Urban outlet water quality and quantity prediction method and device, electronic equipment and storage medium

Abstract

The present application relates to the technical field of urban drainage, and discloses a city outlet water quality and quantity prediction method and device, electronic equipment and storage medium, comprising: obtaining hydrological basic data, meteorological data, water quality pollution data and geographic data in a target area; inputting the data into a pre-established water quality and quantity prediction model to obtain a water quality and quantity prediction result, wherein a runoff process simulation function in a runoff module of the water quality and quantity prediction model is obtained by summing up runoff processes of each type of sub-region, the infiltration capacity of different types of sub-regions is different, key parameters in a water quality migration process simulation model in a water quality module are dynamically adjusted through a grading function value, and the grading function value is determined according to rainfall intensity and total rainfall in a rainfall event. The present application partitions the target area according to the infiltration capacity, and optimizes the water quality migration process by using the rainfall intensity and total rainfall in the rainfall event, so that the obtained water quality and quantity prediction result is more accurate.

Description

Technical Field

[0001] This invention relates to the field of urban drainage technology, specifically to methods, devices, electronic equipment, and storage media for predicting water quality and quantity at urban outfalls. Background Technology

[0002] Water quality and quantity forecasting after urban storms is a core element in ensuring water security and ecological resilience. Under the impact of storms, surface runoff carries large amounts of pollutants into water bodies, causing a rapid deterioration in water quality. Without accurate forecasting of water quantity and quality during urban storms, critical windows for interception by reservoirs and regulation by dams will be missed, potentially leading to pollution incidents at water sources. Therefore, dual-dimensional forecasting of water quality and quantity is not only an "early warning system" for pollution control but also a "lifeline" for building urban resilience. However, current water quality and quantity forecasts lack improvements for the complex underlying runoff generation within different sub-catchments of urban outfalls, and do not offer adaptive classification methods for water quantity and quality parameters to reflect more realistic water quality migration under different conditions of large, medium, and small rainfall events, resulting in inaccurate forecasts. Summary of the Invention

[0003] This invention provides a method, device, electronic equipment, and storage medium for predicting the water quality and quantity of urban outfalls, in order to solve the problem of inaccurate prediction results for water quality and quantity.

[0004] In a first aspect, the present invention provides a method for predicting the water quality and quantity of urban outfalls, comprising: acquiring basic hydrological data, meteorological data, water pollution data, and geographical data within a target area, wherein the geographical data includes the area of ​​different types of sub-regions, and the infiltration capacity of different types of sub-regions is different; inputting the basic hydrological data, meteorological data, water pollution data, and geographical data into a pre-established water quality and quantity prediction model to obtain the water quality and quantity prediction results, wherein the water quality and quantity prediction model integrates a runoff generation module and a water quality module, wherein the runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation processes of each type of sub-region, and the key parameters in the water quality migration process simulation model in the water quality module are dynamically adjusted through a grading function value, wherein the grading function value is determined based on the rainfall intensity and total rainfall in the rainfall event.

[0005] In one alternative implementation, the different types of sub-regions include an over-permeable runoff generation zone and a full runoff generation zone, and the runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation process of the over-permeable runoff generation zone and the runoff generation process of the full runoff generation zone.

[0006] In one optional implementation, for the super-infiltrative runoff generation zone, when the rainfall intensity is greater than the infiltration capacity of the super-infiltrative runoff generation zone, the runoff generation process of the super-infiltrative runoff generation zone is determined based on the rainfall intensity and infiltration capacity at each time point within the super-infiltrative runoff generation zone; for the fully saturated runoff generation zone, when the average soil water demand of the fully saturated runoff generation zone is greater than the average water storage capacity, the runoff generation process of the super-infiltrative runoff generation zone is determined based on the rate of change between net rainfall intensity and soil water storage within the fully saturated runoff generation zone.

[0007] In one optional implementation, the step of determining the grading function value includes: determining a pollutant transport grading index based on the rainfall amount and maximum rainfall intensity of the rainfall event; substituting the grading index into the grading function to calculate the grading function value, wherein the grading function is used to smoothly map the grading index to the grading intensity range.

[0008] In one alternative implementation, the flow generation process within the target area is as follows:

[0009]

[0010] in, It is a deep runoff; , This represents the area integral of the over-permeable runoff generation zone and the saturated runoff generation zone. It is time The intensity of rainfall at any given moment; Indicates Horton's infiltration capacity; Indicates the time when the excessively permeable runoff zone begins to produce runoff; Indicates the evaporation term; This represents the average soil water storage capacity of the runoff-producing area. This represents the rate of change between net rainfall intensity and soil water storage. This indicates the average water storage capacity of the runoff-producing area when... When this value is 0, no flow is generated. When this value is 1, abortion begins; It is the time when the runoff-producing zone is full and runoff begins to occur, and H() is the infiltration curve formula. This represents the infiltration intensity from the start of runoff generation in the super-runoff zone up to time t.

[0011] In one alternative implementation, the pollutant transport classification index is calculated using the following formula:

[0012]

[0013] Among them, the index and The nonlinear weighted average represents the influence of rainfall amount and maximum rainfall intensity. The total rainfall in a typical event in the study area R represents the typical rainfall intensity threshold, and R represents the total rainfall. This indicates the maximum rainfall intensity during the specified period.

[0014] In one alternative implementation, the grading function value is calculated using the following formula:

[0015]

[0016] in, For continuous range function values, the range is usually in [ ]between, A classification index for pollutant transport; for The upper and lower limits; For the midpoint parameter, corresponding =( The PTTI value at ) / 2, For function kernel, It is the steepness coefficient.

[0017] In one optional implementation, the water quality and quantity prediction results include predicted runoff, predicted peak flow, and predicted pollutant concentration. The method further includes: acquiring measured runoff, measured peak flow, measured pollutant concentration, traditional predicted runoff, traditional predicted peak flow, and traditional predicted pollutant concentration for the target area, wherein the traditional predicted runoff, traditional predicted peak flow, and traditional predicted pollutant concentration are predicted by a traditional water quality and quantity prediction model; calculating a comprehensive water quantity improvement index based on the measured runoff, traditional predicted runoff, predicted runoff, measured peak flow, traditional predicted peak flow, and predicted peak flow; calculating a comprehensive water quality improvement index based on the measured pollutant concentration, traditional predicted pollutant concentration, and predicted pollutant concentration; and determining the comprehensive evaluation result of the water quality and quantity prediction model based on the comprehensive water quantity improvement index and the comprehensive water quality improvement index.

[0018] Secondly, the present invention provides a device for predicting the water quality and quantity of urban outfalls, comprising: a data acquisition module for acquiring basic hydrological data, meteorological data, water pollution data, and geographical data within a target area, wherein the geographical data includes the area of ​​different types of sub-regions, and the infiltration capacity of different types of sub-regions is different; and a water quality and quantity prediction module for inputting the basic hydrological data, meteorological data, water pollution data, and geographical data into a pre-established water quality and quantity prediction model to obtain water quality and quantity prediction results, wherein the water quality and quantity prediction model integrates a runoff generation module and a water quality module, wherein the runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation processes of each type of sub-region, and the key parameters in the water quality migration process simulation model in the water quality module are dynamically adjusted through a grading function value, which is determined based on the rainfall intensity and total rainfall in the rainfall event.

[0019] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the urban outfall water quality and quantity prediction method described in the first aspect or any corresponding embodiment thereof.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the urban outfall water quality and quantity prediction method described in the first aspect or any corresponding embodiment thereof.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the urban outfall water quality and quantity prediction method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0022] 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.

[0023] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the first process of the method for predicting the water quality and quantity of urban outfalls according to an embodiment of the present invention.

[0025] Figure 3This is a structural block diagram of an urban outfall water quality and quantity prediction device according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0027] 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.

[0028] 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.

[0029] As an optional application scenario of this invention, such as Figure 1 As shown, the 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.

[0030] 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.

[0031] According to an embodiment of the present invention, an embodiment of a method for predicting the water quality and quantity of urban outfalls 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.

[0032] This embodiment provides a method for predicting the water quality and quantity of urban outfalls, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 2 This is a flowchart of a method for predicting the water quality and quantity of urban outfalls according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0033] Step S201: Obtain basic hydrological data, meteorological data, water pollution data, and geographic data within the target area. The geographic data includes the area of ​​different types of sub-regions, and the infiltration capacity of different types of sub-regions varies.

[0034] In one alternative implementation, the target area can be the urban drainage catchment area.

[0035] In one optional implementation, since the embodiments of the present invention require prediction of water quality and quantity, when collecting data, hydrological basic data, meteorological data, water pollution data and geographical data related to rainfall runoff and pollution in water zones can be collected.

[0036] In one alternative implementation, meteorological data includes rainfall, rainfall intensity, pre-rain dry period, rainfall duration, temperature, and atmospheric deposition (characterized by PM2.5). Meteorological data can be obtained through an online meteorological monitoring system.

[0037] In one alternative implementation, the geographic data includes pipeline networks, topography, soil types, land use, etc. Soil types can be used to determine the infiltration capacity of different areas, thereby dividing the target area into multiple sub-regions of different types. The geographic data can be obtained through remote sensing and GIS processing.

[0038] In one alternative implementation, the basic hydrological data includes discharge outlet flow rate, liquid level, etc. This basic hydrological data can be monitored in real time using devices such as flow meters.

[0039] In one alternative implementation, the water pollution data includes the concentrations of pollutants such as COD, SS, TN, TP, and NH3-N at the discharge outlet. Pollution data can be obtained from literature or through small-scale on-site sampling and testing.

[0040] In one optional implementation, after acquiring basic hydrological data, meteorological data, water pollution data, and geographic data within the target area, all attribute data can be organized into a time-series dataset, recording the meteorological, runoff, and pollution data corresponding to each rainfall event, and mapping them one-to-one with geospatial data through relevant attributes. Based on the above data, a comprehensive spatiotemporal database D of storm runoff and its pollution is designed and established. Database D includes timestamps, spatial locations, and monitoring values. The intrinsic relationships between different datasets are established through spatiotemporal dimensions and key identifiers, which may include drainage zone IDs, monitoring point IDs, etc. The comprehensive spatiotemporal database D of storm runoff and its pollution established based on the above data can be schematically represented as follows:

[0041]

[0042] Where D represents the entire dataset, M, G, H, and P represent meteorological, geographical, hydrological, and pollution datasets, respectively, T represents the time dimension, and S represents the spatial dimension. Represents the Cartesian product. This represents the spatiotemporal intersection constraint.

[0043] Database D stores all factor data related to storm runoff and its pollution. It performs data integrity verification, consistency checks and cleaning to ensure the reliability of subsequent input data into the SWMM model. At the same time, it provides a standardized data access interface to efficiently serve the model's parameter input, boundary condition setting and calibration verification process.

[0044] Step S202: Input the basic hydrological data, meteorological data, water pollution data, and geographic data into the pre-established water quality and quantity prediction model to obtain the water quality and quantity prediction results. The water quality and quantity prediction model integrates a runoff generation module and a water quality module. The runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation processes of each type of sub-region. The key parameters in the water quality migration process simulation model in the water quality module are dynamically adjusted through grading function values, which are determined based on the rainfall intensity and total rainfall in the rainfall event.

[0045] In one alternative implementation, the different types of sub-regions include an over-permeable runoff generation zone and a full runoff generation zone, and the runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation process of the over-permeable runoff generation zone and the runoff generation process of the full runoff generation zone.

[0046] In one optional implementation, the water quality migration process simulation model includes a pollutant accumulation model, a scouring model, etc., and key parameters include maximum accumulation, accumulation constant, scouring coefficient, and scouring index, etc.

[0047] When the target area is an urban area, the urban underlying surface is extremely complex. A single catchment area may simultaneously contain different types of regions, such as infiltrative runoff zones and saturated runoff zones. Infiltrative runoff zones are paved surfaces, rooftops, etc., with paved surfaces including asphalt and concrete. These zones are characterized by limited infiltration capacity; runoff is generated when rainfall intensity exceeds this capacity. The traditional Horton infiltration curve method is suitable for describing the dynamic runoff process in such areas. Saturated runoff zones are characterized by relatively moist soil or green spaces with shallow groundwater levels. Once the soil's water storage capacity reaches saturation, almost all subsequent rainfall becomes runoff. The traditional Horton method significantly underestimates infiltration and overestimates runoff volume in these areas. Therefore, using a single runoff method to generalize the entire mixed underlying surface sub-catchment area can lead to significant deviations in overall runoff prediction due to incorrect characterization of saturated runoff zone behavior, thus affecting subsequent runoff and water quality simulation predictions. Therefore, in this embodiment of the invention, the runoff generation simulation function in the runoff generation module is obtained by summing the runoff generation processes of each type of sub-region. The runoff generation processes of different types of sub-regions are described in different ways, and then the runoff generation processes of each type of sub-region are summed to obtain the overall runoff generation process of the target area. This method more reasonably describes the coexistence of different runoff generation mechanisms in the complex underlying surface space within the urban drainage sub-catchment and their contribution to the total runoff in a physically sound manner. It also more completely describes the physical essence of the runoff generation forms in the sub-catchment under the mixed urban underlying surface in a mathematically sound manner. Therefore, the runoff generation module in this embodiment of the invention can more accurately simulate the runoff generation process within the target area.

[0048] Furthermore, in water quality calculations, pollutant accumulation and flushing processes are typically represented by empirical formulas, with key parameters usually set as constants or varying over long timescales. However, the actual transport intensity of water pollutants in a single rainfall event strongly depends on the characteristics of that rainfall event, particularly the amount and intensity. Light or low-intensity rainfall may primarily flush away easily migratable fine particulate pollutants from the surface, while heavy or high-intensity rainfall can transport more coarse particulate pollutants and even flush away sediments from pipes. Fixed parameters are insufficient to accurately reflect these differences in pollutant output characteristics determined by rainfall dynamics. Therefore, in this embodiment of the invention, a grading function value is calculated based on the rainfall intensity and total amount of rainfall in the rainfall event. This grading function value is then used to dynamically adjust key parameters in the water quality migration process simulation model. The grading function value comprehensively characterizes the transport potential of a rainfall event for pollutant migration, thus using it to dynamically adjust key parameters better reflects the actual pollutant accumulation / flushing process.

[0049] In one optional implementation, for the super-infiltrative runoff generation zone, when the rainfall intensity is greater than the infiltration capacity of the super-infiltrative runoff generation zone, the runoff generation process of the super-infiltrative runoff generation zone is determined based on the rainfall intensity and infiltration capacity at each time point within the super-infiltrative runoff generation zone; for the fully saturated runoff generation zone, when the average soil water demand of the fully saturated runoff generation zone is greater than the average water storage capacity, the runoff generation process of the super-infiltrative runoff generation zone is determined based on the rate of change between net rainfall intensity and soil water storage within the fully saturated runoff generation zone.

[0050] In one alternative implementation, the flow generation process within the target area is as follows:

[0051]

[0052] in, It is a deep runoff; , Represents the area integral of the over-permeable runoff generation zone and the saturated runoff generation zone, where Area(A) / Area(S) = Area(B) / Area(S) = ; It is time The intensity of rainfall at any given moment; Indicates Horton's infiltration capacity; Indicates the time when the hyperosmolarity-induced runoff zone begins to produce runoff, when hour, This ensures that the contribution before the start of the flow is zero. express Evaporation term; This represents the average soil water storage capacity of the runoff-producing area. This represents the rate of change between net rainfall intensity and soil water storage. When the soil is saturated and net rainfall is positive, this item is the runoff rate. This indicates the average water storage capacity of the runoff-producing area when... When it means unsaturated, this item is 0, and no flow is generated. When this value is 1, it means saturation, and flow generation begins. It is the time when the runoff-producing zone begins to produce runoff after it has reached saturation.

[0053] In one alternative implementation, the step of determining the grading function value includes:

[0054] Step a1: Determine and calculate the pollutant transport classification index based on the rainfall amount and maximum rainfall intensity during the rainfall event.

[0055]

[0056] Among them, the index and The nonlinear weighted average represents the influence of rainfall amount and maximum rainfall intensity. This indicates that the marginal utility of R is decreasing. express The marginal impact increases, emphasizing high-intensity scouring, and the index and It needs to be matched with the pollutant transport mechanism. For example, if the focus is more on rainfall intensity for particulate pollutants, then it can be set... If the dissolved state is more concerned with rainfall, then it can be set as follows: Larger. Total rainfall in typical events representing the study area The threshold representing typical rainfall intensity needs to be determined based on the characteristics of the catchment area and the critical conditions for pollutant migration. A simple approach is to use statistical analysis of long-term historical rainfall event data, such as calculating the median total rainfall by analyzing minute-level rainfall data from at least two years of rainfall events in the region. Median rainfall intensity as Alternatively, frequency analysis or actual pollutant mutations can be used to determine the cause. and Key parameters can be determined based on pollutant characteristics and regional data.

[0057] Step a2: Substitute the grading index into the grading function to calculate the grading function value. The grading function is used to smoothly map the grading index to the grading intensity range.

[0058] In one alternative implementation, the grading function value is calculated using the following formula:

[0059]

[0060] in, For continuous range function values, the range is usually in [ Between ], for example, it can be [ ], continuously characterizing the transport potential of pollutants; A classification index for pollutant transport; for The upper and lower limits; For the midpoint parameter, corresponding =( The PTTI value at ) / 2 is the baseline for "medium migration potential"; For function kernel, It is the steepness coefficient, which controls... Follow The speed of change. The larger the value, the steeper the transition, and the closer it is to discrete gradation; The smaller the value, the smoother the transition; this function is based on nonlinearity. The index, through a parameterized Logistic transformation Smoothly and continuously mapped to the gear intensity range [ ]; Key parameters can be determined based on pollutant characteristics and regional data.

[0061] In one alternative implementation, the continuous gradation function values It was used for adaptive water quality parameter settings, based on the rainfall event. The value dynamically adjusts key parameters of the pollutant accumulation model and the scouring model, allowing water quality parameters to "sense" the scouring capacity of current rainfall. Taking the maximum accumulation as an example, the maximum accumulation after adjustment using the tiered function value is:

[0062] in, This represents the maximum cumulative amount of water quality parameters for this rainfall event; The ADC is the value of the grading function; it represents the drought conditions in the early stage, which can be represented by the number of dry days in the early stage. The larger the ADC, the drier the early stage and the greater the potential accumulation. The decay function represents the negative contribution of the ADC to the initial available cumulative amount. It is a decay coefficient, which attempts to simulate that as the number of drought days increases, the cumulative amount tends to saturate. The process; (0 ≤ ≤1) is the weighting coefficient, balancing and The relative importance of the initial available cumulative amount, if If it is close to 1, it mainly depends on the rainfall potential T (PTTI). If it is close to 0, it mainly relies on the potential accumulated in the early stage; This ensures that the calculated value does not exceed the physical maximum cumulative amount. or actual cumulative amount You can refer to the maximum value of the maximum cumulative amount parameter in the SWMM help documentation or literature.

[0063] The above formulas explain when When both the potential for heavy rainfall and the ADC (anti-drought) are large, it indicates that almost all accumulated pollutants can be washed away; when When the potential for rainfall is very small (low rainfall potential) and the ADC is very small (pre-wet), it means that only a small amount of pollutants can be washed away.

[0064] In an optional implementation, the water quality and quantity prediction results obtained in step S202 above include peak flow prediction values ​​and pollutant concentration prediction values. The method provided in this embodiment of the invention further includes:

[0065] Step b1: Obtain the measured values ​​of peak flow, pollutant concentration, traditional predicted values ​​of peak flow and pollutant concentration for the target area. The traditional predicted values ​​of peak flow and pollutant concentration are obtained through the traditional water quality and quantity prediction model.

[0066] Step b2: Calculate the comprehensive water volume improvement index based on the measured runoff value, the traditional predicted runoff value, the predicted runoff value, the measured peak flow value, the traditional predicted peak flow value, and the predicted peak flow value.

[0067]

[0068] in, Let be the measured runoff value at time t. and These are the runoff prediction values ​​obtained in step S202 and the traditional runoff prediction values ​​obtained by the traditional method, respectively. This is the measured value of the peak flow. and These are the flood peak flow prediction value obtained in step S202 and the flood peak flow traditional prediction value obtained by the traditional method, respectively. and For example, the weighting coefficient can be taken as... T represents the total number of time points.

[0069] Step b3: Calculate the comprehensive water quality improvement index based on the measured pollutant concentration, the traditionally predicted pollutant concentration, and the predicted pollutant concentration.

[0070]

[0071] in, This represents the reduction in the relative error of the predicted pollutant concentration value compared to the conventional predicted pollutant concentration value in the i-th rainfall event; This is the attenuation coefficient (usually taken as 1.0), used to control the impact of the reduction in error on the exponent; The pollutant transport classification index for the i-th rainfall event; The number of rainfall events to be verified.

[0072] Step b4: Determine the comprehensive evaluation results of the water quality and quantity prediction model based on the comprehensive water quantity improvement index and the comprehensive water quality improvement index.

[0073] This invention proposes a comprehensive water quantity improvement index and a comprehensive water quality improvement index to comprehensively evaluate the degree of improvement in runoff process curves and peak flow rates, as well as the degree of improvement in pollutant concentration simulation and prediction. It also considers the impact of the PTTI (Precipitation-to-Temperature Interval) effect. Compared to traditional evaluation indicators, this index can achieve a comprehensive assessment integrating multiple factors and can adjust the importance of each factor. It is specifically designed to evaluate the effectiveness of improvement methods and directly demonstrates the degree of improvement. The comprehensive water quality improvement index simultaneously couples the scour effect with rainfall characteristics, enabling intelligent evaluation where "the improvement effect of high-intensity rainfall events has a higher weight."

[0074] This embodiment also provides a device for predicting the water quality and quantity of urban outfalls. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs 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.

[0075] This embodiment provides a device for predicting the water quality and quantity of urban outfalls, such as... Figure 3 As shown, it includes:

[0076] The data acquisition module 301 is used to acquire basic hydrological data, meteorological data, water pollution data and geographic data within the target area. The geographic data includes the area of ​​different types of sub-regions, and the infiltration capacity of different types of sub-regions is different.

[0077] The water quality and quantity prediction module 302 is used to input hydrological basic data, meteorological data, water pollution data and geographic data into a pre-established water quality and quantity prediction model to obtain water quality and quantity prediction results. The water quality and quantity prediction model integrates a runoff generation module and a water quality module. The runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation processes of various types of sub-regions. The key parameters in the water quality migration process simulation model in the water quality module are dynamically adjusted through grading function values, which are determined based on the rainfall intensity and total rainfall in the rainfall event.

[0078] The urban outfall water quality and quantity prediction device provided in this embodiment of the invention can execute the urban outfall water quality and quantity 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.

[0079] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0080] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable 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.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0081] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 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.

[0082] 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 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the urban outfall water quality and quantity prediction method of the embodiments of the present invention.

[0083] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0084] 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 urban outfall water quality and quantity prediction method shown in the above embodiments is implemented.

[0085] 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.

[0086] 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 all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for predicting the water quality and quantity of urban outfalls, characterized in that, The method includes: Acquire basic hydrological data, meteorological data, water pollution data, and geographic data within the target area. The geographic data includes the area of ​​different types of sub-regions, and the infiltration capacity of different types of sub-regions varies. The hydrological data, meteorological data, water pollution data, and geographic data are input into a pre-established water quality and quantity prediction model to obtain water quality and quantity prediction results. The water quality and quantity prediction model integrates a runoff generation module and a water quality module. The runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation processes of various types of sub-regions. The key parameters in the water quality migration process simulation model in the water quality module are dynamically adjusted through grading function values, which are determined based on the rainfall intensity and total rainfall in the rainfall event. The different types of sub-regions include the over-permeable runoff generation zone and the full runoff generation zone. The runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation process of the over-permeable runoff generation zone and the runoff generation process of the full runoff generation zone. The flow generation process within the target area is as follows: in, It is a deep runoff; , This represents the area integral of the over-permeable runoff generation zone and the saturated runoff generation zone. It is time The intensity of rainfall at any given moment; Indicates Horton's infiltration capacity; Indicates the time when the excessively permeable runoff zone begins to produce runoff; Indicates the evaporation term; This represents the average soil water storage capacity of the runoff-producing area. This represents the rate of change between net rainfall intensity and soil water storage. This indicates the average water storage capacity of the runoff-producing area when... When this value is 0, no flow is generated. When this value is 1, abortion begins; It is the time when the runoff-producing zone is full and runoff begins to occur, and H() is the infiltration curve formula. This represents the infiltration intensity from the start of runoff generation in the super-runoff zone up to time t; The steps for determining the gradation function value include: The pollutant transport classification index is determined based on the rainfall amount and the maximum rainfall intensity during the rainfall event. The grading index is substituted into the grading function to calculate the grading function value. The grading function is used to smoothly map the grading index to the grading intensity range. The pollutant transport classification index is calculated using the following formula: Among them, the index and The nonlinear weighted average represents the influence of rainfall amount and maximum rainfall intensity. The total rainfall in a typical event in the study area R represents the typical rainfall intensity threshold, and R represents the total rainfall. Indicates the maximum rainfall intensity during the specified period; The grading function value is calculated using the following formula: in, For continuous range function values, the range is [ ]between, A classification index for pollutant transport; for The upper and lower limits; For the midpoint parameter, corresponding =( PTTI value at ) / 2, For function kernel, It is the steepness coefficient.

2. The method according to claim 1, characterized in that, For the super-permeable runoff generation zone, when the rainfall intensity is greater than the infiltration capacity of the super-permeable runoff generation zone, the runoff generation process of the super-permeable runoff generation zone is determined based on the rainfall intensity and infiltration capacity at each moment within the super-permeable runoff generation zone; For a fully saturated runoff-producing zone, when the average soil water demand in the fully saturated runoff-producing zone is greater than the average water storage capacity, the runoff generation process of the super-infiltrated runoff-producing zone is determined based on the rate of change between net rainfall intensity and soil water storage within the fully saturated runoff-producing zone.

3. The method according to claim 1, characterized in that, The water quality and quantity prediction results include predicted runoff values, predicted peak flow rates, and predicted pollutant concentrations. The method further includes: The measured values ​​of runoff, peak flow, and pollutant concentration in the target area are obtained, along with the traditional predicted values ​​of runoff, peak flow, and pollutant concentration. These values ​​are predicted using a traditional water quality and quantity prediction model. The comprehensive water volume improvement index is calculated based on the measured runoff value, the traditional predicted runoff value, the predicted runoff value, the measured peak flow value, the traditional predicted peak flow value, and the predicted peak flow value. The comprehensive water quality improvement index is calculated based on the measured values ​​of pollutant concentration, the traditional predicted values ​​of pollutant concentration, and the predicted values ​​of pollutant concentration. The comprehensive evaluation result of the water quality and water quantity prediction model is determined based on the comprehensive water quantity improvement index and the comprehensive water quality improvement index.

4. A device for predicting the water quality and quantity of urban outfalls, characterized in that, The device includes: The data acquisition module is used to acquire basic hydrological data, meteorological data, water pollution data and geographic data within the target area. The geographic data includes the area of ​​different types of sub-regions, and the infiltration capacity of different types of sub-regions is different. The water quality and quantity prediction module is used to input the aforementioned hydrological basic data, meteorological data, water pollution data, and geographical data into a pre-established water quality and quantity prediction model to obtain water quality and quantity prediction results. The water quality and quantity prediction model integrates a runoff generation module and a water quality module. The runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation processes of various types of sub-regions. The key parameters in the water quality migration process simulation model in the water quality module are dynamically adjusted through grading function values, which are determined based on the rainfall intensity and total rainfall in the rainfall event. The different types of sub-regions include the over-permeable runoff generation zone and the full runoff generation zone. The runoff generation process simulation function in the runoff generation module is obtained by summing the runoff generation process of the over-permeable runoff generation zone and the runoff generation process of the full runoff generation zone. The flow generation process within the target area is as follows: in, It is a deep runoff; , This represents the area integral of the over-permeable runoff generation zone and the saturated runoff generation zone. It is time The intensity of rainfall at any given moment; Indicates Horton's infiltration capacity; Indicates the time when the excessively permeable runoff zone begins to produce runoff; Indicates the evaporation term; This represents the average soil water storage capacity of the runoff-producing area. This represents the rate of change between net rainfall intensity and soil water storage. This indicates the average water storage capacity of the runoff-producing area when... When this value is 0, no flow is generated. When this value is 1, abortion begins; It is the time when the runoff-producing zone is full and runoff begins to occur, and H() is the infiltration curve formula. This represents the infiltration intensity from the start of runoff generation in the super-runoff zone up to time t; The steps for determining the gradation function value include: The pollutant transport classification index is determined based on the rainfall amount and the maximum rainfall intensity during the rainfall event. The grading index is substituted into the grading function to calculate the grading function value. The grading function is used to smoothly map the grading index to the grading intensity range. The pollutant transport classification index is calculated using the following formula: Among them, the index and The nonlinear weighted average represents the influence of rainfall amount and maximum rainfall intensity. The total rainfall in a typical event in the study area R represents the typical rainfall intensity threshold, and R represents the total rainfall. Indicates the maximum rainfall intensity during the specified period; The grading function value is calculated using the following formula: in, For continuous range function values, the range is [ ]between, A classification index for pollutant transport; for The upper and lower limits; For the midpoint parameter, corresponding =( PTTI value at ) / 2, For function kernel, It is the steepness coefficient.

5. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the urban outfall water quality and quantity prediction method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the urban outfall water quality and quantity prediction method according to any one of claims 1 to 3.

7. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the urban outfall water quality and quantity prediction method according to any one of claims 1 to 3.

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