Analysis method of corridor waterlogging area based on SCS model

Abstract

The invention discloses a corridor waterlogging area analysis method based on an SCS model, and the invention relates to the field of corridor management, and comprises the following steps: setting a corridor waterlogging management platform, acquiring data information in a region, and constructing a stormwater corridor distribution image; presetting a meteorological monitoring period, acquiring rainfall data within the meteorological monitoring period, and adjusting SCS model parameters at corresponding positions in the stormwater corridor distribution image according to the rainfall data; classifying and marking watershed pixels in the stormwater corridor distribution image; setting data processing nodes, and setting corresponding processing priorities according to the classification marks of the watershed pixels; acquiring predicted meteorological data, performing waterlogging analysis on the watershed pixels according to the processing priorities, and acquiring predicted waterlogging areas; performing warning level evaluation according to the analysis results and distribution conditions of the predicted waterlogging areas, and generating waterlogging warning information; the invention improves the accuracy and efficiency of the waterlogging area analysis process to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of corridor management, and in particular to a corridor waterlogging area analysis method based on an SCS model. Background Art

[0002] With the development of society and the improvement of people's living standards, people pay more and more attention to water resource management and soil protection. The SCS model is a model used for water resource management and soil protection, which helps decision makers better understand and manage hydrological processes and soil moisture changes, thereby protecting the environment, improving resource utilization efficiency, and reducing the occurrence of natural disasters.

[0003] The calculation method of the surface water accumulation state of a multi-source non-single depression with publication number CN106295123B discloses a calculation method of the surface water accumulation state of a multi-source non-single depression, including: starting from the water source of the target area, going downward along the slope, finding the point with the lowest slope as the first water accumulation point, and finding other water accumulation points in the target area based on the first water accumulation point; for each water accumulation point in the associated water accumulation point set, calculating the water accumulation amount corresponding to each water accumulation point, when the water accumulation amount exceeds the maximum capacity of the corresponding water accumulation point, using the excess water as the water source of the next one or more water accumulation points to which the water flows when the corresponding water accumulation point overflows, and continuing the calculation until the excess water is reduced to 0; calculating the water accumulation depth and water accumulation range of each water accumulation point based on the water accumulation amount of each water accumulation point, looping through all water accumulation points in the associated water accumulation point set, and performing rapid water accumulation analysis on the water accumulation points with valid water accumulation analysis marks.

[0004] The terminal-based big data analysis and processing method and system with publication number CN113012428B includes a processor, the processor is communicatively connected with a road condition monitoring module, a road water accumulation monitoring module, a street light monitoring module, a traffic light monitoring module and a storage module; the road water accumulation monitoring module is used to detect and analyze the water accumulation on the road surface. The present invention can segment the sunken area on the road surface through the road water accumulation monitoring module, and focus on water accumulation detection in the sunken area on rainy days. When water accumulation is detected in the sunken area, the road water accumulation monitoring module sends the water accumulation area and the image of the water accumulation area to the processor, so as to arrange the staff to deal with the water accumulation as soon as possible. The lighting condition of the road can be analyzed through the street light monitoring module.

[0005] However, when analyzing the waterlogged area, it is currently impossible to analyze and process the waterlogged area according to the soil environment in the current period, which causes deviations in the prediction results. In addition, in the process of analyzing and managing the waterlogged areas in the corridor area, they are analyzed and processed one by one, which reduces the efficiency of the early warning process. Therefore, how to improve the accuracy and efficiency of the waterlogged area prediction process is a problem we need to solve. To this end, a corridor waterlogged area analysis method based on the SCS model is provided. Summary of the invention

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide a corridor waterlogging area analysis method based on the SCS model.

[0007] The purpose of the present invention can be achieved by the following technical solution: A corridor waterlogging area analysis method based on the SCS model comprises the following steps:

[0008] Step S1: Setting up a corridor waterlogging management platform, obtaining terrain data, hydrological characteristic data and historical meteorological data of a corresponding area through the corridor waterlogging management platform; and setting up a rainwater corridor distribution image according to the obtained data information;

[0009] Step S2: Preset a meteorological monitoring period, obtain rainfall data within the meteorological monitoring period, and adjust the SCS model parameters at the corresponding position in the stormwater corridor distribution image according to the rainfall data;

[0010] Step S3: obtaining water level data corresponding to the rainwater corridor distribution image within the historical meteorological monitoring period, and generating high-flow rate pixels and low-flow rate pixels according to the water level data;

[0011] Step S4: multiple data processing nodes are set, a processing area is set according to the distribution of high sink flow pixels and low sink flow pixels, and a processing priority is set for each sink flow pixel in the processing area;

[0012] Step S5: Real-time meteorological data and forecast meteorological data are obtained, and the data processing node performs water accumulation analysis on the corresponding water-collecting pixels according to the processing priority, obtains the predicted water accumulation area, and dynamically adjusts the processing priority according to the distribution of the predicted water accumulation area; analyzes and processes each water-collecting pixel according to the dynamic adjustment result, and obtains the predicted water accumulation area;

[0013] Step S6: Obtain the distribution of the predicted waterlogging area to conduct warning level assessment, and generate waterlogging warning information based on the warning level assessment results.

[0014] Furthermore, the process of setting the stormwater corridor distribution image includes:

[0015] Setting up a corridor waterlogging management platform, the corridor waterlogging management platform acquires data information in a corresponding area, and sets up a stormwater corridor distribution image according to the acquired data information;

[0016] The terrain file data, hydrological characteristic data and historical meteorological data in the corresponding area are input through the corridor waterlogging management platform; the terrain file data includes the elevation data, district land use planning data and land type data in the area; a 3D terrain image is generated according to the terrain file image; the hydrological characteristic data includes the soil infiltration rate, soil moisture and CN value corresponding to different land types; the historical meteorological data includes the total rainfall, direct runoff, initial loss and waterlogging data in the area; the terrain file data, hydrological characteristic data and historical meteorological data are analyzed and processed based on the SCS model to obtain The retention volume data at different positions in the area are preset with a retention volume threshold. When the retention volume data is greater than or equal to the retention volume threshold, the position is set as a catchment pixel, and the obtained catchment pixel is mapped to the 3D terrain image to obtain the catchment pixel distribution network. According to the catchment pixel distribution network, artificial pipe network facilities and corresponding stormwater flow direction raster data are set, and the catchment pixel distribution network, artificial pipe network facilities and stormwater flow direction raster data are integrated to set a stormwater corridor distribution image; the stormwater corridor distribution image includes the corresponding backwater pixel distribution network and its corresponding SCS model and its corresponding model parameters.

[0017] Furthermore, the process of adjusting the SCS model parameters at the corresponding position in the stormwater corridor distribution image includes:

[0018] The corridor waterlogging management platform is provided with a data monitoring terminal, which is used to monitor rainfall data and soil infiltration rate at the corresponding water collection pixel position;

[0019] Preset a meteorological monitoring period, obtain the total rainfall data and soil infiltration rate of the catchment pixels corresponding to the stormwater corridor distribution image within the meteorological monitoring period; obtain the corresponding data information within the historical meteorological monitoring period, construct a historical data set, divide the obtained historical data set into a training set and a validation set, construct a soil infiltration model for the catchment pixels corresponding to the stormwater corridor distribution image based on a deep learning algorithm, analyze and train the soil infiltration model according to the training set until the loss function training is stable, save the network parameters corresponding to the model, analyze and test the soil infiltration model through the validation set until it meets the preset requirements, and output the soil infiltration model for the catchment pixels corresponding to the stormwater corridor distribution image;

[0020] The total rainfall data of the corresponding catchment pixels of the rainwater corridor distribution image during the meteorological monitoring period are obtained, and the data are input into the corresponding soil infiltration model to obtain the soil infiltration rate of the corresponding catchment pixels; the obtained soil infiltration rate is associated with the corresponding SCS model.

[0021] Furthermore, the process of setting the high sink amount pixel and the low sink amount pixel includes:

[0022] The corridor waterlogging management platform stores historical corridor waterlogging data of watershed pixels corresponding to the stormwater corridor distribution image, sets a corridor waterlogging threshold value according to the watershed pixels corresponding to the stormwater corridor distribution image; obtains corridor water level data at corresponding positions of corresponding watershed pixels, compares and analyzes the corridor water level data at corresponding watershed pixels with the corridor waterlogging threshold value, and marks the watershed pixel as abnormal waterlogging when the corridor water level data is greater than or equal to the corridor waterlogging threshold value, and marks the watershed pixel as normal waterlogging when the corridor water level data is less than the corridor waterlogging threshold value;

[0023] The abnormal waterlogging rate and normal waterlogging rate of the corresponding catchment pixel are obtained according to the analysis results corresponding to the corresponding catchment pixel; the abnormal waterlogging rate threshold and the normal waterlogging rate threshold are preset; the abnormal waterlogging rate and the normal waterlogging rate at each catchment pixel in the stormwater corridor distribution image are compared and analyzed with the abnormal waterlogging rate threshold and the normal waterlogging rate threshold respectively; when the abnormal waterlogging rate is greater than or equal to the abnormal waterlogging rate threshold, the catchment pixel is marked as a high-catchment pixel; when the normal waterlogging rate is greater than or equal to the normal waterlogging rate threshold, the catchment pixel is marked as a low-catchment pixel; if both are not met, the catchment pixel is not marked.

[0024] Furthermore, the process of setting the corresponding processing priority for each water catchment pixel includes:

[0025] Obtaining the labeling results of each water catchment pixel in the stormwater corridor distribution image, mapping it to the stormwater corridor distribution image according to the labeling results, and setting a plurality of data processing nodes, wherein the data processing nodes are used to analyze and process the corresponding water catchment pixels in the stormwater corridor distribution image;

[0026] According to the distribution of high-flow pixels and low-flow pixels in the stormwater corridor distribution image, a processing area is set, wherein the processing area includes at least N high-flow pixels and N low-flow pixels; and a data processing node is associated with a corresponding processing area;

[0027] Set high flow pixels as the first processing priority, set low flow pixels as the second processing priority, obtain the water pixels connected to the high flow pixels, set them as the third processing priority, set other water pixels as the fourth processing priority, and store the obtained processing priority data.

[0028] Furthermore, the process of obtaining the predicted waterlogging area includes:

[0029] Acquire real-time meteorological data and forecast meteorological data, obtain the total rainfall in the meteorological monitoring period according to the real-time meteorological data and forecast meteorological data, input the obtained total rainfall into the soil infiltration model, obtain the soil infiltration rate of the corresponding catchment pixel, and correlate the obtained soil infiltration rate with the corresponding SCS model;

[0030] The data processing node is provided with a fixed data processing channel and a random data processing channel. The fixed data processing channel is used to analyze and process the watershed pixels in the processing area according to the processing priority, and the random data processing channel is used to randomly obtain the watershed pixels corresponding to the third processing priority and the fourth processing priority for analysis and processing; in the process of analyzing and processing the watershed pixels by the data processing channel, the soil infiltration rate and total rainfall corresponding to the SCS model corresponding to the watershed pixels are obtained, and the SCS model analyzes and processes the data information of the corresponding watershed pixels to obtain the retention data of the watershed pixels; a pixel retention threshold is preset, and when the retention data is greater than or equal to the pixel retention threshold, the watershed pixel is recorded as a predicted waterlogging area.

[0031] Furthermore, the process of dynamically adjusting the processing priority according to the distribution of the predicted waterlogged areas includes:

[0032] The water catchment pixel whose analysis result in the random data processing channel is the predicted waterlogging area is obtained, the water catchment pixels connected to the water catchment pixel are obtained, and the corresponding water catchment pixels are set to the second processing priority; the fixed data processing channel in the data processing node analyzes and processes the corresponding data information according to the processing priority of each water catchment pixel in the corresponding processing area, and obtains the predicted waterlogging area of ​​each water catchment pixel.

[0033] Furthermore, the process of obtaining the distribution of the predicted waterlogging area and evaluating the warning level, and generating waterlogging warning information according to the warning level evaluation result includes:

[0034] The corridor waterlogging management platform obtains the distribution of the predicted waterlogging area in the stormwater corridor distribution image, obtains the marking type of the watershed pixel corresponding to the predicted waterlogging area, and sets the marking weight coefficient according to the marking type of the watershed pixel; determines the connection relationship between the watershed pixels corresponding to the predicted waterlogging area to set the connection weight coefficient, and obtains the comprehensive danger data in the stormwater corridor distribution image according to the marking weight coefficient and the connection weight coefficient of each predicted waterlogging area;

[0035] A danger level interval is preset, and the danger level is divided into four types: safe, general, serious and very serious. The comprehensive danger data is compared and analyzed with the danger level interval, and the warning level assessment result is obtained according to the danger level interval to which it belongs, and the corresponding water accumulation alarm information is generated.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The soil environment of the corresponding water-collecting pixels in the rainwater corridor distribution image is analyzed and processed through rainfall data, and the model parameters of the SCS model are adjusted according to the soil environment. According to the adjustment results, the SCS model analyzes and processes the predicted meteorological data to obtain the predicted waterlogging area, thereby improving the accuracy of the analysis process of the predicted waterlogging area;

[0038] 2. By classifying and marking the corresponding watershed pixels, setting the corresponding processing priority according to the classification and marking results, and analyzing and processing each watershed pixel according to the processing priority, the efficiency of the data processing process is improved to a certain extent;

[0039] 3. By setting up random data processing channels, water catchment pixels with lower processing priorities are randomly extracted to obtain the processing results of the corresponding water catchment pixels. According to the processing results of the randomly obtained water catchment pixels, the processing priority of the water catchment pixels in the stormwater corridor distribution image is dynamically adjusted, and adjustments are made according to actual conditions, thereby improving the flexibility of the waterlogging area analysis and processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the corridor waterlogging area analysis method based on the SCS model in an embodiment of the present application. DETAILED DESCRIPTION

[0041] like Figure 1 As shown in the figure, the corridor waterlogging area analysis method based on the SCS model includes the following steps:

[0042] Step S1: Setting up a corridor waterlogging management platform, obtaining terrain data, hydrological characteristic data and historical meteorological data of a corresponding area through the corridor waterlogging management platform; and setting up a rainwater corridor distribution image according to the obtained data information;

[0043] Step S2: Preset a meteorological monitoring period, obtain rainfall data within the meteorological monitoring period, and adjust the SCS model parameters at the corresponding position in the stormwater corridor distribution image according to the rainfall data;

[0044] Step S3: obtaining water level data corresponding to the rainwater corridor distribution image within the historical meteorological monitoring period, and generating high-flow rate pixels and low-flow rate pixels according to the water level data;

[0045] Step S4: multiple data processing nodes are set, a processing area is set according to the distribution of high sink flow pixels and low sink flow pixels, and a processing priority is set for each sink flow pixel in the processing area;

[0046] Step S5: Real-time meteorological data and forecast meteorological data are obtained, and the data processing node performs water accumulation analysis on the corresponding water-collecting pixels according to the processing priority, obtains the predicted water accumulation area, and dynamically adjusts the processing priority according to the distribution of the predicted water accumulation area; analyzes and processes each water-collecting pixel according to the dynamic adjustment result, and obtains the predicted water accumulation area;

[0047] Step S6: Obtain the distribution of the predicted waterlogging area to conduct warning level assessment, and generate waterlogging warning information based on the warning level assessment results.

[0048] It should be further explained that, in the specific implementation process, the corridor waterlogging management platform is set up, and the terrain data, hydrological characteristic data and historical meteorological data of the corresponding area are obtained through the corridor waterlogging management platform; the rainwater corridor distribution image is set according to the obtained data information, and the process includes:

[0049] Obtaining corresponding terrain file data in a corresponding area through the data entry terminal, wherein the terrain file data includes corresponding elevation data, district land use planning data and land type data, analyzing and processing the obtained terrain file data and the corresponding terrain file data therein based on the TIN algorithm, generating a 3D terrain image, performing regional division on the obtained 3D terrain image, dividing the relevant land types according to the land use planning, wherein the land types include water areas, road squares, industrial land, urban residential land, rural residential areas, forests, bare land, grasslands, shrubs and cultivated land, etc., mapping the corresponding land types in the terrain file data to the 3D terrain image, and marking them in the 3D terrain image;

[0050] The hydrological characteristic data and historical meteorological data of the corresponding position in the 3D terrain graph are obtained through the data entry terminal; the hydrological characteristic data include soil infiltration rate, soil moisture and CN value corresponding to different land types; the historical meteorological data include total rainfall, direct runoff, initial loss and water accumulation data in the region; the hydrological characteristic data and historical meteorological data in the region are analyzed and processed based on the SCS model, and calculated based on the water balance equation and the proportional equality assumption and the initial loss value (the maximum potential retention at that time). The specific implementation process includes:

[0051] P=Ia+F+Q(1)

[0052] QP-Ia=FS(2)

[0053] Ia=λ·S(3)

[0054] When P ≥ Ia: Q = (P-Ia)2P + S-Ia (4)

[0055] When P<Ia:Q=0(5)

[0056] Formula (4) is used to calculate S, introducing a parameter CN

[0057] S=25400 / CN-25

[0058] Where: P is the total rainfall, in mm; Q is the direct runoff, in mm; Ia is the initial loss, in mm, including interception, surface water storage, etc.; F is the cumulative infiltration excluding Ia, in mm; S is the maximum possible retention at that time, in mm; λ is the ratio of initial loss to potential infiltration in the basin, calculated by taking λ = 0.2; CN is the curve value, which is a dimensionless parameter used to describe the relationship between rainfall and runoff, reflecting the runoff capacity of the underlying surface unit in the basin. The influencing factors mainly include the previous soil wetness, soil type and land use type, etc.

[0059] Based on the SCS model, terrain file data, hydrological characteristic data, and historical meteorological data are analyzed and processed to obtain retention data at different locations in the area, and a retention threshold is preset. When the retention data is greater than or equal to the retention threshold, the location is set as a watershed pixel, and the obtained watershed pixel is mapped to a 3D terrain image to obtain a watershed pixel distribution network. According to the watershed pixel distribution network, artificial pipe network facilities and corresponding stormwater flow direction raster data are set, and the watershed pixel distribution network, artificial pipe network facilities and stormwater flow direction raster data are integrated to set a stormwater corridor distribution image; the stormwater corridor distribution image includes the corresponding watershed pixel distribution network and its corresponding SCS model and its corresponding model parameters, and the model parameters include the corresponding CN value, initial loss and cumulative infiltration.

[0060] It should be further explained that, in the specific implementation process, the preset meteorological monitoring period, obtaining rainfall data within the meteorological monitoring period, and adjusting the SCS model parameters at the corresponding position in the stormwater corridor distribution image according to the rainfall data, the process includes:

[0061] The corridor waterlogging management platform is provided with a data monitoring terminal, which is used to monitor rainfall data and soil infiltration rate at the corresponding water collection pixel position;

[0062] Preset a meteorological monitoring period, obtain the total rainfall data and soil infiltration rate of the catchment pixels corresponding to the stormwater corridor distribution image within the meteorological monitoring period; obtain the corresponding data information within the historical meteorological monitoring period, construct a historical data set, divide the obtained historical data set into a training set and a validation set, construct a soil infiltration model for the catchment pixels corresponding to the stormwater corridor distribution image based on a deep learning algorithm, analyze and train the soil infiltration model according to the training set until the loss function training is stable, save the network parameters corresponding to the model, analyze and test the soil infiltration model through the validation set until it meets the preset requirements, and output the soil infiltration model for the catchment pixels corresponding to the stormwater corridor distribution image;

[0063] The total rainfall data of the corresponding catchment pixels of the rainwater corridor distribution image during the meteorological monitoring period are obtained, and the data are input into the corresponding soil infiltration model to obtain the soil infiltration rate of the corresponding catchment pixels; the obtained soil infiltration rate is associated with the corresponding SCS model.

[0064] It should be further explained that, in the specific implementation process, the water level data corresponding to the rainwater corridor distribution image in the historical meteorological monitoring period is obtained, and high flow rate pixels and low flow rate pixels are generated according to the water level data, and the process includes:

[0065] According to the rain and flood corridor distribution image, the corridor waterlogging threshold is set, the corridor water level data at the corresponding position of the corresponding watershed pixel is obtained, and the corridor water level data at the corresponding watershed pixel is compared and analyzed with the corridor waterlogging threshold. When the corridor water level data is greater than or equal to the corridor waterlogging threshold, the watershed pixel is marked as abnormal waterlogging, and when the corridor water level data is less than the corridor waterlogging threshold, the watershed pixel is marked as normal waterlogging; the waterlogging abnormality rate and the waterlogging normality rate at the corresponding position in the rain and flood corridor during the historical meteorological monitoring period are obtained, and the waterlogging abnormality rate threshold and the waterlogging normality rate threshold are preset, and the waterlogging abnormality rate and the waterlogging abnormality rate threshold at the corresponding position in the rain and flood corridor distribution image are compared and analyzed. When the waterlogging abnormality rate is greater than or equal to the waterlogging abnormality rate threshold, the position is marked as a high-flow pixel; the waterlogging normality rate and the waterlogging normality threshold at the corresponding position in the rain and flood corridor distribution image are compared and analyzed. When the waterlogging normality rate is greater than or equal to the waterlogging normality threshold, the position is marked as a low-flow pixel;

[0066] The corresponding high-flow pixels and low-flow pixels in the stormwater corridor distribution image are marked, and the marked stormwater corridor distribution image is marked.

[0067] It should be further explained that, in the specific implementation process, the setting of multiple data processing nodes, setting a processing area according to the distribution of high-flow pixels and low-flow pixels, and setting a processing priority for each water-collecting pixel in the processing area, the process includes:

[0068] The process of setting the corresponding processing priority for each water catchment pixel includes:

[0069] Obtaining the labeling results of each water catchment pixel in the stormwater corridor distribution image, mapping it to the stormwater corridor distribution image according to the labeling results, and setting a plurality of data processing nodes, wherein the data processing nodes are used to analyze and process the corresponding water catchment pixels in the stormwater corridor distribution image;

[0070] According to the distribution of high-flow pixels and low-flow pixels in the stormwater corridor distribution image, a processing area is set, wherein the processing area includes at least N high-flow pixels and N low-flow pixels; and a data processing node is associated with a corresponding processing area;

[0071] The high-confluence pixel is set as the first processing priority, the low-confluence pixel is set as the second processing priority, the water-confluence pixel connected to the high-confluence pixel is obtained, and the water-confluence pixel is set as the third processing priority, and the other water-confluence pixels are set as the fourth processing priority, and the obtained processing priority data is stored;

[0072] The priorities corresponding to the first processing priority, the second processing priority, the third processing priority and the fourth processing priority decrease in sequence.

[0073] It should be further explained that, in the specific implementation process, the real-time meteorological data and the predicted meteorological data are obtained, and the data processing node performs water accumulation analysis on the corresponding watershed pixels according to the processing priority to obtain the predicted waterlogging area, and dynamically adjusts the processing priority according to the distribution of the predicted waterlogging area; and analyzes and processes each watershed pixel according to the dynamic adjustment result to obtain the predicted waterlogging area, and the process includes:

[0074] Acquire real-time meteorological data and forecast meteorological data, obtain the total rainfall in the meteorological monitoring period according to the real-time meteorological data and forecast meteorological data, input the obtained total rainfall into the soil infiltration model, obtain the soil infiltration rate of the corresponding catchment pixel, and correlate the obtained soil infiltration rate with the corresponding SCS model;

[0075] The data processing node is provided with a fixed data processing channel and a random data processing channel, the fixed data processing channel is used to analyze and process the water catchment pixels in the processing area according to the processing priority, and the random data processing channel is used to randomly obtain the water catchment pixels corresponding to the third processing priority and the fourth processing priority for analysis and processing; in the process of analyzing and processing the water catchment pixels by the data processing channel, the soil infiltration rate and total rainfall corresponding to the SCS model corresponding to the water catchment pixels are obtained, and the SCS model analyzes and processes the data information of the corresponding water catchment pixels to obtain the retention data of the water catchment pixels; a pixel retention threshold is preset, and when the retention data is greater than or equal to the pixel retention threshold, the water catchment pixel is recorded as a predicted waterlogging area;

[0076] It should be further explained that, in the specific implementation process, the pixel retention threshold is set according to different watershed pixels, different watershed pixels have different corresponding pixel retention thresholds, and the pixel retention threshold corresponding to each watershed pixel is obtained based on scientific experimental analysis.

[0077] The water catchment pixel whose analysis result in the random data processing channel is the predicted waterlogging area is obtained, the water catchment pixels connected to the water catchment pixel are obtained, and the corresponding water catchment pixels are set to the second processing priority; the fixed data processing channel in the data processing node analyzes and processes the corresponding data information according to the processing priority of each water catchment pixel in the corresponding processing area, and obtains the predicted waterlogging area of ​​each water catchment pixel.

[0078] It should be further explained that, in the specific implementation process, the distribution of the predicted waterlogging area is obtained to conduct warning level assessment, and waterlogging warning information is generated according to the warning level assessment result, and the process includes:

[0079] The corridor waterlogging management platform obtains the distribution of the predicted waterlogging area in the stormwater corridor distribution image, obtains the marking type of the watershed pixel corresponding to the predicted waterlogging area, and sets the marking weight coefficient according to the marking type of the watershed pixel; determines the connection relationship between the watershed pixels corresponding to the predicted waterlogging area to set the connection weight coefficient, and obtains the comprehensive danger data in the stormwater corridor distribution image according to the marking weight coefficient and the connection weight coefficient of each predicted waterlogging area;

[0080] It should be further explained that, in the specific implementation process, the process of obtaining the comprehensive hazard data is as follows: the comprehensive hazard data is recorded as ZW; according to the type of catchment pixel, the high catchment pixel, the low catchment pixel and the ordinary catchment pixel are marked as HG, HD and HP respectively; the weight coefficients corresponding to the catchment pixels of the corresponding type are marked as QG, QD and QP; wherein QD>QP>QG; the connection relationship between the corresponding predicted waterlogging area and other catchment pixels is obtained, and the process of setting the connection weight coefficient according to the connection relationship is as follows: the connection weight coefficient is marked as LQ; the number of connections and the constant term are obtained, and they are recorded as LS and β respectively; wherein LQ=LS*β; the number of corresponding catchment pixels is marked as i;

[0081] ZW=∑ i HG i ×QG i ×LQ+HD i ×QD i ×LQ+HP i ×QP i ×LQ;

[0082] A danger level interval is preset, and the danger level is divided into four types: safe, general, serious and very serious. The comprehensive danger data is compared and analyzed with the danger level interval, and the warning level assessment result is obtained according to the danger level interval to which it belongs, and the corresponding water accumulation alarm information is generated.

[0083] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The corridor waterlogging area analysis method based on the SCS model is characterized by: The following steps are involved: Step S1: Setting up a corridor waterlogging management platform, obtaining terrain data, hydrological characteristic data and historical meteorological data of a corresponding area through the corridor waterlogging management platform; and setting up a rainwater corridor distribution image according to the obtained data information; Step S2: Preset a meteorological monitoring period, obtain rainfall data within the meteorological monitoring period, and adjust the SCS model parameters at the corresponding position in the stormwater corridor distribution image according to the rainfall data; Step S3: obtaining water level data corresponding to the rainwater corridor distribution image within the historical meteorological monitoring period, and generating high-flow rate pixels and low-flow rate pixels according to the water level data; Step S4: multiple data processing nodes are set, a processing area is set according to the distribution of high sink flow pixels and low sink flow pixels, and a processing priority is set for each sink flow pixel in the processing area; Step S5: Real-time meteorological data and forecast meteorological data are obtained, and the data processing node performs water accumulation analysis on the corresponding water-collecting pixels according to the processing priority, obtains the predicted water accumulation area, and dynamically adjusts the processing priority according to the distribution of the predicted water accumulation area; analyzes and processes each water-collecting pixel according to the dynamic adjustment result, and obtains the predicted water accumulation area; Step S6: Obtain the distribution of the predicted waterlogging area to conduct warning level assessment, and generate waterlogging warning information based on the warning level assessment results.

2. The corridor waterlogging area analysis method based on the SCS model according to claim 1 is characterized in that: The process of setting the stormwater corridor distribution image includes: Setting up a corridor waterlogging management platform, the corridor waterlogging management platform acquires data information in a corresponding area, and sets up a stormwater corridor distribution image according to the acquired data information; The terrain file data, hydrological characteristic data and historical meteorological data in the corresponding area are input through the corridor waterlogging management platform; the terrain file data includes the elevation data, district land use planning data and land type data in the area; a 3D terrain image is generated according to the terrain file image; the hydrological characteristic data includes the soil infiltration rate, soil moisture and CN value corresponding to different land types; the historical meteorological data includes the total rainfall, direct runoff, initial loss and waterlogging data in the area; the terrain file data, hydrological characteristic data and historical meteorological data are analyzed and processed based on the SCS model to obtain The retention volume data at different positions in the area are preset with a retention volume threshold. When the retention volume data is greater than or equal to the retention volume threshold, the position is set as a catchment pixel, and the obtained catchment pixel is mapped to the 3D terrain image to obtain the catchment pixel distribution network. According to the catchment pixel distribution network, artificial pipe network facilities and corresponding stormwater flow direction raster data are set, and the catchment pixel distribution network, artificial pipe network facilities and stormwater flow direction raster data are integrated to set a stormwater corridor distribution image; the stormwater corridor distribution image includes the corresponding catchment pixel distribution network and its corresponding SCS model and its corresponding model parameters.

3. The corridor waterlogging area analysis method based on the SCS model according to claim 2 is characterized in that: The process of adjusting the SCS model parameters at the corresponding position in the stormwater corridor distribution image includes: The corridor waterlogging management platform is provided with a data monitoring terminal, which is used to monitor rainfall data and soil infiltration rate at the corresponding water collection pixel position; Preset a meteorological monitoring period, obtain the total rainfall data and soil infiltration rate of the catchment pixels corresponding to the stormwater corridor distribution image within the meteorological monitoring period; obtain the corresponding data information within the historical meteorological monitoring period, construct a historical data set, divide the obtained historical data set into a training set and a validation set, construct a soil infiltration model for the catchment pixels corresponding to the stormwater corridor distribution image based on a deep learning algorithm, analyze and train the soil infiltration model according to the training set until the loss function training is stable, save the network parameters corresponding to the model, analyze and test the soil infiltration model through the validation set until it meets the preset requirements, and output the soil infiltration model for the catchment pixels corresponding to the stormwater corridor distribution image; The total rainfall data of the corresponding catchment pixels of the rainwater corridor distribution image during the meteorological monitoring period are obtained, and the data are input into the corresponding soil infiltration model to obtain the soil infiltration rate of the corresponding catchment pixels; the obtained soil infiltration rate is associated with the corresponding SCS model.

4. The corridor waterlogging area analysis method based on the SCS model according to claim 3 is characterized in that: The process of setting high and low flow sink cells involves: The corridor waterlogging management platform stores historical corridor waterlogging data of watershed pixels corresponding to the stormwater corridor distribution image, sets a corridor waterlogging threshold value according to the watershed pixels corresponding to the stormwater corridor distribution image; obtains corridor water level data at corresponding positions of corresponding watershed pixels, compares and analyzes the corridor water level data at corresponding watershed pixels with the corridor waterlogging threshold value, and marks the watershed pixel as abnormal waterlogging when the corridor water level data is greater than or equal to the corridor waterlogging threshold value, and marks the watershed pixel as normal waterlogging when the corridor water level data is less than the corridor waterlogging threshold value; The abnormal waterlogging rate and normal waterlogging rate of the corresponding catchment pixel are obtained according to the analysis results corresponding to the corresponding catchment pixel; the abnormal waterlogging rate threshold and the normal waterlogging rate threshold are preset; the abnormal waterlogging rate and the normal waterlogging rate at each catchment pixel in the stormwater corridor distribution image are compared and analyzed with the abnormal waterlogging rate threshold and the normal waterlogging rate threshold respectively; when the abnormal waterlogging rate is greater than or equal to the abnormal waterlogging rate threshold, the catchment pixel is marked as a high-catchment pixel; when the normal waterlogging rate is greater than or equal to the normal waterlogging rate threshold, the catchment pixel is marked as a low-catchment pixel; if both are not met, the catchment pixel is not marked.

5. The corridor waterlogging area analysis method based on the SCS model according to claim 4 is characterized in that: The process of setting the corresponding processing priority for each water catchment pixel includes: Obtaining the labeling results of each water catchment pixel in the stormwater corridor distribution image, mapping it to the stormwater corridor distribution image according to the labeling results, and setting a plurality of data processing nodes, wherein the data processing nodes are used to analyze and process the corresponding water catchment pixels in the stormwater corridor distribution image; According to the distribution of high-flow pixels and low-flow pixels in the stormwater corridor distribution image, a processing area is set, wherein the processing area includes at least N high-flow pixels and N low-flow pixels; and a data processing node is associated with a corresponding processing area; Set high flow pixels as the first processing priority, set low flow pixels as the second processing priority, obtain the water pixels connected to the high flow pixels, set them as the third processing priority, set other water pixels as the fourth processing priority, and store the obtained processing priority data.

6. The corridor waterlogging area analysis method based on the SCS model according to claim 5 is characterized in that: The process of obtaining the predicted waterlogging area includes: Acquire real-time meteorological data and forecast meteorological data, obtain the total rainfall in the meteorological monitoring period according to the real-time meteorological data and forecast meteorological data, input the obtained total rainfall into the soil infiltration model, obtain the soil infiltration rate of the corresponding catchment pixel, and correlate the obtained soil infiltration rate with the corresponding SCS model; The data processing node is provided with a fixed data processing channel and a random data processing channel. The fixed data processing channel is used to analyze and process the watershed pixels in the processing area according to the processing priority, and the random data processing channel is used to randomly obtain the watershed pixels corresponding to the third processing priority and the fourth processing priority for analysis and processing; in the process of analyzing and processing the watershed pixels by the data processing channel, the soil infiltration rate and total rainfall corresponding to the SCS model corresponding to the watershed pixels are obtained, and the SCS model analyzes and processes the data information of the corresponding watershed pixels to obtain the retention data of the watershed pixels; a pixel retention threshold is preset, and when the retention data is greater than or equal to the pixel retention threshold, the watershed pixel is recorded as a predicted waterlogging area.

7. The corridor waterlogging area analysis method based on the SCS model according to claim 6 is characterized in that: The process of dynamically adjusting the processing priority according to the distribution of the predicted waterlogged areas includes: The water catchment pixel whose analysis result in the random data processing channel is the predicted waterlogging area is obtained, the water catchment pixels connected to the water catchment pixels are obtained, and the corresponding water catchment pixels are set as the second processing priority; the fixed data processing channel in the data processing node analyzes and processes the corresponding data information according to the processing priority of each water catchment pixel in the corresponding processing area, and obtains the predicted waterlogging area of ​​each water catchment pixel.

8. The corridor waterlogging area analysis method based on the SCS model according to claim 7 is characterized in that: The process of obtaining the distribution of the predicted waterlogging area to perform warning level assessment and generating waterlogging warning information according to the warning level assessment result includes: The corridor waterlogging management platform obtains the distribution of the predicted waterlogging area in the stormwater corridor distribution image, obtains the marking type of the watershed pixel corresponding to the predicted waterlogging area, and sets the marking weight coefficient according to the marking type of the watershed pixel; determines the connection relationship between the watershed pixels corresponding to the predicted waterlogging area, sets the connection weight coefficient, and obtains the comprehensive danger data in the stormwater corridor distribution image according to the marking weight coefficient and the connection weight coefficient of each predicted waterlogging area; A danger level interval is preset, and the danger level is divided into four types: safe, general, serious and very serious. The comprehensive danger data is compared and analyzed with the danger level interval, and the warning level assessment result is obtained according to the danger level interval to which it belongs, and the corresponding water accumulation alarm information is generated.

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