Method, device, equipment, medium and program product for predicting rainfall runoff pollution

Abstract

This invention relates to the field of pollution load prediction technology, and discloses a method, apparatus, equipment, medium, and program product for predicting rainfall runoff pollution. The method for predicting rainfall runoff pollution includes: dividing the rainfall runoff area into spatial grids and obtaining the basic hydrological data of each spatial grid unit; obtaining the target pollutant accumulation process and the target pollutant flushing process based on the basic hydrological data of each spatial grid unit; constructing a rainwater runoff pollution prediction model based on historical rainfall data, the target pollutant accumulation process, and the target pollutant flushing process; and predicting the rainfall runoff pollutant load for the prediction period based on the rainfall data and the rainwater runoff pollution prediction model, thereby obtaining the target rainfall runoff pollutant load. This invention improves the accuracy of rainfall runoff pollutant load prediction through the analysis of gridded pollutant accumulation and pollutant flushing processes.

Description

Technical Field

[0001] This invention relates to the field of pollution load prediction technology, specifically to methods, devices, equipment, media, and program products for predicting rainfall-runoff pollution. Background Technology

[0002] Urban rainwater runoff pollution is a key factor leading to the deterioration of water quality in receiving water bodies. Therefore, it is necessary to predict urban rainwater runoff pollution in order to carry out precise water environment management.

[0003] In related technologies, methods for predicting urban rainfall runoff pollution mainly rely on traditional statistical models or simple physical models. While these methods can predict rainfall runoff pollution to some extent, their parameter settings typically treat land use types as homogeneous units, ignoring the spatial heterogeneity caused by differences in underlying surface characteristics, human activity intensity, and other factors. This leads to inaccurate simulations of the complex urban surface pollutant accumulation and scouring process, resulting in inaccurate and unrealistic predictions of urban rainfall runoff pollution. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and program product for predicting rainfall runoff pollution, in order to solve the problem that the prediction results of related technologies for predicting urban rainfall runoff pollution are not accurate enough and do not conform to the actual situation.

[0005] In a first aspect, the present invention provides a method for predicting rainfall runoff pollution, comprising: dividing a rainfall runoff area into spatial grids and obtaining basic hydrological data for each spatial grid unit; analyzing the pollutant accumulation process and the pollutant flushing process based on the basic hydrological data of each spatial grid unit to obtain the target pollutant accumulation process and the target pollutant flushing process; constructing a rainwater runoff pollution prediction model based on historical rainfall data, the target pollutant accumulation process, and the target pollutant flushing process; the rainwater runoff pollution prediction model is used to predict rainwater runoff pollutant data by simulating the accumulation and flushing of rainwater runoff pollutants; and predicting the rainfall runoff pollutant load for the prediction period based on the rainfall data for the prediction period and the rainwater runoff pollution prediction model to obtain the target rainfall runoff pollutant load.

[0006] The rainfall runoff pollution prediction method of this invention divides the rainfall runoff area into spatial grids and obtains the hydrological baseline data for each spatial grid unit. By decomposing a large-scale rainfall runoff area into refined grid units, the method accurately acquires the hydrological baseline data for each grid, avoiding errors caused by large-area averaging and improving the spatial resolution and accuracy of subsequent analyses. Based on the hydrological baseline data of each spatial grid unit, this invention analyzes the pollutant accumulation and flushing processes to obtain the target pollutant accumulation and flushing processes. It captures the impact of spatial heterogeneity on the accumulation and flushing processes of rainwater runoff pollution, more accurately reflecting the spatiotemporal distribution of actual pollutants under complex land use scenarios, and more accurately simulating complex rainwater runoff pollution migration processes. This invention constructs a stormwater runoff pollution prediction model based on historical rainfall data, the accumulation process of target pollutants, and the flushing process of target pollutants. Based on rainfall data for the prediction period and the model, the pollutant load in the stormwater runoff for that period is predicted, yielding the target stormwater runoff pollutant load. By combining historical rainfall data with measured pollution processes, the model possesses stronger physical mechanisms and data support, improving the accuracy of stormwater runoff pollutant load prediction for the predicted period. Compared with related technologies, this invention improves the precision of stormwater runoff pollution migration processes, effectively capturing the impact of spatial heterogeneity on stormwater runoff pollution migration processes, and thus enhancing the accuracy of stormwater runoff pollutant load prediction.

[0007] In one optional implementation, spatial grid division of the rainfall runoff area includes: acquiring the spatial characteristics of the rainfall runoff area, determining the target spatial grid resolution based on the spatial characteristics, and dividing the rainfall runoff area into spatial grids according to the target spatial grid resolution.

[0008] In one optional implementation, the hydrological baseline data includes grid area data, soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, scalability factor, maximum pollutant accumulation for each land use type, pollutant accumulation rate for each land use type, scour coefficient for each land use type, and scour index for each land use type.

[0009] In one optional implementation, based on the hydrological baseline data of each spatial grid cell, the pollutant accumulation process and the pollutant flushing process are analyzed to obtain the target pollutant accumulation process and the target pollutant flushing process. This includes: determining the area proportion of each land use type in each spatial grid cell based on grid area data; analyzing the pollution potential of each spatial grid cell based on soil impermeability factors, pollution characteristic factors, land use intensity factors, surface retention and redistribution factors, and scalability factors to obtain a land use composite pollution potential index; determining the target enhancement weight of each spatial grid cell based on the product of the area proportion and the land use composite pollution potential index; analyzing the gridded pollutant parameters based on the target enhancement weights of multiple spatial grid cells, the maximum pollutant accumulation amount corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the flushing coefficient corresponding to each land use type, and the flushing index corresponding to each land use type to obtain gridded pollutant accumulation parameters and gridded pollutant flushing parameters; and analyzing the pollutant accumulation process and the pollutant flushing process based on the gridded pollutant accumulation parameters and the gridded pollutant flushing parameters to obtain the target pollutant accumulation process and the target pollutant flushing process.

[0010] This invention determines the area proportion of each land use type in each spatial grid unit based on grid area data; it analyzes the pollution potential of each spatial grid unit based on soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, and scalability factor, and obtains the land use composite pollution potential index. The land use composite pollution potential index breaks through the single-dimensional parameter calculation mode that only uses land use area for weighting. By weighted integration of multiple key driving factors such as soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, and scalability factor, it comprehensively evaluates the true pollution potential within the grid unit. Based on the product of area ratio and land use composite pollution potential index, the target enhancement weight of each spatial grid unit is determined. The gridded pollutant parameters are analyzed based on the target enhancement weights of multiple spatial grid units, the maximum pollutant accumulation amount corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the scour coefficient corresponding to each land use type, and the scour index corresponding to each land use type. The pollutant accumulation process and the pollutant scour process are analyzed based on the gridded pollutant accumulation parameters and the gridded pollutant scour parameters, resulting in the target pollutant accumulation process and the target pollutant scour process. This achieves a dual-weighted analysis of grid-level accumulation and scour parameters, making the impact of small areas of high-pollution-potential land quantifiable and enabling a spatially refined expression of runoff pollution accumulation and scour parameters on complex and interwoven underlying surfaces.

[0011] In one optional implementation, a stormwater runoff pollution prediction model is constructed based on historical rainfall data, the target pollutant accumulation process, and the target pollutant flushing process. This includes inputting historical rainfall data, the target pollutant accumulation process, and the target pollutant flushing process into a preset stormwater runoff pollution model for hydrological simulation to obtain the stormwater runoff pollution prediction model.

[0012] In one optional implementation, the target rainfall runoff pollutant load is predicted based on rainfall data for the predicted time period and a stormwater runoff pollution prediction model. This includes: inputting rainfall data for the predicted time period into the stormwater runoff pollution prediction model to obtain runoff flow rate and concentration of each pollutant; and integrating the product of runoff flow rate and concentration of each pollutant to obtain the target rainfall runoff pollutant load.

[0013] Secondly, the present invention provides a device for predicting rainfall runoff pollution, comprising: a grid division module for spatially dividing a rainfall runoff area into grids and acquiring basic hydrological data for each spatial grid unit; a pollutant analysis module for analyzing the pollutant accumulation process and the pollutant flushing process based on the basic hydrological data of each spatial grid unit, to obtain the target pollutant accumulation process and the target pollutant flushing process; a prediction model construction module for constructing a rainwater runoff pollution prediction model based on historical rainfall data, the target pollutant accumulation process, and the target pollutant flushing process; the rainwater runoff pollution prediction model is used to predict rainwater runoff pollutant data by simulating the accumulation and flushing of rainwater runoff pollutants; and a pollution load prediction module for predicting the rainfall runoff pollutant load for the prediction period based on the rainfall data and the rainwater runoff pollution prediction model, to obtain the target rainfall runoff pollutant load.

[0014] Thirdly, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the rainfall runoff pollution prediction method of the first aspect or any corresponding embodiment described above.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the rainfall runoff pollution prediction method of the first aspect or any corresponding embodiment thereof.

[0016] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the method for predicting rainfall runoff pollution according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a method for predicting rainfall runoff pollution according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for predicting rainfall runoff pollution according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the method for predicting rainfall runoff pollution according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a rainfall runoff pollution prediction device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0021] As an optional application scenario of this invention, such as Figure 1 As shown, the rainfall runoff pollution prediction system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0022] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0023] This invention provides a method for predicting rainfall runoff pollution, which improves the accuracy of rainfall runoff pollutant load prediction by analyzing the gridded pollutant accumulation and flushing processes.

[0024] According to an embodiment of the present invention, a method for predicting rainfall runoff pollution is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] This embodiment provides a method for predicting rainfall runoff pollution, which can be used with computer equipment. Figure 2 This is a first flowchart of a method for predicting rainfall runoff pollution according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Divide the rainfall-runoff area into spatial grids and obtain the basic hydrological data for each spatial grid unit.

[0026] Among them, the rainfall runoff area is the catchment area that generates surface runoff after rainfall and may carry pollutants into water bodies; spatial grid division is to divide the rainfall runoff area into regular grids of equal area; spatial grid unit is a single spatial grid obtained after spatial grid division through the rainfall runoff area; hydrological basic data are the underlying parameters that drive subsequent pollution process analysis. Specifically, the hydrological basic data includes grid area data, soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, scalability factor, maximum pollutant accumulation amount corresponding to each land use type, pollutant accumulation rate corresponding to each land use type, scour coefficient corresponding to each land use type, and scour index corresponding to each land use type.

[0027] Step S202: Based on the hydrological data of each spatial grid unit, analyze the pollutant accumulation process and the pollutant flushing process to obtain the target pollutant accumulation process and the target pollutant flushing process.

[0028] Among them, the target pollutant accumulation process is the dynamic process in which pollutants are gradually deposited and accumulated on the surface over time during rainfall intervals; the target pollutant flushing process is the dynamic process in which surface runoff strips, transports, and flows into water bodies when rainfall occurs.

[0029] Step S203: Based on historical rainfall data, the accumulation process of target pollutants, and the flushing process of target pollutants, a stormwater runoff pollution prediction model is constructed. The stormwater runoff pollution prediction model is used to predict stormwater runoff pollutant data by simulating the accumulation and flushing of pollutants in rainfall runoff.

[0030] Among them, historical rainfall data refers to the rainfall amount at each time interval of a historical rainfall event in the rainfall runoff area, or the design rainfall time series generated by the rainstorm intensity formula.

[0031] Step S204: Based on the rainfall data and stormwater runoff pollution prediction model for the predicted time period, the pollutant load of the stormwater runoff for the predicted time period is predicted to obtain the target stormwater runoff pollutant load.

[0032] The forecast period can be set according to actual needs. For example, the forecast period can be the whole year, one month, or one day; the target rainfall runoff pollutant load is the total amount of pollutants entering the water body through runoff per unit time.

[0033] The rainfall runoff pollution prediction method provided in this embodiment divides the rainfall runoff area into spatial grids and obtains the hydrological baseline data for each spatial grid unit. By decomposing a large-scale rainfall runoff area into refined grid units, the method accurately acquires the hydrological baseline data for each grid, avoiding errors caused by large-area averaging and improving the spatial resolution and accuracy of subsequent analyses. Based on the hydrological baseline data of each spatial grid unit, this embodiment analyzes the pollutant accumulation and flushing processes to obtain the target pollutant accumulation and flushing processes. It captures the impact of spatial heterogeneity on the accumulation and flushing processes of rainwater runoff pollution, more accurately reflecting the spatiotemporal distribution of actual pollutants under complex land use scenarios, and more accurately simulating complex rainwater runoff pollution migration processes. This invention constructs a stormwater runoff pollution prediction model based on historical rainfall data, the accumulation process of target pollutants, and the flushing process of target pollutants. Based on rainfall data for the prediction period and the model, the pollutant load in the stormwater runoff for that period is predicted, yielding the target stormwater runoff pollutant load. By combining historical rainfall data with measured pollution processes, the model possesses stronger physical mechanisms and data support, improving the accuracy of stormwater runoff pollutant load prediction for the predicted period. Compared with related technologies, this invention improves the precision of stormwater runoff pollution migration processes, effectively captures the impact of spatial heterogeneity on stormwater runoff pollution migration processes, and enhances the accuracy of stormwater runoff pollutant load prediction.

[0034] This embodiment provides a method for predicting rainfall runoff pollution, which can be used with computer equipment. Figure 3 This is a second flowchart of a method for predicting rainfall runoff pollution according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Divide the rainfall-runoff area into spatial grids and obtain the basic hydrological data for each spatial grid unit.

[0035] Specifically, step S301 includes: Step S3011: Obtain the spatial characteristics of the rainfall runoff area, and determine the target spatial grid resolution based on the spatial characteristics.

[0036] Specifically, based on the area and spatial heterogeneity of the rainfall runoff region, a corresponding target spatial grid resolution is set for the rainfall runoff region. For example, a correspondence between spatial features and target spatial grid resolution is pre-defined. Based on the spatial features, the corresponding target spatial grid resolution is determined in the correspondence. The target spatial grid resolution can be 10m×10m or 30m×30m.

[0037] Step S3012: Divide the rainfall-runoff area into spatial grids according to the target spatial grid resolution, and obtain the basic hydrological data of each spatial grid unit.

[0038] Specifically, the target spatial grid resolution and rainfall-runoff region are imported into the grid generation software to perform spatial grid generation on the rainfall-runoff region.

[0039] In some optional implementations, the hydrological baseline data includes grid area data, soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, scalability factor, maximum pollutant accumulation for each land use type, pollutant accumulation rate for each land use type, scour coefficient for each land use type, and scour index for each land use type.

[0040] Specifically, high-resolution remote sensing imagery is used to classify land use / cover types; digital elevation models are used to extract topographic factors such as slope and aspect; soil type distribution maps are used to determine soil permeability and structure; long-term, high-temporal-resolution rainfall monitoring data are used to analyze rainfall events and intensity; and field-collected rainwater runoff water quality data (such as concentrations of various pollutants, including chemical oxygen demand, suspended solids, ammonia nitrogen, total nitrogen, and total phosphorus) are acquired. The hydrological data are unified into a single spatial reference and grid system, forming a multi-dimensional spatiotemporal database. The core of this database is that each spatial grid unit is not merely a geometric shape, but a comprehensive data entity carrying information on land use type, soil properties, topographic features, historical rainfall, and pollution monitoring. This lays the data foundation for the next step of calculating refined runoff pollutant accumulation and scour parameters.

[0041] Step S302: Based on the hydrological data of each spatial grid unit, analyze the pollutant accumulation process and the pollutant flushing process to obtain the target pollutant accumulation process and the target pollutant flushing process.

[0042] Specifically, step S302 includes: Step S3021: Based on the grid area data, determine the area ratio of each land use type in each spatial grid cell.

[0043] For example, the formula for determining the area proportion of each land use type is:

[0044] in, In each spatial grid cell, the first The area ratio of different land use types In each spatial grid cell, the first Land area of ​​various land use types This represents the total area of ​​the spatial grid cells.

[0045] Step S3022: Based on soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, and scalability factor, the pollution potential of each spatial grid unit is analyzed to obtain the land use composite pollution potential index.

[0046] The land use composite pollution potential index is used to characterize the inherent potential for different land use types to accumulate / wash away pollutants within a specific grid. For example, the formula for determining the land use composite pollution potential index is:

[0047] in, For the first Land use composite pollution potential index for various land use types For the first Weighting of soil impermeability by land use type Soil impermeability factors can be obtained based on pollutant monitoring data and literature. For the first Pollution characteristic weights of land use types The pollution characteristic factors can be obtained based on pollutant monitoring data and literature. For the first The weighting of land use intensity for different land use types Land use intensity factors can be obtained through socioeconomic data such as population density. For the first The weight of surface retention and redistribution of land use types Surface retention and redistribution factors, quantifying the impact of surface roughness and microtopography on pollutant retention capacity, can be obtained through topographic interpretation. For the first The scalability weight of land use type integration The scalability factor, which defaults to 1, can be defined as a different function when other factors need to be considered. For the number of land use types, , , , The sum of is 1.

[0048] In some alternative implementations, the land use composite pollution potential index is a normalized relative value, with a value greater than 1 indicating that the pollution potential of the land use type is higher than the average level within the grid, and a value less than 1 indicating that it is lower than the average level.

[0049] In this embodiment of the invention, the land use composite pollution potential index is comprehensively evaluated by the product of five key factors to avoid the defects of single factor dominance and better reflect the interaction between factors. It is more comprehensive and this multi-dimensional evaluation method is more in line with the actual situation and can capture the differences in pollutant accumulation / scouring characteristics of the same land use type under different situations.

[0050] Step S3023: Determine the target enhancement weight for each spatial grid unit based on the product of the area ratio and the land use composite pollution potential index.

[0051] The enhancement weight is obtained by multiplying the area ratio and the land use composite pollution potential index. The enhancement weight is then normalized to obtain the target enhancement weight.

[0052] For example, the formula for determining the target enhancement weights is:

[0053]

[0054] in, For the first Increase the weight of land use types. In each spatial grid cell, the first The area ratio of different land use types For the first Land use composite pollution potential index for various land use types For the number of land use types, For the first The target of land use type is given increased weight.

[0055] Step S3024: Based on the target enhancement weights of multiple spatial grid cells, the maximum cumulative amount of pollutants corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the scour coefficient corresponding to each land use type, and the scour index corresponding to each land use type, the gridded pollutant parameters are analyzed to obtain the gridded pollutant accumulation parameters and the gridded pollutant scour parameters.

[0056] Among them, the gridded pollutant accumulation parameters include the comprehensive maximum accumulation amount and the comprehensive accumulation rate, and the gridded pollutant scouring parameters include the comprehensive scouring coefficient and the comprehensive scouring index.

[0057] For example, the formula for determining the maximum cumulative total is:

[0058] in, To achieve the maximum cumulative amount, For the first Increase the weight of land use type targets. For the first Maximum cumulative amount of pollutants for each land use type This refers to the number of land use types.

[0059] In some alternative implementations, the formula for determining the overall cumulative rate is:

[0060] in, For the overall cumulative rate, For the first Increase the weight of land use type targets. For the first Pollutant accumulation rates for different land use types This refers to the number of land use types.

[0061] In some alternative implementations, the formula for determining the overall scour coefficient is:

[0062] in, To take into account the overall scour coefficient, For the first Increase the weight of land use type targets. For the first The erosion coefficient of land use type This refers to the number of land use types.

[0063] In some alternative implementations, the formula for determining the comprehensive scour index is:

[0064] in, To comprehensively assess the scouring index, For the first Increase the weight of land use type targets. For the first The erosion index of land use type This refers to the number of land use types.

[0065] Step S3025: Based on the gridded pollutant accumulation parameters and gridded pollutant flushing parameters, analyze the pollutant accumulation process and the pollutant flushing process to obtain the target pollutant accumulation process and the target pollutant flushing process.

[0066] The accumulation process of the target pollutant can be represented as:

[0067] in, For the accumulation process of target pollutants, To achieve the maximum cumulative amount, For the overall cumulative rate, This is the dry period before the rain.

[0068] In some alternative implementations, the target pollutant flushing process can be expressed as a pollutant flushing rate, and the formula for determining the pollutant flushing rate is:

[0069] in, For the pollutant flushing rate, To take into account the overall scour coefficient, For the accumulation process of target pollutants, For rainfall intensity data, The comprehensive scouring index.

[0070] The process of determining the target pollutant accumulation and flushing processes in this invention organically combines the pollution potential of land use with the area ratio, achieving a refined expression of the pollutant accumulation and flushing processes under complex and overlapping land use conditions. The pollutant accumulation and flushing parameters of the grid are no longer fixed empirical values, but rather a comprehensive result of weighting the parameters corresponding to all land use types within the grid using both area weight and pollution potential weight. This means that a small area of ​​land with high pollution potential can significantly increase the overall pollution accumulation capacity of its grid. This method achieves downscaling and refinement of parameters from the land use type level to the grid level, enabling a refined expression of the pollutant accumulation and flushing processes under complex and overlapping land use conditions. It enhances the model's ability to express spatial heterogeneity and, compared to the traditional area-weighted method, better reflects the actual pollution characteristics of the grid, providing a scientific basis for refined stormwater runoff pollution prediction.

[0071] Step S303: Based on historical rainfall data, the accumulation process of target pollutants, and the flushing process of target pollutants, a stormwater runoff pollution prediction model is constructed. The stormwater runoff pollution prediction model is used to predict stormwater runoff pollutant data by simulating the accumulation and flushing of pollutants in rainfall runoff.

[0072] Specifically, step S303 includes: Step S3031: Input historical rainfall data, target pollutant accumulation process and target pollutant flushing process into the preset stormwater runoff pollution model for hydrological simulation to obtain stormwater runoff pollution prediction model.

[0073] The preset stormwater runoff pollution model can be an SWMM (Storm Water Management Model), which uses historical rainfall data as the driving input. The preset stormwater runoff pollution model simulates the accumulation and flushing processes of target pollutants, resulting in a stormwater runoff pollution prediction model. For example, the expression for the stormwater runoff pollution prediction model is:

[0074] in, For pollutant concentration, For runoff flow, This is historical rainfall data. For the accumulation process of target pollutants, This represents the pollutant scouring rate.

[0075] In some alternative implementations, the output data of the stormwater runoff pollution prediction model is compared with measured data, and quantitative analysis is performed using various hydrological and water quality evaluation indicators to assess the reliability of the stormwater runoff pollution prediction model.

[0076] For example, the efficiency coefficient and relative error are used to evaluate the stormwater runoff pollution prediction model, which can be specifically:

[0077]

[0078] in, For efficiency coefficient, This is a relative error. These are actual measured data. These are predicted values.

[0079] Step S304: Based on the rainfall data and stormwater runoff pollution prediction model for the predicted time period, the pollutant load of the stormwater runoff for the predicted time period is predicted to obtain the target stormwater runoff pollutant load.

[0080] Specifically, step S304 includes: Step S3041: Input the rainfall data for the predicted time period into the stormwater runoff pollution prediction model to obtain the runoff flow and the concentration of each pollutant.

[0081] Step S3042: Integrate the product of runoff flow rate and concentration of each pollutant to obtain the target rainfall runoff pollutant load.

[0082] The product of runoff flow and concentration of each pollutant is integrated to obtain the runoff pollutant load of each rainfall event. The runoff pollutant loads of multiple rainfall events are summed to obtain the target runoff pollutant load.

[0083] For example, the formula for determining the pollutant load of rainfall runoff is:

[0084] in, For the first The first rainfall The first generated within the spatial grid cell No. Rainfall runoff pollutant load of type 2 pollutants For the first The first rainfall The first generated within the spatial grid cell No. The runoff flow corresponding to each type of pollutant For the first The first rainfall The first generated within the spatial grid cell No. Pollutant concentration of type 1 pollutant The start time of the rainfall event. This refers to the end time of the rainfall event. For time step.

[0085] In some alternative implementations, the formula for determining the target rainfall runoff pollutant load is:

[0086] in, To predict the amount of rainfall runoff generated in the region during the forecast period. Target rainfall runoff pollutant load for pollutants of class 2 For the first The first rainfall The first generated within the spatial grid cell No. Rainfall runoff pollutant load of type 2 pollutants This represents the number of rainfall events.

[0087] The rainfall-runoff pollution prediction method provided in this embodiment determines the area proportion of each land use type in each spatial grid unit based on grid area data. It analyzes the pollution potential of each spatial grid unit based on soil impermeability factors, pollution characteristic factors, land use intensity factors, surface retention and redistribution factors, and scalability factors, obtaining a land use composite pollution potential index. This index breaks through the single-dimensional parameter calculation model that only weights land use area. By weighted integration of multiple key driving factors such as soil impermeability factors, pollution characteristic factors, land use intensity factors, surface retention and redistribution factors, and scalability factors, it comprehensively evaluates the true pollution potential within the grid unit. Based on the product of area ratio and land use composite pollution potential index, the target enhancement weight of each spatial grid unit is determined. The gridded pollutant parameters are analyzed based on the target enhancement weights of multiple spatial grid units, the maximum pollutant accumulation amount corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the scour coefficient corresponding to each land use type, and the scour index corresponding to each land use type. The pollutant accumulation process and the pollutant scour process are analyzed based on the gridded pollutant accumulation parameters and the gridded pollutant scour parameters, resulting in the target pollutant accumulation process and the target pollutant scour process. This achieves a dual-weighted analysis of grid-level accumulation and scour parameters, making the impact of small areas of high-pollution-potential land quantifiable and enabling a spatially refined expression of runoff pollution accumulation and scour parameters on complex and interwoven underlying surfaces.

[0088] This embodiment provides a method for predicting rainfall runoff pollution, which can be used with computer equipment. Figure 4 This is a third flowchart of a rainfall runoff pollution prediction method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Construction of a high-resolution spatiotemporal database of runoff pollution; improvement of gridded parameters based on LUCPPI; construction of a land use composite pollution potential index; analysis of gridded cumulative function parameters and gridded scour function parameters; dynamic adjustment method for runoff pollutant parameters oriented towards complex cross-land use characteristics; simulation of pollutant accumulation process and pollutant scour process; prediction and performance evaluation of stormwater runoff pollution; accurate calculation of pollution load.

[0089] This embodiment also provides a rainfall runoff pollution prediction device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0090] This embodiment provides a device for predicting rainfall runoff pollution, such as... Figure 5 As shown, it includes: The grid division module 501 is used to divide the rainfall-runoff area into spatial grids and obtain the basic hydrological data of each spatial grid unit.

[0091] The pollutant analysis module 502 is used to analyze the pollutant accumulation process and the pollutant flushing process based on the hydrological basic data of each spatial grid cell, and to obtain the target pollutant accumulation process and the target pollutant flushing process.

[0092] The prediction model building module 503 is used to build a stormwater runoff pollution prediction model based on historical rainfall data, the accumulation process of target pollutants, and the flushing process of target pollutants. The stormwater runoff pollution prediction model is used to predict stormwater runoff pollutant data by simulating the accumulation and flushing of pollutants in rainfall runoff.

[0093] The pollution load prediction module 504 is used to predict the pollutant load of the rainfall runoff during the prediction period based on the rainfall data and the rainwater runoff pollution prediction model during the prediction period, so as to obtain the target rainfall runoff pollutant load.

[0094] In some alternative implementations, the mesh generation module 501 includes: The resolution determination unit is used to acquire the spatial characteristics of the rainfall-runoff area and determine the target spatial grid resolution based on the spatial characteristics.

[0095] The grid division unit is used to divide the rainfall runoff area into spatial grids according to the target spatial grid resolution.

[0096] In some alternative implementations, the contaminant analysis module 502 includes: The area ratio determination unit is used to determine the area ratio of each land use type in each spatial grid unit based on the grid area data.

[0097] The pollution potential analysis unit is used to analyze the pollution potential of each spatial grid unit based on soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, and scalability factor, and obtain the land use composite pollution potential index.

[0098] The enhanced weight determination unit is used to determine the target enhanced weight of each spatial grid unit based on the product of the area ratio and the land use composite pollution potential index.

[0099] The parameter determination unit is used to analyze the gridded pollutant parameters based on the target enhancement weights of multiple spatial grid cells, the maximum cumulative amount of pollutants corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the scour coefficient corresponding to each land use type, and the scour index corresponding to each land use type, so as to obtain the gridded pollutant accumulation parameters and the gridded pollutant scour parameters.

[0100] The process analysis unit is used to analyze the pollutant accumulation process and the pollutant scouring process based on the gridded pollutant accumulation parameters and the gridded pollutant scouring parameters, so as to obtain the target pollutant accumulation process and the target pollutant scouring process.

[0101] In some alternative implementations, the prediction model building module 503 includes: The prediction model building unit is used to input historical rainfall data, the accumulation process of target pollutants, and the flushing process of target pollutants into a preset stormwater runoff pollution model for hydrological simulation, so as to obtain a stormwater runoff pollution prediction model.

[0102] In some optional implementations, the pollution load prediction module 504 includes: The data determination unit is used to input rainfall data for the predicted time period into the stormwater runoff pollution prediction model to obtain runoff flow and concentration of each pollutant.

[0103] The pollution load prediction unit is used to integrate the product of runoff flow and the concentration of each pollutant to obtain the target rainfall runoff pollutant load.

[0104] The precipitation runoff pollution prediction device provided in this embodiment of the invention can execute the precipitation runoff pollution prediction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0105] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0106] The following is a detailed reference. Figure 6This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0107] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0108] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the rainfall runoff pollution prediction method of the embodiments of the present invention.

[0109] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0110] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the precipitation runoff pollution prediction method shown in the above embodiments is implemented.

[0111] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0112] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for predicting rainfall runoff pollution, characterized in that, The method includes: The rainfall-runoff area is divided into spatial grids, and the basic hydrological data of each spatial grid unit is obtained. Based on the hydrological data of each spatial grid cell, the pollutant accumulation process and the pollutant flushing process are analyzed to obtain the target pollutant accumulation process and the target pollutant flushing process. Based on historical rainfall data, the accumulation process of the target pollutants, and the flushing process of the target pollutants, a stormwater runoff pollution prediction model is constructed; the stormwater runoff pollution prediction model is used to predict stormwater runoff pollutant data by simulating the accumulation and flushing of pollutants in rainfall runoff. Based on the rainfall data for the predicted time period and the stormwater runoff pollution prediction model, the pollutant load of the stormwater runoff for the predicted time period is predicted to obtain the target stormwater runoff pollutant load. The step of analyzing the pollutant accumulation and scouring processes based on the hydrological data of each spatial grid unit to obtain the target pollutant accumulation and scouring processes includes: determining the area proportion of each land use type in each spatial grid unit based on the grid area data; analyzing the pollution potential of each spatial grid unit based on soil impermeability factors, pollution characteristic factors, land use intensity factors, surface retention and redistribution factors, and scalability factors to obtain a land use composite pollution potential index; and determining the area proportion of each spatial grid unit based on the product of the area proportion and the land use composite pollution potential index. The target enhancement weights are determined; based on the target enhancement weights of multiple spatial grid cells, the maximum cumulative amount of pollutants corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the scour coefficient corresponding to each land use type, and the scour index corresponding to each land use type, the gridded pollutant parameters are analyzed to obtain gridded pollutant accumulation parameters and gridded pollutant scour parameters; based on the gridded pollutant accumulation parameters and the gridded pollutant scour parameters, the pollutant accumulation process and the pollutant scour process are analyzed to obtain the target pollutant accumulation process and the target pollutant scour process; The formula for determining the land use composite pollution potential index is as follows: in, For the first Land use composite pollution potential index for various land use types For the first Weighting of soil impermeability by land use type Soil impermeability factor, For the first Pollution characteristic weights of land use types Pollution characteristic factors, For the first The integration of land use types and land use intensity weights Land use intensity factor For the first The weight of surface retention and redistribution of land use types Surface retention and redistribution factors, For the first The scalability weight of land use type integration The scalability factor, which is 1 by default, For the number of land use types, , , , The sum of is 1.

2. The method according to claim 1, characterized in that, The spatial grid division of the rainfall runoff area includes: The spatial characteristics of the rainfall-runoff area are obtained, and the target spatial grid resolution is determined based on the spatial characteristics. The rainfall runoff area is divided into spatial grids according to the target spatial grid resolution.

3. The method according to claim 1 or 2, characterized in that, The basic hydrological data includes grid area data, soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, scalability factor, maximum pollutant accumulation for each land use type, pollutant accumulation rate for each land use type, scour coefficient for each land use type, and scour index for each land use type.

4. The method according to claim 1 or 2, characterized in that, The step of constructing a stormwater runoff pollution prediction model based on historical rainfall data, the accumulation process of the target pollutant, and the flushing process of the target pollutant includes: The historical rainfall data, the target pollutant accumulation process, and the target pollutant flushing process are input into a preset stormwater runoff pollution model for hydrological simulation to obtain the stormwater runoff pollution prediction model.

5. The method according to claim 1 or 2, characterized in that, The step of predicting the pollutant load of rainfall runoff during the predicted time period based on rainfall data and the stormwater runoff pollution prediction model to obtain the target rainfall runoff pollutant load includes: The rainfall data for the predicted time period is input into the stormwater runoff pollution prediction model to obtain runoff flow and concentration of each pollutant; The target rainfall runoff pollutant load is obtained by integrating the product of the runoff flow rate and the concentration of each pollutant.

6. A device for predicting rainfall runoff pollution, characterized in that, The device includes: The grid division module is used to divide the rainfall-runoff area into spatial grids and obtain the basic hydrological data of each spatial grid unit. The pollutant analysis module is used to analyze the pollutant accumulation process and the pollutant flushing process based on the hydrological basic data of each spatial grid cell, and to obtain the target pollutant accumulation process and the target pollutant flushing process. The prediction model building module is used to construct a stormwater runoff pollution prediction model based on historical rainfall data, the accumulation process of the target pollutant, and the flushing process of the target pollutant; the stormwater runoff pollution prediction model is used to predict stormwater runoff pollutant data by simulating the accumulation and flushing of pollutants in rainfall runoff. The pollution load prediction module is used to predict the pollutant load of the rainfall runoff during the prediction period based on the rainfall data and the rainwater runoff pollution prediction model during the prediction period, so as to obtain the target rainfall runoff pollutant load. The pollutant analysis module includes: an area ratio determination unit, used to determine the area ratio of each land use type in each spatial grid cell based on grid area data; a pollution potential analysis unit, used to analyze the pollution potential of each spatial grid cell based on soil impermeability factor, pollution characteristic factor, land use intensity factor, surface retention and redistribution factor, and scalability factor, to obtain a land use composite pollution potential index; an enhancement weight determination unit, used to determine the target enhancement weight of each spatial grid cell based on the product of the area ratio and the land use composite pollution potential index; a parameter determination unit, used to analyze the gridded pollutant parameters based on the target enhancement weights of multiple spatial grid cells, the maximum pollutant accumulation amount corresponding to each land use type, the pollutant accumulation rate corresponding to each land use type, the scour coefficient corresponding to each land use type, and the scour index corresponding to each land use type, to obtain gridded pollutant accumulation parameters and gridded pollutant scour parameters; and a process analysis unit, used to analyze the pollutant accumulation process and pollutant scour process based on the gridded pollutant accumulation parameters and gridded pollutant scour parameters, to obtain the target pollutant accumulation process and target pollutant scour process. The formula for determining the land use composite pollution potential index is as follows: in, For the first Land use composite pollution potential index for various land use types For the first Weighting of soil impermeability by land use type Soil impermeability factors can be obtained based on pollutant monitoring data and literature. For the first Pollution characteristic weights of land use types Pollution characteristic factors, For the first The integration of land use types and land use intensity weights Land use intensity factors can be obtained through socioeconomic data such as population density. For the first The weight of surface retention and redistribution of land use types Surface retention and redistribution factors, For the first The scalability weight of land use type integration The scalability factor, which is 1 by default, For the number of land use types, , , , The sum of is 1.

7. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the precipitation runoff pollution prediction method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method for predicting rainfall runoff pollution according to any one of claims 1 to 5.

9. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method for predicting rainfall runoff pollution as described in any one of claims 1 to 5.

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