Road icing real-time early warning system based on big data

Abstract

The invention discloses a road icing real-time early warning system based on big data, which comprises a data acquisition module, a processing module, an image recognition and comparison module and a prediction module, and is characterized in that the data acquisition module comprises a real-time monitoring module and a historical data acquisition module; the data acquisition module is the basis of the processing module and is used for acquiring real-time monitoring data and historical data; the image recognition and comparison module is used for recognizing a specific moving target in the shot road picture. The invention belongs to the technical field of road icing real-time early warning, and aims to solve the problems that in the prior art, whether a whole road is iced or not can only be roughly predicted, and the pavement area in a specified range on the road cannot be accurately predicted. The method has the technical effects that the road icing rate of the road surface area in the specified range on the road can be calculated, and the deviation between the prediction result and the actual result is reduced in the range of the road icing threshold value F in historical data.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time warning of road icing, and in particular to a real-time warning system for road icing based on big data. Background Art

[0002] Road icing in meteorology refers to the accumulation of snow or ice caused by rain, snow, sleet or fog falling on the ground at a temperature below 0℃. At the same time, humidity and wind speed are also the main factors affecting road icing. Under high humidity conditions, water vapor in the air is more likely to condense on the road surface and form an ice layer. When the relative humidity approaches or reaches 100%, the possibility of road icing increases greatly. At the same time, the increase in wind speed will accelerate the heat loss on the road surface and promote the icing process. Road icing will greatly reduce the friction coefficient between the vehicle tires and the road surface, affecting the normal driving of the vehicle, and it is easy for the vehicle to slip, causing road traffic accidents. The brightness is lowest when the road surface is dry and the color is gray. When it is frozen, the road surface becomes gray or transparent. According to statistics, traffic accidents caused by road icing account for 35% of the total traffic accidents in winter. Therefore, a real-time road icing warning system is needed to predict road icing conditions and reduce the occurrence of traffic accidents.

[0003] Currently, most real-time warning systems for road icing only use a single meteorological sensor to collect road condition data and meteorological information to roughly predict whether the entire road is icy. They are unable to accurately predict the road surface area within a specified range on the road, resulting in the inability to guarantee the accuracy of the final prediction. Summary of the invention

[0004] To this end, the present invention provides a real-time road icing warning system based on big data to solve the above-mentioned problems in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] According to a first aspect of the present invention, a real-time road icing warning system based on big data comprises:

[0007] A data acquisition module, a processing module, an image recognition and comparison module, and a prediction module, wherein the data acquisition module includes a real-time monitoring module and a historical data acquisition module;

[0008] The data acquisition module is the basis of the processing module and is used to obtain real-time monitoring data and historical data;

[0009] The image recognition and comparison module is used to identify specific moving targets in the road pictures taken, and can further extract depth information. It can compare images taken at different times, different perspectives or under different conditions to find the same or similar images;

[0010] The specific implementation steps of the real-time road icing warning system include the following steps:

[0011] Step 1: The real-time monitoring module collects the temperature and humidity condition information, road surface wind speed information, road surface pictures, and air pressure data of the predicted road surface at each time period, obtains the temperature and humidity condition information and road surface wind speed information data at each time period and makes them into curves, and based on the big data historical data, the historical data acquisition module obtains the historical data and gets the historical data curve;

[0012] Step 2: Both the real-time monitoring information and the historical data are transmitted to the processing module. The processing module analyzes and compares the historical data curve and the curve made in Step 1, determines the road icing threshold F in the historical data and the corresponding countermeasures for the road icing threshold F, predicts the relationship between the road condition and road icing, and constructs a road icing calculation model;

[0013] The calculation formula of the road icing calculation model is:

[0014]

[0015] Among them, A is the road icing rate, C is the precipitation within the time period, V is the density of moist air, is a fixed coefficient value (J / kg), R is the number of road base layers (R≥0), r is the number of road surface layers (r≥1), and D is the monitored road surface area;

[0016] The prediction module compares the road icing rate A calculated by the road icing calculation model with the road icing threshold F. If F + 1 > A > F, it is judged that the road icing rate A is a moderate icing warning. If A≥F + 1, it is judged that the road icing rate A is a severe icing warning. If A≤F, it is judged that the road icing rate A is a severe icing warning.

[0017] Furthermore, the processing module screens the road surface picture data taken by the camera at each time period, uses the Gaussian mixture model to extract and delete the moving targets, retains the road surface pictures containing the complete road surface and transmits them to the image recognition and comparison module. The image recognition and comparison module selects the road surface pictures with relatively clear image saturation and hue at each time period, and through processing operations such as parameter calculation, parameter image reconstruction, difference, and filtering, and performs binary classification to determine the threshold F1 for image comparison.

[0018] Furthermore, when the brightness of the real-time image obtained in the processing module < F1, it is predicted that the road surface in this time period is a dry road surface and the picture is deleted. When the brightness of the real-time image ≥ F1, it is predicted that the road surface in this time period is an icing road surface, and the road icing calculation model is used to calculate the road surface icing situation, which is convenient to improve the accuracy of the prediction result and provide timely and accurate road condition information.

[0019] Furthermore, the prediction module releases warning information through broadcasting, the Internet, and mobile phone APP channels to remind drivers and relevant departments to ensure the safety of travel in icy weather to the greatest extent.

[0020] Furthermore, the calculation formula of the humid air density V is:

[0021]

[0022] Where P is the total pressure during the time period, T is the Celsius temperature +273.15 during the time period, and K is the relative humidity during the time period.

[0023] Furthermore, the processing module is used to extract, convert, calculate and analyze valuable data information from a large amount of raw data, and can also compare the historical data curve with the curve prepared in step one.

[0024] The present invention has the following advantages:

[0025] 1. Through the real-time monitoring module and the image recognition and comparison module, the temperature and humidity module, the wind speed sensor module, the camera and the atmospheric pressure sensor module can respectively collect the temperature and humidity information of the road surface in each time period, the road surface wind speed information, the road surface picture and the air pressure data, so as to realize the real-time monitoring and early warning of the road icing situation and provide accurate road condition information;

[0026] 2. Through the road icing calculation model, the road icing rate of the road surface area within a specified range on the road can be calculated, the accuracy of the overall prediction can be improved, and the deviation between the predicted results and the actual results can be reduced within the range of the road icing threshold F in the historical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a real-time road icing warning system based on big data is provided for some embodiments of the present invention. DETAILED DESCRIPTION

[0028] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Example 1

[0030] like Figure 1As shown, a road icing real-time warning system based on big data in the first aspect of the present invention includes: a data acquisition module, a processing module, an image recognition and comparison module and a prediction module, the data acquisition module includes a real-time monitoring module and a historical data acquisition module, and the modules are connected through electrical signals;

[0031] The data acquisition module is the basis of the processing module and is used to obtain real-time monitoring data and historical data;

[0032] The processing module is used to extract, convert, calculate and analyze valuable data information from a large amount of raw data, and can also compare the historical data curve with the curve made in step 1;

[0033] The image recognition and comparison module is used to identify specific moving targets in the road pictures taken, and can further extract depth information. It can compare images taken at different times, different perspectives or under different conditions to find the same or similar images;

[0034] The specific implementation steps of the road icing real-time warning system include the following steps:

[0035] Step 1: respectively collect the temperature and humidity condition information of the predicted road surface in each time period, the road surface wind speed information, the road surface picture and the air pressure data through the temperature and humidity module, the wind speed sensor module, the camera and the atmospheric pressure sensor module, obtain the temperature and humidity condition information and the road surface wind speed information data in each time period and make a curve, and obtain the historical data through the historical data acquisition module based on the big data historical data to obtain the historical data curve;

[0036] Step 2: The real-time monitoring information and historical data are transmitted to the processing module, which analyzes and compares the historical data curve and the curve prepared in step 1, determines the road icing threshold F in the historical data, and the corresponding countermeasures of the road icing threshold F, predicts the relationship between road conditions and road icing, and constructs a road icing calculation model;

[0037] S1. The calculation formula of the road icing calculation model is:

[0038]

[0039] Among them, A is the road icing rate, C is the precipitation in the time period, V is the density of moist air, 3.34*10 5 is a fixed coefficient value (J / kg), R is the number of road subgrade layers (R≥0), r is the number of road pavement layers (r≥1), and D is the monitored pavement area;

[0040] S2. The calculation formula of moist air density V is:

[0041]

[0042] Wherein, P is the total pressure within the time period, T is the Celsius temperature within the time period plus 273.15, and K is the relative humidity within the time period;

[0043] Step Three: The processing module screens the road surface picture data captured by the camera in each time period, uses the Gaussian mixture model to extract and delete moving targets, retains the road surface pictures containing the complete road surface, and transmits them to the image recognition and comparison module. The image recognition and comparison module selects the road surface pictures with relatively clear image saturation and hue in each time period, and performs processing operations such as parameter calculation, parameter image reconstruction, difference, and filtering, and conducts binary classification to determine the threshold F1 for image comparison. When the brightness of the acquired real-time image < F1, it is predicted that the road surface in this time period is a dry road surface, and the picture is deleted. When the real-time image brightness ≥ F1, it is predicted that the road surface in this time period is an icy road surface, and the road icing condition of the road surface is calculated through the road icing calculation model to improve the accuracy of the prediction results and provide timely and accurate road condition information;

[0044] Step Four: The prediction module compares the road icing rate A calculated by the road icing calculation model with the road icing threshold F. If F + 1 > A > F, it is determined that the road icing rate A is a moderate icing warning. If A ≥ F + 1, it is determined that the road icing rate A is a severe icing warning. If A ≤ F, it is determined that the road icing rate A is a severe icing warning;

[0045] Step Five: The warning information is released through channels such as radio, Internet, and mobile phone APP to remind drivers and relevant departments, and to ensure the safety of traveling on roads prone to icing to the greatest extent. By integrating modern sensor technology, data processing technology, and intelligent image recognition technology, real-time monitoring and warning of road icing conditions can be achieved, and accurate road condition information can be provided, thereby reducing traffic accidents caused by road icing.

Claims

1. A real-time road icing warning system based on big data, characterized in that: Including: A data acquisition module, a processing module, an image recognition and comparison module, and a prediction module. The data acquisition module includes a real-time monitoring module and a historical data acquisition module; The data acquisition module is the basis of the processing module and is used to acquire real-time monitoring data and historical data; The image recognition and comparison module is used to identify specific moving targets in the captured road pictures, and can further extract depth information. It can compare images taken at different times, from different perspectives, or under different conditions to find the same or similar images; The specific implementation steps of this real-time road icing warning system include the following steps: Step 1: The real-time monitoring module collects the temperature and humidity condition information, road surface wind speed information, road surface pictures, and air pressure data of the predicted road surface at each time period, obtains the temperature and humidity condition information and road surface wind speed information data at each time period and makes them into curves. Based on big data historical data, the historical data acquisition module obtains historical data and gets a historical data curve; Step 2: Both the real-time monitoring information and the historical data are transmitted to the processing module. The processing module analyzes and compares the historical data curve and the curve made in Step 1 to determine the road icing threshold F in the historical data and the corresponding countermeasures for the road icing threshold F, predicts the relationship between the road condition and road icing, and constructs a road icing calculation model; The calculation formula of the road icing calculation model is: Among them, A is the road icing rate, C is the precipitation within the time period, V is the density of moist air, is a fixed coefficient value (J / kg), R is the number of road base layers (R≥0), r is the number of road surface layers (r≥1), and D is the monitored road surface area; The prediction module compares the road icing rate A calculated by the road icing calculation model with the road icing threshold F. If F + 1 > A > F, it is judged that the road icing rate A is a moderate icing warning. If A≥F + 1, it is judged that the road icing rate A is a severe icing warning. If A≤F, it is judged that the road icing rate A is a severe icing warning.

2. The real-time road icing warning system based on big data according to claim 1 is characterized in that: The processing module screens the road surface picture data taken by the camera at each time period, uses the Gaussian mixture model to extract and delete moving targets, retains the road surface pictures containing the complete road surface and transmits them to the image recognition and comparison module. The image recognition and comparison module selects the road surface pictures with relatively clear image saturation and hue at each time period, and through processing operations such as parameter calculation, parametric image reconstruction, difference, and filtering, and performs binary classification to determine the threshold F1 for image comparison.

3. The real-time road icing warning system based on big data according to claim 2 is characterized in that: When the brightness of the real-time image obtained in the processing module <F1, it is predicted that the road surface in this time period is a dry road surface and the picture is deleted. When the brightness of the real-time image ≥F1, it is predicted that the road surface in this time period is an icing road surface, and the road icing calculation model is used to calculate the road surface icing situation, so as to improve the accuracy of the prediction result and provide timely and accurate road condition information.

4. The real-time road icing warning system based on big data according to claim 1 is characterized in that: The prediction module publishes warning information through channels such as radio, the Internet, and mobile phone APPs to remind drivers and relevant departments to ensure the safety of traveling on roads prone to icing to the greatest extent.

5. The real-time road icing warning system based on big data according to claim 1 is characterized in that: The calculation formula of the density V of moist air is: Where P is the total pressure during the time period, T is the Celsius temperature +273.15 during the time period, and K is the relative humidity during the time period.

6. The real-time road icing warning system based on big data according to claim 1 is characterized in that: The processing module is used to extract, convert, calculate and analyze valuable data information from a large amount of raw data, and can also compare the historical data curve with the curve made in step one.

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