CN117237832B - Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium - Google Patents
Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium Download PDFInfo
- Publication number
- CN117237832B CN117237832B CN202311517618.0A CN202311517618A CN117237832B CN 117237832 B CN117237832 B CN 117237832B CN 202311517618 A CN202311517618 A CN 202311517618A CN 117237832 B CN117237832 B CN 117237832B
- Authority
- CN
- China
- Prior art keywords
- bridge
- course
- time
- vehicle
- detected
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 230000004044 response Effects 0.000 claims abstract description 138
- 238000001514 detection method Methods 0.000 claims abstract description 125
- 238000006073 displacement reaction Methods 0.000 claims abstract description 105
- 230000005484 gravity Effects 0.000 claims abstract description 55
- 230000001133 acceleration Effects 0.000 claims abstract description 43
- 239000003550 marker Substances 0.000 claims abstract description 38
- 238000005259 measurement Methods 0.000 claims abstract description 22
- 230000008859 change Effects 0.000 claims description 17
- 239000011159 matrix material Substances 0.000 claims description 11
- 230000008878 coupling Effects 0.000 claims description 6
- 238000010168 coupling process Methods 0.000 claims description 6
- 238000005859 coupling reaction Methods 0.000 claims description 6
- 238000012567 pattern recognition method Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- 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 invention discloses a bridge vibration mode identification method, an unmanned aerial vehicle and a computer readable storage medium, wherein the method comprises the following steps: the unmanned aerial vehicle acquires and detects video data of the vehicle when the vehicle runs in a measuring interval corresponding to the bridge; determining pixel coordinates of the marker on the detected vehicle and the bridge characteristic point in each video frame based on the video data; determining a first displacement time-course response of the bridge characteristic points in the gravity direction by taking the detection vehicle marker as a coordinate system according to the pixel coordinates; acquiring second displacement time-course responses of the detected vehicle in the gravity direction of the vehicle markers corresponding to all the measurement intervals on the bridge to be detected, and acceleration time-course responses of the detected vehicle in all the measurement intervals; and obtaining target displacement through the two time-course responses, and determining a vibration mode recognition result corresponding to the bridge to be detected by calculating a subspace recognition method on a time-course signal consisting of the target displacement, the acceleration time-course response and the detection vehicle modal parameters. And the vibration mode identification precision and efficiency of the non-contact bridge are improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a vibration mode identification method of a bridge, an unmanned aerial vehicle and a computer readable storage medium.
Background
In order to ensure the structural safety of the bridge, the bridge needs to be detected regularly. And the dynamic characteristic detection of the bridge is the most basic detection item in bridge detection items.
In the vibration mode identification method of the related bridge, a large number of vibration sensors are usually installed at different positions of the bridge, acceleration time-course response of the bridge in a vibration state is collected through the vibration sensors, and finally the acceleration time-course response is analyzed by a computer to obtain a bridge dynamic characteristic detection result. However, the sensor applied to bridge detection has the defects of complex installation and inflexible fixed measuring points, and more sensors need to be deployed, so that more time and more equipment need to be deployed when bridge vibration detection is performed, and the current bridge dynamic characteristic detection is time-consuming and labor-consuming.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a vibration mode identification method for a bridge, an unmanned aerial vehicle and a computer readable storage medium, which solve the problems of time and labor consumption in bridge dynamic characteristic detection in the prior art and improve detection efficiency.
In order to achieve the above object, the present invention provides a method for identifying a vibration mode of a bridge, the method for identifying a vibration mode of a bridge comprising the steps of:
the method comprises the steps that an unmanned aerial vehicle collects video data of a detected vehicle when the detected vehicle runs in a measuring interval corresponding to a bridge to be detected, wherein the bridge to be detected is divided into a plurality of measuring intervals along the length direction;
determining corresponding pixel coordinate time course data of the marker and the bridge characteristic point on the detected vehicle in each video frame within a duration period based on the video data;
determining a first displacement time-course response of the bridge characteristic point in the gravity direction in a detection coordinate system according to the pixel coordinate time-course data, wherein the detection coordinate system takes the marker as an origin, and the first displacement time-course response in the gravity direction is a displacement response of the bridge characteristic point to be detected relative to the origin of the detection coordinate system;
acquiring second displacement time-course responses of the detection vehicle in the gravity direction corresponding to all measurement intervals on the bridge to be detected and acceleration time-course responses measured by the acceleration sensors of the detection vehicle in all measurement intervals, wherein the second displacement time-course responses of the gravity direction are calculated according to the acceleration time-course responses;
calculating a target displacement time-course response of the bridge characteristic point in the gravity direction according to the first displacement time-course response in the gravity direction and the second displacement time-course response in the gravity direction, wherein the target displacement time-course response is described in a physical unit;
and calculating all the target displacement time-course response, the acceleration time-course response and time-course signals consisting of modal parameters of the detected vehicle by using a subspace recognition method, and determining a vibration mode recognition result corresponding to the bridge to be detected.
Optionally, before the step of determining the first displacement time-course response of the bridge feature point in the gravity direction in the detection coordinate system according to the pixel coordinate time-course data, the method further includes:
acquiring the internal and external parameter data, distortion coefficients and image information of the video data at a certain moment;
determining the real size of the marker, and correcting the pixel size of the marker in the image information based on the distortion coefficient;
and determining the shooting distance between the camera and the detection vehicle according to the proportion between the real size and the pixel size.
Optionally, the step of determining a first displacement time-course response of the bridge feature point in the gravity direction in the detection coordinate system according to the pixel coordinate time-course data includes:
according to the internal and external parameter data and the shooting distance, converting the pixel coordinate time-course data of the bridge characteristic points into detection coordinate time-course data under the detection coordinate system;
determining the detection coordinate change distance of the bridge characteristic points at different moments based on the detection coordinate time course data;
and determining the first displacement time-course response of the bridge characteristic point under the detection coordinate system according to the detection coordinate change distance.
Optionally, the step of determining the vibration pattern recognition result corresponding to the bridge to be detected by calculating the subspace recognition method on all the time-course signals consisting of the target displacement time-course response, the acceleration time-course response and the modal parameters of the detected vehicle includes:
according to the target displacement time-course response of each measuring interval of the bridge to be detected, the acceleration time-course response of the detected vehicle and the modal parameters of the detected vehicle, combining a vehicle-bridge coupling kinematics equation, detecting the vehicle and the related information of the bridge by separating the kinematics equation, and forming a new time-course signal only comprising the bridge information in a time domain space;
and applying the subspace identification method to the time-course signal, and calculating to obtain a system matrix of the bridge to be detected, so as to further calculate a vibration mode curve of the bridge to be detected, wherein the vibration mode curve is the vibration mode identification result.
In addition, in order to achieve the above object, the present invention also provides an unmanned aerial vehicle, the unmanned aerial vehicle including a memory, a processor, and a vibration pattern recognition program of a bridge stored on the memory and operable on the processor, the vibration pattern recognition program of the bridge implementing the steps of the vibration pattern recognition method of the bridge as described above when executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a vibration pattern recognition program of a bridge, which when executed by a processor, implements the steps of the vibration pattern recognition method of a bridge as described above.
The embodiment of the invention provides a vibration mode identification method of a bridge, an unmanned aerial vehicle and a computer readable storage medium, wherein the vibration mode identification method of the bridge, the unmanned aerial vehicle and the computer readable storage medium are characterized in that after the bridge is integrally divided into a plurality of detection intervals according to the comprehensive factors such as the bridge length, the field of view of a camera of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the bridge, and the like, a detection vehicle with a marker is driven on the bridge to be detected, then the video data are obtained by hovering from a fixed position shooting of the unmanned aerial vehicle, the frame of the video data comprises the bridge to be detected interval and the detection vehicle, the vehicle acceleration time response is measured by a vehicle-mounted acceleration sensor, one bridge self-characteristic point in the bridge detection interval is selected based on the video data, the pixel coordinate time-distance data corresponding to the marker on the detection vehicle and the characteristic point of the bridge self-characteristic point at different moments is determined, and the first displacement time-distance response of the gravity point of the bridge self-characteristic point in a pixel detection coordinate system is determined according to the pixel coordinate time-distance data, the detection coordinate time-distance response of the bridge self-characteristic point in the gravity time-distance in the pixel detection coordinate system is obtained, the detection time-distance response of the marker is the gravity time-distance response in the first displacement time-distance-of the bridge in the first displacement time-coordinate system is calculated in the direction relative to the initial point in the detection time-point in the direction of the movement time-point in the detection time-of the moment of the detection of the point in the movement of the detection of the coordinate of the movement of the point in the detection of the coordinate of the detection of the movement of the point in the first position of the bridge to be detected in the coordinate, and then calculating target displacement time-course response of the bridge characteristic points in the gravity direction according to the first displacement time-course response in the gravity direction and the second displacement time-course response in the gravity direction, wherein the target displacement time-course response is described in a physical unit, and finally calculating time-course signals consisting of all the target displacement time-course response, the acceleration time-course response and the modal parameters of the detected vehicle by using a subspace recognition method to determine a vibration mode recognition result corresponding to the bridge to be detected. It can be seen that video data of a detected vehicle and vibration information of the detected vehicle with a marker in the vehicle-bridge coupling system are shot through the unmanned aerial vehicle, so that a vibration mode identification result of a bridge to be detected can be obtained based on the video data, the problem that a detection period is too long due to the fact that more sensors are required to be installed in a traditional detection mode is avoided, indirect identification of the vibration mode of the bridge is achieved, the effects of improving vibration detection efficiency and detection precision of the bridge to be detected are achieved, and the cost of bridge vibration mode identification can be reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a first embodiment of a method for identifying vibration mode of a bridge according to the present invention;
fig. 2 is a schematic diagram of a refinement flow of step S60 of the first embodiment of the vibration mode identification method of the bridge according to the present invention;
FIG. 3 is a flow chart of a second embodiment of a method for identifying vibration mode of a bridge according to the present invention;
FIG. 4 is a schematic flow chart of a second embodiment of a method for identifying vibration mode of a bridge according to the present invention;
fig. 5 is a schematic diagram of a terminal hardware structure of each embodiment of the vibration mode identification method of the bridge of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the vibration mode identification method of the related bridge, a large number of vibration sensors are usually installed at different positions of the bridge, acceleration time-course response of the bridge in a vibration state is collected through the vibration sensors, and finally the acceleration time-course response is analyzed by a computer to obtain a bridge dynamic characteristic detection result. However, the sensor applied to bridge detection has the defects of complex installation and inflexible fixed measuring points, and more sensors need to be deployed, so that more time and more equipment need to be deployed when bridge vibration detection is performed, and the current bridge dynamic characteristic detection is time-consuming and labor-consuming.
In order to solve the above-mentioned drawbacks, an embodiment of the present invention provides a method for identifying a vibration mode of a bridge, which mainly includes the following steps:
the method comprises the steps that an unmanned aerial vehicle collects video data of a detected vehicle when the detected vehicle runs in a measuring interval corresponding to a bridge to be detected, wherein the bridge to be detected is divided into a plurality of measuring intervals along the length direction;
determining corresponding pixel coordinate time course data of the marker and the bridge characteristic point on the detected vehicle in each video frame within a duration period based on the video data;
determining a first displacement time-course response of the bridge characteristic point in the gravity direction in a detection coordinate system according to the pixel coordinate time-course data, wherein the detection coordinate system takes the marker as an origin, and the first displacement time-course response in the gravity direction is a displacement response of the bridge characteristic point to be detected relative to the origin of the detection coordinate system;
acquiring second displacement time-course responses of the detection vehicle in the gravity direction corresponding to all measurement intervals on the bridge to be detected and acceleration time-course responses measured by the acceleration sensors of the detection vehicle in all measurement intervals, wherein the second displacement time-course responses of the gravity direction are calculated according to the acceleration time-course responses;
calculating a target displacement time-course response of the bridge characteristic point in the gravity direction according to the first displacement time-course response of the gravity direction and the second displacement time-course response of the gravity direction, wherein the real gravity direction displacement is described in a physical unit;
and calculating all the target displacement time-course response, the acceleration time-course response and time-course signals consisting of modal parameters of the detected vehicle by using a subspace recognition method, and determining a vibration mode recognition result corresponding to the bridge to be detected.
According to the invention, the vibration mode identification result of the bridge to be detected can be completed through the unmanned aerial vehicle, the detection vehicle and the bridge characteristic points of the bridge to be detected, so that the problem that the detection period is too long due to the fact that more sensors are required to be installed in the traditional detection mode is avoided, and the effect of improving the vibration detection efficiency of the bridge to be detected is achieved.
In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure 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.
Referring to fig. 1, fig. 1 is a flow chart of a first embodiment of a vibration mode identification method of a bridge according to the present invention.
The scheme of this embodiment is applied to unmanned aerial vehicle system, wherein, unmanned aerial vehicle system includes unmanned aerial vehicle, unmanned aerial vehicle on high accuracy camera, on-vehicle spirit level, on-vehicle acceleration sensor, detects the sign thing of vehicle and wait to detect the feature point etc.. The execution body of the embodiment is an unmanned aerial vehicle.
In this embodiment, the method for identifying the vibration mode of the bridge includes the following steps:
step S10, an unmanned aerial vehicle collects video data of a detected vehicle when the detected vehicle runs in a measurement interval corresponding to a bridge to be detected, wherein the bridge to be detected is divided into a plurality of measurement intervals along the length direction;
in this embodiment, during the detection, the bridge needs to be divided into a plurality of detection areas, for example, the bridge is divided into a plurality of measurement areas along the length direction of the bridge with the leftmost bridge pier of the bridge as the starting point. The unmanned aerial vehicle hovers at one side of the bridge to shoot the bridge, and the position can be the position of the middle area of the bridge measurement interval, and the hovering shooting point is at the same level as the characteristic point of the bridge. The video data refers to a driving video of the detected vehicle corresponding to each detection area when the unmanned aerial vehicle reaches the current detection point, that is, the video data is video data corresponding to all detection areas of the bridge, and it is noted that the video data refers to a whole video.
Specifically, when the unmanned aerial vehicle hovers on the side face of the bridge to be tested and the current position is the vibration shooting point of the bridge, the unmanned aerial vehicle starts to wait for shooting. After the detected vehicle starts, a tester can control the unmanned aerial vehicle to shoot the video when the detected vehicle sequentially passes through each measuring interval at a constant speed on the bridge to be detected.
Step S20, determining corresponding pixel coordinate time course data of the marker and the bridge characteristic point on the detected vehicle in each video frame in a duration period based on the video data;
in this embodiment, the marker of the detected vehicle is a quadrilateral marker, and is mounted (attached) on one side of the detected vehicle relative to the unmanned aerial vehicle, and the bridge characteristic point may be a characteristic of the bridge itself or a marker point additionally arranged for the bridge to be detected, and the characteristic point may be a mark, and is mounted on a side wall of the bridge or a characteristic of the bridge side. The pixel coordinate time course data refers to coordinate data in which the pixel coordinates vary with each frame of video over a duration period. For example, at A1 time the pixel coordinate time-course data is (x 1, y 2) and at the next frame, i.e., A2 time, the pixel coordinate time-course data becomes (x 2, y 2).
In actual test, the marker of the detected vehicle is usually used as the origin of coordinates, so that in the video data corresponding to the current test area of the bridge, the marker of the vehicle is the origin of a pixel coordinate system, different pixel coordinate time course data corresponding to the bridge feature points in each frame of video data can be obtained based on the origin of the pixel coordinate time course data, and further the movement information of the bridge feature points relative to the marker of the detected vehicle in the gravity direction can be calculated according to the pixel coordinate time course data corresponding to each frame.
Step S30, determining a first displacement time-course response of the bridge characteristic points in the gravity direction in a detection coordinate system according to the pixel coordinate time-course data, wherein the detection coordinate system takes the marker as an origin, and the first displacement time-course response in the gravity direction is a displacement response of the bridge characteristic points to be detected relative to the origin of the detection coordinate system;
in this embodiment, the detection coordinate system is a world coordinate system (also referred to as a three-dimensional coordinate system), and the world coordinate system also uses the marker of the detection vehicle as the world coordinate origin, and since the marker of the detection vehicle is a quadrilateral marker, a certain point of the quadrilateral marker can be selected as the coordinate origin. In this step, the pixel coordinate time-course data of the bridge feature point in each frame of image in the video data is required to be converted into corresponding world coordinate time-course data, and then the relative change distance of the bridge feature point in the current frame of image compared with the previous frame of image is calculated according to the world coordinate time-course data of each frame of image, namely, the first displacement time-course response of the bridge feature point in the gravity direction in the detection coordinate system is calculated. The first displacement time-course response in the gravity direction refers to the displacement response of the bridge to be detected relative to the origin of the detection coordinate system.
Step S40, acquiring second displacement time-course responses of the detected vehicle in the gravity direction corresponding to all the measurement intervals on the bridge to be detected and acceleration time-course responses measured by the acceleration sensors of the detected vehicle in all the measurement intervals, wherein the second displacement time-course responses of the gravity direction are calculated according to the acceleration time-course responses;
in this embodiment, in the process of recognizing the vibration mode of the bridge, calculation is required according to the real displacement time-course response of the bridge in the gravity direction and the acceleration time-course response measured by the acceleration sensor. The real displacement time-course response needs to be calculated based on the first displacement time-course response of the bridge characteristic points and the second displacement time-course response of the vehicle, so that the second displacement time-course response and the acceleration time-course response of the vehicle need to be acquired.
Specifically, the second displacement time-course response of the detection in the corresponding gravity direction in the measurement interval may be calculated according to the acceleration time-course response, that is, the calculation mode may be a displacement-acceleration formula. The second displacement time response can be obtained by a calculation module for detecting the interior of the vehicle based on the time corresponding to the acceleration time response data after the acceleration time response is obtained.
Step S50, calculating a target displacement time-course response of the bridge characteristic point in the gravity direction according to the first displacement time-course response in the gravity direction and the second displacement time-course response in the gravity direction, wherein the target displacement time-course response is described in a physical unit;
in this embodiment, after the first displacement time-course response and the second displacement time-course response of a certain measurement interval are obtained, the data of the first displacement time-course response and the data of the second displacement time-course response may be added in an adding manner, so as to obtain the target displacement time-course response of the bridge feature point. The target displacement time-course response refers to the target displacement corresponding to the bridge characteristic point in a certain measurement interval, and the measurement interval corresponding to the first displacement time-course response and the second displacement time-course response is the same.
Alternatively, the target displacement time-course response may also be calculated by means of integration and different weight ratios.
And step S60, calculating the time course signals consisting of all target displacement time course responses, all acceleration time course responses and all modal parameters of the detected vehicle by using a subspace recognition method, and determining a vibration mode recognition result corresponding to the bridge to be detected.
In this embodiment, the target displacement time-course response, the acceleration time-course response of the detected vehicle, and the number of modes may be processed in combination with the vehicle-bridge coupled kinematics equation. Specifically, referring to fig. 2, step S60 includes:
step S61, according to the target displacement time-course response of each measuring interval of the bridge to be detected, the acceleration time-course response of the detected vehicle and the modal parameters of the detected vehicle, combining a vehicle-bridge coupling kinematics equation, detecting the related information of the vehicle and the bridge in the kinematics equation by separating the related information of the vehicle and the bridge, and forming a new time-course signal only comprising the bridge information in a time domain space;
in this embodiment, by combining the vehicle-bridge coupled kinematic equation, by separating the vehicle and the bridge related information in the kinematic equation, new time-course information including only the bridge information can be formed in the time-domain space, so that the signal can be processed by the subspace identification method to further correspond to the matrix information.
Step S62, applying the subspace identification method to the time-course signal, and calculating to obtain a system matrix of the bridge to be detected, so as to further calculate a vibration mode curve of the bridge to be detected, wherein the vibration mode curve is the vibration mode identification result.
In this embodiment, the subspace identification method can project a plurality of data into the same plane, after a time-course signal is obtained, the time-course signal is calculated by the subspace identification method, in the calculation process, the influence of time-varying characteristics of the vehicle-bridge coupling system on the subspace identification method can be ignored, and further a system matrix for calculating the bridge characteristics can be obtained, and then a vibration mode curve of the bridge to be detected is calculated based on the matrix information, and the vibration mode curve is used as a vibration mode identification result. The vibration mode refers to the current vibration type of the bridge to be detected, and the vibration mode curve refers to visual vibration data of the bridge to be detected. After the vibration mode or vibration mode curve corresponding to the whole bridge is obtained. The structural condition of the bridge can be analyzed based on the data of the vibration mode and the vibration mode curve, and whether the bridge is in a safe state or not is judged.
In the technical scheme disclosed by the embodiment, video data corresponding to the vehicle driving on each detection area of the bridge is shot and detected through an unmanned aerial vehicle, pixel coordinates are calculated based on the video data, a first displacement time response of bridge characteristic points corresponding to a measurement area in the gravity direction is calculated according to the pixel coordinates, then a second displacement time response and an acceleration time response of the vehicle in the gravity direction corresponding to the measurement area are obtained, the target displacement time response obtained by adding the first displacement time response and the second displacement time response is taken as an input parameter of a subspace identification method, and finally vibration signals obtained by taking the target displacement time response and the acceleration time response as input parameters of a subspace identification method are obtained, so that a tester can analyze abnormal detection areas in existence of the vibration pattern or the vibration pattern curve of the bridge, unidirectional vibration response identification of the bridge characteristic points in air-ground cooperation is realized while the detection efficiency is improved, the period of bridge vibration detection efficiency is reduced, and the accuracy of bridge vibration detection is improved.
Referring to fig. 3, in the second embodiment, if a first displacement time-course response of the bridge feature point in the gravity direction under the detection coordinate system is to be obtained based on the first embodiment, it is necessary to determine internal reference data of a camera of the unmanned aerial vehicle and a shooting distance between the unmanned aerial vehicle and the bridge, and further coordinate conversion between the pixel coordinate system and the detection coordinate system is performed based on the distance, and further the first displacement time-course response is obtained based on the converted detection coordinate system, that is, before step S30, the method further includes:
step S70, obtaining internal reference data, distortion coefficients and image information of the video data at a certain moment;
the internal and external parameter data of the unmanned aerial vehicle camera respectively comprise a rotation matrix of the camera, an offset vector of the camera and an internal parameter matrix, wherein the internal and external parameter data are mainly used for conversion between coordinates. And the distortion coefficient is used to repair the distortion condition of the video data caused by external factors. The image information at a certain moment can be information corresponding to any frame of image in the video data, and is mainly used for calculating the real pixel area of the marker for detecting the vehicle.
Step S80, determining the real size of the marker, and correcting the pixel size of the marker in the image information based on the distortion coefficient;
specifically, before bridge vibration detection, a tester is required to mount (attach) a marker corresponding to the marker on a detection vehicle, for example, 40cm in area 2 Is mounted on a test vehicle and then transmits the area information of the red panel to the unmanned aerial vehicle. Thus, the true size of the markers is known size information that can be directly obtained by the drone. In the image information corrected by the distortion coefficient, the pixel area can be determined by the pixel coordinates corresponding to the four coordinate points of the marker in the image information currently shot, and also can be determined by the area proportion occupied in the image.
And step S90, determining the shooting distance between the camera and the detection vehicle according to the proportion between the real size and the pixel size.
After the real area and the pixel area are obtained, the proportionality coefficient between the real area and the pixel area can be determinedThe method comprises the following steps:
，
the scaling factor can then be establishedLinear regression model with shooting distance: />The shooting distance Zc is calculated based on the regression model.
Further, referring to fig. 4, in this embodiment, after obtaining the inside and outside parameter data and the shooting distance of the unmanned aerial vehicle camera, the pixel coordinates of the bridge feature points can be converted into world coordinates in the world coordinate system based on the inside and outside parameter data and the shooting distance, and further, the first displacement time response is obtained by calculating according to the coordinates, that is, step S30 specifically includes:
step S31, converting the pixel coordinate time course data of the bridge characteristic points into detection coordinate time course data under the detection coordinate system according to the internal and external parameter data and the shooting distance;
in this embodiment, the pixel coordinates of the bridge feature points may be converted into coordinates in the world coordinate system based on the following calculation formula:
，
wherein R is a camera rotation matrix;is a camera offset vector; k is a camera internal reference matrix; zc is the distance between the bridge characteristic point and the unmanned aerial vehicle camera; (u, v) are pixel coordinates of the bridge feature points in certain frame of image information of the video data; (Xw, yw, zw) is the coordinates of the bridge feature points in the world coordinate system.
Based on the calculation mode, world coordinate time course data corresponding to the pixel coordinate time course data of each frame of image of the bridge characteristic points in the video data can be calculated.
Step S32, determining the detection coordinate change distances of the bridge characteristic points at different moments based on the detection coordinate time course data;
specifically, the detected coordinate change distances at different moments refer to the coordinate change distances between the three-dimensional coordinates of the bridge feature points in the current frame of image and the three-dimensional coordinates corresponding to the bridge feature points in the previous frame of image. The detection coordinate variation distance can be calculated by information of coordinate points corresponding to the two detection coordinate time course data.
And step S33, determining the first displacement time-course response of the bridge characteristic point under the detection coordinate system according to the detection coordinate change distance.
In this embodiment, the obtained detection coordinate change distance is actually a three-dimensional change distance of world coordinates, and in order to facilitate subsequent detection, the three-dimensional change distance needs to be mapped into a world coordinate plane to obtain displacement of the bridge feature point along the mapping direction of the world coordinate system, that is, the first displacement time-course response of the bridge feature point in the gravity direction in the detection coordinate system.
In the technical scheme disclosed by the embodiment, through acquiring the shot internal and external parameter data, distortion data, image information and the like, the image information is subjected to pixel calibration based on the real size of the detection vehicle, so that the shooting distance between the unmanned aerial vehicle and the detection vehicle is obtained, the unmanned aerial vehicle can convert the pixel coordinate time course data into the detection coordinate time course data under the detection coordinate system according to the shooting distance, the change distances of bridge characteristic features at different moments are obtained based on the time course data at different moments, the first displacement time course response is determined based on the change distances, the accuracy of the first displacement time course response is improved based on the change distances, and the accuracy of the whole bridge detection process is improved.
Referring to fig. 5, fig. 5 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 5, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 5 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 5, an operating system, a network communication module, a user interface module, and a vibration mode identification program of a bridge may be included in the memory 1004 as one type of computer storage medium.
In the terminal shown in fig. 5, the network interface 1003 is mainly used for connecting to a background server, and performing data communication with the background server; the processor 1001 may call a bridge vibration mode identification program stored in the memory 1005 and perform the following operations:
the method comprises the steps that an unmanned aerial vehicle collects video data of a detected vehicle when the detected vehicle runs in a measuring interval corresponding to a bridge to be detected, wherein the bridge to be detected is divided into a plurality of measuring intervals along the length direction;
determining corresponding pixel coordinate time course data of the marker and the bridge characteristic point on the detected vehicle in each video frame within a duration period based on the video data;
determining a first displacement time-course response of the bridge characteristic point in the gravity direction in a detection coordinate system according to the pixel coordinate time-course data, wherein the detection coordinate system takes the marker as an origin, and the first displacement time-course response in the gravity direction is a displacement response of the bridge characteristic point to be detected relative to the origin of the detection coordinate system;
acquiring second displacement time-course responses of the detection vehicle in the gravity direction corresponding to all measurement intervals on the bridge to be detected and acceleration time-course responses measured by the acceleration sensors of the detection vehicle in all measurement intervals, wherein the second displacement time-course responses of the gravity direction are calculated according to the acceleration time-course responses;
calculating a target displacement time-course response of the bridge characteristic point in the gravity direction according to the first displacement time-course response in the gravity direction and the second displacement time-course response in the gravity direction, wherein the target displacement time-course response is described in a physical unit;
and calculating all the target displacement time-course response, the acceleration time-course response and time-course signals consisting of modal parameters of the detected vehicle by using a subspace recognition method, and determining a vibration mode recognition result corresponding to the bridge to be detected.
Further, the processor 1001 may call a bridge vibration mode identification program stored in the memory 1005, and further perform the following operations:
acquiring the internal and external parameter data, distortion coefficients and image information of the video data at a certain moment;
determining the real size of the marker, and correcting the pixel size of the marker in the image information based on the distortion coefficient;
and determining the shooting distance between the camera and the detection vehicle according to the proportion between the real size and the pixel size.
Further, the processor 1001 may call a bridge vibration mode identification program stored in the memory 1005, and further perform the following operations:
according to the internal and external parameter data and the shooting distance, converting the pixel coordinate time-course data of the bridge characteristic points into detection coordinate time-course data under the detection coordinate system;
determining the detection coordinate change distance of the bridge characteristic points at different moments based on the detection coordinate time course data;
and determining the first displacement time-course response of the bridge characteristic point under the detection coordinate system according to the detection coordinate change distance.
Further, the processor 1001 may call a bridge vibration mode identification program stored in the memory 1005, and further perform the following operations:
according to the target displacement time-course response of each measuring interval of the bridge to be detected, the acceleration time-course response of the detected vehicle and the modal parameters of the detected vehicle, combining a vehicle-bridge coupling kinematics equation, detecting the vehicle and the related information of the bridge by separating the kinematics equation, and forming a new time-course signal only comprising the bridge information in a time domain space;
and applying the subspace identification method to the time-course signal, and calculating to obtain a system matrix of the bridge to be detected, so as to further calculate a vibration mode curve of the bridge to be detected, wherein the vibration mode curve is the vibration mode identification result.
Furthermore, it will be appreciated by those of ordinary skill in the art that implementing all or part of the processes in the methods of the above embodiments may be accomplished by computer programs to instruct related hardware. The computer program comprises program instructions, and the computer program may be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the control terminal to carry out the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a computer-readable storage medium storing a vibration pattern recognition program of a bridge, which when executed by a processor, implements the respective steps of the vibration pattern recognition method of a bridge as described in the above embodiments.
It should be noted that, because the storage medium provided in the embodiments of the present application is a storage medium used to implement the method in the embodiments of the present application, based on the method described in the embodiments of the present application, a person skilled in the art can understand the specific structure and the modification of the storage medium, and therefore, the description thereof is omitted herein. All storage media used in the methods of the embodiments of the present application are within the scope of protection intended in the present application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts 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.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (4)
1. The method for identifying the vibration mode of the bridge is characterized by comprising the following steps of:
the method comprises the steps that an unmanned aerial vehicle collects video data of a detected vehicle when the detected vehicle runs in a measuring interval corresponding to a bridge to be detected, wherein the bridge to be detected is divided into a plurality of measuring intervals along the length direction;
determining corresponding pixel coordinate time course data of the marker and the bridge characteristic point on the detected vehicle in each video frame within a duration period based on the video data;
according to the inside and outside parameter data of the camera and the shooting distance between the camera and the detection vehicle, converting the pixel coordinate time-course data of the bridge characteristic points into detection coordinate time-course data under the detection coordinate system;
determining the detection coordinate change distance of the bridge characteristic points at different moments based on the detection coordinate time course data;
determining a first displacement time-course response of the bridge characteristic point in the gravity direction under the detection coordinate system according to the detection coordinate change distance, wherein the detection coordinate system takes the marker as an origin, and the first displacement time-course response in the gravity direction is a displacement response of the bridge characteristic point to be detected relative to the origin of the detection coordinate system;
acquiring second displacement time-course responses of the detection vehicle in the gravity direction corresponding to all measurement intervals on the bridge to be detected and acceleration time-course responses measured by the acceleration sensors of the detection vehicle in all measurement intervals, wherein the second displacement time-course responses of the gravity direction are calculated according to the acceleration time-course responses;
calculating a target displacement time-course response of the bridge characteristic point in the gravity direction according to the first displacement time-course response in the gravity direction and the second displacement time-course response in the gravity direction, wherein the target displacement time-course response is described in a physical unit;
according to the target displacement time-course response of each measuring interval of the bridge to be detected, the acceleration time-course response of the detected vehicle and the modal parameters of the detected vehicle, combining a vehicle-bridge coupling kinematics equation, detecting the vehicle and the related information of the bridge by separating the kinematics equation, and forming a new time-course signal only comprising the bridge information in a time domain space;
and applying a subspace identification method to the time-course signal, calculating to obtain a system matrix of the bridge to be detected, and further calculating a vibration mode curve of the bridge to be detected, wherein the vibration mode curve is the vibration mode identification result.
2. The method for recognizing the vibration mode of a bridge according to claim 1, wherein the step of converting the pixel coordinate time course data of the bridge characteristic point into the detection coordinate time course data in the detection coordinate system according to the inside and outside parameter data of the camera and the shooting distance between the camera and the detection vehicle further comprises:
acquiring the internal and external parameter data, distortion coefficients and image information of the video data at a certain moment of the video;
determining the real size of the marker, and correcting the pixel size of the marker in the image information based on the distortion coefficient;
and determining the shooting distance between the camera and the detection vehicle according to the proportion between the real size and the pixel size.
3. An unmanned aerial vehicle, characterized in that the unmanned aerial vehicle comprises: memory, processor and the bridge vibration identification program stored on the memory and executable on the processor, the bridge vibration pattern identification program when executed by the processor implementing the steps of the bridge vibration pattern identification method according to any one of claims 1 to 2.
4. A computer-readable storage medium, wherein a vibration pattern recognition program of a bridge is stored on the computer-readable storage medium, which when executed by a processor, implements the steps of the vibration pattern recognition method of a bridge according to any one of claims 1 to 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311517618.0A CN117237832B (en) | 2023-11-15 | 2023-11-15 | Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311517618.0A CN117237832B (en) | 2023-11-15 | 2023-11-15 | Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117237832A CN117237832A (en) | 2023-12-15 |
| CN117237832B true CN117237832B (en) | 2024-02-09 |
Family
ID=89088449
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311517618.0A Active CN117237832B (en) | 2023-11-15 | 2023-11-15 | Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117237832B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110502855A (en) * | 2019-08-28 | 2019-11-26 | 湘潭大学 | The cross-section continuous beam damnification recognition method of faulted condition influence line of reactions curvature |
| WO2021119947A1 (en) * | 2019-12-16 | 2021-06-24 | 哈尔滨工业大学（深圳） | Method for quick detection of damage to bridge, and related device |
| CN113392789A (en) * | 2021-06-21 | 2021-09-14 | 暨南大学 | Bridge full-bridge complete modal shape recognition method utilizing small amount of sensor information |
| WO2022231989A1 (en) * | 2021-04-29 | 2022-11-03 | Skygrid, Llc | Unmanned aerial vehicle response to object detection |
| CN115375924A (en) * | 2022-09-14 | 2022-11-22 | 湖南大学 | Bridge health monitoring method and system based on image recognition |
| CN115761487A (en) * | 2022-11-13 | 2023-03-07 | 福州大学 | Method for quickly identifying vibration characteristics of small and medium-span bridges based on machine vision |
-
2023
- 2023-11-15 CN CN202311517618.0A patent/CN117237832B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110502855A (en) * | 2019-08-28 | 2019-11-26 | 湘潭大学 | The cross-section continuous beam damnification recognition method of faulted condition influence line of reactions curvature |
| WO2021119947A1 (en) * | 2019-12-16 | 2021-06-24 | 哈尔滨工业大学（深圳） | Method for quick detection of damage to bridge, and related device |
| WO2022231989A1 (en) * | 2021-04-29 | 2022-11-03 | Skygrid, Llc | Unmanned aerial vehicle response to object detection |
| CN113392789A (en) * | 2021-06-21 | 2021-09-14 | 暨南大学 | Bridge full-bridge complete modal shape recognition method utilizing small amount of sensor information |
| CN115375924A (en) * | 2022-09-14 | 2022-11-22 | 湖南大学 | Bridge health monitoring method and system based on image recognition |
| CN115761487A (en) * | 2022-11-13 | 2023-03-07 | 福州大学 | Method for quickly identifying vibration characteristics of small and medium-span bridges based on machine vision |
Non-Patent Citations (1)
| Title |
|---|
| 基于时频分析的运营桥梁模态参数识别方法;侯立群;欧进萍;;振动工程学报(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117237832A (en) | 2023-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111856963B (en) | Parking simulation method and device based on vehicle-mounted looking-around system | |
| CN109471096B (en) | Multi-sensor target matching method and device and automobile | |
| RU2016140057A (en) | TEST STAND FOR DETERMINING THE BORDERS OF THE MOTION LANE IN THE VIRTUAL DRIVING ENVIRONMENT | |
| CN103985131B (en) | Camera fast-calibration method for highway lane departure warning system | |
| CN106370435A (en) | Road spectrum acquisition system, vehicle and road spectrum acquisition method | |
| WO2020022042A1 (en) | Deterioration diagnosis device, deterioration diagnosis system, deterioration diagnosis method, and storage medium for storing program | |
| CN106382946B (en) | Parameter calibrating method and device | |
| US20220055652A1 (en) | Device and method for data fusion between heterogeneous sensors | |
| CN109360228A (en) | Pose method for registering between monocular cam and millimetre-wave radar | |
| CN115808324B (en) | Light safety management monitoring method and system for small and medium span bridges | |
| CN114639085A (en) | Traffic signal lamp identification method and device, computer equipment and storage medium | |
| KR101272571B1 (en) | Simulator for stereo vision system of intelligent vehicle and camera calibration method using the same | |
| CN111492320A (en) | Behavior model of environmental sensor | |
| CN113198166B (en) | Long jump detection method and detection device | |
| CN103743425A (en) | Method for detecting dynamic response capability of instrument pointer | |
| CN117237832B (en) | Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium | |
| CN107767366B (en) | A kind of transmission line of electricity approximating method and device | |
| CN105741260A (en) | Action positioning device and positioning method thereof | |
| CN110426034B (en) | Indoor positioning method based on map information auxiliary inertial navigation array | |
| JP2021154935A (en) | Vehicle simulation system, vehicle simulation method and computer program | |
| CN113340405B (en) | Bridge vibration mode measuring method, device and system | |
| CN116203976A (en) | Indoor inspection method and device for transformer substation, unmanned aerial vehicle and storage medium | |
| CN111862146A (en) | Target object positioning method and device | |
| CN114879168A (en) | Laser radar and IMU calibration method and system | |
| CN113513984A (en) | Parking space identification precision detection method and device, electronic equipment and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |