A sewage pollution diffusion prediction method and system

Abstract

The application relates to the technical field of water pollution diffusion prediction, and particularly provides a sewage pollution diffusion prediction method and system, which comprises the following steps: acquiring riverbed topographic feature information, real-time hydrological data and sewage discharge information of a river basin; dividing the river basin into multiple reaches according to the riverbed topographic feature information, and then acquiring local hydrodynamic parameters corresponding to each reach according to the riverbed topographic feature information and the real-time hydrological data; for each reach, acquiring the dynamic characteristics of the bottom mud according to the riverbed topographic feature information and the local hydrodynamic parameters, and then determining the bottom mud adsorption parameters and the bottom mud release parameters according to the dynamic characteristics of the bottom mud; and performing pollution diffusion prediction according to the bottom mud adsorption parameters, the bottom mud release parameters, the real-time hydrological data and the sewage discharge information to obtain a diffusion prediction result; the method can effectively solve the problem of inaccurate prediction caused by the fact that the bottom mud adsorption parameters and the bottom mud release parameters cannot dynamically reflect changes in hydrological conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water pollution diffusion prediction, in particular to a sewage pollution diffusion prediction method and system. BACKGROUND

[0002] One of the core responsibilities of river basin management agencies is to respond to water pollution incidents and predict the diffusion path and concentration changes of pollutants in water bodies to guide environmental protection decisions and emergency responses. To this end, the agency deploys a sewage pollution diffusion prediction system. The core of the system is a mathematical model that simulates the entire process of pollutants migrating, diffusing in water under the action of water flow, and interacting with river bed sediments (bottom mud). The model takes into account the effects of pollutants being adsorbed by the bottom mud and subsequently released back into the water body from the bottom mud. In the early stages of system construction, technical personnel obtain data on the physical and chemical properties of the bottom mud by sampling and analyzing the riverbed mud, and set a set of static parameters representing the adsorption and release capacity of the bottom mud to pollutants based on these data. This prediction system performs pollution diffusion prediction based on these pre-set static parameters in daily operation.

[0003] However, the hydrological characteristics of the present river basin exhibit seasonal dynamic changes. In the dry season, the flow rate of the river decreases, and the fine-grained sediment and organic matter carried in the water body settle on the riverbed, especially in areas with slow flow, forming a layer of surface bottom mud containing fine-grained and organic matter. These newly formed surface bottom muds have high specific surface area and many adsorption sites, and exhibit adsorption capacity for soluble pollutants, especially heavy metal pollutants. As the dry season continues, the thickness of this layer of mud increases, and its adsorption capacity and retention effect on pollutants increase.

[0004] On the contrary, when the river enters the wet season, the runoff increases and the flow rate increases. This change in hydrodynamic conditions causes the fine-grained sediment and organic matter on the riverbed surface to be resuspended and transported downstream. In some river sections, the original surface bottom mud containing fine particles is even scoured and stripped, exposing the underlying coarse sand, gravel or bedrock with large particle size, small specific surface area and low organic matter content. Therefore, in the wet season, the adsorption capacity of the riverbed mud decreases and the retention effect on pollutants weakens.

[0005] The natural hydrological cycle of dry-season deposition and wet-season erosion causes the particle size composition, organic matter content, and corresponding pollutant adsorption and release characteristics of the surface layer of riverbed sediment to exhibit seasonal dynamic changes and spatial heterogeneity. The static parameters preset in existing models based on sediment samples obtained during a specific period cannot reflect the changes in sediment characteristics with the seasons and hydrological conditions. Environmental protection departments conduct regular sediment monitoring, but due to the limitations of sampling costs and technical conditions, the monitoring frequency is low, such as one to two comprehensive sampling analyses per year. This low-frequency monitoring cannot capture the changes in the physical and chemical properties of the sediment between different hydrological periods. Therefore, during most of the operating time, the pollution diffusion prediction model uses parameters that do not match the current actual sediment conditions for calculation, resulting in deviations between the prediction results and the actual situation.

[0006] For example, shortly after a river enters the wet season, a leakage accident occurs at an upstream enterprise, causing soluble heavy metal pollutants to enter the river. The environmental protection department initiates an emergency response and operates the existing pollution diffusion prediction system. The system calculates based on the sediment parameters stored and set during the dry season, which reflect a high adsorption capacity of the sediment. However, at this time, the river is in the wet season, and the surface layer of the riverbed sediment is being eroded, with the actual adsorption capacity being lower than the level represented by the model parameters. The output results of the model show that the pollutants are adsorbed and retained by the sediment, the peak concentration decreases with the increase of the migration distance, and the pollutants migrate downstream at a stable rate. Based on this, the emergency command center formulates response measures.

[0007] However, the real-time data transmitted back by the water quality monitoring sites deviate from the prediction results of the model. In the river section where the actual adsorption capacity of the sediment is reduced, the monitoring station records a higher peak concentration of pollutants than the model's prediction, and the arrival time is earlier than expected, because the pollutants are not retained by the sediment and pass through the water flow. This causes the drinking water intake downstream to be invaded by contaminated water before receiving the warning closure instruction, causing a risk of water supply. At the same time, in the area predicted by the model that the pollutants are adsorbed by the sediment, the actual monitoring finds that the pollutant concentration does not decay enough, and the pollution duration exceeds the expected value, affecting the local aquatic ecosystem. Due to the inaccuracy of the model prediction, the effectiveness of the emergency response measures is reduced, causing economic losses and ecological damage, and damaging the credibility of the environmental protection department. SUMMARY

[0008] The purpose of the present application is to provide a sewage pollution diffusion prediction method and system that can effectively solve the problem of inaccurate prediction caused by the inability of sediment adsorption and release parameters to dynamically reflect changes in hydrological conditions.

[0009] In a first aspect, the present application provides a sewage pollution diffusion prediction method, which comprises the following steps:

[0010] S1, acquire river bed topographic feature information, real-time hydrological data and sewage discharge information of a river basin;

[0011] S2, divide the river basin into multiple reaches according to the river bed topographic feature information, and then acquire local hydrodynamic parameters of each reach according to the river bed topographic feature information and the real-time hydrological data;

[0012] S3, for each reach, acquire sediment dynamic characteristics according to the river bed topographic feature information and the local hydrodynamic parameters, and then determine sediment adsorption parameters and sediment release parameters according to the sediment dynamic characteristics;

[0013] S4, perform pollution diffusion prediction according to the sediment adsorption parameters, the sediment release parameters, the real-time hydrological data and the sewage discharge information to obtain a diffusion prediction result.

[0014] In a second aspect, the present application further provides a sewage pollution diffusion prediction system, comprising:

[0015] An information acquisition module is configured to acquire river bed topographic feature information, real-time hydrological data and sewage discharge information of a river basin;

[0016] A dynamic parameter acquisition module is configured to divide the river basin into multiple reaches according to the river bed topographic feature information, and then acquire local hydrodynamic parameters of each reach according to the river bed topographic feature information and the real-time hydrological data;

[0017] A parameter confirmation module is configured to, for each reach, acquire sediment dynamic characteristics according to the river bed topographic feature information and the local hydrodynamic parameters, and then determine sediment adsorption parameters and sediment release parameters according to the sediment dynamic characteristics;

[0018] A diffusion prediction module is configured to perform pollution diffusion prediction according to the sediment adsorption parameters, the sediment release parameters, the real-time hydrological data and the sewage discharge information to obtain a diffusion prediction result.

[0019] As can be seen from the above, the sewage pollution diffusion prediction method and system provided by the present application can dynamically consider the adsorption and release of pollutants by sediment by combining real-time hydrological data, local hydrodynamic parameters and river bed topographic feature information, dynamically acquiring sediment characteristics and determining sediment adsorption parameters and sediment release parameters. Therefore, the present application can effectively solve the problem of inaccurate prediction caused by the fact that sediment adsorption parameters and sediment release parameters cannot dynamically reflect changes in hydrological conditions, thereby effectively improving the accuracy of pollution diffusion prediction. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of a sewage pollution diffusion prediction method provided by an embodiment of the present application.

[0021] Figure 2 Fig. 1 is a structural schematic diagram of a sewage pollution diffusion prediction system provided by an embodiment of the present application.

[0022] Fig. 1 is a structural schematic diagram of a sewage pollution diffusion prediction system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0024] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0025] In a first aspect, as shown in the accompanying drawings, the present application provides a sewage pollution diffusion prediction method, which comprises the following steps: Figure 1

[0026] S1, obtaining riverbed topographic feature information, real-time hydrological data and sewage discharge information of a river basin;

[0027] S2, dividing the river basin into multiple reaches according to the riverbed topographic feature information, and then obtaining local hydrodynamic parameters corresponding to each reach according to the riverbed topographic feature information and the real-time hydrological data;

[0028] S3, for each reach, obtaining sediment dynamic characteristics according to the riverbed topographic feature information and the local hydrodynamic parameters, and then determining sediment adsorption parameters and sediment release parameters according to the sediment dynamic characteristics;

[0029] S4, performing pollution diffusion prediction according to the sediment adsorption parameters, the sediment release parameters, the real-time hydrological data and the sewage discharge information, to obtain a diffusion prediction result.

[0030] ​The river bed topography feature information of step S1 refers to the spatial attributes of the geometry, elevation distribution, slope, roughness, etc. of the river bed. In this embodiment, the river bed topography feature information can be obtained by using high-precision topographic survey, laser radar scanning, multi-beam depth sounder, etc., and stored in the form of digital elevation model (DEM) or triangular mesh (TIN), etc. The real-time hydrological data of step S1 refers to the current hydrological state data of the river, which preferably includes the flow rate, water level, runoff and water sediment content of the river. In this embodiment, the real-time hydrological data can be obtained by using flow meters, water level gauges, flowmeters, turbidity sensors, etc. arranged along the river to collect real-time data. The sewage discharge information of step S1 refers to the relevant data of the sewage discharged by the pollution source into the river, which preferably includes the discharge amount and pollutant type. In this embodiment, the sewage discharge information can be obtained by using an enterprise sewage outlet online monitoring system, periodic sampling analysis by an environmental monitoring station or manual input, etc.

[0031] The step S2 of dividing the river basin into multiple reaches according to the river bed topography feature information refers to dividing the continuous river basin into several sub-regions with relatively uniform characteristics according to the geometry, elevation distribution, slope, roughness, etc. of the river bed. In this embodiment, the river basin can be divided into multiple reaches according to the river bed topography feature information by using geographic information system (GIS) tools, combining the bending degree, slope change, tributary confluence point, position of hydraulic structure, etc. of the river topography feature to realize manual or semi-automatic division. The local hydrodynamic parameters corresponding to each reach are calculated or estimated. The local hydrodynamic parameters refer to the flow motion characteristics of a specific river reach under the current hydrological conditions, including instantaneous flow velocity vector, water depth, shear stress and turbulent energy dissipation rate. In this embodiment, the local hydrodynamic parameters can be obtained by using two-dimensional or three-dimensional hydrodynamic models (such as MIKE 21, HEC-RAS2D / 3D) to simulate and calculate according to the river bed topography feature information and real-time hydrological data. Specifically, the river bed topography feature information and real-time hydrological data are used as the boundary conditions and driving force of the hydrodynamic model. In this embodiment, the local hydrodynamic parameters can also be calculated according to the river bed topography feature information and real-time hydrological data by using empirical formula, which represent the scouring or deposition effect of the flow on the bottom mud of each reach.

[0032] The obtaining of the bottom sediment dynamic characteristics in step S3 refers to evaluating the physical and chemical state and behavior of the bottom sediment under the current hydrological conditions. The bottom sediment dynamic characteristics can include the state of deposition, erosion, resuspension, etc. of the bottom sediment and the changes in its physical and chemical properties (such as bottom sediment particle size distribution, organic matter content, porosity, and oxidation-reduction potential). This embodiment can obtain the bottom sediment dynamic characteristics by using a water dynamics-sediment transport model to simulate according to the river bed topographic feature information and the local hydrodynamic parameters. The model can predict the erosion, deposition, or resuspension process of the bottom sediment according to the local hydrodynamic parameters such as shear stress and flow velocity of the water flow and the river bed topographic feature information. This embodiment can also achieve the obtaining of the bottom sediment dynamic characteristics according to the river bed topographic feature information and the local hydrodynamic parameters by using a lookup table method. The bottom sediment dynamic characteristics can reflect the real-time changes in the adsorption and release capacity of the bottom sediment for pollutants. The determining of the bottom sediment adsorption parameter and the bottom sediment release parameter refers to dynamically adjusting the coefficient of the distribution of pollutants between the bottom sediment and the water body according to the dynamic characteristics of the bottom sediment. This embodiment can determine the bottom sediment adsorption parameter and the bottom sediment release parameter by using an empirical formula, a lookup table, or a machine learning model based on the bottom sediment dynamic characteristics (such as the particle size composition of the surface bottom sediment, the organic matter content, the oxidation-reduction potential, etc.). For example, when the bottom sediment is in a deposition state and the organic matter content increases, the adsorption parameter can be adjusted to a higher value. When the bottom sediment is in an erosion state and coarse particles are exposed, the adsorption parameter can be adjusted to a lower value. This step is closely related to the local hydrodynamic parameters obtained in step S2. The hydrodynamic parameters directly affect the dynamic characteristics of the bottom sediment, and further affect the determination of the adsorption and release parameters. Through this dynamic adjustment, the problem of static bottom sediment parameters in existing models is solved, so that the prediction model can reflect the dynamic characteristics of the bottom sediment changing with seasons and hydrological conditions, thereby improving the consistency of the prediction results with the actual situation.

[0033] Step S4 can achieve the pollution diffusion prediction by using a mathematical model to simulate the migration, diffusion, and transformation process of pollutants in the water body according to the bottom sediment adsorption parameter, the bottom sediment release parameter, the real-time hydrological data, and the sewage discharge information. The mathematical model can be an advection-diffusion model, a Lagrangian particle tracking model, or a three-dimensional water quality model (such as CE-QUAL-W2, EFDC). These mathematical models input the sewage discharge information as a pollution source, input the real-time hydrological data as a water flow field driver, and input the bottom sediment adsorption parameter and the release parameter as the boundary conditions of the interaction between the pollutants and the bottom sediment. The diffusion prediction result of step S4 refers to the concentration distribution, migration path, arrival time, peak concentration, etc. of the pollutants in the river changing with time. It can be presented in the form of a concentration contour map, a time series curve, a three-dimensional visualization, etc.

[0034] The core innovation of the application is that the adsorption and release of pollutants by the sediment are dynamically considered by combining real-time hydrological data, local hydrodynamic parameters and river bed topographic feature information, dynamically obtaining the characteristics of the sediment and determining the adsorption parameters and release parameters of the sediment, so that the application can effectively solve the problem of inaccurate prediction caused by the fact that the adsorption parameters and release parameters of the sediment cannot dynamically reflect the changes in hydrological conditions, thereby effectively improving the accuracy of pollution diffusion prediction.

[0035] Specifically, the method first acquires riverbed topographic feature information of a river basin, real-time hydrological data and sewage discharge information. Specifically, the riverbed topographic feature information provides a spatial basis for subsequent river section division and water dynamic analysis, the real-time hydrological data provides dynamic environmental information of the current water body to help understand the behavior of pollutants and sediments, and the sewage discharge information clarifies the characteristics of the pollution source. Then, the river basin is divided into multiple river sections according to the riverbed topographic feature information, and then the local water dynamic parameters corresponding to each river section are acquired according to the riverbed topographic feature information and the real-time hydrological data. This step decomposes the complex river system into manageable units, and through river section division according to the riverbed topographic feature information, fine analysis can be performed on the physical characteristics of different regions. According to the riverbed topographic feature information and the real-time hydrological data, the local water dynamic parameters corresponding to each river section are acquired, which reflect the actual motion state of the water flow in different river sections, and are the key driving force for understanding the migration of pollutants in the water body and the dynamic change of sediments. Subsequently, for each river section, the dynamic characteristics of the sediment are acquired according to the riverbed topographic feature information and the local water dynamic parameters, and then the sediment adsorption parameters and the sediment release parameters are determined according to the dynamic characteristics of the sediment, which means that the physical and chemical properties of the sediment are no longer fixed values, but change in real time according to the current hydrological and topographic conditions, i.e. the dynamic characteristics of the sediment in this scheme can reflect the actual behavior of the sediment under the current hydrological conditions in real time. Then, the sediment adsorption parameters and the sediment release parameters are dynamically determined according to the acquired dynamic characteristics of the sediment, i.e. this method no longer uses fixed sediment adsorption parameters and sediment release parameters, which means that this scheme can adjust the adsorption and release capacity of the sediment to pollutants according to the actual hydrological conditions of the river, so as to more accurately simulate the interaction between pollutants and sediments, so as to overcome the problem of prediction deviation caused by the staticization of sediment parameters in existing models. Finally, the pollution diffusion prediction is performed according to the sediment adsorption parameters, the sediment release parameters, the real-time hydrological data and the sewage discharge information to obtain the diffusion prediction result, which combines the dynamically acquired sediment adsorption parameters and release parameters with the real-time hydrological data and the sewage discharge information, and inputs them into the pollution diffusion model for calculation. Since the sediment parameters are dynamically adjusted, the prediction result can more accurately reflect the diffusion path, concentration change and arrival time of the pollutants under the actual hydrological conditions, thereby significantly improving the accuracy and reliability of the prediction, and providing more accurate basis for environmental protection decision and emergency response. Compared with the model in the background art which presets static sediment parameters, the method dynamically adjusts the sediment adsorption and release parameters, so that the prediction result can more truly reflect the actual behavior of the pollutants under different hydrological conditions, avoiding the prediction deviation caused by the mismatch of parameters, and thereby improving the effectiveness of environmental protection decision and emergency response.

[0036] As a preferred embodiment, the scheme of the present application is implemented as follows: First, the flow rate, water level, runoff, and sediment content of the river are collected in real time by a network of hydrological monitoring stations deployed in the river basin, and the digital elevation model (DEM) data of the river bed is obtained through the geographic information system (GIS), while the sewage discharge and pollutant type information are obtained from the sewage enterprises. Then, using GIS tools combined with DEM data, the river basin is automatically divided into multiple river sections with similar hydraulic characteristics according to the topographic features (such as slope, width change). For each river section, combined with real-time hydrological data, the instantaneous flow velocity vector, water depth, shear stress, and turbulent energy dissipation rate of the river section are calculated and output by running a three-dimensional hydrodynamic model (such as MIKE 3 or Delft3D). Then, for each river section, based on its local hydrodynamic parameters (such as shear stress size) and river bed topographic feature information (such as river bed roughness), combined with empirical models, the deposition or erosion state of the sediment is dynamically evaluated to obtain the dynamic physical and chemical properties of the sediment (sediment dynamic characteristics). According to the sediment dynamic characteristics, through a pre-set adsorption / release model (such as a parameterized model based on Langmuir or Freundlich isotherm), the sediment adsorption parameters and sediment release parameters under the current conditions of the river section are calculated and determined in real time. Finally, the dynamically determined sediment adsorption parameters and sediment release parameters, together with real-time hydrological data and sewage discharge information, are input into a pollutant transport and diffusion model (such as QUAL2K or WASP7) to simulate the migration, transformation, and diffusion of pollutants, thereby obtaining the concentration distribution prediction results of pollutants at different locations and times in the river.

[0037] Through the above scheme, the present application can dynamically obtain the adsorption and release parameters of the river sediment, so that they are consistent with the real-time hydrological conditions and sediment state, thereby solving the problem of inaccurate prediction caused by the static sediment parameters in the existing method. This makes the pollution diffusion prediction results more accurately reflect the actual migration, diffusion, and interaction process of pollutants in the river, thereby improving the reliability of pollution diffusion prediction, and further providing more accurate decision basis for water pollution emergency response and environmental management, and reducing the environmental risk and economic loss caused by prediction deviation.

[0038] In some preferred embodiments, the real-time hydrological data includes the flow velocity, water level, runoff and sediment content of the river. The flow velocity of the river refers to the distance passed by the water flow per unit time, which can be measured by Doppler flow meter or float method. The water level refers to the height of the water surface relative to a certain datum, which can be monitored by ultrasonic water level meter or pressure sensor. The runoff refers to the volume of water passing through a certain section of the river per unit time, which can be calculated according to the flow velocity and the cross-sectional area of the water or directly measured by a flow meter. The sediment content of the water refers to the mass or volume of sediment contained in a unit volume of water, which can be determined by turbidimeter or sampling and weighing method.

[0039] In some embodiments of the present application, the bottom mud dynamic characteristics are obtained according to the riverbed topographic feature information and the local hydrodynamic parameters, and then the bottom mud adsorption parameters and the bottom mud release parameters are determined according to the bottom mud dynamic characteristics. However, in the implementation process, the pollutant adsorption and release characteristics of the riverbed mud are not static and unchangeable, but dynamically change with the changes of real-time environmental factors such as season and hydrological conditions. For example, environmental parameters such as water temperature, pH value and ionic strength can significantly affect the adsorption and release capacity of the bottom mud to pollutants. If the adsorption and release parameters are determined only according to the bottom mud dynamic characteristics without fully considering the influence of these real-time environmental parameters on the adsorption and release capacity of the bottom mud, the determined bottom mud adsorption parameters and bottom mud release parameters may not accurately reflect the actual behavior of the bottom mud under the current water body environment, thereby reducing the reliability of the prediction system and the effectiveness of the decision guidance.

[0040] To solve the technical problem, in some preferred embodiments, step S3 comprises:

[0041] S31, for each river section, obtaining the bottom mud dynamic characteristics according to the riverbed topographic feature information and the local hydrodynamic parameters;

[0042] S32, for each river section, determining the preliminary adsorption parameters and the preliminary release parameters according to the bottom mud dynamic characteristics, and obtaining the real-time environmental parameters of the river section, the real-time environmental parameters including water temperature, pH value and ionic strength;

[0043] S33, for each river section, adjusting the preliminary adsorption parameters and the preliminary release parameters according to the real-time environmental parameters to obtain the bottom mud adsorption parameters and the bottom mud release parameters.

[0044] Real-time environmental parameters are key dynamic factors affecting the migration and transformation of pollutants in water bodies and their interaction with sediments. Specifically, water temperature affects chemical reaction rates and solubility of substances. In this embodiment, the temperature sensor of the online water quality monitoring station can be used to obtain the water temperature in real time. The pH value changes the ionic form of pollutants and the surface charge characteristics of sediments, i.e., the pH value affects the effectiveness of adsorption sites. In this embodiment, the pH sensor of the online water quality monitoring station can be used to obtain the pH value in real time. Ionic strength affects the shielding effect of ions in solution and adsorption equilibrium. In this embodiment, the conductivity sensor of the online water quality monitoring station can be used to obtain the conductivity in real time, and then the ionic strength can be obtained based on the conductivity through empirical formula or ion composition analysis conversion. The adjustment of the preliminary adsorption parameters and the preliminary release parameters according to the real-time environmental parameters in step S33 is to correct the preliminary adsorption parameters and the preliminary release parameters determined in S32 according to the real-time environmental parameters, so that they are more consistent with the sediment adsorption or release behavior under the actual water body environment. Specifically, an empirical correction model, a machine learning model or a physical and chemical model based on experimental data can be used to perform the correction, for example, inputting the water temperature, pH value, ionic strength and preliminary parameters into a pre-established multivariate regression model to make the model output adjusted parameters, or correcting by consulting a database of pollutant adsorption or desorption coefficients under different environmental conditions. The sediment adsorption parameters and the sediment release parameters refer to the parameters that are finally used in the pollution diffusion prediction model and can truly reflect the adsorption and release capacity of sediments to pollutants under the current water body environment after adjustment by real-time environmental parameters. These parameters can be distribution coefficients, adsorption rate constants, desorption rate constants, etc.

[0045] Specifically, the present scheme effectively improves the accuracy of the adsorption and release parameters of the sediment by introducing real-time environmental parameters for dynamic adjustment. First, after obtaining the dynamic characteristics of the sediment, the system will determine a set of preliminary adsorption and release parameters based on the dynamic characteristics of the sediment. These parameters reflect the adsorption and release capacity of the sediment under ideal or benchmark conditions. At the same time, the system will obtain real-time environmental parameters such as water temperature, pH value, and ionic strength, which are key environmental factors affecting the migration and transformation of pollutants in water and their interaction with sediment. These environmental parameters have a significant impact on the distribution behavior of pollutants between water and sediment. Subsequently, these real-time environmental parameters are used to dynamically correct the preliminary adsorption and release parameters. For example, when the water temperature rises, the desorption rate of some pollutants may accelerate, and the adsorption parameter may need to be adjusted downward, and the release parameter may need to be adjusted upward. When the pH value changes, the surface charge of the pollutants or the sediment changes, and the adsorption capacity also changes. This dynamic adjustment based on real-time environmental parameters greatly improves the accuracy and timeliness of the sediment parameters, enabling the entire pollution diffusion prediction model to more accurately simulate the migration and fate of pollutants in complex dynamic environments, thereby effectively improving the accuracy and reliability of the diffusion prediction results.

[0046] As a specific implementation, first, the sediment transport model is used to analyze the sediment dynamic characteristics based on the river bed topography information and local hydrodynamic parameters. Then, the adsorption isotherm and kinetic model based on the laboratory adsorption and desorption experimental results of the sediment sample under standard conditions are used to determine the preliminary adsorption and release parameters based on the sediment dynamic characteristics. At the same time, real-time environmental parameters are collected by multi-parameter water quality sensors deployed in each river section. Finally, the preliminary adsorption and release parameters are adjusted using the obtained real-time environmental parameters. For example, a pre-established correction model based on experimental data can be used: adjusted distribution coefficient = preliminary distribution coefficient * f (water temperature) * g (pH value) * h (ionic strength); adjusted desorption rate constant = preliminary desorption rate constant * F (water temperature) * G (pH value) * H (ionic strength); where f, g, h, F, G, H are correction functions fitted according to experimental data.

[0047] By the technical solution, the application solves the problem that the adsorption parameters and the release parameters determined only according to the dynamic characteristics of the bottom mud may not fully reflect the adsorption and release behaviors of the bottom mud to the pollutants in the actual water body environment. Since the embodiment takes into account the significant influence of real-time environmental parameters such as water temperature, pH value and ionic strength on the adsorption process and the release process, the determined bottom mud adsorption parameters and bottom mud release parameters can effectively avoid deviation from the actual situation, thereby effectively improving the accuracy and reliability of the pollution diffusion prediction, so that the sewage pollution diffusion prediction method can better adapt to the complex changes of the actual water body environment and help avoid prediction deviation and emergency response failure caused by inaccurate parameters.

[0048] In the implementation process, only considering real-time environmental parameters may not be enough to accurately reflect the adsorption and release characteristics of the bottom mud to different types of pollutants. Different types of pollutants, such as heavy metals or organic pollutants, have different interaction mechanisms with the bottom mud and different responses to environmental parameters. If the parameter adjustment process does not fully consider the specific types of pollutants, the obtained bottom mud adsorption parameters and bottom mud release parameters may not accurately characterize the actual behavior of a specific pollutant under the current environmental conditions, resulting in deviation of the pollution diffusion prediction results from the actual situation and affecting the accuracy of the prediction and the effectiveness of the emergency response.

[0049] To solve the technical problem, in some preferred embodiments, the sewage discharge information includes the type of pollutant, and step S33 includes:

[0050] S331, for each river segment, determining a parameter adjustment strategy according to the type of pollutant, the dynamic characteristics of the bottom mud and the real-time environmental parameters, and then adjusting the preliminary adsorption parameters and the preliminary release parameters according to the parameter adjustment strategy to obtain the bottom mud adsorption parameters and the bottom mud release parameters.

[0051] The parameter adjustment strategy refers to a set of rules, models or algorithms for determining how to correct the preliminary adsorption parameters and the preliminary release parameters according to the input information (such as the type of pollutant, the dynamic characteristics of the bottom mud and the real-time environmental parameters), which can be implemented in various ways, for example, the parameter adjustment strategy can be a rule base constructed based on expert experience, which contains parameter correction coefficients for different combinations of pollutants, bottom mud types and environmental conditions; the parameter adjustment strategy can also be a prediction model trained by machine learning method, which can directly output the adjusted parameter values or adjustment factors according to the input features.

[0052] The present scheme no longer relies solely on real-time environmental parameters when determining the sediment adsorption parameters and sediment release parameters, but by introducing pollutant species information and comprehensively considering the dynamic characteristics of the sediment and real-time environmental parameters, a more accurate parameter adjustment strategy is determined. Specifically, on the basis of obtaining the preliminary adsorption parameters and preliminary release parameters and real-time environmental parameters, the present scheme further obtains the pollutant species in the sewage discharge information, and then the pollutant species, the dynamic characteristics of the sediment and the real-time environmental parameters are jointly used as inputs to determine a parameter adjustment strategy for the current river section, the current pollutant and the current environmental conditions. For example, for heavy metal pollutants, their adsorption behavior may be greatly affected by pH value and sediment organic matter content; while for some organic pollutants, temperature and sediment particle size distribution may be more critical. By comprehensively analyzing these factors, the system can identify the dominant mechanism affecting the adsorption and release behavior of a specific pollutant, and generate a refined adjustment strategy accordingly, and then use the adjustment strategy to correct the preliminary adsorption parameters and preliminary release parameters. This correction process enables the final sediment adsorption parameters and sediment release parameters to more accurately reflect the actual adsorption and release behavior of a specific pollutant under specific river conditions, specific sediment conditions and specific environmental conditions. This method of considering multiple dimensions of information makes the adjustment of parameters more targeted and accurate, thereby significantly improving the reliability of pollution diffusion prediction. In this way, the present scheme further refines the parameter adjustment logic on the basis of the existing technology, enabling the prediction model to more realistically simulate the migration and transformation process of pollutants in complex environments, effectively avoiding prediction bias caused by inaccurate parameters.

[0053] In a specific embodiment, for each river segment, the process of determining the parameter adjustment strategy can be implemented as follows: first, the system receives the pollutant species (e.g., "cadmium" or "benzene"), at the same time, the system has obtained the sediment dynamic characteristics of this river segment, and the real-time environmental parameters (e.g., water temperature is 20 degrees Celsius, pH value is 7.5, ionic strength is 0.01 M). In order to determine the parameter adjustment strategy, the system internally maintains a multi-dimensional lookup table or a pre-trained decision tree model, which takes the pollutant species, sediment dynamic characteristics (e.g., organic matter content of surface sediment, median particle size) and real-time environmental parameters (e.g., water temperature, pH value) as input variables, for example, if the pollutant is cadmium, the sediment organic matter content is high, and the water temperature is between 15-25 degrees Celsius, the pH value is between 7-8, the lookup table may indicate a specific adjustment factor (adjustment strategy), such as increasing the adsorption parameter by 10% and reducing the release parameter by 5%. Or the decision tree model will output a specific adjustment strategy according to these input features, such as "based on the current conditions, the adsorption parameter should be multiplied by 1.1, and the release parameter should be multiplied by 0.9". Finally, the system modifies the previously determined preliminary adsorption parameter and preliminary release parameter according to the strategy, for example, if the preliminary adsorption parameter is 0.5 and the preliminary release parameter is 0.01, and the adjustment strategy indicates that the adsorption parameter should be multiplied by 1.1 and the release parameter should be multiplied by 0.9, then the final sediment adsorption parameter will be updated to 0.55 and the sediment release parameter will be updated to 0.009.

[0054] The present scheme introduces pollutant species information in the parameter adjustment process, and combines it with sediment dynamic characteristics and real-time environmental parameters to determine a more accurate parameter adjustment strategy, which enables the obtained sediment adsorption parameters and sediment release parameters to more accurately represent the actual adsorption and release behavior of a specific pollutant under current environmental conditions, thereby significantly improving the accuracy of pollution diffusion prediction results.

[0055] In some embodiments of the present application described above, it is proposed to obtain sediment dynamic characteristics by analyzing river bed topographic feature information and local hydrodynamic parameters. However, in the implementation process, the river bed sediment is not a homogeneous single layer, but may have a multi-layer structure, the physical and chemical properties of different layers of sediment are significantly different, and the adsorption and release of pollutants in the sediment may also occur at different depths. The sediment dynamic characteristics determined from the river bed topographic feature information and the local hydrodynamic parameters cannot finely distinguish this vertical heterogeneity, resulting in inaccurate prediction of the vertical migration and retention behavior of pollutants in the sediment. This may lead to inaccurate determination of the sediment adsorption parameters and the sediment release parameters, and further affect the accuracy of the pollution diffusion prediction results, and cannot effectively cope with the challenge of changes in river basin sediment characteristics with seasons and hydrological conditions.

[0056] In some preferred embodiments, step S31 comprises:

[0057] S311, for each river reach, obtaining historical sediment deposition data, the historical sediment deposition data comprising historical deposition thickness, historical sediment grain size distribution, and historical sediment organic matter content;

[0058] S312, for each river reach, obtaining sediment vertical stratification structure according to river bed topography characteristic information, local hydrodynamic parameters, and historical deposition data analysis, the sediment vertical stratification structure comprising depth range and initial physical and chemical properties corresponding to each layer;

[0059] S313, for each river reach, obtaining dynamic physical and chemical properties corresponding to each layer according to depth range, initial physical and chemical properties, and local hydrodynamic parameters, and then integrating all dynamic physical and chemical properties to obtain sediment dynamic characteristics.

[0060] The historical sediment deposition data refers to the recorded information formed by the deposition process of river sediment in a specific river reach over a period of time, which can be obtained in the form of geological exploration reports, drilling sampling analysis data, remote sensing image historical data, or long-term sediment monitoring records of hydrological monitoring stations. The historical deposition thickness refers to the vertical depth accumulated by the sediment over a certain period of time, the historical sediment grain size distribution refers to the proportion of different size particulate matter (such as clay, silt, sand, gravel, etc.) in the sediment, and the historical sediment organic matter content refers to the mass percentage of organic matter contained in the sediment. The sediment vertical stratification structure refers to the layered distribution of different physical and chemical properties of river sediment in the vertical direction, the depth range refers to the starting depth and ending depth of each independent layer in the sediment vertical stratification structure in the vertical direction, the initial physical and chemical properties refer to the inherent physical and chemical attributes of each layer in the sediment vertical stratification structure in a specific initial state, such as its inherent grain size distribution, organic matter content, density, porosity, mineral composition, or initial pollutant concentration. The dynamic physical and chemical properties refer to the time-varying characteristics of the physical and chemical attributes (such as grain size distribution, organic matter content, adsorption capacity, release rate, etc.) of each layer in the sediment vertical stratification structure under real-time hydrodynamic conditions, which can be calculated using a hydrodynamic-sediment transport coupling model or an empirical formula. The sediment dynamic characteristics refer to a comprehensive description of the time-varying characteristics of the vertical stratification structure and the physical and chemical properties of each layer of river sediment under different hydrological conditions, which can be represented by a multi-layer sediment model parameter set, a dynamic adsorption-release coefficient matrix, or a machine learning-based sediment state prediction model.

[0061] The scheme aims to more accurately describe the real state of river sediment, thereby improving the accuracy of pollution diffusion prediction. Specifically, the scheme first obtains historical sediment deposition data for each river section, including historical deposition thickness, historical sediment particle size distribution, and historical sediment organic matter content. These historical data are records of the long-term evolution and formation process of sediment, providing important historical background and basic information for understanding the current physical and chemical properties of sediment and its adsorption and release capacity for pollutants. On this basis, for each river section, the vertical stratification structure of the sediment is analyzed and obtained in combination with river bed topography information, local hydrodynamic parameters, and historical deposition data. This analysis mechanism creatively integrates multi-source information: river bed topography information provides physical and spatial constraints for sediment deposition, local hydrodynamic parameters indicate the current flow scouring or deposition effect on sediment, and historical deposition data provide the vertical distribution rule of long-term accumulated sediment. Through this comprehensive analysis, the actual vertical stratification structure of the river section sediment can be identified, and the depth range and initial physical and chemical properties of each layer are determined. This identification mechanism overcomes the limitations of treating sediment as a homogeneous single layer, lays a spatial and initial state foundation for subsequent fine deduction of dynamic characteristics of each layer, and significantly improves the accuracy of the description of the real state of sediment. Further, for each river section, the dynamic physical and chemical properties of each layer are obtained according to the depth range, initial physical and chemical properties, and real-time local hydrodynamic parameters, to consider the influence of current local hydrodynamic parameters on each layer, for example, in the wet season, high flow velocity may cause the scouring of fine-grained surface sediment, thereby changing its physical and chemical properties; while in the dry season, low flow velocity may promote new deposition and form a new surface. By obtaining these layer-level dynamic properties and integrating them, a comprehensive, fine, and real-time reflecting the real state of sediment dynamic characteristics of sediment is finally obtained. The scheme can extract long-term evolution information of sediment from historical deposition data, combine real-time hydrodynamic conditions and river bed topography characteristics, fine identify the vertical stratification structure of sediment, and further calculate the dynamic physical and chemical properties of each layer. This multi-dimensional and dynamic sediment property acquisition method makes the sediment dynamic characteristics more accurately reflect the real adsorption and release capacity of sediment for pollutants, so the embodiment can effectively improve the sediment adsorption parameters and sediment release parameters, thereby effectively improving the accuracy of the diffusion prediction results.

[0062] To further illustrate the above method of obtaining the dynamic characteristics of the bottom sediment, the following embodiments can be considered. For a specific river section in a river basin, first, historical bottom sediment deposition data is obtained from the national geological database, historical monitoring reports of the water conservancy department, or by drilling and sampling the river section and analyzing the core. Second, the historical deposition data, riverbed topography information, and local hydrodynamic parameters are input into a machine learning-based clustering algorithm, which divides the bottom sediment into different vertical layers based on the similarity of the data. For example, at a typical location, three main layers are identified: the surface sediment layer (0-15 cm), with initial physical and chemical properties of median particle size 50 microns, organic matter content 7%, and initial porosity 0.6; the transition layer (15-40 cm), with initial physical and chemical properties of median particle size 150 microns, organic matter content 3%, and initial porosity 0.5; and the base layer (greater than 40 cm), with initial physical and chemical properties of median particle size 300 microns, organic matter content 1%, and initial porosity 0.4. Finally, a hydrodynamic model coupled with a sediment transport module is used to dynamically simulate the changes in physical and chemical properties of each layer based on the depth range, initial physical and chemical properties, and real-time local hydrodynamic parameters. For example, when the local hydrodynamic parameters show an increase in flow velocity and shear stress, the model can calculate the resuspension amount of fine sediment in the surface layer and update its particle size distribution and organic matter content accordingly, thereby obtaining the dynamic physical and chemical properties of the layer under the current hydrological conditions. By calculating and integrating the dynamic physical and chemical properties of all layers, the dynamic characteristics of the bottom sediment in the river section at the current time are obtained.

[0063] This scheme overcomes the limitations of traditional methods that treat the bottom sediment as a homogeneous single layer by introducing a refined description of the vertical stratification structure of the bottom sediment and its dynamic changes. By obtaining historical bottom sediment deposition data and combining riverbed topography information and local hydrodynamic parameters, the vertical stratification structure of the bottom sediment can be accurately identified, and the depth range and initial physical and chemical properties of different layers can be determined. Based on this, the dynamic physical and chemical properties of each layer are further calculated according to real-time hydrodynamic parameters, and they are integrated to obtain a comprehensive and real-time reflection of the real state of the bottom sediment. This enables the subsequent determination of the adsorption and release parameters of the bottom sediment to more accurately reflect the real adsorption and release capacity of the bottom sediment to pollutants.

[0064] In some preferred embodiments, step S2 includes:

[0065] S21, dividing the river basin into multiple river sections according to the riverbed topography information, each river section corresponding to a riverbed topography information;

[0066] S22, obtaining the current hydrological flow state characteristics according to real-time hydrological data, the current hydrological flow state characteristics including flow velocity range and flow rate change rate;

[0067] S23, for each river section, determining a water dynamic parameter acquisition rule according to the river section riverbed topographic feature information and the current hydrological flow state feature, and then acquiring the local water dynamic parameter by using the water dynamic parameter acquisition rule according to the river section riverbed topographic feature and the real-time hydrological data.

[0068] The current hydrological flow state feature refers to the water flow dynamic characteristics exhibited by the river at a specific time point or a short time period. In this embodiment, the current hydrological flow state feature can be acquired by analyzing the real-time hydrological data or extracting key dynamic indicators. The water dynamic parameter acquisition rule refers to a set of logic, algorithm or model used to guide how to calculate or determine the local water dynamic parameter according to the input data. In this embodiment, the water dynamic parameter acquisition rule can be determined by using methods such as empirical formula, numerical simulation model, machine learning algorithm or table lookup method.

[0069] This scheme refines the acquisition process of the local water dynamic parameter, aiming to more accurately capture the dynamic hydrological conditions of the river. Specifically, first, in step S21, the river basin is divided into multiple river sections according to the riverbed topographic feature information, and each river section corresponds to its own riverbed topographic feature information. Second, in step S22, the current hydrological flow state feature is acquired according to the real-time hydrological data, which includes the flow velocity range and the flow rate of change. The system can better understand and respond to the actual water flow conditions of the river under different hydrological periods based on the current hydrological flow state feature, to make up for the possible shortcomings of relying only on general real-time hydrological data. Finally, in step S23, for each river section, a water dynamic parameter acquisition rule is determined according to the river section riverbed topographic feature information and the current hydrological flow state feature, and then the local water dynamic parameter is acquired by using the water dynamic parameter acquisition rule in combination with the river section riverbed topographic feature and the real-time hydrological data. This series of steps combines the specific riverbed topographic features of the river section with the current dynamic hydrological flow state feature to determine a more adaptive water dynamic parameter acquisition rule. This means that the acquisition of the local water dynamic parameter is no longer based on a fixed or universal method, but rather on the customization of the acquisition rule according to the specific topography of each river section and the current dynamic water flow conditions. By using this dynamically determined rule in combination with the river section riverbed topographic feature and the real-time hydrological data, more accurate and more consistent local water dynamic parameters with the current actual water flow conditions can be acquired.

[0070] To further illustrate the implementation details of the present scheme, a specific application scenario can be envisaged. For example, in step S21, using geographic information system (GIS) tools, combined with high-precision digital elevation model (DEM) data and river boundary data, the river basin is automatically divided into multiple river sections with relatively uniform terrain characteristics by setting standards such as terrain slope, river width change rate or water depth threshold. In step S22, the difference between the maximum and minimum flow rates in the past hour or day is calculated as the flow rate range from the flow rate sensor data and flow meter data obtained from real-time hydrological monitoring stations, and the increase or decrease in flow per unit time is calculated as the flow rate change rate to obtain the current hydrological flow regime characteristics. Subsequently, in step S23, for each river section, the river bed terrain feature information of the river section and the current hydrological flow regime characteristics obtained in step S22 are used to determine the water dynamic parameter acquisition rule, for example, if the river bed terrain feature of a certain river section shows that it is wide, shallow and flat, and the current hydrological flow regime characteristics show that the flow rate range is large and the flow rate change rate is high (for example, during the flood season), the system can call a modified model based on the Manning formula or the Chezy formula as the water dynamic parameter acquisition rule, which dynamically adjusts the roughness coefficient according to the current flow rate and water depth, thereby obtaining more accurate instantaneous flow rate vector and shear stress. Conversely, if the river section terrain is deep and narrow, and the flow rate range is small and the flow rate change rate is low (for example, during the dry season), an empirical formula considering the influence of sediment deposition can be used as the rule to obtain local water dynamic parameters reflecting the interaction of bottom mud under low flow rate. In this way, the local water dynamic parameters of each river section can be customized according to its unique geographical conditions and real-time hydrological dynamics.

[0071] In some preferred embodiments, step S21 comprises:

[0072] S211, dividing the river basin according to the river bed terrain feature information to obtain a plurality of initial river sections;

[0073] S212, for each initial river section, evaluating the flow connectivity and flow capacity according to real-time hydrological data and corresponding river bed terrain feature information;

[0074] S213, adjusting the river section division boundary of the river basin according to the evaluation result, and then dividing the river basin according to the river section division boundary to obtain a plurality of river sections.

[0075] Water flow connectivity refers to the state of whether water bodies in different regions of a river can effectively flow and connect with each other under certain hydrological conditions. It can be analyzed based on data such as water depth, flow velocity, water surface elevation, and the presence of obstacles (such as sandbars, exposed riverbeds during dry season), to determine the continuity of water flow paths. For example, by constructing a digital elevation model (DEM) and a hydrodynamic model, the flow paths of water under different water levels can be simulated, and disconnected areas or newly connected channels can be identified. The assessment of water carrying capacity refers to the actual carrying and transporting capacity of a river section under certain hydrological conditions. It can be achieved by calculating parameters such as effective water carrying cross-sectional area, Manning coefficient, hydraulic radius, and flow rate. For example, by combining real-time water level data and cross-sectional topographic data of the river section, the water carrying cross-sectional area and hydraulic radius under the current water level can be calculated, and the water carrying capacity can be evaluated.

[0076] The overall working principle of the scheme is as follows: the scheme introduces a mechanism of dynamically evaluating and adjusting the river section division boundary, solves the problem that the river section division based on only static topographic information cannot adapt to the change of dynamic hydrological conditions of the river, and further affects the accuracy of obtaining local hydrodynamic parameters. Specifically, the scheme first uses the riverbed topographic feature information of the river to preliminarily and basically divide the river basin into multiple initial river sections. On this basis, for each initial river section, the scheme introduces real-time hydrological data as an important basis for evaluating the rationality of river section division, and dynamically evaluates the water flow connectivity and water passing function of each initial river section by combining real-time hydrological data and the riverbed topographic feature information of the initial river section. The water flow connectivity evaluation can identify whether there is effective water flow connection between different regions under the current hydrological conditions, for example, whether there is a dry-up or a newly added connected path; the water passing function evaluation focuses on the actual water carrying capacity and water passing section of the river section under the current hydrological conditions. This evaluation can reveal the dynamic hydrological characteristics that cannot be reflected by only static topographic information, such as the possibility of dry-up in some areas during the dry season, or the significant increase in water carrying capacity in some areas during the wet season, thereby identifying possible deficiencies in the initial division. Finally, the scheme dynamically adjusts the river section division boundary of the river basin based on the evaluation results of the water flow connectivity and the water passing function. This means that if the evaluation finds that the water flow connectivity or water passing function within an initial river section has changed significantly under the current real-time hydrological conditions, or the connectivity with adjacent river sections has changed, the boundary of the initial river section will be adjusted accordingly, for example, an initial river section will be divided into multiple smaller river sections with uniform hydrological characteristics, or multiple initial river sections with high connectivity will be merged into a larger river section. This dynamic adjustment ensures that the multiple river sections finally divided can accurately reflect the actual hydrodynamic characteristics and water flow distribution under the current real-time hydrological conditions, so that the hydrodynamic conditions within each river section are uniform and stable. It is precisely because of this dynamically adaptive river section division that the acquisition of local hydrodynamic parameters can be based on the river section definition that accurately reflects the actual hydrological conditions, providing a reasonable and dynamically adaptive basis for the accurate acquisition of local hydrodynamic parameters, that is, the embodiment can effectively improve the accuracy and reliability of local hydrodynamic parameters, thereby improving the accuracy and reliability of pollution diffusion prediction.

[0077] To further illustrate the implementation details of the present scheme, a specific embodiment is provided as follows: first, the river basin is preliminarily divided into 10 initial river sections by using precise topographic survey data (river bed topographic feature information) and combining historical hydrogeological survey reports. Subsequently, for these initial river sections, the real-time hydrological data is input into a two-dimensional hydrodynamic model (for example, the MIKE 21 model based on the finite element method), and the simulation is performed in combination with the river bed topographic feature information of each initial river section. The simulation results show that there is a local flow cutoff phenomenon between initial river section 3 and initial river section 4 due to low water level and poor actual flow connectivity; while initial river section 7 and initial river section 8 are independent in terms of topography, but in the current dry season, due to the convergence of water flow, their water passing section area and flow rate show high similarity. Finally, based on the simulation results of the hydrodynamic model, the system identifies that the actual water flow is discontinuous between initial river section 3 and initial river section 4, and therefore, a new river section boundary is forcibly set between them to divide them into two independent river sections. At the same time, since the hydrodynamic characteristics of initial river section 7 and initial river section 8 are highly similar under the current hydrological conditions, the system merges these two initial river sections into a new river section.

[0078] Through the above technical scheme, the present application solves the problem that the river basin division method based only on static topographic features cannot fully reflect the dynamic water flow connectivity and water passing function of the river under different real-time hydrological conditions. Thus, the situation that the divided river sections do not match the actual water flow conditions is avoided, and the accuracy of subsequent local hydrodynamic parameter acquisition is improved. Ultimately, the pollution diffusion prediction result is more accurate, and the prediction deviation is reduced.

[0079] In some preferred embodiments, step S23 comprises:

[0080] S231, for each river section, determining a preliminary parameter acquisition rule according to the river bed topographic feature information and the current hydrological flow state characteristics;

[0081] S232, for each river section, acquiring real-time sediment movement state information, and then adjusting the preliminary parameter acquisition rule according to the real-time sediment movement state information to obtain a water movement parameter acquisition rule, the real-time sediment movement state information including suspended sediment concentration and bed surface morphological characteristics;

[0082] S233, for each river section, acquiring local hydrodynamic parameters according to the river bed topographic features and real-time hydrological data by using the water movement parameter acquisition rule.

[0083] The real-time sediment movement state information refers to a comprehensive description of the dynamic distribution of sediment particles in the water body and the surface morphological characteristics of the riverbed. It can be obtained by real-time monitoring of the suspended sediment concentration using an acoustic Doppler current profiler (ADCP) combined with a turbidity sensor, or by obtaining bed surface morphological data using an underwater terrain scanner and a multi-beam depth sounding system, and combining image recognition technology to analyze bed surface sand waves, dunes and other characteristics.

[0084] In obtaining the local hydrodynamic parameters, the present scheme first determines the preliminary parameter acquisition rule for each river section according to the riverbed topographic feature information and the current hydrological flow state characteristics of the river section. This step lays the foundation for the refinement of the hydrodynamic parameter acquisition, which comprehensively considers the inherent geometric shape, roughness and other topographic conditions of the river section, as well as the average velocity, flow rate of change and other macroscopic hydrological flow state, providing a basis for preliminary estimation of the hydrodynamic parameters. On this basis, the present scheme further introduces the consideration of real-time sediment movement state information. For each river section, the real-time sediment movement state information is obtained, and the preliminary parameter acquisition rule is adjusted according to the information to obtain a more accurate hydrodynamic parameter acquisition rule. Since the concentration of suspended sediment affects the density and viscosity of the water body, thereby changing the resistance characteristics of the water flow, and the bed surface morphological characteristics directly affect the roughness of the riverbed and the turbulent structure of the water flow, the real-time sediment movement state information is a dynamic factor that cannot be ignored in the hydrodynamics. By obtaining the real-time sediment movement state information and adjusting the preliminary parameter acquisition rule accordingly, the rule can more accurately reflect the actual situation of the current water flow and riverbed sediment interaction and overcome the possible deviation caused by relying only on static topography and macroscopic hydrological data. This adjustment mechanism enables the hydrodynamic parameter acquisition rule to dynamically adapt to changes in the river environment, thereby significantly improving the applicability and accuracy of the rule. Finally, for each river section, the local hydrodynamic parameters are obtained using the adjusted hydrodynamic parameter acquisition rule in combination with the riverbed topographic features and real-time hydrological data. Since the hydrodynamic parameter acquisition rule used has fully considered the influence of the real-time sediment movement state, the obtained local hydrodynamic parameters will more accurately reflect the true hydrodynamic environment of the river under the current sediment conditions. This more accurate local hydrodynamic parameter provides a more reliable physical basis for the subsequent migration, diffusion and interaction of pollutants in the water body and with the bottom mud. In this way, based on the original determination of the hydrodynamic parameter acquisition rule according to the riverbed topographic feature information and the current hydrological flow state characteristics of the river section, the present scheme dynamically adjusts by introducing the real-time sediment movement state information, making the hydrodynamic parameter acquisition more realistic, thereby providing more solid and accurate data support for the entire pollution diffusion prediction method, and significantly improving the overall accuracy and reliability of the prediction model.

[0085] In a specific embodiment, the method can be applied to a typical river reach. First, in step S231, using the digital elevation model (DEM) data of the river reach and the historical hydrological observation data, combined with the hydraulic empirical formula (such as the Manning formula or the Chezy formula), the water dynamic parameter acquisition rule is initially established. Then, in step S232, in order to obtain real-time sediment movement state information, online monitoring equipment can be deployed at key positions of the river reach, for example, an ultrasonic Doppler current profiler (ADCP) can be installed to monitor the flow velocity profile and suspended sediment concentration (inverted by acoustic echo intensity) in the water body, while underwater sonar or laser scanner is used to regularly scan the riverbed to obtain bed topography data and identify bed form features such as sand waves and dunes. After obtaining these real-time data, they can be input into an adjustment module based on a machine learning model or a physical model, which can be trained by a large amount of experimental data in advance to learn the complex relationship between sediment movement state and water dynamic parameters. For example, when the suspended sediment concentration increases, the model can adjust the effective viscosity coefficient of the water flow; when large sand waves are detected, the model can increase the riverbed roughness coefficient. In this way, the preliminary parameter acquisition rule can be dynamically corrected to more accurately reflect the flow resistance, turbulence intensity, etc. under the current sediment conditions. Finally, in step S233, using the water dynamic parameter acquisition rule adjusted as described above, combined with real-time hydrological data and the latest topographic data of the river reach, the local water dynamic parameters of the river reach are calculated.

[0086] In some preferred embodiments, the local water dynamic parameters include instantaneous flow velocity vector, water depth, shear stress, and turbulent energy dissipation rate. The instantaneous flow velocity vector refers to the speed and direction of any point in the water body at a specific time, which can be obtained by simulating a high-resolution three-dimensional water dynamic model. The water depth refers to the vertical distance from the water surface to the riverbed bottom, which can be calculated by combining topographic data with water level information. The shear stress refers to the tangential force per unit area exerted by the water flow on the riverbed bottom, which can be calculated by the water dynamic model combined with the flow velocity gradient and water viscosity. The turbulent energy dissipation rate refers to the rate at which turbulent energy in the water body is converted into heat energy, reflecting the intensity of turbulence, which can be obtained by simulating a turbulence model.

[0087] In a second aspect, as shown in Figure 2 The present application also provides a sewage pollution diffusion prediction system, which comprises:

[0088] An information acquisition module 1 is configured to acquire riverbed topographic feature information of a river basin, real-time hydrological data, and sewage discharge information.

[0089] The power parameter acquisition module 2 is configured to divide the river basin into a plurality of river sections according to the riverbed topographic feature information, and then acquire local hydrodynamic parameters of each river section according to the riverbed topographic feature information and real-time hydrological data.

[0090] The parameter confirmation module 3 is configured to acquire the dynamic characteristics of the sediment for each river section according to the riverbed topographic feature information and the local hydrodynamic parameters, and then determine the sediment adsorption parameters and the sediment release parameters according to the dynamic characteristics of the sediment.

[0091] The diffusion prediction module 4 is configured to perform pollution diffusion prediction according to the sediment adsorption parameters, the sediment release parameters, the real-time hydrological data and the sewage discharge information, so as to obtain a diffusion prediction result.

[0092] The sewage pollution diffusion prediction system provided in the embodiment includes the information acquisition module 1, the power parameter acquisition module 2, the parameter confirmation module 3 and the diffusion prediction module 4. The sewage pollution diffusion prediction system provided in the embodiment is used to execute the steps in the sewage pollution diffusion prediction method provided in the first aspect, and the principle of the sewage pollution diffusion prediction system provided in the embodiment is the same as that of the sewage pollution diffusion prediction method provided in the first aspect, which will not be discussed in detail here.

[0093] As can be seen from the above, the sewage pollution diffusion prediction method and system provided in the application can dynamically consider the adsorption and release of the sediment to the pollutants by combining the real-time hydrological data, the local hydrodynamic parameters and the riverbed topographic feature information, dynamically acquiring the characteristics of the sediment and determining the sediment adsorption parameters and the sediment release parameters, so that the application can effectively solve the problem of inaccurate prediction caused by the fact that the sediment adsorption parameters and the sediment release parameters cannot dynamically reflect the changes in the hydrological conditions, thereby effectively improving the accuracy of the pollution diffusion prediction.

[0094] In the embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the above units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another robot, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0095] In addition, each functional module in each embodiment of the application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0096] As used in the description herein and throughout the claims that follow, the meaning of "in" includes "in" and "on" and "at" and does not exclude any of the interior portions of such body, surface, or additional structure.

[0097] The above description is embodied in the form of examples only and is not used to limit the protection scope of the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of predicting the spread of sewage pollution, characterized by, The sewage pollution diffusion prediction method comprises the following steps: S1, obtaining river bed topographic feature information, real-time hydrological data and sewage discharge information of a river basin; S2, dividing the river basin into a plurality of reaches according to the river bed topographic feature information, and then obtaining local hydrodynamic parameters of each reach according to the river bed topographic feature information and the real-time hydrological data; S3, for each reach, obtaining sediment dynamic characteristics according to the river bed topographic feature information and the local hydrodynamic parameters, and then determining sediment adsorption parameters and sediment release parameters according to the sediment dynamic characteristics; S4, performing pollution diffusion prediction according to the sediment adsorption parameters, the sediment release parameters, the real-time hydrological data and the sewage discharge information to obtain a diffusion prediction result; Step S2 comprises: S21, dividing the river basin into a plurality of reaches according to the river bed topographic feature information, each reach corresponding to a reach river bed topographic feature information; S22, obtaining current hydrological flow state characteristics according to the real-time hydrological data, the current hydrological flow state characteristics including flow velocity range and flow rate change rate; S23, for each reach, determining a hydrodynamic parameter acquisition rule according to the reach river bed topographic feature information and the current hydrological flow state characteristics, and then obtaining local hydrodynamic parameters according to the reach river bed topographic feature and the real-time hydrological data by using the hydrodynamic parameter acquisition rule.

2. The method according to claim 1, wherein, The real-time hydrological data includes flow velocity, water level, runoff and water body sediment content of the river.

3. The method according to claim 2, wherein, Step S3 comprises: S31, for each reach, obtaining sediment dynamic characteristics according to the river bed topographic feature information and the local hydrodynamic parameters; S32, for each reach, determining preliminary adsorption parameters and preliminary release parameters according to the sediment dynamic characteristics, and obtaining real-time environmental parameters of the reach, the real-time environmental parameters including water temperature, ph value and ionic strength; S33, for each reach, adjusting the preliminary adsorption parameters and the preliminary release parameters according to the real-time environmental parameters to obtain sediment adsorption parameters and sediment release parameters.

4. The method according to claim 3, wherein, The sewage discharge information includes pollutant types, and step S33 comprises: S331, for each reach, determining a parameter adjustment strategy according to the pollutant types, the sediment dynamic characteristics and the real-time environmental parameters, and then adjusting the preliminary adsorption parameters and the preliminary release parameters according to the parameter adjustment strategy to obtain sediment adsorption parameters and sediment release parameters.

5. The method of predicting the spread of sewage pollution according to claim 3, wherein Step S31 comprises: S311, for each reach, obtaining historical sediment deposition data, the historical sediment deposition data including historical deposition thickness, historical sediment particle size distribution and historical sediment organic matter content; S312, for each reach, analyzing and obtaining sediment vertical stratification structure according to the river bed topographic feature information, the local hydrodynamic parameters and the historical sediment deposition data, the sediment vertical stratification structure including depth range and initial physical and chemical properties corresponding to different layers. S313. For each river section, obtain the dynamic physicochemical properties corresponding to each layer based on the depth range, the initial physicochemical properties, and the local hydrodynamic parameters, and then integrate all the dynamic physicochemical properties to obtain the dynamic characteristics of the sediment.

6. The method of predicting the spread of sewage pollution according to claim 1, wherein Step S21 includes: S211. Divide the river basin according to the riverbed topographic features to obtain multiple initial river segments; S212. For each initial river segment, the water flow connectivity and water passage function are evaluated based on the real-time hydrological data and the corresponding riverbed topographic features. S213. Adjust the river segment division boundary of the river basin according to the evaluation results, and then divide the river basin according to the river segment division boundary to obtain multiple river segments.

7. The method of predicting the spread of sewage pollution according to claim 1, wherein Step S23 includes: S231. For each river segment, determine preliminary parameter acquisition rules based on the riverbed topographic features and the current hydrological flow characteristics of the river segment. S232. For each river section, real-time sediment movement status information is obtained, and then the preliminary parameter acquisition rules are adjusted according to the real-time sediment movement status information to obtain water movement parameter acquisition rules. The real-time sediment movement status information includes suspended sediment concentration and bed morphology characteristics. S233. For each of the river sections, local hydrodynamic parameters are obtained based on the riverbed topography features and the real-time hydrological data using the hydrodynamic parameter acquisition rules.

8. The method of predicting the spread of sewage pollution according to claim 1, wherein The local hydrodynamic parameters include instantaneous velocity vector, water depth, shear stress, and turbulent kinetic energy dissipation rate.

9. A sewage pollution spread prediction system characterized by, The wastewater pollution diffusion prediction system is used to perform the steps in the wastewater pollution diffusion prediction method as described in any one of claims 1-8, and the wastewater pollution diffusion prediction system includes: The information acquisition module is used to acquire information on the topographic features of the riverbed, real-time hydrological data, and sewage discharge information in the river basin. The dynamic parameter acquisition module is used to divide the river basin into multiple river segments based on the riverbed topographic features, and then acquire the local hydrodynamic parameters corresponding to each river segment based on the riverbed topographic features and the real-time hydrological data. The parameter confirmation module is used to obtain the dynamic characteristics of the bottom sediment for each river section based on the riverbed topographic features and the local hydrodynamic parameters, and then determine the bottom sediment adsorption parameters and bottom sediment release parameters based on the bottom sediment dynamic characteristics. The diffusion prediction module is used to predict pollution diffusion based on the sediment adsorption parameters, sediment release parameters, real-time hydrological data, and wastewater discharge information to obtain diffusion prediction results.

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