Ecological Pollution Migration Path Analysis and Early Warning Method and System Based on Soil Heavy Metals

Abstract

The present invention discloses an ecological pollution migration path analysis and early warning method and system based on soil heavy metals, specifically relating to the field of ecological pollution technology; by arranging monitoring points around the pollution source, combining micro-CT scanning to analyze the permeability changes of soil under different humidities, and using synchrotron radiation X-ray spectrometers to analyze the dynamic changes of heavy metal chemical forms, comprehensively evaluate the influence of humidity changes on migration paths and morphological transformations, based on this, quantify the deviation degree between the predicted results of the model and the actual migration behavior, generate early warning signals at different levels for high deviation degrees and take corresponding measures, dynamically respond to the complex influence of abnormal soil characteristics on heavy metal migration, so as to effectively prevent and control the harm of soil pollution diffusion to the environment and ecology.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological pollution, and particularly to a method and system for analyzing and warning the migration path of ecological pollution based on heavy metals in soil. Background Art

[0002] The analysis of the migration path of heavy metals in soil for ecological pollution refers to revealing the impact path on the ecological environment by studying the migration and distribution laws of heavy metal pollutants in the soil. This process usually involves multi-disciplinary intersections, including geochemistry, environmental science, and ecology. The focus of the analysis is on how pollutants migrate through various links of the ecosystem such as soil - plant - water body, affecting the food chain and the health of the regional environment. For example, heavy metals may enter other environmental media through rain leaching, groundwater seepage, or wind erosion, further affecting crop growth, water quality, and soil microbial communities.

[0003] Analysis and warning of the migration path is, on the basis of identifying these migration paths, using prediction models or monitoring data to predict the possible diffusion trend of pollution and proposing warning measures. By real-time monitoring of key pollutant indicators such as heavy metal concentration, soil pH value, and precipitation, a dynamic prediction system is established to take prevention and control measures before the pollution spreads. For example, if it is detected that the heavy metal concentration in the soil of a specific area increases rapidly, it may indicate that the pollution will spread to the surrounding area through groundwater or rain runoff, and the warning system can prompt relevant departments to intervene in time to avoid further aggravation of the pollution. This kind of analysis and warning is crucial in pollution prevention and control, helping to reduce ecological damage, protect the environment, and public health.

[0004] The existing technologies have the following deficiencies:

[0005] In some areas, the physical and chemical properties of the soil are abnormal (such as strong expansibility or high organic matter content), which may significantly affect the adsorption, desorption, and migration behaviors of heavy metals. These special soil properties often do not conform to the standard assumptions of the prediction model, resulting in deviation or distortion of the prediction results. For example, high-organic-matter soil may temporarily "lock" heavy metals, delaying their diffusion, but suddenly release them when environmental conditions change (such as increased humidity), causing pollutants to rapidly migrate to key areas such as drinking water sources, and the warning system fails to identify this high-risk situation in advance. At the same time, due to the prediction model underestimating the accelerating effect of abnormal soil properties on the diffusion of heavy metals, potential high-risk areas may not be identified. For example, in a certain area, a large amount of locked heavy metals are released during the wet-dry alternation process of high-organic-matter soil, and the pollution rapidly penetrates into the groundwater or flows into the irrigation system, but the warning system fails to capture this dynamic change, resulting in out-of-control pollution diffusion, seriously threatening the downstream water resources and farmland ecology. Summary of the Invention

[0006] The object of the present invention is to provide a method and system for analyzing and warning the ecological pollution migration path based on soil heavy metals to solve the deficiencies in the background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: A method for analyzing and warning the ecological pollution migration path based on soil heavy metals, comprising the following steps:

[0008] S1: Set a number of monitoring points around the pollution source in different directions and distances, and set multiple depth monitoring areas along the soil profile at each monitoring point;

[0009] S2: For each depth monitoring area, use a micro-CT to regularly scan the soil column under in-situ conditions, measure the permeability coefficient of the soil column at different humidities, and evaluate the influence of humidity change on the water flow and heavy metal migration path;

[0010] S3: When the influence of humidity change on the water flow and heavy metal migration path is serious, use a portable synchrotron radiation X-ray absorption spectrometer to perform high-resolution scanning on the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms;

[0011] S4: According to the influence of humidity change on the water flow and heavy metal migration path and the abnormal change trends of different heavy metal chemical forms, evaluate the deviation degree between the prediction result of the prediction model and the actual migration behavior;

[0012] S5: If the deviation degree is high, further analyze the deviation degree between the model prediction result and the actual migration behavior within a fixed time period, generate warning signals of different levels according to the analysis result, and make corresponding treatments.

[0013] Preferably, in S2, after analyzing the influence degree of the pore connectivity on the migration in the path of heavy metal migration from the surface layer to the deep layer, a pore connectivity migration interference index is generated. The acquisition method of the pore connectivity migration interference index is:

[0014] Model the soil pore network as a weighted graph G(V,E), where: V is the node set, representing pores, E is the edge set, representing the connection channels between pores, and the weight w of each edge e ij represents the migration resistance of heavy metals passing through the channel i→j; use a micro-CT scan to obtain the three-dimensional pore structure of the soil, extract the geometric parameters of the pore network nodes and edges, construct the weighted graph G(V,E), starting node s: the initial position of heavy metal migration, ending node t, the target position of migration; use the shortest path algorithm to calculate the shortest path from the starting point s to the ending point t, and the shortest path weight W ij : W min : W min = min∑( i,j)∈P wij ; P is the set of all paths. Initialize the distances d(s, v) from the starting point s to each node as d(s, v) = ∞, and d(s, s) = 0 for the starting point. Traverse the nodes, select the node u with the shortest distance among the unvisited nodes, and update the distances of the neighbor nodes v of u: d(s, v) = min(d(s, v), d(s, u) + w uv ); until d(s, t) = W is calculated min , the pore connectivity migration interference index quantifies the degree of obstruction of the actual pore connectivity to the heavy metal migration path: In the formula, W ideal is the shortest path weight in the ideal state.

[0015] Preferably, in S3, after analyzing the proportion of heavy metal chemical forms before and after the humidity change, a heavy metal proportion anomaly index is generated. The method for obtaining the heavy metal proportion anomaly index is as follows:

[0016] Obtain the time series data during the humidity change, including the proportion of each heavy metal chemical form, where: t is the time point, k is the chemical form category, is the proportion of chemical form k at time t. Select the sliding window size w, and the window slides on the time series, moving one unit time Δt each time. For the proportion of each chemical form k Calculate the change rate within the sliding window [t, t + w - 1] The relative change rate of chemical form k within the window [t, t + w - 1], is the proportion of chemical form k at the end point t + w - 1 of the window; define the anomaly threshold τ: when , it is marked as an anomaly, and calculate the cumulative anomaly weight W k : is the indicator function, which takes the value of 1 when , and 0 otherwise; normalize the cumulative anomaly weights of all chemical forms, and calculate the heavy metal proportion anomaly index AS. The expression is: In the formula, K is the total number of chemical form categories, and max(W k ) is the maximum cumulative anomaly weight among all chemical forms k, which is used for normalization.

[0017] Preferably, in S4, according to the influence of humidity change on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the deviation degree between the prediction result of the prediction model and the actual migration behavior. Specifically:

[0018] The pore connectivity migration interference index and the heavy metal proportion anomaly index were normalized, and the deviation degree between the prediction model prediction results and the actual migration behavior was calculated by the normalized pore connectivity migration interference index and the heavy metal proportion anomaly index.

[0019] Preferably, the obtained deviation value between the prediction result of the prediction model and the actual migration behavior is compared with a reference threshold of the deviation value under normal conditions preset according to historical data. If the deviation value between the prediction result of the prediction model and the actual migration behavior is greater than or equal to the reference threshold of the deviation value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is high, and an abnormal signal is generated at this time; if the deviation value between the prediction result of the prediction model and the actual migration behavior is less than the reference threshold of the deviation value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is low, and a normal signal is generated at this time.

[0020] Preferably, in S5, if the degree of deviation is high, further analysis is performed on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed, specifically:

[0021] If the degree of deviation is high, that is, the deviation value between the prediction results of the prediction model generated within a fixed time period and the actual migration behavior is greater than or equal to the deviation value reference threshold, the deviation values ​​between the prediction results of the prediction model generated within a subsequent fixed time period that are greater than or equal to the deviation value reference threshold and the actual migration behavior are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, different levels of warning signals are generated according to the analysis results, and corresponding processing is made.

[0022] Preferably, if the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, a first-level warning signal is generated, indicating that the high deviation is persistent and stable, and emergency response measures need to be taken quickly;

[0023] If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a secondary warning signal is generated, indicating that the degree of high deviation fluctuates greatly and requires key monitoring and model adjustment;

[0024] If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a third-level warning signal is generated, indicating that the overall deviation is low but the fluctuation is large, and the model is optimized;

[0025] If the mean value of the anomaly coefficient is less than the reference threshold of the mean value of the anomaly coefficient, and the standard deviation of the anomaly coefficient is less than the reference threshold of the standard deviation of the anomaly coefficient, no warning signal is generated at this time, indicating a low and stable deviation degree, and routine monitoring continues.

[0026] The present invention also provides an early warning system for analyzing the ecological pollution migration path based on soil heavy metals, including a monitoring point layout module, a penetration evaluation module, a chemical form analysis module, a deviation evaluation module, and an early warning response module:

[0027] Monitoring point layout module: Set a number of monitoring points around the pollution source in different directions and distances, and set multiple depth monitoring areas along the soil profile at each monitoring point;

[0028] Penetration evaluation module: For each depth monitoring area, use a micro-CT to regularly scan the soil column under in-situ conditions, measure the permeability coefficient of the soil column at different humidities, and evaluate the influence of humidity change on the water flow and the heavy metal migration path;

[0029] Chemical form analysis module: When the influence of humidity change on the water flow and the heavy metal migration path is serious, use a portable synchrotron radiation X-ray absorption spectrometer to perform high-resolution scanning on the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trend of different heavy metal chemical forms;

[0030] Deviation evaluation module: Evaluate the deviation degree between the prediction result of the prediction model and the actual migration behavior according to the influence of humidity change on the water flow and the heavy metal migration path and the abnormal change trend of different heavy metal chemical forms;

[0031] Early warning response module: If the deviation degree is high, further analyze the deviation degree between the model prediction result and the actual migration behavior within a fixed time period, generate warning signals of different levels according to the analysis result, and make corresponding treatments.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention:

[0033] 1. By constructing a dynamic analysis and early warning system based on the ecological pollution migration path of soil heavy metals, the present invention solves the problem of prediction deviation or distortion caused by abnormal soil characteristics in the prior art. This method comprehensively captures the dynamic influence of humidity change on the migration path and morphological transformation by setting monitoring points, using micro-CT to scan and analyze the permeability change, and synchrotron radiation X-ray spectrometer to analyze the chemical forms of heavy metals, and combines the pore connectivity migration interference index and the heavy metal proportion anomaly index to accurately evaluate the deviation degree between the model prediction and the actual migration behavior, thereby greatly improving the recognition ability of potential high-risk areas and the adaptability of the prediction model.

[0034] 2. The present invention can generate multi - level warning signals according to the deviation degree, and take measures such as emergency response, key monitoring or model optimization respectively to ensure the precision and classification of response measures. At the same time, the areas with low deviation degree are continuously monitored routinely to reduce the system burden. The overall method has high flexibility and practicability, not only improving the prediction accuracy of soil heavy metal migration, but also providing scientific support for drinking water source protection, farmland ecological security and regional environmental governance, effectively reducing the ecological environment risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0036] Figure 1 It is the flowchart of the method of the present invention.

[0037] Figure 2 It is the system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] Example 1. Please refer to Figure 1 and Figure 2 As shown, the ecological pollution migration path analysis and warning method based on soil heavy metals in this embodiment includes the following steps:

[0040] S1: Set a number of monitoring points around the pollution source in different directions and distances, and set multiple depth monitoring areas along the soil profile at each monitoring point;

[0041] S2: For each depth monitoring area, use a micro - CT to regularly scan the soil column in - situ, measure the permeability coefficient of the soil column at different humidities, and evaluate the influence of humidity change on the water flow and heavy metal migration path;

[0042] S3: When the impact of humidity changes on water flow and heavy metal migration paths is severe, use a portable synchrotron radiation X-ray absorption spectrometer to perform high-resolution scanning on the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms;

[0043] S4: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the deviation degree between the prediction results of the prediction model and the actual migration behavior;

[0044] S5: If the deviation degree is high, further analyze the deviation degree between the model prediction results and the actual migration behavior within a fixed time period, generate warning signals at different levels according to the analysis results, and make corresponding treatments.

[0045] In S1, set several monitoring points around the pollution source in different directions and distances, and set multiple depth monitoring areas along the soil profile at each monitoring point. Specifically:

[0046] Centering on the pollution source, set radial monitoring points in different directions (such as east, south, west, north, or according to topographic features). According to the estimated diffusion range and migration rate, select multiple groups of distances with different radii for monitoring point layout (such as 50 meters, 100 meters, 500 meters). Focus on strengthening the monitoring point density in high-risk areas such as the downwind direction of the pollution source, the direction of groundwater flow, and low-lying areas. If the pollution diffusion direction is unknown, the points can be initially arranged in a uniform manner and the distribution can be optimized according to the results later. Combine the pollution source type and the surrounding environmental zoning (such as industrial area, agricultural area, residential area) to set monitoring points with different densities in different areas.

[0047] The selection of profile depths includes: shallow layer (0 - 20 cm): Monitor the heavy metal concentration in the surface soil and pay attention to the risks of surface runoff and biological contact. Middle layer (20 - 50 cm): Monitor the dynamic penetration and adsorption behavior of heavy metals. Deep layer (> 50 cm): Monitor the diffusion of heavy metals in the groundwater recharge layer.

[0048] Set multiple monitoring units along the profile at each monitoring point (such as setting a monitoring unit every 10 cm) to obtain complete soil profile information. Humidity and temperature sensors: Buried in soil units at different depths to record the soil environmental changes in real time. Heavy metal monitoring sensors: Such as ion-selective electrodes or micro-CT sensors, used to dynamically monitor the heavy metal migration behavior. Install micro-pore water samplers in the deep monitoring area to regularly extract soil pore water samples and analyze the dynamic changes of dissolved heavy metals. Set sampling channels in each monitoring unit to facilitate obtaining soil samples or using instruments for on-site analysis (such as portable synchrotron radiation X-ray instruments).

[0049] Analyze the heavy metal concentration distribution and migration direction based on the preliminary monitoring results, appropriately increase the monitoring points in the high-concentration areas, and reduce the points in the low-risk areas. During long-term dynamic monitoring, optimize the monitoring points and profiles in combination with environmental changes (such as seasonal precipitation) to enhance the representativeness and timeliness of the data.

[0050] S2: For each depth monitoring area, use micro-CT to regularly scan the soil column in-situ, measure the permeability coefficient of the soil column at different humidities, and evaluate the impact of humidity changes on the water flow and heavy metal migration path.

[0051] Select typical in-situ soil columns in each depth unit of the monitoring area. The diameter and height of the soil column are based on the technical requirements of the monitoring equipment (usually 5-10 cm in diameter and the height not exceeding 20 cm). Obtain a complete soil column through undisturbed sampling technology to keep its pore structure and physical properties undamaged. Fix the soil column in the scanning chamber of the micro-CT equipment, ensure that there are no cracks and external damages on the surface of the soil column, and at the same time mark the direction of the soil column (such as up and down, east, south, west, and north).

[0052] Set a high resolution (such as 10 μm or higher) according to the target pore size to clearly capture the pore structure changes. Scan the entire soil column, covering from the surface layer to the deep layer. Determine the scanning period according to the humidity change rate (such as scanning once every 4 hours in the initial stage and then it can be extended to once a day).

[0053] Conduct a baseline scan of the soil column in the dry state to obtain the initial pore structure image. Gradually increase the environmental humidity (such as from 10% to 90%), repeat the scan under different humidity conditions, and record the dynamic changes of the soil pores.

[0054] Reconstruct the three-dimensional pore structure of the soil column through micro-CT scan data. Calculate the porosity (pore volume / total volume), analyze the pore connectivity (the interconnection status between pores), and evaluate the changes in the crack width and depth.

[0055] Calculate the permeability coefficient (k) using Darcy's law combined with the pore structure data. The expression is: Q is the amount of water flowing through per unit time, L is the height of the soil column, A is the cross-sectional area, and ΔP is the head difference. Compare the permeability coefficients under different humidity conditions and analyze the dynamic impact of humidity changes on soil permeability.

[0056] After analyzing the influence degree of pore connectivity on the migration when heavy metals migrate from the surface layer to the deep layer, generate a pore connectivity migration interference index. The method for obtaining the pore connectivity migration interference index is:

[0057] The soil pore network is modeled as a weighted graph G(V, E), where: V is the set of nodes representing pores, and E is the set of edges representing the connected channels between pores. For each edge e ij the weight w ij represents the migration resistance of heavy metals through the channel i→j;

[0058] The weight can be determined by pore geometry and soil properties: The channel length between pores iii and jjj, k ij is the permeability coefficient of the channel (related to pore size, connectivity, etc.), A ij is the cross-sectional area of the channel.

[0059] The three-dimensional pore structure of the soil is obtained using micro-CT scanning, and the geometric parameters of the nodes and edges of the pore network are extracted to construct the weighted graph G(V, E). The starting node s: the initial position of heavy metal migration (surface pores), and the ending node t: the target position of migration (deep pores). The shortest path algorithm (such as Dijkstra's algorithm) is used to calculate the shortest path from the starting point s to the ending point t. The shortest path weight W min : W min = min∑ i,j)∈P w ij ; P is the set of all possible paths. The distance d(s, v) from the starting point s to each node is initialized to ∞, and d(s, s) = 0 for the starting point. The nodes are traversed, and the node u with the shortest distance among the unvisited nodes is selected to update the distance of its neighbor node v: d(s, v) = min(d(s, v), d(s, u)+w uv ); until d(s, t) = W min is calculated. The pore connectivity migration interference index quantifies the degree of obstruction of the actual pore connectivity to the heavy metal migration path: where W ideal is the shortest path weight in the ideal state (i.e., the theoretical minimum weight when there is no obstruction to connectivity).

[0060] When the pore connectivity migration interference index is larger, it indicates that the connectivity of the soil pores is poor and the degree of obstruction of the migration path is higher. In this case, the influence of humidity changes on water flow and heavy metal migration paths may be more significant. For example, when the humidity increases, due to the poor pore connectivity, the water flow may concentrate and flow along the fissures or deviate, resulting in a more irregular heavy metal migration path or even sudden migration phenomena. This non-uniform migration may exacerbate the pollution diffusion in high-risk areas and is difficult to accurately predict through conventional models.

[0061] Conversely, when the pore connectivity migration interference index is smaller, it indicates that the connectivity of soil pores is better, the migration path is smoother and more regular. In this case, the influence of humidity change on the water flow and heavy metal migration path is relatively gentle, and the migration behavior tends to be linear or diffusive. This shows that the water flow and heavy metal migration are more likely to gradually penetrate to the deep layer through the uniformly distributed pore network, and the pollution diffusion range and rate are easier to be predicted and controlled, reducing the risk of sudden pollution events.

[0062] S3: When the influence of humidity change on the water flow and heavy metal migration path is severe, use a portable synchrotron radiation X-ray absorption spectrometer to perform high-resolution scanning on the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms.

[0063] Based on the dynamic monitoring results of the influence of humidity change on the permeability coefficient, pore connectivity and migration path, when the following conditions occur, it is considered that the influence is severe: the pore connectivity migration interference index (PCMII) increases significantly, indicating that humidity causes a significant change in the migration path. The migration depth or diffusion range of heavy metals shows an abnormal increase or deviation.

[0064] For the high-risk areas with significant changes in the migration path among the monitoring points, preferentially select representative soil columns or profiles for morphological analysis. Collect samples from the soil profiles significantly affected by humidity change, covering the surface layer (0 - 20 cm), middle layer (20 - 50 cm), and deep layer (> 50 cm). When collecting samples, keep the soil moist and intact to avoid the transformation of heavy metal forms due to environmental changes. Put the collected soil samples into the special sample chamber of the portable synchrotron radiation XAS instrument to ensure uniform distribution to improve the accuracy of spectral data.

[0065] Light source wavelength range: Select the absorption edge of heavy metal elements (such as the L edge of Pb, the K edge of As). Set it to the high-resolution mode (such as 0.1 - 0.2 eV) to accurately separate the characteristic peaks of different chemical states. Perform multi-point scanning on the soil samples to obtain high-resolution spectra of the heavy metal chemical forms at different positions. Record the X-ray absorption spectra of the samples, focusing on the edge absorption region (XANES) and fine structure (EXAFS).

[0066] Using XANES data, identify the main chemical forms of different heavy metals: Soluble state: free ions or complexes (easily migratory). Oxidized state: oxides or hydroxides bound to the mineral surface (less migratory). Organically bound state: bound to organic matter or humus (migratory related to environmental conditions). Residual state: strongly bound in the mineral lattice (extremely low migratory). Based on the absorption spectrum database of standard samples, use spectral fitting algorithms (such as linear combination fitting method, LCF) to calculate the proportion of different chemical forms. Correlate the analysis results with the soil profile depth to generate the distribution map of heavy metal chemical forms at different depths and positions.

[0067] After analyzing the proportion of heavy metal chemical forms before and after the humidity change, generate the heavy metal proportion anomaly index. The method for obtaining the heavy metal proportion anomaly index is as follows:

[0068] Obtain the time series data during the humidity change, including the proportion of each heavy metal chemical form where: t is the time point (or the number of humidity conditions, such as different humidity gradients), k is the chemical form category (such as soluble state, oxidized state, organically bound state, residual state), is the proportion of chemical form k at time t. Select the sliding window size w (for example, w = 3 means every three time points as a window). The window slides on the time series, moving one unit time Δt each time, for the proportion of each chemical form k Calculate the change rate within the sliding window [t, t + w - 1] The relative change rate (percentage) of chemical form k within the window [t, t + w - 1], is the proportion of chemical form k at the window end point t + w - 1. Define the anomaly threshold τ: when it is marked as an anomaly. Calculate the cumulative anomaly weight W of chemical form k k : is the indicator function, which takes the value of 1 when and 0 otherwise. Normalize the cumulative anomaly weights of all chemical forms to calculate the heavy metal proportion anomaly index AS, and the expression is: In the formula, K is the total number of chemical form categories, and max(W k ) is the maximum cumulative anomaly weight among all chemical forms k, which is used for normalization.

[0069] When the abnormal index of the heavy metal proportion is larger, it indicates that significant abnormal changes have occurred in the proportions of different heavy metal chemical forms. This trend shows that the influence of humidity change on certain forms is stronger. For example, the proportion of the soluble state increases significantly, the oxidized state decreases rapidly, or the organically bound state is suddenly released, which may lead to easier migration or release of heavy metals into the environment. Such drastic changes usually indicate the existence of potential unstable chemical conditions in the soil, such as changes in redox potential or fluctuations in pH caused by increased humidity, which may greatly increase the ecological risk of heavy metals.

[0070] On the contrary, when the abnormal index of the heavy metal proportion is smaller, it indicates that the changes in the proportions of different heavy metal chemical forms tend to be stable. This trend shows that the influence of humidity change on chemical forms is relatively limited, the conversion rate between forms is low, and the morphological distribution of heavy metals in the soil remains in a relatively balanced state. At this time, the migration risk is low, the environmental release ability of heavy metals is weak, and the soil has good self-stabilization ability.

[0071] S4: According to the influence of humidity change on the water flow and heavy metal migration path and the abnormal change trend of different heavy metal chemical forms, evaluate the deviation degree between the prediction result of the prediction model and the actual migration behavior.

[0072] Normalize the pore connectivity migration interference index and the abnormal index of heavy metal proportion, and calculate the deviation degree value between the prediction result of the prediction model and the actual migration behavior through the normalized pore connectivity migration interference index and the abnormal index of heavy metal proportion.

[0073] For example, the present invention can use the following formula to calculate the deviation degree value between the prediction result of the prediction model and the actual migration behavior. The calculation expression is: LK = ln(a 1 QS + a 2 AS + 1); where LK is the deviation degree value between the prediction result of the prediction model and the actual migration behavior, QS is the pore connectivity migration interference index, AS is the abnormal index of heavy metal proportion, a 1 、a 2 are the proportionality coefficients of the pore connectivity migration interference index and the abnormal index of heavy metal proportion, and a 2 > a 1 > 0.

[0074] Compare the deviation degree value between the prediction result of the obtained prediction model and the actual migration behavior with the deviation degree value reference threshold under the normal state preset according to historical data. If the deviation degree value between the prediction result of the prediction model and the actual migration behavior is greater than or equal to the deviation degree value reference threshold, it indicates that the deviation degree between the prediction result of the prediction model and the actual migration behavior is high, and an abnormal signal is generated at this time; if the deviation degree value between the prediction result of the prediction model and the actual migration behavior is less than the deviation degree value reference threshold, it indicates that the deviation degree between the prediction result of the prediction model and the actual migration behavior is low, and a normal signal is generated at this time.

[0075] When a normal signal is generated, it indicates that the deviation degree between the prediction model and the actual migration behavior is low, and the system is in a low-risk state. At this time, the current monitoring data can be recorded as a model verification sample, and the model parameters can be updated to further improve the prediction accuracy. At the same time, maintain the regular monitoring frequency, continuously track the humidity change and heavy metal migration situation to prevent potential mutations. In addition, the monitoring resources can be preferentially allocated to high-risk areas, and by dynamically adjusting the warning threshold and optimizing the monitoring strategy, the system operation efficiency can be improved, and data support can be provided for long-term soil treatment and warning policy improvement.

[0076] S5: If the deviation degree is high, further analyze the deviation degree between the model prediction result and the actual migration behavior within a fixed time period, generate warning signals of different levels according to the analysis result, and make corresponding treatments.

[0077] If the deviation degree is high, that is, the deviation degree value between the prediction result of the prediction model generated within a fixed time period and the actual migration behavior is greater than or equal to the deviation degree value reference threshold, collect the deviation degree values between the prediction results of the prediction model that are greater than or equal to the deviation degree value reference threshold and the actual migration behavior generated within the subsequent fixed time period, establish a corresponding data set, calculate the mean and standard deviation of the data set, and after analyzing it, generate warning signals of different levels according to the analysis result, and make corresponding treatments.

[0078] If the mean value of the abnormal coefficient in the data set is greater than or equal to the reference threshold of the mean value of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, a first-level warning signal is generated at this time, indicating that the high deviation persists stably, and urgent countermeasures need to be taken quickly;

[0079] If the mean value of the abnormal coefficient is greater than or equal to the reference threshold of the mean value of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a second-level warning signal is generated at this time, indicating that the high deviation degree fluctuates greatly, and key monitoring and model adjustment are required;

[0080] If the mean of the anomaly coefficient is less than the reference threshold of the mean of the anomaly coefficient, and the standard deviation of the anomaly coefficient is greater than or equal to the reference threshold of the standard deviation of the anomaly coefficient, a third-level warning signal is generated at this time, indicating that the overall deviation degree is low but the fluctuation is large, and the model can be appropriately concerned about and optimized;

[0081] If the mean of the anomaly coefficient is less than the reference threshold of the mean of the anomaly coefficient, and the standard deviation of the anomaly coefficient is less than the reference threshold of the standard deviation of the anomaly coefficient, no warning signal is generated at this time, indicating that the deviation degree is low and stable, and continue with routine monitoring.

[0082] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding handling measures according to different warning signal levels.

[0083] In this embodiment, first, monitoring points are set around the pollution source in different directions and distances, and multiple depth monitoring areas are arranged along the soil profile; subsequently, the soil columns in each depth monitoring area are scanned in situ by using a micro-CT to measure the permeability coefficient under humidity changes and evaluate its influence on the water flow and heavy metal migration path; when the influence of humidity changes is serious, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning on the chemical forms of heavy metals in the soil, analyze the proportion of different forms and analyze the abnormal change trend; based on the above data, evaluate the deviation degree between the prediction model and the actual migration behavior; finally, further analyze the high-deviation situation, generate warning signals of different levels and take corresponding handling measures to ensure the prediction accuracy and environmental risk prevention and control ability.

[0084] Embodiment 2, the ecological pollution migration path analysis and warning system based on soil heavy metals described in this embodiment includes a monitoring point layout module, a permeability evaluation module, a chemical form analysis module, a deviation evaluation module, and a warning response module:

[0085] Monitoring point layout module: A number of monitoring points are set around the pollution source in different directions and distances, and multiple depth monitoring areas are set along the soil profile at each monitoring point;

[0086] Permeability evaluation module: For each depth monitoring area, the soil column is scanned regularly in situ by using a micro-CT to measure the permeability coefficient of the soil column under different humidities and evaluate the influence of humidity changes on the water flow and heavy metal migration path;

[0087] Chemical form analysis module: When the influence of humidity changes on the water flow and heavy metal migration path is serious, the chemical forms of heavy metals in the soil are scanned at high resolution by using a portable synchrotron radiation X-ray absorption spectrometer, the proportion of different forms is quantitatively analyzed, and the abnormal change trend of different heavy metal chemical forms is analyzed;

[0088] Deviation evaluation module: Evaluate the deviation degree between the prediction result of the prediction model and the actual migration behavior according to the influence of humidity change on the water flow and heavy metal migration path and the abnormal change trend of different heavy metal chemical forms;

[0089] Early warning response module: If the deviation degree is high, further analyze the deviation degree between the model prediction result and the actual migration behavior within a fixed time period, generate early warning signals of different levels according to the analysis results, and make corresponding treatments.

[0090] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0091] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context.

[0092] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0093] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. An early warning method for ecological pollution migration path analysis based on soil heavy metals, characterized by: The following steps are involved: S1: Set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point; S2: For each depth monitoring area, the soil column was scanned regularly under in situ conditions using micro-CT to determine the permeability of the soil column at different humidity levels and to evaluate the impact of humidity changes on water flow and heavy metal migration pathways; S3: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms; S4: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, evaluate the degree of deviation between the prediction model prediction results and the actual migration behavior; S5: If the degree of deviation is high, further analysis is conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed.

2. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 1 is characterized in that: In S2, after analyzing the influence of pore connectivity on the migration of heavy metals from the surface to the deep layer, the pore connectivity migration interference index is generated. The method for obtaining the pore connectivity migration interference index is as follows: The soil pore network is modeled as a weighted graph G(V,E), where: V is a node set representing pores, E is an edge set representing the connecting channels between pores, and the weight wij of each edge eij represents the migration resistance of heavy metals through channel i→j; micro-CT scanning is used to obtain the three-dimensional pore structure of the soil, and the geometric parameters of the pore network nodes and edges are extracted to construct a weighted graph G(V,E), where the starting node s is the initial position of heavy metal migration and the end node t is the target position of migration; the shortest path algorithm is used to calculate the shortest path from the starting point s to the end point t, and the shortest path weight Wmin is: Wmin=min∑(i,j)∈Pwij; P is the set of all paths, and the distance from the starting point s to each node is initialized as d(s,v)=∞, and the starting point d(s,s)=0; traverse the nodes, select the node u with the shortest distance among the unvisited nodes, and update the distance of u's neighbor node v: d(s,v)=min(d(s,v),d(s,u)+w uv );until d(s,t)=W is calculated min , the pore connectivity migration interference index quantifies the degree of obstruction of the actual pore connectivity to the migration path of heavy metals: Where W ideal is the shortest path weight under ideal conditions.

3. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 2 is characterized by: In S3, the heavy metal proportion anomaly index is generated by analyzing the proportion of heavy metal chemical forms before and after humidity changes. The method for obtaining the heavy metal proportion anomaly index is as follows: Obtain time series data during humidity changes, including the proportion of each heavy metal chemical form, where t is the time point, k is the chemical form category, is the proportion of chemical form k at time t, select the sliding window size w, the window slides on the time series, each time moving a unit time Δt, the proportion of each chemical form k Calculate the rate of change within the sliding window [t, t+w-1] The relative change rate of chemical form k in the window [t, t+w-1], is the proportion of chemical form k at the end point of the window t+w-1; define the abnormal threshold τ: when When , it is marked as abnormal, and the cumulative abnormal weight W of chemical form k is calculated. k : is the indicator function, when The value is 1 when the chemical form is positive, otherwise it is 0; the accumulated abnormal weights of all chemical forms are normalized to calculate the heavy metal proportion abnormal index AS, which is expressed as: Where K is the total number of chemical form categories, max(W k ) is the maximum cumulative anomaly weight among all chemical forms k, which is used for normalization.

4. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 3 is characterized by: In S4, based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, the degree of deviation between the prediction model prediction results and the actual migration behavior is evaluated, specifically: The pore connectivity migration interference index and the heavy metal proportion anomaly index were normalized, and the deviation degree between the prediction model prediction results and the actual migration behavior was calculated by the normalized pore connectivity migration interference index and the heavy metal proportion anomaly index.

5. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 4 is characterized in that: The obtained deviation value between the prediction result of the prediction model and the actual migration behavior is compared with the deviation value reference threshold value under normal conditions preset according to historical data. If the deviation value between the prediction result of the prediction model and the actual migration behavior is greater than or equal to the deviation value reference threshold value, it means that the deviation degree between the prediction result of the prediction model and the actual migration behavior is high, and an abnormal signal is generated at this time; If the deviation between the prediction model prediction result and the actual migration behavior is less than the deviation value reference threshold, it means that the deviation between the prediction model prediction result and the actual migration behavior is low, and a normal signal is generated at this time.

6. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 1 is characterized by: In S5, if the degree of deviation is high, further analysis is performed on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and warning signals of different levels are generated according to the analysis results, and corresponding processing is performed, specifically: If the degree of deviation is high, that is, the deviation value between the prediction results of the prediction model generated within a fixed time period and the actual migration behavior is greater than or equal to the deviation value reference threshold, the deviation values ​​between the prediction results of the prediction model generated within a subsequent fixed time period that are greater than or equal to the deviation value reference threshold and the actual migration behavior are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, different levels of warning signals are generated according to the analysis results, and corresponding processing is made.

7. The method for early warning of ecological pollution migration path analysis based on soil heavy metals according to claim 6 is characterized by: If the mean of the abnormal coefficients in the data set is greater than or equal to the reference threshold of the mean of the abnormal coefficients, and the standard deviation of the abnormal coefficients is less than the reference threshold of the standard deviation of the abnormal coefficients, a first-level warning signal is generated, indicating that the high deviation is persistent and stable, and emergency response measures need to be taken quickly; If the mean of the abnormal coefficient is greater than or equal to the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a secondary warning signal is generated, indicating that the degree of high deviation fluctuates greatly and requires key monitoring and model adjustment; If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is greater than or equal to the reference threshold of the standard deviation of the abnormal coefficient, a third-level warning signal is generated, indicating that the overall deviation is low but the fluctuation is large, and the model is optimized; If the mean of the abnormal coefficient is less than the reference threshold of the mean of the abnormal coefficient, and the standard deviation of the abnormal coefficient is less than the reference threshold of the standard deviation of the abnormal coefficient, no warning signal is generated, indicating that the degree of deviation is low and stable, and routine monitoring continues.

8. An early warning system for analyzing the migration path of ecological pollution based on heavy metals in soil, used to implement the early warning method for analyzing the migration path of ecological pollution based on heavy metals in soil according to any one of claims 1 to 7, characterized in that: It includes monitoring point layout module, penetration assessment module, chemical form analysis module, deviation assessment module and early warning response module: Monitoring point layout module: set up several monitoring points in different directions and distances around the pollution source, and set up multiple depth monitoring areas along the soil profile at each monitoring point; Infiltration assessment module: For each depth monitoring area, micro-CT is used to regularly scan the soil column under in situ conditions to determine the permeability coefficient of the soil column at different humidity levels and evaluate the impact of humidity changes on water flow and heavy metal migration paths; Chemical form analysis module: When humidity changes have a serious impact on water flow and heavy metal migration paths, a portable synchrotron radiation X-ray absorption spectrometer is used to perform high-resolution scanning of the chemical forms of heavy metals in the soil, quantitatively analyze the proportion of different forms, and analyze the abnormal change trends of different heavy metal chemical forms; Deviation assessment module: Based on the impact of humidity changes on water flow and heavy metal migration paths and the abnormal change trends of different heavy metal chemical forms, the deviation between the prediction model prediction results and the actual migration behavior is evaluated; Early warning response module: If the degree of deviation is high, further analysis will be conducted on the degree of deviation between the model prediction results and the actual migration behavior within a fixed time period, and early warning signals of different levels will be generated based on the analysis results, and corresponding processing will be carried out.

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