A method and system for tracing the source of water pollution in a river basin by integrating spatiotemporal big data

Abstract

The present invention provides a basin water quality pollution source tracing analysis method and system integrating spatiotemporal big data, which relates to the field of pollution analysis technology, including collecting water quality, meteorology, surface runoff and other data, and generating a data set through spatiotemporal label association. Based on the data set, a pollutant migration and diffusion model including pollutant concentration attenuation and flow direction prediction sub-models is constructed to calculate the pollutant concentration change rate and migration path. Finally, the pollution sources are divided into point sources, surface sources and endogenous pollution through the pollution source identification model, the contribution rate of each type of pollution source is calculated, and a pollution source tracing analysis report is generated. The present invention can comprehensively consider the impact of various factors on water quality, realize the precise positioning and quantitative analysis of pollution sources, and improve the efficiency and accuracy of tracing.

Description

Technical Field

[0001] The present invention relates to pollution analysis technology, and in particular to a watershed water quality pollution source tracing analysis method and system integrating spatiotemporal big data. Background Art

[0002] Tracing the source of water pollution in a river basin is an important part of environmental management and water pollution prevention and control. Determining the source, migration path and contribution rate of pollutants is crucial for formulating effective control measures. Traditional tracing methods mainly rely on field surveys, sampling analysis and simple mathematical models, which have many limitations.

[0003] The existing technology mainly has the following defects and shortcomings:

[0004] 1. The scope of data collection is limited, and it is difficult to reflect the overall pollution status of the basin. Traditional monitoring methods mainly rely on manual sampling and laboratory analysis, which are costly and inefficient. In addition, the monitoring points are limited and cannot fully cover the entire basin, resulting in insufficient data information and difficulty in accurately reflecting the temporal and spatial variation characteristics of the basin's water quality.

[0005] 2. Lack of fusion analysis of multi-source heterogeneous data. Watershed water pollution is a complex process, which is affected by many factors, such as rainfall, runoff, meteorological conditions, etc. Traditional source tracing methods usually only consider a single pollutant or a single pollution source, lack of comprehensive analysis of multi-source heterogeneous data (such as water quality, meteorology, hydrology, etc.), and it is difficult to reveal the migration and transformation laws of pollutants and the interaction relationship between pollution sources.

[0006] 3. Low accuracy in pollution source identification, making it difficult to accurately quantify pollution contributions. Traditional pollution source identification methods mainly rely on empirical judgment and simple mathematical models, lacking detailed simulation of the migration and diffusion process of pollutants, making it difficult to accurately identify the type, location and pollution contribution rate of pollution sources, resulting in low accuracy in source tracing results and difficulty in supporting accurate pollution control decisions. Summary of the invention

[0007] The embodiments of the present invention provide a method and system for tracing the source of water pollution in a watershed by integrating spatiotemporal big data, which can solve the problems in the prior art.

[0008] A first aspect of an embodiment of the present invention provides a method for tracing the source of water pollution in a river basin by integrating spatiotemporal big data, including:

[0009] Construct a multi-source heterogeneous spatiotemporal big data acquisition system, the multi-source heterogeneous spatiotemporal big data acquisition system includes an online water quality monitoring station, a meteorological monitoring station, and a surface runoff monitoring station set up in the basin, the online water quality monitoring station collects water body physical and chemical index data, the meteorological monitoring station collects rainfall data and temperature data, and the surface runoff monitoring station collects flow velocity data and flow data; the water body physical and chemical index data, the rainfall data, the temperature data, the flow velocity data, and the flow data are associated through spatiotemporal tags to generate a spatiotemporal associated data set;

[0010] Based on the spatiotemporal correlation data set, a pollutant migration and diffusion model is constructed, wherein the pollutant migration and diffusion model includes a pollutant concentration attenuation submodel and a pollutant flow direction prediction submodel, wherein the pollutant concentration attenuation submodel calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, and the pollutant flow direction prediction submodel calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data;

[0011] Based on the concentration change rate of pollutants and the migration path of pollutants, the pollution sources in the basin are divided into three categories: point source pollution, non-point source pollution and internal source pollution through the pollution source identification model. The pollution contribution rate of each type of pollution source is calculated to generate a pollution source tracing analysis report.

[0012] In an optional embodiment,

[0013] The pollutant concentration attenuation sub-model calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, including:

[0014] Collecting temperature data and rainfall data of the water area where the pollutants are located, setting an influence function of an environmental factor and a value of the environmental factor according to the temperature data and the rainfall data, and calculating an environmental factor coupling coefficient based on the influence function of the environmental factor and the value of the environmental factor;

[0015] A pollutant concentration attenuation kinetic model is established according to the environmental factor coupling coefficient, wherein the pollutant concentration attenuation kinetic model adopts the product form of the first-order reaction kinetic equation and the environmental factor coupling coefficient to obtain the derivative of the pollutant concentration with respect to time;

[0016] Collect spatial location data of pollutant detection points, calculate distance data between adjacent detection points according to the spatial location data, and construct a spatial weight matrix based on the distance data, wherein the spatial weight matrix is ​​in the form of a product of the inverse of the square of the distance and an exponential decay function of the distance;

[0017] The pollutant concentration attenuation kinetic model is combined with the spatial weight matrix to obtain a spatiotemporal coupled diffusion model, which is composed of a concentration attenuation term and a spatial diffusion term, wherein the concentration attenuation term includes the environmental factor coupling coefficient, and the spatial diffusion term includes the spatial weight matrix. The concentration change rate of pollutants at different spatial locations is obtained through the spatiotemporal coupled diffusion model.

[0018] In an optional embodiment,

[0019] A pollutant concentration attenuation kinetic model is established according to the environmental factor coupling coefficient. The pollutant concentration attenuation kinetic model adopts the product form of the first-order reaction kinetic equation and the environmental factor coupling coefficient. The derivative of the pollutant concentration with respect to time is obtained, including:

[0020] ;

[0021] Among them, a i represents the baseline impact coefficient of the i-th environmental factor, b i represents the adjustment coefficient of the i-th environmental factor, E i,max represents the maximum value of the i-th environmental factor, E i,min represents the minimum value of the i-th environmental factor;

[0022] ;

[0023] Among them, β represents the environmental factor coupling coefficient, n represents the total number of environmental factors, and f i represents the influence function of the i-th environmental factor, E i represents the value of the i-th environmental factor;

[0024] ;

[0025] Where C represents the pollutant concentration, k represents the intrinsic reaction rate constant of pollutant degradation, Indicates the rate of change of pollutant concentration over time;

[0026] The spatiotemporal coupled diffusion model is shown in the following formula:

[0027] ;

[0028] Where D represents the diffusion coefficient matrix, represents the concentration gradient vector, R(x,y,t) represents the rate term of increase or decrease of pollutants in the x and y directions at time t during the diffusion process, W(x,y) represents the spatial weight function, Represents the flow field velocity vector.

[0029] In an optional embodiment,

[0030] The pollutant flow direction prediction sub-model calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow rate data, including:

[0031] Acquire the hydrological parameters of the watershed, establish the Saint-Venant continuity equation and the Saint-Venant momentum equation according to the hydrological parameters of the watershed, the Saint-Venant continuity equation includes a water flow cross-sectional area parameter, a flow parameter and a lateral inflow parameter, the Saint-Venant momentum equation includes a riverbed slope parameter and an energy slope parameter, establish a cross-sectional flow model according to the water flow cross-sectional area parameter and the flow parameter; based on the cross-sectional flow model and the energy slope parameter, use the Manning formula to calculate the cross-sectional average flow velocity and hydraulic radius, and obtain the watershed hydrodynamic characteristic data;

[0032] Constructing a pollutant transport field based on the watershed hydrodynamic characteristic data, decomposing the average flow velocity of the cross section into a horizontal flow velocity component and a vertical flow velocity component, establishing a two-dimensional pollutant convection diffusion equation based on the horizontal flow velocity component and the vertical flow velocity component, and introducing a horizontal diffusion coefficient and a vertical diffusion coefficient into the two-dimensional pollutant convection diffusion equation;

[0033] The four-order Runge-Kutta algorithm is used to numerically solve the two-dimensional pollutant convection-diffusion equation. The four-order Runge-Kutta algorithm iteratively calculates the pollutant displacement through four weighted coefficients to obtain the pollutant point motion trajectory data as the migration path of the pollutant in the watershed.

[0034] In an optional embodiment,

[0035] The Saint-Venant continuity equation is as follows:

[0036] ;

[0037] Where A is the cross-sectional area of ​​the water flow, represents the rate of change of cross-sectional area over time, Q is the flow rate, x represents the length, represents the rate of change of flow along the way, q l is the lateral inflow per unit length;

[0038] The Saint-Venant momentum equation is as follows:

[0039] ;

[0040] in, represents the rate of change of flow rate over time, represents the inertia force term, represents the pressure gradient term, g represents the acceleration of gravity, h represents the water depth, S0 represents the riverbed slope, S f represents the energy slope;

[0041] The two-dimensional pollutant convection-diffusion equation is as follows:

[0042] ;

[0043] Where C represents the pollutant concentration, represents the rate of change of pollutant concentration over time, u represents the velocity component in the x direction, and v represents the velocity component in the y direction. , They represent the first-order derivatives of pollutant concentration in the x and y directions, i.e., the convection terms, D x , D y represent the diffusion coefficient in the x direction and the diffusion coefficient in the y direction respectively, , They represent the second-order derivatives of pollutant concentration in the x and y directions, respectively, which are also diffusion terms.

[0044] In an optional embodiment,

[0045] Based on the concentration change rate of pollutants and the migration path of pollutants, the pollution sources in the basin are divided into three categories: point source pollution, non-point source pollution and internal source pollution through the pollution source identification model, and the pollution contribution rate of each type of pollution source is calculated, including:

[0046] Obtain the pollutant concentration time series data of the monitoring point, calculate the pollutant concentration change rate, record the location coordinates of the monitoring point, calculate the spatial gradient of the pollutant concentration of the monitoring point, and construct a pollution feature vector containing the pollutant concentration, concentration change rate, location coordinates and concentration gradient;

[0047] Based on the constructed pollution feature vector, a point source pollution discriminant function is constructed in the form of a distance attenuation index, wherein the point source pollution discriminant function includes the pollutant concentration of the monitoring point, the distance from the pollution source, and the attenuation coefficient; and the spatial distribution characteristic value of the point source pollution discriminant function is calculated;

[0048] Construct a non-point source pollution flux equation including a diffusion flux term and a convection flux term, wherein the diffusion flux term is determined by the diffusion coefficient and the concentration gradient, and the convection flux term is determined by the transport velocity vector and the pollutant concentration; calculate the spatial distribution of the non-point source pollution flux in combination with the acquired pollutant concentration gradient data;

[0049] Based on the established non-point source pollution flux equation, a sediment release kinetic equation including endogenous pollution concentration, sediment storage, release coefficient and sedimentation coefficient was constructed; the non-point source pollution flux equation was coupled with the sediment release kinetic equation to calculate the contribution flux of endogenous pollution to the water body;

[0050] Using the obtained point source pollution discriminant function values, non-point source pollution fluxes and contribution fluxes of endogenous pollution to water bodies, a three-dimensional pollution source characteristic discriminant matrix is ​​constructed, wherein the matrix elements of the discriminant matrix are the discriminant weights of different pollution characteristics for various types of pollution sources; the pollution contribution rate of each type of pollution source is determined according to the product of each matrix element in the three-dimensional pollution source characteristic discriminant matrix and the time-varying function.

[0051] In an optional embodiment,

[0052] The calculation formula for the spatial distribution characteristic value of the point source pollution discriminant function is as follows:

[0053] ;

[0054] Among them, F p represents the spatial distribution characteristic value of the point source pollution discriminant function, m represents the number of pollutants, r j represents the distance to the jth pollutant, C j represents the pollutant concentration of the jth pollutant, α represents the attenuation coefficient, V j Represents the pollution feature vector corresponding to the jth pollutant;

[0055] The non-point source pollution flux equation is as follows:

[0056] ;

[0057] ;

[0058] ;

[0059] Among them, J s represents the total non-point source pollution flux, J d represents the diffusion flux term, J c represents the convective flux term, D represents the diffusion coefficient matrix, represents the concentration gradient vector, D x represents the diffusion coefficient in the x direction, D y represents the diffusion coefficient in the y direction, , They represent the concentration gradient in the x direction and the concentration gradient in the y direction respectively; represents the velocity vector of the flow field, C represents the pollutant concentration, v x 、v y They represent the x-direction velocity component and the y-direction velocity component respectively;

[0060] The sediment release kinetic equation is shown in the following formula:

[0061] ;

[0062] The calculation of the contribution flux of internal pollution to water bodies is shown in the following formula:

[0063] ;

[0064] in, Indicates the concentration of endogenous pollution C in The derivative with time, k1, k2 represent the sediment release coefficient and sedimentation coefficient respectively, S d represents the sediment pollutant reserves; J in represents the contribution flux of endogenous pollution to the water body, Represents the changing gradient of non-point source pollution flux.

[0065] A second aspect of an embodiment of the present invention provides a watershed water pollution source tracing analysis system integrating spatiotemporal big data, including:

[0066] The first unit is used to construct a multi-source heterogeneous spatiotemporal big data acquisition system, which includes an online water quality monitoring station, a meteorological monitoring station, and a surface runoff monitoring station set up in the basin, wherein the online water quality monitoring station collects water body physical and chemical index data, the meteorological monitoring station collects rainfall data and temperature data, and the surface runoff monitoring station collects flow velocity data and flow data; the water body physical and chemical index data, the rainfall data, the temperature data, the flow velocity data, and the flow data are associated through spatiotemporal tags to generate a spatiotemporal associated data set;

[0067] The second unit is used to construct a pollutant migration and diffusion model based on the spatiotemporal correlation data set, wherein the pollutant migration and diffusion model includes a pollutant concentration attenuation submodel and a pollutant flow direction prediction submodel, wherein the pollutant concentration attenuation submodel calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, and the pollutant flow direction prediction submodel calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data;

[0068] The third unit is used to classify the pollution sources in the basin into three categories: point source pollution, non-point source pollution and internal source pollution based on the concentration change rate of pollutants and the migration path of pollutants through the pollution source identification model, calculate the pollution contribution rate of each type of pollution source, and generate a pollution source tracing analysis report.

[0069] The beneficial effects of this application are as follows:

[0070] It can accurately locate and trace the source of water pollution in the river basin. It can distinguish point source, non-point source and internal source pollution, and calculate the contribution rate of various pollution sources, providing a scientific basis for pollution control. The comprehensive use of multi-source heterogeneous data improves the accuracy and reliability of pollution source tracing analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of a flow chart of a method for tracing the source of water pollution in a watershed by integrating spatiotemporal big data according to an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of the structure of a watershed water pollution source tracing analysis system that integrates spatiotemporal big data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0075] Figure 1 FIG. 1 is a flow chart of a method for tracing the source of water pollution in a watershed by integrating spatiotemporal big data according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0076] S101. Construct a multi-source heterogeneous spatiotemporal big data acquisition system, the multi-source heterogeneous spatiotemporal big data acquisition system includes an online water quality monitoring station, a meteorological monitoring station, and a surface runoff monitoring station set up in the basin, the online water quality monitoring station collects water body physical and chemical index data, the meteorological monitoring station collects rainfall data and temperature data, and the surface runoff monitoring station collects flow velocity data and flow data; the water body physical and chemical index data, the rainfall data, the temperature data, the flow velocity data, and the flow data are associated through spatiotemporal tags to generate a spatiotemporal associated data set;

[0077] S102. Based on the spatiotemporal correlation data set, a pollutant migration and diffusion model is constructed, wherein the pollutant migration and diffusion model includes a pollutant concentration attenuation submodel and a pollutant flow direction prediction submodel, wherein the pollutant concentration attenuation submodel calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, and the pollutant flow direction prediction submodel calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data;

[0078] S103. Based on the concentration change rate of pollutants and the migration path of pollutants, the pollution sources in the basin are divided into three categories: point source pollution, non-point source pollution and internal source pollution through the pollution source identification model, and the pollution contribution rate of each type of pollution source is calculated to generate a pollution source tracing analysis report.

[0079] In an optional implementation, the pollutant concentration attenuation sub-model calculates the concentration change rate of pollutants at different spatial locations based on the water body physical and chemical index data, the rainfall data and the temperature data, including:

[0080] Collecting temperature data and rainfall data of the water area where the pollutants are located, setting an influence function of an environmental factor and a value of the environmental factor according to the temperature data and the rainfall data, and calculating an environmental factor coupling coefficient based on the influence function of the environmental factor and the value of the environmental factor;

[0081] A pollutant concentration attenuation kinetic model is established according to the environmental factor coupling coefficient, wherein the pollutant concentration attenuation kinetic model adopts the product form of the first-order reaction kinetic equation and the environmental factor coupling coefficient to obtain the derivative of the pollutant concentration with respect to time;

[0082] Collect spatial location data of pollutant detection points, calculate distance data between adjacent detection points according to the spatial location data, and construct a spatial weight matrix based on the distance data, wherein the spatial weight matrix is ​​in the form of a product of the inverse of the square of the distance and an exponential decay function of the distance;

[0083] The pollutant concentration attenuation kinetic model is combined with the spatial weight matrix to obtain a spatiotemporal coupled diffusion model, which is composed of a concentration attenuation term and a spatial diffusion term, wherein the concentration attenuation term includes the environmental factor coupling coefficient, and the spatial diffusion term includes the spatial weight matrix. The concentration change rate of pollutants at different spatial locations is obtained through the spatiotemporal coupled diffusion model.

[0084] First, collect water-related data. It is necessary to collect temperature data, rainfall data, and physical and chemical index data of the water body where the pollutants are located, such as pH value, dissolved oxygen, turbidity, etc. Suppose that the temperature data of a certain water area for three consecutive days are 25℃, 28℃, and 26℃, and the rainfall data are 10mm, 5mm, and 0mm. The pH values ​​are 7.5, 7.8, and 7.6, respectively, the dissolved oxygen is 8mg / L, 7mg / L, and 7.5mg / L, respectively, and the turbidity is 5NTU, 8NTU, and 6NTU, respectively.

[0085] Then, the environmental factor coupling coefficient is calculated. The environmental factor coupling coefficient is calculated based on the collected temperature and rainfall data. The influence function of the environmental factor and the environmental factor value are set according to the temperature data and the rainfall data, and the environmental factor coupling coefficient is calculated based on the influence function of the environmental factor and the environmental factor value.

[0086] Exemplarily, first determine the effective range of temperature, set the minimum temperature threshold and the maximum temperature threshold. Within this range, a linear function is used to describe the impact of temperature on the migration and transformation of pollutants, and the corresponding relationship between temperature and impact degree is established through coefficient fitting. When the actual temperature exceeds the threshold range, the impact degree takes a fixed value. Similarly, determine the effective range of rainfall, set the minimum rainfall threshold and the maximum rainfall threshold. A linear function is used to describe the impact degree of rainfall within this range, and the function coefficient is determined by data fitting. When the actual rainfall exceeds the threshold range, the impact degree takes a fixed value. According to the measured rainfall data, when the rainfall is lower than the minimum threshold, the value is 0; when the rainfall is higher than the maximum threshold, the value is 1; within the threshold range, the specific value is calculated according to the impact function.

[0087] Next, a pollutant concentration decay kinetic model is established. Using the environmental factor coupling coefficient, a pollutant concentration decay kinetic model is established. This model uses the product of the first-order reaction kinetic equation and the environmental factor coupling coefficient to describe the rate of change of pollutant concentration over time. For example, assuming that the initial concentration of the pollutant is 10 mg / L, the rate of change of the pollutant concentration over time can be calculated based on the first-order reaction kinetic equation and the calculated environmental factor coupling coefficient.

[0088] Subsequently, spatial location data is collected and a spatial weight matrix is ​​constructed. Spatial location data of pollutant detection points, such as longitude and latitude coordinates, are collected. The distance between adjacent detection points is calculated based on these coordinate data. Then, a spatial weight matrix is ​​constructed based on the distance data. The matrix takes the form of the product of the inverse of the square of the distance and the exponential decay function of the distance. For example, assuming there are two detection points with a distance of 100 meters, the spatial weight between them can be calculated based on the inverse of the square of the distance and the exponential decay function of the distance.

[0089] Finally, a spatiotemporal coupled diffusion model is constructed and the concentration change rate is calculated. The pollutant concentration decay kinetic model is combined with the spatial weight matrix to obtain a spatiotemporal coupled diffusion model. The model consists of a concentration decay term and a spatial diffusion term. The concentration decay term contains the environmental factor coupling coefficient, and the spatial diffusion term contains the spatial weight matrix. By solving the spatiotemporal coupled diffusion model, the concentration change rate of pollutants at different spatial locations can be obtained. For example, the change in pollutant concentration at different detection points at a specific time point can be calculated.

[0090] The beneficial effects of this pollutant concentration attenuation sub-model are reflected in the following three aspects:

[0091] Improve the prediction accuracy of pollutant concentrations: The model comprehensively considers environmental factors such as temperature and rainfall as well as spatial diffusion effects, and can more accurately predict the changes in pollutant concentrations in different time and space, avoiding the limitations of traditional models that only consider a single factor, thereby improving prediction accuracy.

[0092] Enhance pollution warning and emergency response capabilities: Through real-time monitoring data and model predictions, potential pollution risks can be discovered in a timely manner, early warning measures can be taken in advance, and a scientific basis can be provided for emergency response, thereby effectively reducing the harm caused by pollution.

[0093] Support environmental management and decision-making: The model can provide environmental management departments with dynamic information on the temporal and spatial distribution of pollutants, which will help to formulate more scientific and reasonable pollution control strategies and environmental management measures, optimize resource allocation, and improve environmental management efficiency.

[0094] In an optional embodiment, a pollutant concentration decay kinetic model is established according to the environmental factor coupling coefficient. The pollutant concentration decay kinetic model adopts the product form of the first-order reaction kinetic equation and the environmental factor coupling coefficient, and the derivative of the pollutant concentration with respect to time is obtained, which includes:

[0095] ;

[0096] Among them, a i represents the baseline impact coefficient of the i-th environmental factor, b i represents the adjustment coefficient of the i-th environmental factor, E i,max represents the maximum value of the i-th environmental factor, E i,min represents the minimum value of the i-th environmental factor;

[0097] ;

[0098] Among them, β represents the environmental factor coupling coefficient, n represents the total number of environmental factors, and f i represents the influence function of the i-th environmental factor, E i represents the value of the i-th environmental factor;

[0099] ;

[0100] Where C represents the pollutant concentration, k represents the intrinsic reaction rate constant of pollutant degradation, It represents the rate of change of pollutant concentration over time;

[0101] The spatiotemporal coupled diffusion model is shown in the following formula:

[0102] ;

[0103] Where D represents the diffusion coefficient matrix, represents the concentration gradient vector, R(x,y,t) represents the rate term of increase or decrease of pollutants in the x and y directions at time t during the diffusion process, W(x,y) represents the spatial weight function, Represents the flow field velocity vector.

[0104] The pollutant concentration decay prediction method can more accurately predict the concentration changes of pollutants in the environment, thereby providing a more reliable basis for environmental governance and risk assessment. The core of this method is to introduce the environmental factor coupling coefficient to quantify and integrate the impact of multiple environmental factors on pollutant degradation.

[0105] First, determine the environmental factors that affect the degradation of pollutants. These environmental factors can include temperature, pH, dissolved oxygen, light intensity, etc. The environmental factors to be selected depend on the specific pollutants and environmental conditions. For example, for organic pollutants, temperature and dissolved oxygen are usually important factors; while for heavy metal pollutants, pH and redox potential are more critical.

[0106] Next, determine the influence function of each environmental factor. The influence function describes the degree of influence of environmental factors on the degradation rate of pollutants. The influence function can be linear, exponential, logarithmic or other forms, and the specific form needs to be determined based on experimental data or theoretical models. For example, the effect of temperature on many chemical reactions follows the Arrhenius equation, so the effect of temperature can be described by an exponential function.

[0107] Then, determine the baseline impact coefficient and adjustment coefficient of each environmental factor. The baseline impact coefficient indicates the degree of influence of the environmental factor under standard conditions, while the adjustment coefficient reflects the regulatory effect of the environmental factor value range on the degree of influence. These coefficients can be obtained by fitting experimental data. For example, by measuring the degradation rate of pollutants at different temperatures, the baseline impact coefficient and adjustment coefficient of temperature can be fitted.

[0108] Let's take a specific case to illustrate. Assume that the degradation of a certain pollutant in water is affected by temperature and pH value. Through experimental measurement, the following data are obtained: when the temperature is 25 degrees Celsius and the pH value is 7, the degradation rate of the pollutant decreases by 10% every day; when the temperature is 30 degrees Celsius and the pH value is 7, the degradation rate of the pollutant decreases by 15% every day; when the temperature is 25 degrees Celsius and the pH value is 8, the degradation rate of the pollutant decreases by 8% every day. Based on these data, the influence function, baseline influence coefficient and adjustment coefficient of temperature and pH value can be fitted.

[0109] Finally, based on the environmental factor coupling coefficient and the intrinsic reaction rate constant of pollutant degradation, a pollutant concentration decay kinetic model is established. This model can be used to predict the concentration changes of pollutants under different environmental conditions. For example, the concentration of pollutants in water bodies in the future can be predicted, thus providing a decision-making basis for environmental governance.

[0110] The beneficial effects of this method are reflected in three aspects:

[0111] First, the accuracy of pollutant concentration prediction is improved. By considering the influence of multiple environmental factors, this method can more accurately reflect the degradation process of pollutants in the actual environment, thereby improving the reliability of the prediction results. Second, the scientific nature of environmental risk assessment is enhanced. This method provides a method for quantitatively evaluating the impact of environmental factors on pollutant degradation, which can provide a more scientific basis for environmental risk assessment. Third, the formulation of environmental governance strategies is optimized. Based on the prediction results of this method, more effective environmental governance strategies can be formulated to better control and reduce environmental pollution risks.

[0112] In an optional implementation, the pollutant flow direction prediction sub-model calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data, including:

[0113] Acquire the hydrological parameters of the watershed, establish the Saint-Venant continuity equation and the Saint-Venant momentum equation according to the hydrological parameters of the watershed, the Saint-Venant continuity equation includes a water flow cross-sectional area parameter, a flow parameter and a lateral inflow parameter, the Saint-Venant momentum equation includes a riverbed slope parameter and an energy slope parameter, establish a cross-sectional flow model according to the water flow cross-sectional area parameter and the flow parameter; based on the cross-sectional flow model and the energy slope parameter, use the Manning formula to calculate the cross-sectional average flow velocity and hydraulic radius, and obtain the watershed hydrodynamic characteristic data;

[0114] Constructing a pollutant transport field based on the watershed hydrodynamic characteristic data, decomposing the average flow velocity of the cross section into a horizontal flow velocity component and a vertical flow velocity component, establishing a two-dimensional pollutant convection diffusion equation based on the horizontal flow velocity component and the vertical flow velocity component, and introducing a horizontal diffusion coefficient and a vertical diffusion coefficient into the two-dimensional pollutant convection diffusion equation;

[0115] The four-order Runge-Kutta algorithm is used to numerically solve the two-dimensional pollutant convection-diffusion equation. The four-order Runge-Kutta algorithm iteratively calculates the pollutant displacement through four weighted coefficients to obtain the pollutant point motion trajectory data as the migration path of the pollutant in the watershed.

[0116] The pollutant flow prediction method can accurately simulate the migration path of pollutants in the watershed, providing a scientific basis for environmental risk assessment and emergency management. This method is based on velocity and flow data, combined with hydrodynamic models and pollutant transport models to predict the migration path of pollutants.

[0117] First, obtain the hydrological parameters of the target basin, such as the length, width, depth, slope, roughness, etc. of the river, as well as rainfall, evaporation and other data in the basin. Take a river as an example, its length is 100 kilometers, the average width is 50 meters, the average depth is 2 meters, the riverbed slope is 0.001, and the roughness is 0.03.

[0118] Then, based on the obtained basin hydrological parameters, the Saint-Venant equations describing the movement of water flow are established. The Saint-Venant equations include the continuity equation and the momentum equation. The continuity equation describes the conservation of mass of water flow, taking into account parameters such as the cross-sectional area, flow rate, and lateral inflow. The momentum equation describes the conservation of momentum of water flow, taking into account parameters such as the riverbed slope and energy slope.

[0119] Next, based on the cross-sectional area and flow parameters, a cross-sectional flow model is established to describe the relationship between the cross-sectional flow and the water level. For example, a power function model can be used to describe the relationship.

[0120] Based on the cross-sectional flow model and energy slope parameters, the Manning formula is used to calculate the cross-sectional average velocity and hydraulic radius. The Manning formula is an empirical formula that relates the cross-sectional average velocity to the hydraulic radius, riverbed slope and roughness. Through calculation, the hydrodynamic characteristic data of the basin, such as velocity, water depth, hydraulic radius, etc., can be obtained. Assume that the cross-sectional average velocity of a river section calculated according to the Manning formula is 1 m / s and the hydraulic radius is 1.5 m.

[0121] After obtaining the watershed hydrodynamic characteristic data, the pollutant transport field is constructed. The average flow velocity of the section is decomposed into the horizontal flow velocity component and the vertical flow velocity component. For example, assuming that the horizontal flow velocity component is 0.8 m / s and the vertical flow velocity component is 0.6 m / s.

[0122] According to the horizontal velocity component and the vertical velocity component, a two-dimensional pollutant convection diffusion equation is established. This equation describes the convection and diffusion process of pollutants under the action of water flow. The horizontal diffusion coefficient and the vertical diffusion coefficient are introduced into the equation. For example, the horizontal diffusion coefficient is 0.1 square meters / second and the vertical diffusion coefficient is 0.01 square meters / second to reflect the diffusion degree of pollutants in different directions.

[0123] The fourth-order Runge-Kutta algorithm is used to numerically solve the two-dimensional pollutant convection-diffusion equation. The fourth-order Runge-Kutta algorithm is a commonly used numerical calculation method. It iteratively calculates the displacement of pollutants through four weighted coefficients to obtain the movement trajectory data of pollutant points, that is, the migration path of pollutants in the watershed. For example, the time step can be set to 1 second, and the displacement of pollutants can be iteratively calculated to obtain the location information of pollutants at different times.

[0124] The beneficial effects of this method are reflected in the following three aspects:

[0125] 1. Improve prediction accuracy: This method comprehensively considers the hydrodynamic characteristics and pollutant diffusion process, and can more accurately predict the migration path of pollutants in the basin, avoiding the errors caused by the simplified assumptions of traditional methods.

[0126] 2. Enhanced practicality: The method is based on readily available velocity and flow data, is easy to implement, and can be widely applied to different types of watersheds.

[0127] 3. Support refined management: This method can provide refined simulation results of pollutant migration paths, provide a scientific basis for environmental risk assessment, emergency management and pollution control, and help improve the level of environmental management.

[0128] In an optional embodiment, the Saint-Venant continuity equation is as follows:

[0129] ;

[0130] Where A is the cross-sectional area of ​​the water flow, represents the rate of change of cross-sectional area over time, Q is the flow rate, x represents the length, represents the rate of change of flow along the way, q l is the lateral inflow per unit length;

[0131] The Saint-Venant momentum equation is as follows:

[0132] ;

[0133] in, represents the rate of change of flow rate over time, represents the inertia force term, represents the pressure gradient term, g represents the acceleration of gravity, h represents the water depth, S0 represents the riverbed slope, S f represents the energy slope;

[0134] The two-dimensional pollutant convection-diffusion equation is as follows:

[0135] ;

[0136] Where C represents the pollutant concentration, represents the rate of change of pollutant concentration over time, u represents the velocity component in the x direction, and v represents the velocity component in the y direction. , They represent the first-order derivatives of pollutant concentration in the x and y directions, i.e., the convection terms, D x , D y represent the diffusion coefficient in the x direction and the diffusion coefficient in the y direction respectively, , They represent the second-order derivatives of pollutant concentration in the x and y directions, respectively, which are also diffusion terms.

[0137] The water flow and water quality simulation method and system based on the Saint-Venant equation and the two-dimensional pollutant convection-diffusion equation can more accurately simulate the river flow and pollutant transport process.

[0138] First, obtain the geometric data of the river, including riverbed topography, cross-sectional shape and size, etc. For example, use measurement tools to obtain the width, depth and other data of a section every 100 meters from upstream to downstream of a river, and use digital elevation model (DEM) to obtain riverbed elevation data. Next, divide the calculation grid and divide the river into several small calculation units. For example, divide the above river into a rectangular grid with a length of 100 meters and a width of 10 meters.

[0139] Then, determine the initial conditions and boundary conditions. The initial conditions include the water depth, flow rate, and pollutant concentration of each grid at the initial moment. For example, the water depth of all grids is set to 2 meters, the flow rate is set to 0.5 meters per second, and the pollutant concentration is set to 0 mg / L at the initial moment. The boundary conditions include the water depth, flow rate, and pollutant concentration of the upstream boundary, downstream boundary, and side boundary. For example, set the flow rate of the upstream boundary to 10 cubic meters per second and the pollutant concentration to 10 mg / L; the water depth of the downstream boundary to 1 meter; and no inflow to the side boundary.

[0140] After that, the water flow simulation calculation is performed. According to the initial conditions and boundary conditions determined in the previous step, the Saint-Venant equation is used to calculate the water depth and flow velocity of each grid at each time step. The Saint-Venant equation takes into account factors such as the cross-sectional area of ​​the water flow, flow rate, lateral inflow, inertia force, pressure gradient, gravity, riverbed slope and energy slope, and can simulate the non-constant and non-uniform flow of water more accurately. For example, assuming that the time step is 1 second, according to the initial conditions and boundary conditions in the previous step, the Saint-Venant equation is used to calculate the water depth and flow velocity of each grid in the first time step, and then the calculation results are used as the initial conditions of the next time step, and the calculation continues until the end of the simulation.

[0141] Next, the pollutant transport simulation calculation is performed. According to the water depth and flow velocity obtained by the water flow simulation calculation, the pollutant concentration of each grid at each time step is calculated using the two-dimensional pollutant convection diffusion equation. The two-dimensional pollutant convection diffusion equation takes into account the convection term and the diffusion term, and can simulate the transport and diffusion process of pollutants under the action of water flow. For example, the water depth and flow velocity calculated in the previous step are substituted into the two-dimensional pollutant convection diffusion equation, the pollutant concentration of each grid at the first time step is calculated, and then the calculation result is used as the initial condition of the next time step, and the calculation continues until the end of the simulation.

[0142] Finally, the simulation results are output. The simulated water depth, flow velocity, and pollutant concentration data are visualized in the form of charts, animations, etc., so as to analyze and evaluate the water flow and water quality of the river. For example, the pollutant concentration of each grid at each time step is plotted into a concentration distribution map to intuitively understand the transport and diffusion of pollutants.

[0143] The method and system have the following beneficial effects:

[0144] 1. Higher accuracy: This method is based on the Saint-Venant equation and the two-dimensional pollutant convection-diffusion equation, and can more accurately simulate the non-constant and non-uniform flow of water in rivers and the transport and diffusion of pollutants. 2. Wider applicability: This method is applicable to various types of rivers, including natural rivers, artificial canals, etc., and can simulate water flow and water quality under different hydrological conditions. 3. More practical: This method can provide a scientific basis for the management and protection of rivers. For example, it can be used to predict the diffusion range of pollutants in rivers, assess the impact of pollution accidents, and formulate water pollution prevention and control measures.

[0145] In an optional implementation, based on the concentration change rate of pollutants and the migration path of pollutants, the pollution sources in the basin are divided into three categories: point source pollution, non-point source pollution and internal source pollution through a pollution source identification model, and the pollution contribution rate of each type of pollution source is calculated, including:

[0146] Obtain the pollutant concentration time series data of the monitoring point, calculate the pollutant concentration change rate, record the location coordinates of the monitoring point, calculate the spatial gradient of the pollutant concentration of the monitoring point, and construct a pollution feature vector containing the pollutant concentration, concentration change rate, location coordinates and concentration gradient;

[0147] Based on the constructed pollution feature vector, a point source pollution discriminant function is constructed in the form of a distance attenuation index, wherein the point source pollution discriminant function includes the pollutant concentration of the monitoring point, the distance from the pollution source, and the attenuation coefficient; and the spatial distribution characteristic value of the point source pollution discriminant function is calculated;

[0148] Construct a non-point source pollution flux equation including a diffusion flux term and a convection flux term, wherein the diffusion flux term is determined by the diffusion coefficient and the concentration gradient, and the convection flux term is determined by the transport velocity vector and the pollutant concentration; calculate the spatial distribution of the non-point source pollution flux in combination with the acquired pollutant concentration gradient data;

[0149] Based on the established non-point source pollution flux equation, a sediment release kinetic equation including endogenous pollution concentration, sediment storage, release coefficient and sedimentation coefficient was constructed; the non-point source pollution flux equation was coupled with the sediment release kinetic equation to calculate the contribution flux of endogenous pollution to the water body;

[0150] Using the obtained point source pollution discriminant function values, non-point source pollution fluxes and contribution fluxes of endogenous pollution to water bodies, a three-dimensional pollution source characteristic discriminant matrix is ​​constructed, wherein the matrix elements of the discriminant matrix are the discriminant weights of different pollution characteristics for various types of pollution sources; the pollution contribution rate of each type of pollution source is determined according to the product of each matrix element in the three-dimensional pollution source characteristic discriminant matrix and the time-varying function.

[0151] Obtain monitoring data and construct pollution feature vectors. First, select several monitoring points in the basin, and sample each monitoring point over a period of time to obtain the data on the change of pollutant concentration over time at each monitoring point, as well as the spatial position coordinates (such as longitude and latitude) of each monitoring point. Then, calculate the rate of change of pollutant concentration at each monitoring point. For example, it can be expressed by dividing the concentration difference between the two sampling times by the time interval. Next, calculate the spatial change trend of pollutant concentration at each monitoring point. For example, it can be expressed by dividing the concentration difference between adjacent monitoring points by the distance between them. Finally, integrate the pollutant concentration, concentration change rate, location coordinates and concentration gradient of each monitoring point together to construct a pollution feature vector for subsequent pollution source identification and contribution rate calculation. Among them, the calculation of pollutant concentration change rate and the calculation of spatial gradient of pollutant concentration at monitoring points can refer to the calculation method given above; pollutant concentration time series data and recording monitoring point location coordinates can be directly obtained, and the construction of pollution feature vectors is to directly combine the above parameters.

[0152] Identify point source pollution and calculate spatial distribution characteristic values. Construct a point source pollution discriminant function based on the pollutant concentration in the pollution characteristic vector, the distance from the monitoring point to the potential pollution source, and the pre-set attenuation coefficient (reflecting the degree to which the pollutant concentration decays with distance). The larger the function value, the greater the impact of point source pollution on the monitoring point. The attenuation coefficient can be adjusted according to the characteristics of the pollutants and environmental conditions. Calculate the spatial distribution characteristic value of the discriminant function in the entire watershed. For example, the average or maximum value of the discriminant function values ​​of all monitoring points can be calculated.

[0153] For example, assume that the potential pollution source is located at the latitude and longitude (100.5, 30.5). The distance from point A to the pollution source is about 0.7 km. Assuming the attenuation coefficient is 0.5, the point source pollution discriminant function value of point A is 12 * exp(-0.5 *0.7) ≈ 8.2. Similarly, the point source pollution discriminant function values ​​of points B and C can be calculated, and the spatial distribution characteristic value of the point source pollution discriminant function of the entire basin can be calculated.

[0154] Among them, the calculation formula for the spatial distribution characteristic value of the point source pollution discriminant function is as follows:

[0155] ;

[0156] Among them, F p represents the spatial distribution characteristic value of the point source pollution discriminant function, m represents the number of pollutants, r j represents the distance to the jth pollutant, C j represents the pollutant concentration of the jth pollutant, α represents the attenuation coefficient, V j Represents the pollution feature vector corresponding to the j-th pollutant.

[0157] Calculate the spatial distribution of non-point source pollution flux. Construct a non-point source pollution flux equation, which contains diffusion flux terms and convective flux terms. The diffusion flux term is determined by the diffusion coefficient and the concentration gradient, and represents the diffusion migration of pollutants due to concentration differences. The convective flux term is determined by the transport velocity vector (such as water flow velocity) and the pollutant concentration, and represents the flux of pollutants migrating with the water flow. Combined with the acquired pollutant concentration gradient data and water flow velocity data, calculate the spatial distribution of non-point source pollution flux.

[0158] For example, suppose the water flow velocity near point A is (0.1, 0.2) km / h and the diffusion coefficient is 0.01 km^2 / h. Then the diffusion flux at point A is 0.01 * (-1.4) ≈ -0.014 mg / (h·km), and the convection flux is (0.1, 0.2) *12 = (1.2, 2.4) mg / (h·km). Similarly, the non-point source pollution flux at other monitoring points can be calculated.

[0159] Calculate the flux contribution of endogenous pollution. Based on the established non-point source pollution flux equation, construct the sediment release kinetic equation, which includes the endogenous pollution concentration, sediment storage, release coefficient and sedimentation coefficient. The release coefficient represents the rate at which pollutants in the sediment are released into the water body, and the sedimentation coefficient represents the rate at which pollutants in the water body settle into the sediment. Couple the non-point source pollution flux equation with the sediment release kinetic equation to calculate the contribution flux of endogenous pollution to the water body.

[0160] For example, assuming that the sediment storage at point A is 100 kg, the endogenous pollution concentration is 1 mg / kg, the release coefficient is 0.001 / h, and the sedimentation coefficient is 0.002 / h. Then the endogenous pollution release flux at point A is 100 * 1 * 0.001 = 0.1 mg / h, and the sedimentation flux is 12 * 0.002 = 0.024 mg / h. Therefore, the net contribution flux of endogenous pollution to point A is 0.1- 0.024 = 0.076 mg / h.

[0161] Among them, the endogenous pollution release flux is defined as the release flux, which refers to the amount of pollutants released from unit area of ​​sediment to overlying water body per unit time; the endogenous pollution sedimentation flux is defined as the sedimentation flux, which refers to the amount of pollutants in unit area of ​​water body deposited into the sediment per unit time.

[0162] Calculate the contribution rate of pollution sources. Use the obtained point source pollution discriminant function values, non-point source pollution fluxes and the contribution flux of internal pollution to water bodies to construct a three-dimensional pollution source characteristic discriminant matrix. Each element of the matrix represents the discrimination weight of different pollution characteristics for each type of pollution source. Determine the pollution contribution rate of each type of pollution source based on the product of each element in the matrix and the time-varying function. The time-varying function can reflect the changes in pollution contribution of different pollution sources in different time periods.

[0163] For example, suppose that at a certain point in time, the weights of point source pollution, non-point source pollution and internal source pollution on the pollution at point A are 0.5, 0.3 and 0.2 respectively. Then the contribution rate of point source pollution to the total pollutant concentration at point A is 8.2 * 0.5 / (8.2 *0.5 + ...). Similarly, the contribution rates of non-point source pollution and internal source pollution can be calculated.

[0164] Beneficial effects:

[0165] More accurate pollution source identification: This method comprehensively considers multiple information such as pollutant concentration, concentration change rate, spatial location and concentration gradient, and can more accurately identify different types of pollution sources, avoiding the limitations of traditional methods that rely on a single indicator. More comprehensive pollution contribution rate assessment: This method can simultaneously consider the contribution of point source pollution, non-point source pollution and endogenous pollution to water bodies, and combined with time-varying functions, it can more comprehensively evaluate the pollution contribution rates of different pollution sources in different time periods. More effective pollution control strategy formulation: Based on more accurate pollution source identification and more comprehensive pollution contribution rate assessment results, pollution control strategies can be formulated more specifically, pollution control efficiency can be improved, and a more scientific decision-making basis can be provided for environmental protection.

[0166] In an optional implementation, the calculation formula for the spatial distribution characteristic value of the point source pollution discriminant function is as follows:

[0167] ;

[0168] Among them, F p represents the spatial distribution characteristic value of the point source pollution discriminant function, m represents the number of pollutants, r j represents the distance to the jth pollutant, C j represents the pollutant concentration of the jth pollutant, α represents the attenuation coefficient, V j Represents the pollution feature vector corresponding to the jth pollutant;

[0169] The non-point source pollution flux equation is as follows:

[0170] ;

[0171] ;

[0172] ;

[0173] Among them, J s represents the total non-point source pollution flux, J d represents the diffusion flux term, J c represents the convective flux term, D represents the diffusion coefficient matrix, represents the concentration gradient vector, D x represents the diffusion coefficient in the x direction, D y represents the diffusion coefficient in the y direction, , They represent the concentration gradient in the x direction and the concentration gradient in the y direction respectively; represents the velocity vector of the flow field, C represents the pollutant concentration, v x 、v y They represent the x-direction velocity component and the y-direction velocity component respectively;

[0174] The sediment release kinetic equation is shown in the following formula:

[0175] ;

[0176] The calculation of the contribution flux of internal pollution to water bodies is shown in the following formula:

[0177] ;

[0178] in, Indicates the concentration of endogenous pollution C in The derivative with time, k1, k2 represent the sediment release coefficient and sedimentation coefficient respectively, S d represents the sediment pollutant reserves; J in represents the contribution flux of endogenous pollution to the water body, Represents the changing gradient of non-point source pollution flux.

[0179] Pollution source identification and contribution flux calculation method

[0180] The method is used to identify the sources of water pollution and quantify the contribution of point, non-point and internal sources of pollution to water pollution.

[0181] First, construct a point source pollution discriminant function. Traverse each pollutant, calculate the distance between the pollutant and all other pollutants, and obtain the concentration of the pollutant and the corresponding pollution characteristic vector. Integrate the distance, concentration, attenuation coefficient and pollution characteristic vector to calculate the discriminant function value of each pollutant. The higher the discriminant function value of a pollutant, the greater the contribution of the pollutant to water pollution.

[0182] For example: Assume that there are three pollutants A, B and C in the water body. Measure the distance between A and B, A and C, B and A, B and C, C and A, C and B. Obtain the concentrations of the three pollutants A, B, and C, and determine the attenuation coefficient (for example, set to 0.5), as well as the pollution feature vector corresponding to each pollutant (for example, A is [0.2, 0.8], B is [0.5, 0.5], and C is [0.9, 0.1]). Based on these data, calculate the discriminant function values ​​of A, B, and C respectively. Assuming that the calculated results A is 1.5, B is 0.8, and C is 2.2, then C contributes the most to water pollution.

[0183] Next, calculate the nonpoint source pollution flux. First, calculate the concentration gradient vector. Calculate the concentration gradient of the pollutant in the x-direction and y-direction respectively. Then, construct a diffusion coefficient matrix, which contains the diffusion coefficients in the x-direction and y-direction. Multiply the diffusion coefficient matrix with the concentration gradient vector to obtain the diffusion flux term. At the same time, obtain the flow field velocity vector, which contains the flow velocity components in the x-direction and y-direction, as well as the pollutant concentration. Multiply the flow velocity vector with the concentration to obtain the convective flux term. Finally, add the diffusion flux term and the convective flux term to obtain the total nonpoint source pollution flux.

[0184] For example, assuming that the concentration gradient in the x-direction is 0.1, the concentration gradient in the y-direction is 0.2, the diffusion coefficient in the x-direction is 0.01, the diffusion coefficient in the y-direction is 0.02, the velocity component in the x-direction is 0.5, the velocity component in the y-direction is 0.3, and the pollutant concentration is 10. Then the diffusion flux term is [0.01, 0.02]*[0.1, 0.2]=0.005, the convection flux term is [0.5, 0.3]*10 = 8, and the total nonpoint source pollution flux is 0.005+8=8.005.

[0185] Then, calculate the contribution flux of endogenous pollution to the water body. First, obtain the sediment pollutant reserves, sediment release coefficient and sedimentation coefficient. Multiply the sediment pollutant reserves by the release coefficient, and then subtract the endogenous pollution concentration multiplied by the sedimentation coefficient to obtain the rate of change of the endogenous pollution concentration over time. At the same time, calculate the change gradient of the non-point source pollution flux. Finally, add the rate of change of the endogenous pollution concentration over time and the change gradient of the non-point source pollution flux to obtain the contribution flux of endogenous pollution to the water body.

[0186] For example, assuming that the sediment pollutant reserve is 100, the release coefficient is 0.01, the sedimentation coefficient is 0.005, the endogenous pollution concentration is 2, and the gradient of the non-point source pollution flux is 0.1. Then the rate of change of the endogenous pollution concentration over time is 100*0.01-2*0.005=0.99. The contribution flux of endogenous pollution to the water body is 0.99+0.1=1.09.

[0187] The beneficial effects are as follows:

[0188] 1. Accurate identification of pollution sources: By constructing a point source pollution discrimination function, the contribution of different pollution sources to water bodies can be effectively distinguished, and accurate pollution source identification can be achieved. 2. Comprehensive calculation of pollution flux: Point source, non-point source and internal source pollution are comprehensively considered, and corresponding calculation models are established respectively, which can comprehensively evaluate the pollution load of different pollution sources on water bodies. 3. Pollution control strategy optimization: Based on accurate pollution source identification and comprehensive pollution flux calculation results, it can provide a scientific basis for formulating more effective pollution control strategies, thereby better protecting the water environment.

[0189] Figure 2 FIG. 1 is a schematic diagram of the structure of a watershed water pollution source tracing analysis system integrating spatiotemporal big data according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0190] The first unit is used to construct a multi-source heterogeneous spatiotemporal big data acquisition system, which includes an online water quality monitoring station, a meteorological monitoring station, and a surface runoff monitoring station set up in the basin, wherein the online water quality monitoring station collects water body physical and chemical index data, the meteorological monitoring station collects rainfall data and temperature data, and the surface runoff monitoring station collects flow velocity data and flow data; the water body physical and chemical index data, the rainfall data, the temperature data, the flow velocity data, and the flow data are associated through spatiotemporal tags to generate a spatiotemporal associated data set;

[0191] The second unit is used to construct a pollutant migration and diffusion model based on the spatiotemporal correlation data set, wherein the pollutant migration and diffusion model includes a pollutant concentration attenuation submodel and a pollutant flow direction prediction submodel, wherein the pollutant concentration attenuation submodel calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, and the pollutant flow direction prediction submodel calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data;

[0192] The third unit is used to classify the pollution sources in the basin into three categories: point source pollution, non-point source pollution and internal source pollution based on the concentration change rate of pollutants and the migration path of pollutants through the pollution source identification model, calculate the pollution contribution rate of each type of pollution source, and generate a pollution source tracing analysis report.

[0193] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A basin water pollution source tracing analysis method integrating spatiotemporal big data is characterized by: include: Construct a multi-source heterogeneous spatiotemporal big data acquisition system, the multi-source heterogeneous spatiotemporal big data acquisition system includes an online water quality monitoring station, a meteorological monitoring station, and a surface runoff monitoring station set up in the basin, the online water quality monitoring station collects water body physical and chemical index data, the meteorological monitoring station collects rainfall data and temperature data, and the surface runoff monitoring station collects flow velocity data and flow data; the water body physical and chemical index data, the rainfall data, the temperature data, the flow velocity data, and the flow data are associated through spatiotemporal tags to generate a spatiotemporal associated data set; Based on the spatiotemporal correlation data set, a pollutant migration and diffusion model is constructed, wherein the pollutant migration and diffusion model includes a pollutant concentration attenuation submodel and a pollutant flow direction prediction submodel, wherein the pollutant concentration attenuation submodel calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, and the pollutant flow direction prediction submodel calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data; Based on the concentration change rate of pollutants and the migration path of pollutants, the pollution sources in the basin are divided into three categories: point source pollution, non-point source pollution and internal source pollution through the pollution source identification model. The pollution contribution rate of each type of pollution source is calculated to generate a pollution source tracing analysis report.

2. The method according to claim 1, characterized in that The pollutant concentration attenuation sub-model calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, including: Collecting temperature data and rainfall data of the water area where the pollutants are located, setting an influence function of an environmental factor and a value of the environmental factor according to the temperature data and the rainfall data, and calculating an environmental factor coupling coefficient based on the influence function of the environmental factor and the value of the environmental factor; A pollutant concentration attenuation kinetic model is established according to the environmental factor coupling coefficient, wherein the pollutant concentration attenuation kinetic model adopts the product form of the first-order reaction kinetic equation and the environmental factor coupling coefficient to obtain the derivative of the pollutant concentration with respect to time; Collect spatial location data of pollutant detection points, calculate distance data between adjacent detection points according to the spatial location data, and construct a spatial weight matrix based on the distance data, wherein the spatial weight matrix is ​​in the form of a product of the inverse of the square of the distance and an exponential decay function of the distance; The pollutant concentration attenuation kinetic model is combined with the spatial weight matrix to obtain a spatiotemporal coupled diffusion model, which is composed of a concentration attenuation term and a spatial diffusion term, wherein the concentration attenuation term includes the environmental factor coupling coefficient, and the spatial diffusion term includes the spatial weight matrix. The concentration change rate of pollutants at different spatial locations is obtained through the spatiotemporal coupled diffusion model.

3. The method according to claim 2, characterized in that A pollutant concentration attenuation kinetic model is established according to the environmental factor coupling coefficient. The pollutant concentration attenuation kinetic model adopts the product form of the first-order reaction kinetic equation and the environmental factor coupling coefficient. The derivative of the pollutant concentration with respect to time is obtained, including: ； Among them, a i represents the baseline impact coefficient of the i-th environmental factor, b i represents the adjustment coefficient of the i-th environmental factor, E i,max represents the maximum value of the i-th environmental factor, E i,min represents the minimum value of the i-th environmental factor; ； Among them, β represents the environmental factor coupling coefficient, n represents the total number of environmental factors, and f i represents the influence function of the i-th environmental factor, E i represents the value of the i-th environmental factor; ； Where C represents the pollutant concentration, k represents the intrinsic reaction rate constant of pollutant degradation, It represents the rate of change of pollutant concentration over time; The spatiotemporal coupled diffusion model is shown in the following formula: ； Where D represents the diffusion coefficient matrix, represents the concentration gradient vector, R(x,y,t) represents the rate term of increase or decrease of pollutants in the x and y directions at time t during the diffusion process, W(x,y) represents the spatial weight function, Represents the flow field velocity vector.

4. The method according to claim 1, characterized in that: The pollutant flow direction prediction sub-model calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow rate data, including: Acquire the hydrological parameters of the watershed, establish the Saint-Venant continuity equation and the Saint-Venant momentum equation according to the hydrological parameters of the watershed, the Saint-Venant continuity equation includes a water flow cross-sectional area parameter, a flow parameter and a lateral inflow parameter, the Saint-Venant momentum equation includes a riverbed slope parameter and an energy slope parameter, establish a cross-sectional flow model according to the water flow cross-sectional area parameter and the flow parameter; based on the cross-sectional flow model and the energy slope parameter, use the Manning formula to calculate the cross-sectional average flow velocity and hydraulic radius, and obtain the watershed hydrodynamic characteristic data; Constructing a pollutant transport field based on the watershed hydrodynamic characteristic data, decomposing the average flow velocity of the cross section into a horizontal flow velocity component and a vertical flow velocity component, establishing a two-dimensional pollutant convection diffusion equation based on the horizontal flow velocity component and the vertical flow velocity component, and introducing a horizontal diffusion coefficient and a vertical diffusion coefficient into the two-dimensional pollutant convection diffusion equation; The four-order Runge-Kutta algorithm is used to numerically solve the two-dimensional pollutant convection-diffusion equation. The four-order Runge-Kutta algorithm iteratively calculates the pollutant displacement through four weighted coefficients to obtain the pollutant point motion trajectory data as the migration path of the pollutant in the watershed.

5. The method according to claim 4, characterized in that The Saint-Venant continuity equation is as follows: ； Where A is the cross-sectional area of ​​the water flow, represents the rate of change of cross-sectional area over time, Q is the flow rate, x represents the length, represents the rate of change of flow along the way, q l is the lateral inflow per unit length; The Saint-Venant momentum equation is as follows: ； in, represents the rate of change of flow rate over time, represents the inertia force term, represents the pressure gradient term, g represents the acceleration of gravity, h represents the water depth, S0 represents the riverbed slope, S f represents the energy slope; The two-dimensional pollutant convection-diffusion equation is as follows: ； Where C represents the pollutant concentration, represents the rate of change of pollutant concentration over time, u represents the velocity component in the x direction, and v represents the velocity component in the y direction. , They represent the first-order derivatives of pollutant concentration in the x and y directions, i.e., the convection terms, D x , D y represent the diffusion coefficient in the x direction and the diffusion coefficient in the y direction respectively, , They represent the second-order derivatives of pollutant concentration in the x and y directions, respectively, which are also diffusion terms.

6. The method according to claim 1, characterized in that Based on the concentration change rate of pollutants and the migration path of pollutants, the pollution sources in the basin are divided into three categories: point source pollution, non-point source pollution and internal source pollution through the pollution source identification model, and the pollution contribution rate of each type of pollution source is calculated, including: Obtain the pollutant concentration time series data of the monitoring point, calculate the pollutant concentration change rate, record the location coordinates of the monitoring point, calculate the spatial gradient of the pollutant concentration of the monitoring point, and construct a pollution feature vector containing the pollutant concentration, concentration change rate, location coordinates and concentration gradient; Based on the constructed pollution feature vector, a point source pollution discriminant function is constructed in the form of a distance attenuation index, wherein the point source pollution discriminant function includes the pollutant concentration of the monitoring point, the distance from the pollution source, and the attenuation coefficient; and the spatial distribution characteristic value of the point source pollution discriminant function is calculated; Construct a non-point source pollution flux equation including a diffusion flux term and a convection flux term, wherein the diffusion flux term is determined by the diffusion coefficient and the concentration gradient, and the convection flux term is determined by the transport velocity vector and the pollutant concentration; calculate the spatial distribution of the non-point source pollution flux in combination with the acquired pollutant concentration gradient data; Based on the established non-point source pollution flux equation, a sediment release kinetic equation including endogenous pollution concentration, sediment storage, release coefficient and sedimentation coefficient was constructed; the non-point source pollution flux equation was coupled with the sediment release kinetic equation to calculate the contribution flux of endogenous pollution to the water body; Using the obtained point source pollution discriminant function values, non-point source pollution fluxes and contribution fluxes of endogenous pollution to water bodies, a three-dimensional pollution source characteristic discriminant matrix is ​​constructed, wherein the matrix elements of the discriminant matrix are the discriminant weights of different pollution characteristics for various types of pollution sources; the pollution contribution rate of each type of pollution source is determined according to the product of each matrix element in the three-dimensional pollution source characteristic discriminant matrix and the time-varying function.

7. The method according to claim 6, characterized in that The calculation formula for the spatial distribution characteristic value of the point source pollution discriminant function is as follows: ； Among them, F p represents the spatial distribution characteristic value of the point source pollution discriminant function, m represents the number of pollutants, r j represents the distance to the jth pollutant, C j represents the pollutant concentration of the jth pollutant, α represents the attenuation coefficient, V j Represents the pollution feature vector corresponding to the jth pollutant; The non-point source pollution flux equation is as follows: ； ； ； Among them, J s represents the total non-point source pollution flux, J d represents the diffusion flux term, J c represents the convective flux term, D represents the diffusion coefficient matrix, represents the concentration gradient vector, D x represents the diffusion coefficient in the x direction, D y represents the diffusion coefficient in the y direction, , They represent the concentration gradient in the x direction and the concentration gradient in the y direction respectively; represents the velocity vector of the flow field, C represents the pollutant concentration, v x 、v y They represent the x-direction velocity component and the y-direction velocity component respectively; The sediment release kinetic equation is shown in the following formula: ； The calculation of the contribution flux of internal pollution to water bodies is shown in the following formula: ； in, Indicates the concentration of endogenous pollution C in The derivative with time, k1, k2 represent the sediment release coefficient and sedimentation coefficient respectively, S d represents the sediment pollutant reserves; J in represents the contribution flux of endogenous pollution to the water body, Represents the changing gradient of non-point source pollution flux.

8. A basin water pollution source tracing analysis system integrating spatiotemporal big data, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to construct a multi-source heterogeneous spatiotemporal big data acquisition system, which includes an online water quality monitoring station, a meteorological monitoring station, and a surface runoff monitoring station set up in the basin, wherein the online water quality monitoring station collects water body physical and chemical index data, the meteorological monitoring station collects rainfall data and temperature data, and the surface runoff monitoring station collects flow velocity data and flow data; the water body physical and chemical index data, the rainfall data, the temperature data, the flow velocity data, and the flow data are associated through spatiotemporal tags to generate a spatiotemporal associated data set; The second unit is used to construct a pollutant migration and diffusion model based on the spatiotemporal correlation data set, wherein the pollutant migration and diffusion model includes a pollutant concentration attenuation submodel and a pollutant flow direction prediction submodel, wherein the pollutant concentration attenuation submodel calculates the concentration change rate of pollutants at different spatial locations according to the water body physical and chemical index data, the rainfall data and the temperature data, and the pollutant flow direction prediction submodel calculates the migration path of pollutants in the watershed according to the flow velocity data and the flow data; The third unit is used to classify the pollution sources in the basin into three categories: point source pollution, non-point source pollution and internal source pollution based on the concentration change rate of pollutants and the migration path of pollutants through the pollution source identification model, calculate the pollution contribution rate of each type of pollution source, and generate a pollution source tracing analysis report.

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