Method for modifying ssc-cn model, runoff simulation method and device

Abstract

The application provides a correction method of an SCS-CN model, a runoff simulation method and device. The runoff simulation method comprises the following steps: obtaining a configured storm intensity model parameter and a clogging condition; inputting the storm intensity model parameter into a storm intensity model to predict rainfall data of each unit time within a preset rainfall duration range; inputting the clogging condition and the rainfall data of each unit time within the preset rainfall duration range into a corrected SCS-CN model to predict surface runoff of each unit time within the preset rainfall duration range under the clogging condition; and visually displaying the rainfall data of each unit time within the preset rainfall duration range and / or the surface runoff of each unit time within the preset rainfall duration range under different clogging conditions in at least one of a curve graph, a column graph and a table. The application can more accurately predict the runoff process of the permeable brick under different clogging scenarios.

Description

Technical Field

[0001] This invention relates to the field of hydrological prediction technology, and in particular to a method for correcting the SCS-CN model, a method for simulating runoff generation, and an apparatus. Background Technology

[0002] Because the hydraulic conductivity of the underlying soil in permeable pavement is much lower than that of its structural layer, commonly used stormwater models (e.g., SWMM 5.0) often employ the retention-storage method in their Low Impact Development (LID) modules for calculating runoff generation in permeable pavement systems. However, for permeable brick systems with low initial permeability, since the difference between the hydraulic conductivity of the structural layer and the underlying soil is relatively small, it is more reasonable to treat them as soil-like and directly use the Soil Retention Service Curve Number (SCS-CN) method for runoff generation calculation. However, clogging issues resulting from the increasing service life of permeable bricks can affect their runoff generation patterns. Existing stormwater models, due to the infiltration-runoff parameters already incorporated into the SCS-CN curve method, cannot accurately predict the runoff generation throughout the entire lifecycle of permeable brick pavement clogging. Summary of the Invention

[0003] This invention provides a correction method, a runoff simulation method, and an apparatus for the SCS-CN model, which solves the problem that existing stormwater models cannot accurately predict the runoff situation throughout the entire life cycle of permeable brick pavement blockage because the infiltration-runoff parameters have been embedded in the SCS-CN curve method. This invention enables more accurate prediction of the runoff process of permeable bricks under different blockage scenarios.

[0004] This invention provides a method for correcting the SCS-CN model, comprising:

[0005] The initial SCS-CN model was used to fit the runoff data of all rainfall events to obtain the first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events were determined.

[0006] An exponential function was used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and a second fitting result was obtained.

[0007] The relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement was fitted using an exponential function to obtain the third fitting result;

[0008] Based on the second fitting result and the third fitting result, the initial SCS-CN model is corrected to obtain the corrected SCS-CN model.

[0009] According to a correction method for an SCS-CN model provided by the present invention, the initial SCS-CN model includes: a first expression, a second expression, and a third expression; wherein, the first expression is used to characterize the relationship between cumulative surface runoff, cumulative rainfall, initial loss value of rainfall before surface runoff generation, and maximum possible water storage; the second expression is used to characterize the relationship between the initial loss value of rainfall before surface runoff generation, the initial loss rate, and the maximum possible water storage; and the third expression is used to characterize the relationship between the maximum possible water storage and the CN value.

[0010] According to a correction method for an SCS-CN model provided by the present invention, the step of using an initial SCS-CN model to fit the runoff data of all rainfall events to obtain a first fitting result, and determining the initial loss rate and CN value of all rainfall events based on the first fitting result, includes:

[0011] The first expression in the initial SCS-CN model was used to fit the runoff data of all rainfall events, and the first fitting result was obtained.

[0012] Using the second expression in the initial SCS-CN model, the initial loss rate of all rainfall events is calculated based on the first fitting result;

[0013] Using the third expression in the initial SCS-CN model, the CN values ​​for all rainfall events are calculated based on the first fitting results.

[0014] This invention also provides a method for simulating runoff generation under the scenario of blockage in permeable brick pavement, comprising:

[0015] Obtain the configured rainstorm intensity model parameters and blockage conditions;

[0016] The parameters of the rainstorm intensity model are input into the rainstorm intensity model to predict the rainfall at each moment within the preset rainfall duration range, and the cumulative rainfall at each moment within the preset rainfall duration range is calculated based on the rainfall at each moment.

[0017] Based on the blockage conditions, the corresponding permeability coefficient is determined. The permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration are input into any of the above-mentioned modified SCS-CN models to predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage conditions. The surface runoff at each moment within the preset rainfall duration under the blockage conditions is obtained based on the difference between the cumulative surface runoff at each two adjacent moments.

[0018] At least one of the following within the preset rainfall duration range, namely, the rainfall amount at each moment, the cumulative rainfall amount at each moment, the cumulative surface runoff at each moment under different blockage conditions, and the surface runoff at each moment under different blockage conditions, will be visualized in at least one of the following methods: curve graph, bar chart, and table.

[0019] According to the present invention, a method for simulating runoff generation under a clogging scenario of permeable brick pavement is provided, the method further comprising:

[0020] Using at least one selected data format, export at least one of the following: rainfall at each moment within the preset rainfall duration, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions.

[0021] The present invention also provides a correction device for an SCS-CN model, comprising:

[0022] The first fitting module is used to fit the runoff data of all rainfall events using the initial SCS-CN model to obtain the first fitting result, and to determine the initial loss rate and CN value of all rainfall events based on the first fitting result.

[0023] The second fitting module is used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement using an exponential function, and obtain the second fitting result.

[0024] The third fitting module is used to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement using an exponential function, and obtain the third fitting result.

[0025] The model correction module is used to correct the initial SCS-CN model based on the second fitting result and the third fitting result to obtain the corrected SCS-CN model.

[0026] The present invention also provides a flow generation simulation device for permeable brick pavement blockage scenarios, comprising:

[0027] The parameter configuration module is used to obtain the configured rainstorm intensity model parameters and blockage conditions;

[0028] The rainfall prediction module is used to input the parameters of the rainstorm intensity model into the rainstorm intensity model, predict the rainfall at each moment within a preset rainfall duration, and calculate the cumulative rainfall at each moment within the preset rainfall duration based on the rainfall at each moment.

[0029] The surface runoff prediction module is used to determine the corresponding permeability coefficient based on the blockage condition, input the permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration into any of the above-mentioned modified SCS-CN models, predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage condition, and obtain the surface runoff at each moment within the preset rainfall duration under the blockage condition based on the difference between the cumulative surface runoff at each two adjacent moments.

[0030] The visualization module is used to visualize at least one of the following within the preset rainfall duration range: rainfall at each moment, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions, in at least one of the following ways: curve graph, bar chart, and table.

[0031] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the correction method of the SCS-CN model as described above, or the flow generation simulation method under the blockage scenario of permeable brick pavement as described above.

[0032] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the correction method of the SCS-CN model as described above, or the flow generation simulation method under the blockage scenario of permeable brick pavement as described above.

[0033] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the correction method for the SCS-CN model as described above, or the flow generation simulation method for the blockage scenario of permeable brick pavement as described above.

[0034] This invention provides a method for correcting the SCS-CN model. First, an initial SCS-CN model is used to fit the runoff data of all rainfall events, obtaining a first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events are determined. Second, an exponential function is used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, obtaining a second fitting result. An exponential function is then used to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, obtaining a third fitting result. That is, the relationship between the initial loss rate and CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement is established. Finally, based on the second and third fitting results, the initial SCS-CN model is corrected to obtain a corrected SCS-CN model, which is applicable to the runoff simulation of permeable brick pavements with different degrees of blockage.

[0035] This invention also provides a method for simulating runoff generation under clogging scenarios in permeable brick pavements. First, the configured rainstorm intensity model parameters and clogging conditions are obtained. Second, the rainstorm intensity model parameters are input into the rainstorm intensity model to predict the rainfall at each moment within a preset rainfall duration, and the cumulative rainfall within the preset rainfall duration is calculated based on the rainfall at each moment. Based on the clogging conditions, the corresponding permeability coefficient is determined, and the permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration are input into a modified SCS-CN model to predict the cumulative surface runoff at each moment under the clogging conditions within the preset rainfall duration. The preset rainfall duration is obtained based on the difference between the cumulative surface runoff at each two adjacent moments. The surface runoff at each moment under the blockage condition within the range is calculated. Since the modified SCS-CN model is applicable to the runoff simulation of permeable brick pavements with different degrees of blockage, the surface runoff and cumulative surface runoff at each moment under different blockage conditions within the preset rainfall duration range can be predicted based on the modified SCS-CN model. Finally, at least one of the following—rainfall at each moment, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions—is visualized in at least one of the following methods: curve graph, bar chart, and table. This allows for more accurate prediction of the permeable brick runoff process under different blockage scenarios. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the correction method for the SCS-CN model provided by the present invention;

[0038] Figure 2 This is a schematic diagram of the fitting curves between cumulative runoff and cumulative rainfall under multiple rainfall intensities and multiple different permeability coefficients provided by the present invention;

[0039] Figure 3 This is a schematic diagram of the fitting curves between the initial loss rate and CN value of all rainfall events provided by this invention and different permeability coefficients;

[0040] Figure 4 This is a flowchart illustrating the flow generation simulation method for permeable brick pavement blockage scenarios provided by the present invention.

[0041] Figure 5 This is a visual schematic diagram of the runoff simulation data provided by the present invention;

[0042] Figure 6a This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 0.0%.

[0043] Figure 6b This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 15.0%.

[0044] Figure 6c This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 24.8%.

[0045] Figure 6d This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 28.3%.

[0046] Figure 6e This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 39.8%.

[0047] Figure 6f This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 46.0%.

[0048] Figure 6g This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 55.8%.

[0049] Figure 6h This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 69.0%.

[0050] Figure 6i This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage level of 80.5%.

[0051] Figure 6j This is a schematic diagram comparing the predicted and measured values ​​of the traditional stormwater model SWMM5.0 and the modified SCS-CN model provided by this invention at a blockage rate of 90.3%.

[0052] Figure 7 This is a schematic diagram of the structure of the correction device for the SCS-CN model provided by the present invention;

[0053] Figure 8 This is a schematic diagram of the flow generation simulation device for permeable brick pavement blockage scenarios provided by the present invention;

[0054] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0056] The following is combined Figure 1 Figure 6 illustrates the correction method of the SCS-CN model and the flow generation simulation method under the blockage scenario of permeable brick pavement according to the present invention.

[0057] Please refer to Figure 1 , Figure 1This is a flowchart illustrating the correction method for the SCS-CN model provided by this invention. Figure 1 As shown, the correction method for the SCS-CN model provided by this invention may include the following steps:

[0058] Step 101: Using the initial SCS-CN model, fit the runoff data of all rainfall events to obtain the first fitting result, and determine the initial loss rate and CN value of all rainfall events based on the first fitting result.

[0059] Step 102: Use an exponential function to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and obtain the second fitting result;

[0060] Step 103: Use an exponential function to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and obtain the third fitting result;

[0061] Step 104: Based on the second and third fitting results, the initial SCS-CN model is corrected to obtain the corrected SCS-CN model.

[0062] In step 101, the initial SCS-CN model may include: a first expression, a second expression, and a third expression; wherein, the first expression is used to characterize the relationship between cumulative surface runoff, cumulative rainfall, the initial loss value of rainfall before surface runoff generation, and the maximum possible water storage; the second expression is used to characterize the relationship between the initial loss value of rainfall before surface runoff generation, the initial loss rate, and the maximum possible water storage; and the third expression is used to characterize the relationship between the maximum possible water storage and the CN value.

[0063] Specifically, the initial SCS-CN model may include:

[0064] First expression:

[0065] Second expression: I a =λS (2)

[0066] Third expression:

[0067] Where R represents cumulative surface runoff (mm); P represents cumulative rainfall (mm); I aλ represents the initial loss value (mm) of rainfall before surface runoff occurs, and is taken from the cumulative infiltration at the time of runoff occurrence; S represents the maximum possible water storage (mm); λ represents the initial loss rate; CN represents the curve value, which is a dimensionless parameter with a theoretical value range of 0 to 100. This value is a spatial parameter that incorporates the influence and contribution of land use, soil, vegetation, slope, and previous hydrological conditions.

[0068] Optionally, step 101 above may include the following sub-steps:

[0069] Step 1011: Using the first expression in the initial SCS-CN model, fit the runoff data of all rainfall events to obtain the first fitting result;

[0070] Step 1012: Using the second expression in the initial SCS-CN model, calculate the initial loss rate of all rainfall events based on the first fitting result;

[0071] Step 1013: Using the third expression in the initial SCS-CN model, calculate the CN value for all rainfall events based on the first fitting result.

[0072] In step 1011, the first fitting result is the fitting result obtained by fitting the runoff data of all rainfall events to the first expression in the initial SCS-CN model. Optionally, the first fitting result is a fitting curve between cumulative runoff and cumulative rainfall. For example, as... Figure 2 As shown, the first fitting result can be: fitting curves between cumulative runoff and cumulative rainfall for multiple rainfall intensities and multiple different permeability coefficients; wherein, the multiple rainfall intensities are 35.8 mm / h, 49.7 mm / h, 64.8 mm / h and 73.6 mm / h; the multiple different permeability coefficients are 0%, 15.0%, 24.8%, 28.3%, 39.8%, 46.0%, 55.8%, 69.0%, 80.5% and 90.3%.

[0073] In step 1012, the data from the first fitting result are substituted into the second expression in the initial SCS-CN model to calculate the initial loss rate of all rainfall events.

[0074] In step 1013, the data from the first fitting result are substituted into the third expression in the initial SCS-CN model to calculate the CN value for all rainfall events.

[0075] In this step, the initial loss rate and CN value of all rainfall events are calculated using the initial SCS-CN model.

[0076] In step 102, the second fitting result is the fitting result obtained by using an exponential function to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement. Specifically, as shown... Figure 3 As shown, the initial loss rate of all rainfall events is correlated with the permeability coefficient corresponding to the degree of clogging of each permeable brick pavement. An exponential function is used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of clogging of each permeable brick pavement, resulting in a second fitting result with a fitting coefficient of 0.707. This second fitting result can be described by expression four:

[0077] Expression 4: λ = 0.16402e -2.43236k (4)

[0078] Where k represents the permeability coefficient (mm·min) corresponding to the degree of blockage of the permeable brick pavement. -1 ).

[0079] In step 103, the third fitting result is the fitting result obtained by using an exponential function to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement. Specifically, as shown... Figure 3 As shown, the CN values ​​of all rainfall events are correlated with the permeability coefficients corresponding to the degree of clogging of each permeable brick pavement. An exponential function is used to fit the relationship between the CN values ​​of all rainfall events and the permeability coefficients corresponding to the degree of clogging of each permeable brick pavement, resulting in a third fitting result with a fitting coefficient of 0.909. This third fitting result can be described by expression five:

[0080] Expression 5: CN = -6.24447e 2.01664k +107.27361 (5)

[0081] Where CN represents the CN value.

[0082] In step 104, the second fitting result (i.e., expression four) and the third fitting result (i.e., expression five) are substituted into the initial SCS-CN model to obtain the modified SCS-CN model. Specifically, the modified SCS-CN model may include:

[0083] Expression Six:

[0084] Expression 7:

[0085] In this step, the modified SCS-CN model can be applied to the flow generation simulation of permeable brick pavements with different degrees of blockage.

[0086] This embodiment provides a method for correcting the SCS-CN model. First, an initial SCS-CN model is used to fit the runoff data of all rainfall events, obtaining a first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events are determined. Second, an exponential function is used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, obtaining a second fitting result. An exponential function is then used to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, obtaining a third fitting result. That is, the relationship between the initial loss rate and CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement is established. Finally, based on the second and third fitting results, the initial SCS-CN model is corrected to obtain a corrected SCS-CN model, which is applicable to the runoff simulation of permeable brick pavements with different degrees of blockage.

[0087] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the flow generation simulation method for permeable brick pavement blockage scenarios provided by the present invention. Figure 4 As shown, the flow generation simulation method for permeable brick pavement blockage scenarios provided by the present invention may include the following steps:

[0088] Step 401: Obtain the configured rainstorm intensity model parameters and blockage conditions;

[0089] Step 402: Input the parameters of the rainstorm intensity model into the rainstorm intensity model to predict the rainfall at each moment within the preset rainfall duration range, and calculate the cumulative rainfall at each moment within the preset rainfall duration range based on the rainfall at each moment.

[0090] Step 403: Determine the corresponding permeability coefficient based on the blockage condition, input the permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration into the modified SCS-CN model, predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage condition, and obtain the surface runoff at each moment within the preset rainfall duration under the blockage condition based on the difference between the cumulative surface runoff at each two adjacent moments.

[0091] Step 404: Visualize at least one of the following within the preset rainfall duration range: rainfall at each moment, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions, using at least one of the following methods: curve graph, bar chart, and table.

[0092] In step 401, the parameters of the rainstorm intensity model may include: rainfall return period, rainfall duration, etc., and the blockage conditions may include: initial permeability coefficient, degree of blockage of permeable brick pavement, etc.

[0093] In step 402, the rainfall intensity model may include:

[0094]

[0095] Where i represents rainfall intensity (mm·min) -1 A1 represents the rainfall parameter, C represents the rainfall variation parameter, p represents the rainfall return period, t represents the rainfall duration, b represents the duration correction parameter, and n represents the rainfall attenuation index.

[0096] The instantaneous rainfall intensity before and after the rain peak is:

[0097]

[0098]

[0099] Where i(t) b ) represents the instantaneous rainfall intensity before the rain peak, t b Indicates the corresponding duration, i(t) a ) represents the instantaneous rainfall intensity after the rain peak, t a The corresponding duration is represented by r, which represents the peak ratio. If it is uniform rainfall intensity, r is 1, and A = A1(1 + ClgP).

[0100] Optionally, a uniform intensity can be used to generate the rainfall process line, or a rainfall peak can be formed based on the peak coefficient.

[0101] It should be noted that, in addition to the rainstorm intensity model illustrated above, other rainstorm intensity models can also be used, and this embodiment does not impose any specific limitations.

[0102] For example, a rainstorm intensity model can be:

[0103]

[0104] By inputting the parameters of the rainstorm intensity model into the model, the rainfall at each moment within the preset rainfall duration is predicted. For each moment, the sum of the rainfall at the current moment and the rainfall at each moment before the current moment within the preset rainfall duration is calculated to obtain the cumulative rainfall at the current moment within the preset rainfall duration.

[0105] Specifically, assuming a preset rainfall duration of 120 minutes and a preset step size of 1 minute, by inputting the rainfall intensity model parameters into the rainfall intensity model, the rainfall amount per minute within 120 minutes, i.e., the rainfall intensity, can be obtained. Based on the rainfall amount per minute within 120 minutes, the cumulative rainfall P within 120 minutes is calculated.

[0106] In step 403, the modified SCS-CN model can be obtained by using any of the above-mentioned modification methods for the SCS-CN model.

[0107] Specifically, based on the blockage condition, the corresponding permeability coefficient k is determined, and the permeability coefficient k is substituted into Expression 7 above to obtain the maximum possible water storage capacity S. The maximum possible water storage capacity S and the cumulative rainfall P at each moment within the preset rainfall duration are substituted into Expression 6 above to predict the cumulative surface runoff R at each moment within the preset rainfall duration under the blockage condition. The difference between the cumulative surface runoff at each two adjacent moments is calculated to obtain the surface runoff at each moment within the preset rainfall duration under the blockage condition.

[0108] Specifically, assuming a preset rainfall duration of 120 minutes and a preset step size of 1 minute, by inputting the blockage condition and the cumulative rainfall P per minute under the blockage condition within 120 minutes into the modified SCS-CN model, the cumulative surface runoff R per minute under the blockage condition within 120 minutes can be obtained. The surface runoff per minute under the blockage condition within 120 minutes is then obtained based on the difference between the cumulative surface runoff R of every two adjacent minutes.

[0109] In step 404, the visualization methods include: line graphs, bar charts, and tables. At least one of the following parameters is visualized using at least one of the following methods: rainfall at each moment within a preset rainfall duration, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions.

[0110] Specifically, assuming a preset step size of 1 minute and a preset rainfall duration of 120 minutes, such as Figure 5 As shown, the left side is the graphical area, and the right side is the table area. The upper left figure shows the minute-by-minute surface runoff curve and minute-by-minute rainfall bar chart over 120 minutes. The lower left figure shows the cumulative surface runoff curve and cumulative rainfall bar chart over 120 minutes. The cumulative surface runoff curve describes the cumulative surface runoff per minute, and the cumulative rainfall bar chart describes the cumulative rainfall per minute. The table area on the right shows the minute-by-minute rainfall and minute-by-minute surface runoff. The lower right figure shows the current permeability coefficient, the cumulative rainfall for the entire rainfall event, and the cumulative runoff.

[0111] Furthermore, the visualization of runoff simulation data is updated in real time when the model parameters and / or blockage conditions of the rainstorm intensity model change.

[0112] In this step, the production flow simulation data can be visualized.

[0113] In this embodiment, since the modified SCS-CN model is applicable to the flow generation simulation of permeable brick pavements with different degrees of blockage, the flow generation simulation of permeable brick pavements under blockage scenarios based on the modified SCS-CN model can more accurately predict the flow generation process of permeable bricks under different blockage scenarios.

[0114] Optionally, at least one of the following can be exported using at least one selected data format: rainfall at each moment within a preset rainfall duration, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions. Specifically, multiple selectable data formats are provided to export the runoff simulation data to a selected file storage location using at least one selected data format, facilitating the backup of the runoff simulation data.

[0115] Optionally, the exported runoff simulation data in at least one data format can be imported into other software, such as the Storm Water Management Model (SWMM).

[0116] The flow generation simulation method under the blockage scenario of permeable brick pavement in this embodiment will be verified below.

[0117] The runoff of permeable brick pavement during the 5-year return period of rainfall was predicted using the traditional stormwater model SWMM5.0 and the modified SCS-CN model, respectively, and the results were compared with the measured values.

[0118] The model efficiency coefficient E can be used to evaluate the predictive performance of both models. E is commonly used in hydrology to assess model performance. Typically, the value of E can range from 1 to negative infinity. A lower E value indicates that the model predictions deviate significantly from the actual observations.

[0119] Specifically, the model efficiency coefficient E can be calculated using the following expression:

[0120]

[0121] Where E represents the model efficiency coefficient, n represents the total number of observations, and Q i Let Q represent the depth of the i-th observed runoff. ave Q represents the average observed runoff depth. pi This represents the predicted runoff depth for the i-th time.

[0122] Through Table 1 and Figures 6a-6j This demonstrates the predictive performance of the traditional stormwater model SWMM5.0 and the modified SCS-CN model under different levels of blockage.

[0123] Table 1. Evaluation of the prediction performance of the SWMM model and the modified SCS-CN model

[0124]

[0125] Through Table 1 and Figures 6a-6j It can be seen that the modified SCS-CN model has a better model efficiency coefficient than the traditional stormwater model SWMM5.0 under different levels of blockage.

[0126] The following describes the correction device for the SCS-CN model and the flow generation simulation device under the blockage scenario of permeable brick pavement provided by the present invention. The correction device for the SCS-CN model and the flow generation simulation device under the blockage scenario of permeable brick pavement described below can be referred to in correspondence with the correction method for the SCS-CN model and the flow generation simulation method under the blockage scenario of permeable brick pavement described above.

[0127] Please refer to Figure 7 , Figure 7 This is a schematic diagram of the structure of the correction device for the SCS-CN model provided by the present invention. Figure 7 As shown, the correction device for the SCS-CN model provided by the present invention may include:

[0128] The first fitting module 701 is used to fit the runoff data of all rainfall events using the initial SCS-CN model to obtain the first fitting result, and to determine the initial loss rate and CN value of all rainfall events based on the first fitting result.

[0129] The second fitting module 702 is used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement using an exponential function, and obtain the second fitting result.

[0130] The third fitting module 703 is used to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement using an exponential function, and obtain the third fitting result.

[0131] The model correction module 704 is used to correct the initial SCS-CN model based on the second fitting result and the third fitting result to obtain the corrected SCS-CN model.

[0132] Optionally, the initial SCS-CN model includes: a first expression, a second expression, and a third expression; wherein, the first expression is used to characterize the relationship between cumulative surface runoff, cumulative rainfall, the initial loss value of rainfall before surface runoff generation, and the maximum possible water storage; the second expression is used to characterize the relationship between the initial loss value of rainfall before surface runoff generation, the initial loss rate, and the maximum possible water storage; and the third expression is used to characterize the relationship between the maximum possible water storage and the CN value.

[0133] Optionally, the first fitting module 701 includes:

[0134] The fitting unit is used to fit the runoff data of all rainfall events using the first expression in the initial SCS-CN model to obtain the first fitting result;

[0135] The first calculation unit is used to calculate the initial loss rate of all rainfall events based on the first fitting result using the second expression in the initial SCS-CN model.

[0136] The second calculation unit is used to calculate the CN value of all rainfall events based on the first fitting result, using the third expression in the initial SCS-CN model.

[0137] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the flow generation simulation device for permeable brick pavement blockage scenarios provided by the present invention. Figure 8 As shown, the flow generation simulation device for permeable brick pavement blockage scenarios provided by the present invention may include:

[0138] The parameter configuration module 801 is used to obtain the configured rainstorm intensity model parameters and blockage conditions;

[0139] The rainfall prediction module 802 is used to input the rainstorm intensity model parameters into the rainstorm intensity model, predict the rainfall at each moment within a preset rainfall duration range, and calculate the cumulative rainfall at each moment within the preset rainfall duration range based on the rainfall at each moment.

[0140] The surface runoff prediction module 803 is used to determine the corresponding permeability coefficient based on the blockage condition, input the permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration into the modified SCS-CN model described above, predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage condition, and obtain the surface runoff at each moment within the preset rainfall duration under the blockage condition based on the difference between the cumulative surface runoff at each two adjacent moments.

[0141] The visualization module 804 is used to visualize at least one of the following within the preset rainfall duration range: rainfall at each moment, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions, in at least one of the following ways: curve graph, bar chart, and table.

[0142] Optionally, the device further includes:

[0143] The data export module is used to export at least one of the following data within the preset rainfall duration range: rainfall at each moment, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions, using at least one selected data format.

[0144] Figure 9 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 9 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840.

[0145] The processor 810 can call logical instructions in the memory 830 to execute a correction method for the SCS-CN model, the method including:

[0146] The initial SCS-CN model was used to fit the runoff data of all rainfall events to obtain the first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events were determined.

[0147] An exponential function was used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and a second fitting result was obtained.

[0148] The relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement was fitted using an exponential function to obtain the third fitting result;

[0149] Based on the second fitting result and the third fitting result, the initial SCS-CN model is corrected to obtain the corrected SCS-CN model.

[0150] Processor 810 can also call logic instructions in memory 830 to execute a flow generation simulation method under the scenario of permeable brick pavement blockage, the method including:

[0151] Obtain the configured rainstorm intensity model parameters and blockage conditions;

[0152] The parameters of the rainstorm intensity model are input into the rainstorm intensity model to predict the rainfall at each moment within the preset rainfall duration range, and the cumulative rainfall at each moment within the preset rainfall duration range is calculated based on the rainfall at each moment.

[0153] Based on the blockage conditions, the corresponding permeability coefficient is determined. The permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration are input into any of the above-mentioned modified SCS-CN models to predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage conditions. The surface runoff at each moment within the preset rainfall duration under the blockage conditions is obtained based on the difference between the cumulative surface runoff at each two adjacent moments.

[0154] At least one of the following within the preset rainfall duration range, namely, the rainfall amount at each moment, the cumulative rainfall amount at each moment, the cumulative surface runoff at each moment under different blockage conditions, and the surface runoff at each moment under different blockage conditions, will be visualized in at least one of the following methods: curve graph, bar chart, and table.

[0155] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the correction method for the SCS-CN model provided by the above methods, the method comprising:

[0157] The initial SCS-CN model was used to fit the runoff data of all rainfall events to obtain the first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events were determined.

[0158] An exponential function was used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and a second fitting result was obtained.

[0159] The relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement was fitted using an exponential function to obtain the third fitting result;

[0160] Based on the second fitting result and the third fitting result, the initial SCS-CN model is corrected to obtain the corrected SCS-CN model.

[0161] The computer can also execute the flow generation simulation methods for permeable brick pavement blockage scenarios provided by the above methods, which include:

[0162] Obtain the configured rainstorm intensity model parameters and blockage conditions;

[0163] The parameters of the rainstorm intensity model are input into the rainstorm intensity model to predict the rainfall at each moment within the preset rainfall duration range, and the cumulative rainfall at each moment within the preset rainfall duration range is calculated based on the rainfall at each moment.

[0164] Based on the blockage conditions, the corresponding permeability coefficient is determined. The permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration are input into any of the above-mentioned modified SCS-CN models to predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage conditions. The surface runoff at each moment within the preset rainfall duration under the blockage conditions is obtained based on the difference between the cumulative surface runoff at each two adjacent moments.

[0165] At least one of the following within the preset rainfall duration range, namely, the rainfall amount at each moment, the cumulative rainfall amount at each moment, the cumulative surface runoff at each moment under different blockage conditions, and the surface runoff at each moment under different blockage conditions, will be visualized in at least one of the following methods: curve graph, bar chart, and table.

[0166] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a method for correcting the SCS-CN model provided by the methods described above, the method comprising:

[0167] The initial SCS-CN model was used to fit the runoff data of all rainfall events to obtain the first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events were determined.

[0168] An exponential function was used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and a second fitting result was obtained.

[0169] The relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement was fitted using an exponential function to obtain the third fitting result;

[0170] Based on the second fitting result and the third fitting result, the initial SCS-CN model is corrected to obtain the corrected SCS-CN model.

[0171] When executed by a processor, this computer program can also implement a flow generation simulation method for permeable brick pavement blockage scenarios provided by the above methods, which includes:

[0172] Obtain the configured rainstorm intensity model parameters and blockage conditions;

[0173] The parameters of the rainstorm intensity model are input into the rainstorm intensity model to predict the rainfall at each moment within the preset rainfall duration range, and the cumulative rainfall at each moment within the preset rainfall duration range is calculated based on the rainfall at each moment.

[0174] Based on the blockage conditions, the corresponding permeability coefficient is determined. The permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration are input into any of the above-mentioned modified SCS-CN models to predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage conditions. The surface runoff at each moment within the preset rainfall duration under the blockage conditions is obtained based on the difference between the cumulative surface runoff at each two adjacent moments.

[0175] At least one of the following within the preset rainfall duration range, namely, the rainfall amount at each moment, the cumulative rainfall amount at each moment, the cumulative surface runoff at each moment under different blockage conditions, and the surface runoff at each moment under different blockage conditions, will be visualized in at least one of the following methods: curve graph, bar chart, and table.

[0176] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0177] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for correcting the runoff curve number (SCS-CN) model, characterized in that, include: The initial SCS-CN model was used to fit the runoff data of all rainfall events to obtain the first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events were determined. An exponential function was used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement, and a second fitting result was obtained. The relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement was fitted using an exponential function to obtain the third fitting result; Based on the second fitting result and the third fitting result, the initial SCS-CN model is corrected to obtain the corrected SCS-CN model.

2. The correction method for the SCS-CN model according to claim 1, characterized in that, The initial SCS-CN model includes: a first expression, a second expression, and a third expression; wherein, the first expression is used to characterize the relationship between cumulative surface runoff, cumulative rainfall, the initial loss value of rainfall before surface runoff generation, and the maximum possible water storage; the second expression is used to characterize the relationship between the initial loss value of rainfall before surface runoff generation, the initial loss rate, and the maximum possible water storage; and the third expression is used to characterize the relationship between the maximum possible water storage and the CN value.

3. The correction method for the SCS-CN model according to claim 2, characterized in that, The initial SCS-CN model is used to fit the runoff data of all rainfall events to obtain a first fitting result. Based on the first fitting result, the initial loss rate and CN value of all rainfall events are determined, including: The first expression in the initial SCS-CN model was used to fit the runoff data of all rainfall events, and the first fitting result was obtained. Using the second expression in the initial SCS-CN model, the initial loss rate of all rainfall events is calculated based on the first fitting result; Using the third expression in the initial SCS-CN model, the CN values ​​for all rainfall events are calculated based on the first fitting results.

4. A method for simulating flow generation under the scenario of blockage in permeable brick pavement, characterized in that, include: Obtain the configured rainstorm intensity model parameters and blockage conditions; The parameters of the rainstorm intensity model are input into the rainstorm intensity model to predict the rainfall at each moment within the preset rainfall duration range, and the cumulative rainfall at each moment within the preset rainfall duration range is calculated based on the rainfall at each moment. Based on the blockage conditions, the corresponding permeability coefficient is determined, and the permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration are input into the modified SCS-CN model according to any one of claims 1 to 3 to predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage conditions. The surface runoff at each moment within the preset rainfall duration under the blockage conditions is obtained based on the difference between the cumulative surface runoff at each two adjacent moments. At least one of the following within the preset rainfall duration range, namely, the rainfall amount at each moment, the cumulative rainfall amount at each moment, the cumulative surface runoff at each moment under different blockage conditions, and the surface runoff at each moment under different blockage conditions, will be visualized in at least one of the following methods: curve graph, bar chart, and table.

5. The method for simulating runoff generation under the scenario of blockage in permeable brick pavement according to claim 4, characterized in that, The method further includes: Using at least one selected data format, export at least one of the following: rainfall at each moment within the preset rainfall duration, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions.

6. A correction device for an SCS-CN model, characterized in that, include: The first fitting module is used to fit the runoff data of all rainfall events using the initial SCS-CN model to obtain the first fitting result, and to determine the initial loss rate and CN value of all rainfall events based on the first fitting result. The second fitting module is used to fit the relationship between the initial loss rate of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement using an exponential function, and obtain the second fitting result. The third fitting module is used to fit the relationship between the CN value of all rainfall events and the permeability coefficient corresponding to the degree of blockage of each permeable brick pavement using an exponential function, and obtain the third fitting result. The model correction module is used to correct the initial SCS-CN model based on the second fitting result and the third fitting result to obtain the corrected SCS-CN model.

7. A device for simulating flow generation under the scenario of blockage in permeable brick pavement, characterized in that, include: The parameter configuration module is used to obtain the configured rainstorm intensity model parameters and blockage conditions; The rainfall prediction module is used to input the parameters of the rainstorm intensity model into the rainstorm intensity model, predict the rainfall at each moment within a preset rainfall duration, and calculate the cumulative rainfall at each moment within the preset rainfall duration based on the rainfall at each moment. The surface runoff prediction module is used to determine the corresponding permeability coefficient based on the blockage condition, input the permeability coefficient and the cumulative rainfall at each moment within the preset rainfall duration into the modified SCS-CN model according to any one of claims 1 to 3, predict the cumulative surface runoff at each moment within the preset rainfall duration under the blockage condition, and obtain the surface runoff at each moment within the preset rainfall duration under the blockage condition based on the difference between the cumulative surface runoff at each two adjacent moments. The visualization module is used to visualize at least one of the following within the preset rainfall duration range: rainfall at each moment, cumulative rainfall at each moment, cumulative surface runoff at each moment under different blockage conditions, and surface runoff at each moment under different blockage conditions, in at least one of the following ways: curve graph, bar chart, and table.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the correction method for the SCS-CN model as described in any one of claims 1 to 3, or the flow generation simulation method for the permeable brick pavement blockage scenario as described in claim 4 or 5.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the correction method for the SCS-CN model as described in any one of claims 1 to 3, or the flow generation simulation method for the permeable brick pavement blockage scenario as described in claim 4 or 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the correction method for the SCS-CN model as described in any one of claims 1 to 3, or the flow generation simulation method for the permeable brick pavement blockage scenario as described in claim 4 or 5.

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