CN115506216A - Pavement evenness analysis method and maintenance inspection system - Google Patents
Pavement evenness analysis method and maintenance inspection system Download PDFInfo
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- CN115506216A CN115506216A CN202211290203.XA CN202211290203A CN115506216A CN 115506216 A CN115506216 A CN 115506216A CN 202211290203 A CN202211290203 A CN 202211290203A CN 115506216 A CN115506216 A CN 115506216A
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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Abstract
The pavement evenness analysis method comprises the steps of converting a three-axis coordinate system; step two, data preprocessing; thirdly, analyzing the road flatness based on a machine learning classification algorithm, extracting characteristic values capable of representing different road flatness by performing preliminary processing and analysis on the preprocessed data, and outputting road bump point information; step four, calculating the photo capturing time of the video camera; and step five, identifying and analyzing the disease type of the bumping point based on the image. A road flatness maintenance inspection system is a light-weight vehicle-mounted road flatness maintenance inspection system based on an intelligent vehicle-mounted terminal OBU and a video camera, and a platform server outputs a road flatness analysis result through a road flatness analysis module. The invention realizes low-cost and rapid detection of the type, position, degree and the like of the road pavement diseases, saves the daily maintenance inspection cycle and cost, and provides a guidance basis for road maintenance decisions.
Description
Technical Field
The invention relates to the technical field of road flatness detection, in particular to a road flatness analysis method and a light vehicle-mounted road flatness maintenance and inspection system based on an intelligent vehicle-mounted terminal OBU and a video camera.
Background
With the basic construction of highway networks in China, the problems of road surface aging, subgrade settlement, local potholes, road surface cracks, pot holes, humps and the like are gradually shown when China walks into highway maintenance and repair high-tide periods, the driving safety, the passing efficiency, the driving comfort and the like of vehicles are influenced to different degrees, and the existing road surface diseases further aggravate the deterioration of the road surface. Daily maintenance and inspection of highway pavements is an important basis for maintenance and management decisions, and according to statistics, the total mileage of the highways, national and provincial roads and rural roads in 2021 is 534.7 kilometers in total, thereby providing extremely high challenges for the working quality and efficiency of maintenance and inspection.
The common road surface detection work is completed by manual walking, a semi-automatic detection vehicle and a full-automatic detection vehicle. In the manual inspection process, each inspector can only complete about 10km of inspection volume every day, the time and the labor are consumed, large potential safety hazards exist, and large-scale maintenance and inspection work cannot be dealt with. The semi-automatic detection vehicle collects road surface images in the driving process, and then the defects are checked and identified manually, so that the defect positioning precision is low, and misjudgment and missed judgment are easy to occur in subjective judgment. The full-automatic detection vehicle has the problems of various devices, complex operation, high cost, difficult maintenance and the like, and is not beneficial to large-scale popularization and application.
In recent years, pavement maintenance and inspection technology research is increasing, and the research mainly focuses on aspects such as pavement damage AI video identification, three-axis acceleration pavement evenness index analysis, and navigation path planning considering pavement bump, and the like, and a certain progress is achieved, but certain problems still exist. Firstly, limited by image recognition technology, tree shadows, cracks, light spots, pits and the like are difficult to distinguish accurately, and the AI video recognition accuracy rate of the pavement diseases is only 65-80%. Secondly, the road surface flatness analysis based on the triaxial acceleration does not consider the influence of factors such as vehicle types, vehicle speeds and sensor placement angles, and the road surface unevenness cannot distinguish whether the road surface is caused by road surface diseases or well covers, speed bumps, obstacles and the like, and does not have the capacity of representing the road surface quality. Thirdly, the positioning error of the current pavement damage identification is generally large, which is caused by the low positioning accuracy of the smart phone or the GPS sensor, about 10 m. Fourthly, the vehicle-mounted pavement maintenance patrol equipment is large in quantity, large in occupied space, complex in installation and high in cost, and is not suitable for large-scale assembly and use.
Based on the above, a light vehicle-mounted maintenance and inspection system for road flatness is needed, which can meet the requirements of simple equipment, low cost, low consumption, high frequency and the like, can also realize the functions of inspection recording, automatic detection, intelligent analysis and the like, saves the maintenance and inspection cycle and cost, and improves the road maintenance efficiency.
There are some cases of patents in the prior art:
the patent: a road quality recorder and a method thereof (patent application number: 201010574907.0). A road quality recorder is composed of speed sensor, vibration sensor, navigator, processor and memory, and features that the data about speed, jolt degree and GPS are collected to build up a road quality database, and the routes are sorted according to the jolt degree of road. The main disadvantage is that the road quality database is still the actual running time of the vehicle, and has a small relation with the degree of jolt and a large relation with factors such as vehicle speed, flow and weather. Secondly, the degree of road bump and the specific points cannot be quantified.
The patent: vehicle-mounted road surface unevenness acquisition system based on acceleration sensor and working method (patent application number: 201510126564.4). A vehicle-mounted road surface unevenness acquisition system based on an acceleration sensor and a working method are provided. The system consists of a voltage stabilizing circuit, an acceleration sensor, a signal processing circuit, a GPS module, a singlechip, a Bluetooth module and an upper computer. The method comprises the steps of fixing an acceleration sensor on a suspension of an automobile close to a left rear wheel, collecting X, Y and Z three-axis acceleration signals and GPS data, transmitting the signals to an upper computer through Bluetooth, modeling, and calculating an IRI value of a road surface unevenness index. The method has the disadvantages that the influence of the vehicle speed, the vehicle type, the sensor arrangement angle and the like on the IRI value is not considered; the IRI value can not identify whether the reasons caused by uneven pavement are pavement diseases or facilities such as well covers and speed bumps, and the guidance for pavement maintenance decision is limited.
The patent: a road surface quality rating method based on a built-in three-axis acceleration sensor smart phone (patent application number: 201611173513.8). A road surface quality grading method based on a built-in three-axis acceleration sensor smart phone is provided, and a Logitics road surface quality grade division regression model is established based on three-axis acceleration data of the smart phone. The method has the defects that the positioning precision of the mobile phone is limited; vertically placing the mobile phone on a front instrument panel, wherein the mobile phone is likely to deviate in position in the driving process of a vehicle; the coordinate system of the built-in triaxial acceleration sensor of the mobile phone is not corrected, which may cause distortion of the detection result.
The patent: a method and a system for determining a road comfort index (patent application number: 202011406155.7). A method and a system for determining a road comfort index are provided, wherein road characteristic values (including an average road height value, a pit and bag frequency, a 1-degree steering return rate, an average steering angle and an average noise sound pressure level) are analyzed based on data such as a GPS (global positioning system), a vehicle longitudinal acceleration, a suspension dynamic stroke, a steering wheel corner, a vehicle speed, a tire noise sound pressure level and the like, and the road comfort index is calculated. The method has the defects that the raised parts and the depressed parts of the road surface are extremely easy to offset by the average road surface elevation value, and the comfort index of the road surface cannot be accurately reflected; the sound pressure level of the tire noise is related to various factors such as road environment, road surface materials, vehicle types and the like, and the road surface comfort index cannot be accurately reflected.
Therefore, the proposal of a light vehicle-mounted road flatness maintenance patrol system based on an intelligent vehicle-mounted terminal OBU and a video camera is urgently needed.
Disclosure of Invention
According to the problems brought forward by the background technology, the invention provides a road flatness maintenance inspection system, which is a light-weight vehicle-mounted road flatness maintenance inspection system based on an intelligent vehicle-mounted terminal OBU and a video camera.
The pavement flatness analysis method comprises the following steps:
step one, starting a system to start working;
step two, the OBU outputs vibration data, positioning data and time data to a memory in real time, and the memory outputs the data to a processor for preprocessing and uploads the data to a platform system; the video camera equipment outputs video data and time data to the memory in real time, and the memory outputs the data to the processor for preprocessing and then uploads the data to the platform system;
step three, the platform system judges whether the vehicle is positioned in the electronic fence or not according to the positioning data, and if so, the platform system jumps to step four; if not, the pavement evenness maintenance inspection system stops or suspends working;
step four, the platform system performs three-axis coordinate system conversion, converts three-axis acceleration sensor coordinates into vehicle coordinates, performs filtering processing on vibration data, performs pavement vibration bump point analysis including time, position and degree, and extracts corresponding time of the pavement vibration bump point;
calculating the driving speed of the vehicle by the platform system according to the vehicle positioning data, calculating the video bump point time by combining the road surface vibration bump point time, extracting video pictures according to the bump point time, and identifying and analyzing the disease type of the bump point based on the images;
step six, combining the analysis of the vibration and bump points of the road surface and the analysis of the types of the road surface diseases to output a road surface flatness analysis result;
and step seven, judging whether the vehicle finishes the patrol task, if so, stopping or suspending the work of the road flatness maintenance patrol system, and if not, continuing the work of the step two to the step six.
Further, in the fourth step, the three-axis coordinate system conversion is implemented by calculating euler angles: ji sanThe transverse axis, the longitudinal axis and the vertical axis of the axial acceleration sensor are respectively an X axis, a Y axis and a Z axis, the Euler angles comprise a rolling angle alpha, a pitch angle beta and a yaw angle gamma, the alpha, the beta and the gamma are sequentially rotated around the X axis, the Y axis and the Z axis respectively, and the coordinates of the three-axis acceleration sensor are converted into vehicle coordinates; assuming that the triaxial acceleration acquired by the triaxial acceleration sensor is [ a ] x ,a y ,a z ]Then α, β, and γ can be calculated by the following formula:
according to the calculated Euler angle, the three-axis acceleration [ a 'of the vehicle can be obtained through conversion' x ，a′ y ，a′ z ]：
Further, the filtering processing of the vibration data is to perform noise reduction processing on the vertical vibration data acquired by the sensor by adopting mean filtering: assuming that the acceleration value acquired by the sensor at the time t is a (t), and taking 2n data before and after the a (t) for average calculation, the corrected acceleration at the time t is obtained as follows:
further, in the fourth step, the analysis of the vibration and bump point of the road surface comprises the following steps:
adopting a K nearest neighbor algorithm to construct a relation model between the data characteristic value and the road surface flatness index:
collecting pavement data with different flatness grades, marking typical data, extracting data distribution characteristics of different pavement flatness, and performing standardized processing to form a training data set;
inputting newly acquired data, and finding k instances closest to the instances in the training set according to a given distance measurement, wherein the most pavement evenness grades to which the k instances belong are the pavement evenness grades corresponding to the acquired data;
finding and inputting examples in a training data set T according to a given distance metric
K nearest points to be covered
Is recorded as
From
In the method, the decision is made according to a classification decision rule
Class y of (2):
wherein I is an indicator function: i (true) =1, I (false) =0; for y i (i =1, 2.., N) only
Is considered.
Further, in the fifth step, the method for calculating the video bump point time and extracting the video photos according to the bump point time comprises the following steps:
when the time when the vehicle passes through the bump point is recorded as t, the longitude, the latitude and the altitude of the vehicle at the time t are respectively (lat _ t, lng _ t and alt _ t), the longitude, the latitude and the altitude of the vehicle at the time t-delta t are respectively (lat _ (t-delta t), lng _ (t-delta t) and alt _ (t-delta t)), and then the instantaneous speed v (t) of the vehicle is as follows:
assuming that the vehicle is at a position x (m) away from the bump point, the video camera can clearly see the state of the bump point, and the video camera faces the direction of the road surface, the corresponding time t' is:
preferably, if the video camera faces backward to the road surface, the corresponding time t' is:
recording the frame rate of the video camera as f (fps), then
The multiple of (a) is a time interval, and a plurality of moments before and after t' are taken as photo capturing time of the video camera.
The pavement flatness maintenance inspection system comprises an intelligent vehicle-mounted terminal OBU, a video sensor and a platform system, wherein the intelligent vehicle-mounted terminal OBU is installed on a front windshield of a vehicle, a video camera faces the pavement direction, and the intelligent vehicle-mounted terminal OBU and the video camera keep time synchronization;
the intelligent vehicle-mounted terminal OBU is used for collecting and uploading vehicle running vibration information and positioning information;
the video camera is used for recording and uploading video and image information in the vehicle running process;
the platform system comprises a maintenance patrol service module and a pavement flatness analysis module, and the two modules are both arranged on the platform server; the maintenance and inspection service module is used for recording daily maintenance and inspection work, and comprises an electronic fence function for setting an inspection area and a non-inspection area, when a daily maintenance and inspection vehicle runs in the inspection area, the intelligent vehicle-mounted OBU and the video camera work normally, and data are collected and uploaded; when the daily maintenance patrol vehicle runs in the non-patrol area, the intelligent vehicle-mounted OBU and the video camera pause working, and stop collecting and uploading data; the road surface flatness analysis module is used for identifying road surface bump points, evaluating road flatness grades and calculating photo intercepting time of the video camera.
Has the beneficial effects that: compared with the prior art, the method solves the problems of time and labor consumption, high cost, low accuracy, multiple devices, complex operation and the like in the pavement maintenance inspection process in the prior art, realizes low-cost and rapid detection of the types, positions, degrees and the like of the pavement diseases of the highway, saves the daily maintenance inspection cycle and cost, and provides a guidance basis for the road maintenance decision.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings required to be used in the embodiments will be briefly described below, 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 that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a functional structure diagram of a light-weight vehicular pavement flatness maintenance inspection system according to the present invention;
fig. 2 is a schematic flow chart of the road flatness analysis according to the present invention.
Detailed Description
The present invention will be further illustrated by the following detailed description, which is to be understood as merely a few examples of the invention, rather than as a complete example. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative efforts all belong to the protection scope of the present invention.
The present embodiment is based on the following assumptions:
1) The intelligent vehicle-mounted terminal OBU and the video camera can be adapted to all types of vehicles, including cars, trucks, buses and the like;
2) The OBU and the video camera of the intelligent vehicle-mounted terminal are used for storing and uploading data in real time, communication modes are not interfered with each other, the transmission rate is normal, and no data packet is lost in the transmission process.
Referring to the attached drawing 1, the pavement evenness maintenance inspection system comprises two parts, namely intelligent vehicle-mounted equipment and a platform system.
The road flatness maintenance inspection system comprises an intelligent vehicle-mounted terminal OBU11, a video sensor 12 and a platform system 13, and external equipment of the system comprises an intelligent mobile phone 14 and a tablet personal computer 15.
To intelligent mobile unit, including intelligent vehicle mounted terminal OBU and video camera, be common mobile unit on the vehicle.
The intelligent vehicle-mounted terminal OBU is mounted on a front windshield of a vehicle;
the video camera can be a vehicle data recorder and is arranged on the front windshield of the vehicle and faces the road surface; or a high-definition pan-tilt camera is selected and installed on the front top or the rear top outside the vehicle and faces the direction of the road surface;
and, intelligent vehicle mounted terminal OBU and video camera keep time synchronization each other.
The intelligent vehicle-mounted terminal OBU11 is used for collecting and uploading vehicle running vibration information and positioning information, and comprises a three-axis acceleration sensor 111, a high-precision positioning module 112, a clock module 113, a memory 114, a Wifi module 115, a processor 116, a 4G module 117 and a power module 118. Wherein:
the three-axis acceleration sensor is used for recording vibration data in the running process of a vehicle, outputting three-axis acceleration (including X-axis acceleration, Y-axis acceleration and Z-axis acceleration) and storing the three-axis acceleration in the memory, wherein the three-axis acceleration refers to a space rectangular coordinate system of the acceleration sensor, and the X-axis, the Y-axis and the Z-axis respectively represent a transverse axis, a longitudinal axis and a vertical axis of the sensor and are different from an OBU space coordinate system and a vehicle space coordinate system of the intelligent vehicle-mounted terminal;
the high-precision positioning module is used for acquiring vehicle positioning information, outputting longitude, latitude and altitude information of a vehicle and storing the longitude, latitude and altitude information in the memory, and supports RTK differential positioning with positioning precision of centimeter level;
the 4G module is used for uploading vehicle running vibration information and positioning information to the platform server and receiving electronic fence information issued by the platform server;
the Wifi module is used for connecting the intelligent vehicle-mounted terminal OBU with the intelligent mobile phone or the tablet personal computer, and is convenient for receiving patrol tasks, reading OBU data or modifying OBU configuration;
the clock module is used for time service of an OBU (on-board unit) of the intelligent vehicle-mounted terminal, supports network time synchronization and verification, outputs time information and stores the time information in the memory;
the memory is used for storing vibration information, positioning information and time information of the OBU, and is generally suitable for storing 1h data;
the processor is used for carrying out OBU data preprocessing on the intelligent vehicle-mounted terminal and controlling the data acquisition on or off state according to the electronic fence information;
the power module supplies power for the whole equipment by being connected with a vehicle-mounted OBD power supply.
The video camera 12 is used for recording and uploading video and image information in the driving process of the vehicle, and includes a video sensor 121, a clock module 122, a memory 123, a Wifi module 124, a processor 125, a 4G module 126, and a power module 127. Wherein:
the video sensor is used for recording video information in the driving process of the vehicle, outputting video data right ahead of the driving direction of the vehicle and storing the video data in the memory, the effective pixels of the video sensor are preferably not less than 200 ten thousand pixels, and the frame rate is preferably not less than 30 fps;
the 4G module is used for uploading shot video or photos and time information to the platform server;
the Wifi module is used for connecting the video camera with the smart phone or the tablet personal computer, and is convenient for reading data of the video camera or modifying the configuration of the video camera;
the clock module is used for time service of the video camera, supports network time synchronization and verification, outputs time information and stores the time information in the memory;
the memory is used for storing video information and picture information of the video camera, and is generally suitable for storing 1h of data;
the processor is used for carrying out OBU data preprocessing on the intelligent vehicle-mounted terminal and controlling the data acquisition on or off state according to the electronic fence information;
the power module supplies power for the whole equipment by being connected with a vehicle-mounted OBD power supply.
The platform system comprises a maintenance patrol service module and a road flatness analysis module, and the two modules are both installed on the platform server.
The maintenance patrol service module is used for recording daily maintenance patrol work, and comprises a patrol task management function, a patrol historical record function, an electronic fence function and the like.
The patrol task management function comprises patrol personnel, patrol time, patrol road sections, patrol requirements, execution states and the like, the patrol task management function is automatically generated by the system according to a patrol plan or manually input by the manager, and the patrol task management function is sent to the intelligent vehicle-mounted OBU and the video camera through the 4G network and can be checked on the intelligent mobile phone or the tablet computer.
The execution state is divided into three states of waiting for execution, executing and finished, and the finished inspection task record is synchronously updated into the inspection history record. The patrol historical record functions comprise patrol personnel, patrol time, patrol road sections, patrol requirements, execution states, patrol original data, patrol analysis results and the like.
The electronic fence function is used for setting a patrol area and a non-patrol area, and is manually divided by a manager; when a daily maintenance inspection vehicle runs in the inspection area, the intelligent vehicle-mounted OBU and the video camera work normally, and data are collected and uploaded; when the daily maintenance inspection vehicle runs in the non-inspection area, the intelligent vehicle-mounted OBU and the video camera pause, stop collecting and uploading data.
The road surface flatness analysis module is used for identifying road surface bump points, evaluating road flatness grades and calculating photo intercepting time of the video camera; the identification of the road bump points refers to outputting positions, types, degrees and time of all bump points; the road flatness grade evaluation refers to the evaluation of the flatness grade of all unit road sections, and the output of the positions, the flatness grade and the time of the road sections, wherein the flatness grade generally comprises the flatness, the relatively bumpiness and the very bumpy road surface.
The step of calculating the photo intercepting time of the video camera refers to the video time range within which the video camera can shoot the bumpy point, which is calculated according to the time of the bumpy point on the road and the driving speed, and a plurality of photo intercepting times are selected within the video time range to ensure that the bumpy point can be completely shot.
The pavement flatness analysis method comprises the following steps:
step one, three-axis coordinate system conversion.
The pavement flatness maintenance inspection system collects vibration acceleration data in the vehicle running process through a three-axis acceleration sensor arranged in an intelligent vehicle-mounted terminal OBU. Because the position and the angle of intelligent vehicle-mounted terminal OBU when installing on the vehicle are different, lead to there being the difference between acceleration sensor's coordinate and the vehicle coordinate, consequently, need convert the coordinate of sensor into the coordinate that the vehicle was located, guarantee that the vertical acceleration data of gathering are measured with the coordinate system that the vehicle was located.
The invention realizes the conversion between the sensor coordinate and the vehicle coordinate by calculating the Euler angle: the transverse axis, the longitudinal axis and the vertical axis of the triaxial acceleration sensor are recorded as an X axis, a Y axis and a Z axis respectively, the Euler angles comprise a rolling angle alpha, a pitch angle beta and a yaw angle gamma, the alpha, the beta and the gamma are rotated around the X axis, the Y axis and the Z axis respectively in sequence, and the coordinates of the triaxial acceleration sensor are converted into vehicle coordinates.
Assuming that the triaxial acceleration acquired by the triaxial acceleration sensor is [ a ] x ，a y ，a z ]Then α, β, and γ can be calculated by the following formula:
according to the calculated Euler angle, the three-axis acceleration [ a 'of the vehicle can be obtained through conversion' x ，a′ y ，a′ z ]：
And step two, preprocessing the data.
Lightweight vehicular road flatness maintenance inspection system through the built-in triaxial acceleration sensor of intelligent vehicle mounted terminal OBU, gathers the vibration acceleration data of the vehicle in-process of traveling. Because the vibration data of intelligent vehicle-mounted terminal OBU output receives the interference of factors such as vehicle model, road environment and sensor error, consequently, need carry out filtering noise reduction to the vibration data of gathering.
The invention adopts mean value filtering to carry out noise reduction processing on vertical vibration data acquired by a sensor, supposing that the acceleration value acquired by the sensor at the moment t is a (t), and taking 2n data before and after the a (t) to carry out mean value calculation, the acceleration at the moment t after correction is obtained as follows:
and step three, analyzing the road flatness based on a machine learning classification algorithm.
By carrying out preliminary processing and analysis on the preprocessed data, characteristic values capable of representing the flatness of different roads can be extracted. On the basis, the method adopts a K-nearest neighbor (KNN, K-nearest neighbor) algorithm to construct a relational model between the data characteristic value and the pavement evenness index, and realizes the identification of the pavement evenness.
Firstly, collecting pavement data with different flatness grades, marking typical data, extracting data distribution characteristics of different pavement flatness, and carrying out standardization processing to form a training data set. On the basis of the input of newly acquired data, k instances closest to the instance are found in the training set according to a given distance metric. The most pavement evenness levels to which the k examples belong are the pavement evenness levels corresponding to the collected data.
The calculation method for the pavement evenness analysis comprises the following steps: finding and inputting instances in a training data set T according to a given distance metric
K nearest neighbors. Will cover the k points
Is recorded as
From
In accordance with a classification decision rule (e.g., majority voting)
Class y of (c):
wherein I is an indication function: i (true) =1, I (false) =0; for y i (i =1, 2.., N) only
Is considered.
And step four, calculating the photo intercepting time of the video camera.
Noting that the moment when the vehicle passes through the bump point is t, the longitude, the latitude and the altitude of the vehicle at the moment t are (lat _ t, lng _ t and alt _ t), respectively, and the longitude, the latitude and the altitude of the vehicle at the moment t-delta t are (lat _ (t-delta t), lng _ (t-delta t) and alt _ (t-delta t)), the instantaneous speed v (t) of the vehicle is as follows:
assuming that the vehicle is at a position x (m) before the bump point, the video camera can clearly see the state of the bump point, and the video camera faces forward to the road surface, the corresponding time t' is:
preferably, if the video camera faces backward to the road surface, the corresponding time t' is:
recording the frame rate of the video camera as f (fps), then
The multiple of (a) is a time interval, a plurality of moments before and after t' are taken as photo clips of the video cameraAnd (5) taking time. Taking 5 photos as an example, the corresponding 5 photo capturing times are respectively:
referring to fig. 1, the data flow mode of the intelligent on-board terminal OBU11 is as follows:
the triaxial acceleration sensor 111 outputs triaxial acceleration vibration data of the vehicle to the memory 114 in real time for storage.
The high-precision positioning module 112 outputs the vehicle positioning data to the memory 114 in real time for storage;
the clock module 113 outputs vehicle time data to the memory 114 in real time for storage;
after the vibration data, the positioning data and the time data in the memory 114 are preprocessed by the processor 116, the preprocessed data are uploaded to the platform system 13 in real time through the 4G module 117;
the platform system 13 sends network time to the clock module through the 4G module 117, and performs network time service;
the platform system 13 sends system start, pause and stop commands to the processor 116 through the 4G module 117;
when the platform system 13 sends a start work command to the processor 116 through the 4G module 117, indicating that the maintenance patrol system starts working, the processor 116 executes data preprocessing work; when the platform system 13 sends a work pause command to the processor 116 through the 4G module 117, it indicates that the maintenance patrol vehicle exceeds the range of the electronic fence and needs to pause data acquisition work, and the processor 116 preprocesses the positioning data and uploads the preprocessed data to the platform system 13 through the 4G module in real time; while the processor 116 issues instructions to the memory 114 to stop collecting and storing vibration data and positioning data; when the platform system 13 sends a stop work command to the processor 116 through the 4G module 117, indicating that the maintenance patrol system is finished working, the processor 116 issues an instruction to the memory 114 to stop collecting and storing vibration data, positioning data and time data;
the intelligent vehicle-mounted terminal OBU11 is connected with the intelligent mobile phone 14 or the tablet computer 15 through the Wifi module 115, and transmits and displays data.
The data stream mode of the video camera 12 is as follows:
the video sensor 121 outputs road video data to the memory 123 in real time for storage;
the clock module 122 outputs the vehicle time data to the memory 123 in real time for storage;
the video data and the time data in the memory 123 are pre-processed by the processor 125, and then the pre-processed data are uploaded to the platform system 13 in real time through the 4G module 126;
the platform system 13 sends network time to the clock module through the 4G module 126 to perform network time service;
the platform system 13 sends system start, pause and stop commands to the processor 125 through the 4G module 126;
when the platform system 13 sends a start work command to the processor 125 through the 4G module 126, indicating that the maintenance patrol system starts working, the processor 125 performs preprocessing work of the video data and the time data; when the platform system 13 sends a work pause command to the processor 125 through the 4G module 126, which indicates that the maintenance patrol vehicle exceeds the range of the electronic fence and needs to pause the data acquisition work, the processor 125 issues an instruction to the memory 123 to stop acquiring and storing the video data and the time data; when the platform system 13 sends a stop command to the processor 125 through the 4G module 126, indicating that the maintenance patrol system is finished working, the processor 125 issues an instruction to the memory 123 to stop collecting and storing video data and time data;
the video camera 12 is connected with the smart phone 14 or the tablet computer 15 through the Wifi module 124, and transmits and displays data.
The maintenance patrol service module 131 is used for recording daily maintenance patrol work, and includes a patrol task management function, a patrol history recording function, an electronic fence function, and the like, and the specific implementation method is as follows:
the patrol task management function refers to that the system automatically generates or a manager manually inputs patrol tasks, including a patrol worker-name, patrol time-certain day of a certain month and a certain year, patrol road sections-certain roads (starting point stake number-ending point stake number), execution state-to-be-executed/executing/completed, remarks and the like;
the patrol task management function can be sent to the intelligent vehicle-mounted OBU and the video camera through a 4G network;
the patrol task management function can be sent to a smart phone or a tablet computer through a 4G network for checking;
the execution state is divided into three states of waiting for execution, executing and finished, and the finished inspection task record is synchronously updated into the inspection historical record;
the patrol historical record function comprises a patrol person-name, patrol time-certain day of certain month of a year (certain minute to certain minute), patrol road section-certain road (starting point stake number to terminal point stake number), execution state-completed, patrol original data, patrol analysis result, remark and the like;
the electronic fence function is used for setting a patrol area and a non-patrol area, and is manually divided by a manager;
when a daily maintenance inspection vehicle runs in the inspection area, the intelligent vehicle-mounted OBU and the video camera work normally, and data are collected and uploaded; when the daily maintenance inspection vehicle runs in the non-inspection area, the intelligent vehicle-mounted OBU and the video camera pause, stop collecting and uploading data.
Referring to fig. 2, the complete working flow of the road flatness maintenance and inspection system of the present invention is schematically illustrated.
(1) The method comprises the following steps: the pavement flatness maintenance inspection system starts working (201);
(2) Step two: the method comprises the steps that an intelligent vehicle-mounted terminal OBU device is started (202), vibration data, positioning data and time data are output to a memory (203) in real time in the vehicle running process, the memory outputs the data to a processor for preprocessing (204), and then the data are uploaded to a platform system (208) through a 4G module;
(3) Step three: the video camera equipment and the intelligent vehicle-mounted terminal OBU equipment are started simultaneously (205), video data and time data are output to a memory (206) in real time in the running process of a vehicle, the memory outputs the data to a processor for preprocessing (207), and then the data are uploaded to a platform system (208) through a 4G module;
(4) Step four: the platform system judges whether the vehicle is positioned in the electronic fence or not according to the positioning data (209), and if so, the platform system jumps to the fifth step; if not, the pavement evenness maintenance inspection system stops or suspends the operation (220);
(5) Step five: the platform system performs three-axis coordinate system conversion (210), converts three-axis acceleration sensor coordinates into vehicle coordinates, performs filtering processing (211) on vibration data, performs analysis (212) on road surface vibration and bump points, including time, position, degree and the like, and extracts corresponding time (213) of the road surface vibration and bump points;
(6) Step six: the platform system calculates the vehicle running speed (214) according to the vehicle positioning data, calculates the video bump point moment (215) by combining the road surface vibration bump point corresponding moment (213), extracts a video photo (216) according to the bump point moment, and analyzes the road surface disease type (217) based on image recognition;
(7) Step seven: combining the analysis (212) of the vibration and bump points of the road surface and the analysis (217) of the types of the road surface diseases to output a result (218) of the analysis of the flatness of the road surface;
(8) Step eight: and (5) judging whether the vehicle finishes the patrol task (219), if so, stopping or suspending the road flatness maintenance patrol system (220), and if not, continuing the work of the second step to the seventh step.
The method solves the problems of time and labor consumption, high cost, low accuracy, multiple equipment, complex operation and the like in the pavement maintenance and inspection process in the prior art, realizes low-cost and rapid detection of the type, position, degree and the like of the pavement diseases of the highway, saves the daily maintenance and inspection cycle and cost, and provides a guide basis for the pavement maintenance decision.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The method for analyzing the road flatness is characterized by comprising the following steps of:
step one, starting a system to start working;
step two, the OBU outputs vibration data, positioning data and time data to a memory in real time, and the memory outputs the data to a processor for preprocessing and uploads the data to a platform system; the video camera equipment outputs video data and time data to the memory in real time, and the memory outputs the data to the processor for preprocessing and then uploads the data to the platform system;
step three, the platform system judges whether the vehicle is positioned in the electronic fence or not according to the positioning data, and if so, the platform system jumps to step four; if not, the pavement evenness maintenance inspection system stops or suspends working;
step four, the platform system performs three-axis coordinate system conversion, converts three-axis acceleration sensor coordinates into vehicle coordinates, performs filtering processing on vibration data, performs pavement vibration bump point analysis including time, position and degree, and extracts corresponding time of the pavement vibration bump point;
calculating the vehicle running speed by the platform system according to the vehicle positioning data, calculating the moment of a video bump point by combining the moment corresponding to the road surface vibration bump point, extracting a video photo according to the moment of the bump point, and identifying and analyzing the disease type of the bump point based on the image;
step six, combining the analysis of the vibration and bump points of the road surface and the analysis of the types of the road surface diseases to output a road surface flatness analysis result;
and step seven, judging whether the vehicle finishes the patrol task, if so, stopping or suspending the work of the pavement evenness maintenance patrol system, and if not, continuing the work of the step two to the step six.
2. Road flatness according to claim 1The analysis method is characterized in that in the fourth step, the three-axis coordinate system conversion is realized by calculating Euler angles: recording a transverse axis, a longitudinal axis and a vertical axis of the triaxial acceleration sensor as an X axis, a Y axis and a Z axis respectively, wherein Euler angles comprise a rolling angle alpha, a pitch angle beta and a yaw angle gamma, and sequentially rotating the alpha, the beta and the gamma around the X axis, the Y axis and the Z axis respectively to convert the coordinates of the triaxial acceleration sensor into vehicle coordinates; assuming that the triaxial acceleration acquired by the triaxial acceleration sensor is [ a ] x ,a y ,a z ]Then α, β, and γ can be calculated by the following formula:
according to the calculated Euler angle, the three-axis acceleration [ a 'of the vehicle can be obtained through conversion' x ,a' y ,a' z ]：
3. The method for analyzing the flatness of the road surface according to claim 2, wherein in the fourth step, the filtering of the vibration data is to perform noise reduction on the vertical vibration data collected by the sensor by using mean filtering: assuming that the acceleration value acquired by the sensor at the time t is a (t), taking 2n data before and after the a (t) for mean value calculation, the corrected acceleration at the time t is obtained as follows:
4. the method for analyzing the flatness of the road surface according to claim 3, wherein in the fourth step, the analysis of the vibration and bump point of the road surface comprises the following steps:
adopting a K nearest neighbor algorithm to construct a relation model between the data characteristic value and the road surface flatness index:
collecting pavement data with different flatness grades, marking typical data, extracting data distribution characteristics of different pavement flatness, and performing standardized processing to form a training data set;
inputting newly acquired data, and finding k instances closest to the instances in the training set according to given distance measurement, wherein the most pavement evenness levels of the k instances are the pavement evenness levels corresponding to the acquired data;
finding and inputting instances in a training data set T according to a given distance metric
K nearest neighbor points which will cover the k points
Is recorded as
From
In the method, the decision is made according to a classification decision rule
Class y of (2):
wherein I is an indicator function: i (future) =1, I (false) =0; for y i (i =1,2, \ 8230;, N) is only
Is considered.
5. The method for analyzing the flatness of the road according to claim 1, wherein in the fifth step, the method for calculating the moment of the video bump point and extracting the video picture according to the moment of the bump point comprises the following steps:
noting that the moment when the vehicle passes through the bump point is t, the longitude, the latitude and the altitude of the vehicle at the moment t are (lat _ t, lng _ t and alt _ t), respectively, and the longitude, the latitude and the altitude of the vehicle at the moment t-delta t are (lat _ (t-delta t), lng _ (t-delta t) and alt _ (t-delta t)), the instantaneous speed v (t) of the vehicle is as follows:
assuming that the vehicle is at a position x (m) away from the bump point, the video camera can clearly see the state of the bump point;
if the video camera head faces the road surface, the corresponding time t' is:
if the video camera faces backwards to the road surface, the corresponding time t' is as follows:
recording the frame rate of the video camera as f (fps), and then
The multiple of (a) is a time interval, and more times are taken before and after tThe individual moment is the photo capturing time of the video camera.
6. Road flatness maintenance inspection system, its characterized in that includes: the system comprises an intelligent vehicle-mounted terminal OBU, a video sensor and a platform system, wherein the intelligent vehicle-mounted terminal OBU is installed on a front windshield of a vehicle, a video camera faces the direction of a road surface, and the intelligent vehicle-mounted terminal OBU and the video camera keep time synchronization;
intelligence vehicle mounted terminal OBU is used for gathering and uploading vehicle vibration information and locating information that travel, contains: the three-axis acceleration sensor is used for recording vibration data in the running process of the vehicle, outputting three-axis acceleration and storing the three-axis acceleration in the memory; the high-precision positioning module is used for collecting vehicle positioning information, outputting longitude, latitude and altitude information of the vehicle and storing the longitude, latitude and altitude information in the memory; the communication module is used for uploading vehicle running vibration information and positioning information to the platform server and receiving electronic fence information issued by the platform server; the Wifi module is used for connecting the intelligent vehicle-mounted terminal OBU and the intelligent mobile phone or the tablet personal computer, and is convenient for receiving the patrol task, reading OBU data or modifying the OBU configuration; the clock module is used for time service of the OBU of the intelligent vehicle-mounted terminal, supporting network time synchronization and verification, outputting time information and storing the time information in the memory; the memory is used for storing vibration information, positioning information and time information of the OBU; the processor is used for carrying out OBU data preprocessing on the intelligent vehicle-mounted terminal and controlling the data acquisition to be in an on or off state according to the electronic fence information; the power supply module is connected with a vehicle-mounted OBD power supply to supply power to the whole equipment;
the video camera is used for recording and uploading video and image information in the driving process of the vehicle, and comprises the following components: the video sensor is used for recording video information in the running process of the vehicle, outputting video data right ahead of the running direction of the vehicle and storing the video data in the memory; the communication module is used for uploading shot video or photos and time information to the platform server; the Wifi module is used for connecting the video camera with the smart phone or the tablet computer; the clock module is used for time service of the video camera, supporting network time synchronization and verification, outputting time information and storing the time information in the memory; the memory is used for storing the video information and the picture information of the video camera; the processor is used for carrying out OBU data preprocessing on the intelligent vehicle-mounted terminal and controlling the data acquisition to be in an opening or closing state according to the electronic fence information; the power supply module is connected with a vehicle-mounted OBD power supply to supply power to the whole equipment;
the platform system comprises a maintenance patrol service module and a pavement flatness analysis module, and the two modules are both arranged on the platform server;
the maintenance and inspection service module is used for recording daily maintenance and inspection work, and comprises an electronic fence function for setting an inspection area and a non-inspection area, when a daily maintenance and inspection vehicle runs in the inspection area, the intelligent vehicle-mounted OBU and the video camera work normally, and data are collected and uploaded; when the daily maintenance inspection vehicle runs in the non-inspection area, the intelligent vehicle-mounted OBU and the video camera pause to stop collecting and uploading data;
the road surface flatness analysis module is used for identifying road surface bump points, evaluating road flatness grades, calculating photo intercepting time of the video camera and analyzing disease types of the bump points based on image identification.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116176594A (en) * | 2023-04-26 | 2023-05-30 | 禾多科技（北京）有限公司 | Driving environment sensing method and system for automatic driving vehicle |
| CN116448016A (en) * | 2023-04-26 | 2023-07-18 | 成都智达万应科技有限公司 | Intelligent rapid detection system and detection vehicle with same |
| CN116758757A (en) * | 2023-08-18 | 2023-09-15 | 福建智涵信息科技有限公司 | Highway maintenance inspection method, medium and equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116176594A (en) * | 2023-04-26 | 2023-05-30 | 禾多科技（北京）有限公司 | Driving environment sensing method and system for automatic driving vehicle |
| CN116448016A (en) * | 2023-04-26 | 2023-07-18 | 成都智达万应科技有限公司 | Intelligent rapid detection system and detection vehicle with same |
| CN116758757A (en) * | 2023-08-18 | 2023-09-15 | 福建智涵信息科技有限公司 | Highway maintenance inspection method, medium and equipment |
| CN116758757B (en) * | 2023-08-18 | 2023-11-14 | 福建智涵信息科技有限公司 | Highway maintenance inspection method, medium and equipment |
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