CN111127906A - Intelligent road surface management system and method based on Internet of things - Google Patents
Intelligent road surface management system and method based on Internet of things Download PDFInfo
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Abstract
本发明公开了一种基于物联网的智能路面管理系统及其方法，通过移动客户端(即智能手机)采集车辆的三轴加速度、位置和速度信息，并将这些数据发送到物联网云端的数据储存模块进行储存，物联网云端的数据处理模块根据这些数据进行分析，并得到车辆已行驶路段的路面质量结果，物联网云端的服务处理模块将该路面质量结果反馈至移动客户端，或者，储存路面质量结果为移动客户端提供检索服务。由此，用户利用智能手机应用程序可以显示路网不同路段的路面质量品质，重点标注路面品质不良路段所在位置，以便司机选择行车路线和公路管理部门及时进行路面维修养护。
The invention discloses an intelligent pavement management system based on the Internet of Things and a method thereof, which collects three-axis acceleration, position and speed information of a vehicle through a mobile client (ie, a smart phone), and sends the data to the data of the Internet of Things cloud The storage module stores, and the data processing module of the IoT cloud analyzes the data according to the data, and obtains the road surface quality results of the road sections the vehicle has traveled. The service processing module of the IoT cloud feeds back the road surface quality results to the mobile client, or stores The pavement quality results provide retrieval services for mobile clients. As a result, the user can use the smartphone application to display the road surface quality of different sections of the road network, and focus on marking the location of the road section with poor road quality, so that the driver can choose the driving route and the road management department can carry out timely road maintenance and maintenance.
Description
Technical Field
The invention belongs to the technical field of pavement quality monitoring, and particularly relates to an intelligent pavement management system and method based on the Internet of things.
Background
Road condition monitoring is a challenging problem in the world's world area of road traffic infrastructure. Harsh road conditions may damage the vehicle, endanger the driver, and even cause traffic accidents. Maintaining and monitoring the road infrastructure is a challenging task for governments and road management departments throughout the country. The task needs to collect a large amount of road network condition data, and the data has very important significance for road surface maintenance and repair and safe trip of drivers.
In the road network condition detection data, the road surface running quality is one of the most important indexes for controlling the road construction quality and the operation and maintenance. The road Running Quality Index (RQI) is derived from the International flatness index (IRI). In order to test IRI, detection equipment such as a level gauge, a 3m ruler, a laser flatness detector and the like are mainly adopted at home. Detection equipment such as a level gauge, a 3m ruler and the like has low detection efficiency, is complex to operate, consumes time and labor, and is generally used for detecting the flatness in the construction process. The laser flatness detector is the most widely used detection equipment for road flatness, the equipment is expensive, special vehicles need to be equipped, although the specialization and the precision are greatly improved, the detection price is expensive, and the detection frequency is low (1 year/time of a highway), so that the scientific decision of road network maintenance and repair by using big data in the road operation process cannot be fundamentally solved.
Chinese patent publication No. CN 105426587B discloses a method for completing road surface condition collection and monitoring based on a smart phone, which provides a method for completing collection, basic processing and transmission based on an acceleration sensor and a gyroscope of a smart phone as a data sensing source and using the smart phone, uploading the collected data to the cloud, and completing calculation and evaluation of relatively real-time road surface smoothness (IRI) and road surface Running Quality (RQI) by more centralized data storage and establishment of a road condition model and using strong calculation capability. However, the method sets a vertical acceleration threshold value, takes the average z-axis acceleration of the latest 1-15 seconds as a basis for judging whether the road section is smooth or not, does not give direct connection between the z-axis acceleration and the international flatness index, only can test whether a road surface abnormality (such as a pit or a hug) exists at a certain point or not, and cannot objectively evaluate the road surface quality level of different road sections of the whole road network. Due to the influences of the road surface deceleration marked lines, the well cover and the like, the reasonable threshold value is difficult to determine, and the method has high possibility of misjudgment. Furthermore, for a location fix at a point, the out-of-flatness location may have significant errors given the GPS accuracy limitations. And if only the z-axis acceleration is measured, the mobile phone is placed in the vehicle in a flat mode, so that the collection of the mobile phone information of the whole road network is limited (the mobile phone placing directions of different vehicles can be completely different).
Chinese patent publication No. CN 104164829B discloses a "road flatness detection method based on mobile terminal and an intelligent road information real-time monitoring system", which obtains the accumulated displacement in the vertical direction during the driving of a vehicle by analyzing data collected by a sensor integrated with the mobile terminal; carrying out statistical regression analysis by combining each influence factor and accumulated displacement, and establishing an international flatness index prediction model; and detecting the international flatness index of the detected road section through the established international flatness index prediction model. However, the vertical acceleration is subjected to twice integration in the frequency domain to obtain the displacement, the frequency domain integration is greatly influenced by the low frequency and has low-frequency sensitivity, and the low frequency is also a frequency band with poor sensor precision determined by the principle of the acceleration sensor arranged in the mobile phone, so that the integration operation inevitably generates large errors if the low frequency is not processed. However, if high-pass filtering is used, it is highly likely that the valid signal related to the displacement will be filtered out, resulting in distortion of the displacement after integration. The vertical acceleration is closely related to the vehicle speed and the vehicle type, and the influence of different driving speeds and vehicle types on the road flatness is not considered. Similarly, the invention only measures the vertical acceleration, which greatly limits the application of the mobile phone in the intelligent pavement system. In addition, the method tries to establish a model parameter of accumulated displacement and damping information, a model parameter of environmental information and an international flatness index multiple regression formula in specific time, but because the regression parameters are more, IRI obtained by prediction of the regression formula and actually measured IRI are different greatly, so that the road flatness test loses practical significance.
Disclosure of Invention
The invention aims to provide an intelligent road surface management system and method based on the Internet of things, which can facilitate a driver to select a route and a road surface management department to timely and effectively carry out maintenance work of road infrastructure.
In order to solve the above technical problems, an aspect of the present invention provides an intelligent road surface management system based on the internet of things, which includes:
the mobile client is provided with a three-axis accelerometer, a GPS (global positioning system) and a road surface intelligent detection system application program, and a user is enabled to select a vehicle type on the application program before the road surface intelligent detection system application program starts; during the driving process of the vehicle, the application program of the road surface intelligent detection system records acceleration data from the three-axis accelerometer and position data and speed data of the GPS, sends the acceleration data, the position data and the speed data to the Internet of things cloud, and then receives and displays a road surface quality result returned by the Internet of things cloud;
the internet of things cloud end is provided with a data storage module, a data processing module and a service processing module, wherein the data storage module is in communication connection with the mobile client end and is used for storing acceleration data, position data and speed data sent by the mobile client end; the data processing module is in communication connection with the data storage module and is used for analyzing the data received by the data storage module and obtaining a road surface quality result of a road section on which the vehicle runs; the service processing module is in communication connection with the data processing module and the mobile client respectively, and is used for sending the road quality result obtained by the data processing module to the mobile client, or storing the road quality result obtained by the data processing module and providing retrieval service for the mobile client.
Preferably, the mobile client is a smart phone.
In addition, the invention also provides an intelligent road surface management method based on the Internet of things, which comprises the following steps:
(1) before the application program of the road surface intelligent detection system of the mobile client starts, a user selects a vehicle type on the application program of the road surface intelligent detection system;
(2) in the driving process of the vehicle, an application program of a 'road surface intelligent detection system' records acceleration data of a three-axis accelerometer from a mobile client, and position data and speed data of a GPS (global positioning system) of the mobile client;
(3) the mobile client sends acceleration data, position data, speed data and vehicle types to the cloud end of the Internet of things through an application program of a pavement intelligent detection system;
(4) the data storage module of the Internet of things cloud stores acceleration data, position data, speed data and vehicle types sent by the mobile client;
(5) the data processing module of the cloud of the Internet of things analyzes the data received by the data storage module and obtains a road surface quality result of a road section on which the vehicle runs;
(6) the service processing module of the Internet of things cloud sends the road surface quality result obtained by the data processing module to the mobile client, or stores the road surface quality result obtained by the data processing module and provides retrieval service for the mobile client;
(7) the user checks the road surface quality result of the road section on which the vehicle has run through the application program of the road surface intelligent detection system of the mobile client, or retrieves the road surface quality result of the road section on which the vehicle is to run.
Preferably, the mobile client is a smart phone.
As an optimal scheme of the intelligent road surface management method based on the internet of things, the step of analyzing the data received by the data storage module by the data processing module at the cloud end of the internet of things specifically comprises the following steps:
(1) unifying the acceleration of the X axis, the acceleration of the Y axis and the acceleration of the Z axis into a synthetic acceleration a,
(2) converting the time domain signal of the synthesized acceleration a into a frequency domain signal by using fast Fourier transform, and calculating the acceleration amplitude of 0 Hz-50 Hz according to the sampling rate of 10 Hz;
(3) removing low-frequency components by using high-pass filtering, comparing regression formulas of acceleration accumulated values of 0 Hz-50 Hz, 0Hz-10Hz, 10Hz-20Hz, 20 Hz-30 Hz and 30 Hz-50 Hz, average vehicle speed and international flatness index according to field detection practice, and finding that the correlation coefficient of the regression formula is the largest under the condition of 30 Hz-50 Hz; therefore, the acceleration amplitude of 30 Hz-50 Hz is adopted;
(4) calculating the accumulated value and the average speed of the synthesized acceleration within each 10 meters by adopting the acceleration amplitude under the condition of 30 Hz-50 Hz, establishing a regression formula of the accumulated value, the average speed and the international flatness index of the synthesized acceleration of different types of vehicles through a calibration program, and predicting the road surface flatness index IRI of each 10 meters; calculating a road running quality index RQI according to the road flatness index IRI, and dividing the detected road into different grades;
in the formula (I); a is0High speed and first level highway0.026 is selected, and 0.0185, a is selected for highways of other grades1The highway and first level highway is 0.65, and the other level highway is 0.58.
As a preferred scheme of the intelligent road surface management method based on the Internet of things, the road conditions in the GIS map are displayed by the application program of the intelligent road surface detection system of the mobile client, the road conditions in different levels are marked with different colors, and the road sections with inferior or inferior quality levels are marked with bold and striking colors.
As an optimal scheme of the intelligent road surface management method based on the Internet of things, the method comprises the following steps of establishing a regression formula of the accumulated values of the synthetic accelerations, the average vehicle speed and the international flatness index of different types of vehicles, wherein the regression formula specifically comprises the following steps:
(1) selecting road sections with different flatness levels of not less than 5 sections and enough acceleration and deceleration lengths according to the range that the change amplitude of the road surface flatness index IRI of each calibrated road section is not less than 1.0;
(2) selecting a straight line section with smaller gradient change;
(3) taking a precision level gauge as a standard gauge tool, measuring and calibrating the elevation of the longitudinal section of two wheel tracks of a road section, wherein the sampling interval is required to be 250mm, and the elevation testing precision is 0.5 mm; then, performing model calculation on the measured value of the longitudinal section of each wheel track by adopting a road flatness index IRI calculation program to obtain the road flatness index IRI value of the wheel track, wherein the average value of the two wheel track IRI values is the measured value of the road flatness index IRI of the road section;
(4) establishing a regression formula of three-axis acceleration accumulated values, driving speeds and international flatness indexes of different types of vehicles by using measured values of the road flatness indexes IRI of all calibrated road sections and a mathematical statistical method, wherein the correlation coefficient of the regression formula is not less than 0.90;
(5) the method is characterized in that a car is used as a standard car for detecting the flatness, the measured values of the road flatness indexes IRI detected by other types of vehicles are compared with the measured values of the road flatness indexes IRI detected by the car, a vehicle correction coefficient is introduced, and the measured values of the road flatness indexes IRI detected by different types of vehicles are unified with the measured values of the road flatness indexes IRI of the car, so that the flatness detection results of the same road section by the different types of vehicles are the same.
Preferably, the running speed of the vehicle is not less than 20 km/h.
Compared with the prior art, the intelligent road surface management system and method based on the Internet of things have the following beneficial effects:
according to the invention, the three-axis acceleration, position and speed information of the vehicle is acquired through the mobile client (namely, a smart phone), and the data are sent to the data storage module of the Internet of things cloud for storage, the data processing module of the Internet of things cloud analyzes according to the data and obtains the road surface quality result of the road section on which the vehicle runs, and the service processing module of the Internet of things cloud feeds the road surface quality result back to the mobile client, or the road surface quality result is stored to provide retrieval service for the mobile client. Therefore, the user can display the road surface quality of different road sections of the road network by using the smart phone application program, and the positions of the road sections with poor road surface quality are marked in a key way, so that a driver can select a driving route and a road management department can timely carry out effective road surface maintenance work.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly described below.
Fig. 1 is a schematic diagram of an intelligent road surface management system based on the internet of things.
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.
As shown in fig. 1, the preferred embodiment of the intelligent road surface management system based on the internet of things provided by the present invention includes:
the mobile client is provided with a three-axis accelerometer, a GPS (global positioning system) and a road surface intelligent detection system application program, and a user is enabled to select a vehicle type on the application program before the road surface intelligent detection system application program starts; during the driving process of the vehicle, the application program of the road surface intelligent detection system records acceleration data from the three-axis accelerometer and position data and speed data of the GPS, sends the acceleration data, the position data and the speed data to the Internet of things cloud, and then receives and displays a road surface quality result returned by the Internet of things cloud;
the internet of things cloud end is provided with a data storage module, a data processing module and a service processing module, wherein the data storage module is in communication connection with the mobile client end and is used for storing acceleration data, position data and speed data sent by the mobile client end; the data processing module is in communication connection with the data storage module and is used for analyzing the data received by the data storage module and obtaining a road surface quality result of a road section on which the vehicle runs; the service processing module is in communication connection with the data processing module and the mobile client respectively, and is used for sending the road quality result obtained by the data processing module to the mobile client, or storing the road quality result obtained by the data processing module and providing retrieval service for the mobile client.
Preferably, the mobile client is a smart phone.
Therefore, according to the intelligent road surface management system based on the Internet of things, the three-axis acceleration, position and speed information of a vehicle is acquired through a mobile client (namely a smart phone), the data are sent to the data storage module of the cloud end of the Internet of things to be stored, the data processing module of the cloud end of the Internet of things analyzes the data according to the data, the road surface quality result of the running road section of the vehicle is obtained, and the service processing module of the cloud end of the Internet of things feeds the road surface quality result back to the mobile client or stores the road surface quality result to provide retrieval service for the mobile client. Therefore, the user can display the road surface quality of different road sections of the road network by using the smart phone application program, and the positions of the road sections with poor road surface quality are marked in a key way, so that a driver can select a driving route and a road management department can timely carry out effective road surface maintenance work.
In addition, the invention also provides an intelligent road surface management method based on the Internet of things, which comprises the following steps:
(1) before the application program of the road surface intelligent detection system of the mobile client starts, a user selects a vehicle type on the application program of the road surface intelligent detection system;
(2) in the driving process of the vehicle, an application program of a 'road surface intelligent detection system' records acceleration data of a three-axis accelerometer from a mobile client, and position data and speed data of a GPS (global positioning system) of the mobile client; wherein the data recording is performed at 0.01 second intervals or at a frequency rate of 10 Hz;
(3) the mobile client sends acceleration data, position data, speed data and vehicle types to the cloud end of the Internet of things through an application program of a pavement intelligent detection system;
(4) the data storage module of the Internet of things cloud stores acceleration data, position data, speed data and vehicle types sent by the mobile client;
(5) the data processing module of the cloud of the Internet of things analyzes the data received by the data storage module and obtains a road surface quality result of a road section on which the vehicle runs;
(6) the service processing module of the Internet of things cloud sends the road surface quality result obtained by the data processing module to the mobile client, or stores the road surface quality result obtained by the data processing module and provides retrieval service for the mobile client;
(7) the user checks the road surface quality result of the road section on which the vehicle has run through the application program of the road surface intelligent detection system of the mobile client, or retrieves the road surface quality result of the road section on which the vehicle is to run.
Preferably, the mobile client is a smart phone.
Therefore, according to the intelligent road surface management method based on the Internet of things, the three-axis acceleration, position and speed information of a vehicle is acquired through a mobile client (namely a smart phone), the data are sent to the data storage module of the cloud end of the Internet of things to be stored, the data processing module of the cloud end of the Internet of things analyzes the data according to the data, the road surface quality result of the running road section of the vehicle is obtained, and the service processing module of the cloud end of the Internet of things feeds the road surface quality result back to the mobile client or stores the road surface quality result to provide retrieval service for the mobile client. Therefore, the user can display the road surface quality of different road sections of the road network by using the smart phone application program, and the positions of the road sections with poor road surface quality are marked in a key way, so that a driver can select a driving route and a road management department can timely carry out effective road surface maintenance work.
Illustratively, in order to accurately monitor the road surface quality of each road section of the road network, the data processing module at the cloud end of the internet of things analyzes the data received by the data storage module, and the steps specifically include the following steps:
(1) unifying the acceleration of the X axis, the acceleration of the Y axis and the acceleration of the Z axis into a synthetic acceleration a,
it should be noted that the acceleration data acquired by the smart phone is a time domain signal. The acceleration acquisition process inevitably comprises irrelevant data (such as low-frequency noise generated by an engine and a driver). Converting the time domain signal to a frequency domain signal facilitates processing of uncorrelated signals. The smartphone senses acceleration in three axes, namely, the x-axis, the y-axis, and the z-axis. In order to avoid artificially fixing the direction of the mobile phone, the three-axis acceleration is unified into the synthesized acceleration a through the formula (1), so that no special requirement is made on the placing direction of the mobile phone, only proper fixation is required, and the road surface quality detection result is not influenced even when the mobile phone is placed in a pocket of a driver.
(2) Converting the time domain signal of the synthesized acceleration a into a frequency domain signal by using fast Fourier transform, and calculating the acceleration amplitude of 0 Hz-50 Hz according to the sampling rate of 10 Hz;
(3) because both vehicle personnel and a vehicle engine can generate low-frequency noise in the running process of the vehicle, low-frequency components are removed by utilizing high-pass filtering, the regression formulas of the acceleration accumulated values of 0 Hz-50 Hz, 0Hz-10Hz, 10Hz-20Hz, 20 Hz-30 Hz and 30 Hz-50 Hz, the average vehicle speed and the international flatness index are compared according to field detection practice, and the correlation coefficient of the regression formula under the condition of 30 Hz-50 Hz is found to be the maximum; therefore, the acceleration amplitude of 30 Hz-50 Hz is adopted;
(4) calculating the accumulated value and the average speed of the synthesized acceleration within each 10 meters by adopting the acceleration amplitude under the condition of 30 Hz-50 Hz, establishing a regression formula of the accumulated value, the average speed and the international flatness index of the synthesized acceleration of different types of vehicles through a calibration program, and predicting the road surface flatness index IRI of each 10 meters; calculating a road running quality index RQI (see formula 2 specifically) according to the road flatness index IRI, and dividing the detected road into different grades according to the table 1;
in the formula (I); a is0The highway and the first-level highway are 0.026, and the other-level highway is 0.0185, a1The highway and first level highway is 0.65, and the other level highway is 0.58.
TABLE 1 evaluation criteria for road surface running quality
For example, the mobile client's "road surface intelligent detection system" application will display the road conditions in the GIS map, with the different levels of road conditions marked as different colors, and for the road segments with inferior or poor quality levels, marked with bold and striking colors. If the user needs to select between routes, the road condition can be checked from anywhere through a "road intelligent detection system" application in the smartphone. Meanwhile, the road management department can master the road surface quality condition of the road network through the application program of the road surface intelligent detection system, thereby providing a basis for scientifically maintaining the road surface.
Illustratively, the step of establishing a regression formula of the accumulated values of the composite accelerations, the average vehicle speed and the international flatness index of the vehicles of different types specifically includes the following steps:
(1) selecting road sections with different flatness levels of not less than 5 sections and enough acceleration and deceleration lengths according to the range that the change amplitude of the road surface flatness index IRI of each calibrated road section is not less than 1.0, wherein the length of each road section is not less than 300 m;
(2) a straight line section with small gradient change is selected, the traffic volume of the road section is small, and dispersion is facilitated;
(3) taking a precision level gauge as a standard gauge tool, measuring and calibrating the elevation of the longitudinal section of two wheel tracks of a road section, wherein the sampling interval is required to be 250mm, and the elevation testing precision is 0.5 mm; then, performing model calculation on the measured value of the longitudinal section of each wheel track by adopting a road flatness index IRI calculation program to obtain the road flatness index IRI value of the wheel track, wherein the average value of the two wheel track IRI values is the measured value of the road flatness index IRI of the road section;
(4) establishing a regression formula of three-axis acceleration accumulated values, driving speeds and international flatness indexes of different types of vehicles by using measured values of the road flatness indexes IRI of all calibrated road sections and a mathematical statistical method, wherein the correlation coefficient of the regression formula is not less than 0.90;
(5) the method comprises the steps of taking a car as a standard car for detecting the flatness, comparing the measured value of the road flatness index IRI detected by other types of vehicles with the measured value of the road flatness index IRI detected by the car, introducing a vehicle correction coefficient, and unifying the measured value of the road flatness index IRI detected by different types of vehicles with the measured value of the road flatness index IRI of the car so that the flatness detection results of the same road section by the different types of vehicles are the same.
TABLE 1 correction coefficient of representative vehicle type and flatness vehicle type of each vehicle
The key point of detecting the road flatness of each road section is to establish a regression formula of the accumulated value of the composite acceleration of different types of vehicles, the average vehicle speed and the international flatness index, provide a road flatness calibration program for objectively evaluating the road running quality index, and effectively improve the credibility of the detection data.
For example, since too slow a vehicle speed will result in too small an acceleration, the running speed of the vehicle is preferably not less than 20km/h in order to reduce test errors.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (8)
1. The utility model provides an intelligence road surface management system based on thing networking which characterized in that includes:
the mobile client is provided with a three-axis accelerometer, a GPS (global positioning system) and a road surface intelligent detection system application program, and a user is enabled to select a vehicle type on the application program before the road surface intelligent detection system application program starts; during the driving process of the vehicle, the application program of the road surface intelligent detection system records acceleration data from the three-axis accelerometer and position data and speed data of the GPS, sends the acceleration data, the position data and the speed data to the Internet of things cloud, and then receives and displays a road surface quality result returned by the Internet of things cloud;
the internet of things cloud end is provided with a data storage module, a data processing module and a service processing module, wherein the data storage module is in communication connection with the mobile client end and is used for storing acceleration data, position data and speed data sent by the mobile client end; the data processing module is in communication connection with the data storage module and is used for analyzing the data received by the data storage module and obtaining a road surface quality result of a road section on which the vehicle runs; the service processing module is in communication connection with the data processing module and the mobile client respectively, and is used for sending the road quality result obtained by the data processing module to the mobile client, or storing the road quality result obtained by the data processing module and providing retrieval service for the mobile client.
2. The internet of things-based intelligent road surface management system of claim 1, wherein the mobile client is a smart phone.
3. An intelligent road surface management method based on the Internet of things is characterized by comprising the following steps:
(1) before the application program of the road surface intelligent detection system of the mobile client starts, a user selects a vehicle type on the application program of the road surface intelligent detection system;
(2) in the driving process of the vehicle, an application program of a 'road surface intelligent detection system' records acceleration data of a three-axis accelerometer from a mobile client, and position data and speed data of a GPS (global positioning system) of the mobile client;
(3) the mobile client sends acceleration data, position data, speed data and vehicle types to the cloud end of the Internet of things through an application program of a pavement intelligent detection system;
(4) the data storage module of the Internet of things cloud stores acceleration data, position data, speed data and vehicle types sent by the mobile client;
(5) the data processing module of the cloud of the Internet of things analyzes the data received by the data storage module and obtains a road surface quality result of a road section on which the vehicle runs;
(6) the service processing module of the Internet of things cloud sends the road surface quality result obtained by the data processing module to the mobile client, or stores the road surface quality result obtained by the data processing module and provides retrieval service for the mobile client;
(7) the user checks the road surface quality result of the road section on which the vehicle has run through the application program of the road surface intelligent detection system of the mobile client, or retrieves the road surface quality result of the road section on which the vehicle is to run.
4. The intelligent road surface management method based on the internet of things of claim 3, wherein the mobile client is a smart phone.
5. The intelligent road surface management method based on the internet of things of claim 3 or 4, wherein the step of analyzing the data received by the data storage module by the data processing module at the cloud end of the internet of things specifically comprises the following steps:
(1) unifying the acceleration of the X axis, the acceleration of the Y axis and the acceleration of the Z axis into a synthetic acceleration a,
(2) converting the time domain signal of the synthesized acceleration a into a frequency domain signal by using fast Fourier transform, and calculating the acceleration amplitude of 0 Hz-50 Hz according to the sampling rate of 10 Hz;
(3) removing low-frequency components by using high-pass filtering, comparing regression formulas of acceleration accumulated values of 0 Hz-50 Hz, 0Hz-10Hz, 10Hz-20Hz, 20 Hz-30 Hz and 30 Hz-50 Hz, average vehicle speed and international flatness index according to field detection practice, and finding that the correlation coefficient of the regression formula is the largest under the condition of 30 Hz-50 Hz; therefore, the acceleration amplitude of 30 Hz-50 Hz is adopted;
(4) calculating the accumulated value and the average speed of the synthesized acceleration within each 10 meters by adopting the acceleration amplitude under the condition of 30 Hz-50 Hz, establishing a regression formula of the accumulated value, the average speed and the international flatness index of the synthesized acceleration of different types of vehicles through a calibration program, and predicting the road surface flatness index IRI of each 10 meters; calculating a road running quality index RQI according to the road flatness index IRI, and dividing the detected road into different grades;
in the formula (I); a is0The highway and the first-level highway are 0.026, and the other-level highway is 0.0185, a1The highway and first level highway is 0.65, and the other level highway is 0.58.
6. The intelligent road surface management method based on the internet of things as claimed in claim 5, wherein a road surface intelligent detection system application program of the mobile client displays road conditions in a GIS map, the road conditions of different levels are marked with different colors, and road sections with inferior or inferior quality levels are marked with bold and striking colors.
7. The intelligent road surface management method based on the internet of things of claim 5, wherein the step of establishing a regression formula of the accumulated value of the composite acceleration of different types of vehicles, the average vehicle speed and the international flatness index specifically comprises the following steps:
(1) selecting road sections with different flatness levels of not less than 5 sections and enough acceleration and deceleration lengths according to the range that the change amplitude of the road surface flatness index IRI of each calibrated road section is not less than 1.0;
(2) selecting a straight line section with smaller gradient change;
(3) taking a precision level gauge as a standard gauge tool, measuring and calibrating the elevation of the longitudinal section of two wheel tracks of a road section, wherein the sampling interval is required to be 250mm, and the elevation testing precision is 0.5 mm; then, performing model calculation on the measured value of the longitudinal section of each wheel track by adopting a road flatness index IRI calculation program to obtain the road flatness index IRI value of the wheel track, wherein the average value of the two wheel track IRI values is the measured value of the road flatness index IRI of the road section;
(4) establishing a regression formula of three-axis acceleration accumulated values, driving speeds and international flatness indexes of different types of vehicles by using measured values of the road flatness indexes IRI of all calibrated road sections and a mathematical statistical method, wherein the correlation coefficient of the regression formula is not less than 0.90;
(5) the method is characterized in that a car is used as a standard car for detecting the flatness, the measured values of the road flatness indexes IRI detected by other types of vehicles are compared with the measured values of the road flatness indexes IRI detected by the car, a vehicle correction coefficient is introduced, and the measured values of the road flatness indexes IRI detected by different types of vehicles are unified with the measured values of the road flatness indexes IRI of the car, so that the flatness detection results of the same road section by the different types of vehicles are the same.
8. The intelligent road surface management method based on the internet of things as claimed in claim 7, wherein the running speed of the vehicle is not less than 20 km/h.
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