Quantitative evaluation decision-making method and system for soil acidification treatment effect

Abstract

The invention relates to the technical field of environmental governance project supervision and prediction, and discloses a soil acidification governance effect quantitative evaluation decision-making method and system, and the method comprises the steps: obtaining a system state variable sequence and an external disturbance intensity parameter of a governance project discrete node, by calculating a state variable transition rate and utilizing a steady-state weight factor to hedge extrinsic fluctuations, an intrinsic loss coefficient of a governance object stress-resistant buffer system is extracted, an efficiency attenuation prediction data chain is deduced to calculate a risk time margin, an intervention priority mapping relation is established, a governance resource reset direction scheduling matrix is updated, and the risk time margin of the governance object stress-resistant buffer system is calculated. According to the method, a calculation mechanism for stripping environmental noise from discrete sampling data is established, the recessive efficiency degradation trend of a governed land parcel is identified, and the problem of resource mismatching in administrative decision making is effectively solved.

Description

Technical Field

[0001] This invention relates to a method and system for quantitatively assessing the effectiveness of soil acidification remediation, belonging to the field of environmental remediation project monitoring and prediction technology. Background Technology

[0002] Currently, static comparison technology based on discrete nodes is commonly used. This technology acquires data on pH and organic matter content at the start and acceptance stages of remediation, and assesses compliance based on numerical differences. This data serves as the basis for issuing assessment reports and acceptance instructions. The resilience and stability of complex environmental systems are determined by endogenous buffer capacity and external disturbance loads. The essence of remediation investment lies in restoring chemical buffer capacity. However, existing comparison architectures treat target sites as static evolution objects, ignoring the hysteresis decay law after environmental intervention. When the system is in complex conditions such as large areas of farmland or ecological restoration areas, the assessment process often masks the risk of performance rebound, causing regulatory resource allocation to deviate from the actual decay curve.

[0003] To improve monitoring accuracy, methods such as increasing sampling frequency or introducing multidimensional variable modeling are commonly used. However, due to the large amount of random noise interference in agricultural production environments and the high cost of high-frequency on-site sampling, these linear improvement methods cannot effectively isolate transient data fluctuations caused by precipitation or fertilization. Therefore, existing solutions struggle to extract intrinsic indicators characterizing the degradation pattern of remediation effectiveness from low-frequency sampling data. For example, Chinese invention patent application CN121072957A discloses a knowledge graph-based evaluation system for improving the effect of acidified soil in orchards. This system constructs a dynamic knowledge graph and utilizes a causal reasoning model to measure... While these technologies can improve the intensity of environmental protection, in actual administrative supervision scenarios, their technical logic heavily relies on continuous time-series data provided by sensor networks and lacks the ability to linearly decouple from highly random external disturbances such as rainfall and fertilization. In agricultural production environments, these intelligent methods struggle to remove transient environmental noise from low-frequency sampling data and cannot accurately extract the intrinsic degradation patterns that reflect the true resilience of the treated land. Existing technologies mostly stop at the level of effect evaluation and fail to establish a closed-loop mapping from the attenuation potential energy of the land to the reset of administrative resources and scheduling. This results in a systemic lag in the allocation of regulatory resources and makes it difficult to solve the problem of resource misallocation in administrative decision-making.

[0004] Therefore, the technical problem to be solved by this invention is how to determine the intrinsic degradation law of governance effectiveness based on discrete data, and adjust the resource scheduling matrix accordingly to achieve precise alignment between administrative management instructions and land degradation risks. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A method for quantitatively evaluating and deciding on the effectiveness of soil acidification remediation, comprising the following steps: Step S101: Obtain the system state variable sequence of the environmental governance project at multiple discrete time nodes, as well as the external disturbance intensity parameter corresponding to each discrete time node; the system state variable sequence includes hydrogen ion activity characterization quantity, organic matter loading and exchangeable cation capacity; the external disturbance intensity parameter is generated based on the natural environment input data and artificial intervention load data within the preset time window, through equivalent conversion rules. Step S102: Calculate the transition rate of the system state variable sequence between each adjacent time node, use the system steady-state weighting factor to correct the transition rate, offset the non-intrinsic numerical fluctuations caused by the external disturbance intensity parameter, and extract the intrinsic loss coefficient that reflects the degradation state of the anti-resistance buffer system of the governance project object. Step S103: Based on the intrinsic loss coefficient, deduce the performance degradation prediction data chain of the environmental governance project on the time axis, perform collision matching between the performance degradation prediction data chain and the preset safety threshold, calculate the prediction time point when the performance degradation prediction data chain touches the preset safety threshold, and extract the time margin characterizing the failure risk of governance effectiveness. Step S104: Establish a monotonically decreasing mapping relationship between time margin and intervention priority weight. Update the resource allocation sequence in the governance resource reset scheduling matrix according to the monotonically decreasing mapping relationship. The resource allocation sequence defines the priority of fiscal appropriations and the proportion of engineering material allocation for each regulatory unit within the preset governance cycle. Generate scheduling instructions for the total budget and governance force deployment in the administrative management system.

[0006] Preferably, the logic for generating the external disturbance intensity parameter in step S101 is as follows: Step S1011, collect the cumulative precipitation and frequency of human intervention for the environmental governance project, and map the frequency of human intervention to the acid neutralization capacity load equivalent according to the preset intensity level; Step S1012, perform diffusion intensity correction on the acid neutralization capacity load equivalent based on the cumulative precipitation, and generate a scalar characterizing the total intensity of external intervention as the external disturbance intensity parameter.

[0007] Preferably, in step S101, the sampling period for obtaining the system state variable sequence is not less than 90 days, and the sampling environmental humidity deviation at each discrete time node is less than 15%.

[0008] Preferably, the logic for correcting the transition rate using the system steady-state weight factor in step S102 is as follows: Step S1021, set the system steady-state weight factor benchmark value according to the initial physical texture ratio of the environmental governance project; Step S1022, divide the transition rate by the product of the system steady-state weight factor benchmark value and the external disturbance intensity parameter to complete the linear decoupling and noise reduction of the transition rate.

[0009] Preferably, the method for calculating the predicted time point in step S103 is as follows: Step S1031, apply the Markov state transition probability matrix to simulate the state drift trajectory of the environmental governance project under the action of the intrinsic loss coefficient; Step S1032, when the predicted value of the state drift trajectory coincides with the preset safety threshold, extract the corresponding time abscissa as the predicted time point.

[0010] Preferably, the criterion for establishing the mapping relationship between time margin and intervention priority weight in step S104 is: the shorter the time margin, the higher the corresponding intervention priority weight; when the time margin is lower than the preset management warning time limit, the response slope of the intervention priority weight is increased by the preset emergency factor.

[0011] Preferably, the process of updating the governance resource reset scheduling matrix in step S104 includes: step S1041, arranging the intervention priority weights of multiple evaluation grids in descending order to generate an administrative decision priority sequence; step S1042, allocating governance resource shares sequentially from the top of the administrative decision priority sequence downwards according to the total budget in the administrative management system.

[0012] Preferably, the method further includes the following steps: Step S105, monitoring the feedback value of the system state variable after the dispatch instruction is issued; if the recovery rate of the feedback value is lower than the preset benchmark in two consecutive sampling periods, then re-triggering steps S102 to S104 to correct the weight allocation factor in the dispatch instruction; the environmental governance project is divided into multiple regulatory units with independent administrative numbers; each regulatory unit independently applies steps S101 to S104 to determine the asymmetric fine-grained dispatch scheme of governance resources in the management system.

[0013] A decision-making system for quantitatively evaluating the effectiveness of soil acidification remediation includes: The data acquisition module is used to acquire the system state variable sequence of the environmental governance project at multiple discrete time points, as well as the external disturbance intensity parameter corresponding to each discrete time point; The intrinsic analysis module is used to calculate the rate of change of the system state variable sequence between adjacent time nodes, and to apply the system steady-state weighting factor to correct the rate of change in order to extract the intrinsic loss coefficient that reflects the degradation state of the anti-resistance buffer system of the governance project object. The risk prediction module is used to extrapolate the effectiveness decay prediction data chain of environmental governance projects on the time axis based on the intrinsic loss coefficient. By matching the effectiveness decay prediction data chain with the preset safety threshold, the time margin characterizing the failure risk of governance effectiveness is extracted. The decision-making and scheduling module is used to establish a monotonically decreasing mapping relationship between time margin and intervention priority weight, and to update the resource allocation sequence in the governance resource reset scheduling matrix according to the monotonically decreasing mapping relationship, thereby generating scheduling instructions for the total budget and governance force deployment in the administrative management system.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. In the quantitative evaluation of the effectiveness of soil acidification control, by coupling the slope of the characterization data at adjacent time points with the corresponding external cumulative disturbance load, the system establishes a computational mechanism that can remove transient environmental noise from discrete sampling data. By introducing basic anti-disturbance weight parameters to offset the numerical fluctuations caused by short-term rainfall or fertilization, the intrinsic loss coefficient reflecting the true resilience of the treated plot is extracted. This data processing logic changes the traditional regulatory system's reliance on static numerical comparison, enabling the management terminal to identify the hidden performance degradation trend within the intervened plot under low-frequency sampling engineering conditions.

[0015] 2. Collision calculations are performed between the performance decay prediction data chain generated based on the intrinsic loss coefficient and the safety threshold to extract the time margin representing the failure risk of governance effectiveness. This process transforms the originally isolated acceptance node values ​​into time-oriented risk probabilities, providing time-oriented early warning indicators for administrative supervision. This mechanism enables the decision-making system to move away from passively auditing the failure facts and instead intervene in advance based on the predicted decay potential energy, effectively shortening the decision response time of the administrative management system in complex environmental governance projects.

[0016] 3. Establish a monotonic logical relationship between time margin, intervention priority weight, and governance resource reset scheduling matrix to achieve automatic mapping from technical forecast data to administrative resource allocation instructions. By updating the resource allocation sequence in the scheduling matrix in real time, the system ensures that fiscal budgets and governance forces are accurately allocated to the grids with the shortest time margins to be intervened, preventing the delayed reinvestment of ineffective resources. This cross-mechanism linkage approach solves the resource misallocation problem caused by information asymmetry in large-scale land governance projects from the underlying architecture, and improves the overall operational accuracy of public governance funds in long-term monitoring projects. Attached Figure Description

[0017] Figure 1 This is a flowchart of the overall process for the quantitative evaluation and pre-decision decision-making method for soil acidification remediation effectiveness of the present invention. Figure 2 This invention presents a system decision-making logic diagram that integrates multi-dimensional parameter calibration and dynamic allocation of financial materials.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] A method for quantitatively evaluating and making decisions on the effectiveness of soil acidification remediation includes the following steps: Step S101: Obtain the system state variable sequence of the environmental governance project at multiple discrete time nodes, as well as the external disturbance intensity parameter corresponding to each discrete time node; the system state variable sequence includes hydrogen ion activity characterization quantity, organic matter loading and exchangeable cation capacity; the external disturbance intensity parameter is generated based on the natural environment input data and artificial intervention load data within the preset time window, through equivalent conversion rules. Step S102: Calculate the transition rate of the system state variable sequence between each adjacent time node, use the system steady-state weighting factor to correct the transition rate, offset the non-intrinsic numerical fluctuations caused by the external disturbance intensity parameter, and extract the intrinsic loss coefficient that reflects the degradation state of the anti-resistance buffer system of the governance project object. Step S103: Based on the intrinsic loss coefficient, deduce the performance degradation prediction data chain of the environmental governance project on the time axis, perform collision matching between the performance degradation prediction data chain and the preset safety threshold, calculate the prediction time point when the performance degradation prediction data chain touches the preset safety threshold, and extract the time margin characterizing the failure risk of governance effectiveness. Step S104: Establish a monotonically decreasing mapping relationship between time margin and intervention priority weight. Update the resource allocation sequence in the governance resource reset scheduling matrix according to the monotonically decreasing mapping relationship. The resource allocation sequence defines the priority of fiscal appropriations and the proportion of engineering material allocation for each regulatory unit within the preset governance cycle. Generate scheduling instructions for the total budget and governance force deployment in the administrative management system.

[0021] Preferably, the logic for generating the external disturbance intensity parameter in step S101 is as follows: Step S1011, collect the cumulative precipitation and frequency of human intervention for the environmental governance project, and map the frequency of human intervention to the acid neutralization capacity load equivalent according to the preset intensity level; Step S1012, perform diffusion intensity correction on the acid neutralization capacity load equivalent based on the cumulative precipitation, and generate a scalar characterizing the total intensity of external intervention as the external disturbance intensity parameter.

[0022] Preferably, in step S101, the sampling period for obtaining the system state variable sequence is not less than 90 days, and the sampling environmental humidity deviation at each discrete time node is less than 15%.

[0023] Preferably, the logic for correcting the transition rate using the system steady-state weight factor in step S102 is as follows: Step S1021, set the system steady-state weight factor benchmark value according to the initial physical texture ratio of the environmental governance project; Step S1022, divide the transition rate by the product of the system steady-state weight factor benchmark value and the external disturbance intensity parameter to complete the linear decoupling and noise reduction of the transition rate.

[0024] Preferably, the intrinsic loss factor The extraction logic follows these rules: ,in, This represents the rate of change of system state variables between adjacent time points. This is the baseline value of the system's steady-state weighting factor. For external disturbance intensity parameters, It is a logical smoothing operator preset based on the coefficient of variation of historical data.

[0025] Preferably, the method for calculating the predicted time point in step S103 is as follows: Step S1031, apply the Markov state transition probability matrix to simulate the state drift trajectory of the environmental governance project under the action of the intrinsic loss coefficient; Step S1032, when the predicted value of the state drift trajectory coincides with the preset safety threshold, extract the corresponding time abscissa as the predicted time point.

[0026] Preferably, the criterion for establishing the mapping relationship between time margin and intervention priority weight in step S104 is: the shorter the time margin, the higher the corresponding intervention priority weight; when the time margin is lower than the preset management warning time limit, the response slope of the intervention priority weight is increased by the preset emergency factor.

[0027] Preferably, the process of updating the governance resource reset scheduling matrix in step S104 includes: step S1041, arranging the intervention priority weights of multiple evaluation grids in descending order to generate an administrative decision priority sequence; step S1042, allocating governance resource shares sequentially from the top of the administrative decision priority sequence downwards according to the total budget in the administrative management system.

[0028] Preferably, the method further includes the following steps: Step S105, monitoring the feedback value of the system state variable after the dispatch instruction is issued; if the recovery rate of the feedback value is lower than the preset benchmark in two consecutive sampling periods, then re-triggering steps S102 to S104 to correct the weight allocation factor in the dispatch instruction; the environmental governance project is divided into multiple regulatory units with independent administrative numbers; each regulatory unit independently applies steps S101 to S104 to determine the asymmetric fine-grained dispatch scheme of governance resources in the management system.

[0029] A decision-making system for quantitatively evaluating the effectiveness of soil acidification remediation includes: The data acquisition module is used to acquire the system state variable sequence of the environmental governance project at multiple discrete time points, as well as the external disturbance intensity parameter corresponding to each discrete time point; The intrinsic analysis module is used to calculate the rate of change of the system state variable sequence between adjacent time nodes, and to apply the system steady-state weighting factor to correct the rate of change in order to extract the intrinsic loss coefficient that reflects the degradation state of the anti-resistance buffer system of the governance project object. The risk prediction module is used to extrapolate the effectiveness decay prediction data chain of environmental governance projects on the time axis based on the intrinsic loss coefficient. By matching the effectiveness decay prediction data chain with the preset safety threshold, the time margin characterizing the failure risk of governance effectiveness is extracted. The decision-making and scheduling module is used to establish a monotonically decreasing mapping relationship between time margin and intervention priority weight, and to update the resource allocation sequence in the governance resource reset scheduling matrix according to the monotonically decreasing mapping relationship, thereby generating scheduling instructions for the total budget and governance force deployment in the administrative management system.

[0030] Example 1: In the scenario of administrative supervision and fiscal audit of a large-scale agricultural land soil acidification remediation project covering multiple independent administrative numbering regulatory units, high-frequency sampling at agricultural sites is costly and faces implementation obstacles. The administrative management system relies on discrete time nodes with a sampling period of no less than 90 days to obtain environmental indicator data. During this long sampling period, the superposition of dense natural rainfall and irregular artificial fertilization causes non-intrinsic value fluctuations in system state variables. Traditional management systems that rely on discrete node value comparison cannot penetrate short-term fluctuation noise to identify the implicit degradation trend of the resilience buffer system of the remediated plots, resulting in a systemic lag in the issuance of administrative management instructions and causing misallocation and inefficient reinvestment of fiscal governance resources. When the system faces the above conditions, the data acquisition module acquires the system state variable sequence of the environmental remediation project at multiple discrete time nodes and the external disturbance intensity parameters corresponding to each discrete time node. The system state variable sequence includes hydrogen ion activity, organic matter loading, and exchangeable cation capacity. Simultaneously, the data acquisition module collects the cumulative precipitation and frequency of human intervention in environmental remediation projects. The frequency of human intervention is converted into an acid neutralization capacity load equivalent according to a preset intensity level. The diffusion intensity of the acid neutralization capacity load equivalent is corrected based on the cumulative precipitation, generating a scalar representing the total intensity of external intervention as an external disturbance intensity parameter. The physical mechanism follows the proton mass conservation ion diffusion kinetics law of the soil buffer system. The data acquisition module reads historical datasets recorded by environmental monitoring terminals deployed within the grid, extracting cumulative precipitation and human intervention frequency. In specific implementation, the data acquisition module extracts the average mass of single applications of quicklime from historical agricultural records within the area, calculates the corresponding total proton neutralization molars based on the hydroxide ion mass ratio, and divides this total by the physical dry weight of the soil at the corresponding grid depth. This rigorous material mass calculation replaces simple action count statistics, quantifying each discrete construction operation into a defined acid neutralization capacity load equivalent, specifically the external disturbance intensity parameter. Determined according to the following formula: In the formula, the variable A dimensionless scalar, representing the total chemical load imposed by the external environment; a constant. To pre-calibrate the acid neutralization capacity conversion factor based on the historical fertilization characteristics of the plot; variables To generate discrete load equivalents by mapping the frequency of human intervention, a constant is needed. For the corresponding empirical constant of surface runoff diffusion characteristics in the assessment area; variables To obtain the cumulative precipitation within a preset time window, the calculation process objectively decouples and extracts the physical diffusion effect caused by water leaching and the chemical proton release effect caused by artificial fertilization. The external disturbance intensity parameter output by this module provides the necessary prerequisite data for subsequent noise reduction and decoupling, enabling the management perspective to be transformed from independent detection value comparison to dynamic variable tracking that includes time series and external intervention load dimensions.

[0031] The intrinsic analysis module calculates the transition rate of the system state variable sequence between adjacent time nodes, and uses the system steady-state weighting factor to correct the transition rate to offset non-intrinsic numerical fluctuations. The intrinsic loss coefficient extraction logic follows the formula. ,in, This is the intrinsic loss factor. This represents the rate of change of system state variables between adjacent time points. This is the baseline value for the system steady-state weighting factor set based on the initial physical composition ratio of the environmental remediation project. For external disturbance intensity parameters, Based on a pre-defined logical smoothing operator using historical data variation coefficients, the intrinsic analysis module fuses rate data representing state transitions with the total intensity scalar representing external interventions using the aforementioned formula. It removes transient physical environment interference noise from low-frequency discrete sampling data and extracts the intrinsic loss coefficient reflecting the degradation state of the resilience buffer system of the governance project. This processing link resolves the technical contradiction between the coarsening of low-frequency sampling and the high-fidelity attenuation characteristics required for management decisions within a single data processing architecture. The risk prediction module, based on the intrinsic loss coefficient, extrapolates the effectiveness attenuation prediction data chain of environmental governance projects over time. It applies a Markov state transition probability matrix to simulate the state drift trajectory of environmental governance projects under the influence of the intrinsic loss coefficient. When the measured value coincides with a preset safety threshold, the corresponding time abscissa is extracted as the prediction time point. Based on this, the time margin representing the risk of governance effectiveness failure is calculated and output. This underlying computational process converts the difference attribute parameters of the physical detection stage into a time-oriented quantitative defense line for administrative prediction and supervision purposes. The decision-making and scheduling module establishes a monotonically decreasing mapping relationship between time margin and intervention priority weight. It sets that a shorter time margin corresponds to an increase in intervention priority weight. When the time margin is lower than the preset management warning time limit, the response slope of the preset emergency factor increases the intervention priority weight. The decision-making and scheduling module arranges the intervention priority weights of multiple assessment grids in descending order to generate an administrative decision priority sequence. According to the total budget in the administrative management system, it allocates governance resources from the top of the administrative decision priority sequence downwards, updates the resource allocation sequence in the scheduling matrix, and generates scheduling instructions that define the priority of fiscal appropriations and the proportion of engineering material allocation for each regulatory unit within the preset governance cycle. The underlying logic of the management system imports governance funds and intervention materials into the regulatory grid with the shortest time margin according to the scheduling instructions. This assessment and scheduling mechanism is no longer limited to static numerical compliance judgment. Through the feedback fusion of temporal evolution and resource constraints, the environmental risk assessment process is transformed into a multi-dimensional dynamic optimization allocation process of resources.

[0032] Example 2: In the administrative audit scenario of acceptance and fund disbursement for agricultural land soil acidification remediation projects covering multiple independent administrative numbering regulatory units, the static numerical acceptance standard includes non-intrinsic numerical fluctuations caused by the superposition of dense natural precipitation and artificial fertilization over a long period. Issuing administrative management instructions based on this static numerical acceptance standard results in time lags and misallocation of fiscal governance resources. This experiment uses a publicly available dataset of agricultural land grid monitoring as the basic environmental input source. This dataset covers the historical sequences of hydrogen ion activity and organic matter loading of 50 independent assessment grids over the past 360 days. To reproduce transient disturbances in real engineering scenarios, random white noise with a preset variance is injected into the original sequence to simulate sudden natural precipitation. Superimposed step signals are used to simulate artificial fertilization. The sampling period needs to achieve a technical balance between the real-time requirements of administrative auditing and the cost of grid-based deployment. When the estimated cost of a single sampling exceeds the existing management accounting benchmark and the physical grid density crosses a specific area threshold, the sampling period parameter tends to the upper limit of the set range. Accordingly, 90 days is selected as the sampling period setting value for discrete time nodes. The data acquisition module extracts the cumulative precipitation and the frequency of artificial intervention under this period, converts the frequency of artificial intervention into acid neutralization capacity load equivalent according to the preset intensity level, corrects the diffusion intensity of acid neutralization capacity load equivalent based on the cumulative precipitation, and generates a scalar characterizing the total intensity of external intervention as an external disturbance intensity parameter, providing basic data for offsetting transient physical environment interference noise.

[0033] The experiment constructed a multi-dimensional control system, establishing an experimental group employing complete analytical logic, a control group 1 lacking the steady-state weighting factor and perturbation parameter fusion step, and an out-of-range control group 2 with a sampling period extended to 240 days. External perturbation intensity parameters with gradient-increasing strength were injected into the input data of each group. Test data showed that under low-perturbation conditions, the deviation of the calculated transition rates between the experimental group and control group 1 was less than 5.2%. When the external perturbation intensity parameter crossed the medium load threshold and continued to increase, the system state variable sequence exhibited a nonlinear response inflection point. The system state variable sequence output by control group 1 showed a rebound fluctuation of up to 42.5% due to the influence of precipitation and fertilization pulses, causing the static evaluation module to output a governance compliance judgment. Under the same input conditions, the intrinsic analytical module of the experimental group, based on the formula... Calculate the intrinsic loss factor, where, This is the intrinsic loss factor. This represents the rate of change of system state variables between adjacent time points. This is the baseline value of the system steady-state weighting factor set based on the initial physical texture proportion. For external disturbance intensity parameters, The experimental group used a pre-defined logical smoothing operator based on the coefficient of variation of historical data to extract the intrinsic loss coefficient reflecting the degradation state of the stress-bearing buffer system of the governance project object. The output numerical volatility was stable in the range of 4.1% to 4.6%. This comparative data confirms that the experimental group has removed transient physical environment interference noise from the low-frequency discrete sampling data.

[0034] The risk prediction module receives the intrinsic degradation coefficient, extrapolates the performance degradation prediction data chain along the time axis, and applies the Markov state transition probability matrix to simulate the state drift trajectory of the environmental remediation project under the influence of the intrinsic degradation coefficient. After inputting data containing historical vectors of high-frequency acid precipitation, the predicted state drift trajectory value of the experimental group coincides with the preset safety threshold on day 275. The corresponding time abscissa is extracted, and the time margin is calculated to be 85 days. The control group 2, due to the distorted initial state matrix caused by the sampling period set to 240 days, failed to reach the safety threshold before the actual physical characteristics deteriorated, resulting in prediction omission. The decision-making and scheduling module receives time slack data and converts the 85-day time slack into intervention priority weights based on a monotonically decreasing mapping relationship. Since 85 days is lower than the preset 90-day management alert time limit, an emergency factor is triggered, and the response slope of the intervention priority weights is increased. The intervention priority weight of this assessment grid is ranked first in the administrative decision priority sequence. The logical link updates the governance resource reset direction scheduling matrix according to this sequence and issues a scheduling instruction containing the proportion of the first batch of fiscal appropriations and engineering material allocations to this grid. The environmental assessment parameters are converted into multi-dimensional resource dynamic optimization allocation instructions through time-series evolution and resource constraints.

[0035] Example 3: In a scenario of predicting and allocating resources for soil acidification control covering multiple levels of administrative grids, the stable operation of the system depends on the quantitative calibration of the underlying parameters and the structural determination of the evolution model. Traditional prediction systems set the state evolution probability matrix and weighting factors as static empirical values. This static model produces prediction trajectory distortion and timeliness calculation errors when dealing with the differentiated physical texture distribution and historical variation patterns among different geographical grids. These errors may lead to the risk of erroneous triggering of fiscal intervention instructions. Regarding the benchmark value of the system's steady-state weighting factors... With logical smoothing operator The intrinsic analysis module introduces a direct parameter calibration step, and the data acquisition module obtains the historical background state sequence of the evaluation grid under no external intervention conditions, extracting the component proportion vectors of sand, silt, and clay particles. The intrinsic analysis module multiplies the component proportion vectors with the pre-stored steady-state buffer coefficient matrix to calculate the benchmark value of the system's steady-state weight factor. For logical smoothing operators The intrinsic analysis module extracts the natural environment input data sequence within a preset time window, calculates the ratio of the standard deviation to the mean of the sequence, and generates the historical data variation coefficient. The intrinsic analysis module then multiplies the historical data variation coefficient by a preset scaling factor to generate a logical smoothing operator. The above calibration steps anchor the parameter values ​​to objective geophysical characteristics and statistical sequences. Regarding the construction mechanism of the Markov state transition probability matrix, the risk prediction module performs matrix initialization and dynamic evolution steps. The risk prediction module divides the effectiveness status of the soil acidification remediation project into four discrete level spaces, extracting frequency data of the drift from one state level to another within the past 1800 days of the evaluation grid. Specifically, the system establishes three incrementally increasing physical boundary nodes based on the measured soil hydrogen ion activity values, sequentially dividing the ion activity interval from low to high into four physical attribute domains: robust phase, sub-healthy phase, compensated phase, and decompensated phase. Based on the numerical interval into which the real-time hydrogen ion activity data uploaded at discrete time nodes falls, the system determines and marks the discrete level space where the current effectiveness is located. The risk prediction module normalizes the frequency data and calculates and generates the initial state transition probability matrix.

[0036] In obtaining the intrinsic loss factor Then, the risk prediction module calculates the intrinsic loss factor. The deviation rate from the preset intrinsic loss mean is calculated based on the probability perturbation principle of stochastic process theory, transforming the attenuation characteristics of the continuous system into perturbation components of the discrete state transition matrix. The operational risk prediction module applies the formula... Update the transition probabilities, where the variable After the representation is updated, the system is in state. Transfer to a worse state Probability values, whose values ​​are mathematically constrained and range from 0 to 1; variables The reference element for the corresponding row and column coordinates in the initial state transition probability matrix; a constant. A preset probability perturbation adjustment factor is used to limit the convergence step size of a single update; variables To obtain the average intrinsic loss coefficient of the target regulatory grid over the past twelve consecutive sampling periods, Post-risk prediction module calculates status For the remaining transition probability residual space within the corresponding row, the normalized proportional allocation algorithm is applied to proportionally reduce the values ​​of the remaining elements, maintaining the total probability of a single row at 1. The risk prediction module uses this deviation rate as a non-linear penalty term, superimposed on the transition probability elements pointing to the deteriorating state in the initial state transition probability matrix. Based on the updated Markov state transition probability matrix, the risk prediction module calculates the evolution distribution of the current state vector step by step. The aforementioned calibration and evolution steps construct the internal architecture and quantified data traceability path of the system state variable prediction model. The acquisition mechanism of environmental assessment parameters is freed from the dependence on empirical thresholds, so that the prediction points of the Markov state drift trajectory correspond to the specific geological endowment and disturbance history of each assessment grid. The quantitative processing of the system's underlying logic reduces the extrapolation error of the performance decay prediction data chain from the data source, transforming the prediction behavior into quantifiable and traceable engineering calculation steps, and outputting stable risk time margin data.

[0037] Example 4: In a new monitoring grid scenario where the soil acidification remediation effectiveness quantitative assessment decision system is deployed across regions to a new grid with unknown base buffering characteristics, the predicted value of the state drift trajectory caused by the static foundation preset safety threshold deviates from the physical failure node. Before the system is officially put into operation, a pre-deployment calibration procedure is initiated. The data acquisition module selects test plots in the target deployment area, injects acidic tracer reagents of varying concentrations into the test plots, and extracts the critical time series of the unstable mutation of hydrogen ion activity characterization in the test plots. The injection of high-concentration reagents in a localized area causes surface chemical instability response with hourly characteristics, while the actual physical and chemical buffering evolution of the entire plot exhibits long-period characteristics. The intrinsic analysis module reads the measured soil porosity steady-state water conductivity in the assessment grid and applies Darcy's law solute porosity diffusion kinetics model to extract spatial scale. Specifically, in this model, the intrinsic diffusion of ions at the surface scale follows Fick's law. This surface diffusion is amplified by mechanical dispersion within the overall porous media framework. The system uses Darcy velocity as the boundary input condition to calculate the overall hydrodynamic dispersion coefficient within the pore channels. The ratio of this overall dispersion coefficient to the static ion molecular diffusion coefficient is then established as the required spatial scale conversion coefficient. Specifically, the intrinsic analysis module extracts the critical time series and multiplies it by the spatial scale conversion coefficient. It then physically calculates the hysteresis compensation required for ions to reach diffusion equilibrium in the overall media volume, smoothing out the order-of-magnitude difference between local rapid response and large-scale slow degradation in the spatiotemporal dimension, generating a reference time quantity reflecting the actual degradation cycle under real-world conditions. The intrinsic analysis module calculates the resilience buffer time constant of this critical time series according to the formula... Determine the preset safety threshold, where, To preset a safety threshold, As the time constant for the anti-reverse buffer, To establish a geological confidence factor based on the proportion of clay particles in the soil of the target deployment area, the risk prediction module imports a preset safety threshold into the underlying configuration library, replacing the system's default judgment boundary.

[0038] The decision-making and scheduling module initiates the calibration and data filling procedures for emergency factors in offline mode. This module reads the total governance resource consumption of the target deployment area for five consecutive fiscal years and the corresponding response lag time set for environmental restoration cycles. It constructs a resource dissipation penalty function with the response lag time set as the independent variable and the total governance resource consumption as the dependent variable. During function construction, the system uses a second-order polynomial fitting algorithm to perform continuous regression processing on the extracted historical scatter plot, thereby generating a smooth function model with a continuous mathematical analytical expression. The decision-making and scheduling module extracts the extreme points that cross the resource mismatch boundary on the derivative curve of the resource dissipation penalty function, determines the corresponding slope compensation amount as the emergency factor, and writes the emergency factor into the local policy control library. This writing action limits the calculation benchmark of the intervention priority weight to the physical decay law and financial resource distribution constraints of the target deployment area. The system's underlying logic link receives time margin data and updates the governance resource reset scheduling matrix according to the emergency factors in the local policy control library. To ensure the physical comparability of the system state variable sequences collected at each discrete time node, the data acquisition module initiates a sampling environment consistency assessment program within a preset sampling window. This module reads the real-time humidity data from temperature and humidity sensors deployed within the assessment grid, calculates the relative deviation between the current sampling environment humidity and the historical average humidity for the same period, and if it is not less than 15%, the sampling action is suspended until the real-time humidity data returns to within the preset fluctuation threshold. At the same time, the intrinsic analysis module acquires the topographic slope data of the assessment grid, multiplies the cumulative precipitation by the sine function value of the topographic slope to generate a surface runoff loss scalar, and uses the surface runoff loss scalar to perform diffusion intensity correction on the acid neutralization capacity load equivalent, thereby compensating for the material spatial migration loss caused by precipitation scouring at the data processing level.

[0039] Example 5: In the offline calibration scenario of a soil acidification remediation prediction and resource scheduling system covering multiple levels of administrative grids, the system faces the task of converting time-domain risk indicators into resource allocation weights. The data acquisition module reads the failure sample set within a preset environmental remediation cycle, extracts the interval span from the triggering of the risk warning to the occurrence of physical degradation for each sample in the failure sample set, and arranges the interval span in descending order to form a baseline time series. The decision scheduling module applies a normalized exponential decay function to process the baseline time series. This calculation logic is based on the formula... Established, among which, For intervention priority weighting, As a time margin, To determine the attenuation control coefficient based on the failure sample set, the decision scheduling module calculates the root mean square error by traversing the failure sample set using the least squares method. When the error reaches its minimum value, the value of the attenuation control coefficient is locked. This algorithm constructs a monotonically decreasing mapping relationship with exponential response characteristics. The intervention priority weight exhibits a non-linear increasing state when the time margin approaches the preset management warning time limit. In specific calculations, the system extracts the actual consumption ratio of the total amount of fiscal funds received by the corresponding historical grid in the failure sample set before the actual physical degradation occurred to the total regional budget during the same period as the benchmark verification label. The root mean square is obtained by subtracting the objective intervention priority weight value calculated by the current mapping formula from this benchmark verification label. The subjectively planned attenuation weight is forcibly fitted and aligned to the objectively occurring administrative resource dissipation, ensuring that the locking of the control coefficient has a rigorous data basis.

[0040] Regarding the conversion rules for artificial intervention frequency to acid neutralization capacity load equivalent, the data acquisition module reads an offline database containing records of all alkaline material applications, extracts the average effective alkalinity dosage of a single standard construction operation as a benchmark conversion factor, multiplies the collected artificial intervention frequency by this benchmark conversion factor to generate the initial scalar value of acid neutralization capacity load equivalent, and simultaneously extracts the cumulative precipitation within the corresponding time window, calculates the ratio of this cumulative precipitation to the preset soil saturated water holding capacity to generate a water leaching infiltration factor, and divides the initial scalar value of acid neutralization capacity load equivalent by this water leaching infiltration factor to calculate the external disturbance intensity parameter. The aforementioned offline calibration process transforms the mapping relationship in the computational logic into a quantitative calculation program with a data traceability path, and the system state variable prediction model and decision scheduling module are associated based on the above algorithm rules. The system uses physical intervention statistics and time-series dissipation data to output an administrative decision priority sequence with nonlinear risk warning characteristics. In the terminal execution stage of generating scheduling instructions, the decision scheduling module introduces a closed-loop verification procedure for the allocation of governance resources to the scheduling matrix. This module reads the total budget quota of the current governance cycle of the administrative management system, calculates the target fiscal allocation vector based on the weight ratio of each regulatory grid in the administrative decision priority sequence, and injects the target fiscal allocation vector into the budget column vector of the governance resource reset scheduling matrix. At the same time, it extracts the existing material storage volume of the warehouse corresponding to the evaluation grid, and updates the material allocation parameters in the governance resource reset scheduling matrix using the difference algorithm between the target allocation volume and the existing storage volume. This procedure converts the administrative ranking results into terminal execution instructions that define the specific fiscal allocation amount and material delivery tonnage of each regulatory unit.

[0041] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for quantitatively evaluating and making decisions on the effectiveness of soil acidification remediation, characterized in that, Includes the following steps: Step S101: Obtain the system state variable sequence of the environmental governance project at multiple discrete time nodes, as well as the external disturbance intensity parameter corresponding to each discrete time node; the system state variable sequence includes hydrogen ion activity characterization quantity, organic matter loading and exchangeable cation capacity; the external disturbance intensity parameter is generated based on the natural environment input data and artificial intervention load data within the preset time window, through equivalent conversion rules. Step S102: Calculate the transition rate of the system state variable sequence between each adjacent time node, use the system steady-state weighting factor to correct the transition rate, offset the non-intrinsic numerical fluctuations caused by the external disturbance intensity parameter, and extract the intrinsic loss coefficient that reflects the degradation state of the anti-resistance buffer system of the governance project object. Step S103: Based on the intrinsic loss coefficient, deduce the performance degradation prediction data chain of the environmental governance project on the time axis, perform collision matching between the performance degradation prediction data chain and the preset safety threshold, calculate the prediction time point when the performance degradation prediction data chain touches the preset safety threshold, and extract the time margin characterizing the failure risk of governance effectiveness. Step S104: Establish a monotonically decreasing mapping relationship between time margin and intervention priority weight. Update the resource allocation sequence in the governance resource reset scheduling matrix according to the monotonically decreasing mapping relationship. The resource allocation sequence defines the priority of fiscal appropriations and the proportion of engineering material allocation for each regulatory unit within the preset governance cycle. Generate scheduling instructions for the total budget and governance force deployment in the administrative management system.

2. The method for quantitatively evaluating and making decisions on the effectiveness of soil acidification remediation according to claim 1, characterized in that, The logic for generating the external disturbance intensity parameter in step S101 is as follows: Step S1011: Collect the cumulative precipitation and frequency of human intervention for the environmental governance project, and map the frequency of human intervention to the acid neutralization capacity load equivalent according to the preset intensity level; Step S1012: Correct the diffusion intensity of the acid neutralization capacity load equivalent according to the cumulative precipitation, and generate a scalar characterizing the total intensity of external intervention as the external disturbance intensity parameter.

3. The method for quantitatively evaluating and making decisions on the effectiveness of soil acidification remediation according to claim 1, characterized in that, In step S101, the sampling period for obtaining the system state variable sequence is not less than 90 days, and the sampling environmental humidity deviation at each discrete time node is less than 15%.

4. The method for quantitative evaluation and decision-making on the effectiveness of soil acidification remediation according to claim 1, characterized in that, The logic for correcting the transition rate using the system steady-state weight factor in step S102 is as follows: Step S1021, set the benchmark value of the system steady-state weight factor according to the initial physical texture ratio of the environmental governance project; Step S1022, divide the transition rate by the product of the benchmark value of the system steady-state weight factor and the external disturbance intensity parameter to complete the linear decoupling and noise reduction of the transition rate.

5. The method for quantitative evaluation and decision-making on the effectiveness of soil acidification remediation according to claim 1, characterized in that, The method for calculating the predicted time point in step S103 is as follows: Step S1031, apply the Markov state transition probability matrix to simulate the state drift trajectory of the environmental governance project under the action of the intrinsic loss coefficient; Step S1032, when the predicted value of the state drift trajectory coincides with the preset safety threshold, extract the corresponding time abscissa as the predicted time point.

6. The method for quantitative evaluation and decision-making of soil acidification remediation effectiveness according to claim 1, characterized in that, The criteria for establishing the mapping relationship between time margin and intervention priority weight in step S104 are as follows: the shorter the time margin, the higher the corresponding intervention priority weight; when the time margin is lower than the preset management warning time limit, the response slope of the intervention priority weight is increased by the preset emergency factor.

7. The method for quantitative evaluation and decision-making on the effectiveness of soil acidification remediation according to claim 1, characterized in that, The process of updating the governance resource reset scheduling matrix in step S104 includes: step S1041, arranging the intervention priority weights of multiple evaluation grids in descending order to generate an administrative decision priority sequence; step S1042, allocating governance resource shares sequentially from the top of the administrative decision priority sequence downwards according to the total budget in the administrative management system.

8. The method for quantitative evaluation and decision-making on the effectiveness of soil acidification remediation according to claim 1, characterized in that, It also includes the following steps: Step S105: Monitor the feedback value of the system state variable after the dispatch command is issued. If the recovery rate of the feedback value is lower than the preset benchmark in two consecutive sampling periods, then re-trigger steps S102 to S104 to correct the weight allocation factor in the dispatch command. The environmental governance project is divided into multiple regulatory units with independent administrative numbers. Each regulatory unit independently applies steps S101 to S104 to determine the asymmetric fine-grained dispatch scheme of governance resources in the management system.

9. A soil acidification remediation effectiveness quantitative assessment and decision-making system, used to implement the soil acidification remediation effectiveness quantitative assessment and decision-making method described in claim 1, characterized in that, include: The data acquisition module is used to acquire the system state variable sequence of the environmental governance project at multiple discrete time points, as well as the external disturbance intensity parameter corresponding to each discrete time point; The intrinsic analysis module is used to calculate the rate of change of the system state variable sequence between adjacent time nodes, and to apply the system steady-state weighting factor to correct the rate of change in order to extract the intrinsic loss coefficient that reflects the degradation state of the anti-resistance buffer system of the governance project object. The risk prediction module is used to extrapolate the effectiveness decay prediction data chain of environmental governance projects on the time axis based on the intrinsic loss coefficient. By matching the effectiveness decay prediction data chain with the preset safety threshold, the time margin characterizing the failure risk of governance effectiveness is extracted. The decision-making and scheduling module is used to establish a monotonically decreasing mapping relationship between time margin and intervention priority weight, and to update the resource allocation sequence in the governance resource reset scheduling matrix according to the monotonically decreasing mapping relationship, thereby generating scheduling instructions for the total budget and governance force deployment in the administrative management system.

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