basic information characteristic value of the coastal saline-alkali land plantation area to be detected is as follows: Wherein, ω is the basic information characteristic value of the coastal saline-alkali land planted forest area to be detected, nl is the forest age of the coastal saline-alkali land planted forest area to be detected, ms is the area of ​​the coastal saline-alkali land planted forest area to be detected, gd is the average height of vegetation in the coastal saline-alkali land planted forest area to be detected, μ1 is the compensation factor of the set forest age of the coastal saline-alkali land planted forest area to be detected, μ2 is the compensation factor of the set area of ​​the coastal saline-alkali land planted forest area to be detected, and μ3 is the compensation factor of the set average height of vegetation in the coastal saline-alkali land planted forest area to be detected.

4. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 1, characterized in that: The soil aggregate classification results of the samples in the coastal saline-alkali land planted forest area to be detected and the forest age of the coastal saline-alkali land planted forest area to be detected are stored as designated tags. The specific analysis process is as follows: Obtaining the soil aggregate level of the samples of the coastal saline-alkali land planted forest area to be tested after separation and the forest age of the coastal saline-alkali land planted forest area to be tested corresponding to the soil aggregate level of the samples of the coastal saline-alkali land planted forest area to be tested after separation; The soil aggregate grade of samples in the coastal saline-alkali land planted forest area to be detected and the forest age of the coastal saline-alkali land planted forest area to be detected are stored as designated labels.

5. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 1, characterized in that: Based on the obtained soil aggregate soil microorganism data set corresponding to the designated label, the soil aggregate soil microorganism characteristic value is obtained by comprehensive analysis. The specific analysis process is as follows: Obtain a soil microorganism dataset corresponding to a soil aggregate with a specified label. The soil microorganism dataset corresponding to a soil aggregate with a specified label specifically includes the total amount of soil microorganisms corresponding to a soil aggregate with a specified label, the Shannon diversity index of soil microorganisms corresponding to a soil aggregate with a specified label, and the respiration rate of soil microorganisms corresponding to a soil aggregate with a specified label; Based on the soil microbial dataset corresponding to the specified labels, the soil microbial characteristic values ​​of soil aggregates were obtained through comprehensive analysis. The soil microbial characteristic values ​​of soil aggregates were used as the analysis basis for comparing the soil carbon content of soil aggregates.

6. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 5, characterized in that: The soil aggregate soil microorganism characteristic value, the specific analysis process is: Wherein, α is the characteristic value of soil microorganisms in soil aggregates, zn is the total amount of soil microorganisms in soil aggregates corresponding to the specified label, xn is the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified label, hv is the respiration rate of soil microorganisms in soil aggregates corresponding to the specified label, σ1 is the compensation factor for the total amount of soil microorganisms in soil aggregates corresponding to the specified label, σ2 is the compensation factor for the Shannon diversity index of soil microorganisms in soil aggregates corresponding to the specified label, and σ3 is the compensation factor for the respiration rate of soil microorganisms in soil aggregates corresponding to the specified label.

7. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 1, characterized in that: Based on the obtained soil aggregate organic carbon content data set corresponding to the specified label, the characteristic value of soil aggregate organic carbon content is obtained by comprehensive analysis. The specific analysis process is as follows: Get the soil aggregate organic carbon content data set corresponding to the specified label. The soil aggregate organic carbon content data set corresponding to the specified label specifically includes the total amount of plant residues corresponding to the soil aggregate, the total amount of dissolved organic carbon corresponding to the soil aggregate, and the total amount of humus corresponding to the soil aggregate. Based on the obtained soil aggregate organic carbon content dataset corresponding to the specified label, the characteristic value of soil aggregate organic carbon content was obtained through comprehensive analysis. The characteristic value of soil aggregate organic carbon content was used as the analysis basis for comparing the soil carbon content of soil aggregates.

8. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 7 is characterized in that: The specific analysis process of the characteristic value of soil aggregate organic carbon content is as follows: Where β is the characteristic value of organic carbon content in soil aggregates, ct is the total amount of plant residues in soil aggregates corresponding to the specified label, rj is the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, fz is the total amount of humus in soil aggregates corresponding to the specified label, τ1 is the compensation factor for the total amount of plant residues in soil aggregates corresponding to the specified label, τ2 is the compensation factor for the total amount of dissolved organic carbon in soil aggregates corresponding to the specified label, τ3 is the compensation factor for the total amount of humus in soil aggregates corresponding to the specified label, and e is a natural constant.

9. The method for determining organic carbon content in coastal saline-alkali soil aggregates based on knowledge graph according to claim 1, characterized in that: The comparison obtains the soil carbon content of the soil aggregate corresponding to the specified label, and integrates the soil carbon content of the soil aggregate corresponding to each specified label into a knowledge graph. The specific analysis process is as follows: The soil aggregate soil microbial characteristic values ​​and soil aggregate organic carbon content characteristic values ​​are introduced into the soil aggregate soil carbon content assessment model, and the soil aggregate soil carbon content assessment value is obtained through comprehensive analysis. The soil aggregate soil carbon content assessment value is used as the analysis basis for comparing the soil aggregate soil carbon content. Compare the soil aggregate soil carbon content assessment value with the soil aggregate soil carbon content corresponding to each soil aggregate soil carbon content assessment value stored in the database to obtain the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value, and mark the soil aggregate soil carbon content corresponding to the soil aggregate soil carbon content assessment value as the soil aggregate soil carbon content corresponding to the designated label corresponding to the soil aggregate soil carbon content assessment value; The soil carbon content of soil aggregates corresponding to each specified label is integrated into a knowledge graph.

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