WO2023016036A1 - Multi-factor composite early warning and forecasting method for municipal road ponding - Google Patents

Abstract

Disclosed in the present invention are a multi-factor composite early warning and forecasting method for municipal road ponding. The method comprises: acquiring engineering design data and a BIM model; inputting the engineering design data and the BIM model into a waterlogging analysis software system, and establishing a waterlogging ponding numerical analysis model; inputting rainfall pattern parameters of typical rainstorms into the waterlogging ponding numerical analysis model, and outputting corresponding ponding situations and ponding point positions; arranging miniature meteorological stations and liquid level monitoring apparatuses at the ponding point positions, so as to acquire actual measurement data; and comparing the actual measurement data and the current forecast meteorological data with the rainfall pattern parameters, performing screening to select a typical rainstorm having the closest rainfall pattern, and calling a ponding situation and a ponding point position, which correspond to the typical rainstorm, for early warning and forecasting. By means of the present invention, real-time numerical simulation of real rainfall is not required, thereby omitting the installation of numerical simulation software in a server, and reducing the performance requirements for the server; and if the current condition of an urban area is changed, only a numerical simulation database needs to be updated, and a prediction result is still accurate.

Description

Multi-factor composite early warning and forecast method for municipal road water accumulation technical field

The invention relates to the field of urban road water early warning. More specifically, the present invention relates to a multi-factor composite early warning and forecasting method for municipal road water accumulation.

Background technique

Cities play an important role in social activities. Along with the population flow under the rapid economic development of the country, it has greatly promoted the urbanization process of the country. Due to the complexity of urban areas, coupled with the increasing trend of the level and frequency of urban rainstorms, the urban waterlogging frequency and damage caused by heavy rainstorms are increasing year by year, making waterlogging disasters one of the important reasons hindering the healthy development of cities. Therefore, it is particularly important to establish an urban municipal road water early warning system.

At present, the municipal road water early warning system is basically divided into three categories: the early warning method based on the neural network algorithm and big data, the rainstorm water early warning method based on the rainstorm model, and the Internet of Things monitoring technology, that is, by monitoring the water level of the river and drainage system. Or the situation of water accumulation at points prone to water accumulation combined with the weather forecast for early warning.

Among them, the early warning method based on neural network algorithm and big data has the disadvantage that cities are always changing, especially in China, where cities change rapidly, and urban pipe networks, topography, underlying surfaces and even landforms will change within a few years. Large changes, this kind of change will obviously affect the surface production and confluence, but the early warning method based on big data does not take these factors into account, so when there is a large change in a city, using this method will cause bias in the prediction. The storm water early warning method based on the rainstorm model uses numerical calculations. For short-term heavy rainfall weather, it is difficult to analyze the risk of road waterlogging through the model, and it is impossible to provide accurate early warning. The simple Internet of Things monitoring technology is currently in some cities in low-lying areas such as underpass tunnels, where electronic water gauges and large screens are installed to indicate the depth of water during heavy rains, so as to remind vehicles to pay attention to the wading depth when passing. This type of method does not carry out numerical simulation or big data analysis and prediction, and its timeliness is relatively weak.

Contents of the invention

It is an object of the present invention to solve at least the above-mentioned problems and to provide at least the advantages which will be described later.

Another purpose of the present invention is to provide a multi-factor composite early warning and forecasting method for water accumulation on municipal roads, which carries out numerical simulations of a large number of historical rain patterns in advance, and reserves the big data of road water accumulation in various situations, rather than the current rainfall. Real-time numerical simulation eliminates the need to install numerical simulation software in the server, reducing the performance requirements for the server. However, if the current urban conditions change, such as pipe network expansion, it is only necessary to update the numerical simulation database to ensure the accuracy of the prediction results. Therefore, the road water accumulation prediction method in this study has important practical engineering significance.

In order to achieve these objects and other advantages according to the present invention, a multi-factor composite early warning and forecast method for municipal road water accumulation is provided, comprising the following steps:

Step 1. Obtain engineering design data of the target area and a BIM model based on the engineering design data, wherein the engineering design data includes at least terrain data, rainwater pipe network data, and historical meteorological data of the target area;

Step 2. Select the waterlogging analysis software system, input the engineering design data obtained in step 1 and the corresponding BIM model to the waterlogging analysis software system, and establish the waterlogging numerical analysis model of the target area;

Step 3: Input the rain pattern parameters of typical rainstorms into the numerical analysis model of waterlogging and waterlogging, and output the waterlogging conditions and locations of waterlogging points under each typical rainstorm;

Step 4. Arrange micro-weather stations and liquid level monitoring devices at the water accumulation points calculated in step 3 to obtain on-site measured data;

Step 5. Compare the measured data and/or the data calculated based on the measured data and the current forecast meteorological data with the rain pattern parameters of each typical rainstorm, and select a typical rainstorm whose rain pattern parameters are closest to the measured data and the current forecast meteorological data. Rainstorm, and call the water accumulation situation and water accumulation point location calculated in step 3 of the typical rainstorm for early warning and forecasting.

Preferably, the step 6 is further included, making a forecast according to the measured data of the arranged micro-weather station and the liquid level monitoring device and/or the calculated data based on the measured data.

Preferably, the measured data includes the water depth of the rainwater tube well and the expected duration of rainfall, and the calculated data from the measured data includes the water level rise rate.

Preferably, the rain pattern parameters of a typical rainstorm include: return period, total rainfall, average rainfall, and peak rainfall.

Preferably, the water accumulation situation includes: the theoretical water accumulation occurrence time, the accumulation water duration, the accumulation water depth, and the accumulation water range of each accumulation water position point.

Preferably, it also includes step 7, setting the on-site rainwater tube well water depth threshold, water level rise threshold, and early warning and forecast time. Based on the measured data, the early warning and forecast will be carried out according to the early warning and forecast time.

Preferably, step 8 is also included, if the measured water depth value of the rainwater pipe well or the water level rise rate _V1 is greater than the corresponding threshold value, then start the measures of pushing the early warning information and eliminating the accumulated water.

Preferably, the water depth threshold of the rainwater tube well is a distance between the water level and the manhole cover between 0.3 and 1.0m, and the water level rise rate threshold is a dynamic change value, which is (threshold value of the water depth of the rainwater tube well)/(predicted rainfall duration).

Preferably, the content of the early warning and forecast includes the coordinates, range, water accumulation time and water accumulation duration of the forecast area.

Preferably, the output value form of the waterlogging numerical analysis model is a CAD cloud map of the water accumulation area and a depth table of the water accumulation point.

The present invention at least includes the following beneficial effects:

First, based on numerical simulation (that is, inputting the rain pattern parameters of each typical rainstorm into the numerical analysis model of waterlogging and waterlogging for calculation), the database information of the target area is formed. Taking the rain pattern parameters of each typical rainstorm in the target area as the input condition, first simulate the water accumulation situation in the target area under different rainstorm conditions. The big data of accumulated water, instead of the real-time numerical simulation of the current rainfall, saves the installation of numerical simulation software in the server and reduces the performance requirements for the server. However, if the current conditions of the urban area change, such as the expansion of the pipeline network, only the numerical simulation database needs to be updated to ensure the accuracy of the prediction results. Therefore, the road water accumulation prediction method in this study has important practical engineering significance.

Second, the formation of theoretical calculations and measured data, including weather, water level, time and other multi-factor composite early warning and forecasting methods. The typical rainstorm similar to the local rainfall is selected by comparing the on-site measured and forecasted meteorological data, and then the local rainfall is forecasted based on the numerical simulation results of the typical rainstorm, and combined with the monitoring information of the rainwater pipe network, the real-time monitoring data is calculated and analyzed. Analysis, fully consider the combination of multiple factors, and improve the accuracy of early warning.

Third, the forecast time is accurate to the minute, and the accuracy is accurate to the centimeter level, which improves the accuracy and timeliness of the municipal road water forecast in the case of rainfall.

Other advantages, objectives and features of the present invention will partly be embodied through the following descriptions, and partly will be understood by those skilled in the art through the study and practice of the present invention.

Description of drawings

Fig. 1 is the overall flow chart of the early warning and forecast of one of the technical solutions of the present invention;

Fig. 2 is the BIM model diagram of the target area A of the present invention;

Fig. 3 is the rainwater pipe network figure of target area A of the present invention;

Fig. 4 is the road accumulation water cloud diagram under the 100-year rainfall intensity in the target area of the present invention.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

It should be noted that the experimental methods described in the following embodiments, unless otherwise specified, are conventional methods, and the reagents and materials, if not otherwise specified, can be obtained from commercial sources; in the description of the present invention, The orientation or positional relationship indicated by the term is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the referred device or element must have a specific orientation, use a specific Azimuth configuration and operation, therefore, should not be construed as limiting the invention.

As shown in Figures 1 to 4, the present invention provides a multi-factor composite early warning and forecasting method for municipal road water accumulation, comprising the following steps:

Step 1. Obtain engineering design data of the target area and a BIM model based on the engineering design data, wherein the engineering design data includes at least terrain data, rainwater pipe network data, and historical meteorological data of the target area;

Step 2. Select the waterlogging analysis software system, input the engineering design data obtained in step 1 and the corresponding BIM model to the waterlogging analysis software system, and establish the waterlogging numerical analysis model of the target area;

Step 3: Input the rain pattern parameters of typical rainstorms into the numerical analysis model of waterlogging and waterlogging, and output the waterlogging situation and the location of waterlogging points under typical rainstorms;

Step 4. Arrange micro-weather stations and liquid level monitoring devices at the water accumulation points calculated in step 3 to obtain on-site measured data;

Step 5. Compare the measured data and/or the data calculated based on the measured data and the current forecast meteorological data with the rain pattern parameters of each typical rainstorm, and select a typical rainstorm whose rain pattern parameters are closest to the measured data and the current forecast meteorological data. Rainstorm, and call the water accumulation situation and water accumulation point location calculated in step 3 of the typical rainstorm for early warning and forecasting.

In the above technical solution, the existing waterlogging analysis software system is fully utilized, and the database information of the target area is formed based on numerical simulation (that is, inputting various rain pattern parameters into the waterlogging numerical analysis model for calculation). Specifically, taking the rain pattern parameters of each typical rainstorm in the target area as the input condition, first simulate the water accumulation situation in the target area under different rainstorm conditions. The big data of road water accumulation, instead of the real-time numerical simulation of the current rainfall, saves the installation of numerical simulation software in the server and reduces the performance requirements for the server. However, if the current conditions of the urban area change, such as the expansion of the pipeline network, only the numerical simulation database needs to be updated to ensure the accuracy of the prediction results. Therefore, the road water accumulation prediction method in this study has important practical engineering significance.

When predicting a certain current rain, it is not simply based on weather forecast data, nor is it purely selected empirical data, but to obtain the position of the water accumulation point based on the previous data simulation, only need to calculate the water accumulation point position, and Not all rainwater well pipes, deploy on-site monitoring devices, obtain measured data, and combine the measured data and weather forecast data to screen and match rain patterns to form theoretical calculations and measured data, including weather, water level, time and other multi-factor composite early warning and forecasting methods . The typical rainstorm similar to the local rainfall is selected by comparing the on-site measured and forecasted meteorological data, and then the local rainfall is forecasted based on the numerical simulation results of the typical rainstorm, and combined with the monitoring information of the rainwater pipe network, the real-time monitoring data is calculated and analyzed. Analysis, fully consider the combination of multiple factors, and improve the accuracy of early warning.

In another technical solution, it also includes step 6, making a forecast according to the measured data of the arranged micro-weather station and the liquid level monitoring device and/or the calculated data based on the measured data. Improve the scope and accuracy of early warning and forecasting.

In another technical solution, the content of the early warning and forecast also includes measured data, the measured data includes the water depth of the rainwater tube well and the expected duration of rainfall, and the calculated data includes the water level rise rate.

In another technical solution, the rain pattern parameters of a typical rainstorm include: return period, total rainfall, average rainfall, and peak rainfall. Using the above theoretical parameters can accurately reflect the nature of each rain event, which is helpful for the analysis of accumulated water.

In another technical solution, the water accumulation situation includes: the theoretical water accumulation occurrence time, the accumulation water duration, the accumulation water depth, and the accumulation water range of each accumulation water position point. It can describe the accumulation of water in a more three-dimensional and detailed manner, and can accurately forecast it.

In another technical solution, it also includes step seven, setting the on-site rainwater pipe well water depth threshold, water level rise threshold, early warning and forecast time, if the on-site rainwater pipe well water depth value or water level rise speed value in the measured data is greater than the set When the threshold is reached, the actual measured data shall prevail, and the early warning and forecast shall be carried out according to the early warning and forecast time, and the feedback shall be given. First set the threshold, and then continuously feed back the numerical analysis model of waterlogging and waterlogging through the on-site measured data, and continuously revise the original set value, thereby continuously improving the prediction accuracy of the numerical analysis model of waterlogging and waterlogging.

In another technical solution, step 8 is also included: if the measured water depth value or water level rise rate of the rainwater pipe well is greater than the corresponding threshold value set, then start the measures of pushing the early warning information and eliminating the accumulated water. Timely early warning can be carried out, and drainage measures can be taken in time to avoid the occurrence of waterlogging.

In another technical solution, the water depth threshold of the rainwater pipe well is the distance between the water level and the manhole cover is between 0.3 and 1.0m, and the water level rise rate threshold is a dynamic change value, which is (threshold water depth of the rainwater pipe well)/(predicted rainfall duration). The above threshold as one of the basis for judging whether flooding occurs will enhance the accuracy of system early warning and forecasting.

In another technical solution, the content of the early warning and forecast includes the coordinates, range, water accumulation time and water accumulation duration of the forecast area. Make the early warning and forecast position more accurate.

In another technical scheme, the output value form of the numerical analysis model of waterlogging and waterlogging is CAD waterlogging area cloud map and waterlogging point depth table. The output content is more intuitive and visible for easy viewing.

Taking the target area A as an example, the specific implementation steps of the present invention are set forth below:

Step 1. Collect the design data of the municipal road engineering where the project is located, including BIM model, terrain data, rainwater pipe network data and local historical meteorological data and other related data; the BIM model mainly refers to the digital design model, see Figure 2; terrain data includes elevation , terrain and features, etc.; rainwater pipe network data includes pipe diameter, buried depth, direction, and slope, see Figure 3, etc.; local meteorological data refers to rainfall statistics, temperature, wind speed and direction, etc.;

Step 2. Select Hongye's HYSWMM rainstorm simulation system, and use the engineering design data and the corresponding BIM model as input conditions to establish a numerical analysis model for waterlogging;

Step 3. Through numerical model simulation, that is, input the rain pattern parameters of typical rainstorms in the target area into the waterlogging numerical analysis model, and obtain the water accumulation situation and the location of the water accumulation points in the target area, that is, A area under various typical rainstorms estimate;

Among them, in Step 3, the rain pattern parameters of each typical rainstorm include theoretical parameters such as return period, total rainfall, average rainfall, and peak rainfall, as shown in Table 1. To improve the accuracy of early warning.

Table 1

return periodP	Total rainfall (mm)	Average rainfall (mm/min)	Peak rainfall (mm/min)
5a	91.209	0.760	2.570
10a	108.781	0.907	5.089
20a	128.591	1.072	6.085
30a	140.722	1.173	6.833
50a	156.415	1.303	8.156
100a	180.468	1.504	12.827

The numerical model simulates and calculates the theoretical ponding occurrence time, ponding duration, ponding depth and waterlogging range of each ponding location point under various theoretical return period rain patterns, as shown in Figure 4.

Step 4. Install devices such as micro-weather stations and rainwater tube well liquid level sensors on the spot at the water accumulation point calculated in step 3 to obtain measured data;

Step 5. Select a typical rainstorm similar to the local rainfall based on the actual measured data, the calculated data of the actual measured data, and the current forecast meteorological data, and then use the numerical simulation results of the typical rainstorm to predict the accumulation of water under the local rainfall. , for early warning and forecasting;

Step 6. In order to further improve the accuracy of early warning and forecasting, a comprehensive analysis is also carried out based on multiple factors such as the depth of the on-site rainwater pipe well, the rising speed of the water level, and the expected duration of rainfall, and finally an accurate prediction of whether the municipal roads will be flooded.

Step 7. Set the on-site rainwater pipe well water depth threshold, water level rise threshold and early warning and forecast time. If the local measured on-site rainwater pipe well water depth or water level rise speed value is greater than the set corresponding threshold, feedback prediction is performed.

At the same time, it is necessary to compare the actual rainfall duration with the theoretical accumulation time in the numerical model calculation to further improve the accuracy of early warning.

Step 8: If the measured local water depth of the on-site rainwater pipe well or the water level rise rate is greater than the corresponding threshold value set, start the push of early warning information and the measures to eliminate the accumulated water.

Wherein, the water depth threshold of the rainwater pipe well is that the distance between the water level and the manhole cover ranges from 0.3 to 1.0 m, and the water level rise threshold is a dynamic change value, which is (threshold water depth of the rainwater pipe well)/(predicted rainfall duration).

The push of early warning information shall not be less than 30 minutes before the actual event, and measures to eliminate accumulated water include strong drainage station information linkage, etc.

Although the embodiment of the present invention has been disclosed as above, it is not limited to the use listed in the specification and implementation, it can be applied to various fields suitable for the present invention, and it can be easily understood by those skilled in the art Therefore, the invention is not limited to the specific details and examples shown and described herein without departing from the general concept defined by the claims and their equivalents.

Claims (10)

1. The multi-factor composite early warning and forecasting method for municipal road water accumulation is characterized in that it includes the following steps:

   Step 1. Obtain engineering design data of the target area and a BIM model based on the engineering design data, wherein the engineering design data includes at least terrain data, rainwater pipe network data, and historical meteorological data of the target area;

   Step 2. Select the waterlogging analysis software system, input the engineering design data obtained in step 1 and the corresponding BIM model to the waterlogging analysis software system, and establish the waterlogging numerical analysis model of the target area;

   Step 3: Input the rain pattern parameters of typical rainstorms into the numerical analysis model of waterlogging and waterlogging, and output the waterlogging conditions and locations of waterlogging points under each typical rainstorm;

   Step 4. Arrange micro-weather stations and liquid level monitoring devices at the water accumulation points calculated in step 3 to obtain on-site measured data;

   Step 5. Compare the measured data and/or the data calculated based on the measured data and the current forecast meteorological data with the rain pattern parameters of each typical rainstorm, and select a typical rainstorm whose rain pattern parameters are closest to the measured data and the current forecast meteorological data. Rainstorm, and call the water accumulation situation and water accumulation point location calculated in step 3 of the typical rainstorm for early warning and forecasting.
2. The multi-factor composite early warning and forecasting method for water accumulation on municipal roads according to claim 1, further comprising step 6, according to the measured data of the arranged micro-weather station and the liquid level monitoring device and/or after calculating based on the measured data data for early warning and forecasting.
3. The multi-factor compound early warning and forecasting method for water accumulation on municipal roads according to claim 2, wherein the measured data includes the water depth of rainwater tube wells and the expected duration of rainfall, and the calculated data from the measured data includes the water level rise rate.
4. The multi-factor composite early warning and forecasting method for water accumulation on municipal roads according to claim 1, wherein the rain pattern parameters of typical rainstorms include: return period, total rainfall, average rainfall, and peak rainfall.
5. The multi-factor composite early warning and forecasting method for water accumulation on municipal roads as claimed in claim 4, wherein the water accumulation conditions include: the theoretical accumulation water occurrence time, accumulation duration, accumulation depth, Water range.
6. The multi-factor composite early warning and forecasting method for municipal road water accumulation as claimed in claim 1, further comprising step seven, setting the on-site rainwater tube well water depth threshold, water level rising speed threshold, early warning and forecasting time, if the actual measured data When the water depth value or water level rise rate of the on-site rainwater pipe well is greater than the corresponding threshold value set, the actual measured data shall prevail, and the early warning forecast shall be carried out according to the early warning forecast time.
7. The multi-factor composite early warning and forecasting method for municipal road water accumulation according to claim 6, further comprising step 8, if the measured rainwater pipe well water depth or water level rise rate is greater than the corresponding threshold value set, then start the early warning Information push and measures to eliminate stagnant water.
8. The multi-factor composite early warning and forecasting method for water accumulation on municipal roads as claimed in claim 7, wherein the water depth threshold of the rainwater tube well is between 0.3 and 1.0m from the water level to the manhole cover, and the water level rising speed threshold is dynamically changed Value, the value is (rainwater tube well water depth threshold)/(predicted rainfall duration).
9. The multi-factor compound early warning and forecasting method for municipal road water accumulation according to claim 1, wherein the early warning and forecasting content includes the coordinates, range, water accumulation time and water accumulation duration of the forecast area.
10. The multi-factor composite early warning and forecasting method for water accumulation on municipal roads according to claim 1, wherein the output value form of the waterlogging numerical analysis model is a CAD water accumulation area cloud map and a water accumulation point depth table.

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