CN112329582A - Road ponding depth monitoring method and system based on big data analysis and mechanism model cooperation - Google Patents

Road ponding depth monitoring method and system based on big data analysis and mechanism model cooperation Download PDF

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CN112329582A
CN112329582A CN202011179110.0A CN202011179110A CN112329582A CN 112329582 A CN112329582 A CN 112329582A CN 202011179110 A CN202011179110 A CN 202011179110A CN 112329582 A CN112329582 A CN 112329582A
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area
depth
ponding
road ponding
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CN112329582B (en
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黄伟
赵亦欣
袁海波
邢镔
朱林全
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Chongqing Industrial Big Data Innovation Center Co ltd
Southwest University
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Abstract

The invention discloses a road ponding depth monitoring method based on big data analysis and mechanism model cooperation, which comprises the following steps: s1, collecting road ponding information; s2, identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth; and S3, outputting the road accumulated water depth. A road ponding depth monitoring system with big data analysis and mechanism model cooperation comprises a parameter acquisition module, a data processing module and an output module. The method can accurately calculate the maximum depth and forecast the maximum depth of the road ponding area, effectively prevent the situations of misinformation and misreport of the road ponding depth, make up the defects of other road ponding monitoring modes, and provide powerful reference for road drainage construction.

Description

Road ponding depth monitoring method and system based on big data analysis and mechanism model cooperation
Technical Field
The invention relates to the field of road monitoring, in particular to a road accumulated water depth monitoring method and system based on big data analysis and mechanism model cooperation.
Background
With the continuous promotion of urbanization construction in China, the urban climate and the subsurface conditions are obviously changed. Under the influence of construction lag of an urban drainage pipe network system and unreasonable planning of the drainage system, once severe weather such as typhoon, rainstorm and the like is met, accumulated water is formed on urban roads, and the traveling of urban residents is seriously influenced.
The existing urban road ponding monitoring modes mainly include equipment monitoring and data mining analysis. The equipment monitoring monitors the accumulated water on the road by deploying a water level sensor; the data mining analysis is used for processing the reason and the process of urban road formation and judging the possibility of road ponding generation, the road ponding depth and the like by combining the urban road surface elevation data, the road drainage pipe network and other information.
However, the equipment monitoring mode can only measure the depth of the accumulated water at the measuring position of the sensor, the maximum depth or the effective depth of the accumulated water area of the urban road is difficult to accurately measure, and false alarm and false report are easy to generate. The data mining analysis needs all high-precision digital elevation data of urban roads, urban drainage pipe networks and other information, and the problems of poor data acquisition, incomplete data, complex data, poor data updating and insufficient identification precision exist.
Disclosure of Invention
In view of the above, the present invention aims to overcome the defects in the prior art, and provide a road accumulated water depth monitoring method and system with the cooperation of big data analysis and mechanism model, which can accurately calculate the maximum depth and predict the maximum depth of the urban road accumulated water area, effectively prevent the occurrence of the situations of false and wrong reports of road accumulated water depth, make up for the defects of other road accumulated water monitoring modes, and provide a powerful reference for road drainage construction.
The invention discloses a road accumulated water depth monitoring method based on big data analysis and mechanism model cooperation, which comprises the following steps:
s1, collecting road ponding information; the road ponding information comprises rainfall, road single-point ponding depth and road ponding area images;
s2, identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth; the road ponding depth comprises the maximum depth and the maximum prediction depth of a road ponding area;
and S3, outputting the road accumulated water depth.
Further, in step S2, identifying the road ponding area image to obtain an actual area of the road ponding area, specifically including:
acquiring a road ponding area sample, and performing characteristic marking processing on the road ponding area sample to obtain a processed road ponding area sample;
inputting the processed road ponding area sample to a vector machine for training and learning to obtain a road ponding recognition model;
and identifying the road ponding area image by using the road ponding identification model to obtain the actual area of the road ponding area.
Further, the actual area S of the road ponding area is determined according to the following formula:
S＝k·Spixel；
Wherein k is a conversion coefficient of the actual area of the road waterlogging area; sPixelThe pixel area of the road water accumulation area in the image is shown; conversion coefficient of actual area of road water accumulation area
Figure BDA0002749593200000021
SReference objectReference 1 m.times.1 m inArea of pixels in the image.
Further, in step S2, the maximum depth of the road water accumulation region is determined according to the following formula:
Figure BDA0002749593200000022
wherein H (m) is the maximum depth of the road water accumulation area at the mth minute; h (m) is the road single-point water accumulation depth at the mth minute;
Figure BDA0002749593200000031
is an auxiliary parameter;
determining the maximum prediction depth of the road water accumulation area according to the following formula:
Figure BDA0002749593200000032
wherein H (M + M) is the maximum predicted depth of the road water accumulation area at the mth minute after M minutes;
Figure BDA0002749593200000033
and
Figure BDA0002749593200000034
are all auxiliary parameters; r (j) is the rainfall at the j minute; (m) is the actual area of the road waterlogging area in the mth minute;
determining the auxiliary parameter according to the following equation
Figure BDA0002749593200000035
And
Figure BDA0002749593200000036
Figure BDA0002749593200000037
wherein x is1iIs i is descendingAccumulating the measured rainfall after the rain begins; x is the number of2iCalculating the difference value between the rainfall starting time and the current time obtained by the ith calculation; x is the number of3iCalculating the actual area of the road waterlogged area for the ith time; y isiCalculating the product of the actual area of the road ponding area and the road single-point ponding depth for the ith time; n is the total number of measurements or calculations; i is 1,2, …, n.
A road ponding depth monitoring system with big data analysis and mechanism model cooperation comprises a parameter acquisition module, a data processing module and an output module;
the parameter acquisition module is used for acquiring road ponding information; the road ponding information comprises rainfall, road single-point ponding depth and road ponding area images;
the data processing module is used for processing the road ponding information to obtain the road ponding depth; the road ponding depth comprises the maximum depth and the maximum prediction depth of a road ponding area;
and the output module is used for outputting the road ponding depth.
Further, the parameter acquisition module comprises a tipping bucket type rainfall sensor, a camera and a pressure liquid level sensor;
the tipping bucket type rainfall sensor is used for measuring rainfall;
the camera is used for shooting road ponding area images;
and the pressure liquid level sensor is used for measuring the single-point accumulated water depth of the road.
Further, the data processing module comprises an embedded processing module and a remote data transmission module in communication connection with the embedded processing module;
the embedded processing module is used for identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth;
and the remote data transmission module is used for receiving the road ponding depth and sending rainfall forecast information to the embedded processing module.
Further, the embedded processing module communicates with the remote data transmission module through an NB-IoT communication module.
Further, the embedded processing module determines the maximum depth of the road ponding area by the following method:
Figure BDA0002749593200000041
wherein H (m) is the maximum depth of the road water accumulation area at the mth minute; h (m) is the road single-point water accumulation depth at the mth minute;
Figure BDA0002749593200000042
is an auxiliary parameter;
the embedded processing module determines the maximum prediction depth of the road ponding area by the following method:
Figure BDA0002749593200000043
wherein H (M + M) is the maximum predicted depth of the road water accumulation area at the mth minute after M minutes;
Figure BDA0002749593200000051
and
Figure BDA0002749593200000052
are all auxiliary parameters; r (j) is the rainfall at the j minute; (m) is the actual area of the road waterlogging area in the mth minute;
the embedded processing module determines the auxiliary parameter by the following method
Figure BDA0002749593200000053
And
Figure BDA0002749593200000054
Figure BDA0002749593200000055
wherein x is1iThe measured rainfall is accumulated after the beginning of rainfall for the ith time; x is the number of2iCalculating the difference value between the rainfall starting time and the current time obtained by the ith calculation; x is the number of3iCalculating the actual area of the road waterlogged area for the ith time; y isiCalculating the product of the actual area of the road ponding area and the road single-point ponding depth for the ith time; n is the total number of measurements or calculations; i is 1,2, …, n.
Further, the actual area s (m) of the road waterlogging area at the m-th minute is determined according to the following formula:
S(m)＝k·Spixel(m)；
Wherein k is a conversion coefficient of the actual area of the road waterlogging area; sPixel(m) is the pixel area of the road ponding region in the image at the mth minute; conversion coefficient of actual area of road water accumulation area
Figure BDA0002749593200000056
SReference objectThe area of a pixel in the image of a reference 1m × 1 m.
The invention has the beneficial effects that: according to the road accumulated water depth monitoring method and system based on the cooperation of the big data analysis and the mechanism model, the machine learning is used for carrying out big data analysis on the collected road accumulated water area image, the reliability of the obtained actual area of the road accumulated water area is guaranteed, the actual area of the road accumulated water area, the rainfall and the road single-point accumulated water depth are processed through the rigorous and scientific mechanism model, the maximum accumulated water depth and the predicted maximum depth of the road accumulated water area are accurately calculated, the situations of wrong report and wrong report of the road accumulated water depth are effectively prevented, the defects of other road accumulated water monitoring modes are overcome, and the powerful reference is provided for road drainage construction.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a schematic structural diagram of a road accumulated water monitoring terminal of the invention;
wherein, 1-support the lamp pole; 2-tipping bucket rainfall sensor; 3-road accumulated water monitoring terminal; 4-a camera; 5-water accumulation information display screen; 6-pressure liquid level sensor.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the road accumulated water depth monitoring method based on the cooperation of big data analysis and mechanism model, as shown in figure 1, comprises the following steps:
s1, collecting road ponding information; the road ponding information comprises rainfall, road single-point ponding depth and road ponding area images;
s2, identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth; the road ponding depth comprises the maximum depth and the maximum prediction depth of a road ponding area;
and S3, outputting the road accumulated water depth.
According to the method, the collected road ponding information data is subjected to big data analysis processing to obtain the data after analysis processing; and calculating the data after analysis and treatment by using a mechanism model so as to obtain the maximum depth and the maximum prediction depth of the road water accumulation area. The big data comprises structured data and unstructured data, and the structured data comprises rainfall and road single-point ponding depth; the unstructured data comprises a road ponding area image; the mechanism model includes a calculation formula of the maximum depth of the road water-collecting area and a calculation formula of the maximum predicted depth of the road water-collecting area.
In this embodiment, in step S2, identifying the road ponding area image to obtain an actual area of the road ponding area specifically includes:
acquiring an image of a road water accumulation area, and further acquiring a large number of positive samples (road water accumulation areas) and negative samples (road non-water accumulation areas), wherein the positive samples and the negative samples are obviously different in color characteristics and texture characteristics, and marking the color characteristics and the texture characteristics of the positive samples and the negative samples of the road water accumulation areas respectively to acquire processed road water accumulation positive area samples and processed road water accumulation negative area samples;
adopting a mode of combining HSV color space recognition and SVM texture feature recognition to recognize positive and negative samples of the processed road ponding area to obtain a road ponding recognition model; the HSV color space recognition and the SVM texture feature recognition adopt the prior art, and are not described again;
and identifying the road ponding area image by using the road ponding identification model to obtain the actual area of the road ponding area, thereby realizing the big data analysis based on the road ponding area image.
In this embodiment, the actual area S of the road waterlogged area is determined according to the following formula:
S＝k·Spixel；
Wherein k is a conversion coefficient of the actual area of the road waterlogging area; sPixelThe pixel area of the road water accumulation area in the image is shown; conversion coefficient of actual area of road water accumulation area
Figure BDA0002749593200000071
SReference objectThe area of a pixel in the image of a square reference object of size 1m x 1 m.
In this embodiment, in step S2, the maximum depth of the road water accumulation area is determined according to the following formula:
Figure BDA0002749593200000072
wherein H (m) is the road product at the m minuteMaximum depth of water zone; h (m) is the road single-point water accumulation depth at the mth minute;
Figure BDA0002749593200000073
is an auxiliary parameter;
determining the maximum prediction depth of the road water accumulation area according to the following formula:
Figure BDA0002749593200000074
wherein H (M + M) is the maximum predicted depth of the road water accumulation area at the mth minute after M minutes;
Figure BDA0002749593200000081
and
Figure BDA0002749593200000082
are all auxiliary parameters; r (j) is the rainfall at the j minute, wherein the rainfall at a certain moment between the M minute and the M + M minute is the predicted rainfall provided by the rainfall forecast; (m) is the actual area of the road waterlogging area in the mth minute; and the actual area S (m) of the road waterlogging area at the m minute is measured at the m minute.
Determining the auxiliary parameter according to the following equation
Figure BDA0002749593200000083
And
Figure BDA0002749593200000084
Figure BDA0002749593200000085
wherein x is1iThe rainfall is the cumulative rainfall measured after the beginning of the rainfall for the ith time, and the cumulative rainfall measured after the beginning of the rainfall is
Figure BDA0002749593200000086
t0Time of onset of rainfall, tcIs the current time; x is the number of2iCalculating the difference value between the rainfall starting time and the current time for the ith time, wherein the difference value between the rainfall starting time and the current time is t0-tc；x3iThe negative value of the actual area of the road waterlogging area of the current time obtained by the ith calculation is S (t)c) Corresponding to a negative value of-S (t)c) Said S (t)c) Is at tcMeasuring the actual area S of the road ponding area at any moment; y isiCalculating the product of the actual area of the road ponding area at the current time and the road single-point ponding depth for the ith time, wherein the product of the actual area of the road ponding area at the current time and the road single-point ponding depth is S (t)c)·h(tc)，h(tc) The road single-point water accumulation depth at the current time is obtained; n is the total number of measurements or calculations; 1,2, …, n; in order to ensure the effectiveness and accuracy of measurement or calculation, the value of n is not less than 4.
A road ponding depth monitoring system with big data analysis and mechanism model cooperation comprises a parameter acquisition module, a data processing module and an output module;
the parameter acquisition module is used for acquiring road ponding information; the road ponding information comprises rainfall, road single-point ponding depth and road ponding area images;
the data processing module is used for processing the road ponding information to obtain the road ponding depth; the road ponding depth comprises the maximum depth and the maximum prediction depth of a road ponding area;
the output module is used for outputting the road ponding depth; in this embodiment, as shown in fig. 2, output module is ponding information display screen 5, ponding information display screen 5 is an LED display screen, and its information that shows has 5 lines, and the content is from top to bottom in proper order: ponding prompt information, road single-point ponding depth, road ponding area maximum depth, road ponding area predicted maximum depth and local real-time.
In this embodiment, as shown in fig. 2, the parameter acquisition module includes a tipping bucket rainfall sensor 2, a camera 4 and a pressure liquid level sensor 6; the tipping bucket type rainfall sensor 2 and the camera 4 are respectively arranged on a first cantilever and a second cantilever which support the lamp pole 1, and the lengths of the first cantilever and the second cantilever are both 1 meter; the pressure liquid level sensor 6 is arranged on the road surface; support lamp pole 1 and be the equipment commonly used of road lighting, the height of supporting lamp pole 1 is in 8 ~ 12 meters within ranges, and it is installed on one side of the road.
The tipping bucket type rainfall sensor 2 is a pulse type rainfall sensor, is connected with the data processing module, and is used for measuring the rainfall of a road ponding road section and inputting the rainfall into the data processing module;
the camera 4 is connected with the data processing module and is used for monitoring the road ponding area, periodically recording a road ponding area video or shooting a road ponding area image and inputting the road ponding area image into the data processing module; the number of pixels of the camera 4 is 500 ten thousand, and the range can be expanded to 800 ten thousand or more; the recorded road ponding area video can be divided into a plurality of static images, and then the images are processed;
and the pressure liquid level sensor 6 is connected with the data processing module and used for measuring the road single-point ponding depth and inputting the road single-point ponding depth into the data processing module.
In this embodiment, the data processing module includes an embedded processing module and a remote data transmission module in communication connection with the embedded processing module;
as shown in fig. 2 and 3, the embedded processing module is a road waterlogging monitoring terminal 3, and the road waterlogging monitoring terminal 3 includes an embedded microcontroller and a plurality of communication interfaces arranged on the embedded microcontroller; the plurality of communication interfaces comprise a display screen interface, a power supply interface, a tipping bucket rainfall sensor interface, a pressure liquid level sensor interface, a camera interface and a USART serial interface; the embedded microcontroller has image processing functionality, which may be raspberry pi 3b, 3b +, 4b, or 4b +.
The road accumulated water monitoring terminal 3 receives rainfall input by the tipping bucket rainfall sensor 2 through a tipping bucket rainfall sensor interface; the road ponding monitoring terminal 3 receives road ponding area images input by the camera 4 through the camera interface; and the road accumulated water monitoring terminal 3 receives the road single-point accumulated water depth input by the pressure liquid level sensor 6 through the pressure liquid level sensor interface.
The road accumulated water monitoring terminal 3 receives power supply of an external power supply through the power supply interface, wherein the external power supply is a commercial power 220V alternating current power supply; and the road accumulated water monitoring terminal 3 inputs the road accumulated water depth into an accumulated water information display screen 5 through the display screen interface.
The road ponding monitoring terminal 3 is used for identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth;
the remote data transmission module is a cloud service platform, and the cloud service platform is used for receiving the road accumulated water depth input by the road accumulated water monitoring terminal 3 and sending rainfall forecast information to the road accumulated water monitoring terminal 3; the rainfall forecast information comprises rainfall of the location of the road ponding area; the cloud service platform adopts the prior art, and is not described herein again.
Road ponding monitor terminal 3 and ponding information display screen 5 all installs on supporting lamp pole 1.
In this embodiment, the road waterlogging monitoring terminal 3 communicates with the cloud service platform through an NB-IoT communication module. The NB-IoT communication module is accessed to the road ponding monitoring terminal 3 through a USART serial interface; the NB-IoT communication module is a Narrow-Band Internet of Things (NB-IoT), and supports more accessed devices, lower functional loss and stronger signal coverage capability, so that stability and reliability of information transmission between the road ponding monitoring terminal 3 and the cloud service platform are ensured. The NB-IoT communication module adopts the prior art, and is not described herein again.
In this embodiment, the road ponding monitoring terminal 3 determines the maximum depth of the road ponding area by the following method:
Figure BDA0002749593200000101
wherein H (m) is the maximum depth of the road water accumulation area at the mth minute; h (m) is the road single-point water accumulation depth at the mth minute;
Figure BDA0002749593200000111
is an auxiliary parameter;
the road ponding monitoring terminal 3 determines the maximum predicted depth of the road ponding area by the following method:
Figure BDA0002749593200000112
wherein H (M + M) is the maximum predicted depth of the road water accumulation area at the mth minute after M minutes;
Figure BDA0002749593200000113
and
Figure BDA0002749593200000114
are all auxiliary parameters; r (j) is the rainfall at the j minute, wherein the rainfall at a certain moment between the M minute and the M + M minute is the predicted rainfall provided by the rainfall forecast; (m) is the actual area of the road waterlogging area in the mth minute;
the road accumulated water monitoring terminal 3 determines the auxiliary parameters by the following method
Figure BDA0002749593200000115
And
Figure BDA0002749593200000116
Figure BDA0002749593200000117
wherein x is1iThe rainfall is the cumulative rainfall measured after the beginning of the rainfall for the ith time, and the cumulative rainfall measured after the beginning of the rainfall is
Figure BDA0002749593200000118
t0Time of onset of rainfall, tcThe rainfall is measured by the tipping bucket rainfall sensor 2 at the current time; x is the number of2iCalculating the difference value between the rainfall starting time and the current time for the ith time, wherein the difference value between the rainfall starting time and the current time is t0-tc；x3iThe negative value of the actual area of the road waterlogging area of the current time obtained by the ith calculation is S (t)c) Corresponding to a negative value of-S (t)c) Said S (t)c) Is at tcMeasuring the actual area S of the road ponding area at any moment; y isiCalculating the product of the actual area of the road ponding area at the current time and the road single-point ponding depth for the ith time, wherein the product of the actual area of the road ponding area at the current time and the road single-point ponding depth is S (t)c)·h(tc)，h(tc) The road single-point water accumulation depth at the current time, h (t)c) Is at tcThe time is measured by a pressure liquid level sensor 6; n is the total number of measurements or calculations; 1,2, …, n; in order to ensure the effectiveness and accuracy of measurement or calculation, the value of n is not less than 4.
In the embodiment, the actual area s (m) of the road waterlogged area at the mth minute is determined according to the following formula:
S(m)＝k·Spixel(m)；
Wherein k is a conversion coefficient of the actual area of the road waterlogging area; sPixel(m) is the pixel area of the road ponding region in the image at the mth minute; conversion coefficient of actual area of road water accumulation area
Figure BDA0002749593200000121
SReference objectThe area of a pixel in the image of a square reference object of size 1m x 1 m.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (10)

1. A road accumulated water depth monitoring method based on big data analysis and mechanism model cooperation is characterized in that: the method comprises the following steps:
s1, collecting road ponding information; the road ponding information comprises rainfall, road single-point ponding depth and road ponding area images;
s2, identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth; the road ponding depth comprises the maximum depth and the maximum prediction depth of a road ponding area;
and S3, outputting the road accumulated water depth.
2. The big data analysis and mechanism model coordinated road ponding depth monitoring method according to claim 1, characterized in that: in step S2, identifying the road ponding region image to obtain an actual area of the road ponding region, specifically including:
acquiring a road ponding area sample, and performing characteristic marking processing on the road ponding area sample to obtain a processed road ponding area sample;
inputting the processed road ponding area sample to a vector machine for training and learning to obtain a road ponding recognition model;
and identifying the road ponding area image by using the road ponding identification model to obtain the actual area of the road ponding area.
3. The big data analysis and mechanism model coordinated road ponding depth monitoring method according to claim 2, characterized in that: determining the actual area S of the road ponding area according to the following formula:
S＝k·Spixel；
Wherein k is a conversion coefficient of the actual area of the road waterlogging area; sPixelThe pixel area of the road water accumulation area in the image is shown; conversion coefficient of actual area of road water accumulation area
Figure FDA0002749593190000011
SReference objectThe area of a pixel in the image of a reference 1m × 1 m.
4. The big data analysis and mechanism model coordinated road ponding depth monitoring method according to claim 1, characterized in that: in step S2, the maximum depth of the road water accumulation region is determined according to the following formula:
Figure FDA0002749593190000021
wherein H (m) is the maximum depth of the road water accumulation area at the mth minute; h (m) is the road single-point water accumulation depth at the mth minute;
Figure FDA0002749593190000022
is an auxiliary parameter;
determining the maximum prediction depth of the road water accumulation area according to the following formula:
Figure FDA0002749593190000023
wherein, H (M + M)) The maximum prediction depth of the road water accumulation area after M minutes at the mth minute is obtained;
Figure FDA0002749593190000024
and
Figure FDA0002749593190000025
are all auxiliary parameters; r (j) is the rainfall at the j minute; (m) is the actual area of the road waterlogging area in the mth minute;
determining the auxiliary parameter according to the following equation
Figure FDA0002749593190000026
And
Figure FDA0002749593190000027
Figure FDA0002749593190000028
wherein x is1iThe measured rainfall is accumulated after the beginning of rainfall for the ith time; x is the number of2iCalculating the difference value between the rainfall starting time and the current time obtained by the ith calculation; x is the number of3iCalculating the actual area of the road waterlogged area for the ith time; y isiCalculating the product of the actual area of the road ponding area and the road single-point ponding depth for the ith time; n is the total number of measurements or calculations; i is 1,2, …, n.
5. The utility model provides a road ponding degree of depth monitoring system that big data analysis and mechanism model are collaborative which characterized in that: the device comprises a parameter acquisition module, a data processing module and an output module;
the parameter acquisition module is used for acquiring road ponding information; the road ponding information comprises rainfall, road single-point ponding depth and road ponding area images;
the data processing module is used for processing the road ponding information to obtain the road ponding depth; the road ponding depth comprises the maximum depth and the maximum prediction depth of a road ponding area;
and the output module is used for outputting the road ponding depth.
6. The big data analysis and mechanism model coordinated roadway water accumulation depth monitoring system according to claim 5, wherein: the parameter acquisition module comprises a tipping bucket type rainfall sensor, a camera and a pressure liquid level sensor;
the tipping bucket type rainfall sensor is used for measuring rainfall;
the camera is used for shooting road ponding area images;
and the pressure liquid level sensor is used for measuring the single-point accumulated water depth of the road.
7. The big data analysis and mechanism model coordinated roadway water accumulation depth monitoring system according to claim 5, wherein: the data processing module comprises an embedded processing module and a remote data transmission module in communication connection with the embedded processing module;
the embedded processing module is used for identifying the road ponding area image to obtain the actual area of the road ponding area, and processing the rainfall, the road single-point ponding depth and the actual area of the road ponding area to obtain the road ponding depth;
and the remote data transmission module is used for receiving the road ponding depth and sending rainfall forecast information to the embedded processing module.
8. The big data analysis and mechanism model coordinated roadway water depth monitoring system according to claim 7, wherein: the embedded processing module communicates with the remote data transmission module through an NB-IoT communication module.
9. The big data analysis and mechanism model coordinated roadway water depth monitoring system according to claim 7, wherein: the embedded processing module determines the maximum depth of the road water accumulation area by the following method:
Figure FDA0002749593190000041
wherein H (m) is the maximum depth of the road water accumulation area at the mth minute; h (m) is the road single-point water accumulation depth at the mth minute;
Figure FDA0002749593190000042
is an auxiliary parameter;
the embedded processing module determines the maximum prediction depth of the road ponding area by the following method:
Figure FDA0002749593190000043
wherein H (M + M) is the maximum predicted depth of the road water accumulation area at the mth minute after M minutes;
Figure FDA0002749593190000044
and
Figure FDA0002749593190000045
are all auxiliary parameters; r (j) is the rainfall at the j minute; (m) is the actual area of the road waterlogging area in the mth minute;
the embedded processing module determines the auxiliary parameter by the following method
Figure FDA0002749593190000046
And
Figure FDA0002749593190000047
Figure FDA0002749593190000048
wherein x is1iThe measured rainfall is accumulated after the beginning of rainfall for the ith time; x is the number of2iCalculating the difference value between the rainfall starting time and the current time obtained by the ith calculation; x is the number of3iCalculating the actual area of the road waterlogged area for the ith time; y isiCalculating the product of the actual area of the road ponding area and the road single-point ponding depth for the ith time; n is the total number of measurements or calculations; i is 1,2, …, n.
10. The big data analysis and mechanism model coordinated roadway water depth monitoring system according to claim 9, wherein: determining the actual area S (m) of the road waterlogging area at the m minute according to the following formula:
S(m)＝k·Spixel(m)；
Wherein k is a conversion coefficient of the actual area of the road waterlogging area; sPixel(m) is the pixel area of the road ponding region in the image at the mth minute; conversion coefficient of actual area of road water accumulation area
Figure FDA0002749593190000051
SReference objectThe area of a pixel in the image of a reference 1m × 1 m.
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