Method and system for evaluating biological health condition of cultivated land soil based on exploratory factor analysis

Abstract

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil biological health status assessment, and in particular to a method and system for assessing the biological health status of cultivated land soil based on exploratory factor analysis. Background Art

[0002] As agricultural production continues to increase its requirements for soil quality, accurate evaluation of the biological health status of cultivated soil has become the key to ensuring soil ecological functions and sustainable agricultural development. However, existing soil health assessment methods mostly focus on single physical or chemical indicators, lack comprehensive consideration of soil microbial community structure and its biological regulation, and are unable to fully reflect the actual functional status of soil ecosystems. At the same time, traditional evaluations often rely on simple indicator superposition or linear normalization, failing to effectively analyze the complex causal relationship between soil biological regulation mechanisms and environmental conditions, resulting in limitations in the scientific nature and application guidance of the evaluation results. In particular, in key ecological processes such as the nitrogen cycle, the lack of an indicator system for microbial functions and food web structure makes it impossible to accurately reveal the dynamic changes and potential risks of soil ecological functions, limiting the effectiveness of measures such as soil health management and precision fertilization.

[0003] Publication number CN113822592A discloses a method for diagnosing farmland soil barrier factors and cultivating soil health, including the following steps: a barrier factor diagnosis step, which first determines soil attribute indicators, then converts the soil attribute indicators into indicator scores using a scoring function, determines the weights of the soil attribute indicators, and calculates the soil health index using the multiplication principle; a barrier factor elimination step, which first sorts and evaluates the scores of the soil barrier factors, screens out the barrier factors with the greatest impact on the soil, and then designs solutions for these soil barrier factors; and a productivity assessment step, which repeats the barrier factor diagnosis and elimination steps on the soil treated with the solution until the soil barrier factors are eliminated, and compares the soil productivity before and after the regulation. While this method can quickly determine soil health, it lacks in-depth exploration of biological regulation mechanisms and the nitrogen cycle. Its indicator system is relatively simple, and its scoring function lacks nonlinearity and causal path analysis, making it difficult to accurately reveal the causal relationship between soil ecological functions and the environment. The evaluation lacks scientificity and explanatory power, and its application is weak.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for evaluating the biological health status of cultivated land soil based on exploratory factor analysis to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for evaluating the biological health status of cultivated soil based on exploratory factor analysis includes the following steps: S1: Collect multi-dimensional indicator data of the area to be evaluated to reflect the soil biological health status from the synergistic direction of nitrogen conversion and biological regulation; S2: Based on the collected multidimensional indicator data, exploratory factor analysis was used to extract nitrogen biological regulation factors, and representative and non-representative indicators of nitrogen biological regulation factors were screened. A causal relationship model was constructed in combination with structural equations to enhance the mechanistic explanatory power between nitrogen biological regulation factors and representative indicators. S3: Score non-representative indicators based on the cumulative normal distribution curve to generate a soil environmental health index score, and generate a comprehensive nitrogen cycle function score based on the contribution model of representative indicators to nitrogen biological regulation factors; S4: Weight the nitrogen cycle function comprehensive score and soil environmental health index score to generate a comprehensive soil biological health score; S5: Based on the comprehensive soil biological health score and the preset grading interval, the soil biological health status of the evaluated area is graded and the corresponding soil health evaluation results are generated.

[0007] Preferably, the multidimensional indicator data includes three dimensions: biological, physical, and chemical, wherein: Multidimensional indicator data of the biological dimension include, but are not limited to: microbial function indicators, including the abundance of ammonia-oxidizing archaea, the expression of nitrogen-fixing bacteria genes, nematode indicators, and enzyme activity indicators, including the ratio of bacteria-feeding nematodes to fungi-feeding nematodes, and enzyme activity indicators including dehydrogenase activity and urease temperature sensitivity; Multidimensional indicator data of the physical dimension include but are not limited to: soil bulk density, soil porosity; The multidimensional indicator data of the chemical dimension include but are not limited to: soil organic matter, soil total nitrogen, and soil pH value.

[0008] Preferably, the logic for extracting nitrogen biological regulation factors and screening out representative and non-representative indicators of nitrogen biological regulation factors is: Standardize the multidimensional indicator data and use the standardized multidimensional indicator data to construct a correlation matrix, which is used to reflect the correlation between different indicators; The principal component analysis method was used to extract the principal components with eigenvalues ​​greater than 1 from the correlation matrix, which were used as nitrogen biological regulation factors. The factors were then combined with the factor loading matrix for biological interpretation to characterize the corresponding nitrogen biological regulation mechanism. The varimax rotation method was used to perform orthogonal rotation on the nitrogen biological regulation factors to optimize the factor structure. If the loading of a certain multidimensional indicator data on the nitrogen biological regulation factor was greater than 0.5, it was marked as a representative indicator of the nitrogen biological regulation factor, otherwise it was marked as a non-representative indicator.

[0009] Preferably, the structural equation of the causal relationship model includes a measurement model and a structural model, and the expressions of the two are respectively: ; ; In the formula Represents the vector form of representative indicators, represents the endogenous latent variable, represents an exogenous latent variable, represents the factor loading matrix, represents the measurement error, represents the causal path coefficient matrix, represents the structural error term.

[0010] Preferably, the non-representative indicators are scored to generate the soil environmental health index score as follows: ; In the formula Indicates the Soil environmental health index scores corresponding to non-representative indicators, Indicates the The first of the non-representative indicators Group normalized values, 、 Respectively represent The mean and standard deviation of non-representative indicators, subscript 、 The data category index and data number index of non-representative indicators are represented respectively. Distribution function representing the cumulative normal distribution curve.

[0011] Preferably, for non-representative indicators in multi-dimensional indicator data of different dimensions, distinction is made based on the nature of the indicator data. For non-representative indicators of different categories, the distribution function of the cumulative normal distribution curve adopts a differentiated design: For non-representative indicators that need to maintain high values, the cumulative normal distribution curve adopts an increasing type; For non-representative indicators that need to maintain the indicator data at a low value, the cumulative normal distribution curve adopts the decreasing property; For non-representative indicators that need to maintain indicator data within a specific numerical range, the optimal cumulative normal distribution curve is used.

[0012] Preferably, the contribution model expression of representative indicators to nitrogen biological regulation factors is: ; In the formula Indicates the Comprehensive score of nitrogen cycle function of nitrogen biological regulatory factors, Indicates the The number of types of representative indicators corresponding to group nitrogen biological regulation factors, Indicates the Nitrogen bioregulatory factors The weights of representative indicators, Indicates the Nitrogen bioregulatory factors The expression function of representative indicators, Indicates the Nitrogen bioregulatory factors The first representative indicator Group normalized values, 、 Respectively represent Data category index and data number index of representative indicators corresponding to group nitrogen biological regulation factors.

[0013] Preferably, the soil biological health comprehensive score is calculated as follows: ; In the formula represents the comprehensive score of soil biological health, 、 They represent the weights of soil environmental health index score and nitrogen cycle function comprehensive score respectively. The sizes of the two are determined according to different application scenarios. Indicates the number of types of non-representative indicators, represents the total number of nitrogen biological regulation factors, Indicates the The sub-weights of non-representative indicators, Indicates the The sub-weights of the nitrogen biological regulation factors.

[0014] Preferably, the logic for grading the soil biological health status and generating the corresponding soil health evaluation results is: Based on the interval threshold method, the grading intervals were constructed for the comprehensive score of soil biological health, soil environmental health index score, and nitrogen cycle function comprehensive score, respectively, to achieve a 1-5 level assessment of the three scores, and the corresponding health levels were: unhealthy, relatively unhealthy, sub-healthy, relatively healthy, and healthy; When the soil biological health comprehensive score is rated 1 to 3, the area to be evaluated is considered to be in an unhealthy state. The soil environmental health index score and the nitrogen cycle function comprehensive score are compared with the soil biological health comprehensive score. If the score is not lower than the soil biological health comprehensive score, it is considered normal; otherwise, it is considered abnormal. Based on the comparison results, an analysis of the causes of the unhealthy state is generated: The soil environment health index score was abnormal after comparison, and the reasons for the unhealthy state were analyzed as follows: soil environment deterioration; The comprehensive score of nitrogen cycle function was abnormal after comparison, and the reasons for the unhealthy state were analyzed as follows: the nitrogen cycle microbial function was damaged; After the cause analysis is completed, the soil biological health comprehensive score, soil environmental health index score, nitrogen cycle function comprehensive score grade determination, health grade, and the reason analysis of the unhealthy state of the evaluated area are output as the soil health assessment results.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention achieves accurate analysis of the complex interactive relationship between soil microbial functions and environmental factors by constructing a soil biological health assessment system based on a nitrogen biological regulation collaborative network; through the fusion of multidimensional indicators and in-depth mining of factor models, it effectively identifies and quantifies key biological functional factors, thereby improving the scientific evaluation ability of soil ecological functions; combined with causal path analysis, the scheme can reveal the impact of environmental pressure on microbial regulation mechanisms, and realize intelligent attribution and traceability of abnormal health conditions; based on the dynamic fusion and hierarchical judgment of functional and environmental scores, the scheme supports accurate health status assessment and tiered intervention, enhances the early warning capability and scientific decision-making of soil management, and significantly improves the level of agricultural sustainable development guarantee. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the overall method of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0019] Example: See also Figure 1 , the present invention provides a technical solution: A method for evaluating the biological health status of cultivated soil based on exploratory factor analysis includes the following steps: S1: Collect indicators to collect multidimensional indicator data of the area to be evaluated, which is used to reflect the health status of soil organisms from the synergistic direction of nitrogen conversion and biological regulation.

[0020] Multidimensional indicator data includes three dimensions: biological, physical, and chemical, among which: Multidimensional indicator data of the biological dimension include but are not limited to: microbial function indicators, including the abundance of ammonia-oxidizing archaea and the expression of nitrogen-fixing bacteria genes; nematode indicators, including the ratio of bacteria-feeding nematodes to fungi-feeding nematodes; enzyme activity indicators, including dehydrogenase activity and urease temperature sensitivity; Multidimensional indicator data of the physical dimension include but are not limited to: soil bulk density, soil porosity; The multidimensional indicator data of the chemical dimension include but are not limited to: soil organic matter, soil total nitrogen, and soil pH value.

[0021] When collecting soil samples, the sampling depth is uniformly 0-20 cm, representing the cultivated layer area. The sampling points are arranged in a grid pattern with a spacing of 10m×10m, which is adjusted according to the size of the area to be evaluated. Three samples are collected at each sampling point and combined using the mixed sampling method to ensure sample representativeness. The recommended sampling time is in the middle of the crop growing season when the climatic conditions are stable. To monitor dynamic changes, sampling is carried out at least twice a year, in spring and autumn respectively. Immediately after collection, the soil samples are refrigerated and stored at 4°C. Microbial-related indicators must be sent to the testing laboratory for analysis within 48 hours. Soil physical and chemical indicators can be air-dried or frozen to avoid changes in sample properties.

[0022] For the detection of biological dimension indicators, the abundance of ammonia-oxidizing archaea can be detected by quantitative PCR (qPCR) technology; the expression of nitrogen-fixing bacteria genes can be determined by reverse transcription quantitative PCR (RT-qPCR) to determine the mRNA expression level of nitrogen-fixing-related genes (nifH) in the soil; the ratio of bacteria-feeding nematodes to fungi-feeding nematodes can be calculated by separating soil nematodes according to the Baermann funnel method and counting and identifying bacteria-feeding and fungi-feeding nematodes using an optical microscope; dehydrogenase activity can be detected by the 2,3,5-triphenyltetrazolium chloride (TTC) reduction method; the temperature sensitivity of urease is obtained by measuring the changes in urease activity at different temperatures (such as 20°C, 30°C, and 40°C) and calculating the sensitivity index of activity under the temperature gradient.

[0023] For the detection of physical dimension indicators, soil bulk density can be obtained by collecting several groups of soil blocks using the ring knife method, weighing them after drying to calculate the soil mass per unit volume and taking the average value; soil porosity can be calculated based on soil bulk density and particle density, and the particle density is determined using the pycnometer method.

[0024] For the detection of chemical dimension indicators, the soil organic matter can be determined by heating with potassium dichromate and sulfuric acid to digest the organic carbon content, and then the organic matter content can be calculated; the soil total nitrogen can be determined by the Kjeldahl method; the soil pH value can be determined by mixing the soil with pure water in a ratio of 1:2.5 (w / v), letting it stand for 30 minutes, and then using a pH meter.

[0025] In this step, a multidimensional indicator system was constructed to comprehensively reflect the ecological functions of the soil. Specifically, biological indicators encompass microbial functions (such as gene expression of ammonia-oxidizing archaea and nitrogen-fixing bacteria), key links in the food web (proportion of nematodes), and enzyme activity. These indicators directly reflect the structure of soil microbial communities and key metabolic activities, and serve as the biological basis for the nitrogen cycle in soil ecosystems. Physical indicators, such as soil bulk density and porosity, influence soil gas exchange, water status, and microhabitat structure, regulating microbial activity and the efficiency of biological processes. Chemical indicators (organic matter, total nitrogen, and pH) determine the soil's nutrient supply and chemical environment, and serve as the material foundation for the realization of microbial functions.

[0026] Furthermore, existing technologies typically monitor final nitrogen forms (e.g., ammonium and nitrate) directly, failing to distinguish between chemical and microbially driven transformations during nitrogen conversion. However, the biological dimension indicators designed in this invention include: diazotrophic gene expression, which acts on the input link of the nitrogen cycle and quantifies biological nitrogen fixation capacity; ammonia-oxidizing archaea abundance, which acts on the nitrification link of the nitrogen cycle and reflects the strength of the nitrifying microbial community; urease temperature sensitivity, which acts on the organic nitrogen mineralization link and indicates the environmental adaptability of enzyme activity; and nematode proportion, which characterizes microbial-nematode energy transfer. This design allows the indicators to directly focus on key biological and environmental factors related to the nitrogen cycle, facilitating a precise characterization of the health of the biological regulation of nitrogen. This design not only avoids excessive redundancy and information noise, but also integrates biological indicators into the core evaluation system, particularly including microbial gene expression and food web structure indicators. This enriches the indicator content, enhances insight into soil ecological processes, and improves the biological interpretability and application relevance of the evaluation results.

[0027] S2: Based on the collected multidimensional indicator data, the exploratory factor analysis method was used to extract nitrogen biological regulation factors, screen out representative and non-representative indicators of nitrogen biological regulation factors, and construct a causal relationship model in combination with the structural equation to enhance the mechanistic explanatory power between nitrogen biological regulation factors and representative indicators.

[0028] In step S2, the logic for extracting nitrogen biological regulation factors and screening out representative and non-representative indicators of nitrogen biological regulation factors is as follows: Standardize the multidimensional indicator data and use the standardized multidimensional indicator data to construct a correlation matrix; The principal component analysis method was used to extract the principal components with eigenvalues ​​greater than 1, which were used as nitrogen biological regulation factors. The factors were then combined with the factor loading matrix for biological interpretation to characterize the corresponding nitrogen biological regulation mechanism. The varimax rotation method was used to perform orthogonal rotation on the nitrogen biological regulation factors to optimize the factor structure. If the loading of a certain multidimensional indicator data on the nitrogen biological regulation factor was greater than 0.5, it was marked as a representative indicator of the nitrogen biological regulation factor, otherwise it was marked as a non-representative indicator.

[0029] Principal component analysis (PCA) was used to extract eigenvalues ​​from high-latitude, multivariate data. This approach can uncover commonalities and potential correlations between indicators, simplify the data structure, and highlight key regulatory factors. Combined with a factor loading matrix for biological interpretation, the extracted factors clearly correspond to the biological regulation mechanisms of soil nitrogen, ensuring that the statistical model is compatible with soil ecological processes. Furthermore, SEM was used to construct a causal model, clarifying the measurement and structural relationships between representative indicators and factors regulating nitrogen biological regulation. This approach achieves a mechanistic correspondence between indicators and factors, enhancing the scientific depth of the evaluation system.

[0030] It can be understood that representative indicators are indicators that contribute significantly to a potential nitrogen biological regulation factor, have strong representativeness and explanatory power, and are used in this scheme to reflect the core components of specific nitrogen biological regulation factors, embody the key functions and states of related ecological processes, and correspond to the main observation indicators of latent variables; while non-representative indicators are difficult to be clearly attributed to latent variables. In this scheme, they are used to reflect other ecological information in soil health that is not fully explained by the current factor structure, so as to supplement the evaluation of other aspects of soil health and prevent the omission of indicator importance and explanatory power.

[0031] In existing technologies, exploratory factor analysis methods directly treat all indicators uniformly, often remaining at the level of simple statistical analysis or the superposition of extensive indicators. This lacks in-depth understanding of the causal relationships between ecological processes and the hierarchical identification of indicator compliance thresholds and correlations. In contrast, this approach decouples representative and non-representative indicators, constructing a mechanism-interpretable two-stage factor screening-verification framework. This ensures that the set of representative indicators is composed purely of microbial functional parameters that directly regulate the nitrogen cycle, enabling a better focus on the impact of organisms on the nitrogen cycle. At the same time, a set of non-representative indicators is retained to independently assess the basic state of the soil, making the subsequent comprehensive score not only statistically reasonable but also ecologically meaningful.

[0032] The structural equation of the causal model includes the measurement model and the structural model, and the expressions of the two are: ; ; In the formula Represents the vector form of representative indicators, represents the endogenous latent variable, represents an exogenous latent variable, represents the factor loading matrix, represents the measurement error, represents the causal path coefficient matrix, represents the structural error term.

[0033] The factor loading matrix Reflects the contribution of representative indicators to nitrogen biological regulation factors. Indicators with high loads are the main biological regulation parameters. They are calculated by the maximum likelihood estimation method. Load values ​​greater than 0.5 are considered significant contributions. The specific values ​​are determined in combination with the results of factor analysis. The measurement errors are assumed to be independent and identically distributed and have no correlation with the latent variables. Causal path coefficient matrix It shows how environmental latent variables drive the nitrogen biological regulation mechanism. Positive coefficients indicate a positive promotion relationship, and negative coefficients reflect an inhibitory effect. It is estimated through model fitting optimization using the maximum likelihood method or weighted least squares method. The structural error term reflects the randomness or potential factors in the ecological regulation process that are not captured by the model. It is assumed to have a zero mean, finite variance, and is independent of the exogenous latent variables.

[0034] In this embodiment, structural equation model analysis software (such as AMOS, LISREL, R package lavaan) can be used to estimate the model. Model fit indicators include but are not limited to: chi-square statistics ( ), comparative fit index (CFI), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), etc.; and when the fitting indices meet the following requirements: CFI>0.90, RMSEA<0.08, SRMR<0.08, it indicates that the model fits well.

[0035] During the specific verification and adjustment process of the model, we first used the cross-validation method to randomly divide the samples into training and validation sets to ensure the generalization ability of the model. We then adjusted or eliminated the poorly fitting paths, combined with ecological theory and statistical significance to modify the model. Finally, through the modification index (Modification Indices) recommendations, we rationally added or deleted paths to enhance the explanatory power of the model.

[0036] The structural equation model can not only handle the measurement relationship between latent variables and observed indicators (measurement model), but also characterize the causal path between latent variables (structural model), and realize the systematic analysis of the complex internal relationship of the biological regulation mechanism of soil nitrogen. Endogenous latent variables refer to latent variables affected by exogenous latent variables and other endogenous latent variables, and exogenous latent variables refer to latent variables that are not affected by variables within the model, serving as causal starting points. Specifically, in this scheme, endogenous latent variables can be set as nitrogen biological regulation factors, representing specific biological regulation mechanisms in the soil. They are the response variables of the model and may be affected by other latent variables to form a regulatory network. Exogenous latent variables can be understood as soil environmental base condition latent variables (such as potential environmental factors in physical and chemical dimensions), which are the input driving factors of the model, and these factors can also be extracted from observed indicators through exploratory factor analysis methods. Causal path coefficient matrix in the structural model It is used to reveal the influence path between endogenous latent variables and exogenous latent variables, to clarify the causal relationship and action mechanism between endogenous latent variables and exogenous latent variables in nitrogen biological regulation, and to effectively reveal the influence path and action mechanism of environmental factors on nitrogen biological regulation mechanism, thereby enhancing the connection and causal influence between biological regulation process and environmental factors, so as to improve the interpretability and scientificity of the overall model.

[0037] S3: Score non-representative indicators based on the cumulative normal distribution curve to generate a soil environmental health index score, and generate a comprehensive nitrogen cycle function score based on the contribution model of representative indicators to nitrogen biological regulation factors.

[0038] The calculation method for scoring non-representative indicators and generating soil environmental health index scores is as follows: ; In the formula Indicates the Soil environmental health index scores corresponding to non-representative indicators, Indicates the The first of the non-representative indicators Group normalized values, 、 Respectively represent The mean and standard deviation of non-representative indicators, subscript 、 The data category index and data number index of non-representative indicators are represented respectively. Distribution function representing the cumulative normal distribution curve.

[0039] For non-representative indicators in multidimensional indicator data of different dimensions, distinction is made based on the nature of the indicator data. For non-representative indicators of different categories, the distribution function of the cumulative normal distribution curve adopts a differentiated design: For non-representative indicators that need to maintain high values, the cumulative normal distribution curve adopts an increasing type; For non-representative indicators that need to maintain the indicator data at a low value, the cumulative normal distribution curve adopts the decreasing property; For non-representative indicators that need to maintain indicator data within a specific numerical range, the optimal cumulative normal distribution curve is used.

[0040] Specifically, the increasing distribution function can be set as: ; In the formula represents the cumulative distribution function of the standard normal distribution, Indicates the The total number of data for the non-representative indicators, All data in a non-representative indicator are scored separately, and the mean value is used as the soil environmental health indicator score corresponding to the non-representative indicator. The larger the indicator value, the better the soil health status. For example, high values ​​of microbial abundance and enzyme activity usually indicate a healthier soil ecology, which means that the indicator data needs to be maintained at a high value for non-representative indicators, so an increasing distribution function is used; The decreasing distribution function can be set as: ; The larger the value of this indicator, the worse the soil health. For example, excessive soil bulk density reflects soil compaction, which is a non-representative indicator that needs to maintain a low value. Therefore, a decreasing distribution function is used.

[0041] The optimal distribution function can be set as: ; In the formula Represents The corresponding optimal value (i.e., ecologically optimal state) is obtained. This function ensures that the highest score is obtained when the indicator value is close to the optimal value, and the further away from the optimal value, the lower the score. For example, pH and nutrient content are not good when they are too high or too low. In other words, it is necessary to maintain the indicator data within a specific numerical range for non-representative indicators. Therefore, the optimal distribution function is used.

[0042] Here, the standardized values ​​of non-representative indicators are mapped using a cumulative normal distribution function, converting multidimensional indicators with varying dimensions and distribution characteristics into scores between 0 and 1, facilitating subsequent weighting and comprehensive processing. Due to the distinct ecological roles and health implications of biological, physical, and chemical indicators, three different cumulative normal distribution curves—increasing, decreasing, and optimal—are used to correspond to the scores. This rationally embeds the ideal state and evaluation objectives of the indicators, enhancing the scientific nature and accuracy of the scores.

[0043] Specifically, ascending scores reflect a "higher, healthier" relationship; descending scores represent "lower, healthier"; and optimal scores reflect the ecological principle of "a balance is optimal." It should be understood that the "higher, healthier" and "lower, healthier" relationships here refer to relationships within a certain range, not an unrestricted "higher is better" or "lower is better." This range, as well as the distinction between non-representative indicators, can be determined by expert experience or relevant research literature. This differentiated design ensures that the scores accurately reflect the actual impact of the indicator on soil health and its optimal state, preventing a single scoring curve from misleading evaluation results.

[0044] The contribution model of representative indicators to nitrogen biological regulation factors is expressed as follows: ; In the formula Indicates the Comprehensive score of nitrogen cycle function of nitrogen biological regulatory factors, Indicates the The number of types of representative indicators corresponding to group nitrogen biological regulation factors, Indicates the Nitrogen bioregulatory factors The weights of representative indicators, Indicates the Nitrogen bioregulatory factors The expression function of representative indicators, Indicates the Nitrogen bioregulatory factors The first representative indicator Group normalized values, 、 Respectively represent Data category index and data number index of representative indicators corresponding to group nitrogen biological regulation factors.

[0045] It can be seen from the contribution model of the comprehensive score of nitrogen cycle function that its essence is to quantify the actual contribution of each indicator to the biological regulation function of nitrogen by weighted summation, and converting the standardized values ​​of multiple representative indicators through expression function. The expression function is used to convert the standardized values ​​of representative indicators into scores reflecting their ecological function performance. It is responsible for mapping the original variation range of indicator data to the response range of the indicator's contribution to the ecological function. The specific forms include but are not limited to linear functions, logarithmic functions, step functions, Sigmoid functions, etc., which are similar to the processing method of the distribution function. Nitrogen bioregulatory factors The calculation is performed on all data for each representative indicator, and the mean is used as the calculation result for that representative indicator. The specific form of the expression function can be determined based on the ecological characteristics of the indicator, empirical data fitting, or expert judgment to ensure that the score truly reflects the indicator's contribution to the nitrogen cycle function and its nonlinear response.

[0046] S4: Weight the comprehensive score of nitrogen cycle function and soil environmental health index score to jointly generate a comprehensive score of soil biological health.

[0047] The soil biological health composite score is calculated as follows: ; In the formula represents the comprehensive score of soil biological health, 、 Respectively represent the weights of soil environmental health index score and nitrogen cycle function comprehensive score, and the sizes of the two are determined according to different application scenarios: if the emphasis is on environmental protection and soil ecosystem restoration, then If we focus on nitrogen use efficiency and microbial function in agricultural production, then . Indicates the number of types of non-representative indicators, represents the total number of nitrogen biological regulation factors, Indicates the The sub-weights of multi-dimensional indicator data, Indicates the The sub-weights of the nitrogen biological regulation factors.

[0048] This step combines the weighted scores of the non-representative soil environmental health indicators with the representative nitrogen cycle function scores, combining the ecological function (nitrogen cycle) and environmental condition scores. This integrates the environmental foundation and ecological function indicators, avoiding biased judgments resulting from reliance on a single indicator. This improves the scientific integrity and practical guidance of the evaluation, resulting in a more comprehensive comprehensive assessment of soil biological health. Furthermore, the hierarchical configuration of global and sub-weights allows the scoring system to adjust weight proportions based on different regions, objectives, or priorities, reflecting the flexibility and targeted nature of the evaluation and supporting customized management needs.

[0049] S5: Based on the comprehensive soil biological health score and the preset grading interval, the soil biological health status of the evaluated area is graded and the corresponding soil health evaluation results are generated.

[0050] The logic for grading the soil biological health status and generating the corresponding soil health evaluation results is as follows: Based on the interval threshold method, grading intervals were constructed for the comprehensive score of soil biological health, soil environmental health index score, and comprehensive score of nitrogen cycle function, respectively, to achieve a 1-5 grade assessment of the three scores, and the corresponding health levels are: unhealthy, relatively unhealthy, sub-healthy, relatively healthy, and healthy.

[0051] The threshold in the interval threshold method can be determined in many ways, for example: Statistical distribution method: Based on the score distribution of a large number of healthy soil sample data, the quantile method (such as the 20th, 40th, 60th, and 80th percentiles) is used to determine the threshold value to ensure that the classification is consistent with the actual ecological conditions; Ecological optimal interval method: combining the ecological optimal range of each indicator and regional soil environmental conditions to determine the dividing point between healthy and unhealthy; Expert experience method: Through expert consultation and historical research, combined with the soil ecosystem management needs of the region, the threshold is reasonably adjusted.

[0052] When the soil biological health comprehensive score is rated 1 to 3, the area to be evaluated is considered to be in an unhealthy state. The soil environmental health index score and the nitrogen cycle function comprehensive score are compared with the soil biological health comprehensive score. If the score is not lower than the soil biological health comprehensive score, it is considered normal; otherwise, it is considered abnormal. Based on the comparison results, an analysis of the causes of the unhealthy state is generated: The soil environment health index score was abnormal after comparison, and the reasons for the unhealthy state were analyzed as follows: soil environment deterioration; The comprehensive score of nitrogen cycle function was abnormal after comparison, and the reasons for the unhealthy state were analyzed as follows: the nitrogen cycle microbial function was damaged; After the cause analysis is completed, the soil biological health comprehensive score, soil environmental health index score, nitrogen cycle function comprehensive score grade determination, health grade, and the reason analysis of the unhealthy state of the evaluated area are output as the soil health assessment results.

[0053] Environmental degradation and impaired microbial function can be independent of each other, but they can also be causally linked. Environmental degradation often leads to degradation of microbial community function, which in turn exacerbates soil nutrient depletion and structural deterioration. This study uses hierarchical comparison to distinguish the primary causes, avoiding simplistic and vague diagnoses. This helps target interventions, improves diagnostic accuracy and application, and promotes soil ecosystem restoration and sustainable management.

[0054] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0055] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0056] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0057] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis, characterized in that: The specific steps include: S1: Collect multi-dimensional indicator data of the area to be evaluated to reflect the soil biological health status from the synergistic direction of nitrogen conversion and biological regulation; S2: Based on the collected multidimensional indicator data, exploratory factor analysis was used to extract nitrogen biological regulation factors, and representative and non-representative indicators of nitrogen biological regulation factors were screened. A causal relationship model was constructed in combination with structural equations to enhance the mechanistic explanatory power between nitrogen biological regulation factors and representative indicators. S3: Score non-representative indicators based on the cumulative normal distribution curve to generate a soil environmental health index score, and generate a comprehensive nitrogen cycle function score based on the contribution model of representative indicators to nitrogen biological regulation factors; S4: Weight the nitrogen cycle function comprehensive score and soil environmental health index score to generate a comprehensive soil biological health score; S5: Based on the comprehensive soil biological health score and the preset grading interval, the soil biological health status of the evaluated area is graded and the corresponding soil health evaluation results are generated.

2. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 1, characterized in that: The multi-dimensional indicator data includes three dimensions: biological, physical, and chemical, among which: Multidimensional indicator data of the biological dimension include, but are not limited to: microbial function indicators, including the abundance of ammonia-oxidizing archaea, the expression of nitrogen-fixing bacteria genes, nematode indicators, and enzyme activity indicators, including the ratio of bacteria-feeding nematodes to fungi-feeding nematodes, and enzyme activity indicators including dehydrogenase activity and urease temperature sensitivity; Multidimensional indicator data of the physical dimension include but are not limited to: soil bulk density, soil porosity; The multidimensional indicator data of the chemical dimension include but are not limited to: soil organic matter, soil total nitrogen, and soil pH value.

3. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 2, characterized in that: The logic for extracting nitrogen biological regulation factors and screening out representative and non-representative indicators of nitrogen biological regulation factors is as follows: Standardize the multidimensional indicator data and use the standardized multidimensional indicator data to construct a correlation matrix, which is used to reflect the correlation between different indicators; The principal component analysis method was used to extract the principal components with eigenvalues ​​greater than 1 from the correlation matrix, which were used as nitrogen biological regulation factors. The factors were then combined with the factor loading matrix for biological interpretation to characterize the corresponding nitrogen biological regulation mechanism. The varimax rotation method was used to perform orthogonal rotation on the nitrogen biological regulation factors to optimize the factor structure. If the loading of a certain multidimensional indicator data on the nitrogen biological regulation factor was greater than 0.5, it was marked as a representative indicator of the nitrogen biological regulation factor, otherwise it was marked as a non-representative indicator.

4. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 3, characterized in that: The structural equation of the causal relationship model includes a measurement model and a structural model, and the expressions of the two are: ； ； In the formula Represents the vector form of representative indicators, represents the endogenous latent variable, represents an exogenous latent variable, represents the factor loading matrix, represents the measurement error, represents the causal path coefficient matrix, represents the structural error term.

5. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 3, characterized in that: The calculation method for scoring non-representative indicators and generating soil environmental health index scores is as follows: ； In the formula Indicates the Soil environmental health index scores corresponding to non-representative indicators, Indicates the The first of the non-representative indicators Group normalized values, 、 Respectively represent The mean and standard deviation of non-representative indicators, subscript 、 The data category index and data number index of non-representative indicators are represented respectively. Distribution function representing the cumulative normal distribution curve.

6. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 5, characterized in that: For non-representative indicators in multidimensional indicator data of different dimensions, distinction is made based on the nature of the indicator data. For non-representative indicators of different categories, the distribution function of the cumulative normal distribution curve adopts a differentiated design: For non-representative indicators that need to maintain high values, the cumulative normal distribution curve adopts an increasing type; For non-representative indicators that need to maintain the indicator data at a low value, the cumulative normal distribution curve adopts the decreasing property; For non-representative indicators that need to maintain indicator data within a specific numerical range, the optimal cumulative normal distribution curve is used.

7. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 5, characterized in that: The contribution model of representative indicators to nitrogen biological regulation factors is expressed as follows: ； In the formula Indicates the Comprehensive score of nitrogen cycle function of nitrogen biological regulatory factors, Indicates the The number of types of representative indicators corresponding to group nitrogen biological regulation factors, Indicates the Nitrogen bioregulatory factors The weights of representative indicators, Indicates the Nitrogen bioregulatory factors The expression function of representative indicators, Indicates the Nitrogen bioregulatory factors The first representative indicator Group normalized values, 、 Respectively represent Data category index and data number index of representative indicators corresponding to group nitrogen biological regulation factors.

8. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 7, characterized in that: The soil biological health comprehensive score is calculated as follows: ； In the formula represents the comprehensive score of soil biological health, 、 They represent the weights of soil environmental health index score and nitrogen cycle function comprehensive score respectively. The sizes of the two are determined according to different application scenarios. Indicates the number of types of non-representative indicators, represents the total number of nitrogen biological regulation factors, Indicates the The sub-weights of non-representative indicators, Indicates the The sub-weights of the nitrogen biological regulation factors.

9. The method for evaluating the biological health status of cultivated land soil based on exploratory factor analysis according to claim 8, characterized in that: The logic for grading the soil biological health status and generating the corresponding soil health evaluation results is as follows: Based on the interval threshold method, the grading intervals were constructed for the comprehensive score of soil biological health, soil environmental health index score, and nitrogen cycle function comprehensive score, respectively, to achieve a 1-5 level assessment of the three scores, and the corresponding health levels were: unhealthy, relatively unhealthy, sub-healthy, relatively healthy, and healthy; When the soil biological health comprehensive score is rated 1 to 3, the area to be evaluated is considered to be in an unhealthy state. The soil environmental health index score and the nitrogen cycle function comprehensive score are compared with the soil biological health comprehensive score. If the score is not lower than the soil biological health comprehensive score, it is considered normal; otherwise, it is considered abnormal. Based on the comparison results, an analysis of the causes of the unhealthy state is generated: The soil environment health index score was abnormal after comparison, and the reasons for the unhealthy state were analyzed as follows: soil environment deterioration; The comprehensive score of nitrogen cycle function was abnormal after comparison, and the reasons for the unhealthy state were analyzed as follows: the nitrogen cycle microbial function was damaged; After the cause analysis is completed, the soil biological health comprehensive score, soil environmental health index score, nitrogen cycle function comprehensive score grade determination, health grade, and the reason analysis of the unhealthy state of the evaluated area are output as the soil health assessment results.

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