CN117373231A - Front touch type monitoring method and system for dynamic response of medium and small bridges under vehicle load - Google Patents
Front touch type monitoring method and system for dynamic response of medium and small bridges under vehicle load Download PDFInfo
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Abstract
The embodiment of the application provides a front touch type monitoring method and a front touch type monitoring system for dynamic response of a middle-small bridge under a vehicle load, which are used for capturing video of an on-road vehicle to be on-road vehicle based on a first video acquisition device arranged on the middle-small bridge to obtain a first video stream; video capturing is carried out on the bridge deck of the middle-small bridge based on a second video acquisition device arranged on the middle-small bridge, so as to obtain a second video stream; performing target recognition on the first video stream to determine the vehicle type of the on-coming vehicle; if the vehicle type corresponds to overrun and/or overload vehicles, generating an early warning signal; based on the early warning signal, triggering a displacement meter arranged below the middle and small bridges to collect deflection data of the middle and small bridges so as to predict the structural integrity and operation safety state of the middle and small bridges after the on-coming vehicles travel on the bridge deck, thereby realizing the advanced perception of operation risk and guaranteeing the safe operation of bridge structures.
Description
Technical Field
The embodiment of the application relates to the technical field of bridge safety monitoring, in particular to a front contact type monitoring method and system for dynamic response of a medium-sized and small bridge under a vehicle load.
Background
The bridge is a key node of a road traffic network, and the bridge safety relationship road network is smooth and closely related to the life and property safety of people. However, during long-term service, the service performance and bearing capacity of the bridge are gradually attenuated under the influence of factors such as vehicle load, external environment, material degradation and the like. With the aging and service condition deterioration of the in-service bridge in China, the operation safety problem of the bridge is increasingly remarkable, and collapse accidents are increasingly increased. Especially for large-scale and broad-range middle and small bridges, the problems of serious structural safety and technical conditions such as limited curing funds, low importance level, weak curing and checking force, untimely disease treatment and the like exist. At present, structural safety and technical condition evaluation of middle and small bridges mainly depend on manual periodic detection, and the problems of low detection frequency, strong subjectivity and weak correlation with structural stress state exist. In this context, medium and small bridge structure monitoring and safety assessment become important development directions.
In the process of realizing the application, the inventor finds that the vehicle load is a main reason for influencing the safety of the middle and small bridge structures in the operation period, and the randomness and the contingency of the vehicle load increase the safety risk of the bridge structures. Compared with long bridges, the dead weight of the middle and small bridges is lighter, and a cable system is not present like the long bridges, so that the live load effect ratio of the middle and small bridges is high, and the middle and small bridges are more sensitive to vehicle loads. In addition, the common assembled small and medium bridges have single plates (beams) stressed on a heavy load road section, and certain hidden danger exists in the structure safety.
Therefore, monitoring the dynamic response of the medium and small bridge structure and acquiring the time-varying performance of the bridge under complex operation conditions become an important means for guaranteeing the safety of the bridge structure. At present, when monitoring a medium-small bridge structure, dynamic response parameters such as deflection of the bridge structure and the like are collected after a vehicle passes through the bridge mainly through a rear triggering method, so that the structural integrity and the operation safety state are evaluated, and the operation risk cannot be perceived in advance.
Disclosure of Invention
The application aims to provide a front touch type monitoring method and system for dynamic response of a medium-sized and small bridge under vehicle load, which are used for solving or overcoming the technical problems in the prior art.
According to a first aspect of embodiments of the present application, there is provided a front-touch monitoring method for dynamic response of a small and medium bridge under a vehicle load, including:
video capturing is carried out on an on-road vehicle to be on-road vehicle running on the road surface based on a first video acquisition device arranged on the middle-small bridge to obtain a first video stream;
video capturing is carried out on the bridge deck of the middle-small bridge based on a second video acquisition device arranged on the middle-small bridge, so as to obtain a second video stream;
performing target recognition on the first video stream to determine the vehicle type of the on-coming vehicle;
if the vehicle type corresponds to overrun and/or overload vehicles, generating an early warning signal;
based on the early warning signal, triggering a displacement meter arranged below the middle-small bridge to acquire deflection data of the middle-small bridge;
performing target recognition on the second video stream to determine a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights;
calculating a predicted position average vector of any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights;
According to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence;
determining a vehicle load estimated value on the middle-small bridge according to the deflection data of the middle-small bridge and the space characteristics of all vehicles on the bridge deck;
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value.
Optionally, the first video acquisition device is adjacent to the second video acquisition device and is arranged at the bridge head position of the middle-small bridge, so that the video acquisition angle of the first video acquisition device faces the road surface, and the video acquisition angle of the second video acquisition device faces the bridge surface.
Optionally, the frame rate of video capturing by the first video capturing device and the second video capturing device is not less than 25fps.
Optionally, the method further comprises: and identifying license plates of vehicles on the bridge deck of the vehicle to be driven on the road surface and/or any vehicle on the bridge deck of the medium-small bridge so as to fuse the space characteristics formed by the vehicle to be driven on the road surface and the space characteristics formed by the vehicle to be driven on the bridge deck to obtain the space fusion characteristics of the vehicle to be driven on the bridge deck.
Optionally, the method further comprises: and aligning the deflection data and the second video stream in time sequence, and enabling the aligned time-course difference value to be less than or equal to 20ms so that the deflection data of the middle-small bridge corresponds to the spatial characteristics of all vehicles on the bridge deck.
Optionally, the method further comprises:
based on the first video stream, identifying a real-time running state vector of the on-road vehicle at the k-1 moment, wherein k is a positive integer more than or equal to 2;
calculating a plurality of predicted running state vectors of the on-coming vehicles on the road surface at the k-1 moment according to the real-time running state vectors of the on-coming vehicles on the road surface at the k-1 moment, and distributing corresponding weights;
Calculating an average predicted running state vector according to a plurality of predicted running state vectors of the vehicle to be driven on the road surface at the kth moment, and comparing the average predicted running state vector with an observed running state vector of the vehicle to be driven on the road surface at the kth moment to adjust weights corresponding to the predicted running state vectors until the distance between the average predicted running state vector and the observed running state vector is within a set vector distance range;
determining a position change sequence of the on-coming vehicle on the road surface according to the average predicted running state vector with the distance from the observed running state vector within a set vector distance range;
determining the spatial characteristics of the on-coming vehicles on the road surface according to the position change sequence;
correspondingly, the predicting the structural integrity and the operation safety state of the medium-small bridge after the vehicle to be on-road is driven onto the bridge deck according to the vehicle load estimated value comprises the following steps:
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value and the spatial characteristics of the on-road vehicle.
Optionally, the determining a plurality of predicted position vectors of the any vehicle on the deck from the historical position vectors includes:
the historical position vector is transformed into a state space according to a set position prediction transformation matrix, and is corrected in the state space based on system noise so as to determine a plurality of predicted position vectors of any vehicle on the bridge deck.
Optionally, the transforming the historical position vector into a state space according to a set position prediction transformation matrix, and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors of the any vehicle on the bridge deck, including:
based on the following formula: s is(s) k ＝Φs k-1 +Γu k-1 K=2, &..n, n is a positive integer greater than 2, transforming the historical position vector into a state space, and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors of the any vehicle on the deck, wherein Φ represents a position prediction transformation matrix corresponding to a k-1 time, u k-1 Represents the system noise corresponding to the k-1 time, Γ represents the noise correction matrix, s k Representing a predicted position vector corresponding to the kth moment, which at least comprises the transverse and longitudinal position coordinates of any vehicle in the bridge deck coordinate system at the kth moment, s k-1 A predicted position vector corresponding to the k-1 time is represented.
Optionally, based on the early warning signal, triggering a displacement meter disposed below the middle-small bridge to collect deflection data of the middle-small bridge, including:
image acquisition taking a target point as a center is carried out before the middle and small bridges are deformed based on the displacement meter so as to form a reference image;
determining a sub-reference image area by taking imaging of the target point on the reference image as a center;
performing image acquisition on the middle and small bridges after deformation based on the displacement meter so as to form a target image;
calculating the correlation coefficient of the target image and the sub-reference image area, and taking an image area formed by pixels of the target image as a sub-target image area when the correlation coefficient is maximum;
taking the central point of the sub-target image area as the position corresponding to the target point after deformation;
And calculating the deflection data of the middle-small bridge according to the position of the target point on the reference image and the position of the target point in the sub-target image area.
According to a second aspect of embodiments of the present application, there is provided a front-touch monitoring system for dynamic response of a small and medium bridge under a vehicle load, comprising:
the first video acquisition device is arranged on the middle-small bridge and is used for capturing video of an upcoming vehicle running on a road surface to obtain a first video stream, carrying out target identification on the first video stream so as to determine the vehicle type of the upcoming vehicle, and generating an early warning signal if the vehicle type corresponds to overrun and/or overload vehicles;
the second video acquisition device is arranged on the middle-small bridge and is used for capturing video of the bridge deck of the middle-small bridge to obtain a second video stream and identifying targets of the second video stream;
the displacement meter is used for collecting deflection data of the middle-small bridge based on triggering of the early warning signal;
edge computing means for performing the steps of:
according to target identification of the second video stream, determining a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights;
Calculating a predicted position average vector of any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights;
according to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence;
determining a vehicle load estimated value on the middle-small bridge according to the deflection data of the middle-small bridge and the space characteristics of all vehicles on the bridge deck;
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value.
According to the front touch type monitoring method and system for the dynamic response of the middle-small bridge under the vehicle load, video capturing is carried out on the vehicles to be on-road bridge running on the road surface based on the first video acquisition device arranged on the middle-small bridge, and a first video stream is obtained; video capturing is carried out on the bridge deck of the middle-small bridge based on a second video acquisition device arranged on the middle-small bridge, so as to obtain a second video stream; performing target recognition on the first video stream to determine the vehicle type of the on-coming vehicle; if the vehicle type corresponds to overrun and/or overload vehicles, generating an early warning signal; based on the early warning signal, triggering a displacement meter arranged below the middle-small bridge to acquire deflection data of the middle-small bridge; performing target recognition on the second video stream to determine a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights; calculating a predicted position average vector of any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights; according to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range; determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range; determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence; determining a vehicle load estimated value on the middle-small bridge according to the deflection data of the middle-small bridge and the space characteristics of all vehicles on the bridge deck; and predicting the structural integrity and the operation safety state of the middle and small bridges after the on-road vehicles run on the bridge deck according to the vehicle load estimated value, so that the early perception of the operation risk is realized, and the safety operation of the bridge structure is ensured.
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Some specific embodiments of the present application will be described in detail below by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
fig. 1 is a schematic flow chart of a front-touch monitoring method for dynamic response of a middle-small bridge under a vehicle load according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a front-touch monitoring system for dynamic response of a middle-small bridge under a vehicle load according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions in the embodiments of the present application, the following descriptions will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the embodiments of the present application shall fall within the scope of protection of the embodiments of the present application.
Fig. 1 is a schematic flow chart of a front-touch monitoring method for dynamic response of a middle-small bridge under a vehicle load according to an embodiment of the present application. As shown in fig. 1, it includes the steps of:
S101, capturing video of an on-road vehicle running on a road surface based on a first video acquisition device arranged on the middle-small bridge to obtain a first video stream;
s102, capturing video of the bridge deck of the middle-small bridge based on a second video acquisition device arranged on the middle-small bridge to obtain a second video stream;
optionally, the first video acquisition device is adjacent to the second video acquisition device and is arranged at the bridge head position of the middle-small bridge, so that the video acquisition angle of the first video acquisition device faces the road surface, and the video acquisition angle of the second video acquisition device faces the bridge surface.
Specifically, for example, a frame may be disposed at the bridge head position of the middle-small bridge, and the first video acquisition device and the second video acquisition device are fixed on the frame in an adjacent manner.
Specifically, in this embodiment, the fixed position of the first video acquisition device on the frame makes the first video acquisition device can monitor the vehicle about to get on the bridge within a road range of 30m from the bridge head, so as to ensure that the vehicle about to get on the bridge can be monitored in time, and meanwhile, the work load of the first video acquisition device is reduced.
Specifically, for example, a frame may be disposed at each bridge head position in the uplink direction and the downlink direction of the middle-small bridge, and the first video capturing device and the second video capturing device are fixed on each frame in a manner that the first video capturing device and the second video capturing device are adjacent to each other, so that video capturing is conveniently performed in the uplink direction and/or the downlink direction, and a first video stream and a second video stream in the uplink direction and a first video stream and a second video stream in the downlink direction are obtained, so that the schemes of the embodiments of the present application may be respectively executed for the first video stream and the second video stream in the uplink direction and the first video stream and the second video stream in the downlink direction, and the middle-small bridge is monitored based on the uplink direction and/or the downlink direction.
Of course, in other embodiments, if the middle-small bridge only runs unidirectionally (in the uplink direction or the downlink direction), and not in both directions, the schemes of the embodiments of the present application may be executed only for the first video stream and the second video stream corresponding to the unidirectional running (in the uplink direction or the downlink direction).
Specifically, in this embodiment, the first video capturing device and the second video capturing device may be, for example, a machine vision camera.
Optionally, the frame rate of video capturing performed by the first video capturing device and the second video capturing device is not lower than 25fps, so that seamless splicing can be performed on a moving track before the same vehicle gets on a bridge and a moving track after the same vehicle gets on the bridge, and the moving track of the vehicle is conveniently monitored, so that a vehicle load estimated value of the middle and small bridge is determined.
S103, carrying out target recognition on the first video stream to determine the vehicle type of the vehicle to be on-coming;
in this embodiment, profile data of a target may be extracted from the first video stream, and a size of the target may be determined based on the profile data, so as to determine a vehicle type of the upcoming vehicle.
S104, if the vehicle type corresponds to overrun and/or overload vehicles, generating an early warning signal;
for example, the overrun and/or overload vehicles include large vans, oversized vehicles, container vehicles, and the like.
S105, triggering a displacement meter arranged below the middle-small bridge to collect deflection data of the middle-small bridge based on the early warning signal;
specifically, in the embodiment of the application, the sampling frequency of deflection data is not lower than 50hz, and the resolution is not lower than 0.05mm, so that the granularity of deflection data acquisition is improved, and the comprehensiveness and accuracy of the deflection data are ensured.
Optionally, based on the early warning signal, triggering a displacement meter arranged below the middle-small bridge to collect deflection data of the middle-small bridge, including the following steps:
image acquisition taking a target point as a center is carried out before the middle and small bridges are deformed based on the displacement meter so as to form a reference image;
determining a sub-reference image area by taking imaging of the target point on the reference image as a center; specifically, for example, in the reference image, a sub-region of (2m+1) × (2m+1) pixels centered on a target point (x, y) is taken as a sub-reference image region, where M is a positive integer greater than or equal to 1, and the specific size of M is determined according to the application scenario.
Performing image acquisition on the middle and small bridges after deformation based on the displacement meter so as to form a target image;
calculating the correlation coefficient of the target image and the sub-reference image area, and taking an image area formed by pixels of the target image as a sub-target image area when the correlation coefficient is maximum;
taking the central point of the sub-target image area as the position corresponding to the target point after deformation;
and calculating the deflection data of the middle-small bridge according to the position of the target point on the reference image and the position of the target point in the sub-target image area.
In the present embodiment, the correlation coefficient C of the target image and the sub-reference image area can be calculated by the following formula (1) f，g ：
Wherein:for the displacement value to be measured, it comprises a horizontal component u and a vertical component v, i.e.>Wherein v represents deflection data;
f (x, y) and g (x ', y') are respectively a sub-reference image region and a target image, corr is an operator for obtaining a phase relation number and is used for evaluating the similarity degree of the sub-reference image region and the target image, wherein (x, y) represents the position of a target point in the reference image, and (x ', y') represents the position of the target point in the target image.
Specifically, when calculating the correlation coefficient of the target image and the sub-reference image region, the correlation coefficient is calculated based on the sub-reference image region and the gray field of the target image, thereby improving the calculation efficiency of the correlation coefficient. For this purpose, in the above formula (1), f (x, y) and g (x ', y') are respectively the gray fields of the sub-reference image region and the target image.
In this embodiment of the present application, the maximum value of the correlation coefficient is calculated based on the above formula (1), and then the corresponding area of the target image is determined, the image area formed by the pixels of the target image is taken as the sub-target image area, and then the center point of the sub-target image area is further taken as the position (x ', y') of the target point on the target image, so that the difference between the position (x ', y') of the target point on the target image and the position (x, y) of the target point in the reference image is obtained, the horizontal component u and the vertical component v of the displacement value of the target point are determined, and the vertical component v is taken as the deflection data of the middle-small bridge.
Optionally, the method further comprises: and aligning the deflection data and the second video stream on a time sequence, and enabling the aligned time interval difference value to be smaller than or equal to 20ms, so that the deflection data of the middle and small bridges correspond to the spatial characteristics of all vehicles on the bridge deck, a mapping relation between the deflection data of the bridges and the spatial characteristics of the vehicles is established, and the monitoring granularity of the middle and small bridges is improved.
In particular, when alignment is performed based on a time series in which time stamps are recorded, alignment can be achieved by establishing a mapping relationship between deflection data and spatial features of the same time stamps.
S106, carrying out target recognition on the second video stream to determine a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights;
in this embodiment, for example, the license plate number of any vehicle on the bridge deck may be identified based on the second video stream, the historical position of the corresponding vehicle on the bridge deck may be monitored based on the license plate number, and the timestamp appearing at the position may be monitored, so that the historical position vector is formed based on the historical position and the timestamp.
In this embodiment, determining a plurality of predicted position vectors of the any vehicle on the bridge deck according to the historical position vectors, and assigning corresponding weights may include: and determining a plurality of predicted position vectors of any vehicle on the bridge deck based on the running speed and the running direction of the any vehicle and the historical position vectors, forming the predicted position vectors according to the possible positions and the time stamps of the any vehicle on the bridge deck, and distributing corresponding weights.
S107, calculating a predicted position average vector of the any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights;
in this embodiment, a weighted average calculation may be performed on a plurality of predicted position vectors and corresponding weights, so as to obtain a predicted position average vector, and the predicted position average vector may be used as an effective predicted position vector of the corresponding vehicle.
S108, according to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold range.
In this embodiment, the second video stream may be analyzed based on a timestamp, so as to obtain an actual observation position vector of the any vehicle on the bridge deck, and the difference value between the prediction position average vector and the actual observation position vector is determined by directly comparing the prediction position average vector with the actual observation position, and further compared with a preset vector difference value threshold range, so as to determine whether the difference value between the prediction position average vector and the actual observation position vector is within the preset vector difference value threshold range, and adjust weights corresponding to a plurality of prediction position vectors according to a gradient descent method until the difference value between the prediction position average vector and the actual observation position vector is within the preset vector difference value threshold range.
S109, determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
in this embodiment, the predicted position average vector when the difference value of the actual observed position vector is within the preset vector difference value threshold value range may be recorded in a time-series manner, so as to obtain a position change sequence of the any vehicle on the bridge deck.
S110, determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence.
In this embodiment, a moving track of any vehicle on the bridge deck may be drawn through a position change sequence, and the moving track is used as the spatial feature, so as to realize dynamic monitoring of the vehicle.
S111, determining a vehicle load estimated value on the middle-small bridge according to deflection data of the middle-small bridge and space characteristics of all vehicles on the bridge deck;
specifically, step S111 may specifically include:
establishing a bridge finite element model according to structural attribute characteristics of a bridge, dividing the nodes and the grids of the components of the bridge finite element model, and endowing each grid material with attribute and boundary conditions;
calibrating the bridge finite element model based on a known vehicle load calibration value and a known space calibration characteristic to obtain theoretical deflection data, and comparing deflection data formed when a vehicle matched with the vehicle load calibration value and the space calibration characteristic runs on the medium-small bridge until the difference between the vehicle load calibration value and the space calibration characteristic is smaller than a set threshold value, and completing calibration;
and carrying out inversion processing according to deflection data formed by all vehicles on the bridge deck on the middle-small bridge and the space characteristics of all vehicles on the bridge deck and combining the calibrated bridge finite element model to obtain a vehicle load estimated value of running on the middle-small bridge.
S112, predicting the structural integrity and the operation safety state of the medium and small bridges after the vehicles to be on the bridge deck travel according to the vehicle load estimated value.
Optionally, the method further comprises: and identifying license plates of vehicles on the bridge deck of the vehicle to be driven on the road surface and/or any vehicle on the bridge deck of the medium-small bridge so as to fuse the space characteristics formed by the vehicle to be driven on the road surface and the space characteristics formed by the vehicle to be driven on the bridge deck to obtain the space fusion characteristics of the vehicle to be driven on the bridge deck.
Specifically, for example, a movement track formed by the vehicle to be on-road and a movement track formed by the vehicle to be on-road after running on the bridge deck can be fused to obtain a whole-course movement track, so that the whole-course monitoring is performed before and after the vehicle is on-road.
Optionally, the method further includes determining a spatial feature of the on-coming vehicle on the road surface, which may specifically include:
based on the first video stream, identifying a real-time running state vector of the on-road vehicle at the k-1 moment, wherein k is a positive integer more than or equal to 2;
Calculating a plurality of predicted running state vectors of the on-coming vehicles on the road surface at the k-1 moment according to the real-time running state vectors of the on-coming vehicles on the road surface at the k-1 moment, and distributing corresponding weights;
calculating an average predicted running state vector according to a plurality of predicted running state vectors of the vehicle to be driven on the road surface at the kth moment, and comparing the average predicted running state vector with an observed running state vector of the vehicle to be driven on the road surface at the kth moment to adjust weights corresponding to the predicted running state vectors until the distance between the average predicted running state vector and the observed running state vector is within a set vector distance range;
determining a position change sequence of the on-coming vehicle on the road surface according to the average predicted running state vector with the distance from the observed running state vector within a set vector distance range;
and determining the spatial characteristics of the on-road vehicles according to the position change sequence.
Here, the above-described determination of the spatial characteristics of the on-coming vehicle on the road surface is similar to the above-described determination of the spatial characteristics of any vehicle on the bridge.
In this embodiment, by determining the spatial feature of the approaching vehicle on the road surface based on the above, a plurality of predicted running state vectors on the road surface at the kth time are calculated based on the observed running state vector at the kth time only, thereby reducing the throughput of data, simplifying the complexity of algorithm design, and improving the efficiency of data processing.
Correspondingly, the predicting the structural integrity and the operation safety state of the medium-small bridge after the vehicle to be on-road is driven onto the bridge deck according to the vehicle load estimated value comprises the following steps:
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value and the spatial characteristics of the on-road vehicle.
In this embodiment, the vehicle load increment value caused by the vehicle to be on-road can be calculated according to the position change sequence of the vehicle to be on-road, and then summed with the vehicle load estimation value, so as to predict the structural integrity and the operation safety state of the medium and small bridges after the vehicle to be on-road is driven onto the bridge deck.
Optionally, the determining a plurality of predicted position vectors of the any vehicle on the deck from the historical position vectors includes:
the historical position vector is transformed into a state space according to a set position prediction transformation matrix, and is corrected in the state space based on system noise so as to determine a plurality of predicted position vectors of any vehicle on the bridge deck.
Optionally, the transforming the historical position vector into a state space according to a set position prediction transformation matrix, and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors of the any vehicle on the bridge deck, including:
based on the following formula: s is(s) k ＝Φs k-1 +Γu k-1 K=2, &..n, n is a positive integer greater than 2, transforming the historical position vector into a state space, and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors of the any vehicle on the deck, wherein Φ represents a position prediction transformation matrix corresponding to a k-1 time, u k-1 Representing the system noise corresponding to time k-1, e.g. expressed asIncluding the system noise in the horizontal coordinate system (x, y) direction of the bridge deck at the k-1 time; Γ represents a noise correction matrix, s k Representing a predicted position vector corresponding to the kth time, which at least comprises any one of the vehiclesCoordinates of the lateral and longitudinal position of the vehicle in the deck coordinate system at the kth moment, s k-1 A predicted position vector corresponding to the k-1 time is represented.
Fig. 2 is a schematic structural diagram of a front-touch monitoring system for dynamic response of a middle-small bridge under a vehicle load according to an embodiment of the present application. As shown in fig. 2, it includes:
the first video acquisition device is arranged on the middle-small bridge and is used for capturing video of an upcoming vehicle running on a road surface to obtain a first video stream, carrying out target identification on the first video stream so as to determine the vehicle type of the upcoming vehicle, and generating an early warning signal if the vehicle type corresponds to overrun and/or overload vehicles;
the second video acquisition device is arranged on the middle-small bridge and is used for capturing video of the bridge deck of the middle-small bridge to obtain a second video stream and identifying targets of the second video stream;
The displacement meter is used for collecting deflection data of the middle-small bridge based on triggering of the early warning signal;
edge computing means for performing the steps of:
according to target identification of the second video stream, determining a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights;
calculating a predicted position average vector of any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights;
according to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
Determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence;
determining a vehicle load estimated value on the middle-small bridge according to the deflection data of the middle-small bridge and the space characteristics of all vehicles on the bridge deck;
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value.
Optionally, the first video acquisition device is further used for identifying license plates of vehicles on the bridge deck of the middle-small bridge and/or any vehicles on the bridge deck of the middle-small bridge, so that the edge calculation device is used for fusing the spatial characteristics formed by the vehicles on the road deck and the spatial characteristics formed by the vehicles on the bridge deck after the vehicles on the bridge deck are driven to obtain the spatial fusion characteristics of the vehicles on the bridge deck.
Optionally, the edge calculating device is further configured to align the deflection data and the second video stream in a time sequence, such that a time-course difference of the alignment is less than or equal to 20ms, so that the deflection data of the middle-to-small bridge corresponds to spatial features of all vehicles on the deck.
Optionally, the edge computing device is further configured to:
based on the first video stream, identifying a real-time running state vector of the on-road vehicle at the k-1 moment, wherein k is a positive integer more than or equal to 2;
calculating a plurality of predicted running state vectors of the on-coming vehicles on the road surface at the k-1 moment according to the real-time running state vectors of the on-coming vehicles on the road surface at the k-1 moment, and distributing corresponding weights;
calculating an average predicted running state vector according to a plurality of predicted running state vectors of the vehicle to be driven on the road surface at the kth moment, and comparing the average predicted running state vector with an observed running state vector of the vehicle to be driven on the road surface at the kth moment to adjust weights corresponding to the predicted running state vectors until the distance between the average predicted running state vector and the observed running state vector is within a set vector distance range;
determining a position change sequence of the on-coming vehicle on the road surface according to the average predicted running state vector with the distance from the observed running state vector within a set vector distance range;
determining the spatial characteristics of the on-coming vehicles on the road surface according to the position change sequence;
Correspondingly, the edge computing device is specifically configured to:
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value and the spatial characteristics of the on-road vehicle.
Optionally, the edge computing device is specifically configured to:
the historical position vector is transformed into a state space according to a set position prediction transformation matrix, and is corrected in the state space based on system noise so as to determine a plurality of predicted position vectors of any vehicle on the bridge deck.
Optionally, the edge computing device is specifically configured to:
based on the following formula: s is(s) k ＝Φs k-1 +Γu k-1 K=2, &..n, n is a positive integer greater than 2, transforming the historical position vector into a state space, and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors of the any vehicle on the deck, wherein Φ represents a position prediction transformation matrix corresponding to a k-1 time, u k-1 Represents the system noise corresponding to the k-1 time, Γ represents the noise correction matrix, s k Representing a predicted position vector corresponding to the kth moment, which at least comprises the transverse and longitudinal position coordinates of any vehicle in the bridge deck coordinate system at the kth moment, s k-1 A predicted position vector corresponding to the k-1 time is represented.
Optionally, the edge computing device is specifically configured to:
image acquisition taking a target point as a center is carried out before the middle and small bridges are deformed based on the displacement meter so as to form a reference image;
determining a sub-reference image area by taking imaging of the target point on the reference image as a center;
performing image acquisition on the middle and small bridges after deformation based on the displacement meter so as to form a target image;
calculating the correlation coefficient of the target image and the sub-reference image area, and taking an image area formed by pixels of the target image as a sub-target image area when the correlation coefficient is maximum;
taking the central point of the sub-target image area as the position corresponding to the target point after deformation;
and calculating the deflection data of the middle-small bridge according to the position of the target point on the reference image and the position of the target point in the sub-target image area.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (10)
1. The front touch type monitoring method for the dynamic response of the medium-small bridge under the load of the vehicle is characterized by comprising the following steps of:
video capturing is carried out on an on-road vehicle to be on-road vehicle running on the road surface based on a first video acquisition device arranged on the middle-small bridge to obtain a first video stream;
video capturing is carried out on the bridge deck of the middle-small bridge based on a second video acquisition device arranged on the middle-small bridge, so as to obtain a second video stream;
performing target recognition on the first video stream to determine the vehicle type of the on-coming vehicle;
if the vehicle type corresponds to overrun and/or overload vehicles, generating an early warning signal;
based on the early warning signal, triggering a displacement meter arranged below the middle-small bridge to acquire deflection data of the middle-small bridge;
performing target recognition on the second video stream to determine a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights;
calculating a predicted position average vector of any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights;
According to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence;
determining a vehicle load estimated value on the middle-small bridge according to the deflection data of the middle-small bridge and the space characteristics of all vehicles on the bridge deck;
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value.
2. The method of claim 1, wherein the first video capture device and the second video capture device are adjacent and positioned at a bridge head location of the medium-small bridge such that a video capture angle of the first video capture device is oriented toward the road surface and a video capture angle of the second video capture device is oriented toward the bridge surface.
3. The method of claim 1, wherein the frame rate at which the first video capture device and the second video capture device capture video is no less than 25fps.
4. The method of claim 1, further comprising: and identifying license plates of vehicles on the bridge deck of the vehicle to be driven on the road surface and/or any vehicle on the bridge deck of the medium-small bridge so as to fuse the space characteristics formed by the vehicle to be driven on the road surface and the space characteristics formed by the vehicle to be driven on the bridge deck to obtain the space fusion characteristics of the vehicle to be driven on the bridge deck.
5. The method of claim 1, further comprising: and aligning the deflection data and the second video stream in time sequence, and enabling the aligned time-course difference value to be less than or equal to 20ms so that the deflection data of the middle-small bridge corresponds to the spatial characteristics of all vehicles on the bridge deck.
6. The method of claim 1, further comprising:
based on the first video stream, identifying a real-time running state vector of the on-road vehicle at the k-1 moment, wherein k is a positive integer more than or equal to 2;
Calculating a plurality of predicted running state vectors of the on-coming vehicles on the road surface at the k-1 moment according to the real-time running state vectors of the on-coming vehicles on the road surface at the k-1 moment, and distributing corresponding weights;
calculating an average predicted running state vector according to a plurality of predicted running state vectors of the vehicle to be driven on the road surface at the kth moment, and comparing the average predicted running state vector with an observed running state vector of the vehicle to be driven on the road surface at the kth moment to adjust weights corresponding to the predicted running state vectors until the distance between the average predicted running state vector and the observed running state vector is within a set vector distance range;
determining a position change sequence of the on-coming vehicle on the road surface according to the average predicted running state vector with the distance from the observed running state vector within a set vector distance range;
determining the spatial characteristics of the on-coming vehicles on the road surface according to the position change sequence;
correspondingly, the predicting the structural integrity and the operation safety state of the medium-small bridge after the vehicle to be on-road is driven onto the bridge deck according to the vehicle load estimated value comprises the following steps:
And predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value and the spatial characteristics of the on-road vehicle.
7. The method of claim 1, wherein said determining a plurality of predicted position vectors for the any vehicle on the deck from the historical position vectors comprises:
the historical position vector is transformed into a state space according to a set position prediction transformation matrix, and is corrected in the state space based on system noise so as to determine a plurality of predicted position vectors of any vehicle on the bridge deck.
8. The method of claim 7, wherein said transforming the historical position vector into a state space according to a set position prediction transformation matrix and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors for the any vehicle on the deck comprises:
based on the following formula: s is(s) k ＝Φs k-1 +Γu k-1 K=2, &..n, n is a positive integer greater than 2, transforming the historical position vector into a state space, and correcting the historical position vector in the state space based on system noise to determine a plurality of predicted position vectors of the any vehicle on the deck, wherein Φ represents a position prediction transformation matrix corresponding to a k-1 time, u k-1 Represents the system noise corresponding to the k-1 time, Γ represents the noise correction matrix, s k Representing a predicted position vector corresponding to the kth moment, which at least comprises the transverse and longitudinal position coordinates of any vehicle in the bridge deck coordinate system at the kth moment, s k-1 A predicted position vector corresponding to the k-1 time is represented.
9. The method according to claim 1, wherein triggering a displacement meter disposed below the medium-small bridge for collection of deflection data of the medium-small bridge based on the pre-warning signal comprises:
image acquisition taking a target point as a center is carried out before the middle and small bridges are deformed based on the displacement meter so as to form a reference image;
determining a sub-reference image area by taking imaging of the target point on the reference image as a center;
performing image acquisition on the middle and small bridges after deformation based on the displacement meter so as to form a target image;
calculating the correlation coefficient of the target image and the sub-reference image area, and taking an image area formed by pixels of the target image as a sub-target image area when the correlation coefficient is maximum;
taking the central point of the sub-target image area as the position corresponding to the target point after deformation;
And calculating the deflection data of the middle-small bridge according to the position of the target point on the reference image and the position of the target point in the sub-target image area.
10. A front-contact monitoring system for dynamic response of a small and medium bridge under a vehicle load, comprising:
the first video acquisition device is arranged on the middle-small bridge and is used for capturing video of an upcoming vehicle running on a road surface to obtain a first video stream, carrying out target identification on the first video stream so as to determine the vehicle type of the upcoming vehicle, and generating an early warning signal if the vehicle type corresponds to overrun and/or overload vehicles;
the second video acquisition device is arranged on the middle-small bridge and is used for capturing video of the bridge deck of the middle-small bridge to obtain a second video stream and identifying targets of the second video stream;
the displacement meter is used for collecting deflection data of the middle-small bridge based on triggering of the early warning signal;
edge computing means for performing the steps of:
according to target identification of the second video stream, determining a historical position vector of any vehicle on the bridge deck of the middle-small bridge, determining a plurality of predicted position vectors of any vehicle on the bridge deck according to the historical position vector, and distributing corresponding weights;
Calculating a predicted position average vector of any vehicle on the bridge deck according to a plurality of predicted position vectors of the any vehicle on the bridge deck and corresponding weights;
according to the predicted position average vector of any vehicle on the bridge deck and the corresponding actual observed position vector, adjusting weights corresponding to a plurality of predicted position vectors of any vehicle on the bridge deck until the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining a position change sequence of any vehicle on the bridge deck according to the predicted position average vector when the difference value between the predicted position average vector and the actual observed position vector is within a preset vector difference value threshold value range;
determining the spatial characteristics of any vehicle on the bridge deck according to the position change sequence;
determining a vehicle load estimated value on the middle-small bridge according to the deflection data of the middle-small bridge and the space characteristics of all vehicles on the bridge deck;
and predicting the structural integrity and the operation safety state of the medium-small bridge after the on-road vehicle runs on the bridge deck according to the vehicle load estimated value.
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