Soil microplastic mobility evaluation system and method

Abstract

The invention provides a soil micro-plastic mobility evaluation system and method, and relates to the technical field of micro-plastic mobility evaluation, and the method comprises the steps: uniformly dividing target monitoring soil into a plurality of three-dimensional grid units, taking the center of each unit as a monitoring point position, collecting endogenous parameters and exogenous parameters, generating a comprehensive soil micro-plastic data set, and calculating the micro-plastic mobility of the soil according to the comprehensive soil micro-plastic data set; performing exception identification on the data set, identifying exceptional migration data points, setting a neighborhood search radius, and filling and correcting exceptional data by using a spatial distance attenuation rule to obtain normal micro-plastic migration data; determining a migration potential index of each monitoring site, constructing an ecological mirror image simulation model by adopting a quantum inspiration algorithm, predicting a dynamic path of the microplastics in the soil, obtaining a migration trajectory prediction result, displaying the migration trajectory prediction result by utilizing a visualization technology, and highlighting a high-risk area in a thermodynamic analysis chart. According to the method, the understanding of decision makers on micro-plastic migration is enhanced, and powerful support is provided for environment management.

Description

Technical Field

[0001] The present invention relates to the technical field of microplastic mobility evaluation, and specifically to a soil microplastic mobility evaluation system and method. Background Art

[0002] The main sources of microplastics include the degradation of plastic products, plastic films used in agricultural activities, and urban and industrial waste. In the soil, microplastics not only affect the physical and chemical properties of the soil, but may also affect plant growth and the health of the soil ecosystem through the food chain. However, current microplastic monitoring technologies mainly rely on traditional point sampling and laboratory analysis. This method has problems such as limited sampling range, poor data representativeness, and long processing cycles. Traditional methods generally lack a comprehensive analysis of the spatial distribution and migration pathways of soil microplastics, and cannot effectively identify and assess the extent of microplastic pollution in different regions.

[0003] In addition, existing technologies also have shortcomings in data processing and analysis. Research on microplastic migration often lacks comprehensive consideration of multiple influencing factors, such as endogenous parameters such as soil pH, organic matter content, salinity, and exogenous parameters such as soil mulch years, ultraviolet radiation intensity, and plant root length. This leads to the one-sidedness of the assessment of microplastic migration potential and cannot truly reflect the dynamic behavior of microplastics in the soil. Existing one-dimensional or two-dimensional models often ignore the three-dimensional spatial characteristics of the soil and the nonlinear diffusion characteristics of microplastic particles when describing the migration of microplastics, making it difficult to accurately predict the migration trajectory of microplastics and their potential impact on the environment. Therefore, there is an urgent need for a new soil microplastic monitoring and assessment system to achieve a comprehensive understanding and scientific management of the dynamic behavior of microplastics in the soil.

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

[0005] The purpose of the present invention is to provide a soil microplastic mobility evaluation system and method to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following soil microplastic mobility evaluation system, which specifically includes: The acquisition module is used to evenly divide the target monitoring soil into multiple three-dimensional grid units, use the center of each three-dimensional grid unit as a monitoring point, and collect endogenous and exogenous parameters for each monitoring point. The endogenous parameters include microplastic concentration, soil pH, organic matter content, and salinity. The exogenous parameters include the age of soil film covering, ultraviolet radiation intensity, and plant root length. The parameters are fused with multi-source data to obtain a comprehensive soil microplastic dataset. The recognition module is used to identify anomalies in the comprehensive soil microplastics dataset, identify abnormal migration data points, and set a neighborhood search radius for the abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, the abnormal migration data points are filled and corrected based on the spatial distance attenuation law to obtain normal microplastic migration data; A simulation module is used to determine the migration potential index of each monitoring point based on the normal microplastic migration data, construct an ecological mirror simulation model using a quantum-inspired algorithm, and use the ecological mirror simulation model to predict the dynamic path of microplastics in the soil to obtain a migration trajectory prediction result, wherein the ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior; A generation module is used to construct a BIM model of the target monitored soil, map each three-dimensional grid unit with the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map, highlighting high-risk areas where microplastic concentration exceeds the preset threshold in the thermal map.

[0007] Furthermore, a three-dimensional grid division method was used to divide the target monitoring soil into several three-dimensional grid units with a size of 5m*5m*0.2m. The geometric center of each three-dimensional grid unit was used as the monitoring point. The three-dimensional coordinates of each monitoring point were calibrated by a positioning system, and the intrinsic and exogenous parameters of each monitoring point were synchronously collected. The intrinsic and exogenous parameters were fused through spatiotemporal alignment and normalization to generate a comprehensive soil microplastic dataset. Mark the three-dimensional coordinates of each monitoring point as ; The index of the monitoring point.

[0008] Furthermore, the intrinsic parameters of each monitoring point are collected based on the following specific logic: Collect 100g of soil from the monitoring point as a sample and perform the following operations: Determination of organic matter content: a portion of the sample soil was placed in a crucible, weighed and recorded, dried at 105°C, taken out and placed in a muffle furnace, burned at 450°C for 24 hours, taken out, cooled and weighed, and the organic matter content was calculated by the difference in specific gravity, recorded as ; Soil pH value determination: Weigh 1g of air-dried soil sample and place it in a glass beaker, add 2.5mL of deionized water, stir and mix for 1min, and measure it with a pH meter after settling for 30min. ; Salinity determination: Mix part of the sample soil into a paste and use a salinometer to measure the salinity value. ; Determination of microplastic concentration: 200g soil sample was collected at the center point of each three-dimensional grid unit using a pollution-free sampler. After air drying and sieving, 50g of sample was taken to remove organic matter by H2O2 oxidation, and microplastics were separated by flotation using ZnCl2 density liquid. The sample was vacuum filtered to After filtering, the polymer type was identified by micro-infrared spectroscopy and the number of particles was counted to obtain the microplastic concentration per unit mass of soil, which was recorded as ; The soil film covering life refers to the continuous use time of the plastic film covering the soil surface; Plant root length refers to the maximum vertical extension depth of plant roots in the soil at the monitoring point, which is used to characterize the physical disturbance and biological adsorption of roots on microplastic migration; Among them, for all monitoring points with the same horizontal and vertical coordinates, their soil film covering years, ultraviolet radiation intensity and plant root length are the same.

[0009] Furthermore, when a monitoring point satisfies any of the following conditions, it is determined to be an abnormal migration data point: There are non-numeric values, empty values, or invalid placeholders in the intrinsic and exogenous parameters of the monitoring point; Any value of the intrinsic and extrinsic parameters at the monitoring point exceeds the effective dynamic range of the instrument calibration; Set the neighborhood search radius of abnormal migration data points. According to the normal migration data points within the neighborhood search radius, the abnormal migration data points are corrected based on the spatial distance attenuation law. The formula is as follows: ; Indicates the value to be filled in for abnormal migration data points, Indicates the The value of a normal migration data point, is the number of normal migration data points within the search radius of the abnormal migration data point neighborhood, is the index of the normal migration data point within the search radius of the abnormal migration data point neighborhood, Indicates that the abnormal migration data point is The distance between normal migration data points, is the standard deviation of the Gaussian function, which is used to control the degree of weight attenuation. is a natural constant; Sure The formula is as follows: ; Where, is the three-dimensional coordinate of the monitoring point corresponding to the abnormal migration data point, For the The three-dimensional coordinates of the monitoring points corresponding to the normal migration data points; The neighborhood search radius is determined by statistically analyzing the spatial distribution of normal migration data points, combining the standard deviation and correlation evaluation of the changes in its exogenous and endogenous parameters, so as to determine the minimum distance that achieves the best filling effect, which is the neighborhood search radius.

[0010] Furthermore, the migration potential index of each monitoring point was determined based on the dimensionless normal microplastic migration data, and the formula is as follows: ; Where, is the migration potential index, is salinity, For soil , is the length of the plant root system, is the UV irradiation intensity, is the soil film covering years, is the concentration of microplastics, is the organic matter content, and is the preset weight, , and satisfies .

[0011] Furthermore, the dynamic path of microplastics was predicted based on the migration potential index of each monitoring point. The specific process was as follows: first, the migration potential index of all monitoring points was Values ​​are mapped into a three-dimensional potential energy field , construct a migration probability model based on the quantum tunneling effect; then treat each microplastic particle as a virtual "living" entity for iterative calculation, and initialize its position to the center coordinate of the three-dimensional grid unit where it is located, and the speed ,in, Represents potential energy field The gradient, is the migration coefficient, .

[0012] Furthermore, in the iterative calculation, the particles are Perform quantum state transition, where the horizontal migration step length for: ; Where, is the first adjustment coefficient, is the time step, is the migration potential index; Vertical migration step Corrected by the length of the plant's root system, based on the formula: ; Where, is the vertical migration step length, is a natural constant, is the length of the plant root system, is the characteristic depth, obtained through soil measurement; Update the coordinates of each particle: ; Where, ( ) represents the updated three-dimensional coordinates, ( ) represents the three-dimensional coordinates of the current particle, is the migration step length of microplastic particles in the horizontal x direction, is the migration step length of microplastic particles in the horizontal y direction, is the vertical migration step length; Repeat the above steps until the set number of iterations is reached; output the microplastic concentration of each three-dimensional grid cell during the prediction period through Monte Carlo simulation and the main migration path set , is the index of the path, is the number of main migration paths.

[0013] Furthermore, a BIM model of the target monitored soil is constructed, each three-dimensional grid unit is mapped to the BIM model, the migration trajectory prediction results are displayed using visualization technology, and a thermal analysis diagram is generated; Set the microplastic concentration threshold at , the microplastic concentration of each three-dimensional grid unit and For comparison, the concentration of microplastics exceeding the preset threshold The three-dimensional grid cells are marked as high-risk areas, and the concentration of microplastics exceeds the preset threshold in the heat map. High-risk areas are highlighted.

[0014] The present invention further provides a soil microplastic mobility evaluation method, which is obtained by using the above-mentioned soil microplastic mobility evaluation system, and includes: Step 1: The target monitoring soil is evenly divided into multiple three-dimensional grid units. The center of each three-dimensional grid unit is used as a monitoring point. The endogenous parameters and exogenous parameters of each monitoring point are collected. The endogenous parameters include microplastic concentration, soil pH, organic matter content and salinity. The exogenous parameters include the age of soil film covering, ultraviolet radiation intensity and plant root length. The parameters are fused with multi-source data to obtain a comprehensive soil microplastic dataset. Step 2: Perform anomaly identification on the comprehensive soil microplastics dataset, identify abnormal migration data points, and set a neighborhood search radius for the abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, fill and correct the abnormal migration data points based on the spatial distance attenuation law to obtain normal microplastic migration data; Step 3: Determine the migration potential index of each monitoring point based on the normal microplastic migration data, construct an ecological mirror simulation model using a quantum-inspired algorithm, and use the ecological mirror simulation model to predict the dynamic path of microplastics in the soil to obtain a migration trajectory prediction result, wherein the ecological mirror simulation model regards microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior; Step 4: Construct a BIM model of the target monitored soil, map each three-dimensional grid unit to the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map. In the thermal map, highlight the high-risk areas where the microplastic concentration exceeds the preset threshold.

[0015] Compared with the prior art, the present invention has the following beneficial effects: Through multi-source data fusion and comprehensive analysis, the present invention provides comprehensive monitoring and dynamic assessment capabilities for soil microplastic pollution, can accurately identify abnormal migration data points and make effective corrections, and ensure the reliability and integrity of the data. At the same time, the ecological mirror simulation model constructed based on the migration potential index and quantum-inspired algorithm can efficiently predict the dynamic migration path of microplastics in the soil, thereby providing a scientific basis for the formulation of targeted governance measures. In addition, through the construction of a three-dimensional BIM model and the visualization of a heat map, the system intuitively identifies high-risk areas, enhances decision makers' understanding of the distribution of microplastic pollution, and provides strong support for environmental management. Overall, this solution not only improves the accuracy and timeliness of soil microplastic monitoring, but also provides innovative technical means for ecological environmental protection and sustainable management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the overall system module of the present invention; Figure 2 Schematic diagram of the overall method flow of the present invention; Figure 3 3D scatter plot of UV radiation intensity, soil film covering years and migration potential index. DETAILED DESCRIPTION

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

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

[0019] Example: See also Figure 1 The present invention provides a soil microplastic mobility evaluation system, which specifically includes: The acquisition module is used to evenly divide the target monitoring soil into multiple three-dimensional grid units, use the center of each three-dimensional grid unit as a monitoring point, and collect endogenous and exogenous parameters for each monitoring point. The endogenous parameters include microplastic concentration, soil pH, organic matter content, and salinity. The exogenous parameters include the age of soil film covering, ultraviolet radiation intensity, and plant root length. The parameters are fused with multi-source data to obtain a comprehensive soil microplastic dataset. In this example, a three-dimensional grid division method was used to divide the target monitoring soil into several three-dimensional grid units with a size of 5m*5m*0.2m. The geometric center of each three-dimensional grid unit was used as the monitoring point. The three-dimensional coordinates of each monitoring point were calibrated by a positioning system, and the intrinsic and exogenous parameters of each monitoring point were synchronously collected. The intrinsic and exogenous parameters were subjected to multi-source data fusion processing through spatiotemporal alignment and normalization to generate a comprehensive soil microplastic dataset. Mark the three-dimensional coordinates of each monitoring point as ; The index of the monitoring point.

[0020] The specific logic for collecting the intrinsic parameters of each monitoring point is as follows: Collect 100g of soil from the monitoring point as a sample and perform the following operations: Determination of organic matter content: a portion of the sample soil was placed in a crucible, weighed and recorded, dried at 105°C, taken out and placed in a muffle furnace, burned at 450°C for 24 hours, taken out, cooled and weighed, and the organic matter content was calculated by the difference in specific gravity, recorded as ; Soil pH value determination: Weigh 1g of air-dried soil sample and place it in a glass beaker, add 2.5mL of deionized water, stir and mix for 1min, and measure it with a pH meter after settling for 30min. ; Salinity determination: Mix part of the sample soil into a paste and use a salinometer to measure the salinity value. ; Determination of microplastic concentration: 200g soil sample was collected at the center point of each three-dimensional grid unit using a pollution-free sampler. After air drying and sieving, 50g of sample was taken to remove organic matter by H2O2 oxidation, and microplastics were separated by flotation using ZnCl2 density liquid. The sample was vacuum filtered to After filtering, the polymer type was identified by micro-infrared spectroscopy and the number of particles was counted to obtain the microplastic concentration per unit mass of soil, which was recorded as ; The soil mulch age refers to the continuous use time of plastic film covering the soil surface, and the plant root length refers to the maximum vertical extension depth of the plant root system in the soil at the monitoring point, which is used to characterize the physical disturbance and biological adsorption effect of the root system on the migration of microplastics; Among them, for all monitoring points with the same horizontal and vertical coordinates, their soil film covering years, ultraviolet radiation intensity and plant root length are the same.

[0021] The acquisition module evenly divides the target soil into multiple three-dimensional grid cells and monitors at the center of each cell, efficiently and accurately acquiring both endogenous and exogenous soil parameters. This meticulous spatial division allows for systematic and standardized data collection, helping to comprehensively reflect the distribution characteristics of soil microplastics. It provides higher spatial resolution and, compared to traditional point sampling methods, can better reveal the migration and accumulation patterns of microplastics in soil.

[0022] Compared to existing technologies, this collection module's benefits lie in its comprehensiveness and accuracy. Traditional methods often rely on a limited number of sample points, making it difficult to fully reflect the distribution of microplastics in soil. This module, by collecting parameters at multiple points and in multiple dimensions, not only improves data credibility but also makes the assessment of microplastic mobility more scientific, better meeting the needs of environmental monitoring and soil protection.

[0023] In this paper, the implementation of the acquisition module provides a solid data foundation for the overall solution. Through systematic parameter collection and data fusion processing, subsequent modules such as anomaly identification, migration potential index calculation, and dynamic path simulation rely on accurate input data. This data-driven approach enhances the predictive power and reliability of the entire system, promoting the comprehensiveness and effectiveness of soil microplastic mobility assessment.

[0024] The recognition module is used to identify anomalies in the comprehensive soil microplastics dataset, identify abnormal migration data points, and set a neighborhood search radius for the abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, the abnormal migration data points are filled and corrected based on the spatial distance attenuation law to obtain normal microplastic migration data; In this embodiment, when a monitoring point satisfies any of the following conditions, the monitoring point is determined to be an abnormal migration data point: There are non-numeric values, empty values, or invalid placeholders in the intrinsic and exogenous parameters of the monitoring point; Any value of the intrinsic and extrinsic parameters at the monitoring point exceeds the effective dynamic range of the instrument calibration; Monitoring points containing non-numeric values, null values, or invalid placeholders, or any values ​​of their endogenous or exogenous parameters that exceed the effective dynamic range of instrument calibration, are considered abnormal migration data points. This determination is primarily based on the reliability and validity of the data. Non-numeric or null values ​​make data analysis impossible, affecting the accuracy of research conclusions. Parameters outside the effective dynamic range of instrument calibration may indicate inaccurate measurements or distorted data, which may affect the calculation and assessment of the microplastic migration potential index. Therefore, to ensure the integrity of the data and the scientific nature of the analysis, these abnormal data points must be excluded to maintain the validity and credibility of the research results.

[0025] Set the neighborhood search radius of abnormal migration data points. According to the normal migration data points within the neighborhood search radius, the abnormal migration data points are corrected based on the spatial distance attenuation law. The formula is as follows: ; Indicates the value to be filled in for abnormal migration data points, Indicates the The value of a normal migration data point, is the number of normal migration data points within the search radius of the abnormal migration data point neighborhood, is the index of the normal migration data point within the search radius of the abnormal migration data point neighborhood, Indicates that the abnormal migration data point is The distance between normal migration data points, is the standard deviation of the Gaussian function, which is used to control the degree of weight attenuation. is a natural constant; In the above formula, the dependent variable Represents the value to be filled in for the abnormal migration data point, that is, the endogenous and exogenous parameters after correction at the position of the abnormal point. The meaning of this dependent variable is to use the information of the normal migration data points in the neighborhood to infer the reasonable value of the abnormal point, thereby supplementing and correcting the data. The technical effect is to improve the accuracy and completeness of the data, making the prediction ability of the entire microplastic migration evaluation system more reliable, facilitating subsequent analysis and decision-making, and effectively reducing the misleading caused by data anomalies. In this formula, the independent variables mainly include the values ​​of the normal migration data points and the distance between the abnormal migration data points These independent variables and dependent variables The relationship between is established through the spatial distance decay law. Specifically, the normal migration data point value in the neighborhood The higher the distance from the outlier The closer the distance is, the more significant the impact on the abnormal migration data points. The correlation of the independent variables is reflected in the fact that when the concentration value of the normal data points in the neighborhood is high and the distance is short, their influence is less significant in calculating the correction value. , it will be given a greater weight, thus effectively filling the value of the abnormal data point. In the formula, the dependent variable With independent variables There is a positive correlation between the two, which means that the higher the value of the normal migration data point is, the higher the value of the abnormal migration data point after correction is. This is because during the calculation process, The value of is directly involved in the correction calculation, and the distance weight factor Ensure that the closer the normal data points are, the Therefore, when more and closer normal data points have higher concentrations, The value will also increase accordingly. The relationship is a negative correlation, that is, the farther the distance, The smaller the value of , the less the normal migration data points This shows that in space, normal data points that are close to each other have a stronger correction effect on abnormal points.

[0026] Sure The formula is as follows: ; Where, is the three-dimensional coordinate of the monitoring point corresponding to the abnormal migration data point, For the The three-dimensional coordinates of the monitoring points corresponding to the normal migration data points; The neighborhood search radius is determined by statistically analyzing the spatial distribution of normal migration data points, combining the standard deviation and correlation evaluation of the changes in its exogenous parameters and endogenous parameters, so as to determine the minimum distance that makes the filling effect the best, that is, the neighborhood search radius. Specifically, first, by calculating the distance between normal data points, a distance matrix is ​​constructed to identify the spatial clustering characteristics of the data points. Then, the standard deviation of the exogenous parameters and endogenous parameters of these data points is analyzed, and the correlation between them is evaluated to determine which parameters are consistent and influential in space. By gradually adjusting the neighborhood search radius, observing the changes in the filling effect, and using the cross-validation method to evaluate the accuracy and consistency of the data after filling, the minimum distance that makes the filling effect the best is finally selected as the neighborhood search radius. This process ensures the scientificity and rationality of the filling data and provides a more accurate basis for subsequent analysis.

[0027] The recognition module's primary advantage lies in its efficient and accurate anomaly detection capabilities. By analyzing a comprehensive soil microplastics dataset, the module rapidly identifies anomalous migration data points and applies fill corrections based on a preset neighborhood search radius. This process not only effectively removes noisy data but also accurately interpolates the spatial distribution of normal data points, ensuring the reliability and accuracy of subsequent data and providing high-quality data support for the entire system.

[0028] Compared with existing technologies, the recognition module's significant benefits lie in its integration of a correction method based on spatial distance decay, significantly improving the scientific nature and effectiveness of abnormal data processing. Traditional methods often rely on simple threshold determination or the treatment of isolated data points, which can easily lead to the loss of important information or misjudgment. However, this module, through statistical analysis based on spatial distribution, can more comprehensively and accurately identify and process abnormal data, improving the overall reliability and adaptability of the system.

[0029] In this paper, the implementation of the identification module provides a crucial quality assurance component for the overall solution. By effectively identifying and correcting anomalous migration data points, the module ensures the accuracy of the data relied upon in subsequent steps, such as the calculation of the migration potential index and dynamic path simulation. This high-quality data assurance not only enhances the scientific nature of microplastic mobility assessment but also improves the overall system's predictive capabilities and decision-making support, thereby promoting the effectiveness of soil environmental protection.

[0030] A simulation module is used to determine the migration potential index of each monitoring point based on the normal microplastic migration data, construct an ecological mirror simulation model using a quantum-inspired algorithm, and use the ecological mirror simulation model to predict the dynamic path of microplastics in the soil to obtain a migration trajectory prediction result, wherein the ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior; In this embodiment, the migration potential index of each monitoring point is determined based on the normal microplastic migration data after dimensionless processing, and the formula used is as follows: ; Where, is the migration potential index, is salinity, For soil , is the length of the plant root system, is the UV irradiation intensity, is the soil film covering years, is the concentration of microplastics, is the organic matter content, and is the preset weight, , .

[0031] set up The real reason is that the chemical properties of the soil, such as pH, salinity, and microplastic concentration, have a more direct and significant impact on the migration of microplastics. These factors directly affect the behavior and mobility of microplastics in the soil. In contrast, although biological and environmental factors such as plant root length, ultraviolet radiation intensity, and the number of years of soil mulch can also affect the migration of microplastics, their impact is relatively small. Therefore, they are given a lower weight in the model to more accurately reflect the dominant role of the soil environment on the migration of microplastics.

[0032] In the above given formula, the dependent variable It represents the migration potential index, which reflects the potential of microplastics at each monitoring point to migrate in the soil. The larger the value, the greater the migration ability of the monitoring point. The specific meaning of this method is to evaluate the migration capacity of microplastics under different environmental conditions by comprehensively considering the chemical properties of the soil, microplastic concentration, and environmental factors. The technical effect is to provide a basis for the management and treatment of microplastic pollution, help identify high-risk areas, and formulate corresponding environmental protection measures to promote soil health and ecological safety.

[0033] The presence of organic matter significantly impacts soil microplastics. Both microplastics and organic matter possess large surface areas, making them highly susceptible to interactions such as adsorption. Adsorption of organic matter onto microplastic surfaces increases their aggregation, hindering their degradation efficiency and migration rate within the soil environment, significantly affecting their distribution within the soil layer. Furthermore, soil salinity also exhibits a significant impact on microplastics, second only to organic matter. Soil salt content is highly correlated with soil structural properties, with salt levels directly affecting soil bulk. Increased soil salt content causes a gradual contraction of the soil structure, resulting in a transient decrease in the pore space between soil particles and reduced pore connectivity. This may even cause changes in the surface charge of soil particles. The migration of microplastics within the soil environment is primarily driven by transport and transformation within soil pores through surface charge generation. Salinity plays a key role in the zeta potential of pore water. Increased salinity alters soil pore distribution, leading to changes in water flow paths and velocity, thus limiting the pathways and efficiency of microplastic transport and transformation within the soil environment. The migration potential index generally increases with increasing pH. This is because a higher pH improves soil chemistry, promoting the dissolution and dispersion of microplastics and reducing their adsorption to soil particles, thereby enhancing their migration. Furthermore, a higher pH may increase microbial activity, accelerating the degradation of microplastics and making them more mobile. Therefore, within a certain range, an increase in pH promotes the migration of microplastics in soil. Plant roots are also a major factor in microplastic migration. Plants with longer roots can transport microplastics over longer distances, and areas with denser root systems have higher microplastic concentrations. Therefore, as plant root length increases, the migration potential of microplastics in the soil increases. Increasing UV radiation intensity accelerates the degradation rate of microplastics because UV rays can break down the chemical structure of microplastics, causing them to fragment and degrade into smaller particles. These smaller microplastic particles are more easily transported in soil, so increasing UV radiation intensity increases the migration potential index of microplastics. With the increase in the age of soil mulching, the soil environment may become more stable and moist. Furthermore, the mulch material may promote the growth of microorganisms, accelerating the degradation of microplastics. In addition, mulching can reduce soil water evaporation and improve the soil's water retention capacity, making microplastics more mobile in the soil. Therefore, increasing the number of years of soil mulching will positively affect the migration potential index of microplastics.

[0034] The formal rationality of this formula is reflected in many aspects. First, the migration potential index consists of two parts, namely the square root and the logarithmic form. These two mathematical expressions can effectively reflect the nonlinear effects of different factors on the migration of microplastics. Combining the chemical properties of the soil with the concentration of microplastics shows that these factors affect the migration capacity of microplastics through interactions; the logarithmic part The superposition effect of plant root length, UV radiation intensity and soil mulch years on microplastic migration is considered. and The formula can flexibly adjust the importance of each factor, making the model more in line with the actual situation, reflecting the complexity and diversity of the microplastic migration process, and reflecting the comprehensive effect of corresponding environmental conditions on migration potential.

[0035] Table 1: Migration potential index statistics

[0036] See also Figure 3 In this data analysis, based on the data from 15 monitoring points, we can observe the relationship between the microplastic migration potential index and various influencing factors. The values ​​of showed obvious fluctuations at different monitoring points, reflecting the complexity of the soil environment and its impact on the migration of microplastics.

[0037] First, factors such as soil pH, salinity and microplastic concentration have an impact on The impact of higher salinity and pH is more significant. Higher salinity and pH are generally associated with a better soil environment, which promotes the migration of microplastics. In addition, plant root length and migration potential index The positive correlation trend suggests that plants with longer root systems can enhance the vertical migration potential of microplastics by improving soil structure and water conditions. UV irradiation intensity also shows a certain effect, especially at higher intensities, which can promote the decomposition and migration of microplastics.

[0038] Secondly, changes in soil film covering years and organic matter content also affect . Longer years of film covering may lead to the deterioration of the soil environment, thereby affecting the migration ability of microplastics. Soil with a higher organic matter content can improve the soil structure to a certain extent and increase the migration potential of microplastics. However, the specific extent of the impact requires in-depth analysis in combination with more detailed measured data. Overall, these results emphasize the importance of soil properties, plant characteristics and external environmental factors in the migration process of microplastics, and provide a scientific basis for subsequent management and governance strategies.

[0039] The dynamic path of microplastics is predicted based on the migration potential index of each monitoring point. The specific process is as follows: first, Values ​​are mapped into a three-dimensional potential energy field , construct a migration probability model based on the quantum tunneling effect; then treat each microplastic particle as a virtual "living" entity for iterative calculation, and initialize its position to the center coordinate of the three-dimensional grid unit where it is located, and the speed ,in, Represents potential energy field The gradient, is the migration coefficient, .in, The value is determined by performing regression analysis on historical microplastic migration data and combining it with the actual migration rate of microplastics under different environmental conditions to optimize the migration model and achieve the best fit.

[0040] potential energy field Indicates the energy state of microplastic migration and the migration potential index Inversely proportional. The larger the value, the stronger the migration potential and the lower the potential energy, and vice versa. The value mapping is a potential energy field, which can intuitively represent the migration potential of different locations, thus providing a basis for the subsequent migration probability model.

[0041] In the iterative calculation, the particles are calculated according to the potential energy gradient Perform quantum state transition, where the horizontal migration step length for: ; Where, is the first adjustment coefficient, , is the time step, is the migration potential index; Vertical migration step Corrected by the length of the plant's root system, based on the formula: ; Where, is the vertical migration step length, is a natural constant, is the length of the plant root system, Characteristic depth refers to the depth range where plant roots have a significant impact on the soil environment. It is determined through soil profile analysis and measured data on root distribution, combined with plant growth characteristics and root extension characteristics.

[0042] Vertical migration step The formula reflects the effect of plant root length on microplastic migration. Its formal rationality is reflected in the following aspects: First, the natural constant is used in the formula The exponential function can effectively describe the nonlinear effect of plant roots on the vertical migration of microplastics, reflecting the significant restriction on migration when the root length is short. As a characteristic depth, it can be obtained through measured data, making the model realistic and able to truly reflect the characteristics of the soil environment. Overall, this formula fully combines the relationship between plant root characteristics and migration step length, and reasonably explains the vertical migration mechanism of microplastics in soil.

[0043] Update the coordinates of each particle: ; Where, ( ) represents the updated three-dimensional coordinates, ( ) represents the three-dimensional coordinates of the current particle, is the migration step length of microplastic particles in the horizontal x direction, is the migration step length of microplastic particles in the horizontal y direction, is the vertical migration step; by updating the coordinates, the changes in the position of each microplastic particle during the iteration process can be tracked, laying the foundation for the final migration path prediction.

[0044] Repeat the above steps until the set number of iterations is reached; output the microplastic concentration of each three-dimensional grid cell during the prediction period through Monte Carlo simulation and the main migration path set , is the index of the path, is the number of main migration paths. Monte Carlo simulation is a random sampling computational method used to evaluate the behavior of complex systems. In this paper, it is used to predict the concentration CSS of microplastics in each three-dimensional grid unit and the set of main migration paths. Through this process, the distribution and dynamic migration paths of microplastics in different regions during the forecast period can be obtained, thus providing a scientific basis for environmental management and pollution control. The simulation module's primary advantage lies in its ability to construct an ecological mirror simulation model based on normal microplastic migration data using a quantum-inspired algorithm, thereby accurately predicting the dynamic migration paths of microplastics in soil. This approach not only accounts for the nonlinear diffusion and deposition behavior of microplastics but also treats microplastic particles as virtual "living" entities, enhancing the model's ability to simulate the migration behavior of microplastics in actual soil environments. This refined simulation enables a more realistic reflection of the behavior and impact of microplastics in soil.

[0045] Compared to existing technologies, the simulation module's beneficial effect lies in its integration of quantum tunneling and ecological mirror models, significantly improving the accuracy and reliability of microplastic migration path predictions. Traditional methods often rely on simple linear or stagnation models, which fail to fully reflect the complex behavior of microplastics in variable soil environments. This module, through a more scientific simulation approach, captures the actual migration dynamics of microplastics in soil, enhancing the scientific nature of predictions and contributing to the development of more effective soil management and remediation strategies.

[0046] In this invention, the implementation of the simulation module provides key dynamic predictive capabilities for the overall solution. By accurately simulating the migration pathways of microplastics, it provides a data basis for risk assessment and management strategy development, helping to identify potential high-risk areas. This data-driven predictive capability not only enhances the practicality of the system but also strengthens the scientific nature and effectiveness of soil microplastic management, thereby actively promoting environmental protection and soil remediation efforts.

[0047] A generation module is used to construct a BIM model of the target monitored soil, map each three-dimensional grid unit to the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map to highlight high-risk areas where microplastic concentrations exceed preset thresholds in the thermal map; In this embodiment, a BIM model of the target monitored soil is constructed, each three-dimensional grid unit is mapped to the BIM model, the migration trajectory prediction results are displayed using visualization technology, and a thermal analysis diagram is generated; Set the microplastic concentration threshold at , the microplastic concentration of each three-dimensional grid unit and For comparison, the concentration of microplastics exceeding the preset threshold The three-dimensional grid cells are marked as high-risk areas, and the concentration of microplastics exceeds the preset threshold in the heat map. High-risk areas are highlighted.

[0048] The microplastic concentration threshold The threshold is determined by combining statistical analysis and expert demonstration based on historical monitoring data and ecological risk assessment results of the target area, soil environmental quality standards and microplastic ecotoxicity data. Specifically, the 90th percentile value of the historical monitoring data is selected as the benchmark, and the final threshold is determined after appropriate adjustments based on the ecological risk characteristics of different polymer types. .

[0049] The main advantage of the generation module is its ability to construct a BIM model of the target soil for monitoring, accurately mapping each three-dimensional grid cell to the BIM model, thereby visualizing the migration trajectory of soil microplastics. This visualization technology not only makes the data more intuitive, but also helps relevant personnel quickly identify and analyze the spatial distribution and dynamic changes of microplastic concentrations, providing strong support for decision-making.

[0050] Compared to existing technologies, the Generate Module's beneficial effects lie in its integration of BIM modeling and visualization, enabling a more comprehensive and in-depth analysis of microplastic migration. Traditional methods typically rely on simple two-dimensional charts or statistical data, failing to provide a detailed spatial perspective. However, the visualization technology employed in this module highlights high-risk areas, enabling managers to implement targeted measures and improving management efficiency and effectiveness.

[0051] In this invention, the implementation of the generation module provides an important intuitive analysis tool for the overall solution. By displaying microplastic migration trajectories and concentration information in the form of heat maps, decision-makers can clearly identify high-risk areas for microplastic contamination and formulate scientific soil remediation and management strategies accordingly. This process not only enhances the practicality of the system but also provides important technical support for soil environmental protection efforts, promoting the scientific and refined management of soil microplastics.

[0052] See also Figure 2 The present invention further provides a method for evaluating the mobility of soil microplastics, comprising: Step 1: The target monitoring soil is evenly divided into multiple three-dimensional grid units. The center of each three-dimensional grid unit is used as a monitoring point. The endogenous parameters and exogenous parameters of each monitoring point are collected. The endogenous parameters include microplastic concentration, soil pH, organic matter content and salinity. The exogenous parameters include the age of soil film covering, ultraviolet radiation intensity and plant root length. The parameters are fused with multi-source data to obtain a comprehensive soil microplastic dataset. Step 2: Perform anomaly identification on the comprehensive soil microplastics dataset, identify abnormal migration data points, and set a neighborhood search radius for the abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, fill and correct the abnormal migration data points based on the spatial distance attenuation law to obtain normal microplastic migration data; Step 3: Determine the migration potential index of each monitoring point based on the normal microplastic migration data, construct an ecological mirror simulation model using a quantum-inspired algorithm, and use the ecological mirror simulation model to predict the dynamic path of microplastics in the soil to obtain a migration trajectory prediction result, wherein the ecological mirror simulation model regards microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior; Step 4: Construct a BIM model of the target monitored soil, map each three-dimensional grid unit to the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map. In the thermal map, highlight the high-risk areas where the microplastic concentration exceeds the preset threshold.

[0053] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0054] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0055] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0056] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A soil microplastic mobility evaluation system, characterized in that: Specifically include: The acquisition module is used to evenly divide the target monitoring soil into multiple three-dimensional grid units, use the center of each three-dimensional grid unit as a monitoring point, and collect endogenous and exogenous parameters for each monitoring point. The endogenous parameters include microplastic concentration, soil pH, organic matter content, and salinity. The exogenous parameters include the age of soil film covering, ultraviolet radiation intensity, and plant root length. The parameters are fused with multi-source data to obtain a comprehensive soil microplastic dataset. The recognition module is used to identify anomalies in the comprehensive soil microplastics dataset, identify abnormal migration data points, and set a neighborhood search radius for the abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, the abnormal migration data points are filled and corrected based on the spatial distance attenuation law to obtain normal microplastic migration data; A simulation module is used to determine the migration potential index of each monitoring point based on the normal microplastic migration data, construct an ecological mirror simulation model using a quantum-inspired algorithm, and use the ecological mirror simulation model to predict the dynamic path of microplastics in the soil to obtain a migration trajectory prediction result, wherein the ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior; A generation module is used to construct a BIM model of the target monitored soil, map each three-dimensional grid unit with the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map, highlighting high-risk areas where microplastic concentration exceeds the preset threshold in the thermal map.

2. A soil microplastic mobility evaluation system according to claim 1, characterized in that: The target soil for monitoring was divided into several 5m*5m*0.2m 3D grid cells using a 3D grid division method. The geometric center of each 3D grid cell was used as the monitoring point. The 3D coordinates of each monitoring point were calibrated using a positioning system. The intrinsic and exogenous parameters of each monitoring point were simultaneously collected. Multi-source data fusion processing was performed on these intrinsic and exogenous parameters through spatiotemporal alignment and normalization to generate a comprehensive soil microplastics dataset. Mark the three-dimensional coordinates of each monitoring point as ; The index of the monitoring point.

3. A soil microplastic mobility evaluation system according to claim 1, characterized in that: The specific logic for collecting the intrinsic parameters of each monitoring point is as follows: Collect 100g of soil from the monitoring point as a sample and perform the following operations: Determination of organic matter content: a portion of the sample soil was placed in a crucible, weighed and recorded, dried at 105°C, taken out and placed in a muffle furnace, burned at 450°C for 24 hours, taken out, cooled and weighed, and the organic matter content was calculated by the difference in specific gravity, recorded as ; Soil pH value determination: Weigh 1g of air-dried soil sample and place it in a glass beaker, add 2.5mL of deionized water, stir and mix for 1min, and measure it with a pH meter after settling for 30min. ; Salinity determination: Mix part of the sample soil into a paste and use a salinometer to measure the salinity value. ; Determination of microplastic concentration: 200g soil sample was collected at the center point of each three-dimensional grid unit using a pollution-free sampler. After air drying and sieving, 50g of sample was taken to remove organic matter by H2O2 oxidation, and microplastics were separated by flotation using ZnCl2 density liquid. The sample was vacuum filtered to After filtering, the polymer type was identified by micro-infrared spectroscopy and the number of particles was counted to obtain the microplastic concentration per unit mass of soil, which was recorded as ; The soil film covering life refers to the continuous use time of the plastic film covering the soil surface; Plant root length refers to the maximum vertical extension depth of plant roots in the soil at the monitoring point, which is used to characterize the physical disturbance and biological adsorption of roots on microplastic migration; Among them, for all monitoring points with the same horizontal and vertical coordinates, their soil film covering years, ultraviolet radiation intensity and plant root length are the same.

4. A soil microplastic mobility evaluation system according to claim 1, characterized in that: When a monitoring point meets any of the following conditions, it is determined to be an abnormal migration data point: There are non-numeric values, empty values, or invalid placeholders in the intrinsic and exogenous parameters of the monitoring point; Any value of the intrinsic and extrinsic parameters at the monitoring point exceeds the effective dynamic range of the instrument calibration; Set the neighborhood search radius of abnormal migration data points. According to the normal migration data points within the neighborhood search radius, the abnormal migration data points are corrected based on the spatial distance attenuation law. The formula is as follows: ； Indicates the value to be filled in for abnormal migration data points, Indicates the The value of a normal migration data point, is the number of normal migration data points within the search radius of the abnormal migration data point neighborhood, is the index of the normal migration data point within the search radius of the abnormal migration data point neighborhood, Indicates that the abnormal migration data point is The distance between normal migration data points, is the standard deviation of the Gaussian function, which is used to control the degree of weight attenuation. is a natural constant; Sure The formula is as follows: ； Where, is the three-dimensional coordinate of the monitoring point corresponding to the abnormal migration data point, For the The three-dimensional coordinates of the monitoring points corresponding to the normal migration data points; The neighborhood search radius is determined by statistically analyzing the spatial distribution of normal migration data points, combining the standard deviation and correlation evaluation of the changes in its exogenous and endogenous parameters, so as to determine the minimum distance that achieves the best filling effect, which is the neighborhood search radius.

5. A soil microplastic mobility assessment system according to claim 1, characterized in that: The migration potential index of each monitoring point is determined based on the dimensionless normal microplastic migration data. The formula is as follows: ； Where, is the migration potential index, is salinity, For soil , is the length of the plant root system, is the UV irradiation intensity, is the soil film covering years, is the concentration of microplastics, is the organic matter content, and is the preset weight, , and satisfies .

6. A soil microplastic mobility assessment system according to claim 5, characterized in that: The dynamic path of microplastics is predicted based on the migration potential index of each monitoring point. The specific process is as follows: first, Values ​​are mapped into a three-dimensional potential energy field , construct a migration probability model based on the quantum tunneling effect; then treat each microplastic particle as a virtual "living" entity for iterative calculation, and initialize its position to the center coordinate of the three-dimensional grid unit where it is located, and the speed ,in, Represents potential energy field The gradient, is the migration coefficient, .

7. A soil microplastic mobility assessment system according to claim 6, characterized in that: In the iterative calculation, the particles are calculated according to the potential energy gradient Perform quantum state transition, where the horizontal migration step length for: ； Where, is the first adjustment coefficient, is the time step, is the migration potential index; Vertical migration step Corrected by the length of the plant's root system, based on the formula: ； Where, is the vertical migration step length, is a natural constant, is the length of the plant root system, is the characteristic depth, obtained through soil measurement; Update the coordinates of each particle: ； Where, ( ) represents the updated three-dimensional coordinates, ( ) represents the three-dimensional coordinates of the current particle, is the migration step length of microplastic particles in the horizontal x direction, is the migration step length of microplastic particles in the horizontal y direction, is the vertical migration step length; Repeat the above steps until the set number of iterations is reached; output the microplastic concentration of each three-dimensional grid cell during the prediction period through Monte Carlo simulation and the main migration path set , is the index of the path, is the number of main migration paths.

8. The soil microplastic mobility evaluation system according to claim 1, characterized in that: Used to construct a BIM model of the target monitored soil, map each three-dimensional grid unit to the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis diagram; Set the microplastic concentration threshold at , the microplastic concentration of each three-dimensional grid unit and For comparison, the concentration of microplastics exceeding the preset threshold The three-dimensional grid cells are marked as high-risk areas, and the concentration of microplastics exceeds the preset threshold in the heat map. High-risk areas are highlighted.

9. A method for evaluating the mobility of soil microplastics, characterized by: The soil microplastic mobility evaluation method is obtained by using the soil microplastic mobility evaluation system according to any one of claims 1 to 8, comprising: Step 1: The target monitoring soil is evenly divided into multiple three-dimensional grid units. The center of each three-dimensional grid unit is used as a monitoring point. The endogenous parameters and exogenous parameters of each monitoring point are collected. The endogenous parameters include microplastic concentration, soil pH, organic matter content and salinity. The exogenous parameters include the age of soil film covering, ultraviolet radiation intensity and plant root length. The parameters are fused with multi-source data to obtain a comprehensive soil microplastic dataset. Step 2: Perform anomaly identification on the comprehensive soil microplastics dataset, identify abnormal migration data points, and set a neighborhood search radius for the abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, fill and correct the abnormal migration data points based on the spatial distance attenuation law to obtain normal microplastic migration data; Step 3: Determine the migration potential index of each monitoring point based on the normal microplastic migration data, construct an ecological mirror simulation model using a quantum-inspired algorithm, and use the ecological mirror simulation model to predict the dynamic path of microplastics in the soil to obtain a migration trajectory prediction result, wherein the ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior; Step 4: Construct a BIM model of the target monitored soil, map each three-dimensional grid unit to the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map. In the thermal map, highlight the high-risk areas where the microplastic concentration exceeds the preset threshold.

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