1. A method for detecting the surface drainage capability of a large-structure asphalt wearing layer is characterized by comprising the following steps of:

obtaining vehicle motion information in rainy days according to the rainy day state information of the road section to be detected based on the rainy day vehicle state model;

obtaining vehicle motion information on a sunny day according to the sunny day state information of the road section to be detected on the basis of the sunny day vehicle state model;

and determining the drainage capacity of the road section to be tested according to the vehicle motion information in the rainy day and the vehicle motion information in the sunny day.

2. The method for detecting the surface drainage capability of the large-structure asphalt wearing layer according to claim 1, wherein the method for establishing the vehicle state model in rainy days comprises the following steps:

acquiring a rainy day test data set; the rainy day test data set comprises historical rainy day state information of a plurality of pairs of road sections to be detected and historical rainy day movement information of vehicles on the road sections to be detected;

and training an artificial neural network according to the rainy day test data set to obtain a rainy day vehicle state model.

3. The method for detecting the surface drainage capability of the large-structure asphalt wearing layer according to claim 2, wherein the historical rainy-day state information comprises wind speed, rainfall, flatness, rutting, surface diseases, longitudinal gradient, transverse gradient and route linear index information of a road section to be detected;

the historical rainy day movement information comprises the steering wheel rotation angle, the running speed, the running acceleration and the running angular speed of the vehicle on the road section to be detected.

4. The method for detecting the surface drainage capability of the large-structure asphalt wearing layer according to claim 1, wherein the method for establishing the vehicle state model in sunny days comprises the following steps:

acquiring a sunny test data set; the sunny test data set comprises historical sunny state information of a plurality of pairs of road sections to be detected and historical sunny motion information of vehicles on the road sections to be detected;

and training an artificial neural network according to the sunny test data set to obtain a sunny vehicle state model.

5. The method for detecting the surface drainage capability of the large-structure asphalt wearing layer according to claim 4, wherein the historical sunny state information comprises flatness, rutting, surface damage, longitudinal gradient, transverse gradient and route linearity index information of a road section to be detected;

the historical sunny motion information comprises the steering wheel rotation angle, the running speed, the running acceleration and the running angular speed of the vehicle on the road section to be tested.

6. The method for detecting the surface drainage capability of the large-structure asphalt wearing layer according to claim 1, wherein the determining the drainage capability of the road section to be detected according to the rainy-day vehicle motion information and the sunny-day vehicle motion information specifically comprises:

subtracting the data of the vehicle motion information in the rainy day from the data of the vehicle motion information in the sunny day to obtain difference data;

and determining the drainage capacity of the road section to be measured according to the difference data.

7. A large structure asphalt wearing layer surface drainage capability detection system is characterized by comprising:

the rainy-day vehicle motion information determining unit is used for obtaining rainy-day vehicle motion information according to the rainy-day state information of the road section to be detected based on the rainy-day vehicle state model;

the clear-sky vehicle motion information determining unit is used for obtaining clear-sky vehicle motion information according to the clear-sky state information of the road section to be detected based on the clear-sky vehicle state model;

and the drainage capacity determining unit is respectively connected with the rainy-day vehicle motion information determining unit and the sunny-day vehicle motion information determining unit and is used for determining the drainage capacity of the road section to be detected according to the rainy-day vehicle motion information and the sunny-day vehicle motion information.

8. The large scale asphalt wearing layer surface drainage ability detection system according to claim 7, wherein the drainage ability determination unit includes:

the computing module is respectively connected with the rainy vehicle motion information determining unit and the sunny vehicle motion information determining unit and is used for subtracting the data of the rainy vehicle motion information and the sunny vehicle motion information to obtain difference data;

and the drainage capacity determining module is connected with the computing unit and used for determining the drainage capacity of the road section to be measured according to the difference data.

Background

The traffic accident risk in rainy days is far greater than that in general weather, rainfall changes the road surface adhesion coefficient, rainfall on the road surface in rainy days is not timely removed through surface runoff, a water film is formed on the road surface, when the water film exceeds a certain thickness, water slip is easily formed, accidents such as automobile deviation or rollover, rear-end collision and the like occur, and the influence of the road surface drainage problem on the road surface anti-slip safety in driving is obvious. At present, a detection and evaluation method for the drainage capacity of a road surface mainly focuses on the research of the thickness of a water film of the road surface, and comprises the calculation of a theoretical water film thickness, the actual measurement of the water film thickness by a sensor and the like.

In the aspect of theoretical calculation research of water film thickness, a slope runoff model is typically established based on a constant total flow continuity equation, water flow of an isolation section is intercepted on the basis, a continuity equation and a momentum equation are derived, and a basic differential equation related to the road surface water film thickness is derived without considering rainwater infiltration loss. At present, researches on a water film thickness sensor are divided into two types, one is that microwave emission microwaves are reflected or absorbed when the microwaves touch an object reflector, the distance between an emission end and a receiving end is S, so that the intensity and the phase of the microwaves are changed, a receiving antenna receives the microwaves reflected by the emitted object and converts the microwaves into electric signals, and then the detection on the water film thickness D is realized; second, after the emitted light is refracted to the upper and lower surfaces of the water film, the change of the optical parameter detected by the receiving end realizes the detection of the thickness of the water film, such as the detection principle shown in fig. 4(a) and the detection structure diagram shown in fig. 4 (b). The roadside water film thickness sensor based on the optical measurement principle is non-invasive to install, is expensive and is easily influenced by severe weather. The microwave reflection water film thickness sensor is installed in an intrusive mode, the cost is relatively low, and the measurement error of the water film thickness at different environmental temperatures is shown in figure 5.

The method has great application difficulty for large asphalt wearing layer pavements, firstly, because the road conditions, weather and the like of operating expressways are complex, the theoretical assumption of the water film thickness has great deviation from the actual condition, and the result is difficult to be accurate; secondly, the macro structure of the surface of the large-structure asphalt wearing layer is large and uneven in distribution, the connectivity of the structure is complex and variable, the actual water film thickness difference of the road surface is very obvious, and the water film thickness in gullies and the water film thickness on the surface of raised crushed stones are difficult to distinguish by adopting an optical or microwave water film thickness sensor, so that the water film thickness result which really affects the driving safety is difficult to accurately reflect.

Disclosure of Invention

The invention aims to provide a method and a system for detecting the drainage capability of the surface of a large-structure asphalt wearing layer, which can accurately detect the drainage capability of the surface of the large-structure asphalt wearing layer.

In order to achieve the purpose, the invention provides the following scheme:

a method for detecting the surface drainage capability of a large-structure asphalt wearing layer comprises the following steps:

obtaining vehicle motion information in rainy days according to the rainy day state information of the road section to be detected based on the rainy day vehicle state model;

obtaining vehicle motion information on a sunny day according to the sunny day state information of the road section to be detected on the basis of the sunny day vehicle state model;

and determining the drainage capacity of the road section to be tested according to the vehicle motion information in the rainy day and the vehicle motion information in the sunny day.

Optionally, the method for establishing the vehicle state model in rainy days includes:

acquiring a rainy day test data set; the rainy day test data set comprises historical rainy day state information of a plurality of pairs of road sections to be detected and historical rainy day movement information of vehicles on the road sections to be detected;

and training an artificial neural network according to the rainy day test data set to obtain a rainy day vehicle state model.

Optionally, the historical rainy day state information includes wind speed, rainfall, flatness, rutting, surface damage, longitudinal gradient, transverse gradient and route linear index information of the road section to be detected;

the historical rainy day movement information comprises the steering wheel rotation angle, the running speed, the running acceleration and the running angular speed of the vehicle on the road section to be detected.

Optionally, the method for establishing a vehicle state model in sunny days includes:

acquiring a sunny test data set; the sunny test data set comprises historical sunny state information of a plurality of pairs of road sections to be detected and historical sunny motion information of vehicles on the road sections to be detected;

and training an artificial neural network according to the sunny test data set to obtain a sunny vehicle state model.

Optionally, the historical sunny-day state information includes flatness, ruts, surface diseases, longitudinal gradients, transverse gradients and route linear index information of the road section to be detected;

the historical sunny motion information comprises the steering wheel rotation angle, the running speed, the running acceleration and the running angular speed of the vehicle on the road section to be tested.

Optionally, the determining, according to the rainy vehicle motion information and the sunny vehicle motion information, a drainage capacity of the road section to be measured specifically includes:

subtracting the data of the vehicle motion information in the rainy day from the data of the vehicle motion information in the sunny day to obtain difference data;

and determining the drainage capacity of the road section to be measured according to the difference data.

In order to achieve the above purpose, the invention also provides the following scheme:

a big structure pitch wearing layer surface drainage ability detecting system, big structure pitch wearing layer surface drainage ability detecting system includes:

the rainy-day vehicle motion information determining unit is used for obtaining rainy-day vehicle motion information according to the rainy-day state information of the road section to be detected based on the rainy-day vehicle state model;

the clear-sky vehicle motion information determining unit is used for obtaining clear-sky vehicle motion information according to the clear-sky state information of the road section to be detected based on the clear-sky vehicle state model;

and the drainage capacity determining unit is respectively connected with the rainy-day vehicle motion information determining unit and the sunny-day vehicle motion information determining unit and is used for determining the drainage capacity of the road section to be detected according to the rainy-day vehicle motion information and the sunny-day vehicle motion information.

Optionally, the drainage capability determination unit includes:

the computing module is respectively connected with the rainy vehicle motion information determining unit and the sunny vehicle motion information determining unit and is used for subtracting the data of the rainy vehicle motion information and the sunny vehicle motion information to obtain difference data;

and the drainage capacity determining module is connected with the computing unit and used for determining the drainage capacity of the road section to be measured according to the difference data.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects: obtaining vehicle motion information in rainy days according to the rainy day state information of the road section to be detected based on the rainy day vehicle state model; obtaining vehicle motion information on a sunny day according to the sunny day state information of the road section to be detected on the basis of the sunny day vehicle state model; and determining the drainage capacity of the road section to be tested according to the vehicle motion information in rainy days and the vehicle motion information in sunny days. From the perspective of actual safety of vehicle running in rainy days, the stability of vehicle running in rainy days is monitored to evaluate the drainage capacity of the asphalt wearing layer, the correlation between the evaluation result and the running safety is high, and the evaluation result is reliable.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a flow chart of the method for detecting the surface drainage capacity of a large asphalt wearing layer according to the invention;

FIG. 2 is a block diagram of collecting vehicle movement information;

FIG. 3 is a block diagram of a system for testing the drainage ability of the surface of a large asphalt wearing layer according to the present invention;

FIG. 4(a) is a schematic diagram of water film thickness detection;

FIG. 4(b) is a view showing an internal detection structure of the water film thickness sensor;

FIG. 5 is a graph showing the measurement error of the thickness of the water film at different environmental temperatures.

Description of the symbols:

the device comprises a rainy vehicle motion information determining unit-1, a sunny vehicle motion information determining unit-2 and a drainage capacity determining unit-3.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The invention aims to provide a method and a system for detecting the surface drainage capability of a large-structure asphalt wearing layer, wherein rainy-day vehicle motion information is obtained according to rainy-day state information of a road section to be detected on the basis of a rainy-day vehicle state model; obtaining vehicle motion information on a sunny day according to the sunny day state information of the road section to be detected on the basis of the sunny day vehicle state model; and determining the drainage capacity of the road section to be tested according to the vehicle motion information in rainy days and the vehicle motion information in sunny days. From the perspective of actual safety of vehicle running in rainy days, the stability of vehicle running in rainy days is monitored to evaluate the drainage capacity of the asphalt wearing layer, the correlation between the evaluation result and the running safety is high, and the evaluation result is reliable.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

As shown in fig. 1, the method for detecting the surface drainage capability of the large-structure asphalt wearing layer of the invention comprises the following steps:

s1: and obtaining the vehicle motion information in the rainy day according to the rainy day state information of the road section to be detected based on the rainy day vehicle state model.

S2: and obtaining the vehicle motion information in the sunny day according to the sunny day state information of the road section to be detected based on the sunny day vehicle state model.

S3: and determining the drainage capacity of the road section to be tested according to the vehicle motion information in the rainy day and the vehicle motion information in the sunny day.

Specifically, the method for establishing the vehicle state model in rainy days comprises the following steps:

acquiring a rainy day test data set; the rainy day test data set comprises historical rainy day state information of a plurality of pairs of road sections to be detected and historical rainy day movement information of vehicles on the road sections to be detected. Preferably, under different rainfall and wind speed environments, the driving detection vehicle naturally runs on the asphalt wearing layer of the road section to be detected, and vehicle motion information in rainy days is obtained.

And training an artificial neural network according to the rainy day test data set to obtain a rainy day vehicle state model.

Preferably, the historical rainy day state information includes wind speed, rainfall, flatness, rutting, surface damage, longitudinal gradient, transverse gradient and route linear index information of the road section to be detected.

In this embodiment, the flatness, ruts, surface defects, longitudinal gradient, transverse gradient, and line shape index of the road segment to be detected are obtained by retrieving periodic detection reports of the asphalt wearing layer of the road segment to be detected.

The historical rainy day movement information comprises the steering wheel rotation angle, the running speed, the running acceleration and the running angular speed of the vehicle on the road section to be detected.

In the present embodiment, a Micro-Electro-mechanical system (MEMS) is adapted on a vehicle for detecting drainage capability of an asphalt wearing course, and a vehicle speed sensor, a wind speed sensor and a rain sensor are mounted on the vehicle.

The running state of the vehicle on the asphalt wearing layer is monitored in real time through the MEMS, and the running state comprises the steering wheel turning angle, the running acceleration and the running angular speed of the vehicle. The vehicle speed sensor monitors the speed of the passing vehicle in real time. And the wind speed sensor monitors the wind speed of the road section where the asphalt wearing layer is located in real time. And the rainfall sensor monitors the rainfall of the road section where the asphalt wearing layer is located in real time.

Preferably, as shown in fig. 2, in the present embodiment, the collection of the vehicle motion state information is implemented by using an acceleration sensor, an angular rate gyro and a micro inertial measurement unit in combination with the MEMS sensor. A micro-inertia measuring unit is designed by utilizing an MEMS (micro-electromechanical systems) inertia sensor, so that the models of a clutch pedal, an accelerator pedal and a brake pedal of a vehicle, front and back and left and right direction signals of a gear shift lever and a steering wheel turning speed signal are measured in real time, and the motion information of the vehicle is obtained.

The MEMS, the vehicle speed sensor, the wind speed sensor and the rainfall sensor are connected with the same computer to realize data sharing.

Further, the method for establishing the vehicle state model in sunny days comprises the following steps:

acquiring a sunny test data set; the sunny test data set comprises historical sunny state information of a plurality of pairs of road sections to be detected and historical sunny motion information of vehicles on the road sections to be detected.

And training an artificial neural network according to the sunny test data set to obtain a sunny vehicle state model.

In order to eliminate the influence of objective reasons such as a route and a road surface condition on the drainage condition of the wearing layer, the motion state of a vehicle on the asphalt wearing layer of the road section to be detected is monitored and tested under the dry environment state of the road surface in a sunny day, and a sunny vehicle state model is constructed.

In this embodiment, an MATLAB-equipped neural network toolbox is used for training both a sunny vehicle state model and a rainy vehicle state model, and guidance is adopted to import and export data, so as to automatically train the neural network model.

Preferably, the historical sunny-day state information includes flatness, ruts, surface diseases, longitudinal gradients, transverse gradients and route linear index information of the road section to be detected.

The historical sunny motion information comprises the steering wheel rotation angle, the running speed, the running acceleration and the running angular speed of the vehicle on the road section to be tested.

In order to eliminate the influence of the objective environmental factors on the vehicle motion state, S3: according to the rainy vehicle motion information and the sunny vehicle motion information, determining the drainage capacity of the road section to be detected, and specifically comprising the following steps:

and subtracting the data of the vehicle motion information in the rainy day from the data of the vehicle motion information in the sunny day to obtain difference data.

And determining the drainage capacity of the road section to be measured according to the difference data.

The method is characterized in that vehicle motion state indexes obtained in rainy days are respectively subtracted from vehicle motion state indexes obtained in sunny days, namely a steering wheel corner obtained in rainy days is subtracted from a steering wheel corner obtained in sunny days, an operation vehicle speed obtained in rainy days is subtracted from an operation vehicle speed obtained in sunny days, an operation acceleration obtained in rainy days is subtracted from an operation acceleration obtained in sunny days, an operation angular speed obtained in rainy days is subtracted from an operation angular speed obtained in sunny days, influences of objective environment influence factor variables such as routes and road surface conditions on the vehicle motion state are eliminated, a result of considering influences of the rainy days on the vehicle motion state on the asphalt wearing layer is obtained, and the result is used as an evaluation reference of the drainage capacity of the asphalt wearing layer.

Because the data before rain and the data after rain have differences, the difference excludes the influence of objective environmental factors and directly reflects the road surface drainage capacity, the larger the difference is, the larger the change of the vehicle motion state is compared with the data before rain after rain, namely the larger the influence of the rain is, the influence of the rain on the vehicle is mainly under the action of road surface water and tires, the more difficult the drainage is, the more the road surface water is, and the larger the influence is.

In addition, the precision of the model can be improved by increasing the data capacity, and the driving state of the vehicle on the asphalt wearing layer in rainy days under complex and changeable environmental conditions can be accurately evaluated;

as shown in fig. 3, the system for detecting the surface drainage capability of the large-structure asphalt wearing layer of the present invention includes: a rainy day vehicle motion information determination unit 1, a sunny day vehicle motion information determination unit 2, and a drainage capacity determination unit 3.

The rainy-day vehicle motion information determining unit 1 is used for obtaining rainy-day vehicle motion information according to the rainy-day state information of the road section to be detected based on the rainy-day vehicle state model.

The clear-sky vehicle motion information determining unit 2 is configured to obtain clear-sky vehicle motion information according to clear-sky state information of a road section to be detected based on a clear-sky vehicle state model.

The drainage capacity determining unit 3 is respectively connected with the rainy vehicle motion information determining unit 1 and the sunny vehicle motion information determining unit 2, and the drainage capacity determining unit 3 is configured to determine the drainage capacity of the road segment to be measured according to the rainy vehicle motion information and the sunny vehicle motion information.

Specifically, the drainage capability determination unit 3 includes: the device comprises a calculation module and a drainage capacity determination module.

The computing module is respectively connected with the rainy vehicle motion information determining unit 1 and the sunny vehicle motion information determining unit 2, and is used for subtracting data of the rainy vehicle motion information and the sunny vehicle motion information to obtain difference data.

The drainage capacity determining module is connected with the computing unit and used for determining the drainage capacity of the road section to be measured according to the difference data.

Compared with the prior art, the system for detecting the surface drainage capability of the large-structure asphalt wearing layer has the same beneficial effects as the method for detecting the surface drainage capability of the large-structure asphalt wearing layer, and the details are not repeated herein.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.