A soil ecological function intelligent evaluation system and method

Abstract

The application discloses a kind of soil ecological function intelligent evaluation system and method, specifically related to environmental science field, including multi-source heterogeneous data acquisition layer, multi-dimensional index calculation engine, multi-cascade analysis controller and intelligent evaluation module.The nonlinear cascade analysis model is innovatively constructed in the application, the ecological phase transition critical point is accurately identified by multi-dimensional coupling calculation of structure-biology-environment, the sensitivity of ecological collapse early warning is effectively improved, the dynamic calibration mechanism is introduced to realize the analysis of chemical stress and structure degradation complex effect, the limitations of isolated parameter analysis in traditional methods are broken through, real-time monitoring data is fused using progressive resilience diagnosis architecture, a spatiotemporal continuous evaluation system is established, and the diagnosis universality under different environmental conditions is significantly enhanced, providing accurate decision support for ecological restoration.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental science and technology, and more particularly, to a soil ecological function intelligent evaluation system and method. BACKGROUND

[0002] The current soil ecological function evaluation technology mainly relies on discrete data collection of physical and chemical detection and biological index monitoring, combined with laboratory analysis and field test for ecological system health degree evaluation. The existing evaluation system usually adopts hierarchical sampling combined with spectral analysis technology, establishes a linear regression model by measuring independent parameters such as porosity and enzyme activity, and generates a comprehensive evaluation index by using principal component analysis method.

[0003] The traditional method follows a linear process of "sampling-laboratory detection-index weighting" in the implementation process: first, standard soil drills are used to obtain hierarchical samples, and structure parameters are measured by X-ray diffraction and chromatography technology; then, microbial culture tests are carried out under in vitro conditions to obtain biological activity data; finally, a fixed weight model is used to weight and sum the indexes to generate a static soil quality index.

[0004] The existing technology has three significant defects: first, the linear weighting model cannot represent the nonlinear coupling effect of pore network and microbial activity, resulting in a lag in the early warning of sudden ecological decline; second, independent index analysis ignores the dynamic antagonistic relationship between nutrient fluctuations and resistance gene expression, making it difficult to identify the critical state of chemical stress; third, the static evaluation system of traditional temperature and erosion parameters cannot reflect the time-varying synergistic effect of environmental factors, causing distortion in the resilience evaluation. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the present application provides a soil ecological function intelligent evaluation system and method, which solves the problems of the prior art relying on linear models that cannot capture ecological mutations, index isolated analysis that ignores dynamic correlation, and static evaluation system that cannot reflect environmental time-varying effects, resulting in early warning lag and diagnostic distortion.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a soil ecological function intelligent evaluation system, comprising:

[0007] Multi-source heterogeneous data acquisition layer: a cross-scale soil ecological data acquisition system is constructed, integrating optical-biochemical-thermodynamic multi-modal sensing technology, collecting basic data of soil structure-biological synergy, biological-chemical dynamic balance, and environmental response resilience, and transmitting the basic data to a multi-dimensional index calculation engine;

[0008] Multi-dimensional indicator calculation engine: Constructs three heterogeneous computing channels, each equipped with a dedicated pipeline architecture to realize parallel calculation of indicator matrices, including structure-biology channel, biochemical dynamics channel and environmental response channel, and transmits the calculated indicators to multi-level joint analysis controller;

[0009] Multi-level cascaded analysis controller: Receives the [4×3] index tensor output by the multi-dimensional index calculation engine, splits it into three data streams—structural, biochemical, and environmental—by a format converter, and adopts a cascaded analysis architecture. The first level calculates the structure-biological synergy index, the second level calculates the metabolism-resistance balance index, and the third level calculates the environmental resilience index, and transmits the calculation results to the intelligent assessment module.

[0010] Intelligent assessment module: Based on the progressive judgment system of soil ecological status, including structure-biological synergy index, metabolism-resistance balance index and environmental resilience index, it adopts nonlinear threshold nesting structure to conduct instruction assessment, including basic resilience assessment, dynamic balance diagnosis, system optimization stage and special regulation situation.

[0011] The technical effects and advantages of this invention are as follows:

[0012] This solution overcomes the limitations of traditional linear models by constructing a multi-dimensional index calculation engine, enabling nonlinear coupled analysis of pore structure, biological metabolism, and environmental response. The cascaded calculation model, designed based on dissipative structure theory and the principle of dynamic system stability, can accurately capture the abrupt change characteristics of soil ecosystems at the phase transition critical point, significantly improving the early warning capability for ecological collapse risks.

[0013] This innovative approach introduces a three-channel interactive computational mechanism involving biochemistry and physics, effectively addressing the shortcomings of isolated indicator analysis in traditional methods. Through dynamic verification of structural synergy and metabolic balance indices, the system can accurately identify the combined mechanisms of chemical stress and structural degradation, providing a reliable basis for targeted repair.

[0014] A progressive diagnostic framework based on the environmental resilience index is adopted to overcome the lack of a time dimension in static assessment systems. By integrating real-time monitoring data on permeability heterogeneity and temperature hysteresis effects, the system can dynamically track the evolution trend of soil resilience, ensuring that assessment results under different climatic conditions have spatiotemporal universality. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0016] Figure 2 This is a schematic diagram of the method structure of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] refer to Figure 1 The soil ecological function intelligent assessment system shown includes:

[0019] Multi-source heterogeneous data acquisition layer: Construct a cross-scale soil ecological data acquisition system, integrate optical-biochemical-thermodynamic multimodal sensing technologies, collect basic data on soil structure-biological synergy, bio-chemical dynamic balance and environmental response resilience, and transmit the basic data to a multi-dimensional index calculation engine.

[0020] The multi-source heterogeneous data acquisition layer used a 5cm diameter stainless steel cylindrical soil sampler to collect samples at three depths: 0-10cm, 20-30cm, and 50-60cm, with five replicates at each depth. Pore structure analysis was performed using CT tomography combined with box counting, with grid sizes at five gradients: 0.1mm, 0.5mm, 1mm, 5mm, and 10mm. Enzyme activity was measured using the fluorescent substrate method, detecting the activities of six enzymes: cellulase, urease, acid phosphatase, peroxidase, dehydrogenase, and protease. The fluorescence intensity of the reaction products was measured using a SpectraMax microplate reader. Root exudates were collected using the rhizosphere chamber method. Metabolite detection was performed using an Agilent 7890B gas chromatography-mass spectrometry system, with targeted quantification of five organic acids: citric acid, malic acid, oxalic acid, succinic acid, and acetic acid.

[0021] The cellulase is specifically β-1,4-glucosidase, and the urease is specifically urea hydrolase.

[0022] The multi-source heterogeneous data acquisition layer was used to determine ammonium nitrogen by Kjeldahl method, available phosphorus by molybdenum antimony colorimetric method, and available potassium by flame photometry at 09:00 every day for 7 consecutive days. Soil samples were taken from 15 cm below the surface. Diurnal metabolic data were collected 24 times every 2 hours using an Oxford Nanopore MinION sequencer to collect total RNA from soil microorganisms. The expression levels of three metabolic genes, namely nitrogen fixation gene nifH, ammonia oxidation gene amoA, and denitrification gene nirS, were targeted for detection. Pollutant half-life was determined by indoor culture experiments. DDT residue was detected by Agilent 1260 HPLC, PAH concentration was detected by Shimadzu GCMS-TQ8050, and phthalate degradation was detected by Waters ACQUITY UPLC.

[0023] The multi-source heterogeneous data acquisition layer used a dual-ring infiltration meter at depths of 0-15 cm, recording infiltration rates at 0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, and 110 minutes. Temperature monitoring used a HOBO MX2301 embedded temperature sensor, recording temperature fluctuations every 2 hours at soil depths of 5 cm, 15 cm, and 25 cm for a continuous 30-day cycle. The erosion test used an artificial rainfall simulation device, with a rainfall intensity set at 80 mm / h for 60 minutes. Five-level aggregates (>2 mm, 1-2 mm, 0.5-1 mm, 0.25-0.5 mm, and <0.25 mm) were collected from the runoff and weighed using a wet sieving method. The organic matter thermal stability analysis used a NETZSCH STA 449F3 simultaneous thermal analyzer, heated from 25 °C to 800 °C at a rate of 10 °C / min under a nitrogen atmosphere.

[0024] Multi-dimensional indicator calculation engine: Constructs three heterogeneous computing channels, each equipped with a dedicated pipeline architecture to realize parallel calculation of indicator matrices, including structure-biological channel, biochemical dynamics channel and environmental response channel, and transmits the calculated indicators to multi-level joint analysis controller.

[0025] The structure-biochannel was obtained by CT scanning of soil slices to capture pore structure images, and the fractal dimension was calculated using box counting, specifically as follows: , N represents the fractal dimension of the pores, ε represents the mesh size sequence, and N represents the pore size. p (ε) represents the number of ε grids required to cover the pore structure; the Shannon entropy change rate was calculated after measuring the activities of 6 key enzymes, and is specifically expressed as follows: ,in , The entropy change index represents the enzyme activity. A represents the percentage of activity of the i-th enzyme. i This represents the specific activity values ​​of six enzymes, where t1 and t2 represent the start and end time points of continuous monitoring. A j This represents the specific activity value of the j-th soil enzyme; the Simpson diversity index is calculated by detecting root exudates and is specifically expressed as: , c represents the diversity coefficient of root exudates. k This represents the concentration of the k-th metabolite. The total secretion concentration is represented by m1, and the number of metabolite species detected is represented by m1. An interspecies interaction network is constructed using metagenomic sequencing, and the modularity is calculated, specifically as follows: , e represents the microbial cooperation coefficient. ii (m） This represents the proportion of internal connected edges of species i. Let E represent the degree percentage of species i, E represent the total number of edges in the network, and n represent the total number of species in the microbial interaction network.

[0026] The biochemical dynamic channel collects nitrogen, phosphorus, and potassium concentrations for 7 consecutive days and calculates the product of the coefficients of variation, which is specifically expressed as follows: , σ represents the nutrient fluctuation index. N σ P σ K The standard deviation of the 7-day concentrations of elements N, P, and K is expressed in μ. N μ P μ K The average concentrations of elements N, P, and K are represented; the diurnal amplitude ratio is calculated by detecting the expression levels of functional genes, specifically expressed as follows: , Indices representing the biological metabolic diurnal rhythm index, g τ The expression level of metabolic genes per hour is represented by τ, where τ represents the time variable, n represents nighttime, and d represents daytime. The harmonic mean of the half-lives of three typical pollutants was determined and is expressed as follows: , The t represents the resonance coefficient of pollutant degradation. DDT This indicates the half-life of the organochlorine pesticide DDT, t PAHs The t represents the half-life of polycyclic aromatic hydrocarbons. phthalate The half-life of phthalate plasticizers is represented; the logarithmic ratio of ARGs / MGEs gene copy numbers is calculated and expressed as follows: , Indicates the abundance ratio of resistance genes. This represents the copy number of the j-th type of resistance gene. q represents the number of copies of the k-th type of mobile element, r represents the total number of detected ARGs, and r represents the total number of detected MGEs.

[0027] The environmental response channel calculates the product of skewness and kurtosis using multi-point penetration test data, specifically expressed as follows: , S represents the permeability heterogeneity coefficient. k (h） K represents the skewness of the 15-point permeability rate. u (h） This indicates the corresponding kurtosis; the product of phase difference and amplitude attenuation in the 24-hour temperature change experiment is calculated as follows: , This represents the temperature buffer hysteresis coefficient. This indicates the phase difference between ambient and soil temperature. Indicates the amplitude of ambient temperature. This represents the soil temperature amplitude; the entropy of aggregate size distribution is calculated after simulated rainfall experiments, specifically expressed as follows: , Indicates erosion resistance entropy, Let m represent the mass percentage of the s-th order aggregate, where m is the mass, and s ∈ {2 mm, 1–2 mm, 0.5–1 mm, 0.25–0.5 mm, <0.25 mm}; the integral representing the proportion of inert components in the thermogravimetric analysis is specifically expressed as: , Indicates the organic matter stability index. This represents the rate of weight loss in thermogravimetric analysis, and the integral interval corresponds to the pyrolysis temperature of the inert component.

[0028] Multi-level cascaded analysis controller: Receives the [4×3] index tensor output by the multi-dimensional index calculation engine, splits it into three data streams—structural, biochemical, and environmental—by a format converter, and adopts a cascaded analysis architecture. The first level calculates the structure-biological synergy index, the second level calculates the metabolism-resistance balance index, and the third level calculates the environmental resilience index, and transmits the calculation results to the intelligent assessment module.

[0029] The multi-stage simultaneous analysis controller employs a third-order series control structure to achieve cross-scale parameter coupling. At the hardware level, it deploys an FPGA+GPU heterogeneous computing platform, where the FPGA is responsible for timing control and parameter pre-calibration, and the GPU parallel computing core performs matrix operations.

[0030] The calculation method for the structure-biological synergy index is specifically expressed as follows: , The structure-biological synergy index, α p β represents the terrain adjustment coefficient. e Indicates the enzyme sensitivity coefficient, γ d This represents the root system compensation coefficient.

[0031] The α p The β value was obtained from the pore connectivity regression analysis. e γ was determined by enzyme kinetic parameter inversion. d =1.15.

[0032] The structure-biosynergy index is based on the positive feedback principle of pore network and microbial cooperation. First, a structure efficiency factor α is established. p D p , with microbial cooperation coefficient C m The product term represents the biomechanical coupling, and the denominator introduces the change in enzyme activity ΔE. e The absolute value of the square root and the diversity of root exudates R d The stability constraint term is constructed, and the experimental data fitting shows that the system exhibits positive cooperation when Q1>1.2.

[0033] The specific method for calculating the metabolic-resistance balance index is as follows: , The metabolic-resistance balance index, λ f μ is the nutrient weighting coefficient. r This represents the resistance inhibition coefficient.

[0034] The λ f Based on the nitrogen, phosphorus, and potassium migration rates, μ r It was determined by partial least squares regression of the expression level of resistance genes and the degradation rate of pollutants.

[0035] The metabolic-resistance balance index will account for nutrient fluctuations N f With metabolic rhythm B c The ratio is taken as the natural logarithm to represent the matter-energy balance, minus the ratio of resistance genes A. r With degradation efficiency R p The arctangent function represents the biochemical antagonistic effect. The formula is based on the Lyapunov stability theory of dynamic systems. The system is in steady state when Q2∈(−0.5,0.8) is verified by 48 hours of continuous monitoring data.

[0036] The calculation method for the environmental resilience index is specifically expressed as follows: , Indicating environmental resilience index, ν h ξ is the hydraulic shape coefficient. t ψ is the thermodynamic damping coefficient. e This is the mechanical dissipation coefficient.

[0037] The ν h ξ was determined through fractal analysis of the infiltration curve. t The ψ value was obtained by fitting the Fourier phase shift parameters from a temperature gradient experiment. e It is calculated based on the work done in breaking up the aggregates.

[0038] The environmental resilience index molecule integrates permeability heterogeneity H i With organic matter stability S o Characterizing the system's resistance to disturbances, the denominator combines the temperature hysteresis T. l and erosion entropy E r Reflecting energy dissipation, the formula is constructed based on dissipative structure theory, and ecological resilience is demonstrated through disaster simulation experiments when Q3>2.1.

[0039] Intelligent assessment module: Based on the progressive judgment system of soil ecological status, including structure-biological synergy index, metabolism-resistance balance index and environmental resilience index, it adopts nonlinear threshold nesting structure to conduct instruction assessment, including basic resilience assessment, dynamic balance diagnosis, system optimization stage and special regulation situation.

[0040] The basic resilience assessment triggers a preliminary judgment when Q3 is below 1.7: When Q3 ≤ 1.2, the soil is in a critical state of ecological collapse, characterized by an ability to resist disturbance below the phase transition threshold. At this time, the permeability heterogeneity coefficient Hᵢ < 1.8 × 10⁻³ mm / s and the inert component of organic matter after pyrolysis < 45%. When Q3 is in the range of (1.2, 1.7), the system exhibits a fragile state, and the temperature hysteresis coefficient T... l >0.33 radians and erosion entropy E r >2.4 bits.

[0041] The dynamic equilibrium diagnosis initiates secondary assessment when Q3 ≥ 1.7: if Q2 < -0.8, it is determined to be a state of chemical stress, characterized by the abundance ratio of resistance genes to A. r >3.2 and metabolic rhythm index B c <0.15, when Q2∈[-0.8,0.5] and Q1<0.9, it belongs to the structural degradation state, and the microbial cooperation coefficient C m <0.65 accompanied by the fractal dimension D of the pore size p <2.4.

[0042] The system optimization phase performs a high-level judgment when Q3 ≥ 2.1 and Q2 > 0.5: Q1 ≥ 1.8 represents the top-level community state, satisfying ν h ·S0>3ξ t ·T l And the entropy change of enzyme activity ΔE e When C ∈ [-0.05, 0.03] / h and Q1 ∈ [1.2, 1.8), it is considered a sub-healthy state and needs to be improved by increasing C. m >0.75 and R d >0.6 to achieve the fix.

[0043] In the special regulation scenario, when Q2 > 1.2 and Q1 > 2.0, the target soil ecology enters a state of biological supersaturation, characterized by a microbial network degree > 8.2 accompanied by a diurnal metabolic amplitude ratio > 4.7, requiring the implementation of pore unblocking and biomass regulation.

[0044] The critical values ​​of the intelligent evaluation module are determined through phase space reconstruction: Q3=1.7 corresponds to the permeability heterogeneity skewness S. k (h) =0.38 and pyrolysis integral S o =58% synergistic effect, Q2=-0.8 associated with the imbalance inflection point where the expression level of resistance genes exceeds that of metabolic genes by 23%, Q1=1.8 derived from the percolation threshold theoretical model of the pore-microbe coupling system.

[0045] refer to Figure 2 A method for intelligent assessment of soil ecological function, comprising:

[0046] S1: Multi-source heterogeneous data acquisition: Construct a cross-scale soil ecological data acquisition system, integrate optical-biochemical-thermodynamic multimodal sensing technologies, and collect basic data on soil structure-biological synergy, bio-chemical dynamic equilibrium, and environmental response resilience.

[0047] S2: Multi-dimensional index calculation: Construct a triple heterogeneous computing channel, each channel is equipped with a dedicated pipeline architecture to realize the parallel calculation of index matrix, including structure-biology channel, biochemical dynamics channel and environmental response channel;

[0048] S3: Multilevel cascade analysis: Based on the [4×3] index tensor output by S2, it is split into three data streams: structural group, biochemical group, and environmental group by a format converter. A cascade analysis architecture is adopted. The first level calculates the structure-biological synergy index, the second level calculates the metabolism-resistance balance index, and the third level calculates the environmental resilience index.

[0049] S4: Intelligent Assessment: Based on the progressive judgment system of soil ecological status, including structure-biological synergy index, metabolism-resistance balance index and environmental resilience index, a nonlinear threshold nested structure is used for instruction assessment, including basic resilience assessment, dynamic balance diagnosis, system optimization stage and special regulation situation.

[0050] This invention first collects soil samples at different depths using standardized sampling tools, analyzes pore structure using CT scanning technology, and measures enzyme activity and root exudate composition using biochemical detection methods, while continuously monitoring dynamic data such as nutrient concentration and gene expression. These data are then input into a computing system to generate three core indicators reflecting soil structural stability, biological metabolic balance, and environmental resistance. Next, a cascaded analysis model is used to perform interactive calculations on these three indicators to derive a comprehensive evaluation index. Finally, based on preset ecological threshold standards, the system first determines whether basic disaster resistance capacity meets the standards, then analyzes the material and energy cycle status, ultimately identifying different soil states such as healthy, sub-healthy, or requiring urgent remediation. An early warning mechanism is triggered for special abnormal situations, forming a complete closed loop from data collection and intelligent analysis to decision-making recommendations.

[0051] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0052] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent assessment system for soil ecological function, characterized in that, include: Multi-source heterogeneous data acquisition layer: Construct a cross-scale soil ecological data acquisition system, integrate optical-biochemical-thermodynamic multimodal sensing technologies, collect basic data on soil structure-biological synergy, bio-chemical dynamic balance and environmental response resilience, and transmit the basic data to a multi-dimensional index calculation engine; Multi-dimensional indicator calculation engine: Constructs three heterogeneous computing channels, each equipped with a dedicated pipeline architecture to realize parallel calculation of indicator matrices, including structure-biology channel, biochemical dynamics channel and environmental response channel, and transmits the calculated indicators to multi-level joint analysis controller; The structure-biochannel was obtained by CT scanning of soil slices to capture pore structure images, and the fractal dimension was calculated using box counting, specifically as follows: , N represents the fractal dimension of the pores, ε represents the mesh size sequence, and N represents the pore size. p (ε) represents the number of ε grids required to cover the pore structure; the Shannon entropy change rate was calculated after measuring the activities of 6 key enzymes, and is specifically expressed as follows: ,in , The entropy change index represents the enzyme activity. A represents the percentage of activity of the i-th enzyme. i This represents the specific activity values ​​of six enzymes, where t1 and t2 represent the start and end time points of continuous monitoring. A j This represents the specific activity value of the j-th soil enzyme; the Simpson diversity index is calculated by detecting root exudates and is specifically expressed as: , c represents the diversity coefficient of root exudates. k This represents the concentration of the k-th metabolite. The total secretion concentration is represented by m1, and the number of metabolite species detected is represented by m1. An interspecies interaction network is constructed using metagenomic sequencing, and the modularity is calculated, specifically as follows: , e represents the microbial cooperation coefficient. i (m） This represents the proportion of connected edges within species i. Let E represent the degree percentage of species i, E represent the total number of edges in the network, and n represent the total number of species in the microbial interaction network. The biochemical dynamic channel collects nitrogen, phosphorus, and potassium concentrations for 7 consecutive days and calculates the product of the coefficients of variation, which is specifically expressed as follows: , σ represents the nutrient fluctuation index. N σ P σ K The standard deviation of the 7-day concentrations of elements N, P, and K is expressed in μ. N μ P μ K The average concentrations of elements N, P, and K are represented; the diurnal amplitude ratio is calculated by detecting the expression levels of functional genes, specifically expressed as follows: , Indices representing the biological metabolic diurnal rhythm index, g τ The expression level of metabolic genes per hour is represented by τ, where τ represents the time variable, n represents nighttime, and d represents daytime. The harmonic mean of the half-lives of three typical pollutants was determined and is specifically expressed as follows: , The t represents the resonance coefficient of pollutant degradation. DDT This indicates the half-life of the organochlorine pesticide DDT, t PAHs The t represents the half-life of polycyclic aromatic hydrocarbons. phthalate The half-life of phthalate plasticizers is represented; the logarithmic ratio of ARGs / MGEs gene copy numbers is calculated and expressed as follows: , Indicates the abundance ratio of resistance genes. This represents the copy number of the j-th type of resistance gene. q represents the number of copies of the k-th type of mobile element, r represents the total number of detected ARGs, and r represents the total number of detected MGEs. The environmental response channel calculates the product of skewness and kurtosis using multi-point penetration test data, specifically expressed as follows: , S represents the permeability heterogeneity coefficient. k (h） K represents the skewness of the 15-point permeability rate. u (h） This indicates the corresponding kurtosis; the product of phase difference and amplitude attenuation in the 24-hour temperature change experiment is calculated as follows: , This represents the temperature buffer hysteresis coefficient. This indicates the phase difference between ambient and soil temperature. Indicates the amplitude of ambient temperature. This represents the soil temperature amplitude; the entropy of aggregate size distribution is calculated after simulated rainfall experiments, specifically expressed as follows: , Indicates erosion resistance entropy, Let m represent the mass percentage of the s-th order aggregate, where m is the mass, and s ∈ {2 mm, 1–2 mm, 0.5–1 mm, 0.25–0.5 mm, <0.25 mm}; the integral representing the proportion of inert components in the thermogravimetric analysis is specifically expressed as: , Indicates the organic matter stability index. This represents the rate of weight loss in thermogravimetric analysis, and the integral interval corresponds to the pyrolysis temperature of the inert component. Multi-level cascaded analysis controller: Receives the [4×3] index tensor output by the multi-dimensional index calculation engine, splits it into three data streams—structural, biochemical, and environmental—by a format converter, and adopts a cascaded analysis architecture. The first level calculates the structure-biological synergy index, the second level calculates the metabolism-resistance balance index, and the third level calculates the environmental resilience index, and transmits the calculation results to the intelligent assessment module. The calculation method for the structure-biological synergy index is specifically expressed as follows: , The structure-biological synergy index, α p β represents the terrain adjustment coefficient. e Indicates the enzyme sensitivity coefficient, γ d Indicates the root system compensation coefficient; The specific method for calculating the metabolic-resistance balance index is as follows: , The metabolic-resistance balance index, λ f μ is the nutrient weighting coefficient. r This is the resistance inhibition coefficient; The calculation method for the environmental resilience index is specifically expressed as follows: , Indicating environmental resilience index, ν h ξ is the hydraulic shape coefficient. t ψ is the thermodynamic damping coefficient. e The mechanical dissipation coefficient; Intelligent assessment module: Based on the progressive judgment system of soil ecological status, including structure-biological synergy index, metabolism-resistance balance index and environmental resilience index, it adopts nonlinear threshold nesting structure to conduct instruction assessment, including basic resilience assessment, dynamic balance diagnosis, system optimization stage and special regulation situation.

2. The intelligent soil ecological function assessment system according to claim 1, characterized in that: The multi-source heterogeneous data acquisition layer used a 5cm diameter stainless steel cylindrical soil sampler to collect samples at three depths: 0-10cm, 20-30cm, and 50-60cm, with five replicates at each depth. Pore structure analysis was performed using CT tomography combined with box counting, with grid sizes at five gradients: 0.1mm, 0.5mm, 1mm, 5mm, and 10mm. Enzyme activity was measured using the fluorescent substrate method, detecting the activities of six enzymes: cellulase, urease, acid phosphatase, peroxidase, dehydrogenase, and protease. The fluorescence intensity of the reaction products was measured using a SpectraMax microplate reader. Root exudates were collected using the rhizosphere chamber method. Metabolite detection was performed using an Agilent 7890B gas chromatography-mass spectrometry system, with targeted quantification of five organic acids: citric acid, malic acid, oxalic acid, succinic acid, and acetic acid.

3. The intelligent soil ecological function assessment system according to claim 1, characterized in that: The multi-source heterogeneous data acquisition layer was used to determine ammonium nitrogen by Kjeldahl method, available phosphorus by molybdenum antimony colorimetric method, and available potassium by flame photometry at 09:00 every day for 7 consecutive days. Soil samples were taken from 15 cm below the surface. Diurnal metabolic data were collected 24 times every 2 hours using an Oxford Nanopore MinION sequencer to collect total RNA from soil microorganisms. The expression levels of three metabolic genes, namely nitrogen fixation gene nifH, ammonia oxidation gene amoA, and denitrification gene nirS, were targeted for detection. Pollutant half-life was determined by indoor culture experiments. DDT residue was detected by Agilent 1260 HPLC, PAH concentration was detected by Shimadzu GCMS-TQ8050, and phthalate degradation was detected by Waters ACQUITY UPLC.

4. The intelligent soil ecological function assessment system according to claim 1, characterized in that: The multi-source heterogeneous data acquisition layer used a dual-ring infiltration meter at depths of 0-15 cm, recording infiltration rates at 0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, and 110 minutes. Temperature monitoring used a HOBO MX2301 embedded temperature sensor, recording temperature fluctuations every 2 hours at soil depths of 5 cm, 15 cm, and 25 cm for a continuous 30-day cycle. The erosion test used an artificial rainfall simulation device, with a rainfall intensity set at 80 mm / h for 60 minutes. Five-level aggregates (>2 mm, 1-2 mm, 0.5-1 mm, 0.25-0.5 mm, and <0.25 mm) were collected from the runoff and weighed using a wet sieving method. The organic matter thermal stability analysis used a NETZSCHSTA 449F3 simultaneous thermal analyzer, heated from 25 °C to 800 °C at a rate of 10 °C / min under a nitrogen atmosphere.

5. The intelligent soil ecological function assessment system according to claim 1, characterized in that: The basic resilience assessment triggers a preliminary judgment when Q3 is below 1.7: When Q3 ≤ 1.2, the soil is in a critical state of ecological collapse, characterized by an ability to resist disturbance below the phase transition threshold. At this time, the permeability heterogeneity coefficient Hᵢ < 1.8 × 10⁻³ mm / s and the inert component of organic matter after pyrolysis < 45%. When Q3 is in the range of (1.2, 1.7), the system exhibits a fragile state, and the temperature hysteresis coefficient T... l >0.33 radians and erosion entropy E r >2.4 bits; The dynamic equilibrium diagnosis initiates secondary assessment when Q3 ≥ 1.7: if Q2 < -0.8, it is determined to be a state of chemical stress, characterized by the abundance ratio of resistance genes to A. r >3.2 and metabolic rhythm index B c <0.15, when Q2∈[-0.8,0.5] and Q1<0.9, it belongs to the structural degradation state, and the microbial cooperation coefficient C m <0.65 accompanied by the fractal dimension D of the pore size p <2.4; The system optimization phase performs a high-level judgment when Q3 ≥ 2.1 and Q2 > 0.5: Q1 ≥ 1.8 represents the top-level community state, satisfying ν h ·S0>3ξ t ·T l And the entropy change of enzyme activity ΔE e When C ∈ [-0.05, 0.03] / h and Q1 ∈ [1.2, 1.8), it is considered a sub-healthy state and needs to be improved by increasing C. m >0.75 and R d >0.6 achieves the fix; In the special regulation scenario, when Q2 > 1.2 and Q1 > 2.0, the target soil ecology enters a state of biological supersaturation, characterized by a microbial network degree > 8.2 accompanied by a diurnal metabolic amplitude ratio > 4.7, requiring the implementation of pore unblocking and biomass regulation.

6. A method for intelligent assessment of soil ecological function, used with the intelligent assessment system for soil ecological function as described in any one of claims 1-5, characterized in that, Includes the following steps: S1: Multi-source heterogeneous data acquisition: Construct a cross-scale soil ecological data acquisition system, integrate optical-biochemical-thermodynamic multimodal sensing technologies, and collect basic data on soil structure-biological synergy, bio-chemical dynamic equilibrium, and environmental response resilience. S2: Multi-dimensional index calculation: Construct a triple heterogeneous computing channel, each channel is equipped with a dedicated pipeline architecture to realize the parallel calculation of index matrix, including structure-biology channel, biochemical dynamics channel and environmental response channel; S3: Multilevel cascade analysis: Based on the [4×3] index tensor output by S2, it is split into three data streams: structural group, biochemical group, and environmental group by a format converter. A cascade analysis architecture is adopted. The first level calculates the structure-biological synergy index, the second level calculates the metabolism-resistance balance index, and the third level calculates the environmental resilience index. S4: Intelligent Assessment: Based on the progressive judgment system of soil ecological status, including structure-biological synergy index, metabolism-resistance balance index and environmental resilience index, a nonlinear threshold nested structure is used for instruction assessment, including basic resilience assessment, dynamic balance diagnosis, system optimization stage and special regulation situation.

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