Method, device, equipment and medium for predicting target runoff pollutant load

Abstract

The present application relates to the technical field of hydrology and water environment, and discloses a target runoff pollutant load prediction method, device, equipment and medium, the target runoff pollutant load prediction method comprising: identifying a rainfall event according to meteorological data, determining a pollution migration coupling driving index according to rainfall and peak rainfall intensity; determining a relative index according to the pollution migration coupling driving index and a benchmark index; determining a grading response factor according to the relationship between the relative index and a preset grading threshold, correcting a preset pollutant migration parameter using the grading response factor, predicting runoff pollution using the target pollutant migration parameter of the rainfall event and rainfall, and determining the target runoff pollutant load according to the runoff flow and pollutant concentration prediction value of the rainfall event. The present application improves the adaptability and accuracy of the preset pollutant migration parameter by correcting the preset pollutant migration parameter, thereby improving the accuracy of the target runoff pollutant load.

Description

Technical Field

[0001] This invention relates to the field of hydrology and water environment technology, specifically to methods, devices, equipment, and media for predicting target runoff pollutant loads. Background Technology

[0002] Under the impact of torrential rain, surface runoff carries a large amount of accumulated pollutants, which are rapidly discharged into receiving water bodies through drainage outlets, causing instantaneous deterioration of water quality and ecological risks. Therefore, accurate prediction of runoff pollution after urban rainstorms is a core aspect of urban water environment management and non-point source pollution control.

[0003] In related technologies, runoff pollution prediction methods employ fixed pollutant flushing parameters to predict runoff pollution. However, the flushing dynamics differ under rainfall events of varying intensities, and sampling fixed pollutant flushing parameters yields inaccurate runoff pollution predictions that do not reflect actual runoff pollution conditions. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for predicting target runoff pollutant loads, in order to solve the problem that runoff pollution prediction methods in related technologies are not accurate enough and do not conform to the actual runoff pollution situation.

[0005] In a first aspect, the present invention provides a method for predicting target runoff pollutant load, comprising: acquiring meteorological data of a target area within a target time period; identifying rainfall events based on the meteorological data; acquiring the rainfall amount and peak rainfall intensity of each rainfall event; analyzing the pollutant scouring potential of each rainfall event based on the rainfall amount and peak rainfall intensity to obtain a pollution migration coupling driving index; the pollution migration coupling driving index being used to characterize the pollutant scouring potential of a rainfall event; acquiring a baseline pollution migration coupling driving index; and determining a relative pollution migration coupling driving index based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index; the relative pollution migration coupling driving index being used to characterize the scouring potential of a rainfall event relative to... The intensity of the baseline scenario is determined; based on the relationship between the relative pollution migration coupling driving index and the preset classification threshold, a classification response factor is determined, and the preset pollutant migration parameters are corrected using the classification response factor to obtain the target pollutant migration parameters; the classification response factor is a dynamic adjustment factor for the preset pollutant migration parameters, which are used to characterize the rate or capacity of pollutants migrating with runoff in the standard scenario; runoff pollution is predicted using the target pollutant migration parameters and rainfall amount for each rainfall event, resulting in predicted runoff flow and pollutant concentration values ​​for each rainfall event; based on the predicted runoff flow and pollutant concentration values ​​for each rainfall event, the target runoff pollutant load for the target area within the target time period is determined.

[0006] This invention provides a method for predicting target runoff pollutant loads. It acquires meteorological data for a target area within a target time period, identifies rainfall events based on this data, and obtains the rainfall amount and peak rainfall intensity for each event. By accurately identifying rainfall events through meteorological data, and extracting the two core driving factors—rainfall amount and peak rainfall intensity—this method provides reliable foundational data for subsequent analysis. Based on the rainfall amount and peak rainfall intensity of each rainfall event, this invention analyzes the pollutant scouring potential of the event, obtaining a pollution migration coupling driving index. This index couples the two key parameters, rainfall amount and peak rainfall intensity, to form a pollution migration coupling driving index, achieving a quantitative characterization of rainfall scouring capacity. This invention obtains a baseline pollution migration coupling driving index. Based on this baseline index, a relative pollution migration coupling driving index is determined. A baseline scenario is introduced for comparison, transforming the absolute pollution migration coupling driving index into a relative one. This makes the scouring potential between different rainfall events more comparable, clearly identifying the differences between the current rainfall event and the standard scenario, and providing a basis for tiered response. This invention determines a graded response factor based on the relationship between the relative pollution migration coupling driving index and a preset graded threshold. The graded response factor is then used to correct preset pollutant migration parameters to obtain target pollutant migration parameters. This dynamic correction of pollutant migration parameters, achieved through the graded response factor, allows pollutant migration prediction to move beyond standard scenarios and adjust pollutant migration rates in runoff in real time according to changes in rainfall intensity. This significantly improves the adaptability and prediction accuracy of target runoff pollutant loads under different rainfall conditions. Furthermore, this invention uses the target pollutant migration parameters and rainfall amount for each rainfall event to predict runoff pollution, obtaining predicted runoff flow and pollutant concentration values ​​for each event. The dynamically adjusted target pollutant migration parameters are combined with rainfall amount to predict runoff flow and pollutant concentration separately, achieving a refined simulation of the pollution generation process. Finally, this invention determines the target runoff pollutant load for a target area within a target time period based on the predicted runoff flow and pollutant concentration values ​​for each rainfall event. By integrating the predicted runoff flow and pollutant concentration values ​​for each rainfall event, the target runoff pollutant load for the entire target time period is obtained, achieving a complete closed loop from single-event rainfall analysis to overall regional load assessment. Compared with related technologies, this invention accurately quantifies the scouring potential of rainfall on pollutants, realizes dynamic adjustment of pollutant migration parameters, improves the adaptability and accuracy of target runoff pollutant load prediction, and makes the target runoff pollutant load more consistent with the actual situation.

[0007] In one optional implementation, identifying rainfall events based on meteorological data and obtaining the rainfall amount and peak rainfall intensity of each rainfall event includes: selecting events with a total rainfall flow greater than a preset rainfall amount threshold, a rainfall intensity greater than a preset rainfall intensity threshold, and a rainfall time interval greater than a preset time interval threshold as rainfall events based on meteorological data; and determining the rainfall amount and peak rainfall intensity of each rainfall event based on the rainfall intensity of each rainfall event.

[0008] This invention selects events as rainfall events based on meteorological data, where the total rainfall flow exceeds a preset rainfall threshold, the rainfall intensity exceeds a preset rainfall intensity threshold, and the rainfall time interval exceeds a preset time interval threshold. The total rainfall flow exceeds the preset rainfall threshold to ensure that the rainfall event has sufficient total rainfall to generate runoff. The rainfall intensity exceeds the preset rainfall intensity threshold to ensure that the rainfall intensity is sufficient to initiate effective flushing of pollutants. The rainfall time interval exceeds the preset time interval threshold to ensure that there is sufficient rainless interval between the rainfall event and the preceding rainfall event, providing the necessary time for pollutant accumulation. Thus, rainfall events are identified, and the rainfall amount and peak rainfall intensity of each rainfall event are determined based on the rainfall intensity of each rainfall event.

[0009] In one optional implementation, the pollutant scouring potential of each rainfall event is analyzed based on the rainfall amount and peak rainfall intensity to obtain a pollution migration coupling driving index, including: obtaining a first product result based on the product of the rainfall amount and peak rainfall intensity of each rainfall event; and determining the pollution migration coupling driving index based on the square root of the first product result.

[0010] This invention derives a first product result by multiplying the rainfall amount and peak rainfall intensity for each rainfall event. This comprehensive consideration of both rainfall amount and peak rainfall intensity allows for a more accurate characterization of the overall scour potential of rainfall events, effectively avoiding the limitations of single factors. The product form captures the synergistic effect of rainfall amount and peak rainfall intensity. Based on the square root of the first product result, a pollution migration coupling driving index is determined. The relationship between pollution load and rainfall characteristics is not a simple linear one; taking the square root is essentially a nonlinear compression. That is, as rainfall amount and peak rainfall intensity increase to a certain level, their marginal contribution to pollution load gradually decreases, making the pollution migration coupling driving index more consistent with reality.

[0011] In one optional implementation, obtaining a baseline pollution migration coupling driving index and determining a relative pollution migration coupling driving index based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index includes: obtaining a baseline rainfall and a baseline peak rainfall intensity; determining the baseline pollution migration coupling driving index based on the square root of the product of the baseline rainfall and the baseline peak rainfall intensity; and determining the relative pollution migration coupling driving index based on the quotient of the pollution migration coupling driving index and the baseline pollution migration coupling driving index.

[0012] In one optional implementation, the preset classification thresholds include a first classification threshold and a second classification threshold. A classification response factor is determined based on the relationship between the relative pollution migration coupling driving index and the preset classification thresholds. The classification response factor is then used to correct the preset pollutant migration parameters to obtain the target pollutant migration parameters. This includes: when the relative pollution migration coupling driving index is less than the first classification threshold, using the first preset response factor as the classification response factor; the first classification threshold is less than the second classification threshold; when the relative pollution migration coupling driving index is greater than or equal to the first classification threshold and less than the second classification threshold, using the second preset response factor as the classification response factor; the second preset response factor is greater than the first preset response factor; when the relative pollution migration coupling driving index is greater than or equal to the second classification threshold, using the third preset response factor as the classification response factor; the third preset response factor is greater than the second preset response factor; and the target pollutant migration parameters are obtained by multiplying the classification response factor by the preset pollutant migration parameters.

[0013] This invention divides the relative pollution migration coupling driving index into three distinct intervals by setting two tiered thresholds, providing a clear judgment standard for subsequent tiered responses. When the relative pollution migration coupling driving index is less than the first tiered threshold, a first preset response factor is used as the tiered response factor; when the relative pollution migration coupling driving index is greater than or equal to the first tiered threshold and less than the second tiered threshold, a second preset response factor is used as the tiered response factor; when the relative pollution migration coupling driving index is greater than or equal to the second tiered threshold, a third preset response factor is used as the tiered response factor. This achieves a tiered response mechanism, matching response factors of different intensities according to the strength of rainfall scour potential, and the response factor increases with the increase of the driving index, ensuring a more proactive response to heavy rainfall events and a more conservative response to weak rainfall events, thus achieving dynamic correction of pollutant migration parameters.

[0014] In one optional implementation, runoff pollution prediction is performed using the target pollutant migration parameters and rainfall amount for each rainfall event to obtain the predicted runoff flow rate and pollutant concentration for each rainfall event. This includes: inputting the target pollutant migration parameters and rainfall amount for each rainfall event into a preset pollutant flushing cumulative simulation model to perform runoff pollution prediction, thereby obtaining the predicted runoff flow rate and pollutant concentration for each rainfall event.

[0015] In one optional implementation, the target runoff pollutant load for the target area within a target time period is determined based on the predicted runoff flow and pollutant concentration for each rainfall event. This includes: obtaining the runoff pollutant flux for each rainfall event by integrating the product of the predicted runoff flow and pollutant concentration for each rainfall event over the rainfall period; and summing the runoff pollutant fluxes for multiple rainfall events in the target area within the target time period to obtain the target runoff pollutant load.

[0016] Secondly, the present invention provides a device for predicting target runoff pollutant load, comprising: a rainfall event identification module, used to acquire meteorological data of a target area within a target time period, identify rainfall events based on the meteorological data, and acquire the rainfall amount and peak rainfall intensity of each rainfall event; a driving index determination module, used to analyze the pollutant scouring potential of each rainfall event based on the rainfall amount and peak rainfall intensity, and obtain a pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant scouring potential of the rainfall event; and a relative driving index determination module, used to acquire a baseline pollution migration coupling driving index, and determine a relative pollution migration coupling driving index based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index; the relative pollution migration coupling driving index is used to characterize the scouring potential of the rainfall event relative to the target runoff pollutant load. The system includes: a baseline scenario intensity level; a migration parameter correction module, used to determine a graded response factor based on the relationship between the relative pollution migration coupling driving index and a preset graded threshold, and to correct the preset pollutant migration parameters using the graded response factor to obtain the target pollutant migration parameters; the graded response factor is a dynamic adjustment factor for the preset pollutant migration parameters, which characterize the rate or capacity of pollutants migrating with runoff in the standard scenario; a runoff pollution prediction module, used to predict runoff pollution using the target pollutant migration parameters and rainfall amount for each rainfall event, obtaining the predicted runoff flow and pollutant concentration values ​​for each rainfall event; and a pollutant load determination module, used to determine the target runoff pollutant load of the target area within the target time period based on the predicted runoff flow and pollutant concentration values ​​for each rainfall event.

[0017] 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 target runoff pollutant load prediction method described in the first aspect or any corresponding embodiment thereof.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method for predicting target runoff pollutant loads described in the first aspect or any corresponding embodiment thereof.

[0019] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the method for predicting target runoff pollutant loads described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0020] 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.

[0021] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the first process of a method for predicting target runoff pollutant load according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of a second process for predicting target runoff pollutant load according to an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the third process of the target runoff pollutant load prediction method according to an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the fourth process of the target runoff pollutant load prediction method according to an embodiment of the present invention;

[0026] Figure 6 This is a structural block diagram of a target runoff pollutant load prediction device according to an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0031] As an optional application scenario of this invention, such as Figure 1 As shown, the target runoff pollutant load 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.

[0032] 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.

[0033] Accurate prediction of runoff pollution after urban rainstorms is a core component of urban water environment management and non-point source pollution control. Under the impact of rainstorms, surface runoff carries large amounts of accumulated pollutants, such as COD (Chemical Oxygen Demand) and SS (Suspended Solids). Ammonia nitrogen (N), total nitrogen (TN), and total phosphorus (TP) rapidly flow into receiving water bodies through drainage outlets, causing instantaneous deterioration of water quality and ecological risks. Inaccurate pollutant predictions will directly impact the design and operational effectiveness of control measures such as stormwater storage tanks and interception facilities, failing to effectively curb black and odorous water bodies and combined sewer overflow pollution.

[0034] Therefore, the refined simulation and load accounting of stormwater runoff pollution are key technical supports for improving the level of refined management of urban water environment governance.

[0035] Currently, methods for predicting runoff pollution mostly focus on the coupling layer of deep learning algorithms, lacking research on the physical processes of runoff pollutant migration. Current studies generally employ fixed pollutant scour parameters, failing to address the core issue of significant differences in scour dynamic characteristics under rainfall events of varying intensities. This leads to systematic biases in load calculations when models handle a wide range of rainfall scenarios, from light to torrential rain. Furthermore, there is a lack of dynamic parameter adjustment mechanisms that are tailored to urban outfall scales, integrate multi-source heterogeneous data, and automatically adapt to different rainfall characteristics.

[0036] This invention provides a method for predicting target runoff pollutant load. By modifying preset pollutant migration parameters, the method aims to improve the adaptability and accuracy of preset pollutant migration parameters, thereby improving the accuracy of target runoff pollutant load.

[0037] According to an embodiment of the present invention, a method for predicting target runoff pollutant load 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.

[0038] This embodiment provides a method for predicting target runoff pollutant loads, which can be used with computer equipment. Figure 2 This is a first flowchart of a method for predicting target runoff pollutant load according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0039] Step S201: Obtain meteorological data of the target area within the target time period, identify rainfall events based on the meteorological data, and obtain the rainfall amount and peak rainfall intensity of each rainfall event.

[0040] The target area is the region where target runoff pollutant load prediction is required, and can be set according to actual conditions; the target time period can be set according to actual needs, for example, the target time period can be one year; meteorological data includes rainfall, rainfall intensity, duration, temperature and other meteorological-related data; rainfall event is an independent rainfall process; rainfall amount is the total precipitation of the rainfall event; peak rainfall intensity is the maximum rainfall intensity during the rainfall process.

[0041] In some alternative implementations, meteorological data is collected from weather stations, remote sensing data, or hydrological monitoring systems, and continuous rainfall is divided into rainfall events based on rainfall intensity thresholds and temporal continuity; the rainfall amount and peak rainfall intensity of each rainfall event are calculated.

[0042] In some alternative implementations, the total rainfall and maximum rainfall intensity for each rainfall event are calculated based on meteorological data.

[0043] In some optional implementations, the method for predicting the target runoff pollutant load further includes: acquiring meteorological data, hydrological data, geospatial data and pollution monitoring data, and establishing a multi-source heterogeneous spatiotemporal fusion database based on the meteorological data, hydrological data, geospatial data and pollution monitoring data.

[0044] Specifically, meteorological, hydrological, geospatial, and pollution monitoring data are all unified to meter-level spatial resolution and minute-level temporal resolution to accurately capture the initial effects and dynamic processes of storm runoff pollution. Meteorological factors include minute-level rainfall intensity, cumulative rainfall, temperature, and wind speed; hydrological data includes minute-level discharge outlet levels and flow rates; geospatial data includes catchment area topography (slope, elevation), land use (impervious surface area ratio, vegetation cover), and pipeline network attributes (pipe diameter, runoff path length); pollution data includes multiple pollutants at the discharge outlet (such as COD, SS, TN, TP, etc.). -N) concentration data were obtained through manual sampling and online sensors.

[0045] In some optional implementations, the above data is constructed into a spatiotemporal cube model through spatiotemporal alignment and data cleaning. Data cleaning includes missing value imputation and outlier removal. The spatiotemporal cube model can be represented as follows:

[0046] ;

[0047] in, It is a spacetime cube model. For three-dimensional space ( coordinate, (Coordinates) - Time cube structure, for The set of coordinates for The set of coordinates The start time of the rainfall event. This refers to the end time of the rainfall event.

[0048] In some alternative implementations, a spacetime cube model is established, providing an interface for subsequent methods to call directly.

[0049] Step S202: Based on the rainfall amount and peak rainfall intensity of each rainfall event, the pollutant flushing potential of the rainfall event is analyzed to obtain the pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant flushing potential of the rainfall event.

[0050] Among them, pollutant scour potential represents the ability of rainfall to wash away and carry pollutants on the surface, determining the initial concentration of pollutants in runoff. The pollution migration coupling driving index is a comprehensive indicator that quantifies rainfall scour potential, determined by both rainfall amount and peak rainfall intensity.

[0051] Step S203: Obtain the baseline pollution migration coupling driving index. Based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index, determine the relative pollution migration coupling driving index. The relative pollution migration coupling driving index is used to characterize the strength of the scouring potential of a rainfall event relative to the baseline scenario.

[0052] Among them, the baseline pollution migration coupling driving index is determined based on the baseline rainfall and the baseline peak rainfall intensity. The baseline pollution migration coupling driving index is used to characterize the reference value of scour potential under the baseline scenario. The relative pollution migration coupling driving index is the ratio of the pollution migration coupling driving index to the baseline pollution migration coupling driving index, which characterizes the strength of the scour intensity of the current rainfall event relative to the baseline scenario.

[0053] Step S204: Based on the relationship between the relative pollution migration coupling driving index and the preset classification threshold, determine the classification response factor, and use the classification response factor to correct the preset pollutant migration parameters to obtain the target pollutant migration parameters; the classification response factor is a dynamic adjustment factor for the preset pollutant migration parameters, and the preset pollutant migration parameters are used to characterize the rate or ability of pollutants to migrate with runoff in the standard scenario.

[0054] Among them, the preset classification threshold is a pre-set critical value for classifying the pollution migration coupling driving index into levels; the classification response factor is a dynamic adjustment factor used to correct the pollutant migration parameters.

[0055] In some optional implementations, the target pollutant migration parameters are obtained by multiplying the graded response factor by a preset pollutant migration parameter.

[0056] Step S205: Use the target pollutant migration parameters and rainfall amount for each rainfall event to predict runoff pollution, and obtain the predicted runoff flow and pollutant concentration for each rainfall event.

[0057] Specifically, the target pollutant migration parameters and rainfall amount for each rainfall event are input into a preset pollutant flushing cumulative simulation model to predict runoff pollution, thereby obtaining the predicted runoff flow and pollutant concentration values ​​for each rainfall event.

[0058] Step S206: Determine the target runoff pollutant load for the target area within the target time period based on the predicted runoff flow and pollutant concentration for each rainfall event.

[0059] This embodiment provides a method for predicting target runoff pollutant loads. It acquires meteorological data for the target area within a target time period, identifies rainfall events based on the meteorological data, and obtains the rainfall amount and peak rainfall intensity for each event. By accurately identifying rainfall events through meteorological data, and extracting the two core driving factors—rainfall amount and peak rainfall intensity—it provides reliable basic data for subsequent analysis. Based on the rainfall amount and peak rainfall intensity of each rainfall event, this embodiment analyzes the pollutant scouring potential of the rainfall event, obtaining a pollution migration coupling driving index. This index couples the two key parameters—rainfall amount and peak rainfall intensity—to form a pollution migration coupling driving index, achieving a quantitative characterization of rainfall scouring capacity. This embodiment obtains a baseline pollution migration coupling driving index. Based on the baseline and the current pollution migration coupling driving index, a relative pollution migration coupling driving index is determined. A baseline scenario is introduced for comparison, transforming the absolute pollution migration coupling driving index into a relative one. This makes the scouring potential between different rainfall events more comparable, clearly indicating the difference between the current rainfall event and the standard scenario, providing a basis for graded responses. This invention, through its embodiments, determines a graded response factor based on the relationship between the relative pollution migration coupling driving index and a preset graded threshold. This graded response factor is then used to correct preset pollutant migration parameters, yielding target pollutant migration parameters. By using the graded response factor, dynamic correction of pollutant migration parameters is achieved, enabling pollutant migration prediction to move beyond standard scenarios and adjust the migration rate of pollutants in runoff in real time according to changes in rainfall intensity. This significantly improves the adaptability and prediction accuracy of target runoff pollutant loads under different rainfall conditions. This invention also uses the target pollutant migration parameters and rainfall amount for each rainfall event to predict runoff pollution, obtaining predicted runoff flow and pollutant concentration values ​​for each event. The dynamically adjusted target pollutant migration parameters are combined with rainfall amount to predict runoff flow and pollutant concentration respectively, achieving a refined simulation of the pollution generation process. Furthermore, this invention determines the target runoff pollutant load for a target area within a target time period based on the predicted runoff flow and pollutant concentration values ​​for each rainfall event. By integrating the predicted runoff flow and pollutant concentration values ​​for each rainfall event, the target runoff pollutant load for the entire target time period is obtained, achieving a complete closed loop from single-event rainfall analysis to overall regional load assessment. Compared with related technologies, the embodiments of the present invention accurately quantify the scouring potential of rainfall on pollutants, realize the dynamic adjustment of pollutant migration parameters, improve the adaptability and accuracy of target runoff pollutant load prediction, and make the target runoff pollutant load more consistent with the actual situation.

[0060] This embodiment provides a method for predicting target runoff pollutant loads, which can be used with computer equipment. Figure 3This is a second flowchart of a method for predicting target runoff pollutant loads according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps:

[0061] Step S301: Obtain meteorological data of the target area within the target time period, identify rainfall events based on the meteorological data, and obtain the rainfall amount and peak rainfall intensity of each rainfall event.

[0062] Specifically, step S301 includes:

[0063] Step S3011: Based on meteorological data, select events with total rainfall flow greater than a preset rainfall threshold, rainfall intensity greater than a preset rainfall intensity threshold, and rainfall time interval greater than a preset time interval threshold as rainfall events.

[0064] Among them, only events that simultaneously meet the following conditions are judged as rainfall events: total rainfall flow is greater than a preset rainfall amount threshold, rainfall intensity is greater than a preset rainfall intensity threshold, and rainfall time interval is greater than a preset time interval threshold. Specifically, a rainfall event can be represented as:

[0065] ;

[0066] in, For the first One rainfall event, For the first The onset time of rainfall in each rainfall event. For the first The end time of rainfall in each rainfall event. for The intensity of rainfall at that time To monitor the time step of the data, To preset the rainfall threshold, The logical AND operation indicates that all conditions must be met simultaneously. To preset the rainfall intensity threshold, For the first The end time of rainfall in each rainfall event. This is a preset time interval threshold.

[0067] In some optional implementations, the preset rainfall threshold, preset rainfall intensity threshold, and preset time interval threshold can be set according to specific circumstances to ensure that the rainfall event has sufficient total rainfall to generate effective runoff, ensure that the rainfall intensity is sufficient to initiate effective flushing of pollutants, and ensure that there is sufficient rainless interval with the preceding rainfall event to provide the necessary time for pollutant accumulation. For example, the preset rainfall threshold, preset rainfall intensity threshold, and preset time interval threshold can be determined based on the mean or median of historical rainfall.

[0068] Step S3012: Determine the rainfall amount and peak rainfall intensity for each rainfall event based on the rainfall intensity of each rainfall event.

[0069] The rainfall intensity of each rainfall event is integrated over the time interval from the start to the end of the event to obtain the rainfall amount. The maximum rainfall intensity of each rainfall event is taken as the peak rainfall intensity. For example, the formula for determining the rainfall amount is:

[0070] ;

[0071] in, This refers to rainfall, used to characterize cumulative rainfall depth. The start time of rainfall. This is the time when the rainfall ends. for The intensity of rainfall at that time.

[0072] For example, the formula for determining peak rainfall intensity is:

[0073] ;

[0074] in, Peak rainfall intensity, The maximum value, for The intensity of rainfall at that time The start time of rainfall. This indicates the time when the rainfall will end.

[0075] In some alternative implementations, the pre-rain dry period, average rainfall intensity, and duration of each rainfall event can also be determined. For example, the formula for determining the pre-rain dry period is:

[0076] ;

[0077] in, This is the pre-rain dry period, characterized by dry conditions in the preceding sub-catchment areas. For the first The onset time of rainfall in each rainfall event. For the first The end time of rainfall in each rainfall event.

[0078] In some alternative implementations, the formula for determining the average rainfall intensity is:

[0079] ;

[0080] in, This represents the average rainfall intensity. For rainfall, The start time of rainfall. This indicates the time when the rainfall will end.

[0081] In some alternative implementations, the formula for determining the duration is:

[0082] ;

[0083] in, For duration, The start time of rainfall. This indicates the time when the rainfall will end.

[0084] Step S302: Based on the rainfall amount and peak rainfall intensity of each rainfall event, the pollutant flushing potential of the rainfall event is analyzed to obtain the pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant flushing potential of the rainfall event.

[0085] Specifically, step S302 includes:

[0086] Step S3021: Obtain the first product result based on the product of the rainfall amount and peak rainfall intensity for each rainfall event.

[0087] Step S3022: Determine the pollution migration coupling driving index based on the square root of the first product result.

[0088] For example, the formula for determining the pollution migration coupling driving index is:

[0089] ;

[0090] in, As a pollution migration coupling driving index, For rainfall, Peak rainfall intensity, This is for square root operations.

[0091] Specifically, this formula comprehensively considers both the amount of rainfall (total energy) and the peak (instantaneous maximum impact force), enabling a more scientific characterization of the overall scouring potential of rainfall. The physical meaning of this formula lies in characterizing the total work or comprehensive potential of the pollutant migration process. It effectively avoids the limitations of a single factor; for example, a large amount of rainfall with moderate intensity may result in insufficient scouring power, preventing pollutants from being effectively activated. The product form captures the synergistic effect of these two driving mechanisms; a small value in either factor will lead to a small product result. Furthermore, the relationship between pollution load and rainfall characteristics is not a simple linear one. Taking the square root is essentially a nonlinear compression; as rainfall amount and peak rainfall intensity increase to a certain level, their marginal contribution to pollution load gradually decreases. Simultaneously, the square root operation normalizes the dimensions and makes the calculation results more numerically stable, better reflecting the nonlinear relationship between scouring work and pollutant migration.

[0092] Step S303: Obtain the baseline pollution migration coupling driving index. Based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index, determine the relative pollution migration coupling driving index. The relative pollution migration coupling driving index is used to characterize the strength of the scouring potential of a rainfall event relative to the baseline scenario.

[0093] Specifically, step S303 includes:

[0094] Step S3031: Obtain the baseline rainfall and the baseline peak rainfall intensity. Determine the baseline pollution migration coupling driving index based on the square root of the product of the baseline rainfall and the baseline peak rainfall intensity.

[0095] In some optional implementations, obtaining the baseline rainfall and the baseline peak rainfall intensity includes: obtaining historical rainfall events; determining the baseline rainfall based on the average or median of the rainfall amounts from the historical rainfall events; and determining the baseline peak rainfall intensity based on the average or median of the peak rainfall intensities from the historical rainfall events. For example, the formulas for determining the baseline rainfall and the baseline peak rainfall intensity are as follows:

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] in, Based on the baseline rainfall, As the baseline peak rainfall intensity, The first historical rainfall event Rainfall amount, This is the average value. The median. The first historical rainfall event Peak rainfall intensity.

[0101] In some alternative implementations, the formula for determining the baseline contamination migration coupling driving index is:

[0102] ;

[0103] in, As a benchmark pollution migration coupling driving index, Based on the baseline rainfall, As the baseline peak rainfall intensity, This is for square root operations.

[0104] Step S3032: Determine the relative pollution migration coupling driving index based on the quotient of the pollution migration coupling driving index and the benchmark pollution migration coupling driving index.

[0105] For example, the formula for determining the relative pollution migration coupling driving index is:

[0106] ;

[0107] in, This is a relative pollution migration coupling driving index. As a pollution migration coupling driving index, The baseline pollution migration coupling driving index.

[0108] Step S304: Based on the relationship between the relative pollution migration coupling driving index and the preset classification threshold, a classification response factor is determined. The preset pollutant migration parameters are then corrected using the classification response factor to obtain the target pollutant migration parameters. The classification response factor is a dynamic adjustment factor for the preset pollutant migration parameters, which characterize the rate or capacity of pollutant migration with runoff in a standard scenario. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0109] Step S305: Runoff pollution is predicted using the target pollutant migration parameters and rainfall amount for each rainfall event, yielding predicted runoff flow and pollutant concentration values ​​for each event. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0110] Step S306: Based on the predicted runoff volume and pollutant concentration for each rainfall event, determine the target runoff pollutant load for the target area within the target time period. For details, please refer to [link to relevant documentation]. Figure 2 Step S206 of the illustrated embodiment will not be described again here.

[0111] The target runoff pollutant load prediction method provided in this embodiment selects events as rainfall events based on meteorological data. These events have a total rainfall flow exceeding a preset rainfall threshold, a rainfall intensity exceeding a preset rainfall intensity threshold, and a rainfall time interval exceeding a preset time interval threshold. The total rainfall flow exceeding the preset rainfall threshold ensures sufficient total rainfall to generate runoff. The rainfall intensity exceeding the preset rainfall intensity threshold ensures sufficient rainfall intensity to initiate effective pollutant flushing. The rainfall time interval exceeding the preset time interval threshold ensures sufficient dry interval between the rainfall event and preceding rainfall events, providing necessary time for pollutant accumulation. Based on the rainfall intensity of each rainfall event, the rainfall amount and peak rainfall intensity of each event are determined. This embodiment of the invention obtains a first product result by multiplying the rainfall amount and peak rainfall intensity of each rainfall event, comprehensively considering both rainfall amount and peak rainfall intensity, and can more accurately characterize the overall flushing potential of rainfall events. This effectively avoids the limitations of single factors. The product form can capture the synergistic effect of rainfall amount and peak rainfall intensity. The pollution migration coupling driving index is determined based on the square root of the first product result. The relationship between pollution load and rainfall characteristics is not a simple linear one. Taking the square root is essentially a nonlinear compression. That is, when the rainfall and peak rainfall intensity increase to a certain extent, their marginal contribution to the pollution load will gradually decrease, making the pollution migration coupling driving index more in line with the actual situation.

[0112] This embodiment provides a method for predicting target runoff pollutant loads, which can be used with computer equipment. Figure 4 This is a third flowchart of a method for predicting target runoff pollutant loads according to embodiments of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0113] Step S401: Obtain meteorological data for the target area within the target time period; identify rainfall events based on the meteorological data; and obtain the rainfall amount and peak rainfall intensity for each rainfall event. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0114] Step S402: Based on the rainfall amount and peak rainfall intensity of each rainfall event, the pollutant flushing potential of the rainfall event is analyzed to obtain the pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant flushing potential of a rainfall event. For details, please refer to... Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0115] Step S403: Obtain the baseline pollution migration coupling driving index. Based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index, determine the relative pollution migration coupling driving index. The relative pollution migration coupling driving index is used to characterize the strength of the scour potential of a rainfall event relative to the baseline scenario. For details, please refer to... Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0116] Step S404: Based on the relationship between the relative pollution migration coupling driving index and the preset classification threshold, determine the classification response factor, and use the classification response factor to correct the preset pollutant migration parameters to obtain the target pollutant migration parameters; the classification response factor is a dynamic adjustment factor for the preset pollutant migration parameters, and the preset pollutant migration parameters are used to characterize the rate or ability of pollutants to migrate with runoff in the standard scenario.

[0117] Specifically, step S404 includes:

[0118] Step S4041: When the relative pollution migration coupling driving index is less than the first grading threshold, the first preset response factor is used as the grading response factor; the first grading threshold is less than the second grading threshold.

[0119] Step S4042: When the relative pollution migration coupling driving index is greater than or equal to the first grading threshold and the relative pollution migration coupling driving index is less than the second grading threshold, the second preset response factor is used as the grading response factor; the second preset response factor is greater than the first preset response factor.

[0120] Step S4043: When the relative pollution migration coupling driving index is greater than or equal to the second grading threshold, the third preset response factor is used as the grading response factor; the third preset response factor is greater than the second preset response factor.

[0121] For example, the formula for determining the graded response factor is:

[0122] ;

[0123] in, For graded response factors, As the first preset response factor, As the second preset response factor, As the third preset response factor, This is a relative pollution migration coupling driving index. The first level threshold, This is the second-level threshold.

[0124] in, The first level threshold, This is the second grading threshold. Both grading thresholds can be set according to actual circumstances. For example... A value of 0.6 indicates that the scouring capacity is only 60% or less of the baseline, which is considered weak scouring. A value of 1.4 indicates that the scouring capacity reaches more than 140% of the benchmark, which is considered strong scouring. The first preset response factor is the weak scouring response factor, which is a constant less than 1 (e.g., 0.7) used to weaken the preset pollutant migration parameters to match a gentler scouring dynamic. The third preset response factor is the strong scouring response factor, which is a constant greater than 1 (e.g., 1.5) used to enhance the preset pollutant migration parameters to match more intense scouring dynamics.

[0125] Step S4044: Obtain the target pollutant migration parameters by multiplying the graded response factor and the preset pollutant migration parameters.

[0126] For example, the formula for determining the migration parameters of the target pollutant is:

[0127] ;

[0128] in, For the target pollutant migration parameters, To preset pollutant migration parameters, This is a graded response factor.

[0129] In some alternative implementations, Approximately equal to At that time, the current rainfall is close to the baseline scenario. It is 1.0. equal ;when hour, It is 0.7. The flushing rate was lowered to avoid overestimating the pollution load from weak rainfall; when hour, It is 1.5. The flushing rate was increased to avoid underestimating the pollution load from heavy rainfall.

[0130] Step S405: Use the target pollutant migration parameters and rainfall amount for each rainfall event to predict runoff pollution, and obtain the predicted runoff flow and pollutant concentration for each rainfall event.

[0131] Specifically, step S405 includes:

[0132] Step S4051: Input the target pollutant migration parameters and rainfall amount for each rainfall event into the preset pollutant flushing cumulative simulation model to predict runoff pollution, and obtain the predicted runoff flow and pollutant concentration for each rainfall event.

[0133] Among them, the preset pollutant flushing accumulation simulation model is a physical model constructed based on historical rainfall data to simulate the accumulation and flushing process of runoff pollution.

[0134] For example, the expression for the pre-defined cumulative pollutant flushing simulation model can be:

[0135] ;

[0136] in, For a moment The predicted runoff volume, For a moment The predicted values ​​of pollutant concentrations To simulate the cumulative impact of pollutant flushing, For rainfall, The target pollutant migration parameters.

[0137] In some alternative implementations, the runoff volume prediction is a high spatiotemporal resolution runoff volume sequence, and the pollutant concentration prediction is a multi-pollutant concentration matrix.

[0138] In some optional implementations, various hydrological and water quality evaluation indicators can be used to quantitatively analyze the predicted runoff flow and pollutant concentration. For example, the formula for quantitative analysis using efficiency coefficient and relative error is as follows:

[0139] ;

[0140] in, In order to utilize the efficiency coefficient, For the observed values, For predicted runoff flow or predicted pollutant concentration, This represents the total number of predicted runoff flow or pollutant concentration values.

[0141] Step S406: Determine the target runoff pollutant load for the target area within the target time period based on the predicted runoff flow and pollutant concentration for each rainfall event.

[0142] Specifically, step S406 includes:

[0143] Step S4061: Based on the product integral of the predicted runoff flow and the predicted pollutant concentration for each rainfall event over the rainfall period, the runoff pollutant flux for each rainfall event is obtained.

[0144] For example, the formula for determining the runoff pollutant flux for each rainfall event is:

[0145] ;

[0146] in, For the first Class of pollutants in the first Runoff pollutant flux of a rainfall event For a moment The predicted runoff volume, For a moment The predicted values ​​of pollutant concentrations The start time of rainfall. This is the time when the rainfall ends. The time step for monitoring data.

[0147] Step S4062: Sum the runoff pollutant fluxes of multiple rainfall events in the target area within the target time period to obtain the target runoff pollutant load.

[0148] For example, the formula for determining the target runoff pollutant load is:

[0149] ;

[0150] in, For the target runoff pollutant load, The total number of rainfall events within the target time period. For the first Class of pollutants in the first Runoff pollutant flux of a rainfall event.

[0151] The target runoff pollutant load prediction method provided in this embodiment divides the relative pollution migration coupling driving index into three distinct intervals by setting two tiered thresholds, providing clear judgment criteria for subsequent tiered responses. When the relative pollution migration coupling driving index is less than the first tiered threshold, the first preset response factor is used as the tiered response factor; when the relative pollution migration coupling driving index is greater than or equal to the first tiered threshold and less than the second tiered threshold, the second preset response factor is used as the tiered response factor; when the relative pollution migration coupling driving index is greater than or equal to the second tiered threshold, the third preset response factor is used as the tiered response factor. This realizes a tiered response mechanism, matching response factors of different intensities according to the strength of rainfall scour potential, and the response factor increases with the increase of the driving index, ensuring a more proactive response to heavy rainfall events and a more conservative response to weak rainfall events, thus achieving dynamic correction of pollutant migration parameters.

[0152] This embodiment provides a method for predicting target runoff pollutant loads, which can be used with computer equipment. Figure 5 This is a fourth flowchart of the target runoff pollutant load prediction method according to embodiments of the present invention, as follows: Figure 5 As shown, the process includes the following steps:

[0153] Construction of a multi-source heterogeneous spatiotemporal database of runoff pollutants and related factors at discharge outlets; automatic segmentation of rainfall processes and automatic identification of runoff pollution characteristics based on long-term meteorological data; intelligent segmentation of rainfall events based on dual thresholds; automatic extraction of runoff pollution driving parameters; dynamic adjustment of runoff pollutant parameters for multiple scenarios and different rainfall characteristics; Rainfall-Runoff Pollution Migration Coupling Driving Index (RPMCDI); Runoff Pollution Parameter Graded Gradient Response Factor (RPGCDF); Runoff pollution prediction and performance evaluation based on dynamic parameter settings; Methods for predicting and calculating runoff pollution loads.

[0154] This embodiment also provides a target runoff pollutant load 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.

[0155] This embodiment provides a device for predicting target runoff pollutant loads, such as... Figure 6 As shown, it includes:

[0156] The rainfall event identification module 601 is used to acquire meteorological data of the target area within the target time period, identify rainfall events based on the meteorological data, and obtain the rainfall amount and peak rainfall intensity of each rainfall event.

[0157] The driving index determination module 602 is used to analyze the pollutant flushing potential of each rainfall event based on the rainfall amount and peak rainfall intensity, and obtain the pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant flushing potential of a rainfall event.

[0158] The relative driving index determination module 603 is used to obtain the baseline pollution migration coupling driving index and determine the relative pollution migration coupling driving index based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index. The relative pollution migration coupling driving index is used to characterize the strength of the scouring potential of the rainfall event relative to the baseline scenario.

[0159] The migration parameter correction module 604 is used to determine the graded response factor based on the relationship between the relative pollution migration coupling driving index and the preset graded threshold, and to correct the preset pollutant migration parameters using the graded response factor to obtain the target pollutant migration parameters. The graded response factor is a dynamic adjustment factor for the preset pollutant migration parameters, which are used to characterize the rate or ability of pollutants to migrate with runoff in a standard scenario.

[0160] The runoff pollution prediction module 605 is used to predict runoff pollution using the target pollutant migration parameters and rainfall amount for each rainfall event, and to obtain the predicted runoff flow and pollutant concentration for each rainfall event.

[0161] The pollutant load determination module 606 is used to determine the target runoff pollutant load of the target area within the target time period based on the predicted runoff flow and pollutant concentration for each rainfall event.

[0162] In some alternative implementations, the rainfall event identification module 601 includes:

[0163] The rainfall event selection unit is used to select events as rainfall events based on meteorological data where the total rainfall flow is greater than a preset rainfall threshold, the rainfall intensity is greater than a preset rainfall intensity threshold, and the rainfall time interval is greater than a preset time interval threshold.

[0164] The data determination unit is used to determine the rainfall amount and peak rainfall intensity of each rainfall event based on the rainfall intensity of each rainfall event.

[0165] In some alternative implementations, the drive index determination module 602 includes:

[0166] The first product unit is used to obtain the first product result based on the product of the rainfall amount and the peak rainfall intensity for each rainfall event.

[0167] An index determination unit is used to determine the pollution migration coupling driving index based on the square root of the first product result.

[0168] In some alternative implementations, the relative drive index determination module 603 includes:

[0169] The benchmark index determination unit is used to obtain the benchmark rainfall and benchmark peak rainfall intensity, and to determine the benchmark pollution migration coupling driving index based on the square root of the product of the benchmark rainfall and the benchmark peak rainfall intensity.

[0170] The relative index determination unit is used to determine the relative pollution migration coupling driving index based on the quotient of the pollution migration coupling driving index and the benchmark pollution migration coupling driving index.

[0171] In some alternative implementations, the migration parameter correction module 604 includes:

[0172] The first factor determination unit is used to determine the first preset response factor as the graded response factor when the relative pollution migration coupling driving index is less than the first grading threshold; the first grading threshold is less than the second grading threshold.

[0173] The second factor determination unit is used to determine the second preset response factor as the graded response factor when the relative pollution migration coupling driving index is greater than or equal to the first graded threshold and the relative pollution migration coupling driving index is less than the second graded threshold; the second preset response factor is greater than the first preset response factor.

[0174] The third factor determination unit is used to determine the third preset response factor as the graded response factor when the relative pollution migration coupling driving index is greater than or equal to the second graded threshold; the third preset response factor is greater than the second preset response factor.

[0175] The migration parameter correction unit is used to obtain the target pollutant migration parameters based on the product of the graded response factor and the preset pollutant migration parameters.

[0176] In some alternative implementations, the runoff pollution prediction module 605 includes:

[0177] The runoff pollution prediction unit is used to input the target pollutant migration parameters and rainfall amount for each rainfall event into a preset pollutant flushing cumulative simulation model to predict runoff pollution, and obtain the predicted runoff flow and pollutant concentration for each rainfall event.

[0178] In some alternative implementations, the pollutant load determination module 606 includes:

[0179] The pollutant load determination unit is used to obtain the runoff pollutant flux for each rainfall event by integrating the product of the predicted runoff flow and the predicted pollutant concentration over the rainfall period.

[0180] The summation unit is used to sum the runoff pollutant fluxes of multiple rainfall events in the target area within the target time period to obtain the target runoff pollutant load.

[0181] The target runoff pollutant load prediction device provided in this embodiment of the invention can execute the target runoff pollutant load 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.

[0182] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0183] The following is a detailed reference. Figure 7 This diagram illustrates a suitable structural schematic 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.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0184] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 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.

[0185] 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 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the target runoff pollutant load prediction method of the embodiments of the present invention.

[0186] Figure 7 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.

[0187] 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 method for predicting the target runoff pollutant load shown in the above embodiments is implemented.

[0188] 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.

[0189] 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 target runoff pollutant load, characterized in that, The method includes: Acquire meteorological data for the target area within the target time period, identify rainfall events based on the meteorological data, and obtain the rainfall amount and peak rainfall intensity for each rainfall event; Based on the rainfall amount and peak rainfall intensity of each rainfall event, the pollutant flushing potential of the rainfall event is analyzed to obtain the pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant flushing potential of the rainfall event. A baseline pollution migration coupling driving index is obtained, and a relative pollution migration coupling driving index is determined based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index; the relative pollution migration coupling driving index is used to characterize the strength of the scour potential of the rainfall event relative to the baseline scenario. Based on the relationship between the relative pollution migration coupling driving index and the preset classification threshold, a classification response factor is determined, and the preset pollutant migration parameters are corrected using the classification response factor to obtain the target pollutant migration parameters; the classification response factor is a dynamic adjustment factor for the preset pollutant migration parameters, and the preset pollutant migration parameters are used to characterize the rate or ability of pollutants to migrate with runoff in a standard scenario. Runoff pollution is predicted using the target pollutant migration parameters and rainfall amount for each rainfall event, resulting in predicted runoff flow and pollutant concentration values ​​for each rainfall event. Based on the predicted runoff flow and the predicted pollutant concentration for each rainfall event, the target runoff pollutant load for the target area during the target time period is determined.

2. The method according to claim 1, characterized in that, The step of identifying rainfall events based on the meteorological data and obtaining the rainfall amount and peak rainfall intensity for each rainfall event includes: Based on the meteorological data, events in which the total rainfall flow is greater than a preset rainfall threshold, the rainfall intensity is greater than a preset rainfall intensity threshold, and the rainfall time interval is greater than a preset time interval threshold are selected as the rainfall events. Based on the rainfall intensity of each rainfall event, the rainfall amount and the peak rainfall intensity of each rainfall event are determined.

3. The method according to claim 1 or 2, characterized in that, The step involves analyzing the pollutant scouring potential of each rainfall event based on its rainfall amount and peak rainfall intensity, to obtain a pollution migration coupling driving index, including: The first product result is obtained by multiplying the rainfall amount and the peak rainfall intensity for each rainfall event; The pollution migration coupling driving index is determined based on the square root of the first product result.

4. The method according to claim 1 or 2, characterized in that, The step of obtaining the baseline pollution migration coupling driving index and determining the relative pollution migration coupling driving index based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index includes: Obtain the baseline rainfall and the baseline peak rainfall intensity, and determine the baseline pollution migration coupling driving index based on the square root of the product of the baseline rainfall and the baseline peak rainfall intensity; The relative pollution migration coupling driving index is determined based on the quotient of the pollution migration coupling driving index and the benchmark pollution migration coupling driving index.

5. The method according to claim 1 or 2, characterized in that, The preset classification thresholds include a first classification threshold and a second classification threshold; the step of determining a classification response factor based on the relationship between the relative pollution migration coupling driving index and the preset classification thresholds, and using the classification response factor to correct the preset pollutant migration parameters to obtain the target pollutant migration parameters includes: When the relative pollution migration coupling driving index is less than the first classification threshold, the first preset response factor is used as the classification response factor; the first classification threshold is less than the second classification threshold. When the relative pollution migration coupling driving index is greater than or equal to the first classification threshold and the relative pollution migration coupling driving index is less than the second classification threshold, the second preset response factor is used as the classification response factor; the second preset response factor is greater than the first preset response factor. When the relative pollution migration coupling driving index is greater than or equal to the second grading threshold, a third preset response factor is used as the grading response factor; the third preset response factor is greater than the second preset response factor. The target pollutant migration parameter is obtained by multiplying the graded response factor and the preset pollutant migration parameter.

6. The method according to claim 1 or 2, characterized in that, The step of using the target pollutant migration parameters and rainfall amount for each rainfall event to predict runoff pollution, and obtaining predicted runoff flow and pollutant concentration values ​​for each rainfall event, includes: The target pollutant migration parameters and rainfall amount for each rainfall event are input into a preset pollutant flushing cumulative simulation model to predict runoff pollution, thereby obtaining the predicted runoff flow rate and the predicted pollutant concentration for each rainfall event.

7. The method according to claim 1 or 2, characterized in that, The step of determining the target runoff pollutant load for the target area within the target time period based on the predicted runoff flow and the predicted pollutant concentration for each rainfall event includes: The runoff pollutant flux for each rainfall event is obtained by integrating the product of the predicted runoff flow and the predicted pollutant concentration for each rainfall event over the rainfall period. The target runoff pollutant load is obtained by summing the runoff pollutant fluxes of multiple rainfall events in the target area within the target time period.

8. A device for predicting target runoff pollutant loads, characterized in that, The device includes: The rainfall event identification module is used to acquire meteorological data of the target area within a target time period, identify rainfall events based on the meteorological data, and acquire the rainfall amount and peak rainfall intensity of each rainfall event. The driving index determination module is used to analyze the pollutant flushing potential of each rainfall event based on the rainfall amount and peak rainfall intensity to obtain a pollution migration coupling driving index; the pollution migration coupling driving index is used to characterize the pollutant flushing potential of a rainfall event. The relative driving index determination module is used to obtain the baseline pollution migration coupling driving index and determine the relative pollution migration coupling driving index based on the pollution migration coupling driving index and the baseline pollution migration coupling driving index; the relative pollution migration coupling driving index is used to characterize the strength of the scour potential of the rainfall event relative to the baseline scenario. The migration parameter correction module is used to determine a graded response factor based on the relationship between the relative pollution migration coupling driving index and the preset graded threshold, and to correct the preset pollutant migration parameters using the graded response factor to obtain the target pollutant migration parameters; the graded response factor is a dynamic adjustment factor for the preset pollutant migration parameters, and the preset pollutant migration parameters are used to characterize the rate or ability of pollutants to migrate with runoff in a standard scenario. The runoff pollution prediction module is used to predict runoff pollution using the target pollutant migration parameters and the rainfall amount for each rainfall event, and to obtain the predicted runoff flow rate and the predicted pollutant concentration for each rainfall event. The pollutant load determination module is used to determine the target runoff pollutant load of the target area within the target time period based on the predicted runoff flow and the predicted pollutant concentration for each rainfall event.

9. 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 method for predicting the target runoff pollutant load according to any one of claims 1 to 7.

10. 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 the target runoff pollutant load according to any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method for predicting the target runoff pollutant load as described in any one of claims 1 to 7.

Images