CN114582135A - Method, device, terminal and storage medium for detecting road surface abnormity - Google Patents

Method, device, terminal and storage medium for detecting road surface abnormity Download PDF

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CN114582135A
CN114582135A CN202111363275.8A CN202111363275A CN114582135A CN 114582135 A CN114582135 A CN 114582135A CN 202111363275 A CN202111363275 A CN 202111363275A CN 114582135 A CN114582135 A CN 114582135A
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data
vibration acceleration
acceleration
abnormal
time point
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CN114582135B (en
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张建波
赵祥
孙建平
雷方舒
徐春玲
朱珊
温慧敏
郭继孚
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Beijing Transport Institute
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Beijing Transport Institute
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3833—Creation or updating of map data characterised by the source of data
    • G01C21/3844—Data obtained from position sensors only, e.g. from inertial navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The application provides a method, a device, a terminal and a storage medium for detecting road surface abnormity. The method comprises the following steps: acquiring first data of a vehicle running on a target road, wherein the first data comprises a first vibration acceleration and a longitude and latitude corresponding to at least one preset time point in a first time length; dividing the first data according to a preset sliding time window to obtain at least one group of second data, wherein the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is the first vibration acceleration which is not within a preset vibration acceleration range, and the duration of the sliding time window is less than the first duration; and sending the second data to a road detection server so that the road detection server determines the abnormal road section in the target road according to the longitude and latitude in the second data. The method and the device can accurately determine the abnormal road section in the target road.

Description

Method, device, terminal and storage medium for detecting road surface abnormity
Technical Field
The present disclosure relates to the field of road surface detection technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for detecting a road surface abnormality.
Background
With the rapid development of road traffic in China, road mileage is continuously increased, and how to detect abnormal roads with damaged road surfaces has important significance for safe driving of motor vehicles.
In the prior art, first data of a vehicle running on a target road is acquired, the first data includes coordinates of specific position points and vibration accelerations corresponding to the position points, the first data is divided into at least one group of second data according to a preset position interval, and the at least one group of second data is sent to a road detection server. And after receiving at least one group of second data, the road detection server determines the root mean square corresponding to each group of second data, and when the root mean square corresponding to the second data is greater than a preset value, determines the road section corresponding to the second data as an abnormal road section, thereby determining all abnormal road sections on the target road.
In the process, the first data are divided according to the preset position interval, and once the second data only comprise the vibration data corresponding to the abnormal road section with a small distance and the preset numerical value is set to be large, the abnormal road section cannot be detected, and the abnormal road section in the target road cannot be accurately determined.
Disclosure of Invention
In view of this, the present application provides a method, an apparatus, a terminal and a storage medium for detecting a road surface abnormality, which can accurately determine an abnormal road segment in a target road.
In order to achieve the above purpose, the present application mainly provides the following technical solutions:
in a first aspect, the present application provides a method of detecting a road surface abnormality, the method comprising:
acquiring first data of a vehicle running on a target road, wherein the first data comprises a first vibration acceleration and a longitude and latitude corresponding to at least one preset time point in a first time length;
dividing the first data according to a preset sliding time window to obtain at least one group of second data, wherein the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is a first vibration acceleration which is not within a preset vibration acceleration range, and the duration of the sliding time window is less than the first duration;
and sending the second data to a road detection server so that the road detection server determines an abnormal road section in the target road according to the longitude and latitude in the second data.
In a second aspect, the present application provides a device for detecting a road surface abnormality, the device including:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring first data of a vehicle running on a target road, and the first data comprises a first vibration acceleration and a longitude and latitude corresponding to at least one preset time point in a first time length;
the dividing unit is used for dividing the first data according to a preset sliding time window to obtain at least one group of second data, wherein the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is a first vibration acceleration which is not within a preset vibration acceleration range, and the duration of the sliding time window is less than the first duration;
and the sending unit is used for sending the second data to a road detection server so that the road detection server can determine the abnormal road section in the target road according to the longitude and the latitude in the second data.
In a third aspect, the present application provides a terminal, where the terminal is configured to run a program, where the terminal executes the method for detecting a road surface abnormality according to the first aspect when running.
In a fourth aspect, the present application provides a storage medium for storing a computer program, wherein the computer program controls an apparatus in which the storage medium is located to execute the method for detecting a road surface abnormality of the first aspect when running.
By means of the technical scheme, the method, the device, the terminal and the storage medium for detecting the road surface abnormity are provided, after first data of a vehicle running on a target road are obtained, the first data are divided according to a preset sliding time window to obtain at least one group of second data, the second data are sent to a road detection server, and then the road detection server determines an abnormal road section in the target road according to the longitude and latitude in the second data. Therefore, the vehicle directly sends the second data corresponding to the abnormal road section to the vehicle, so that the road detection server only needs to determine the road section corresponding to the data, the problem that the data are divided according to the preset position interval in the prior art is solved, and the abnormal road section in the target road can be accurately determined.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for detecting road surface anomalies as disclosed herein;
FIG. 2 is a schematic flow chart of a method for detecting road surface anomalies as disclosed herein;
FIG. 3 is a schematic diagram of a method for detecting a road surface anomaly as disclosed herein;
FIG. 4 is a schematic flow chart of a method for detecting a road surface anomaly disclosed in the present application;
FIG. 5 is a schematic flow chart of a method for detecting a road surface anomaly disclosed in the present application;
FIG. 6 is a schematic illustration of a method of detecting a road surface anomaly as disclosed herein;
fig. 7 is a schematic structural view of a device for detecting a road surface abnormality disclosed in the present application;
fig. 8 is a schematic structural diagram of a device for detecting a road surface abnormality disclosed in the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the prior art, first data of a vehicle running on a target road is acquired, the first data is divided into at least one group of second data according to a preset position interval, and the divided second data is sent to a road detection server. And after receiving at least one group of second data, the road detection server determines the root mean square corresponding to each group of second data, and when the root mean square corresponding to the second data is greater than a preset value, determines the road section corresponding to the second data as an abnormal road section, thereby determining all abnormal road sections on the target road.
The first data is divided according to the preset position interval to obtain the second data. Once the second data only includes the vibration data corresponding to the abnormal road section with a small distance and the preset value is set to be large, the abnormal road section cannot be detected, and the abnormal road section in the target road cannot be accurately determined. In order to solve the above problem, in the embodiment of the present application, the terminal device is adopted to detect the acquired data first, and determine the data corresponding to the abnormal road segment, so that not only the abnormal road segment in the target road can be accurately determined, but also the communication resources and the storage resources of the road detection server can be reduced. The specific execution steps are shown in fig. 1, and include:
step 101, acquiring first data of a vehicle running on a target road.
Wherein the target road is a road on which the vehicle is traveling. The first data comprises a first vibration acceleration and longitude and latitude corresponding to at least one preset time point in a first time length. The first period is a period of time during which the vehicle is traveling on the target road.
In the implementation process of the step, a controller of the vehicle acquires first data of the vehicle running on the target road.
And 102, dividing the first data according to a preset sliding time window to obtain at least one group of second data.
Wherein the second data has at least one abnormal first vibration acceleration. The abnormal first vibration acceleration is a first vibration acceleration which is not within a preset vibration acceleration range, and the normal first vibration acceleration is a first vibration acceleration which is within the preset vibration acceleration range. The second data is data corresponding to the abnormal road section. The duration of the sliding time window is less than the first duration, for example, the duration of the sliding time window is equal to the duration between two adjacent preset time points.
In the embodiment of the present application, the preset vibration acceleration range is set by a technician based on the performance of the vehicle, and the preset vibration acceleration ranges corresponding to different types of vehicles may be the same or different. The preset vibration acceleration ranges corresponding to vehicles of the same type may be the same or different. The preset vibration acceleration range may be fixed or variable for a particular vehicle. For example, the preset vibration acceleration range is widened with the use time period of the vehicle.
And 103, sending the second data to the road detection server so that the road detection server determines the abnormal road section in the target road according to the second data.
In the implementation of this step, the controller of the vehicle sends the second data to the road detection server. And after the road detection server receives the second data, the road detection server determines the abnormal road section in the target road according to the longitude and latitude corresponding to each preset time point in the second data.
Meanwhile, the road detection server stores the second data for subsequent use.
It should be noted that the road detection server in the embodiment of the present application may receive the second data transmitted by the plurality of vehicles. After the road detection server receives second data of a preset vehicle about a certain abnormal road section, the road detection server sets a label of the certain abnormal road section as an abnormal label, and then pushes the road section with the label as the abnormal label to relevant technical personnel, so that the relevant technical personnel maintain the road section.
For example, when the road detection server receives second data of 5 vehicles respectively about an abnormal road section, the tag of the abnormal road section is set as the abnormal tag, and the abnormal road section with the abnormal tag is transmitted to the road administration staff or the motor vehicle driver.
In the embodiment of the application, when the minimum difference value of the longitude and latitude in the two second data is smaller than the preset difference value, the two data are determined to be the data describing the same road surface position.
After first data of a vehicle running on a target road are obtained, the first data are divided according to a preset sliding time window to obtain at least one group of second data, the second data are sent to a road detection server, and then the road detection server determines an abnormal road section in the target road according to the longitude and latitude in the second data. Therefore, the vehicle directly sends the data of the abnormal road section to the vehicle, so that the road detection server only needs to determine the road section corresponding to the data, the problem that the data are divided according to the preset position interval in the prior art is solved, and the abnormal road section in the target road can be accurately determined.
Meanwhile, only the divided second data are sent to the road detection server, all data collected by the vehicle do not need to be sent to the road detection server, the data quantity needing to be transmitted is reduced, and communication resources are saved.
Furthermore, as the vehicle is continuously used, a certain angle deviation exists in a three-dimensional coordinate system between the three-axis accelerometer and the vehicle body, so that the data acquisition precision of the vibration acceleration of the vehicle is influenced, the use difficulty of the equipment is increased, and the data acquisition quality is reduced. In order to solve the above problem, an embodiment of the present application provides a method for correcting data acquired by a three-axis accelerometer, which includes the specific steps shown in fig. 2, including:
step 201, obtaining average acceleration of the triaxial accelerometer corresponding to each axis in a preset time period after the vehicle is started.
The three-axis accelerometer comprises three axes, namely an X axis, a Y axis and a Z axis. The acceleration corresponding to each axis includes accelerations corresponding to the X axis, the Y axis, and the Z axis, respectively. In practice, the controller stores the data of the three-axis accelerometer in the memory of the vehicle every time the controller collects the data until the vehicle runs the entire target road.
In the specific implementation process of this step, the controller of the vehicle first obtains, in the memory, the acceleration corresponding to each axis within a preset time period after the vehicle is started, and performs an averaging process on the acceleration to obtain the average acceleration corresponding to each axis within the preset time period.
For example, the acceleration corresponding to each axis within 5 seconds after the vehicle is started is acquired in the memory, and the average acceleration corresponding to each axis within 5 seconds after the vehicle is started is obtained by averaging the axes.
Step 202, determining a correction value of each axis according to the average acceleration corresponding to each axis.
The correction value is used for correcting the acceleration corresponding to each axis corresponding to each preset time point.
In the specific implementation process of this step, the correction values corresponding to the X-axis, the Y-axis, and the Z-axis are determined according to the average acceleration and the preset formula corresponding to the X-axis, the Y-axis, and the Z-axis, respectively. Preset formula as
Figure BDA0003359674410000061
Wherein the content of the first and second substances,
Figure BDA0003359674410000062
the average acceleration corresponding to the X axis corresponding to each preset time point is defined, cos alpha is the corrected value of the X axis,
Figure BDA0003359674410000063
the average acceleration corresponding to the Y axis corresponding to each preset time point, cos beta is the corrected value of the Y axis,
Figure BDA0003359674410000064
and the cos gamma is a corrected value of the Z axis for the average acceleration corresponding to the Z axis corresponding to each preset time point.
And step 203, multiplying the acceleration corresponding to each axis by the correction value of each axis respectively for each preset time point to obtain the corrected acceleration corresponding to each axis.
In the specific implementation process of this step, for each preset time point, the acceleration of the X axis is multiplied by the correction value of the X axis to obtain a corrected acceleration corresponding to the X axis, the acceleration of the Y axis is multiplied by the correction value of the Y axis to obtain a corrected acceleration corresponding to the Y axis, and the acceleration of the Z axis is multiplied by the correction value of the Z axis to obtain a corrected acceleration corresponding to the Z axis.
And 204, determining a second vibration acceleration corresponding to each preset time point according to the corrected acceleration corresponding to each axis corresponding to each preset time point.
In the specific implementation process of the step, for each preset time point, adding the corrected acceleration corresponding to each axis to obtain an addition result; a difference between the addition result and the gravitational acceleration is determined, and the difference is determined as a second vibration acceleration.
Specifically, for each preset time point, the corrected acceleration of the X axis, the corrected acceleration corresponding to the Y axis, and the corrected acceleration corresponding to the Z axis are added to obtain an addition result, a difference between the addition result and the gravitational acceleration is calculated, and the difference is determined as a second vibration acceleration corresponding to the preset time point.
For example, according to the formula a ═ ax·cosα+ay·cosβ+azCos γ -g, determining the second vibration acceleration. Wherein a' is the second vibration acceleration, axAcceleration of the X axis, ayAcceleration in the Y-axis, azThe acceleration of the Z axis is shown as cos alpha, the corrected value of the X axis is shown as cos beta, the corrected value of the Z axis is shown as cos gamma, and the acceleration of gravity is shown as g.
Step 205, determining a first vibration acceleration corresponding to each preset time point according to the second vibration acceleration corresponding to each preset time point.
In the specific implementation process of the step, filtering and denoising is performed on the second vibration acceleration corresponding to each preset time point to obtain the first vibration acceleration corresponding to each preset time point.
In this embodiment of the application, a kalman filtering formula may be used to perform filtering and noise reduction processing on the first vibration acceleration corresponding to each preset time point. Wherein, the Kalman filtering formula is:
Figure BDA0003359674410000071
Figure BDA0003359674410000072
Figure BDA0003359674410000073
Figure BDA0003359674410000074
Figure BDA0003359674410000075
wherein the content of the first and second substances,
Figure BDA0003359674410000076
is an estimated value of the posterior state at the moment k;
Figure BDA0003359674410000077
is a prior state estimated value at the moment k; u. uk-1In order to control the vector of the system,
Figure BDA0003359674410000078
covariance for a posteriori estimate at time k; pk-1Covariance of prior estimates for time k; h is a conversion matrix from the state variable to the observation variable; z is a radical ofkIs an observed value; kkIs a filter gain matrix; a and B are respectively state transition matrixes; q is the excitation noise covariance; r is the observed noise covariance.
Fig. 3 includes the vibration acceleration without being subjected to the filtering processing and the vibration acceleration subjected to the filtering processing. As can be seen from fig. 3, the vibration acceleration subjected to the filtering process is removed from the vibration acceleration not subjected to the filtering process, and the reliability of the vibration acceleration is improved.
In the embodiment of the application, the acceleration acquired by the triaxial accelerometer is corrected, so that the first vibration acceleration is obtained, the problem of angular deviation between the triaxial accelerometer and a vehicle in the vehicle movement process is solved, and the accuracy of the acquired first vibration acceleration is improved.
Meanwhile, the second vibration acceleration is subjected to filtering and denoising processing, so that the vibration acceleration caused by the vehicle is removed, and the accuracy and the reliability of the acquired first vibration acceleration are improved.
Meanwhile, another preferred embodiment of the present application is to explain in detail the process of dividing the first data by using a sliding time window on the basis of the foregoing fig. 1, and its specific steps are shown in fig. 4, including:
step 401, acquiring first data of a vehicle running on a target road.
The first data comprises a first vibration acceleration and longitude and latitude corresponding to at least one preset time point in a first time length.
This step is similar to step 101 and will not be described herein again.
Step 402, detecting whether an abnormal first vibration acceleration exists in the sliding time window.
The first vibration acceleration corresponding to the preset time point in the first data is sequenced according to time, a sliding time window starts to slide with second duration at the first vibration acceleration in the first data, and the second duration is smaller than the duration of the sliding time window.
In the specific implementation process of this step, a first vibration acceleration corresponding to each preset time point in the first data is obtained, the sliding time window starts to slide at the first vibration acceleration in the first data, and the first vibration acceleration corresponding to each preset time point in the sliding time window is detected, so as to determine whether an abnormal first vibration acceleration exists in the sliding time window.
Step 403, if yes, determining a preset time point corresponding to the first abnormal first vibration acceleration in the sliding time window as an abnormal starting time point.
In the specific implementation process of this step, if an abnormal first vibration acceleration exists in the sliding time window, a preset time point corresponding to the first vibration acceleration, where an abnormality is detected, in the sliding time window is determined as an abnormality start time point.
Or adding a preset time point corresponding to the first vibration acceleration with the first detected abnormality to the duration of the sliding time window, and determining the addition result as the abnormality starting time point.
Step 404, sliding the sliding time window with a second duration, when detecting that there is no abnormal first vibration acceleration in the sliding time window, determining a preset time point corresponding to the last detected abnormal first vibration acceleration, and determining the preset time point corresponding to the last detected abnormal first vibration acceleration as an abnormal ending time point.
In the specific implementation process of this step, the sliding time window is slid by the second duration until no abnormal first vibration acceleration exists in the sliding time window, and a preset time point corresponding to the last detected abnormal first vibration acceleration, that is, the latest abnormal first vibration acceleration corresponding to the preset time point, is determined. And determining a first vibration acceleration corresponding to the latest abnormality at the preset time point as an abnormality ending time point.
Or after the preset time point corresponding to the last abnormal first vibration acceleration is determined, adding the preset time point and the duration of the sliding time window to obtain an abnormal ending time point.
In order to avoid that the determined abnormal ending time point is not in at least one preset time point included in the first time length, after the abnormal ending time point is obtained, the time point corresponding to the last first vibration acceleration in the first data needs to be compared with the abnormal ending time point, the method specifically comprises the following steps: judging whether the abnormal ending time point is larger than a preset time point corresponding to the last first vibration acceleration in the first data or not; and if so, updating the abnormal ending time point to a preset time point. And if the time is less than the preset time, not updating the abnormal ending time point.
According to the embodiment of the application, the validity of the abnormal ending time point is determined by comparing the abnormal ending time point with the preset time point corresponding to the last first vibration acceleration in the first data. And when the abnormal ending time point is invalid, updating the abnormal ending time point so as to accurately divide the first data.
And step 405, intercepting the first data according to the abnormal starting time point and the abnormal ending time point to obtain second data.
In the specific implementation process of the step, the preset time point between the abnormal starting time point and the abnormal ending time point is determined in all the preset time points, and the first vibration acceleration and the corresponding longitude and latitude corresponding to the preset time points are determined as second data.
And step 406, sending the second data to the road detection server, so that the road detection server determines the abnormal road section in the target road according to the longitude and latitude in the second data.
In the embodiment of the application, the method for extracting the abnormal road vibration by adopting the sliding time window solves the problem of incomplete data characteristics caused by dividing the first data in the prior art, so that the abnormal road section in the target road can be accurately determined, and meanwhile, the vehicle data transmission cost and the storage cost of a road detection server are also reduced.
In addition, another preferred embodiment of the present application is based on the foregoing fig. 1, in order to ensure that the second data is data corresponding to an abnormal road segment, the present application may first screen the second data, and then send the screened second data to the road detection server, where as shown in fig. 5, the specific steps of the present application include:
step 501, first data of a vehicle running on a target road are acquired.
The first data comprises a first vibration acceleration, a speed and a longitude and latitude corresponding to at least one preset time point in a first time length.
In the specific implementation process of the step, the controller on the vehicle acquires data of the three-axis accelerometer, the wheel speed device and the GPS at preset time intervals, obtains acceleration, speed and longitude and latitude corresponding to each axis, and further stores the acceleration, speed and longitude and latitude into the memory. After the vehicle runs through the target road, the acceleration, the speed and the longitude and latitude corresponding to each axis corresponding to each preset time point are stored in the memory. And for each preset time point, the controller performs angle self-adaptive correction on the acceleration corresponding to each axis to obtain the corrected acceleration corresponding to each axis, and further obtains a second vibration acceleration based on the corrected acceleration corresponding to each axis. And carrying out filtering and noise reduction treatment on the second vibration acceleration corresponding to each preset time point to obtain a first vibration acceleration corresponding to each preset time point. Thus, the first vibration acceleration, the speed and the longitude and latitude corresponding to each preset time point are used as first data.
Step 502, dividing the first data according to a preset sliding time window to obtain at least one group of second data.
And the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is not in a preset vibration acceleration range, and the duration of the sliding time window is less than the first duration.
Step 502 is similar to step 102 and will not be described here.
It should be noted that, in this step, the second data also includes the speed and the latitude and longitude corresponding to at least one preset time point.
And step 503, determining a first vibration acceleration root mean square and an average speed corresponding to each group of second data.
In a specific implementation of this step, for each set of second data, a first vibration acceleration and velocity included in the set of second data is determined. And determining a first vibration acceleration root mean square based on a preset formula and a first vibration acceleration included in the group of data. And averaging the speeds in the group of second data to obtain an average speed. Thus, by the method, the root mean square and the average speed of the first vibration acceleration corresponding to each second datum are obtained.
The predetermined formula is
Figure BDA0003359674410000111
Wherein RMS is the root mean square of the first vibration acceleration, az,iThe first vibration acceleration of the vehicle at the preset time point is N, and the N is a sample amount of the first vibration acceleration in the second data.
In step 504, second data with corresponding duration longer than a third duration, first vibration acceleration root mean square greater than a preset vibration acceleration root mean square and average speed within a preset speed range are screened out from all the groups of second data, and the screened second data are determined as third data.
For example, when the time length of the second data is not less than 1.2s, and the first vibration acceleration root mean square is not less than 0.075g, and the average speed is between 15km/h and 70km/h, such second data is determined as the third data.
In the actual running process of the vehicle, when the speed of the vehicle exceeds the maximum speed in the preset speed range, the vibration acceleration of the vehicle is increased, in order to avoid the influence of the speed of the vehicle on the vibration acceleration, only second data that the first vibration acceleration root mean square is larger than the preset vibration acceleration root mean square and the average speed is in the preset speed range is obtained. And when the duration corresponding to a section of data is less than the preset duration, which is often caused by a deceleration strip arranged on a road, in order to ensure that the data sent to the road detection server is the data corresponding to the abnormal road section, the second data with the corresponding duration less than the preset duration can be removed.
In the embodiment of the present application, the sliding time window is used to divide the first data to obtain the second data, and the pseudo codes corresponding to the process of screening out the second data, of which the corresponding duration is greater than the third duration, the root mean square of the first vibration acceleration is greater than the preset root mean square of the vibration acceleration, and the average speed is within the preset speed range, from the second data of all the groups are as follows:
input Signal (t, a) # Signal input
if a > extreme _ upper or a < extreme _ lower # -extreme judgment
if t-max (Time _ set) > Window _ size # Time Interval judgement
Signal fragment extraction Signal fragment (Signal [ min (Time _ set) + Window _ size, max (Time _ set) + Window _ size ] #)
if RMS (Segment) > Threshold # Threshold judgment, outputting signal Segment
output:Segment
Clear () # clear set of timestamps
Time _ set.appended (t) # writes a new extremum timestamp
else
Time_set.append(t)
Signal (t, a) in the above process is first data (t is a preset Time point, a is a first vibration acceleration), extreme _ upper is a critical value upper limit in a preset vibration acceleration range, extreme _ lower is a critical value lower limit in the preset vibration acceleration range, Threshold is a preset vibration acceleration root mean square, Window _ size is a duration of a sliding Time Window, min (Time _ set) is a first vibration acceleration of a first detected abnormality in the sliding Time Window, max (Time _ set) is a first vibration acceleration of a last detected abnormality in the sliding Time Window, and RMS is a root mean square function.
Wherein, the length of the time window is 0.8s, and the positive and negative thresholds of the vibration acceleration are 0.16g and-0.15 g respectively.
And 505, sending the third data to the road detection server, so that the road detection server determines an abnormal road section in the target road according to the third data.
Step 505 is similar to step 103 and will not be described herein.
As shown in fig. 6, during actual driving of the vehicle, when the speed of the vehicle exceeds the maximum speed in the preset speed range, the vibration acceleration of the vehicle will be larger and larger, and in order to avoid the influence of the speed of the vehicle on the vibration acceleration, only the second data that the first vibration acceleration root mean square is larger than the preset vibration acceleration root mean square and the average speed is within the preset speed range is obtained. And when the duration corresponding to a section of data is less than the preset duration, which is often caused by a deceleration strip arranged on a road, in order to ensure that the data sent to the road detection server is the data corresponding to the abnormal road section, the second data with the corresponding duration less than the preset duration can be removed.
Further, as an implementation of the method embodiments shown in fig. 1-2 and 4-5, the embodiments of the present application provide a device for detecting a road surface abnormality, which can accurately determine an abnormal road segment in a target road. The embodiment of the apparatus corresponds to the foregoing method embodiment, and details in the foregoing method embodiment are not repeated in this embodiment for convenience of reading, but it should be clear that the apparatus in this embodiment can correspondingly implement all the contents in the foregoing method embodiment. As shown in fig. 7 in detail, the apparatus includes:
a first data obtaining unit 701, configured to obtain first data of a vehicle traveling on a target road, where the first data includes a first vibration acceleration and a longitude and latitude corresponding to at least one preset time point within a first duration;
a dividing unit 702, configured to divide the first data acquired by the first data acquiring unit 701 according to a preset sliding time window to obtain at least one group of second data, where the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is a first vibration acceleration that is not within a preset vibration acceleration range, and a duration of the sliding time window is less than the first duration;
a sending unit 703, configured to send the second data obtained by the dividing unit 702 to a road detection server, so that the road detection server determines an abnormal road segment in the target road according to the longitude and latitude in the second data.
Further, as shown in fig. 8, the apparatus further includes:
an average vibration acceleration obtaining unit 704, configured to obtain an average acceleration corresponding to each axis of the triaxial accelerometer within a preset time period after the vehicle is started;
a first determining unit 705, configured to determine a correction value of each axis according to the average acceleration corresponding to each axis acquired by the average vibration acceleration acquiring unit 704, where the correction value is used to correct the acceleration corresponding to each axis corresponding to each preset time point;
a correcting unit 706, configured to multiply, for each preset time point, the acceleration corresponding to each axis by the correction value of each axis obtained by the first determining unit 705, respectively, to obtain a corrected acceleration corresponding to each axis;
a second determining unit 707, configured to determine, according to the corrected acceleration corresponding to each axis obtained by the correcting unit 706, a second vibration acceleration corresponding to each preset time point;
a third determining unit 708, configured to determine, according to the second vibration acceleration obtained by the second determining unit 707, a first vibration acceleration corresponding to each preset time point.
Further, as shown in fig. 8, the second determining unit 707 includes:
an adding module 7071, configured to add the corrected accelerations corresponding to the axes obtained by the correcting unit 706 to obtain an addition result;
a difference module 7072, configured to determine a difference between the addition result obtained by the addition module 7071 and the gravitational acceleration, and determine the difference as the second vibration acceleration.
Further, the dividing unit 702 includes:
a detecting module 7021, configured to detect whether there is an abnormal first vibration acceleration in the sliding time window, where the first vibration accelerations corresponding to preset time points in the first data are sorted according to time, the sliding time window starts to slide at a second duration at a first vibration acceleration in the first data, and the second duration is less than the duration of the sliding time window;
an abnormal starting time point determining module 7022, configured to determine, if the detecting module 7021 detects that an abnormal first vibration acceleration exists in the sliding time window, a preset time point corresponding to the first abnormal first vibration acceleration in the sliding time window as an abnormal starting time point;
an abnormal ending time point determining module 7023, configured to slide the sliding time window by the second duration after the abnormal starting time point determining module 7022 determines the abnormal starting time point, determine, when it is detected that there is no abnormal first vibration acceleration in the sliding time window, a preset time point corresponding to a last detected abnormal first vibration acceleration, and determine, as the abnormal ending time point, the preset time point corresponding to the last detected abnormal first vibration acceleration;
an intercepting module 7024, configured to intercept the first data according to the abnormal starting time point determined by the abnormal starting time point determining module 7022 and the abnormal ending time point determined by the abnormal ending time point determining module 7023 to obtain the second data.
Further, as shown in fig. 8, the apparatus further includes: the first data further includes a speed corresponding to each preset time point, and after the first data is divided according to a preset sliding time window to obtain at least one group of second data, the apparatus further includes:
a fourth determining unit 709, configured to determine a root mean square and an average speed of the vibration acceleration corresponding to each group of the second data;
a screening unit 710, configured to screen out, from all sets of second data, second data whose corresponding duration is greater than a third duration, whose root mean square of the vibration acceleration determined by the fourth determining unit 709 is greater than a preset root mean square of the vibration acceleration, and whose average speed determined by the fourth determining unit 709 is within a preset speed range, and determine the screened second data as third data;
the sending unit 703 is further configured to send the third data determined by the screening module to the road detection server.
Further, an embodiment of the present application further provides a processor, where the processor is configured to execute a program, where the program executes the method for detecting a road surface abnormality described in fig. 1 to 6.
Further, an embodiment of the present application further provides a storage medium, where the storage medium is used to store a computer program, where the computer program is run to control a device in which the storage medium is located to execute the method for detecting a road surface abnormality described in fig. 1 to 6.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It will be appreciated that the relevant features of the method and apparatus described above are referred to one another. In addition, "first", "second", and the like in the above embodiments are for distinguishing the embodiments, and do not represent merits of the embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, this application is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best mode of use of the present application.
In addition, the memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A method of detecting a road surface abnormality, characterized by comprising:
acquiring first data of a vehicle running on a target road, wherein the first data comprises a first vibration acceleration and a longitude and latitude corresponding to at least one preset time point in a first time length;
dividing the first data according to a preset sliding time window to obtain at least one group of second data, wherein the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is a first vibration acceleration which is not within a preset vibration acceleration range, and the duration of the sliding time window is less than the first duration;
and sending the second data to a road detection server so that the road detection server determines an abnormal road section in the target road according to the longitude and latitude in the second data.
2. The method according to claim 1, characterized in that it comprises:
acquiring the average acceleration of the triaxial accelerometer corresponding to each axis within a preset time period after the vehicle is started;
determining a correction value of each axis according to the average acceleration corresponding to each axis, wherein the correction value is used for correcting the acceleration corresponding to each axis corresponding to each preset time point;
for each preset time point, multiplying the acceleration corresponding to each axis by the correction value of each axis respectively to obtain the correction acceleration corresponding to each axis;
determining a second vibration acceleration corresponding to each preset time point according to the corrected acceleration corresponding to each axis;
and determining the first vibration acceleration corresponding to each preset time point according to the second vibration acceleration.
3. The method according to claim 2, wherein the determining the second vibration acceleration corresponding to each preset time point according to the corrected acceleration corresponding to each axis comprises:
adding the corrected acceleration corresponding to each axis to obtain an addition result;
determining a difference between the addition result and the gravitational acceleration, the difference being determined as the second vibration acceleration.
4. The method according to claim 1, wherein the dividing the first data according to a preset sliding time window to obtain at least one group of second data comprises:
detecting whether an abnormal first vibration acceleration exists in the sliding time window, wherein the first vibration acceleration corresponding to a preset time point in the first data is sequenced according to time, the sliding time window starts to slide with a second duration in the first vibration acceleration in the first data, and the second duration is shorter than the duration of the sliding time window;
if yes, determining a preset time point corresponding to a first abnormal first vibration acceleration in the sliding time window as an abnormal starting time point;
sliding the sliding time window by the second duration, determining a preset time point corresponding to the last abnormal first vibration acceleration when detecting that the abnormal first vibration acceleration does not exist in the sliding time window, and determining the preset time point corresponding to the last abnormal first vibration acceleration as an abnormal ending time point;
and intercepting the first data according to the abnormal starting time point and the abnormal ending time point to obtain the second data.
5. The method according to claim 1, wherein the first data further includes a speed corresponding to each preset time point, and after the first data is divided according to a preset sliding time window to obtain at least one group of second data, the method further includes:
determining a first vibration acceleration root mean square and an average speed corresponding to each group of second data;
screening out second data with corresponding duration being greater than third duration, the first vibration acceleration root mean square being greater than a preset vibration acceleration root mean square and the average speed being within a preset speed range from all groups of second data, and determining the screened out second data as third data;
the sending the second data to a road detection server includes:
and sending the third data to a road detection server.
6. An apparatus for detecting a road surface abnormality, characterized by comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring first data of a vehicle running on a target road, and the first data comprises a first vibration acceleration and a longitude and latitude corresponding to at least one preset time point in a first time length;
the dividing unit is used for dividing the first data according to a preset sliding time window to obtain at least one group of second data, wherein the second data has at least one abnormal first vibration acceleration, the abnormal first vibration acceleration is a first vibration acceleration which is not within a preset vibration acceleration range, and the duration of the sliding time window is less than the first duration;
and the sending unit is used for sending the second data to a road detection server so that the road detection server determines the abnormal road section in the target road according to the longitude and latitude in the second data.
7. The apparatus of claim 6, further comprising:
the device comprises an average vibration acceleration acquisition unit, a vibration acceleration detection unit and a vibration acceleration detection unit, wherein the average vibration acceleration acquisition unit is used for acquiring the average acceleration corresponding to each axis of a triaxial accelerometer within a preset time period after the vehicle is started;
the first determining unit is used for determining a correction value of each axis according to the average acceleration corresponding to each axis, and the correction value is used for correcting the acceleration corresponding to each axis corresponding to each preset time point;
the correction unit is used for multiplying the acceleration corresponding to each axis by the correction value of each axis respectively at each preset time point to obtain the correction acceleration corresponding to each axis;
the second determining unit is used for determining a second vibration acceleration corresponding to each preset time point according to the corrected acceleration corresponding to each axis;
and the third determining unit is used for determining the first vibration acceleration corresponding to each preset time point according to the second vibration acceleration.
8. The apparatus according to claim 7, wherein the second determining unit comprises:
the adding module is used for adding the corrected acceleration corresponding to each axis to obtain an addition result;
and the difference calculating module is used for determining the difference between the addition result and the gravity acceleration and determining the difference as the second vibration acceleration.
9. A terminal, characterized in that the terminal is used for running a program, wherein the terminal executes the method for detecting a road surface abnormality according to any one of claims 1 to 5 when running.
10. A storage medium for storing a computer program, wherein the computer program is operable to control an apparatus in which the storage medium is located to execute the method for detecting a road surface abnormality according to any one of claims 1 to 5.
CN202111363275.8A 2021-11-17 2021-11-17 Method, device, terminal and storage medium for detecting pavement abnormality Active CN114582135B (en)

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