CN112927516A - Road vehicle monitoring method, electronic equipment and storage medium - Google Patents
Road vehicle monitoring method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112927516A CN112927516A CN201911242659.7A CN201911242659A CN112927516A CN 112927516 A CN112927516 A CN 112927516A CN 201911242659 A CN201911242659 A CN 201911242659A CN 112927516 A CN112927516 A CN 112927516A
- Authority
- CN
- China
- Prior art keywords
- road surface
- vehicle
- stress data
- road
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 239000013307 optical fiber Substances 0.000 claims abstract description 55
- 230000008859 change Effects 0.000 claims abstract description 22
- 230000036962 time dependent Effects 0.000 claims abstract description 12
- 238000004590 computer program Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 11
- 239000000835 fiber Substances 0.000 description 11
- 230000003287 optical effect Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000001902 propagating effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- 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
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the invention relates to the technical field of information, and discloses a road vehicle monitoring method, electronic equipment and a storage medium. The invention discloses a road vehicle monitoring method, which comprises the following steps: acquiring a sensing signal fed back by a distributed optical fiber sensing system; the distributed optical fiber sensing system is an optical fiber sensor set laid on a road surface; determining the stress data of the road surface according to the time-dependent change value of the phase data in the sensing signal and the linear relationship between the time-dependent change value of the phase data and the stress generated by the interference of the vehicles on the road surface; and determining vehicle information of the road surface according to the stress data. Carrying out phase analysis on a signal fed back by an optical fiber sensing system preset on a road surface; the stress condition of the road surface is obtained, the monitoring of the road surface vehicles is realized, and the information of the road surface vehicles can be accurately identified.
Description
Technical Field
The embodiment of the invention relates to the technical field of information, in particular to a road vehicle monitoring method, electronic equipment and a storage medium.
Background
In the distributed optical fiber vibration sensing, an OTDR (optical time-domain reflectometer) system based on rayleigh scattering is formed by using a laser source with a sufficiently narrow line width (i.e. a sufficiently long coherence length). Due to the use of the light source with long coherence length and narrow line width, the back scattering light excited by the front edge of the detection light pulse can interfere with the subsequent light of the detection light pulse, and the light wave phase can be fluctuated inevitably when vibration disturbance exists outside, so that the interference light intensity is fluctuated. In general, during signal processing, the existence of the vibration disturbance can be detected by using the weak change of the light intensity before and after the vibration disturbance is generated, and accurate positioning is carried out.
The inventor finds that at least the following problems exist in the prior art: the existing distributed optical fiber vibration detection technology can realize disturbance position identification, namely, the disturbance can be identified at a certain spatial position, but the disturbance source identification has a great problem, and the vehicle characteristics cannot be identified according to the change of a detection signal, so that more vehicle information on the road surface can be acquired.
Disclosure of Invention
An object of embodiments of the present invention is to provide a road vehicle monitoring method, an electronic device, and a storage medium, which can monitor a road vehicle according to a stress condition of a road surface and accurately identify vehicle information.
In order to solve the technical problem, an embodiment of the present invention provides a method for monitoring a road vehicle, including: acquiring a sensing signal fed back by a distributed optical fiber sensing system; the distributed optical fiber sensing system is an optical fiber sensor set laid on a road surface; determining the stress data of the road surface according to the time-dependent change value of the phase data in the sensing signal and the linear relationship between the time-dependent change value of the phase data and the stress generated by the interference of the vehicles on the road surface; and determining vehicle information of the road surface according to the stress data.
An embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described method of on-road vehicle monitoring.
The embodiment of the invention also provides a storage medium which stores a computer program, and the computer program is executed by a processor to realize the road vehicle monitoring method.
Compared with the prior art, the method and the device for acquiring the stress data of the road surface acquire the stress data of the road surface through the phase data change value which is in a linear relation with the stress generated by the interference of the road surface vehicles in the sensing signals fed back by the distributed optical fiber sensing system preset on the road surface, and further acquire the vehicle information of the road surface. The road vehicle monitoring device can monitor the road vehicle according to the stress condition of the road and accurately identify the vehicle information.
Additionally, the stress data of the road surface may be determined by the following formula:
wherein epsilon represents time, j represents position, M is the number of phase data in preset time T, thetaj(i) For the ith phase data of the j-position road surface in the time from epsilon-T to epsilon, FjAnd (epsilon) is the actual stress data of the road surface at the j position at the epsilon moment. Stress data are calculated in a mode of accumulating the modulus absolute values of adjacent phase differences within a period of time, and the method is simpler and more accurate.
In addition, according to the stress data, confirm the vehicle information of the road surface, also include: from the stress data, the weight of the road vehicle is determined. The road vehicle weight can be monitored, vehicle identification is realized, and whether the vehicle of a known vehicle type is overloaded or not can be further judged.
Additionally, the weight of the road vehicle is determined by the following equation:
Wj(ε)＝Kj×[Fj(ε)-Fj(ε)0)]
where ε represents time, j represents position, Wj(ε) is the weight of the vehicle at time ε at position j, Fj(ε) is the actual stress data of the j-position road surface at the time ε, Fj(ε)0Stress data for the j-position road surface without vehicle interference, KjLinear coefficient between vehicle weight and stress data for j position; kjObtained from stress data generated at the j position by a vehicle of known weight. The sensing optical fiber is calibrated and calculated in a whole section, and a constant K can be calculated for each position pointjAnd the vehicle weight calculation is more accurate.
In addition, the phase data includes: phase data corresponding to a plurality of position optical fiber sensors preset on a road surface; after determining the stress data of the road surface, before determining the vehicle information of the road surface according to the stress data, the method further comprises the following steps: acquiring a curve graph of the stress data changing along with the position in real time according to the corresponding relation between the phase data and the road surface position; determining vehicle information of the road surface according to the stress data, further comprising: and determining the vehicle information of a plurality of preset positions on the road surface according to the curve graph obtained in real time. The road vehicle monitoring system has the advantages that the road vehicles on the whole road section can be monitored through the phase data change condition of the whole road section, the road vehicle information can be identified, and the vehicles can be tracked.
In addition, according to the graph acquired in real time, the method for determining the vehicle information of a plurality of preset positions of the road surface comprises the following steps: counting the times of stress data meeting preset conditions at the target position within preset time, and calculating the traffic flow of the target position within the preset time; the preset condition is specifically stress data which represents that vehicles pass through the road surface. The road traffic flow can be monitored in real time.
In addition, the method for acquiring the vehicle information of a plurality of positions preset on the road surface according to the real-time acquired graph comprises the following steps: acquiring a first time interval of a target vehicle passing through a first position and a second position of a road surface according to the curve graph acquired in real time; wherein the distance between the first position and the second position is within a preset range; and calculating the speed of the target vehicle according to the first time interval and the distance between the first position and the second position. The vehicle speed information can be acquired in real time, and whether the vehicle is overspeed or not is judged.
In addition, after calculating the vehicle speed of the target vehicle according to the time interval and the distance between the first position and the second position, the method further includes: acquiring a second time interval of stress data generated by the front wheel and the rear wheel of the target vehicle at the same position respectively according to the curve graph acquired in real time; calculating the axle distance of the target vehicle according to the second time interval and the speed of the target vehicle; and determining the type of the target vehicle according to the axle distance and the number of axles of the target vehicle. The model information of the road vehicle can be acquired.
In addition, after determining the model of the target vehicle according to the axle distance and the number of axles of the target vehicle, the method further comprises the following steps: determining the load range of the vehicle according to the type of the target vehicle; and determining whether the target vehicle is overloaded or not according to the load range of the target vehicle and the current weight of the target vehicle. Whether the road vehicle is overloaded can be judged.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
FIG. 1 is a flow chart of a method of monitoring a road vehicle according to a first embodiment of the invention;
FIG. 2 is a pavement sensing optical fiber laying method provided by the pavement vehicle monitoring method according to the first embodiment of the invention;
FIG. 3 is another pavement sensing optical fiber laying method provided by the pavement vehicle monitoring method according to the first embodiment of the invention;
FIG. 4 is a flow chart of a method of monitoring a road vehicle in accordance with a second embodiment of the invention;
FIG. 5 is a graph of stress data as a function of position on a section of a road surface in accordance with a second embodiment of the invention;
FIG. 6 is a graph of vehicle weight at various locations on a section of a roadway surface in accordance with a second embodiment of the present invention;
FIG. 7 is a schematic view of the same vehicle traversing two adjacent lengths of sensing fiber across a roadway surface in accordance with a second embodiment of the present invention;
FIG. 8 is a flowchart illustrating the substeps of step 204 of a method for monitoring road vehicles in accordance with a second embodiment of the present invention;
FIG. 9 is a schematic diagram of a method for monitoring a road vehicle according to a second embodiment of the present disclosure to determine whether the road vehicle is overloaded;
fig. 10 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not constitute any limitation to the specific implementation manner of the present invention, and the embodiments may be mutually incorporated and referred to without contradiction.
The first embodiment of the invention relates to a road vehicle monitoring method, which comprises the steps of acquiring sensing signals fed back by a distributed optical fiber sensing system; the distributed optical fiber sensing system is an optical fiber sensor set laid on a road surface; determining the stress data of the road surface according to the time-dependent change value of the phase data in the sensing signal and the linear relationship between the time-dependent change value of the phase data and the stress generated by the interference of the vehicles on the road surface; and acquiring the vehicle information of the road surface according to the stress data. Carrying out phase analysis on a signal fed back by an optical fiber sensing system preset on a road surface; the stress condition of the road surface is obtained, the monitoring of the road surface vehicles is realized, and the vehicle information can be accurately identified. The following describes the implementation details of the road vehicle monitoring method of the present embodiment in detail, and the following description is only provided for the convenience of understanding and is not necessary for implementing the present embodiment.
As shown in fig. 1, the method for monitoring road vehicles in the present embodiment specifically includes:
step 101, acquiring a sensing signal fed back by a distributed optical fiber sensing system; the distributed optical fiber sensing system is an optical fiber sensor set laid on a road surface.
Specifically, a sensing optical fiber is laid on a road surface of a preset road section, and a detection point on the laid sensing optical fiber is detected by combining devices such as a narrow-band laser transmitter, an acoustic-optical modulator, an optical coherent receiver, an a/D conversion device, and a data acquisition device included in a distributed optical fiber sensing system, so as to obtain a sensing signal. In one example, the sensing fibers may be laid on a road surface several tens of kilometers long for each single lane in a manner as shown in fig. 2, i.e., the sensing fibers are laid back and forth across the road surface, with a predetermined distance between each length of sensing fibers across the road surface. Assuming that the width of the lane is 1 meter, the longitudinal distance for laying two optical fibers crossing the road surface is 8 meters, and the length of the preset road section is 800 meters, the length for laying the sensing optical fiber is 900 meters. The sensing optical fibers can also be laid on the double lanes in a manner shown in fig. 3, that is, the middle line of the double lanes is used as the main line of the optical fiber conduction, and the sensing optical fibers are respectively laid across the left lane and the right lane in a staggered manner. The laying mode of the sensing optical fiber can reduce unnecessary material waste when a road section with a longer distance is laid. The embodiment only provides the above two optical fiber laying modes, and the laying mode can be set according to the actual situation of the detected road section in practical application, and the embodiment is not limited.
And 102, determining the stress data of the road surface according to the time-dependent change value of the phase data in the sensing signal and the linear relation between the time-dependent change value of the phase data and the stress generated by the road vehicle interference.
Specifically, the phase data in the original sensing signal is obtained after the acquired sensing signal is subjected to quadrature demodulation and low-pass filtering, and then the change value of the phase data along with time is obtained according to the phase data acquired in real time, so that corresponding stress data is obtained. As will be understood by those skilled in the art, when an external vehicle passes through the sensing optical fiber, stress is applied to the sensing optical fiber on the road surface, so that the sensing optical fiber is deformed, and further, a change occurs in an optical signal propagating in the sensing optical fiber, wherein a phase change of the optical signal at a specific position is linearly related to the magnitude of the stress applied to the sensing optical fiber at the position. Therefore, the phase data of the position is extracted, and the real-time stress condition of the road surface can be obtained according to the change value of the phase data.
In one example, the stress data for a road surface may be determined by the following equation:
wherein epsilon represents time, j represents position, M is the number of phase data in preset time T, thetaj(i) For the ith phase data of the j-position road surface in the time from epsilon-T to epsilon, FjAnd (epsilon) is the actual stress data of the road surface at the j position at the epsilon moment.
Specifically, M time points are taken from epsilon-T to epsilon according to j position of the road surface, and corresponding phase data, namely theta, are acquiredj(i) (ii) a Taking the difference between adjacent phase data, i.e. thetaj(i)-θj(i + 1); and then, after the absolute value is obtained, the stress data of the road surface at the j position at the epsilon moment is obtained through accumulation. The time T and the value M in this embodiment may be set according to an actual situation or an empirical value. It will be understood by those skilled in the art that the smaller the value of T, the larger the value of M, and the more accurate the corresponding stress data. Further, the stress data may be calculated by performing a piecewise derivation on the phase data or by other methods, which is not limited in this embodiment.
And 103, determining the vehicle information of the road surface according to the stress data.
Specifically, the stress data is influenced by vehicles on the road surface, when vehicles pass through, the stress is relatively large, and whether the vehicles pass through the road surface can be reflected according to the stress data. For example, the stress data range of the j position is 0-100N when no vehicle passes through the road surface, and the stress data range is 1-500KN when the vehicle passes through the road surface. And judging whether a vehicle passes through the j position at present according to the current stress data of the j position.
Further, the weight of the road vehicle may be determined from the stress data. In particular, the magnitude of the stress data experienced by the sensing fiber is linearly related to the weight of the road vehicle. In one example, the weight of the road vehicle may be determined by the following equation:
Wj(ε)＝Kj×[Fj(ε)-Fj(ε)0)]
where ε represents time, j represents position, Wj(ε) is the weight of the vehicle at time ε at position j, Fj(ε) is the actual stress data of the j-position road surface at the time ε, Fj(ε)0Stress data for the j-position road surface without vehicle interference, KjLinear coefficient between vehicle weight and stress data for j position; kjObtained from stress data generated at the j position by a vehicle of known weight. Specifically, after the sensor optical fiber is laid, the distributed optical fiber sensing system is started when no vehicle runs on the road surface, and reference stress data F borne by the sensor optical fiber under the condition of no disturbance is acquiredj(ε)0(ii) a After the acquisition is finished, a vehicle with a known weight, for example, the weight of 1.5 tons, is selected, the vehicle is driven, the vehicle stops after running for a certain distance along the paved sensing optical fiber lane, and stress data F under the condition of vehicle interference of the distance from the road surface is obtainedj(ε) based on the known vehicle weight Wj(ε) was 1.5 ton, and K corresponding to each position was determinedj。
In the embodiment, the stress data of the road surface is obtained through the phase data change value which is in a linear relation with the stress generated by the interference of the road surface vehicles in the sensing signals fed back by the distributed optical fiber sensing system preset on the road surface, and further the vehicle information of the road surface is obtained. The road vehicle monitoring device can monitor the road vehicle according to the stress condition of the road, accurately identify the vehicle and acquire the vehicle information.
A second embodiment of the invention relates to a method of on-road vehicle monitoring. The second embodiment is substantially the same as the first embodiment, and mainly differs therefrom in that: in the second embodiment, the phase data specifically includes phase data corresponding to a plurality of position optical fiber sensors preset on the road surface. And drawing a curve graph of the stress data along with the change of the positions according to the phase data of the positions, and further acquiring vehicle information corresponding to the positions on the road surface.
As shown in fig. 4, the road vehicle monitoring method in the present embodiment specifically includes:
step 201, acquiring a sensing signal fed back by a distributed optical fiber sensing system; the distributed optical fiber sensing system is an optical fiber sensor set laid on a road surface.
Steps 201 to 202 in this embodiment are similar to steps 101 to 102 in the first embodiment, except that the phase data in the sensing signal fed back by the sensing fiber laid at all the sections in this embodiment includes: phase data for a plurality of locations on a road surface.
And step 203, acquiring a curve graph of the stress data changing along with the position in real time according to the corresponding relation between the phase data and the road surface position.
Specifically, stress data is calculated according to the phase data, and the corresponding relation between the stress data and a plurality of positions is obtained according to the corresponding relation between the phase data and a plurality of preset positions, so that a curve graph of the stress data changing along with the positions is obtained.
In one example, on a road where the sensing fiber is laid in the manner shown in fig. 2, it is assumed that the length of the sensing fiber is 900 meters. If a detection point is selected on the sensing optical fiber every 3 meters, 300 detection points can be selected, and the detection points correspond to 300 road surface positions. And acquiring sensing signals from the distributed optical fiber sensing system, extracting phase data corresponding to the 300 detection points from the sensing signals, and calculating corresponding stress data. The abscissa is taken as the position of the detection point on the sensing optical fiber, the ordinate is taken as stress data, the data corresponding to 300 detection points is displayed in a coordinate graph, and a curve formed by connecting all the points, namely a curve graph of the stress data changing along with the position is shown in fig. 5, which is an example of the curve graph of the stress data on a section of road surface changing along with the position.
And step 204, acquiring vehicle information of a plurality of positions preset on the road surface according to the real-time acquired curve graph.
Specifically, whether a vehicle passes through each position can be reflected according to the stress data corresponding to each position in the graph. Specifically, a vehicle weight graph corresponding to each position of the road surface can be obtained according to a graph of stress data changing along with the position, as shown in fig. 6. The weight of the vehicles at all positions on the road surface can be detected, so that the vehicles can be identified and tracked.
Furthermore, the frequency of stress data meeting preset conditions at the target position in preset time can be counted, and the traffic flow of the target position in the preset time is calculated; the preset condition is specifically stress data which represents that vehicles pass through the road surface. Specifically, the vehicle passes through each section of sensing optical fiber crossing the road surface, which causes obvious disturbance, as shown in fig. 7, each two peaks on the graph correspond to a set of stress data generated by the left wheel and the right wheel of the vehicle at corresponding positions on the road surface, and the distance between the two peaks is the distance between the left wheel and the right wheel. Since the pitch of the right and left wheels of a typical vehicle is not greatly different from each other, the peak distances of the stress data generated corresponding to the right and left wheels are the same. Therefore, the occurrence of the stress data corresponding to two peaks maintained at a specific distance as shown in fig. 7 indicates that the vehicle passes through the corresponding road surface position. The monitoring of the traffic flow is realized by counting the times of the stress data of the target position in a period of time, namely the number of vehicles passing by the period of time.
Furthermore, more road vehicle information can be acquired according to the curve graph acquired in real time. Specifically, the following process is described in detail, and as shown in fig. 8, the process includes:
step 2041, acquiring a first time interval between a first position and a second position of the target vehicle passing through the road surface according to the real-time acquired curve graph; wherein the distance between the first position and the second position is within a preset range.
Step 2042, calculating the speed of the target vehicle according to the first time interval and the distance between the first position and the second position.
Specifically, as shown in FIG. 7, a vehicle passing over each section of sensing fiber across the road surface will generate a corresponding disturbance in the stress data. First position P in the present embodiment1And a second position P2Sensor light for a target vehicle passing two adjacent transverse road surfacesThe position on the fiber, assuming that the longitudinal distance between two optical fibers crossing the road surface is L, AP1And BP2May be based on the first position P1And a second position P2The position on the sensing fiber is obtained, the first position P1And a second position P2Distance P of1P2Can be calculated by the following formula:
the target vehicle passes through the first position P1And past the second position P2Is equal to the target vehicle being in the first position P1Stress data generated by disturbance and at the second position P2A first time interval Δ t of occurrence of disturbance generated stress data; according to P1P2The vehicle speed of the target vehicle can be obtained from the distance of (d) and the first time interval Δ t. Whether the target vehicle is overspeed or not can be further judged according to the vehicle speed of the target vehicle.
It should be noted that, when the first position and the second position are selected, it is required that the distance between the first position and the second position is within a preset range. It will be appreciated by those skilled in the art that the accuracy of the calculated vehicle speed may be low if the first and second positions are too far apart, since there is no guarantee that the vehicle will travel in a straight line between the first and second positions. In this embodiment, the positions where the vehicle passes through two adjacent sensing optical fibers crossing the road surface are selected as the first position and the second position, respectively, and in other embodiments, other positions may be selected, which is not limited in this embodiment.
And step 2044, calculating the axle distance of the target vehicle according to the second time interval and the speed of the target vehicle.
And 2045, determining the type of the target vehicle according to the axle distance and the number of axles of the target vehicle.
Specifically, the axle distance and the number of axles are fixed for each vehicle type, and if the axle distance and the number of axles are determined, the vehicle type can be determined. Through the real-time changing curve graph, the target vehicle can be identified and tracked, the second time interval of the front wheel and the rear wheel of the target vehicle passing through the same position is obtained, the speed of the target vehicle is calculated according to the methods in the steps 2051 to 2052, and further the distance between the front wheel and the rear wheel, namely the axle distance, can be obtained. In addition, according to the stress data of each wheel of the target vehicle in the graph, the number of wheels of the target vehicle can be obtained, and the number of axles can be further determined. The axle distance and the number of the axles are determined, and the corresponding vehicle type can also be determined.
And step 2046, determining the load range of the vehicle according to the type of the target vehicle.
Specifically, each vehicle type has a corresponding load range, and after the vehicle in the curve chart is identified and the vehicle type is determined, whether the target vehicle is overloaded or not can be judged according to the vehicle weight acquired in real time. In one example, vehicles passing through three positions of a road surface are identified, and the types of the vehicles are determined to be a large truck, an SUV (sports utility vehicle) and a car respectively; the corresponding vehicle weight is calculated according to the stress data generated by the vehicle on the road surface, and when the vehicle weight exceeds the specified weight of the vehicle type, the vehicle is overloaded. As shown in FIG. 9, the large truck is judged to be overloaded and the SUV and sedan are not overloaded.
In the embodiment, the phase data of a plurality of positions are acquired to obtain the curve graph of the stress data changing along with the positions, and further the vehicle information corresponding to the positions on the road surface is acquired, so that the road vehicle can be monitored according to the stress condition of the road surface, the vehicle can be accurately identified, and a plurality of pieces of vehicle information including the traffic flow, the vehicle speed, the vehicle type, the vehicle weight and the like can be acquired.
The steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, so long as the same logical relationship is included, which are all within the protection scope of the present patent; it is within the scope of the patent to add insignificant modifications to the algorithms or processes or to introduce insignificant design changes to the core design without changing the algorithms or processes.
A third embodiment of the present invention relates to an electronic apparatus, as shown in fig. 10, including: at least one processor 301; and a memory 302 communicatively coupled to the at least one processor 301; the memory 302 stores instructions executable by the at least one processor 301, and the instructions are executed by the at least one processor 301, so that the at least one processor 301 can execute the road vehicle monitoring method according to any one of the above embodiments.
Where the memory 302 and the processor 301 are coupled in a bus, the bus may comprise any number of interconnected buses and bridges, the buses coupling one or more of the various circuits of the processor 301 and the memory 302. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 301 is transmitted over a wireless medium through an antenna, which further receives the data and transmits the data to the processor 301.
The processor 301 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 302 may be used to store data used by processor 301 in performing operations.
A fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method according to the above embodiments may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (11)
1. A method of on-road vehicle monitoring, comprising:
acquiring a sensing signal fed back by a distributed optical fiber sensing system; the distributed optical fiber sensing system is an optical fiber sensor set paved on a road surface;
determining the stress data of the road surface according to the time-dependent change value of the phase data in the sensing signal and the linear relationship between the time-dependent change value of the phase data and the stress generated by the road vehicle interference;
and determining vehicle information of the road surface according to the stress data.
2. The method of claim 1, wherein the stress data for the roadway is determined by the formula:
where ε represents time and j represents positionM is the number of phase data in a preset time T, thetaj(i) For the ith phase data of the j-position road surface in the time from epsilon-T to epsilon, FjAnd (epsilon) is the actual stress data of the road surface at the j position at the epsilon moment.
3. The method of claim 1, wherein the determining vehicle information for the roadway from the stress data further comprises: determining a weight of the road vehicle based on the stress data.
4. A method of monitoring road vehicles according to claim 3, wherein the weight of the road vehicle is determined by the formula:
Wj(ε)＝Kj×[Fj(ε)-Fj(ε)0)]
where ε represents time, j represents position, Wj(ε) is the weight of the vehicle at time ε at position j, Fj(ε) is the actual stress data of the j-position road surface at the time ε, Fj(ε)0Stress data for the j-position road surface without vehicle interference, KjLinear coefficient between vehicle weight and stress data for j position; kjObtained from stress data generated at the j position by a vehicle of known weight.
5. The method of claim 3, wherein the phase data comprises: phase data corresponding to a plurality of position optical fiber sensors preset on the road surface;
after the determining the stress data of the road surface, before the determining the vehicle information of the road surface according to the stress data, the method further comprises: acquiring a curve graph of the stress data changing along with the position in real time according to the corresponding relation between the phase data and the road surface position;
the determining vehicle information of the road surface according to the stress data further comprises:
and determining vehicle information of a plurality of preset positions on the road surface according to the curve graph acquired in real time.
6. The method for monitoring vehicles on road surface according to claim 5, wherein the step of determining vehicle information of a plurality of preset positions on the road surface according to the graph acquired in real time comprises the following steps:
counting the times of stress data meeting preset conditions at a target position within preset time, and calculating the traffic flow of the target position within the preset time; the preset condition is specifically stress data which represents that vehicles pass through the road surface.
7. The method for monitoring vehicles on road surface according to claim 5, wherein the step of determining vehicle information of a plurality of preset positions on the road surface according to the graph acquired in real time comprises the following steps:
according to the curve graph obtained in real time, obtaining a first time interval of the target vehicle passing through a first position and a second position of the road surface; wherein the distance between the first position and the second position is within a preset range;
and calculating the speed of the target vehicle according to the first time interval and the distance between the first position and the second position.
8. The method of claim 7, further comprising, after the calculating the speed of the target vehicle based on the time interval and the distance of the first location from the second location:
acquiring a second time interval of stress data generated by the front wheel and the rear wheel of the target vehicle at the same position respectively according to the curve graph acquired in real time;
calculating the axle distance of the target vehicle according to the second time interval and the speed of the target vehicle;
and determining the vehicle type of the target vehicle according to the axle distance and the axle number of the target vehicle.
9. The method of claim 8, further comprising, after determining the model of the target vehicle based on the axle spacing and the number of axles of the target vehicle: determining the load range of the vehicle according to the model of the target vehicle;
and determining whether the target vehicle is overloaded or not according to the load range of the target vehicle and the current weight of the target vehicle.
10. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 9.
11. A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911242659.7A CN112927516B (en) | 2019-12-06 | 2019-12-06 | Road vehicle monitoring method, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911242659.7A CN112927516B (en) | 2019-12-06 | 2019-12-06 | Road vehicle monitoring method, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112927516A true CN112927516A (en) | 2021-06-08 |
| CN112927516B CN112927516B (en) | 2024-09-06 |
Family
ID=76161703
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911242659.7A Active CN112927516B (en) | 2019-12-06 | 2019-12-06 | Road vehicle monitoring method, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112927516B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115331455A (en) * | 2022-06-29 | 2022-11-11 | 北京见合八方科技发展有限公司 | Method and system for road vehicle lane level positioning and vehicle situation monitoring |
| CN115410403A (en) * | 2022-04-19 | 2022-11-29 | 北京见合八方科技发展有限公司 | Passive perception-based road vehicle positioning and tracking method and device and readable medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101667711A (en) * | 2008-09-05 | 2010-03-10 | Ofs飞泰尔公司 | Figure eight fiber laser for ultrashort pulse generation |
| CN101706997A (en) * | 2009-08-17 | 2010-05-12 | 昆山敏通光纤传感技术研发中心有限公司 | Distributed optical fiber vehicle comprehensive information detection system and processing method thereof |
| CN103940501A (en) * | 2014-04-11 | 2014-07-23 | 电子科技大学 | BOTDA distributed vibration sensing system based on dynamic phase demodulating |
| WO2015117497A1 (en) * | 2014-08-20 | 2015-08-13 | 中兴通讯股份有限公司 | Driving safety aid method and device |
| CN107730895A (en) * | 2017-10-25 | 2018-02-23 | 同济大学 | A kind of flush type road vehicle type identifying system and method |
| CN109612559A (en) * | 2018-12-28 | 2019-04-12 | 东南大学 | Bridge type dynamic weighing method based on distributed long gauge length optical fibre grating sensor |
-
2019
- 2019-12-06 CN CN201911242659.7A patent/CN112927516B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101667711A (en) * | 2008-09-05 | 2010-03-10 | Ofs飞泰尔公司 | Figure eight fiber laser for ultrashort pulse generation |
| CN101706997A (en) * | 2009-08-17 | 2010-05-12 | 昆山敏通光纤传感技术研发中心有限公司 | Distributed optical fiber vehicle comprehensive information detection system and processing method thereof |
| CN103940501A (en) * | 2014-04-11 | 2014-07-23 | 电子科技大学 | BOTDA distributed vibration sensing system based on dynamic phase demodulating |
| WO2015117497A1 (en) * | 2014-08-20 | 2015-08-13 | 中兴通讯股份有限公司 | Driving safety aid method and device |
| CN107730895A (en) * | 2017-10-25 | 2018-02-23 | 同济大学 | A kind of flush type road vehicle type identifying system and method |
| CN109612559A (en) * | 2018-12-28 | 2019-04-12 | 东南大学 | Bridge type dynamic weighing method based on distributed long gauge length optical fibre grating sensor |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115410403A (en) * | 2022-04-19 | 2022-11-29 | 北京见合八方科技发展有限公司 | Passive perception-based road vehicle positioning and tracking method and device and readable medium |
| CN115410403B (en) * | 2022-04-19 | 2023-11-10 | 北京见合八方科技发展有限公司 | Road vehicle positioning tracking method and device based on passive perception and readable medium |
| CN115331455A (en) * | 2022-06-29 | 2022-11-11 | 北京见合八方科技发展有限公司 | Method and system for road vehicle lane level positioning and vehicle situation monitoring |
| CN115331455B (en) * | 2022-06-29 | 2024-03-01 | 北京见合八方科技发展有限公司 | Highway vehicle lane-level positioning and vehicle situation monitoring method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112927516B (en) | 2024-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10967869B2 (en) | Road surface condition estimation apparatus and road surface condition estimation method | |
| EP2955479B1 (en) | Methods, systems and computer program products for detection of short term irregularities in a road surface | |
| CN205049180U (en) | Highway detecting system that transfinites | |
| CN103576142B (en) | Spacing computing device and method | |
| CN112562405A (en) | Radar video intelligent fusion and early warning method and system | |
| US20220274599A1 (en) | Method and System for the Recognition of the Irregularities of a Road Pavement | |
| US20210312726A1 (en) | Asset and Vehicle Coupling | |
| CN103164962A (en) | Mountain road sharp turn section real-time vehicle speed early warning method | |
| CN112927516B (en) | Road vehicle monitoring method, electronic equipment and storage medium | |
| CN103802826A (en) | Stability prewarning system used during high-speed turning and driving of automobile | |
| CN105206051B (en) | A kind of city road network Rationality Assessment method and device | |
| CN108367651A (en) | The system and method for determining road quality | |
| CN113744538B (en) | Highway dynamic overload control method, computer equipment and readable storage medium | |
| WO2013133752A1 (en) | Method and system for determining vehicle position | |
| CN104933871B (en) | Method and system for detecting traffic vehicles | |
| Zeng et al. | A vibration-based traffic monitoring system using distributed optical sensing technology | |
| DE102022120968A1 (en) | NETWORKED VEHICLE-BASED DETERMINATION OF ROAD SURFACE QUALITY | |
| CN118119542A (en) | Method and device for determining and characterizing lane irregularities | |
| US20230168368A1 (en) | Guardrail estimation method based on multi-sensor data fusion, and vehicle-mounted device | |
| CN113795415A (en) | Method and system for identifying unevenness of road surface | |
| CN117485369A (en) | Real-time detection method for deformation obstacle in logistics robot transportation | |
| CN117008129A (en) | Vehicle track analysis method, device, apparatus, medium and program product | |
| CN112415518B (en) | Passable space detection method based on vehicle-mounted millimeter wave radar | |
| KR100854076B1 (en) | Traffic parameter detection system and method using single vehicles axle sensor | |
| CN115824666B (en) | Method, device and system for testing automatic emergency braking performance of vehicle and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240808 Address after: No.189, Ningdan Road, Yuhuatai District, Nanjing City, Jiangsu Province, 210012 Applicant after: Nanjing Zhongxing New Software Co.,Ltd. Country or region after: China Address before: 518057 Zhongxing building, science and technology south road, Nanshan District hi tech Industrial Park, Guangdong, Shenzhen Applicant before: ZTE Corp. Country or region before: China |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |