A soil microplastic migration assessment system and method

Abstract

This invention provides a soil microplastic migration assessment system and method, belonging to the field of microplastic migration assessment technology. The method includes: uniformly dividing the target monitoring soil into multiple three-dimensional grid units, taking the center of each unit as a monitoring point, collecting endogenous and extrinsic parameters to generate a comprehensive soil microplastic dataset; identifying anomalies in the dataset, identifying anomalous migration data points, setting a neighborhood search radius, and using spatial distance attenuation laws to fill and correct anomalous data to obtain normal microplastic migration data; determining the migration potential index of each monitoring point; constructing an ecological mirror simulation model using a quantum-inspired algorithm to predict the dynamic path of microplastics in the soil, obtaining migration trajectory prediction results; and displaying the migration trajectory prediction results using visualization technology, highlighting high-risk areas in a thermal analysis map. This invention enhances decision-makers' understanding of microplastic migration and provides strong support for environmental management.

Description

Technical Field

[0001] This invention relates to the field of microplastic migration assessment technology, specifically to a soil microplastic migration assessment system and method. Background Technology

[0002] The main sources of microplastics include the degradation of plastic products, plastic films used in agricultural activities, and urban and industrial waste. In soil, microplastics not only affect the physical and chemical properties of the soil but may also impact 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, which suffers from limited sampling scope, poor data representativeness, and long processing cycles. Traditional methods often lack a comprehensive analysis of the spatial distribution and migration pathways of soil microplastics, making it difficult to effectively identify and assess the degree of microplastic pollution in different areas.

[0003] Furthermore, existing technologies have shortcomings in data processing and analysis. Studies on microplastic migration often lack a comprehensive consideration of multiple influencing factors, such as endogenous parameters like soil pH, organic matter content, and salinity, as well as exogenous parameters like soil mulching duration, UV radiation intensity, and plant root length. This leads to a one-sided assessment of microplastic migration potential, failing to accurately reflect the dynamic behavior of microplastics in soil. Existing one-dimensional or two-dimensional models, when describing microplastic migration, often neglect the three-dimensional spatial characteristics of soil and the nonlinear diffusion characteristics of microplastic particles, making it difficult to accurately predict the migration trajectory of microplastics and their potential environmental impact. Therefore, a novel soil microplastic monitoring and assessment system is urgently needed to achieve a comprehensive understanding and scientific management of the dynamic behavior of microplastics in soil.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a soil microplastic migration assessment system and method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a soil microplastic migration assessment system, specifically comprising:

[0007] The data acquisition module is used to uniformly divide the target monitoring soil into multiple three-dimensional grid units, with the center of each three-dimensional grid unit as a monitoring point. It collects the intrinsic and extrinsic parameters of each monitoring point. The intrinsic parameters include microplastic concentration, soil pH, organic matter content and salinity, while the extrinsic parameters include the number of years of soil mulching, ultraviolet radiation intensity and plant root length. The parameters are then subjected to multi-source data fusion processing to obtain a comprehensive soil microplastic dataset.

[0008] The identification module is used to identify anomalies in the comprehensive soil microplastic dataset, identify abnormal migration data points, set the neighborhood search radius of abnormal migration data points, and fill and correct the abnormal migration data points based on the spatial distance decay law according to the normal migration data points within the neighborhood search radius to obtain normal microplastic migration data.

[0009] The 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 the migration trajectory prediction results. The ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior.

[0010] The generation module is used to construct a BIM model of the target monitored soil, establish a mapping between each three-dimensional grid unit and the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a heat map. High-risk areas with microplastic concentrations exceeding a preset threshold are highlighted in the heat map.

[0011] Furthermore, the target monitoring soil is divided into several three-dimensional grid units with a size of 5m*5m*0.2m using a three-dimensional grid division method. The geometric center of each three-dimensional grid unit is used as the monitoring point. The three-dimensional coordinates of each monitoring point are calibrated by a positioning system, and the intrinsic and extrinsic parameters of each monitoring point are collected simultaneously. The intrinsic and extrinsic parameters are fused by multi-source data through spatiotemporal alignment and normalization to generate a comprehensive soil microplastic dataset.

[0012] The three-dimensional coordinates of each monitoring point are marked as follows: ; This is an index for the monitoring points.

[0013] Furthermore, the specific logic for collecting the intrinsic parameters of each monitoring point is as follows:

[0014] Collect 100g of soil from the monitoring point as a sample and perform the following operations:

[0015] Determination of organic matter content: A portion of the soil sample was placed in a crucible, weighed and recorded, dried at 105℃, removed and placed in a muffle furnace, and ignited at 450℃ for 24 hours. After cooling, the sample was removed and weighed again. The organic matter content was calculated by the difference in specific gravity and recorded as follows. ;

[0016] Soil pH measurement: Weigh 1g of air-dried soil sample into a glass beaker, add 2.5mL of deionized water, stir and mix for 1min, allow to settle for 30min, and then measure the pH using a pH meter. Record the result as pH. ;

[0017] Salinity determination: A portion of the soil sample was prepared into a paste, and the salinity value was measured using a salinity meter and recorded as follows. ;

[0018] Determination of microplastic concentration: At the center point of each three-dimensional grid cell, 200g of soil sample was collected using a pollution-free sampler. After air drying and sieving, 50g of sample was taken and organic matter was removed by H2O2 oxidation. Microplastics were separated by ZnCl2 density liquid flotation and then vacuum filtered to... After filtration, the polymer type was identified and the particle number was counted using micro-infrared spectroscopy to obtain the microplastic concentration per unit mass of soil, denoted as . ;

[0019] The soil mulching period refers to the continuous use time of the plastic film covering the soil surface;

[0020] 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 biosorption of microplastics by the roots.

[0021] Among them, for all monitoring points with the same horizontal and vertical axes, the soil mulching years, ultraviolet radiation intensity, and plant root length are the same.

[0022] Furthermore, a monitoring point is determined to be an abnormal migration data point when it meets any of the following conditions:

[0023] The monitoring points contain non-numerical, empty, or invalid placeholder values ​​in their intrinsic and extrinsic parameters.

[0024] The value of either the intrinsic or extrinsic parameter at the monitoring point exceeds the effective dynamic range calibrated by the instrument;

[0025] Set the neighborhood search radius for abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, correct the abnormal migration data points according to the spatial distance decay law. The formula used is as follows:

[0026] ;

[0027] This represents the value to be filled for abnormally migrated data points. Indicates the first The value of a normal migrated data point. This represents the number of normal migrated data points located within the search radius of the neighborhood of the abnormal migrated data points. This is an index for normal migrating data points located within the neighborhood search radius of abnormal migrating data points. This indicates that the abnormal migration data point is related to the first... The distance between normal migrated data points The standard deviation of the Gaussian function is used to control the degree of weight decay. It is a natural constant;

[0028] Sure The formula used is as follows:

[0029] ;

[0030] In the formula, The three-dimensional coordinates of the monitoring points corresponding to the abnormal migration data points. For the first The three-dimensional coordinates of the monitoring point corresponding to each normal migration data point;

[0031] The neighborhood search radius is determined by statistically analyzing the spatial distribution of normal migration data points, and combining the standard deviation and correlation of changes in external and internal parameters to evaluate the minimum distance that results in the best filling effect. This minimum distance is the neighborhood search radius.

[0032] Furthermore, the migration potential index of each monitoring point was determined based on the dimensionless processed normal microplastic migration data, using the following formula:

[0033] ;

[0034] In the formula, As a migration potential index, Salinity For soil , The length of the plant root system. Intensity of ultraviolet radiation, For the number of years of soil mulching, Microplastic concentration, Organic matter content, and For the preset weights, And satisfy .

[0035] Furthermore, the dynamic path of microplastics is predicted based on the migration potential index of each monitoring point. The specific process is as follows: First, all monitoring points... Values ​​are mapped to a three-dimensional potential energy field. A migration probability model based on the quantum tunneling effect is constructed; then, each microplastic particle is treated as a virtual "living" entity for iterative calculation, and its position is initialized as the center coordinate of its respective 3D grid cell, and its velocity is... ,in, Represents the potential energy field gradient, For migration coefficient, .

[0036] Furthermore, in the iterative calculation, the particle is based on the potential energy gradient. To perform quantum state transitions, where the horizontal migration step size for:

[0037] ;

[0038] In the formula, The first adjustment coefficient, For time step, It is a migration potential index;

[0039] Vertical migration step size The formula used to correct for plant root length is:

[0040] ;

[0041] In the formula, This is the vertical migration step size. It is a natural constant. The length of the plant root system. The characteristic depth was obtained through soil field measurements;

[0042] Update the coordinates of each particle:

[0043] ;

[0044] In the formula, ( ) represents the updated 3D coordinates, ( () represents the current three-dimensional coordinates of the particle. Let x be the migration step size of the microplastic particles in the horizontal x-direction. Let be the migration step size of the microplastic particles in the horizontal y-direction. This is the vertical migration step size;

[0045] Repeat the above steps until the set number of iterations is reached; output the microplastic concentration of each 3D mesh cell during the prediction period using Monte Carlo simulation. and the set of main migration paths , For the path index, This represents the number of primary migration paths.

[0046] Furthermore, a BIM model for constructing the target monitoring soil is established, mapping each three-dimensional grid unit to the BIM model, visualization technology is used to display the migration trajectory prediction results, and a thermal analysis diagram is generated.

[0047] Set the microplastic concentration threshold as The concentration of microplastics in each three-dimensional grid unit and The comparison was made based on the concentration of microplastics exceeding a preset threshold. The three-dimensional grid cells are marked as high-risk areas, and the heat map shows areas where the microplastic concentration exceeds a preset threshold. High-risk areas are highlighted.

[0048] The present invention also provides a method for evaluating the migration of soil microplastics, wherein the method is performed using the aforementioned soil microplastic migration evaluation system, and includes:

[0049] Step 1: Divide the target monitoring soil into multiple three-dimensional grid units evenly, and take the center of each three-dimensional grid unit as a monitoring point. Collect the intrinsic and extrinsic parameters of each monitoring point. The intrinsic parameters include microplastic concentration, soil pH, organic matter content and salinity. The extrinsic parameters include soil mulching years, ultraviolet radiation intensity and plant root length. Perform multi-source data fusion processing on the parameters to obtain a comprehensive soil microplastic dataset.

[0050] Step 2: Perform anomaly identification on the comprehensive soil microplastic dataset to identify abnormal migration data points. At the same time, set the neighborhood search radius of the abnormal migration data points. Based on the spatial distance decay law, fill and correct the abnormal migration data points according to the normal migration data points within the neighborhood search radius to obtain normal microplastic migration data.

[0051] Step 3: Based on the normal microplastic migration data, determine the migration potential index of each monitoring point, 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 the migration trajectory prediction results. The ecological mirror simulation model treats microplastic particles as virtual "living" entities to simulate their nonlinear diffusion and deposition behavior.

[0052] Step 4: Construct a BIM model of the target soil for monitoring, establish a mapping between each three-dimensional grid unit and the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map. High-risk areas where the microplastic concentration exceeds the preset threshold are highlighted in the thermal map.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] This invention provides a comprehensive monitoring and dynamic assessment capability for soil microplastic pollution through multi-source data fusion and integrated analysis. It accurately identifies and effectively corrects abnormal migration data points, ensuring data reliability and integrity. Simultaneously, the ecological mirror simulation model, constructed based on migration potential indices and quantum-inspired algorithms, can efficiently predict the dynamic migration paths of microplastics in soil, thus providing a scientific basis for developing targeted remediation measures. Furthermore, through the construction of a 3D BIM model and the visualization of heat maps, the system intuitively identifies high-risk areas, enhancing decision-makers' understanding of microplastic pollution distribution and providing 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. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the overall system modules of the present invention;

[0056] Figure 2 This is a schematic diagram of the overall method flow of the present invention;

[0057] Figure 3 A 3D scatter image of ultraviolet radiation intensity, soil mulch duration, and migration potential index. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] Example:

[0061] Please see Figure 1 This invention provides a soil microplastic migration assessment system, specifically comprising:

[0062] The data acquisition module is used to uniformly divide the target monitoring soil into multiple three-dimensional grid units, with the center of each three-dimensional grid unit as a monitoring point. It collects the intrinsic and extrinsic parameters of each monitoring point. The intrinsic parameters include microplastic concentration, soil pH, organic matter content and salinity, while the extrinsic parameters include the number of years of soil mulching, ultraviolet radiation intensity and plant root length. The parameters are then subjected to multi-source data fusion processing to obtain a comprehensive soil microplastic dataset.

[0063] In this embodiment, the target monitoring soil is divided into several three-dimensional grid units with a size of 5m*5m*0.2m using a three-dimensional grid division method. The geometric center of each three-dimensional grid unit is used as the monitoring point. The three-dimensional coordinates of each monitoring point are calibrated by a positioning system, and the intrinsic and extrinsic parameters of each monitoring point are collected synchronously. The intrinsic and extrinsic parameters are fused by multi-source data through spatiotemporal alignment and normalization to generate a comprehensive soil microplastic dataset.

[0064] The three-dimensional coordinates of each monitoring point are marked as follows: ; This is an index for the monitoring points.

[0065] The specific logic for collecting the intrinsic parameters of each monitoring point is as follows:

[0066] Collect 100g of soil from the monitoring point as a sample and perform the following operations:

[0067] Determination of organic matter content: A portion of the soil sample was placed in a crucible, weighed and recorded, dried at 105℃, removed and placed in a muffle furnace, and ignited at 450℃ for 24 hours. After cooling, the sample was removed and weighed again. The organic matter content was calculated by the difference in specific gravity and recorded as follows. ;

[0068] Soil pH measurement: Weigh 1g of air-dried soil sample into a glass beaker, add 2.5mL of deionized water, stir and mix for 1min, allow to settle for 30min, and then measure the pH using a pH meter. Record the result as pH. ;

[0069] Salinity determination: A portion of the soil sample was prepared into a paste, and the salinity value was measured using a salinity meter and recorded as follows. ;

[0070] Determination of microplastic concentration: At the center point of each three-dimensional grid cell, 200g of soil sample was collected using a pollution-free sampler. After air drying and sieving, 50g of sample was taken and organic matter was removed by H2O2 oxidation. Microplastics were separated by ZnCl2 density liquid flotation and then vacuum filtered to... After filtration, the polymer type was identified and the particle number was counted using micro-infrared spectroscopy to obtain the microplastic concentration per unit mass of soil, denoted as . ;

[0071] The soil mulching period refers to the continuous use time of the plastic film covering the soil surface, and the plant root length refers to the maximum vertical extension depth of the plant roots in the soil at the monitoring point, which is used to characterize the physical disturbance and bioadsorption of microplastics by the roots.

[0072] Among them, for all monitoring points with the same horizontal and vertical axes, the soil mulching years, ultraviolet radiation intensity, and plant root length are the same.

[0073] The data acquisition module efficiently and accurately acquires both intrinsic and extrinsic soil parameters by uniformly dividing the target soil into multiple three-dimensional grid units and monitoring at the center of each unit. This detailed spatial division makes data acquisition systematic and standardized, helping to comprehensively reflect the distribution characteristics of soil microplastics and providing higher spatial resolution. Compared with traditional point sampling methods, it can better reveal the migration and accumulation patterns of microplastics in the soil.

[0074] Compared with existing technologies, the advantages of this data acquisition module 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 the soil. This module, through multi-point and multi-dimensional parameter acquisition, not only improves the reliability of the data but also makes the assessment of microplastic migration more scientific, better meeting the needs of environmental monitoring and soil protection.

[0075] In this invention, the implementation of the data acquisition module provides a solid data foundation for the overall solution. Through systematic parameter acquisition and data fusion processing, it is ensured that 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 ability and reliability of the entire system, promoting the comprehensiveness and effectiveness of soil microplastic migration assessment.

[0076] The identification module is used to identify anomalies in the comprehensive soil microplastic dataset, identify abnormal migration data points, set the neighborhood search radius of abnormal migration data points, and fill and correct the abnormal migration data points based on the spatial distance decay law according to the normal migration data points within the neighborhood search radius to obtain normal microplastic migration data.

[0077] In this embodiment, a monitoring point is determined to be an abnormal migration data point when it meets any of the following conditions:

[0078] The monitoring points contain non-numerical, empty, or invalid placeholder values ​​in their intrinsic and extrinsic parameters.

[0079] The value of either the intrinsic or extrinsic parameter at the monitoring point exceeds the effective dynamic range calibrated by the instrument;

[0080] Monitoring points containing non-numerical values, null values, or invalid placeholders, or whose intrinsic or extrinsic parameters exceed the instrument's calibrated effective dynamic range, are considered anomalous migration data points. This judgment is primarily based on the reliability and validity of the data. Non-numerical or null values ​​prevent data analysis and affect the accuracy of research conclusions. Parameters exceeding the instrument's calibrated effective dynamic range may indicate inaccurate measurements or data distortion, potentially affecting the calculation and evaluation of the microplastic migration potential index. Therefore, to ensure data integrity and the scientific rigor of the analysis, these anomalous data points must be excluded to maintain the validity and credibility of the research results.

[0081] Set the neighborhood search radius for abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, correct the abnormal migration data points according to the spatial distance decay law. The formula used is as follows:

[0082] ;

[0083] This represents the value to be filled for abnormally migrated data points. Indicates the first The value of a normal migrated data point. This represents the number of normal migrated data points located within the search radius of the neighborhood of the abnormal migrated data points. This is an index for normal migrating data points located within the neighborhood search radius of abnormal migrating data points. This indicates that the abnormal migration data point is related to the first... The distance between normal migrated data points The standard deviation of the Gaussian function is used to control the degree of weight decay. It is a natural constant;

[0084] In the above formula, the dependent variable This represents the values ​​to be filled for anomalous migration data points, i.e., the intrinsic and extrinsic parameters after correction at the anomalous point location. The meaning of this dependent variable is that by utilizing information from normal migration data points in the neighborhood, the reasonable values ​​for anomalous points are inferred, thereby supplementing and correcting the data. The technical effect is to improve the accuracy and completeness of the data, making the predictive ability of the entire microplastic migration evaluation system more reliable, aiding subsequent analysis and decision-making, and effectively reducing misleading information caused by data anomalies. In this formula, the independent variables mainly include the values ​​of normal migration data points. and the distance between the abnormal migration data points These independent variables and dependent variables The relationship between them is established through a spatial distance decay law. Specifically, the values ​​of normally migrating data points within the neighborhood... The higher and the distance from the outlier The closer the data points, the more significant their impact on anomalous migration data points. The correlation of the independent variables is reflected in the fact that when the concentration values ​​of neighboring normal data points are high and the distance is short, their influence on the calculation of correction values ​​is less pronounced. These are given greater weight, thus effectively filling in the values ​​of outlier data points. In the formula, the dependent variable... With independent variable There is a positive correlation, meaning that the higher the value of the normal migration data point, the lower the value of the corrected abnormal migration data point. This will also increase accordingly. This is because during the calculation process, The value is directly involved in the correction calculation, and the distance weight factor Ensure that the closer the normal data points are, the better. The greater the impact, the more likely it is that the more recent normal data points have high concentrations. The value will also increase accordingly. And with the independent variable... The relationship is negatively correlated, meaning the greater the distance, the lower the correlation. The smaller the value, the less impact this normal migration data point has on... The influence indicates that, spatially, normal data points that are closer together have a stronger corrective effect on outliers.

[0085] Sure The formula used is as follows:

[0086] ;

[0087] In the formula, The three-dimensional coordinates of the monitoring points corresponding to the abnormal migration data points. For the first The three-dimensional coordinates of the monitoring point corresponding to each normal migration data point;

[0088] The neighborhood search radius is determined by statistically analyzing the spatial distribution of normal migrating data points, combined with the standard deviation and correlation assessment of changes in their external and internal parameters, to identify the minimum distance that yields the best filling effect. Specifically, the process involves: first, constructing a distance matrix by calculating the distances between normal data points to identify their spatial clustering characteristics; then, analyzing the standard deviations of the external and internal parameters of these data points to assess their correlation and determine which parameters have spatial consistency and influence; and finally, gradually adjusting the neighborhood search radius, observing changes in the filling effect, and using cross-validation to evaluate the accuracy and consistency of the filled data, ultimately selecting the minimum distance that yields the best filling effect as the neighborhood search radius. This process ensures the scientific validity and rationality of the filled data, providing a more accurate foundation for subsequent analysis.

[0089] The main advantage of the identification module lies in its efficient and accurate anomaly detection capabilities. This module can quickly identify anomalous migrating data points by analyzing a comprehensive soil microplastic dataset and perform filling correction based on a preset neighborhood search radius. This process not only effectively removes noisy data but also performs accurate interpolation based on the spatial distribution of normal data points, thereby ensuring the reliability and accuracy of subsequent data and providing high-quality data support for the entire system.

[0090] Compared to existing technologies, the beneficial effects of the identification module are mainly reflected in its significantly improved scientific rigor and effectiveness in handling abnormal data by incorporating a correction method based on spatial distance attenuation. Traditional methods often rely on simple threshold judgments or processing isolated data points, which can easily lead to the loss or misjudgment of important information. This module, however, through statistical analysis based on spatial distribution, can more comprehensively and accurately identify and process abnormal data, thereby enhancing the overall reliability and adaptability of the system.

[0091] In this invention, the implementation of the identification module provides crucial quality assurance for the overall solution. By effectively identifying and correcting anomalous migration data points, this module ensures the accuracy of the data upon which subsequent steps, such as the calculation of the migration potential index and dynamic path simulation, rely. This high-quality data assurance not only enhances the scientific rigor of microplastic migration assessment but also improves the predictive capabilities and decision support level of the overall system, thereby promoting the effectiveness of soil environmental protection.

[0092] The 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 the migration trajectory prediction results. The ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior.

[0093] In this embodiment, the migration potential index of each monitoring point is determined based on the dimensionless normal microplastic migration data, using the following formula:

[0094] ;

[0095] In the formula, As a migration potential index, Salinity For soil , The length of the plant root system. Intensity of ultraviolet radiation, For the number of years of soil mulching, Microplastic concentration, Organic matter content, and For the preset weights, , .

[0096] set up The practical reason lies in the chemical properties of the soil, such as pH, salinity, and microplastic concentration, which have a more direct and significant impact on microplastic migration. 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, UV radiation intensity, and the number of years of soil mulching also affect microplastic migration, their influence is relatively small. Therefore, they are given lower weights in the model to more accurately reflect the dominant role of the soil environment in microplastic migration.

[0097] In the given formula above, the dependent variable The migration potential index represents the potential for microplastics to migrate in the soil at each monitoring point. The higher the value, the greater the migration capacity of that monitoring point. Specifically, this means assessing the migration capacity of microplastics under different environmental conditions by comprehensively considering soil chemical properties, microplastic concentration, and environmental factors. The technological benefit lies in providing a basis for the management and remediation of microplastic pollution, helping to identify high-risk areas, thereby enabling the development of corresponding environmental protection measures and promoting soil health and ecological security.

[0098] The presence of organic matter significantly impacts soil microplastics. Both microplastics and organic matter possess large specific surface areas and readily interact through adsorption. The adsorption of organic matter on microplastic surfaces increases their aggregation, hindering their degradation efficiency and migration rate in the soil environment, thus significantly affecting their distribution in the soil layer. In addition, soil salinity also exhibits a significant impact on microplastics, second only to organic matter. There is a strong correlation between soil salinity and soil structural properties; the level of salinity directly affects soil looseness. As soil salinity increases, soil structure gradually shrinks, the porosity between soil particles decreases instantaneously, the connectivity between pore sizes decreases, and it may even cause changes in the surface charge properties of soil particles. The migration of microplastics in the soil environment mainly occurs through surface charge interactions and other processes within soil pores. Salinity plays a crucial role in the zeta potential of pore water; increased salinity alters soil pore distribution, leading to changes in water flow paths and velocities, thus limiting the pathways and efficiency of microplastic transport and transformation in the soil environment. Increased pH generally leads to a higher migration potential index because higher pH values ​​improve soil chemistry, promoting the dissolution and dispersion of microplastics and reducing their adsorption on soil particles, thus enhancing their migration capacity. Furthermore, higher pH values ​​may increase microbial activity, accelerating microplastic degradation and making them more mobile. Therefore, within a certain range, increased pH promotes microplastic migration in soil. Plant roots are also a major factor contributing to microplastic migration; long-rooted plants transport microplastics over longer distances, and areas with dense root systems have higher microplastic content. Therefore, the longer the plant root system, the greater the migration potential of microplastics in the soil. Increased UV intensity accelerates microplastic degradation because UV radiation can disrupt the chemical structure of microplastics, causing them to break down and degrade into smaller particles. These smaller microplastic particles are more easily migrated in the soil; therefore, increased UV intensity increases the microplastic migration potential index. As soil mulching ages, the soil environment may become more stable and moist, and the mulching material may promote microbial growth, thereby accelerating the microplastic degradation process. Furthermore, soil mulching reduces soil moisture evaporation and improves soil moisture retention, making microplastics more mobile within the soil. Therefore, increasing the duration of soil mulching positively impacts the microplastic migration potential index.

[0099] The rationality of this formula is reflected in several aspects. First, the migration potential index consists of two parts: a square root form and a logarithmic form. These two mathematical expressions can effectively reflect the nonlinear influence of different factors on microplastic migration. The square root part... Combining soil chemical properties with microplastic concentration indicates that these factors influence the migration capacity of microplastics through interactions; while the logarithmic part This study considered the cumulative effects of plant root length, UV radiation intensity, and soil mulching duration on microplastic migration. Weights were set accordingly. 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 demonstrating the comprehensive effect of the corresponding environmental conditions on the migration potential.

[0100] Table 1: Statistics on Migration Potential Index

[0101]

[0102] Please see Figure 3 In this data analysis, based on data from 15 monitoring points, the relationship between the microplastic migration potential index and various influencing factors can be observed. Overall, the migration potential index... The values ​​showed significant fluctuations at different monitoring sites, reflecting the complexity of the soil environment and its impact on microplastic migration.

[0103] First, factors such as soil pH, salinity, and microplastic concentration have an impact on... The effects are quite significant. Higher salinity and pH are generally associated with better soil conditions and promote microplastic migration. Furthermore, plant root length is related to the 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 moisture conditions. Ultraviolet radiation intensity also shows an effect, especially at higher intensities, which can promote the decomposition and migration of microplastics.

[0104] Secondly, changes in the number of years of soil mulching and organic matter content also have an impact. Prolonged mulching periods may lead to soil degradation, thereby affecting the migration capacity of microplastics. Soils with higher organic matter content can, to some extent, improve soil structure and increase the migration potential of microplastics. However, the specific extent of the impact requires further analysis using more detailed field data. Overall, these results highlight the importance of soil properties, plant characteristics, and external environmental factors in the microplastic migration process and provide a scientific basis for subsequent management and control strategies.

[0105] The dynamic path of microplastics is predicted based on the migration potential index of each monitoring point. The specific process is as follows: First, all monitoring points... Values ​​are mapped to a three-dimensional potential energy field. A migration probability model based on the quantum tunneling effect is constructed; then, each microplastic particle is treated as a virtual "living" entity for iterative calculation, and its position is initialized as the center coordinate of its respective 3D grid cell, and its velocity is... ,in, Represents the potential energy field gradient, For migration coefficient, .in, The value was determined by performing regression analysis on historical microplastic migration data, combined with the actual migration rate of microplastics under different environmental conditions, in order to optimize the migration model and achieve the best fit.

[0106] Potential energy field The energy state representing 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. By... The value is mapped to a potential energy field, which can intuitively represent the migration potential at different locations, thus providing a foundation for subsequent migration probability models.

[0107] In iterative calculations, particles are determined according to the potential energy gradient. To perform quantum state transitions, where the horizontal migration step size for:

[0108] ;

[0109] In the formula, The first adjustment coefficient, , For time step, It is a migration potential index;

[0110] Vertical migration step size The formula used to correct for plant root length is:

[0111] ;

[0112] In the formula, This is the vertical migration step size. It is a natural constant. 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 by soil profile analysis and measured data on root distribution, combined with plant growth characteristics and root extension characteristics.

[0113] Vertical migration step size The formula reflects the influence of plant root length on microplastic migration, and its rationality is reflected in the following aspects: First, the formula uses natural constants. The exponential function can effectively describe the nonlinear effect of plant roots on the vertical migration of microplastics, reflecting the significant limitation on migration caused by short root length. Secondly, As a feature depth, it can be obtained through measured data, giving the model a realistic basis and enabling it 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 the soil.

[0114] Update the coordinates of each particle:

[0115] ;

[0116] In the formula, ( ) represents the updated 3D coordinates, ( () represents the current three-dimensional coordinates of the particle. Let x be the migration step size of the microplastic particles in the horizontal x-direction. Let be the migration step size of the microplastic particles in the horizontal y-direction. This represents the vertical migration step size; by updating the coordinates, the positional changes of each microplastic particle during the iteration process can be tracked, laying the foundation for the final migration path prediction.

[0117] Repeat the above steps until the set number of iterations is reached; output the microplastic concentration of each 3D mesh cell during the prediction period using Monte Carlo simulation. and the set of main migration paths , For the path index, The Monte Carlo simulation is a computational method using random sampling to evaluate the behavior of complex systems. In this invention, it is used to predict the concentration (CSS) of microplastics in each 3D grid cell, as well as the set of major migration paths. This process allows us to obtain the distribution and dynamic migration paths of microplastics in different regions during the prediction period, thus providing a scientific basis for environmental management and pollution control.

[0118] The main advantage of the simulation module lies in its ability to construct an ecological mirror simulation model based on normal microplastic migration data using quantum-inspired algorithms, thereby accurately predicting the dynamic migration paths of microplastics in soil. This method not only considers the nonlinear diffusion and deposition behavior of microplastics but also treats microplastic particles as virtual "living" entities, enhancing the model's ability to simulate microplastic migration behavior in real soil environments. This refined simulation enables a more realistic reflection of the behavior and impact of microplastics in soil.

[0119] Compared to existing technologies, the simulation module's advantage lies in its combination of quantum tunneling effect and ecological mirror model, which significantly improves the accuracy and reliability of microplastic migration path prediction. Traditional methods often rely on simple linear or stagnant models, failing to fully reflect the complex behavior of microplastics in varying soil environments. This module, through a more scientific simulation approach, captures the actual migration dynamics of microplastics in soil, enhancing the scientific rigor of predictions and contributing to the development of more effective soil management and remediation strategies.

[0120] In this invention, the implementation of the simulation module provides crucial dynamic predictive capabilities for the overall solution. Accurate simulation of microplastic migration paths provides data support for risk assessment and management strategy development, helping to identify potentially high-risk areas. This data-driven predictive capability not only enhances the system's practicality but also strengthens the scientific rigor and effectiveness of soil microplastic management, thus playing a positive role in promoting environmental protection and soil remediation efforts.

[0121] The generation module is used to construct a BIM model of the target monitoring soil, establish a mapping between each three-dimensional grid unit and the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a heat analysis map. High-risk areas with microplastic concentrations exceeding a preset threshold are highlighted in the heat map.

[0122] In this embodiment, a BIM model of the target monitoring 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.

[0123] Set the microplastic concentration threshold as The concentration of microplastics in each three-dimensional grid unit and The comparison was made based on the concentration of microplastics exceeding a preset threshold. The three-dimensional grid cells are marked as high-risk areas, and the heat map shows areas where the microplastic concentration exceeds a preset threshold. High-risk areas are highlighted.

[0124] The microplastic concentration threshold The threshold was determined using the following method: based on historical monitoring data and ecological risk assessment results of the target area, and comprehensively considering soil environmental quality standards and microplastic ecotoxicity data, a combination of statistical analysis and expert consultation was employed. Specifically, the 90th percentile of historical monitoring data was selected as the baseline, and the final threshold was determined after appropriate adjustments based on the ecological risk characteristics of different polymer types. .

[0125] The main advantage of the generation module lies in its ability to construct a BIM model of the target monitored soil, accurately mapping each 3D 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 concentration, providing strong support for decision-making.

[0126] Compared to existing technologies, the benefits of the generative module lie in its combination of BIM model construction and visualization techniques, which enables a more comprehensive and in-depth analysis of the microplastic migration process. Traditional methods typically rely on simple two-dimensional charts or statistical data, failing to provide a detailed spatial perspective. In contrast, the visualization technology in this module highlights high-risk areas, facilitating targeted measures by managers and improving management efficiency and effectiveness.

[0127] In this invention, the implementation of the generation module provides an important and intuitive analytical 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 of microplastic pollution and formulate scientific soil remediation and management strategies accordingly. This process not only enhances the system's practicality but also provides crucial technical support for soil environmental protection work, promoting the scientific and refined management of soil microplastics.

[0128] Please see Figure 2 The present invention also provides a method for evaluating the mobility of soil microplastics, comprising:

[0129] Step 1: Divide the target monitoring soil into multiple three-dimensional grid units evenly, and take the center of each three-dimensional grid unit as a monitoring point. Collect the intrinsic and extrinsic parameters of each monitoring point. The intrinsic parameters include microplastic concentration, soil pH, organic matter content and salinity. The extrinsic parameters include soil mulching years, ultraviolet radiation intensity and plant root length. Perform multi-source data fusion processing on the parameters to obtain a comprehensive soil microplastic dataset.

[0130] Step 2: Perform anomaly identification on the comprehensive soil microplastic dataset to identify abnormal migration data points. At the same time, set the neighborhood search radius of the abnormal migration data points. Based on the spatial distance decay law, fill and correct the abnormal migration data points according to the normal migration data points within the neighborhood search radius to obtain normal microplastic migration data.

[0131] Step 3: Based on the normal microplastic migration data, determine the migration potential index of each monitoring point, 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 the migration trajectory prediction results. The ecological mirror simulation model treats microplastic particles as virtual "living" entities to simulate their nonlinear diffusion and deposition behavior.

[0132] Step 4: Construct a BIM model of the target soil for monitoring, establish a mapping between each three-dimensional grid unit and the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map. High-risk areas where the microplastic concentration exceeds the preset threshold are highlighted in the thermal map.

[0133] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0134] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0135] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0136] 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 migration assessment system, characterized in that, Specifically, it includes: The data acquisition module is used to uniformly divide the target monitoring soil into multiple three-dimensional grid units, with the center of each three-dimensional grid unit as a monitoring point. It collects the intrinsic and extrinsic parameters of each monitoring point. The intrinsic parameters include microplastic concentration, soil pH, organic matter content and salinity, while the extrinsic parameters include the number of years of soil mulching, ultraviolet radiation intensity and plant root length. The parameters are then subjected to multi-source data fusion processing to obtain a comprehensive soil microplastic dataset. The identification module is used to identify anomalies in the comprehensive soil microplastic dataset, identify abnormal migration data points, set the neighborhood search radius of abnormal migration data points, and fill and correct the abnormal migration data points based on the spatial distance decay law according to the normal migration data points within the neighborhood search radius to obtain normal microplastic migration data. The 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 the migration trajectory prediction results. The ecological mirror simulation model treats microplastic particles as virtual "living" entities and simulates their nonlinear diffusion and deposition behavior. The generation module is used to construct a BIM model of the target monitored soil, establish a mapping between each three-dimensional grid unit and the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a heat map. High-risk areas with microplastic concentrations exceeding a preset threshold are highlighted in the heat map.

2. The soil microplastic migration assessment system according to claim 1, characterized in that: The target soil for monitoring was divided into several three-dimensional grid units with a size of 5m*5m*0.2m using a three-dimensional grid division method. 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. The intrinsic and extrinsic parameters of each monitoring point were collected simultaneously. The intrinsic and extrinsic parameters were fused by multi-source data through spatiotemporal alignment and normalization to generate a comprehensive soil microplastic dataset. The three-dimensional coordinates of each monitoring point are marked as follows: ; This is an index for the monitoring points.

3. The soil microplastic migration assessment 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 soil sample was placed in a crucible, weighed and recorded, dried at 105℃, removed and placed in a muffle furnace, and ignited at 450℃ for 24 hours. After cooling, the sample was removed and weighed again. The organic matter content was calculated by the difference in specific gravity and recorded as follows. ; Soil pH measurement: Weigh 1g of air-dried soil sample into a glass beaker, add 2.5mL of deionized water, stir and mix for 1min, allow to settle for 30min, and then measure the pH using a pH meter. Record the result as pH. ; Salinity determination: A portion of the soil sample was prepared into a paste, and the salinity value was measured using a salinity meter and recorded as follows. ; Determination of microplastic concentration: At the center point of each three-dimensional grid cell, 200g of soil sample was collected using a pollution-free sampler. After air drying and sieving, 50g of sample was taken and organic matter was removed by H2O2 oxidation. Microplastics were separated by ZnCl2 density liquid flotation and then vacuum filtered to... After filtration, the polymer type was identified and the particle number was counted using micro-infrared spectroscopy to obtain the microplastic concentration per unit mass of soil, denoted as . ; The soil mulching period 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 biosorption of microplastics by the roots. Among them, for all monitoring points with the same horizontal and vertical axes, the soil mulching years, ultraviolet radiation intensity, and plant root length are the same.

4. The soil microplastic migration assessment system according to claim 1, characterized in that: A monitoring point is considered an abnormal data migration point if it meets any of the following conditions: The monitoring points contain non-numerical, empty, or invalid placeholder values ​​in their intrinsic and extrinsic parameters. The value of either the intrinsic or extrinsic parameter at the monitoring point exceeds the effective dynamic range calibrated by the instrument; Set the neighborhood search radius for abnormal migration data points. Based on the normal migration data points within the neighborhood search radius, correct the abnormal migration data points according to the spatial distance decay law. The formula used is as follows: ； This represents the value to be filled for abnormally migrated data points. Indicates the first The value of a normal migrated data point. This represents the number of normal migrated data points located within the search radius of the neighborhood of the abnormal migrated data points. This is an index for normal migrating data points located within the neighborhood search radius of abnormal migrating data points. This indicates that the abnormal migration data point is related to the first... The distance between normal migrated data points The standard deviation of the Gaussian function is used to control the degree of weight decay. It is a natural constant; Sure The formula used is as follows: ； In the formula, The three-dimensional coordinates of the monitoring points corresponding to the abnormal migration data points. For the first The three-dimensional coordinates of the monitoring point corresponding to each normal migration data point; The neighborhood search radius is determined by statistically analyzing the spatial distribution of normal migration data points, and combining the standard deviation and correlation of changes in external and internal parameters to evaluate the minimum distance that results in the best filling effect. This minimum distance is the neighborhood search radius.

5. The soil microplastic migration assessment system according to claim 1, characterized in that: The migration potential index for each monitoring point was determined based on dimensionless normal microplastic migration data, using the following formula: ； In the formula, As a migration potential index, Salinity For soil , The length of the plant root system. The intensity of ultraviolet radiation. For the number of years of soil mulching, Microplastic concentration, Organic matter content, and For the preset weights, And satisfy .

6. The soil microplastic migration 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, all monitoring points... Values ​​are mapped to a three-dimensional potential energy field. A migration probability model based on the quantum tunneling effect is constructed; then, each microplastic particle is treated as a virtual "living" entity for iterative calculation, and its position is initialized as the center coordinate of its respective 3D grid cell, and its velocity is... ,in, Represents the potential energy field gradient, For migration coefficient, .

7. The soil microplastic migration assessment system according to claim 6, characterized in that: In iterative calculations, particles are determined according to the potential energy gradient. To perform quantum state transitions, where the horizontal migration step size for: ； Where, The first adjustment coefficient, For time step, It is a migration potential index; Vertical migration step size The formula used to correct for plant root length is: ； Where, This is the vertical migration step size. It is a natural constant. The length of the plant root system. The characteristic depth was obtained through soil field measurements; Update the coordinates of each particle: ； In the formula, ( ) represents the updated 3D coordinates, ( () represents the current three-dimensional coordinates of the particle. Let x be the migration step size of the microplastic particles in the horizontal x-direction. Let be the migration step size of the microplastic particles in the horizontal y-direction. This is the vertical migration step size; Repeat the above steps until the set number of iterations is reached; output the microplastic concentration of each 3D mesh cell during the prediction period using Monte Carlo simulation. and the set of main migration paths , For the path index, This represents the number of primary migration paths.

8. The soil microplastic migration assessment system according to claim 1, characterized in that: The BIM model for constructing the target soil for monitoring is used to establish a mapping between each three-dimensional grid unit and the BIM model, and the migration trajectory prediction results are displayed using visualization technology, and a thermal analysis map is generated. Set the microplastic concentration threshold as The concentration of microplastics in each three-dimensional grid unit and The comparison was made based on the concentration of microplastics exceeding a preset threshold. The three-dimensional grid cells are marked as high-risk areas, and the heat map shows areas where the microplastic concentration exceeds a preset threshold. High-risk areas are highlighted.

9. A method for evaluating the migration of soil microplastics, characterized in that: The method for evaluating the migration of soil microplastics is performed using the soil microplastic migration evaluation system according to any one of claims 1-8, and includes: Step 1: Divide the target monitoring soil into multiple three-dimensional grid units evenly, and take the center of each three-dimensional grid unit as a monitoring point. Collect the intrinsic and extrinsic parameters of each monitoring point. The intrinsic parameters include microplastic concentration, soil pH, organic matter content and salinity. The extrinsic parameters include soil mulching years, ultraviolet radiation intensity and plant root length. Perform multi-source data fusion processing on the parameters to obtain a comprehensive soil microplastic dataset. Step 2: Perform anomaly identification on the comprehensive soil microplastic dataset to identify abnormal migration data points. At the same time, set the neighborhood search radius of the abnormal migration data points. Based on the spatial distance decay law, fill and correct the abnormal migration data points according to the normal migration data points within the neighborhood search radius to obtain normal microplastic migration data. Step 3: Based on the normal microplastic migration data, determine the migration potential index of each monitoring point, 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 the migration trajectory prediction results. The ecological mirror simulation model treats microplastic particles as virtual "living" entities to simulate their nonlinear diffusion and deposition behavior. Step 4: Construct a BIM model of the target soil for monitoring, establish a mapping between each three-dimensional grid unit and the BIM model, use visualization technology to display the migration trajectory prediction results, and generate a thermal analysis map. High-risk areas where the microplastic concentration exceeds the preset threshold are highlighted in the thermal map.

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