Garbage retention area analysis method and system based on two-dimensional shallow water equation model

Abstract

The invention belongs to the field of water pollutant treatment, particularly relates to a garbage retention area analysis method and system based on a two-dimensional shallow water equation model, and aims to solve the problem of inaccurate garbage retention area prediction. The method comprises the steps of performing grid division on a target water area, and constructing a hydrodynamic model of the target water area in a preset time period; according to the hydrodynamic model, simulating the motion trail of discrete garbage in the target water area through a discrete particle motion equation; based on the shoreline gradient and the river curvature of the target water area, determining the stranding probability of the discrete garbage under the motion trail; and in a preset time period, determining a garbage retention index of each grid based on the water flow speed, the discrete garbage retention time in each grid and the stranding probability, so as to screen a garbage retention area in the target water area. According to the method, the prediction deviation caused by simplifying the garbage into an ideal tracer in the prior art is overcome, and the prediction accuracy of the garbage transportation path and the final gathering position is remarkably improved.

Description

Technical Field

[0001] This application belongs to the field of water pollutant treatment, and specifically involves a method and system for analyzing garbage retention areas based on a two-dimensional shallow water equation model. Background Technology

[0002] Floating debris in water bodies, such as plastic bottles, foam, and other household waste, poses a significant challenge to water pollution control. Timely cleanup and removal of this floating debris are crucial for effective water environment management. Traditional monitoring methods rely heavily on manual patrols or remote sensing imagery, making it difficult to predict potential debris accumulation areas in real time and accurately. Consequently, cleanup efforts often lack specificity, leading to inefficiency and high costs.

[0003] To improve the scientific rigor of predictions, existing technologies have begun to employ numerical models to simulate the transport and diffusion processes of pollutants. For example, hydrodynamic and water quality models have been widely used in pollutant diffusion studies. However, existing models often simplify solid floating objects into dissolved tracers that drift with the current, neglecting their unique physical behaviors as discrete individuals, such as wind dragging, shoreline grounding, and resuspension. This leads to discrepancies between model predictions and reality, resulting in inaccurate predictions of waste accumulation areas. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, namely the inaccurate prediction of waste retention areas, one embodiment of this application provides a waste retention area analysis method based on a two-dimensional shallow water equation model, comprising:

[0005] The target water area is divided into grids, and a hydrodynamic model of the target water area within a preset time period is constructed based on two-dimensional shallow water equations.

[0006] Based on the hydrodynamic model, the motion trajectory of discrete waste in the target water area is simulated by the discrete particle motion equation, which is determined based on the water flow velocity and the wind-induced flow velocity of the discrete particles.

[0007] Based on the shoreline slope and river curvature of the target water area, the probability of discrete waste stranding is determined under the motion trajectory.

[0008] Within a preset time period, the garbage retention index of each grid is determined based on the water flow velocity, the discrete garbage residence time in each grid, and the grounding probability, in order to screen garbage retention areas in the target water area.

[0009] As a preferred implementation method, the motion trajectory of discrete debris in the target water area is simulated using discrete particle motion equations, including:

[0010] The wind-induced velocity is determined based on the drag coefficient, air density, exposed area of ​​discrete waste, and wind speed.

[0011] By summing the water flow velocity and the wind-induced flow velocity, a discrete particle motion equation that varies with time is constructed;

[0012] Using discrete waste as discrete particles, the initial position of each discrete particle is determined through a hydrodynamic model.

[0013] Starting from the initial position, the simulated positions of discrete particles at different times are obtained according to the discrete particle motion equation, thus obtaining the motion trajectory of discrete waste.

[0014] As a preferred implementation, determining the stranding probability of discrete waste includes:

[0015] Based on the difference between the shoreline slope of the target water area and the preset critical slope, and the difference between the river channel curvature and the preset curvature critical threshold, a stranding probability function is constructed.

[0016] Based on the hydrodynamic model, determine the shoreline boundary of the target water area;

[0017] For any discrete particle, if its position in its trajectory coincides with the shoreline boundary, the stranding probability of the discrete waste corresponding to that discrete particle is determined according to the stranding probability function.

[0018] As a preferred implementation method, the waste retention index of each grid is determined, including:

[0019] Based on the relationship between the average flow velocity within the grid and the global average flow velocity of the target water area, low-velocity zones within the target water area are identified.

[0020] Based on the movement trajectory of each discrete piece of waste, the residence time of discrete waste in each grid is determined in order to screen out high-density particle areas in the target water area;

[0021] Based on the stranding probability, the frequency of discrete waste stranding within the pre-divided shoreline units is determined in order to screen high stranding probability areas in the target waters.

[0022] The waste retention index is determined based on the area proportion of low-velocity zones in each grid, the discrete waste residence time in high-density zones, and the stranding frequency in high-stranding probability zones.

[0023] As a preferred implementation method, determining the waste retention index includes:

[0024] The normalized average residence time of each discrete piece of waste in the high-density zone is calculated to determine the particle density index of each grid in the high-density zone.

[0025] The normalized result of the stranding frequency in the high stranding probability zone within a preset time period is calculated and used as the stranding probability index.

[0026] The waste retention index is obtained by weighting and summing the area ratio, particle density index, and stranding probability index.

[0027] As a preferred implementation, determining the frequency of discrete waste stranding within pre-divided shoreline units includes:

[0028] The shoreline boundary of the target waters is divided into multiple shoreline units;

[0029] The probability of any discrete waste being stranded is compared with a random number. If the probability of stranding is greater than the random number, then any discrete waste is determined to have successfully stranded.

[0030] Within a preset time period, the number of times a ship is successfully grounded in any shoreline unit is determined as the grounding frequency.

[0031] As a preferred implementation method, the screening process for waste retention areas includes:

[0032] The waste retention index is segmented according to at least one preset grading threshold.

[0033] Based on the segmentation results, multiple grids were selected to identify waste retention areas.

[0034] As a preferred embodiment, the method further includes:

[0035] Acquire waste monitoring data of the target water area and construct dynamic source terms based on the waste monitoring data. The dynamic source terms are used to simulate the waste scenario of the target water area under a preset emergency.

[0036] By combining hydrodynamic models and dynamic source terms, the movement trajectory of small particulate waste is simulated.

[0037] As a preferred embodiment, the method further includes:

[0038] Obtain measured garbage distribution data for the target water area;

[0039] Spatial consistency is determined based on the difference in the number of grids between the simulation results of the waste retention area and the measured waste distribution data.

[0040] Based on spatial consistency, one or more parameters in the inversion simulation process are retrieved.

[0041] On the other hand, one embodiment of this application proposes a waste retention area analysis system based on a two-dimensional shallow water equation model, used to perform the above-described waste retention area analysis method based on a two-dimensional shallow water equation model, including:

[0042] The model building module is used to divide the target water area into grids and build a hydrodynamic model of the target water area within a preset time period based on two-dimensional shallow water equations.

[0043] The motion trajectory simulation module is used to simulate the motion trajectory of discrete waste in the target water area based on the hydrodynamic model and the discrete particle motion equation. The discrete particle motion equation is determined based on the water flow velocity and the wind-induced flow velocity of the discrete particles.

[0044] The stranding probability determination module is used to determine the stranding probability of discrete waste based on the shoreline slope and river curvature of the target water area, under the motion trajectory.

[0045] The garbage retention area screening module is used to determine the garbage retention index of each grid within a preset time period based on water flow velocity, discrete garbage residence time in each grid, and grounding probability, so as to screen garbage retention areas in the target water area.

[0046] Compared with the prior art, the technical solution provided in this application has at least one of the following beneficial effects:

[0047] This application simulates the movement trajectory of discrete waste by coupling water flow transport and wind towing, and considers the factor of shoreline stranding. It fuses multi-dimensional information to generate a quantitative waste retention index, which can more closely reflect the behavior of floating waste in real waters. It overcomes the prediction bias caused by simplifying waste into an ideal tracer in existing technologies, thereby significantly improving the prediction accuracy of waste transport paths and final accumulation locations. This provides precise target guidance for subsequent waste cleanup and salvage work, and significantly improves the efficiency and effectiveness of environmental governance. Attached Figure Description

[0048] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0049] Figure 1 This is a flowchart of a method for analyzing waste retention areas based on a two-dimensional shallow water equation model, provided in one embodiment of this application;

[0050] Figure 2 This is a system block diagram of a waste retention area analysis system based on a two-dimensional shallow water equation model provided in one embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the structure of a computer system used to implement the methods, systems, and electronic devices of this application. Detailed Implementation

[0052] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0053] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] This application provides a method for analyzing waste retention areas based on a two-dimensional shallow water equation model. The method involves dividing the target water area into grids and constructing a hydrodynamic model of the target water area within a preset time period based on the two-dimensional shallow water equation. According to the hydrodynamic model, the movement trajectory of discrete waste in the target water area is simulated using discrete particle motion equations, where the discrete particle motion equations are determined based on water flow velocity and wind-induced flow velocity of discrete particles. Based on the shoreline slope and river curvature of the target water area, the grounding probability of discrete waste is determined under the motion trajectory. Within the preset time period, based on water flow velocity, the residence time of discrete waste in each grid, and the grounding probability, a waste retention index is determined for each grid to screen waste retention areas within the target water area. By coupling water flow transport and wind dragging to simulate the movement trajectory of discrete waste, and considering the factor of shoreline grounding, multi-dimensional information is fused to generate a quantified waste retention index. This method can more closely approximate the behavior of floating waste in real water areas, overcoming the prediction bias caused by simplifying waste as an ideal tracer in existing technologies, thereby significantly improving the prediction accuracy of waste transport paths and final accumulation locations.

[0055] To more clearly explain the waste retention area analysis method based on the two-dimensional shallow water equation model in this application, the following will be combined with... Figure 1 The steps in the embodiments of this application are described in detail.

[0056] The method for analyzing waste retention areas based on a two-dimensional shallow water equation model in the first embodiment of this application includes steps S10-S40, each of which is described in detail below:

[0057] Step S10: Divide the target water area into grids and construct a hydrodynamic model of the target water area within a preset time period based on the two-dimensional shallow water equation.

[0058] Optionally, the continuous target water area can be divided into a large number of non-overlapping discrete small units (i.e., a grid) for defining and calculating physical quantities. The grid can be uniform or non-uniform, and its shape can be a geometric shape such as a square or triangle. In most open water areas, a larger base grid can be used; for example, the characteristic side length of the grid can be set to 10 meters.

[0059] As one possible implementation, mesh generation can be achieved through mesh generation software, such as the Surface Water Modeling System (SMS) based on Delaunay triangulation.

[0060] In one embodiment of this application, the entire target water area is divided into multiple unstructured triangular grids. In order to balance the computational accuracy and computational cost, the basic grid size is set to 10 meters, and the grid size is locally densified to 5 meters for preset key areas such as pollution sources, shorelines, and estuaries.

[0061] Understandably, in critical areas where water flow changes drastically or where there is a significant impact on waste transport, higher resolution meshes are needed to capture their detailed hydrodynamic characteristics. Therefore, in this embodiment, these pre-defined critical areas are locally densified, with the mesh size in these areas reduced to 5 meters.

[0062] The identification of critical areas can be based on various criteria. For example, one approach is to define pre-defined geographical features, often empirically recognized as key nodes for hydrodynamics and waste transport, such as areas near direct discharge points of waste pollution sources, shorelines with complex shapes, and estuaries or distributaries where water flows converge. Another approach is to dynamically identify areas based on acquired geographic environmental data; for instance, by analyzing Digital Bathymetric Model (DBM) data, steep bank slopes with topographic gradients (slopes) exceeding a set slope threshold can be identified as critical areas. A third approach is to analyze Point of Interest (POI) data, obtaining the number of POIs per square kilometer as pollution source density, and identifying areas with pollution source density exceeding a set density threshold as critical areas.

[0063] Here, setting the slope threshold can be, for example, as follows: The density threshold can be determined based on the statistical results of the density values ​​of the entire target water area. For example, the density values ​​of the top 5%-15% of the density values ​​in the entire target water area can be selected as the density threshold.

[0064] By employing this localized encryption strategy, the model can significantly improve the simulation accuracy of key areas without substantially increasing the overall computational load, thereby more accurately simulating the complex movement and retention behavior of waste in these areas.

[0065] As one possible implementation method, various water area data of the target water area can be collected before grid division.

[0066] As an example, a high-precision digital elevation model (DEM) is obtained for terrain analysis. In this embodiment, the resolution of the DEM is... 5m.

[0067] As an example, the acquisition of remote sensing imagery for water body extraction and shoreline identification uses multispectral or high-resolution satellite imagery (e.g., Sentinel-2 or Landsat) in the embodiments of this application.

[0068] As an example, POI data (pollution source locations) is obtained from an open map platform (such as OpenStreetMap) in this application embodiment, and the pollution source type (such as campsite, village, tourist spot) is marked.

[0069] As an example, population / tourist statistics are obtained from local statistics departments or tourism platforms in this application embodiment and integrated at a daily or weekly granularity.

[0070] As an example, meteorological data (such as rainfall, wind speed and direction) is acquired. In this embodiment of the application, meteorological data is acquired from meteorological stations or reanalysis data (such as ERA5), with a time resolution of [missing information]. One hour. The ERA5 reanalysis data provides data products at hourly intervals, therefore its time resolution is one hour. For higher time resolution, such as less than one hour, actual data from local encrypted weather stations can be used.

[0071] As an example, a holiday calendar can be retrieved to adjust for peak travel periods.

[0072] The hydrodynamic model is the foundation for all subsequent simulation analyses, aiming to accurately calculate the flow velocity, direction, and depth at any location within the water body at any time. In this embodiment, to improve the accuracy and efficiency of the calculation, the process of constructing the hydrodynamic field preferably employs a two-dimensional shallow water equation model based on an unstructured grid. This two-dimensional shallow water equation model can comprehensively consider various physical forces such as wind stress, seabed friction, and Coriolis forces, thereby simulating the true state of water flow.

[0073] As an example, the depth-averaged two-dimensional shallow water equation can be expressed by the following formula:

[0074] ;

[0075] ;

[0076] ;

[0077] Where h represents water depth, t represents time, u and v represent velocity components, and g represents gravitational acceleration. Indicates the elevation of the subgrade. and These represent the wind stress components in the x and y directions, respectively. This indicates the density of water. Represents the Coriolis force parameter. and These represent the bed friction components in the x and y directions, respectively.

[0078] Wind stress , Indicates air density, The drag coefficient is represented by W, and the wind speed is represented by W; the bed friction is represented by W. 'n' represents the Manning coefficient, determined according to the substrate type, 'U' represents the average cross-sectional velocity of the water flow, and 'Coriolis force' is the parameter. , Represents the Earth's angular velocity of rotation. Indicates latitude.

[0079] As an example, drag coefficient The value can be 0.0013, and the Manning coefficient n can be set to 0.025-0.05 depending on the type of substrate.

[0080] Furthermore, we construct boundaries and drivers:

[0081] As an example, the upstream flow boundary can be a flow time series obtained from a hydrological station or simulated using a rainfall-runoff model. The downstream water level boundary can be obtained from tide gauges or reservoir water level data. Wind field driving can incorporate meteorological models (e.g., Weather Research and Forecasting, WRF) or measured wind field data with a temporal resolution ≤ 1 hour and spatial interpolation to grid points.

[0082] The upstream flow boundary defines how much water (flow rate, in cubic meters per second) flows from the upstream of the river into the simulation area at each moment in the simulation. The downstream water level boundary defines how high the water level should be at the outlet of the simulation area; this water level, in turn, affects the flow velocity and surface slope of the entire area. The wind field drive simulation simulates the wind stress generated by wind blowing on the water surface, which may affect the surface flow direction and exert drag on exposed debris.

[0083] Since the amount of waste generated is not constant but fluctuates with the time rhythm of human activities, for example, the amount generated in tourist spots and campsites is significantly higher than on weekdays and weekends. The wind force varies from day to day, which has different effects on the movement and retention of waste. Therefore, in this embodiment of the application, the hydrodynamic model is constructed on a daily cycle. In other embodiments, the hydrodynamic model can also be constructed on a weekly or monthly cycle according to the local conditions.

[0084] Step S20: Based on the hydrodynamic model, the motion trajectory of discrete waste in the target water area is simulated by the discrete particle motion equation, wherein the discrete particle motion equation is determined based on the water flow velocity and the wind-induced flow velocity of the discrete particles.

[0085] Optionally, during the transport of discrete waste, the transport effect of water flow will cause the discrete waste to move along the water flow calculated by the hydrodynamic model. The drag effect of wind on waste particles cannot be ignored, especially for some waste floating on the water surface, such as plastic bottles. Therefore, by using the discrete particle motion equations determined based on the water flow velocity and the wind-induced flow velocity of discrete particles, the motion trajectory of discrete waste is simulated according to the constructed hydrodynamic model.

[0086] In one embodiment of this application, the wind-induced velocity is determined based on the drag coefficient, air density, exposed area of ​​discrete waste, and wind speed; the water flow velocity and the wind-induced velocity are summed to construct a time-varying discrete particle motion equation; using discrete waste as discrete particles, the initial position of each discrete particle is determined through a hydrodynamic model; starting from the initial position, the simulated position of the discrete particles at different times is obtained according to the discrete particle motion equation, thus obtaining the motion trajectory of the discrete waste.

[0087] As an example, the equations of motion for discrete particles can be expressed by the following formula:

[0088] ;

[0089] ;

[0090] Where x represents the position of the discrete particle. Let x represent the velocity at position x at time t, and u represent the velocity of the water flow. Indicates wind-induced velocity. Indicates the drag coefficient. This represents the exposed area of ​​discrete waste on the water surface. Represents the total area of ​​discrete waste. This indicates the percentage of garbage exposed above the water surface.

[0091] It should be noted that the drag coefficient is assigned according to the type of waste. For example, in this embodiment, the drag coefficient of plastic bottles is determined to be 0.8, that of leaves is 0.3, and that of wood is 0.5 based on experimental data. The percentage of waste exposed above the water surface is generally taken in the range of 0.1-0.5.

[0092] This application employs the Lagrange method to track and simulate the motion trajectories of multiple discrete waste particles. Unlike existing technologies that simplify waste into soluble tracers, this application treats waste as discrete particles with independent physical properties. The simulation of their motion trajectories comprehensively considers multiple key factors, enabling accurate simulation of the motion trajectories of discrete waste.

[0093] Step S30: Based on the shoreline slope and river curvature of the target water area, determine the probability of discrete waste stranding under the motion trajectory.

[0094] Optionally, when discrete particles simulate discrete waste moving to the shore, they may become stranded, temporarily or permanently remaining on the shore, or be carried away by the water flow, resulting in resuspension. By calculating the stranding probability, the situation after the particles move to the shore can be simulated.

[0095] It is understandable that when the discrete particle position is relatively shallow and the shoreline slope is large and the river channel is highly curved, stranding is likely to occur. If a simple judgment is made by setting a water depth threshold, slope threshold or river channel curvature threshold, there will be many false judgments. Therefore, by setting a critical value and constructing a smooth probability function, a comprehensive judgment on stranding situation can be made.

[0096] In one embodiment of this application, a stranding probability function is constructed based on the difference between the shoreline slope of the target water area and the preset critical slope, and the difference between the river channel curvature and the preset curvature critical threshold; the shoreline boundary of the target water area is determined according to the hydrodynamic model; for any discrete particle, if its position in the trajectory coincides with the shoreline boundary, the stranding probability of the discrete waste corresponding to any discrete particle is determined according to the stranding probability function.

[0097] As an example, the stranding probability function can be represented by the following formula:

[0098] ;

[0099] in, Let represent the probability of stranding, k and m represent the mathematical shape parameters of the stranding probability function, exp() represents the exponential function with the natural constant e as the base, and S represents the shoreline slope. This indicates the preset critical slope. Indicates the curvature of the river channel. This indicates the preset curvature critical threshold.

[0100] It should be noted that the mathematical shape parameters k and m are used to adjust the shape of the probability curve, such as an S-shaped or exponential curve, and are calibrated using measured data. In this embodiment, k=0.5 and m=1.0.

[0101] In this embodiment of the application, the river curvature is calculated using the radius of curvature. For example, the reciprocal of the radius of curvature of a meandering river is used as the river curvature. Since the unit of the reciprocal of the radius of curvature is 1 / meter, the same curvature value has completely different physical meanings in rivers of different widths. Therefore, the reciprocal of the radius of curvature is multiplied by the river width to obtain the river curvature, so that it is related to the scale of the river itself. This allows for a direct assessment of the degree of river meandering and the probability of grounding.

[0102] As an example, a preset critical slope Preset curvature critical threshold When the shoreline slope is greater than the preset critical slope and the shoreline channel curvature is greater than the preset curvature critical threshold, the probability of stranding increases significantly.

[0103] It is understandable that the stranding probability is not an inherent property of discrete particles, but rather the probability of a stranding scenario when a discrete particle touches the shoreline. Even if the stranding probability is high, other scenarios (such as waves) may occur that cause the discrete particle to be carried away by the water flow again.

[0104] Therefore, for any discrete particle, the stranding probability calculation will only be triggered if its position in the trajectory coincides with the shoreline boundary obtained by the hydrodynamic model. At this time, a random number is generated and compared with the stranding probability to simulate a random scenario. If the stranding probability is greater than the generated random number, the discrete waste corresponding to the discrete particle is determined to be stranded.

[0105] In some embodiments, a comprehensive judgment can be made based on multiple environmental conditions, such as the real-time water depth at the location of the discrete particle, the slope of the shoreline itself, and the curvature of the river channel, to determine whether the discrete particle is stranded or carried away by the water flow. For example, it can be set that the discrete particle is stranded when the water depth at the location of the discrete particle is less than a certain threshold (e.g., 0.05 meters) and the slope of the shoreline is greater than a preset critical slope.

[0106] Step S40: Within a preset time period, based on the water flow velocity, the discrete garbage residence time in each grid, and the grounding probability, determine the garbage retention index of each grid to screen garbage retention areas in the target water area.

[0107] Optionally, after obtaining the trajectories of a large number of debris particles, the simulation results need to be analyzed to identify potential stagnation areas. Areas with lower water flow velocities, such as backflow zones and the interior of harbors, are more prone to debris accumulation. Areas where discrete particles frequently linger, as well as areas with a higher calculated probability of stranding, are more likely to experience stranding.

[0108] In one embodiment of this application, a waste retention index is determined by identifying low-velocity areas, high-particle-density areas, and high-stranding-probability areas within a target water body to assess the waste retention situation.

[0109] Specifically, based on the relationship between the average flow velocity within the grid and the global average flow velocity of the target water area, low-velocity zones within the target water area are identified.

[0110] The average flow velocity simulated within a preset time period based on the hydrodynamic model. Set dynamic threshold ,in, The value ranges from 0.2 to 0.5, with the specific value determined based on historical data optimization. For example, spatial distribution images of historical garbage accumulation areas are obtained through drone aerial photography or manual inspections. Set a set of candidate values ​​within the range of values. Values, such as [0.2, 0.3, 0.4, 0.5], are used to identify low-velocity areas using dynamic thresholding for any candidate value. The overlap between these areas and the spatial distribution image is then calculated, and the area with the highest overlap is selected. The optimal value is selected to obtain the dynamic threshold. The overlap can be calculated using the intersection-union ratio. For example, the low-velocity area corresponding to the candidate value is extracted as the shadow area, and the garbage accumulation area in the spatial distribution image is extracted as the shadow area. The ratio of the intersection area to the union area of ​​the two shadow areas is calculated as the overlap.

[0111] It should be noted that, The value of is related to the hydrological and geographical characteristics of the water body. For different types of water bodies, such as rivers, lakes, and reservoirs, the optimal value can be obtained by optimizing the method according to their hydrological and geographical characteristics to ensure its applicability.

[0112] For any given grid, if its flow velocity is less than the dynamic threshold, then that grid belongs to the low-velocity region. More precisely, a normalization assignment can be performed based on the flow velocity, and the low-velocity region can be selected according to a set proportion (e.g., 20%) based on the normalization result. The normalization method can be common methods such as linear normalization or standard normalization.

[0113] Based on the movement trajectory of each discrete piece of waste, the residence time of discrete waste in each grid is determined in order to screen out high-density particle areas in the target water area.

[0114] For any given grid, the total residence time of discrete particles within a preset time period is calculated, and the grids with the longest residence time in all grids of the target water area are extracted as the high-density particle areas. For example, the preset proportion can be 10%.

[0115] Based on the stranding probability, the frequency of discrete waste stranding within the pre-divided shoreline units is determined in order to screen high stranding probability areas in the target waters.

[0116] The shoreline boundary of the target water area is divided into multiple shoreline units, for example, one shoreline unit every 50 meters. The frequency of grounding in each shoreline unit within a preset time period is counted. The grids involved in the shoreline units with a grounding frequency greater than the frequency threshold are selected as high grounding probability zones.

[0117] As an example, the frequency threshold can be set to once per day.

[0118] The method for obtaining the stranding frequency is as follows: the shoreline boundary of the target water area is divided into multiple shoreline units; the stranding probability of any discrete waste is compared with a random number, and if the stranding probability is greater than the random number, the stranding of any discrete waste is determined to be successful; within a preset time period, the number of times the waste is successfully stranded in any shoreline unit is determined as the stranding frequency.

[0119] The waste retention index is determined based on the area proportion of low-velocity zones in each grid, the discrete waste residence time in high-density zones, and the stranding frequency in high-stranding probability zones.

[0120] Specifically, the normalized average residence time of each discrete piece of waste in the high-density zone is calculated to determine the particle density index of each grid in the high-density zone; the normalized result of the stranding frequency in the high stranding probability zone within a preset time period is calculated as the stranding probability index; and the waste retention index is obtained by weighted summation of the area ratio, particle density index and stranding probability index.

[0121] As an example, the waste retention index can be determined using the following formula:

[0122] ;

[0123] in, Indicates the waste retention index, This indicates the area proportion of the low-velocity region. Indicates the particle density index. This represents the probability index of stranding. , and Indicates the weight.

[0124] As an example, the weights can be determined using the Analytic Hierarchy Process (AHP), for instance, by constructing a judgment matrix using expert scoring to obtain three weights, as described in this embodiment. , , In other embodiments, the weights can be determined based on the actual aquatic environment. For example, in a reservoir with significant wind action and a tortuous shoreline, the weight of the grounding probability index may be set higher.

[0125] The area ratio of low-velocity zones is used to quantify the risk of waste accumulation in a region due to low flow velocity. The particle density index is used to quantify the degree of particle aggregation in a region, and the stranding probability index is used to quantify the risk of waste stranding in the shoreline area. Through the above calculations, the waste accumulation index is analyzed from three dimensions. Based on hydrodynamic principles, low-velocity zones that are more likely to cause waste aggregation are identified. Based on direct statistics of Lagrange particle tracking results, high-density particle zones in all grids are identified where discrete particles are most likely to stay for extended periods. For shoreline stranding, the high stranding probability zones that are most likely to intercept and adsorb waste are selected by statistically analyzing the frequency of waste particle stranding on shoreline units determined in the simulation.

[0126] Multidimensional fusion analysis was performed on the identified low-velocity zone, high-particle-density zone, and high-stuck-probability zone to determine the final waste retention area. Since these three zones reveal the possibility of waste retention from three different mechanisms—hydrodynamics, particle behavior statistics, and shoreline interaction—fusion analysis yields more comprehensive and reliable results, avoiding the one-sidedness of single-dimensional judgment.

[0127] Furthermore, based on the calculated waste retention index, all areas of the target water body can be classified and determined. In this embodiment, the waste retention index is segmented according to at least one preset classification threshold; based on the segmentation results, waste retention areas composed of multiple grids are selected.

[0128] For example, two preset grading thresholds of 0.3 and 0.7 can be set to divide the water area into three levels: The area was designated as a key cleanup zone, which is the area with the highest risk of being stranded and the most urgent need for cleanup. The area is designated as a secondary priority area, where there is a certain risk of lingering, requiring continued monitoring or regular cleanup. The area is designated as an observation zone, which is an area with a low risk of garbage accumulation.

[0129] After classifying the target water area, a tiered retention area map can be generated for the entire target water area. For example, key cleanup areas, secondary key areas, and observation areas can be marked with different colors, providing precise, intuitive, and actionable target guidance for environmental management departments in making decisions such as scheduling garbage collection vessels and allocating cleanup resources. Combined with weather forecasts, this method can also predict future garbage drift trends and potential accumulation risks, enabling garbage cleanup work to shift from passive to proactive, significantly improving the efficiency and effectiveness of environmental governance.

[0130] In this way, this application can output an intuitive, quantitative, and graded map of waste retention risks, providing strong decision support for environmental management departments to formulate precise and efficient cleanup and salvage strategies.

[0131] In some embodiments, a dynamic source term for waste can be constructed before the start of the entire simulation to simulate how waste enters the water body. This dynamic source term can comprehensively consider the type of waste pollution source (such as tourist attractions, towns and villages), seasonal changes in traffic flow, peak tourist seasons during holidays, and the scouring effect of rainstorms on surface waste, so that the input of waste is more in line with the spatiotemporal pulse characteristics of reality, rather than a simple uniform and constant input.

[0132] Specifically, waste monitoring data of the target water area is acquired, and dynamic source items are constructed based on the waste monitoring data. The dynamic source items are used to simulate the waste scenario of the target water area under preset emergency events.

[0133] As one possible implementation method, a mapping function between pollution sources and waste generation is first established:

[0134] ;

[0135] in, This indicates the amount of waste generated within a preset time period. This indicates the weight of the pollution source types in the target water area. This represents population / tourist statistics within a preset time period. This represents the seasonal correction factor for the target water area. K represents an empirical coefficient, and R represents the rainfall (in millimeters) within a preset time period.

[0136] It should be noted that the weights for pollution source types are determined based on field surveys or literature. For example, the weight for pollution source types in campsites is 1.5, in villages and communities it is 1.0, and in urban areas it is 1.2. Seasonal correction coefficients are fitted based on historical waste data; for example, the seasonal correction coefficient for summer is 1.3, and for winter it is 0.7. The empirical coefficient K can be 0.1.

[0137] In one embodiment of this application, the fitting method for the seasonal correction coefficient is as follows: obtain the actual amount of garbage collected or monitored in the target water area for at least one full year, preferably for three consecutive years or more, as historical garbage data; calculate the total average value of all historical garbage data as the garbage baseline value; group the historical garbage data by season and calculate the average amount of garbage in each season, and use the ratio of the average amount of garbage in each season to the garbage baseline value as the seasonal correction coefficient for each season.

[0138] The source term is set as a spatiotemporal impulse function to simulate event-driven inputs such as peak tourist seasons and torrential rain runoff.

[0139] ;

[0140] in, This represents a dynamic source term, used to describe the input at coordinates (x, y) at time t. This represents a time-intensity function used to simulate pre-set sudden events, such as peak tourist seasons or heavy rainstorms. This represents the spatial distribution function, and the Gaussian kernel function is used to simulate point source diffusion. Indicates the coordinates of the pollution source.

[0141] ;

[0142] Where A represents the event magnitude, Indicates the central time of the event. Indicates the duration of the event.

[0143] It should be noted that the value of the event amplitude A is determined based on the event type. For example, for rainfall events such as heavy rainstorms, the value of A is related to the rainfall intensity. For instance, it can be set as a tiered rule: light rain (daily rainfall <10mm / d), A=1.5; moderate rain (daily rainfall 10-25mm / d), A=2.5; heavy rain or torrential rain (daily rainfall >25mm / d), A=4.0. As another example, for peak tourist season events, the value of A is related to the holiday level. For instance: ordinary weekends, A=2.0; short holidays (e.g., Qingming Festival, Dragon Boat Festival), A=3.5; long holidays (e.g., National Day, Spring Festival), A=5.0. Specific values ​​can be determined based on historical tourist volume data.

[0144] ;

[0145] in, and This represents the diffusion parameter, which can be, for example, 100 meters.

[0146] Furthermore, to make the simulation more closely resemble the composition of real-world waste, this application can also combine hydrodynamic models and dynamic source terms to simulate the movement trajectory of small particulate waste.

[0147] As an example, the equation obtained by simulating the concentration field of small particles using the Euler method is:

[0148] ;

[0149] Where C represents the mass concentration of garbage tracers per unit volume of water (unit: kg / m³). and denoted by , where S represents the dispersion coefficient and S represents the dynamic source term.

[0150] It should be noted that the dispersion coefficient and It can be empirically set based on the characteristics of water flow turbulence, and its acquisition method can be: calculation using theoretical or empirical formulas, for example... ,in It represents the frictional flow velocity; or it can be calibrated through field tracer tests. For example, according to existing research, the dispersion coefficient in river or lake environments is usually in the range of 0.1-10 m² / s, and this range can be used as the initial basis for parameter selection.

[0151] The convection-diffusion process of small particulate waste (such as microplastics and debris) concentration field is simulated by the Eulerian method, and combined with the Lagrange method for simulating large discrete waste, the transport and diffusion behavior of waste of different sizes can be simulated simultaneously.

[0152] In some embodiments, real-world observation data can be used to calibrate and optimize numerical models, making them increasingly more predictive.

[0153] Specifically, the process involves acquiring measured waste distribution data for the target water area; determining spatial consistency based on the difference in the number of grids between the simulation results and the measured waste distribution data for the waste retention area; and retrieving one or more parameters from the simulation process based on the spatial consistency.

[0154] Optionally, measured waste distribution data can be obtained through various modern monitoring technologies, such as using drones equipped with high-definition or multispectral cameras to take aerial photographs of the water surface, utilizing satellite remote sensing imagery, or deploying monitoring cameras at key locations. The acquired image data can be intelligently analyzed using deep learning models (such as YOLO, CNN, etc.) to automatically identify and delineate the location and extent of waste in the images, forming a measured waste distribution map. GPS is used to record waste locations for verification.

[0155] It should be noted that when the drone is equipped with an RGB or multispectral camera for image acquisition, the flight altitude should be ≤100 meters and the resolution ≤0.1 meters. YOLOv5 or Faster R-CNN should be used, and the training dataset should include labeled garbage samples (such as plastic bottles and plastic bags). The training parameters can be set as follows: epochs=100, batch_size=16, and COCO pre-trained weights should be used.

[0156] After obtaining the measured waste distribution data, the spatial consistency between the simulated waste retention area and the measured waste distribution data is evaluated, and the accuracy of the model is quantified.

[0157] Specifically, the simulation results of the model (i.e., the identified waste retention areas) and the measured waste distribution map can be processed into a grid, and then compared grid by grid within the same grid system. By calculating precision and recall and combining them to obtain the F1-score as spatial consistency, or by calculating well-known evaluation indicators in the field such as intersection-over-union ratio (IoU) as spatial consistency, the degree of agreement between the simulation results and the actual situation can be quantitatively evaluated.

[0158] As an example, the formula for calculating the F1 score can be shown below:

[0159] ;

[0160] in, Indicates accuracy. , Indicates recall rate, TP represents the number of grid cells with garbage in both simulation and actual measurement, FP represents the number of grid cells with garbage in simulation but not in actual measurement, and FN represents the number of grid cells without garbage in simulation but with garbage in actual measurement.

[0161] Furthermore, based on the evaluation results of spatial consistency, one or more key parameters used in the simulation process are inverted and optimized.

[0162] If the evaluation results show a large deviation between the simulation and the actual measurements (e.g., the calculated spatial consistency value is less than 0.5), it indicates that some parameters in the model are set unreasonably. In this case, a loss function aimed at reducing the difference between simulation and actual measurements can be constructed, and optimization algorithms (such as genetic algorithms or Bayesian optimization) can be used to automatically search for and adjust key parameters in the model, such as the drag coefficient. The shape parameters k and m of the stranding probability function, and the dispersion coefficient. and We continue until we find the optimal combination of parameters that makes the simulation results closest to the measured data.

[0163] As an example, the constructed loss function can be expressed by the following formula:

[0164] ;

[0165] Where L represents the loss function value, and N represents the number of grid cells involved in the loss calculation. This represents the simulated waste retention index of the i-th grid. This represents the measured waste retention index of the i-th grid. Represents the regularization coefficient. Indicates the parameters to be inverted. This represents the prior data of the parameters to be inverted.

[0166] It should be noted that the actual measured garbage retention index The acquisition method can be as follows: The measured garbage distribution map identified by UAV aerial photography or remote sensing imagery is processed into a grid like the model. The garbage coverage area or garbage quantity within each grid is statistically analyzed. Then, the garbage coverage area or garbage quantity of all grids is normalized using a linear function (Min-Max Normalization). For any grid, a quantized value between [0,1] is obtained, which serves as the measured garbage retention index for that grid. If the measured data is only "present / absent," then the garbage grid... A garbage-free grid .

[0167] The parameters to be inverted are key parameters in the model that have a significant impact on the results and are subject to high uncertainty. In this embodiment, the parameter to be inverted may be the drag coefficient. The shape parameters k and m of the stranding probability function, and the dispersion coefficient. and Prior data refers to the optimal estimates or reasonable ranges of the parameters to be inverted before inversion optimization. Its sources mainly include: empirical values ​​recommended in authoritative literature, values ​​measured in laboratory physical experiments, or parameter values ​​from models successfully applied in other similar water bodies. In the loss function... This is a regularization term used to constrain the optimized values ​​of the parameters to be inverted from deviating excessively from their prior data, thereby avoiding overfitting and ensuring the physical meaning and stability of the inversion results.

[0168] In one embodiment of this application, when the optimization algorithm is a genetic algorithm, the parameters can be set as follows: population size = 50, number of iterations = 100, crossover probability = 0.8, mutation probability = 0.1.

[0169] In one embodiment of this application, when the optimization algorithm is Bayesian optimization, a Gaussian process is used, and the number of iterations can be set to 50.

[0170] Furthermore, the optimization process goes beyond simply finding a fixed set of optimal parameters for the entire model; it involves establishing and updating the response relationships between key parameters and one or more environmental factors. For example, through repeated optimizations at different wind speeds, the drag coefficient can be gradually fitted. The functional relationship between wind speed and wind speed, i.e. In this way, the model evolves from using static parameters to using dynamic parameters, and can automatically adjust its internal physical parameters according to real-time environmental conditions, thereby greatly improving the model's adaptability and prediction accuracy under various working conditions.

[0171] Through continuous iteration of the aforementioned closed-loop optimization process, measured waste distribution data obtained through drones, remote sensing, and other means are compared with the model simulation results. Furthermore, optimization algorithms such as genetic algorithms are used to invert and dynamically calibrate key parameters in the model (such as drag coefficient and stranding probability parameters). This endows the model with self-learning and adaptive capabilities, allowing its prediction accuracy to continuously improve with the accumulation of monitoring data, and enhancing its generalization ability.

[0172] Please see Figure 2 The second embodiment of this application provides a waste retention area analysis system based on a two-dimensional shallow water equation model, which is used to execute the above-mentioned waste retention area analysis method based on a two-dimensional shallow water equation model. It includes: a model construction module 100, a motion trajectory simulation module 200, a stranding probability determination module 300, and a waste retention area screening module 400.

[0173] The model building module 100 is used to divide the target water area into grids and build a hydrodynamic model of the target water area within a preset time period based on the two-dimensional shallow water equation.

[0174] The motion trajectory simulation module 200 is used to simulate the motion trajectory of discrete waste in the target water area based on the hydrodynamic model and the discrete particle motion equation, wherein the discrete particle motion equation is determined based on the water flow velocity and the wind-induced flow velocity of the discrete particles.

[0175] The stranding probability determination module 300 is used to determine the stranding probability of discrete waste based on the shoreline slope and river curvature of the target water area under the motion trajectory.

[0176] The garbage retention area screening module 400 is used to determine the garbage retention index of each grid within a preset time period based on the water flow velocity, the discrete garbage residence time in each grid, and the grounding probability, so as to screen garbage retention areas in the target water area.

[0177] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0178] It should be noted that the above embodiments of the method and system for analyzing waste retention areas based on a two-dimensional shallow water equation model are merely illustrative examples of the division of the functional modules described above. In practical applications, the functions described above can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of this application can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of this application are merely for distinguishing the various modules or steps and are not considered as an improper limitation of this application.

[0179] A device according to a third embodiment of this application includes:

[0180] At least one processor;

[0181] and a memory communicatively connected to at least one of the processors;

[0182] The memory stores instructions that can be executed by the processor to implement the above-described method for analyzing waste retention areas based on a two-dimensional shallow water equation model.

[0183] A computer-readable storage medium according to a fourth embodiment of this application stores computer instructions that are executed by the computer to implement the above-described method for analyzing waste retention areas based on a two-dimensional shallow water equation model.

[0184] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and related descriptions of the electronic devices, computer-readable storage media, and computer program products described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0185] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system for implementing embodiments of the systems, methods, and electronic devices of this application. Figure 3 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0186] like Figure 3As shown, the computer system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0187] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0188] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0189] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0190] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0191] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0192] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0193] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for analyzing waste retention areas based on a two-dimensional shallow water equation model, characterized in that, include: The target water area is divided into grids, and a hydrodynamic model of the target water area within a preset time period is constructed based on two-dimensional shallow water equations. According to the hydrodynamic model, the motion trajectory of discrete waste in the target water area is simulated by the discrete particle motion equation, wherein the discrete particle motion equation is determined based on the water flow velocity and the wind-induced flow velocity of the discrete particles. Based on the shoreline slope and river curvature of the target water area, the probability of the discrete waste being stranded is determined under the motion trajectory. Within the preset time period, based on the water flow velocity, the discrete garbage residence time in each grid, and the stranding probability, the garbage retention index of each grid is determined to screen garbage retention areas in the target water area.

2. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 1, characterized in that, The simulation of the motion trajectory of discrete debris in the target water area using discrete particle motion equations includes: The wind-induced velocity is determined based on the drag coefficient, air density, the exposed area of ​​the discrete waste, and wind speed. Summing the water flow velocity and the wind-induced flow velocity, the time-varying discrete particle motion equation is constructed; Using the discrete waste as discrete particles, the initial position of each discrete particle is determined through the hydrodynamic model; Starting from the initial position, the simulated positions of the discrete particles at different times are obtained according to the discrete particle motion equation, thus obtaining the motion trajectory of the discrete waste.

3. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 1 or 2, characterized in that, Determining the stranding probability of the discrete waste includes: Based on the difference between the shoreline slope of the target water area and the preset critical slope, and the difference between the river curvature and the preset curvature critical threshold, a stranding probability function is constructed. Based on the hydrodynamic model, determine the shoreline boundary of the target water area; For any discrete particle, if its position in the trajectory coincides with the shoreline boundary, the stranding probability of the discrete waste corresponding to that discrete particle is determined according to the stranding probability function.

4. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 1, characterized in that, The determination of the waste retention index for each grid includes: Based on the relationship between the average flow velocity within the grid and the global average flow velocity of the target water area, low-velocity zones within the target water area are identified. Based on the movement trajectory of each discrete piece of debris, the residence time of discrete debris in each grid is determined in order to filter out high-density particle areas in the target water area; Based on the stranding probability, the frequency of discrete waste stranding within the pre-divided shoreline units is determined to screen high stranding probability areas within the target waters. The waste retention index is determined based on the area proportion of the low flow velocity zone in each grid, the discrete waste residence time of the high density zone, and the stranding frequency of the high stranding probability zone.

5. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 4, characterized in that, Determining the waste retention index includes: The normalized average residence time of each discrete piece of waste in the high-density zone is calculated to determine the particle density index of each grid in the high-density zone; The normalized result of the stranding frequency in the high stranding probability zone within a preset time period is calculated and used as the stranding probability index. The waste retention index is obtained by weighted summation of the area ratio, the particle density index, and the stranding probability index.

6. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 4 or 5, characterized in that, Determining the discrete waste stranding frequency within the pre-divided shoreline units includes: The shoreline boundary of the target water area is divided into multiple shoreline units; The stranding probability of any discrete waste is compared with a random number. If the stranding probability is greater than the random number, the stranding of any discrete waste is determined to be successful. Within the preset time period, the number of times a ship is successfully stranded in any shoreline unit is determined as the stranding frequency.

7. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 1, characterized in that, The screening process for the waste retention area includes: The waste retention index is segmented according to at least one preset grading threshold. Based on the segmentation results, multiple grids were selected to identify waste retention areas.

8. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 1, characterized in that, The method further includes: Acquire garbage monitoring data of the target water area, and construct dynamic source items based on the garbage monitoring data, wherein the dynamic source items are used to simulate garbage scenarios in the target water area under preset emergencies; By combining the hydrodynamic model and the dynamic source term, the movement trajectory of small particulate waste is simulated.

9. The method for analyzing waste retention areas based on a two-dimensional shallow water equation model according to claim 1, characterized in that, The method further includes: Obtain measured garbage distribution data for the target water area; Spatial consistency is determined based on the difference in the number of grids between the simulation results of the waste retention area and the measured waste distribution data. Based on the aforementioned spatial consistency, one or more parameters in the inversion simulation process are retrieved.

10. A system for analyzing landfill areas based on a two-dimensional shallow water equation model, used to execute the landfill area analysis method based on a two-dimensional shallow water equation model as described in any one of claims 1-9, characterized in that, include: The model building module is used to divide the target water area into grids and build a hydrodynamic model of the target water area within a preset time period based on two-dimensional shallow water equations. The motion trajectory simulation module is used to simulate the motion trajectory of discrete waste in the target water area based on the hydrodynamic model and the discrete particle motion equation, wherein the discrete particle motion equation is determined based on the water flow velocity and the wind-induced flow velocity of the discrete particles. A stranding probability determination module is used to determine the stranding probability of the discrete waste under the motion trajectory based on the shoreline slope and river curvature of the target water area. The garbage retention area screening module is used to determine the garbage retention index of each grid within the preset time period based on the water flow velocity, the discrete garbage residence time in each grid, and the grounding probability, so as to screen garbage retention areas in the target water area.

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