Petroleum pollutant diffusion dynamic tracking simulation method, device and equipment

Abstract

The invention discloses a petroleum pollutant diffusion dynamic tracking simulation method, device and equipment, and relates to the technical field of pollutant diffusion simulation, and the method comprises the steps: determining a multi-dimensional simulation working condition of pollutant diffusion in a target water body region; establishing an unstructured grid hydrodynamic model of the target water body area according to the multi-dimensional simulation working condition; an oil spill prediction model is constructed based on the unstructured grid hydrodynamic model, and the diffusion process of pollutants is simulated according to the oil spill prediction model; if the pollutants are non-soluble petroleum pollutants, the oil spill prediction model is a four-process coupling model including diffusion motion, wind-induced drift, turbulent diffusion and weathering degradation, and if the pollutants are soluble petroleum pollutants, the oil spill prediction model is a convective diffusion model. The simulation precision of the diffusion process and the influence range of the petroleum pollutants can be improved, the simulation precision is improved by 30% or above, and the method is particularly suitable for emergency decision support of closed water bodies such as reservoirs.

Description

Technical Field

[0001] The present application relates to the technical field of pollutant diffusion simulation, and in particular to a method, device and equipment for dynamic tracking simulation of petroleum pollutant diffusion. Background Art

[0002] With the development of society, ensuring water supply security and maintaining aquatic ecological safety are of paramount importance. Reservoirs are not only important sources of drinking water but also provide crucial support for agricultural and industrial development. However, when reservoirs face sudden point source pollution from petroleum spills, there is still a lack of hydrodynamic and water quality models that can accurately predict pollutant dispersion. This makes it difficult to ensure both water supply and ecological security, resulting in slow response times for reservoir water pollution incidents, making it difficult to protect public health and property, and seriously endangering ecological and social stability. Summary of the Invention

[0003] The purpose of this application is to provide a method, device and equipment for dynamic tracking simulation of the diffusion of petroleum pollutants, which can improve the accuracy of the diffusion process and impact range of pollutants in point source pollution caused by petroleum substance leakage.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for simulating the dynamic tracking of petroleum pollutant diffusion, the method comprising:

[0006] Determine the target water area for multi-dimensional simulation of pollutant diffusion;

[0007] Establishing an unstructured grid hydrodynamic model of the target water area according to the multi-dimensional simulation working condition;

[0008] An oil spill prediction model is constructed based on the unstructured grid hydrodynamic model, and the diffusion process of the pollutants is simulated according to the oil spill prediction model; if the pollutants are non-dissolved petroleum pollutants, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation; if the pollutants are dissolved petroleum pollutants, the oil spill prediction model is a convection-diffusion model.

[0009] In a second aspect, the present application provides a device for simulating the dynamic tracking of the diffusion of petroleum pollutants, the device comprising:

[0010] A simulation condition acquisition module is used to obtain simulation conditions for pollutant diffusion simulation in the target water area; the simulation conditions include pollution source intensity, leakage time, hydrological conditions, wind direction and speed, and water pollution event scenario; the pollution source for pollutant diffusion simulation is a point pollution source caused by petroleum leakage;

[0011] a two-dimensional hydrodynamic model determination module, configured to determine a two-dimensional hydrodynamic model of the target water body area according to the simulation process;

[0012] a diffusion simulation module, configured to determine an oil spill prediction model of pollutants in the target water area based on the two-dimensional hydrodynamic model, and simulate the diffusion process of the pollutants based on the oil spill prediction model;

[0013] The simulation working condition acquisition module determines the multi-dimensional simulation working conditions for pollutant diffusion in the target water area;

[0014] A hydrodynamic model determination module, configured to establish an unstructured grid hydrodynamic model of the target water body area according to the multi-dimensional simulation working condition;

[0015] A diffusion simulation module is used to construct an oil spill prediction model based on the unstructured grid hydrodynamic model, and simulate the diffusion process of the pollutant according to the oil spill prediction model; if the pollutant is a non-soluble petroleum pollutant, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation; if the pollutant is a dissolved petroleum pollutant, the oil spill prediction model is a convection diffusion model.

[0016] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor executes the computer program to implement any of the above-mentioned methods for dynamic tracking and simulation of petroleum pollutant diffusion.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0018] The present application provides a method, device and equipment for dynamic tracking simulation of the diffusion of petroleum pollutants. The present application establishes an unstructured grid hydrodynamic model of the target water body area according to the multi-dimensional simulation working conditions; constructs an oil spill prediction model based on the unstructured grid hydrodynamic model, and simulates the diffusion process of the pollutant according to the oil spill prediction model; if the pollutant is a non-soluble petroleum pollutant, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation; if the pollutant is a dissolved petroleum pollutant, the oil spill prediction model is a convection diffusion model. By collecting multi-dimensional working condition parameters that affect the diffusion of pollutants, oil spill prediction models are created for dissolved and non-dissolved petroleum pollutants respectively, providing a more accurate simulation environment for the diffusion process, thereby improving the simulation accuracy of the diffusion process and impact range of pollutants from sudden point source pollution, and the simulation accuracy is improved by more than 30%, which is particularly suitable for emergency decision support in closed water bodies such as reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a method for simulating the dynamic tracking of petroleum pollutant diffusion provided in one embodiment of the present application;

[0021] Figure 2 This is a principle block diagram of a method for simulating the dynamic tracking of petroleum pollutant diffusion provided in one embodiment of the present application;

[0022] Figure 3 A wind rose diagram of a reservoir provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the arrangement of measuring sections of a reservoir provided in one embodiment of the present application;

[0024] Figure 5 This is a schematic diagram of the distribution results of the oil film at each hour in the first time period of a certain simulated working condition of non-dissolved pollutants provided in one embodiment of the present application;

[0025] Figure 6 A schematic diagram of the distribution results of the oil film at each hour in the second time period of a certain simulated working condition of non-dissolved pollutants provided in one embodiment of the present application;

[0026] Figure 7A schematic diagram of the distribution results of the oil film at each hour in the third time period of a certain simulated working condition of non-dissolved pollutants provided in one embodiment of the present application;

[0027] Figure 8 A schematic diagram of the distribution results of the oil film at each hour in the fourth time period of a certain simulated working condition of non-dissolved pollutants provided in one embodiment of the present application;

[0028] Figure 9 A schematic diagram of the distribution results of the oil film at each hour in the fifth time period of a certain simulated working condition of non-dissolved pollutants provided in one embodiment of the present application;

[0029] Figure 10 This is a schematic diagram of the distribution results of the oil film at each hour in the sixth time period of a certain simulated working condition of non-dissolved pollutants provided in one embodiment of the present application;

[0030] Figure 11 A schematic diagram of the area swept by the oil film after 30 days of leakage of non-dissolved pollutants under a certain simulated working condition provided by one embodiment of the present application;

[0031] Figure 12 A schematic diagram of the area swept by the oil film after 30 days of leakage of dissolved pollutants under a simulated working condition provided in one embodiment of the present application;

[0032] Figure 13 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] An analysis of the reasons for the poor emergency response to water pollution incidents in reservoirs shows two main reasons: (1) Traditional models do not fully integrate multiple factors such as hydrology, meteorology, and fluid mechanics, resulting in insufficient accuracy in simulating the driving force of water flow. This application refines the parameters while constructing a coupled model and sets boundary conditions to accurately improve the accuracy of the simulation. (2) Traditional models often ignore emulsification and turbulent diffusion in order to simplify the diffusion mechanism, causing the prediction results to deviate from reality. The oil spill prediction model of this application decomposes the diffusion process into four parts: diffusion motion, drift motion, turbulent diffusion, and weathering process, which more realistically simulates the drift of oil particles and the diffusion process of oil film, thereby improving the simulation accuracy of the diffusion path.

[0036] This application provides a method for simulating the dynamic tracking of petroleum pollutant diffusion. Figure 1 and Figure 2 As shown, the petroleum pollutant diffusion dynamic tracking simulation method includes steps 101 to 103.

[0037] Step 101: Determine a target water area for multi-dimensional simulation of pollutant diffusion conditions.

[0038] Step 102: establishing an unstructured grid hydrodynamic model of the target water area according to the multi-dimensional simulation working condition.

[0039] Step 103: An oil spill prediction model is constructed based on the unstructured grid hydrodynamic model, and the diffusion process of the pollutant is simulated according to the oil spill prediction model. If the pollutant is an insoluble petroleum pollutant, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation. If the pollutant is a dissolved petroleum pollutant, the oil spill prediction model is a convection-diffusion model.

[0040] This application constructs a two-dimensional hydrodynamic model of the target water area (such as lakes and reservoirs), and on this basis constructs an oil spill prediction model. Specifically, if the pollutant is an insoluble petroleum pollutant, based on the "oil particle" tracking theory, a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation is established; if the pollutant is a dissolved petroleum pollutant, the oil spill prediction model is a convection diffusion model, simulating the migration and transformation process of petroleum pollutants in the reservoir water. The purpose of this application is to accurately simulate the diffusion process of insoluble petroleum substances after leakage on a bridge across a reservoir, to help formulate the most effective emergency response plan for sudden water pollution incidents, and has great application significance and promotion value in terms of maximizing the control and elimination of ecological and environmental risks and reducing the impact and harm of such incidents on the people.

[0041] In an exemplary embodiment, step 101 specifically includes:

[0042] When the target water body area includes a reservoir and an upstream river channel of the reservoir, geographic information data, hydrological and meteorological data, pollution source data and reservoir scheduling and operation data of the target water body area are obtained.

[0043] The multi-dimensional simulation conditions including pollution source parameters and environmental parameters are constructed based on geographic information data, hydrological and meteorological data, pollution source data and reservoir scheduling and operation data.

[0044] Based on a two-dimensional hydrodynamic model, different water quality models are selected for pollutant simulation based on the type and properties of hazardous chemicals involved in the incident. When hazardous petroleum chemicals, such as those caused by traffic accidents, flow into reservoirs, reservoir hydrodynamic conditions play a key role in their diffusion and distribution within the water. A two-dimensional hydrodynamic model of the river and reservoir was established to provide flow driving force support for subsequent petroleum chemical spill simulations.

[0045] In an exemplary embodiment, taking the water conservancy project Q as an example, an oil spill model for insoluble petroleum is constructed based on a two-dimensional hydrodynamic model. This application uses diesel as an example.

[0046] When the target water area includes a reservoir and an upstream river channel of the reservoir, the data required for step 101 of this application mainly include geographic information data of the domain of reservoir Q and its upstream river channel, hydrological and meteorological data, water quality monitoring data, pollution source data, and reservoir scheduling and operation data, etc.

[0047] ① Geographic information data includes reservoir shore and underwater topography, contour maps, river channel topography and cross-section data.

[0048] ② Hydrometeorological data: meteorological data such as rainfall, evaporation, wind speed and direction, temperature, as well as hydrological process data such as reservoir inflow, outflow, water level, etc.; covering rainfall, evaporation, wind speed, etc.

[0049] ③ Water quality monitoring data: generally should include pH, DO, conductivity, turbidity, COD Mn (or COD Cr ), BOD5, NH3-N, TP, TN, chlorophyll a, transparency and other key water quality indicators.

[0050] ④ Pollution source data: Detailed records of potential pollution sources around the reservoir, such as industrial emissions, agricultural emissions, domestic sewage discharge, etc., including information such as the location of the pollution sources, emission volume, and emission concentration.

[0051] ⑤ Reservoir dispatching operation data: reservoir dispatching rules, historical dispatching records, reservoir storage capacity curve, discharge capacity curve, etc.

[0052] The pollution source parameters include pollution source intensity, leakage duration, point source coordinates of the pollution source and petroleum component characteristic vectors.

[0053] The environmental parameters include wind speed at a height of 10 m, wind direction angle, surface velocity, water temperature, turbulence intensity and vertical temperature gradient in the target water area.

[0054] For the set petroleum pollution scenario, a pollution source parameter set A = {a i |i=1~5}, environment parameter set B={b j |j=1-6}, used for subsequent hydrodynamic-water quality model simulations to assess the potential impact of oil pollution incidents on reservoir water quality. a1 is the pollution source intensity (kg / s), a2 is the leakage duration Δt (s), a3 is the point source coordinates (x0, y0), a4 is the oil component characteristic vector [c1, c2]^T, c1 is the mass fraction of the light component, and c2 is the mass fraction of the heavy component. b1 is the wind speed U at a height of 10m 10 (m / s), b2 is the wind direction angle θ (°), b3 is the surface velocity v s (m / s), b4 is water temperature T (℃), b5 is turbulence intensity I t (%), b6 is the vertical temperature gradient T is temperature.

[0055] Pollution source parameters are focused on source intensity, leakage time, and oil composition. Bridge accidents typically result in a high proportion of pollutants leaking into rivers, but the amount leaked can vary. For example, if a tanker truck is severely damaged by a violent collision or rollover, or if the vehicle crashes directly into the water, both the amount of leakage and the amount entering the river are typically large. However, if the collision is minor, the damage is minimal, or the vehicle is partially intercepted by guardrails or bridge runoff collection systems, only a small amount of leakage may occur. Simulations can consider scenarios with higher pollution risks and set the mass of hazardous chemicals leaking into the water. For liquid hazardous chemicals, the rate of leakage from bridge accidents is related to the extent of container damage, typically resulting in a short period of time for the liquid hazardous chemicals to enter the water. Considering the worst-case scenario for hazardous chemical entry into the water, it can be assumed that all hazardous chemicals enter the water within a short period of time after leakage. In oil spill simulations, oil is divided into two components: light and heavy. Light components are defined as hydrocarbons with a molecular weight less than 160 g / mol and a boiling point less than 300°C. Heavy components are defined as hydrocarbons with a molecular weight greater than 160 g / mol and a boiling point between 250°C and 300°C or above.

[0056] Environmental parameters were focused on hydrological conditions and wind dynamics. Considering the differences in flow velocity across different hydrological periods, dynamic tracking simulations of petroleum pollutant dispersion were conducted for both the flood and non-flood seasons. For the non-flood season, the simulations were conducted in months with low inflow, while for the flood season, the simulations were conducted during the main and post-flood seasons. To account for the impact of wind on pollutant dispersion, wind speeds were analyzed based on wind direction and speed data from meteorological stations. The wind speeds were the annual average wind speed and calm wind speed for each hydrological period, and the wind directions were set to several high-frequency wind directions.

[0057] Based on the above data, make further assumptions.

[0058] Description of various factors of simulated working conditions.

[0059] 1) Pollution intensity: The location of the pollution source and the time of leakage are important factors affecting the leakage volume and the diffusion rate of hazardous chemicals.

[0060] 2) Leakage Time: For liquid hazardous chemicals, the rate of leakage in bridge accidents is related to the extent of container damage, typically resulting in liquid hazardous chemicals entering the water within a short period of time. This application considers the worst-case scenario of insoluble petroleum-based hazardous chemicals entering the water. For Reservoir Q, the assumption is that all hazardous chemicals will enter the water within 10 minutes after the leak.

[0061] 3) Hydrological Conditions: Consider different hydrological periods, that is, perform simulations separately for flooding and non-flooding seasons. For the non-flooding season, this application selects the months with the lowest flow rates to lakes and reservoirs. For the flooding season, the main flooding season and the post-flooding season are selected. For Reservoir Q, the main flooding season is June 1st to August 31st, and the post-flooding season is September 1st to September 30th.

[0062] 4) Wind direction and speed: Considering the impact of wind on the diffusion of pollutants, the wind direction and speed data of meteorological stations near lakes and reservoirs are used. The wind speeds are the annual average wind speed and calm wind speed respectively. Two or more high-frequency wind directions can be selected as the wind direction.

[0063] For Reservoir Q, the case data comes from a meteorological station in 2015, taking the average wind speed (1.27m / s) and calm wind respectively, and the wind direction is set to two high-frequency wind directions: (1) west northwest wind (292.5°); (2) east wind (90°). The wind rose diagram of Reservoir Q is as follows: Figure 3 shown.

[0064] 5) Water Pollution Incident Analysis: To fully understand the sudden point source pollution incident caused by the simulated spill of insoluble petroleum substances on the bridge across the reservoir, an in-depth analysis of historical pollution incidents is required. Long-term water quality monitoring data and related information on pollution incidents at the reservoir and upstream over the past few decades are collected. This data is analyzed to identify high-frequency pollution sources (such as specific road sections), the time and concentration of pollutant leaks, and other factors. This helps identify periods of high risk for water pollution incidents and clearly defines river sections potentially affected by sewage. The frequency and duration of pollution incidents, as well as their impact on water quality, are assessed.

[0065] 6) Water Pollution Incident Scenarios: Based on the potential risk of petroleum contamination to reservoirs, including historical data and potential risks, a study area for sudden water pollution incident simulation was selected. Specific pollution release scenarios were constructed, taking into account the most likely pollution sources, and the model boundary and initial conditions were developed. Each scenario detailed the amount of pollutant released, the time of release, the release location, and the possible environmental conditions (e.g., rainfall, flow changes).

[0066] Specific point source pollution scenarios were set for different pollution scenarios, including parameters such as pollutant type, release amount, release location, release time, and duration. These scenarios will be used in subsequent two-dimensional hydrodynamic-water quality model simulations to assess the potential impact of different pollution events on reservoir water quality.

[0067] For Reservoir Q, three potential accident locations are set:

[0068] A1: Middle of Miaoziping Bridge (31.022211°N, 103.544245°E)

[0069] A2: Center of Shoujiang Bridge (30.981523°N, 103.464652°E)

[0070] A3: Baihua Bridge (31.044281°N, 103.476895°E)

[0071] Taking into account the type of hazardous chemicals, leak location, hydrological period, wind speed, etc., 27 emergency accident simulation conditions were developed, as detailed in Tables 1 and 2. Each simulation condition lasted 30 days from the leak.

[0072] Table 1 Various simulated operating conditions when the pollutant is diesel

[0073]

[0074]

[0075] Table 2 Various simulated working conditions when the pollutant is dissolved petroleum

[0076]

[0077]

[0078] In step 102, the required data include geographic information data and hydrological and meteorological data of the target water area. For reservoir Q, the required data include geographic information data and hydrological and meteorological data of reservoir Q and its upstream river.

[0079] ① Geographic information data: reservoir shore and underwater topography, contour maps, river topography and large-section data, etc.

[0080] ② Hydrometeorological data: meteorological data such as rainfall, evaporation, wind speed and direction, temperature, as well as hydrological process data such as reservoir inflow, outflow, water level, etc.; covering rainfall, evaporation, wind speed, etc.

[0081] Specifically, this application uses the daily rainfall, inflow and outflow, and reservoir water level in 2015.

[0082] According to the simulated working conditions obtained in step 101, a two-dimensional model of the target water area is constructed, specifically including:

[0083] ① Two-dimensional mesh generation:

[0084] Based on the bottom elevation distribution data of the target water area (reservoir), the reservoir terrain is digitized and a suitable 2D or 3D grid model is constructed. First, a grid generator is used to horizontally divide the target water area using an unstructured triangular mesh. Local mesh refinement is performed in areas such as the reservoir inlet, shoreline, and sewage outlet. A Sigma / z value hybrid stratification scheme (using a Sigma grid for the upper layer and an elevation z-layer for the lower layer) is then used for vertical meshing, resulting in an unstructured grid, or 2D model.

[0085] ②Boundary conditions and initial conditions:

[0086] Set the inflow and outflow boundary conditions for the 2D model, including the inflow and outflow flow and water quality of the reservoir. Set the initial water quality state of the reservoir based on the latest water quality monitoring data.

[0087] ③Parameter settings:

[0088] Hydrological and hydrodynamic parameters include flow velocity, wind speed, precipitation and evaporation, eddy viscosity coefficient, bed resistance, and parameters related to hydraulic structures; water quality parameters involve pollutant diffusion coefficient, degradation coefficient and kinetic parameters of other related biochemical processes (such as ammonia nitrogen half-saturation constant, phytoplankton growth rate, etc.).

[0089] After constructing a two-dimensional model of the target water area, the application determines a two-dimensional hydrodynamic model based on the two-dimensional model.

[0090] A two-dimensional hydrodynamic model is a mathematical model used to simulate and analyze the movement laws of water flow and related dynamic characteristics in two-dimensional space.

[0091] The two-dimensional hydrodynamic model includes control equations, turbulence models, bottom stress and wind stress. The two-dimensional hydrodynamic model provides water flow driving force support for subsequent petroleum leakage simulations.

[0092] In an exemplary embodiment, 1) the governing equations of this application are the two-dimensional incompressible Reynolds-averaged NS equations based on the Boussinesq assumption and the hydrostatic pressure assumption, i.e., the shallow water equations. The following equations are obtained:

[0093]

[0094] Where t is time; x, y, and z are right-handed Cartesian coordinates; η is the height of the water surface relative to the undisturbed water surface, commonly known as the water level; h is the still water depth; u, v, and w are the components of the flow velocity in the x, y, and z directions, respectively; p a is the local atmospheric pressure; ρ is the water density, ρ0 is the reference water density; f = 2Ωsinφ is the Coriolis force parameter (where Ω = 0.729×10 -4 s -1 is the Earth's rotation angular rate, φ is the geographic latitude); and are the accelerations in the y and x directions caused by the rotation of the Earth; s xx 、s xy 、s yx and s yy is the radiation stress component; T xx 、T xy 、T yx and T yy is the horizontal viscous stress term; S is the source and sink term; (u s , v s ) is the source-sink flow velocity, u s is the source-sink flow velocity in the x direction, v s is the source-sink flow velocity in the y direction, τ sx and τ sy They represent the x- and y-direction components of the shear stress exerted by the wind on the water surface, respectively, and τ bx and τ by They represent the components of the friction between the water flow and the bottom bed in the x and y directions respectively.

[0095] 2) Turbulence modeling uses the Smagorinsky pressure grid scale model from the large eddy simulation method. This model describes subgrid-scale transport using an effective eddy viscosity value related to the characteristic length scale. The subgrid-scale eddy viscosity value is given by the following equation:

[0096]

[0097] Where A is the sub-grid scale eddy viscosity value, c s is a constant, l is the characteristic length, S ij represents the deformation rate, S ij Here, i and j represent the subscripts of spatial directions. When i = 1, it corresponds to the x direction, and when i = 2, it corresponds to the y direction. Similarly, when j = 1, it corresponds to the x direction, and when j = 2, it corresponds to the y direction.

[0098] The deformation rate is given by:

[0099]

[0100] Among them, u i is the velocity in the x direction, u j is the y-direction velocity, x i is the x-direction coordinate, x j is the y-coordinate.

[0101] 3) Bottom stress Following the law of quadratic friction:

[0102]

[0103] in, represents the bottom stress, c f is the drag coefficient, is the flow velocity, u b represents the component of flow velocity in the x direction, v b Represents the component of flow velocity in the y direction.

[0104] For two-dimensional calculations, It is the average speed with respect to water depth. The resistance coefficient can be obtained by the Xie Cai coefficient C or the Manning coefficient M.

[0105]

[0106] Where g is the acceleration due to gravity.

[0107] The Manning number M can be obtained from the substrate roughness height:

[0108]

[0109] Among them, k sIt indicates the substrate roughness height and describes the roughness of the substrate surface.

[0110] 4) Wind stress: In areas without ice cover, surface stress Depends on the wind strength above the surface. The stress magnitude is given by the following empirical formula:

[0111]

[0112] in, represents the surface stress in the region without ice cover, ρ a is the density of air, c d is the air resistance coefficient, is the wind speed 10 m above the sea surface, u w is the wind speed in the x direction 10m above the sea surface, v w is the wind speed in the y direction 10 m above the sea surface.

[0113] The slip velocity is related to the surface stress as follows:

[0114]

[0115] The drag coefficient can be a fixed constant or vary with wind speed. Generally, empirical formulas are used to determine the relevant parameters of the drag coefficient.

[0116]

[0117] where c a , c b , w a and w b are all empirical coefficients, w 10 It refers to the wind speed at a distance of 10m from the sea surface. The default values of these empirical coefficients are c a =1.255·10 -3 , c b =2.425·10 -3 , w a =7m / s and w b =25m / s.

[0118] This application also includes the calibration and verification of model parameters of the two-dimensional hydrodynamic model. Based on the river topography data, typical flood and dry years are selected to carry out calibration and verification of model parameters to obtain parameters such as river roughness and diffusion coefficient in the reservoir area.

[0119] This application sets up 6 water quality monitoring sections (A, B, C, D, E, F) in reservoir Q, such as Figure 4As shown, the vertical line in the middle of the sampling section was taken as the monitoring section, and stratified vertical sampling was carried out once, with samples taken at 0.5m underwater (surface layer), the midpoint of the vertical line (middle layer), and 3 / 4 depth underwater (deep layer). A multi-parameter water quality analyzer was used to measure water temperature, pH, DO, conductivity, turbidity, and chlorophyll a on site, and water samples were taken back to the laboratory for determination of NH3-N, TP, TN, and COD. Mn , BOD5. Compare and analyze the simulated results of water quality indicators of each section and each water layer with the measured data, including the seasonal changes and spatial distribution of water quality factors, and use statistical indicators (such as R 2 , RMSE, NSE, etc.) to quantitatively evaluate the simulation effect.

[0120] For the purposes of this application, oil components are divided into two categories: light and heavy components, based on hydrocarbon molecular weight. Light components are defined as hydrocarbons with a molecular weight less than 160 g / mol and a boiling point less than 300°C. Heavy components are defined as hydrocarbons with a molecular weight greater than 160 g / mol and a boiling point between 250°C and 300°C or higher, including wax and asphalt components.

[0121] When petroleum leaks into water, an oil film forms on the surface. Pollutant diffusion involves four transport processes. The four-process coupling model employed in this application includes gravity-inertial diffusion, wind-current coordinated drift, turbulent random diffusion, and multiphase weathering. The multiphase weathering process includes volatilization, emulsification, and dissolution models.

[0122] 1) Gravity-inertial diffusion process: Oil film diffusion refers to the continuous expansion of the oil film coverage area on a plane under the action of gravity, inertia, viscosity and surface tension. The oil spill prediction model uses the modified Fay gravity-viscosity formula to calculate the oil film expansion:

[0123]

[0124] The oil film volume is:

[0125]

[0126] Among them, A oil is the instantaneous area of the oil film (m 2 ), t is time, K a is the empirical coefficient, which is positively correlated with the API gravity of the oil; V oil is the effective oil film volume (m 3 );R oil is the oil film equivalent diameter (m); the oil film diameter is determined by the two-dimensional hydrodynamic model, h s =Q0 / (πR0 2ρ) is the initial oil film thickness; Q0 is the total leakage volume (kg); and R0 is the initial diffusion radius (m). The oil film is the film formed on the surface of the target water body after the petroleum substance enters the target water area. The oil film diameter changes over time.

[0127] 2) Wind-current coordinated drift: The drift of oil particles is affected by wind, water flow, and the turbulent diffusion of the oil spill itself. The drift force is mainly the drag of water flow and wind. The total drift velocity of oil particles is calculated by the following weight formula:

[0128] U tot =c w (z)·U w +U s (14)

[0129] Among them, U tot is the total drift velocity of oil particles, U w is the wind speed 10m above the water surface; U s is the surface velocity; c w is the wind drift coefficient, c w Typically between 0.02 and 0.04, where z is the depth below the water surface.

[0130] Wind-flow cooperative drift also relies on vertical velocity distribution modeling, wind speed-flow velocity coupling and derivation of friction velocity to overcome the problem of low diffusion simulation accuracy caused by traditional models ignoring vertical velocity changes and simplifying wind field effects.

[0131] Wind field data is obtained from the meteorological department, while the flow field is obtained from the results of a two-dimensional hydrodynamic model. However, the two-dimensional hydrodynamic model generally calculates the vertical average value, and the vertical distribution of the flow velocity must be estimated based on this. Assuming that it conforms to the logarithmic relationship:

[0132]

[0133] where z is the depth below the water surface; V(z) is the logarithmic velocity relationship; κ is the von Karman constant (0.42); k n is the Nikuradse drag coefficient; U f is the friction velocity, defined as:

[0134]

[0135] Where V mean is the average flow velocity.

[0136]

[0137] When the water depth is greater than this location, the model assumes that the convection velocity is 0.

[0138] When z = 0, the surface velocity U can be calculateds :

[0139] U s =V(0) (18)

[0140] The velocity calculation points in the two-dimensional hydrodynamic calculation results are located at each discrete grid point. However, in the "oil particle" model, the oil particles are not exactly at these points most of the time, so the velocity values need to be interpolated. This application uses bilinear interpolation:

[0141] F′=F1+(F2-F1)·y+(F4-F1)·x+(F1-F2+F3-F4)·x·y (19)

[0142] Where F′ is the flow velocity value obtained using bilinear interpolation, F1, F2, F3, and F4 are the known flow velocities at the grid points, and x and y are distances. F′ represents the interpolated flow velocity value located within the grid cell. F1 represents the lower left corner, F2 represents the lower right corner, F3 represents the upper right corner, and F4 represents the upper left corner. x represents the horizontal distance from the interpolated point F′ to the left edge of the grid cell, and y represents the vertical distance from the interpolated point F′ to the lower edge of the grid cell.

[0143] 3) Turbulent random diffusion process: The turbulent diffusion rate of oil particles is calculated using a random step size.

[0144] The turbulent diffusion is expressed as:

[0145]

[0146] Among them, S α is the diffusion distance in the α direction within one time step under the condition of isotropic horizontal diffusion; is a random number in the range of -1 to 1, D α is the diffusion coefficient in the α direction, and Δt is the time variation.

[0147] 4) Multiphase weathering process: The weathering process of oil particles includes evaporation, emulsification, dissolution, etc. During these processes, the composition of the oil particles changes, but the horizontal position of the oil particles does not change. This application only focuses on the non-dissolution process.

[0148] ① Volatility model: Within a few hours to a few days after an oil spill, evaporation is the primary weathering process occurring on the oil film surface. In particular, when the oil contains a large proportion of light components (such as gasoline), evaporation will remove most oil contaminants within 24 hours. However, for crude oils containing a large number of large molecular carbon chains, evaporation removes less oil contaminants, with only approximately 10% to 30% of the oil contaminants being removed through volatilization within 24 hours after the spill.

[0149] This application introduces time-dependent evaporation loss here, and the evaporation process, that is, the volatilization model is expressed as:

[0150] loss(%weight)=(E+B·T)*ln(t′) (21)

[0151] Where, loss is the evaporation amount, specifically the evaporation percentage of the oil film weight, E is the oil characteristic constant, B is the oil temperature characteristic constant, T is the oil temperature, °C, and t′ is the oil age, minutes.

[0152] ②Emulsification model:

[0153] An emulsion forms when two different liquids, seawater and oil, mix after an oil spill. After an oil spill, some fine oil droplets remain suspended in the water (insoluble), creating an emulsion that can occupy more than four times its original volume. Furthermore, the viscous emulsion persists in the environment much longer than crude oil, slowing subsequent weathering.

[0154] The emulsification process occurs under strong winds or waves, often several hours after an oil spill. Existing models view the emulsification process as an equilibrium between oil-in-water and water-in-oil phases. The stability of the emulsion is a key factor in determining emulsification and demulsification. Unstable and apparently stable emulsions will be released back into the water. This application uses a first-order release formula to describe this process. The emulsification model is as follows:

[0155]

[0156] waterrelease=-α·Y w (twenty three)

[0157] Among them, wateruptake represents the rate at which the emulsion absorbs water under wind drive, waterrelease represents the rate at which the emulsion is released back into the water due to its instability, and waterrelease is a rate parameter that describes the speed of the process of emulsion being released back into the water. The unit is the inverse of time. Y w is the water fraction; Y max is the maximum water fraction; U is the wind speed; K em is the emulsification rate constant, K em Usually 2×10 -6 s / m 2 , α is the water release rate, α=0 is a stable emulsion; α>0 is an unstable emulsion.

[0158] The water release rate ɑ is related to the emulsion stability parameter S′ as follows:

[0159]

[0160] Among them, α0 is the water release rate of the unstable emulsion, and α0 is equal to Unit: s -1 corresponding to the time when the emulsion breaks within a few hours under gentle breeze conditions. α 0.67 is the water release rate of the stable emulsion, α 0.67 is equal to Unit: s -1 corresponding to the time when the apparent stable emulsion breaks within a few days under gentle breeze conditions. S′ is the emulsion stability.

[0161] In the formula related to the water release rate ɑ and the emulsion stability S′ parameter, when S′ < 0.67, it is an unstable emulsion, and the water release rate of this part is very high and it will break within a few hours; when 0.67 < S′ < 1.22, it is an apparent stable emulsion and will break slowly within a few days; while when S′ > 1.22, it is a stable emulsion and is in a long-term stable state.

[0162] ③ Dissolution model

[0163] Because some soluble hydrocarbons dissolve into the surrounding water body, the amount of some oil films will decrease. Although this reduces the size of the oil film, it will cause environmental problems because the dissolved oil spill components are the most toxic to marine organisms. Small molecular aromatic hydrocarbons such as benzene and toluene, and other large molecular polycyclic aromatic hydrocarbons (PAHs) such as naphthalene are water-soluble petroleum components, and their toxicity is well-known. [[ID={27]]

[0164] Other factors affecting oil dissolution include the amount of oil spill exposed on the oil film surface, wind, sea level conditions, air temperature, and sunlight intensity. Other factors include the emulsification of the oil film, which will greatly slow down the evaporation rate.

[0165] For the dissolution model described above, the calculation formulas for the dissolution processes of volatile components and heavy components are as follows:

[0166]

[0167]

[0168] Among them, DISS volatile is the dissolution rate of volatile components, DISS_heavy is the dissolution rate of heavy components, k disl is the dissolution rate of volatile components [m / s], k dish is the dissolution rate of heavy components [m / s], M volatile is the mass of volatile component oil particles [kg], M total is the total mass of oil particles [kg], M heavyis the mass of heavy oil particles [kg], A' is the area of oil film of each particle in contact with the water surface [m 2 ],ρ volatile is the density of volatile components [kg / m 3 ],ρ heavy is the density of heavy components [kg / m 3 ], f Disp represents the enhancement factor of chemical dispersant on solubility, f Disp The larger the solubility, the stronger the f Disp For the chemical dispersant effect, the solubility is improved, is the water solubility of the volatile component [kg / kg], is the water solubility of the heavy component [kg / kg].

[0169] The flow-diffusion model is suitable for simulating the transport and diffusion of dissolved substances such as hydrochloric acid, dissolved petroleum, and dissolved nitrobenzene after entering the water body.

[0170] Based on the two-dimensional hydrodynamic model, the convection diffusion model is used to calculate the transport and diffusion of hydrochloric acid after discharge. The convection diffusion model is expressed as:

[0171]

[0172] Where t is time; x, y, and z are the x-axis, y-axis, and z-axis coordinates in the right-handed Cartesian coordinate system, respectively; c is the concentration of the substance, u is the velocity component in the x direction, v is the velocity component in the y direction, h is the water depth, and D x is the diffusion coefficient in the x direction, D y is the diffusion coefficient in the y direction, D z is the diffusion coefficient in the z direction, F is the linear attenuation coefficient, and S is the source and sink term.

[0173] The transport process also includes shoreline absorption. When the oil spill contacts the shoreline, the shoreline will adsorb and desorb the oil film. However, this application is to simulate the farthest distance that the oil spill can reach after leakage, and does not consider shoreline adsorption.

[0174] In one exemplary embodiment, based on the "oil particle" principle, this application calculated the range of the oil film swept by a diesel leak into a reservoir and the distribution of the oil film at each hour within 48 hours after the diesel entered the water. The simulation results, i.e., the simulated diffusion process of the pollutant, provide a basis for emergency response measures. The black area in the simulation result diagram of this application represents the oil film range.

[0175] This application takes the diesel leakage at point A1 + main flood season + calm wind conditions as an example. The oil film distribution in each hour from 1 to 8 hours is as follows: Figure 5 As shown in the figure, the oil film distribution at each hour from 9 to 16 hours is as follows Figure 6As shown, the oil film distribution at each hour from 17 to 24 hours is as follows Figure 7 As shown, the oil film distribution at each hour from 25 to 32 hours is as follows Figure 8 As shown, the oil film distribution at each hour from 33 to 40 hours is as follows Figure 9 As shown, the oil film distribution at each hour from 41 to 48 hours is as follows Figure 10 shown.

[0176] The area swept by the oil film for 30 days under the working conditions of "diesel leakage at point A1 + main flood season + calm wind" can be obtained, see Figure 11 It can be seen that under calm wind conditions during the main flood season at point A1, the oil film reaches the reservoir dam site within 44 hours.

[0177] In an exemplary embodiment, based on the convection diffusion model, the concentration distribution of dissolved petroleum under the working conditions of "diesel leakage at point A1 + non-flood season + calm wind" is obtained. The envelope diagram of dissolved petroleum 30 days after the leakage is as follows: Figure 12 The colored areas in the figure indicate areas exceeding the petroleum concentration threshold for surface water quality classes I to III. In this scenario, petroleum did not reach the dam site during the 30 days of simulation.

[0178] Based on the same inventive concept, embodiments of the present application also provide a device for simulating the dynamic tracking of petroleum pollutant diffusion, for implementing the aforementioned method for simulating the dynamic tracking of petroleum pollutant diffusion. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for simulating the dynamic tracking of petroleum pollutant diffusion provided below can be found in the aforementioned limitations of the method for simulating the dynamic tracking of petroleum pollutant diffusion, and will not be further elaborated here.

[0179] In an exemplary embodiment, the present application provides a petroleum pollutant diffusion dynamic tracking simulation device comprising:

[0180] The simulation working condition acquisition module determines the multi-dimensional simulation working conditions for pollutant diffusion in the target water area;

[0181] A hydrodynamic model determination module, configured to establish an unstructured grid hydrodynamic model of the target water body area according to the multi-dimensional simulation working condition;

[0182] A diffusion simulation module is used to construct an oil spill prediction model based on the unstructured grid hydrodynamic model, and simulate the diffusion process of the pollutant according to the oil spill prediction model; if the pollutant is a non-soluble petroleum pollutant, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation; if the pollutant is a dissolved petroleum pollutant, the oil spill prediction model is a convection diffusion model.

[0183] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 13 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store dynamic tracking simulation data of petroleum pollutant diffusion. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for simulating the dynamic tracking of petroleum pollutant diffusion is implemented.

[0184] Those skilled in the art will understand that Figure 13 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0185] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0186] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0187] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, data processing logic of programmable logic devices, and the like.

[0188] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0189] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for simulating the dynamic tracking of petroleum pollutant diffusion, characterized in that: The petroleum pollutant diffusion dynamic tracking simulation method includes: Determine the target water area for multi-dimensional simulation of pollutant diffusion; Establishing an unstructured grid hydrodynamic model of the target water area according to the multi-dimensional simulation working condition; An oil spill prediction model is constructed based on the unstructured grid hydrodynamic model, and the diffusion process of the pollutants is simulated according to the oil spill prediction model; if the pollutants are non-dissolved petroleum pollutants, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation; if the pollutants are dissolved petroleum pollutants, the oil spill prediction model is a convection-diffusion model.

2. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 1 is characterized in that: Determine the target water area for multi-dimensional simulation conditions of pollutant diffusion, including: When the target water area includes a reservoir and an upstream river of the reservoir, obtaining geographic information data, hydrological and meteorological data, pollution source data, and reservoir scheduling and operation data of the target water area; The multi-dimensional simulation conditions including pollution source parameters and environmental parameters are constructed based on geographic information data, hydrological and meteorological data, pollution source data and reservoir scheduling and operation data.

3. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 2, characterized in that: The geographic information data includes reservoir shore and underwater topography, contour maps, river channel topography and cross-section data; The hydrometeorological data includes meteorological data and hydrological data, wherein the meteorological data includes rainfall, evaporation, wind speed and direction, and temperature, and the hydrological data includes reservoir inflow, outflow, and water level; The reservoir operation data includes reservoir operation rules, historical operation records, reservoir storage capacity curve and discharge capacity curve; The pollution source parameters include pollution source intensity, leakage duration, point source coordinates of the pollution source and petroleum component characteristic vector; The environmental parameters include wind speed at a height of 10 m, wind direction angle, surface velocity, water temperature, turbulence intensity and vertical temperature gradient in the target water area.

4. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 1, characterized in that: The unstructured grid hydrodynamic model is a two-dimensional hydrodynamic model, which includes a control equation, a turbulence model, bottom stress and wind stress.

5. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 4 is characterized in that: The control equation is a shallow water equation, which is expressed as: Where t is time; x, y, and z are the x-axis, y-axis, and z-axis coordinates in the right-handed Cartesian coordinate system, respectively; η is the water level; h is the water depth; u, v, and w are the components of the flow velocity in the x, y, and z directions, respectively; p a is the local atmospheric pressure; ρ is the water density, ρ0 is the reference water density; f = 2Ωsinφ is the Coriolis force parameter, Ω is the Earth's rotation angular rate, and φ is the geographical latitude; and are the accelerations in the y and x directions caused by the rotation of the Earth; s xx 、s xy 、s yx and s yy is the radiation stress component; T xx 、T xy 、T yx and T yy are all horizontal viscous stress terms; S is the source and sink term; τ sx , τ sy They represent the x- and y-direction components of the shear stress exerted by the wind on the water surface, respectively, and τ bx and τ by They represent the components of the friction between the water flow and the bottom bed in the x and y directions respectively.

6. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 1, characterized in that: The four-process coupling model includes a gravity-inertial diffusion process, a wind-flow coordinated drift process, a turbulent random diffusion process and a multiphase weathering process, and the multiphase weathering process includes a volatilization model, an emulsification model and a dissolution model.

7. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 6, characterized in that: The gravity-inertial diffusion process is expressed as: Among them, A oil is the instantaneous area of the oil film, t is the time, K a is the empirical coefficient, V oil is the effective oil film volume, R oil is the oil film diameter, h s is the initial oil film thickness, h s =Q0 / (πR0 2 ρ), Q0 is the total leakage volume, R0 is the initial diffusion radius; the oil film is the oil film formed on the surface of the water body after the petroleum substance enters the target water area; The wind-current coordinated drift is expressed as: U tot =c w (z)·U w +U s ; Among them, U tot is the total drift velocity of oil particles, U w is the wind speed 10m above the water surface; U s is the surface velocity; c w is the wind drift coefficient, z is the depth below the water surface; The turbulent random diffusion process is expressed as: Among them, S α is the diffusion distance in the α direction within one time step under the condition of isotropic horizontal diffusion; is a random number in the range of -1 to 1, D α is the diffusion coefficient in the α direction, Δt is the time variation; The volatility model is expressed as: loss = (E + B·T)·ln(t′); Where, loss is the evaporation amount, E is the oil characteristic constant; B is the oil temperature characteristic constant; T is the oil temperature, and t' is the oil age; The emulsification model is expressed as: waterrelease=-α·Y w ; Among them, wateruptake represents the rate at which the emulsion absorbs water under wind power, waterrelease represents the rate at which the emulsion is released back into the water due to its instability, and Y w is the water fraction; Y max is the maximum water fraction; U is the wind speed; K em is the emulsification rate constant, α is the water release rate, α0 is the release rate of unstable emulsion water, α 0.67 is the release rate of water to stabilize the emulsion, S′ is the emulsion stability; The dissolution model is expressed as: Among them, DISS volatile is the diffusion of volatile components, DISS_heavy is the diffusion of heavy components, k disl is the solubility rate of volatile components, k dish is the solubility rate of heavy components, M volatile is the mass of volatile component oil particles, M total is the total mass of oil particles, M heavy is the mass of heavy oil particles, A' is the area of oil film of each particle in contact with the water surface, ρ volatile is the density of volatile components, ρ heavy is the density of heavy components, f Disp is the chemical dispersant coefficient, is the water solubility of the volatile component, is the water solubility of the heavy component.

8. The petroleum pollutant diffusion dynamic tracking simulation method according to claim 1 is characterized in that: The convection-diffusion model is expressed as: Where t is time; x, y, and z are the x-axis, y-axis, and z-axis coordinates in the right-handed Cartesian coordinate system, respectively; c is the concentration of the substance, u is the velocity component in the x direction, v is the velocity component in the y direction, h is the water depth, and D x is the diffusion coefficient in the x direction, D y is the diffusion coefficient in the y direction, D z is the diffusion coefficient in the z direction, F is the linear attenuation coefficient, and S is the source and sink term.

9. A dynamic tracking simulation device for the diffusion of petroleum pollutants, characterized in that: The petroleum pollutant diffusion dynamic tracking simulation device includes: A simulation condition acquisition module is used to obtain simulation conditions for pollutant diffusion simulation in the target water area; the simulation conditions include pollution source intensity, leakage time, hydrological conditions, wind direction and speed, and water pollution event scenario; the pollution source for pollutant diffusion simulation is a point pollution source caused by petroleum leakage; a two-dimensional hydrodynamic model determination module, configured to determine a two-dimensional hydrodynamic model of the target water body area according to the simulation process; a diffusion simulation module, configured to determine an oil spill prediction model of pollutants in the target water area based on the two-dimensional hydrodynamic model, and simulate the diffusion process of the pollutants based on the oil spill prediction model; The simulation working condition acquisition module determines the multi-dimensional simulation working conditions for pollutant diffusion in the target water area; A hydrodynamic model determination module, configured to establish an unstructured grid hydrodynamic model of the target water body area according to the multi-dimensional simulation working condition; A diffusion simulation module is used to construct an oil spill prediction model based on the unstructured grid hydrodynamic model, and simulate the diffusion process of the pollutant according to the oil spill prediction model; if the pollutant is a non-soluble petroleum pollutant, the oil spill prediction model is a four-process coupling model including diffusion movement, wind-induced drift, turbulent diffusion and weathering degradation; if the pollutant is a dissolved petroleum pollutant, the oil spill prediction model is a convection diffusion model.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for simulating the dynamic tracking of petroleum pollutant diffusion according to any one of claims 1 to 8.

Images