Bridge displacement monitoring and alarming method, terminal and storage medium

Abstract

The invention provides a bridge displacement monitoring and alarming method, a terminal and a storage medium. The method comprises the following steps: extracting position information of different bridge feature points in a current frame image of a target bridge; calculating centroid position information, and determining macroscopic rigid deformation parameters of the target bridge based on the centroid position information of the initial frame and the centroid position information of the current frame; according to the position information of the different bridge feature points and the centroid position information of the current frame, updating a local relative displacement sequence of the target bridge; determining local deformation parameters of different bridge feature points; pCA decomposition is carried out on the local relative displacement sequence, and a principal component contribution rate is extracted; and based on the macroscopic rigid deformation parameter, the local deformation parameter and the principal component contribution rate, performing collapse early warning on the target bridge. According to the scheme, the macroscopic rigid body deformation condition of the bridge can be monitored, and local deformation information can be captured, so that the structural state of the bridge is comprehensively reflected, and the accuracy of bridge monitoring and collapse early warning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge detection, and particularly to a bridge displacement monitoring and alarming method, a terminal, and a storage medium. Background Art

[0002] With the increasingly serious problem of infrastructure aging, bridge structural health monitoring has become an important topic for ensuring public safety. Traditional monitoring methods rely on physical sensors such as accelerometers, strain gauges, displacement sensors, etc., which are costly, complex to deploy, and difficult to achieve large-scale and comprehensive real-time monitoring.

[0003] In recent years, the breakthrough of computer vision technology has provided a new path for non-contact monitoring. The image-based monitoring method can obtain global data by deploying cameras, and has the advantages of low cost, easy expansion, non-invasiveness, etc., and has become a research hotspot in the field of bridge monitoring. The existing image-based bridge monitoring methods usually evaluate the bridge structural state by tracking the displacement changes of key points of the bridge (such as bearings, bridge piers), but this method will miss the structural states of many positions of the bridge, resulting in insufficient accuracy of the overall bridge monitoring. Summary of the Invention

[0004] Embodiments of the present invention provide a bridge displacement monitoring and alarming method, a terminal, and a storage medium to solve the problem of insufficient accuracy in monitoring the overall structural state of a bridge in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a bridge displacement monitoring and alarming method, including: Extracting the position information of different bridge feature points in the current frame image of the target bridge; Calculating the centroid position information in the current frame image, and determining the macroscopic rigid body deformation parameters of the target bridge based on the centroid position information of the initial frame and the centroid position information of the current frame; Updating the local relative displacement sequence of the target bridge according to the position information of different bridge feature points and the centroid position information of the current frame; and determining the local deformation parameters of different bridge feature points; the local deformation parameters include local rigid residuals and local relative displacement parameters; Performing PCA decomposition on the local relative displacement sequence to extract the principal component contribution rate; the local relative displacement sequence includes multiple frames of local relative displacements of each bridge feature point; Performing a collapse warning on the target bridge based on the macroscopic rigid body deformation parameters, the local deformation parameters, and the principal component contribution rate.

[0006] In a second aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method according to any possible implementation manner of the first aspect above are implemented.

[0007] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method according to any possible implementation manner of the first aspect above are implemented.

[0008] An embodiment of the present invention provides a bridge displacement monitoring and alarm method, a terminal, and a storage medium. The method extracts the position information of different bridge feature points in the current frame image of the target bridge; can calculate the centroid position information in the current frame image, and based on the centroid position information of the initial frame and the centroid position information of the current frame, determine the macroscopic rigid body deformation parameters of the target bridge; update the local relative displacement sequence of the target bridge according to the position information of different bridge feature points and the centroid position information of the current frame; and determine the local deformation parameters of different bridge feature points; the local deformation parameters include local rigid residuals and local relative displacement parameters; perform PCA decomposition on the local relative displacement sequence to extract the principal component contribution rate; the local relative displacement sequence includes multiple frames of local relative displacements of each bridge feature point; and finally, based on the macroscopic rigid body deformation parameters, the local deformation parameters, and the principal component contribution rate, perform a collapse warning on the target bridge. The above solution can not only monitor the macroscopic rigid body deformation of the bridge, but also capture local deformation information, so as to comprehensively reflect the structural state of the bridge and improve the accuracy of bridge monitoring and collapse warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0010] Figure 1 is a flowchart of the implementation of the bridge displacement monitoring and alarm method provided by the embodiment of the present invention; Figure 2 is a schematic diagram of the installation position of a camera for photographing the bridge deck of the target bridge provided by the embodiment of the present invention; Figure 3 is a schematic diagram of the structure of the bridge displacement monitoring and alarm device provided by the embodiment of the present invention; Figure 4It is a schematic diagram of the terminal provided by an embodiment of the present invention. Detailed implementation manners

[0011] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures and technologies are presented to thoroughly understand the embodiments of the present invention. However, those skilled in the art should understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0012] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments with reference to the accompanying drawings.

[0013] Refer to Figure 1 , which shows the implementation flowchart of the bridge displacement monitoring and alarm method provided by an embodiment of the present invention, and is described in detail as follows: S101: Extract the position information of different bridge feature points in the current frame image of the target bridge.

[0014] In this embodiment, a camera can be used to capture an image of the target bridge, and this image can be a complete bridge deck image of the target bridge. Figure 2 shows a schematic diagram of the installation position of a camera for photographing the bridge deck of the target bridge. Refer to Figure 2 , the camera 10 is installed on one side of the target bridge 30 through the bracket 20 and shoots the entire bridge deck from above to obtain a bridge deck image. It can be understood that when photographing the bridge deck structure, different lighting conditions and shooting angles (such as the front view, side view, and close-up view of the support of the bridge deck) need to be covered. In this embodiment, a complete bridge deck image of the target bridge can be captured by a single camera. When a single camera cannot capture a complete bridge deck image of the target bridge, multiple cameras can also be used to simultaneously capture bridge deck images at different positions of the target bridge, and a complete bridge deck image of the target bridge in the same frame can be obtained through synchronization technology.

[0015] After setting up the camera, video data containing the bridge structure is captured for the target bridge. Before conducting bridge displacement monitoring, first select the video data as the training set, and use manual annotation tools to label the bridge feature points at the key parts of the bridge to determine the bridge feature points of the target bridge and form a standardized data set. Among them, the manual annotation tools can include CVAT (Computer Vision Annotation Tool) and Labelme (label generation tool), etc. The key parts of the bridge include the core stress-bearing area, the edge and deformation-sensitive area, and the rigid reference area, etc. Among them, the core stress-bearing area includes the mid-span main beam midpoint, the support connection point, and the pier top feature point; the edge and deformation-sensitive area includes the bridge deck edge corner point, the joint position, and the guardrail connection point; the rigid reference area includes stable and immovable abutments or ground fixed points, etc. Then, based on the labeled data, use the YOLOv8-Pose pre-trained model for transfer learning training, and conduct customized training for the bridge feature points to improve the accuracy and robustness of the model for detecting bridge feature points.

[0016] During the actual monitoring process, use the trained YOLOv8-Pose model to perform real-time inference on the video stream and extract the position information of different bridge feature points of the bridge structure.

[0017] Through the above method, this embodiment can generate the time series trajectory of each bridge feature point in the target bridge, and this time series trajectory includes multi-frame position information that changes over time. For the trajectory interruption caused by occlusion or missed detection, an interpolation method is used for completion and repair.

[0018] S102: Calculate the centroid position information in the current frame image, and based on the centroid position information of the initial frame and the centroid position information of the current frame, determine the macroscopic rigid body deformation parameters of the target bridge.

[0019] In a possible implementation manner, the specific implementation process of calculating the centroid position information in the current frame image in S102 includes: S201: Subtract the position information of each bridge feature point in the current frame image from the initial position information of the corresponding bridge feature point to obtain the relative displacement of each bridge feature point.

[0020] Among them, the position information can be position coordinates, and the relative displacement of the bridge feature point is calculated by the following formula:

[0021] Among them, ( , ) is the position coordinate of the i-th bridge feature point at time t, is the initial time, ( , ) is the position coordinate of the i-th bridge feature point at the initial time t0. represents the lateral relative displacement of the i-th bridge feature point at time t, represents the longitudinal relative displacement of the i-th bridge feature point at time t.

[0022] S202: Convert the relative displacements of each bridge feature point on the image into actual relative displacements.

[0023] In this embodiment, for each bridge feature point in each frame of image, draw a line segment passing through the bridge feature point and with the two side contours of the bridge as endpoints along the direction parallel to the width of the bridge deck, and use it as the bridge pixel width line, and measure the pixel length of the line segment in the image ; and calculate the pixel rate corresponding to each bridge feature point according to the actual bridge deck width w known from the bridge design drawing or on-site measurement .

[0024] After determining the pixel rate, normalize and convert the relative displacement of each bridge feature point i in the image at time t according to the pixel rate corresponding to the bridge feature point to obtain the actual relative displacement. The conversion formula is as follows:

[0025] Among them, represents the actual relative lateral displacement of the bridge feature point i at time t, represents the actual relative longitudinal displacement of the bridge feature point i at time t.

[0026] S203: Perform weighted averaging on the actual relative displacements of all bridge feature points in the current frame of image to obtain the centroid position information of the target bridge.

[0027] In a possible implementation manner, the centroid position information includes centroid coordinates; the specific implementation process of S203 includes: Based on the formula Calculate the centroid coordinates of the target bridge; Among them, represents the abscissa of the centroid at time t, represents the ordinate of the centroid at time t; N represents the number of bridge feature points; (t) represents the actual relative lateral displacement of the i-th bridge feature point at time t; (t) represents the actual relative longitudinal displacement of the i-th bridge feature point at time t; represents the weight of the i-th bridge feature point.

[0028] Among them, the weights of the bridge feature points in each part are different, and the weights are assigned according to the importance of the bridge structure parts. Specifically, the weights of the bridge feature points in the mid-span area are greater than those of the bridge feature points in the bearing area, and the weights of the bridge feature points in the bearing area are greater than those of the bridge feature points in the edge area. Higher weights are given to the mid-span area and the area directly above the main girder. The weights of the bridge feature points near the bearings or in the edge area are adjusted according to the force characteristics. In actual applications, the weights of each bridge feature point can be determined according to bridge design drawings, finite element simulations or empirical knowledge.

[0029] In a possible implementation, the macro-rigid body deformation parameters include the centroid displacement, centroid tilt angle, and main vibration frequency; The specific implementation process of determining the macro-rigid body deformation parameters of the target bridge based on the centroid position information of the initial frame and the centroid position information of the current frame in S102 includes: Subtract the centroid position information of the initial frame from the centroid position information of the current frame to obtain the centroid displacement of the current frame; the centroid displacement includes the centroid lateral displacement and the centroid longitudinal displacement; Based on the centroid lateral displacement and the centroid longitudinal displacement of the current frame, determine the centroid tilt angle of the current frame; Perform a fast Fourier transform on the current centroid trajectory sequence to obtain the spectrum of the current centroid trajectory sequence, and extract the main vibration frequency of the spectrum; the current centroid trajectory sequence includes all centroid position information from the initial frame to the current frame.

[0030] In this embodiment, after the centroid position information is calculated according to S201 to S203, the centroid position coordinates of the initial frame are determined and the centroid position coordinates at the current time t , and then subtract the centroid abscissa of the initial frame from the centroid abscissa of the current frame to obtain the centroid lateral displacement of the current frame; subtract the centroid ordinate of the initial frame from the centroid ordinate of the current frame to obtain the centroid longitudinal displacement of the current frame. The specific formula is: , represents the centroid lateral displacement, represents the centroid longitudinal displacement.

[0031] After determining the centroid lateral displacement and the centroid longitudinal displacement, based on the formula determine the centroid tilt angle, where represents the centroid tilt angle at time t. This tilt angle is used to reflect the tendency of the bridge as a whole to rotate or overturn, which is common under uneven settlement and abnormal load effects. If the tilt angle increases sharply in a short period of time, it indicates a risk of structural instability.

[0032] The centroid trajectory includes the centroid displacements of consecutive frames that change over time and can reflect the changes in the overall translational state of the bridge, such as overall settlement, overall lateral displacement, etc. The high-frequency fluctuation components in the centroid trajectory are used to analyze the vibration characteristics of the structure. For the centroid trajectory sequence perform a Fast Fourier Transform (FFT) to extract the main vibration frequencies:

[0033] Analyze the main frequency of the centroid vibration and its changing trend. Under normal circumstances, the centroid vibration frequency of the bridge is relatively stable; when the structural stiffness decreases, such as when cracks appear or the cross-section is weakened, the main frequency of the centroid will drift and decrease.

[0034] S103: Update the local relative displacement sequence of the target bridge according to the position information of different bridge feature points and the centroid position information of the current frame; and determine the local deformation parameters of different bridge feature points; the local deformation parameters include local rigidity residuals and local relative displacement parameters.

[0035] In a possible implementation, the local relative displacement parameters include the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution; The specific implementation process of determining the local deformation parameters of different bridge feature points in S103 includes: Based on the position information of different bridge feature points in the current frame and the centroid position information of the current frame, calculate the local relative displacements of different bridge feature points in the current frame; Based on the local relative displacements of all bridge feature points in the current frame, calculate the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution of the target bridge in the current frame; Divide all bridge feature points into multiple feature point clusters according to the spatial proximity relationship; Based on the position information of each bridge feature point in the current frame and the position information of the initial frame, fit the rigid transformation matrix corresponding to each bridge feature point; For each bridge feature point cluster, calculate the local fitting residual of the feature point cluster based on the position information of each bridge feature point in the current frame, the position information of the initial frame, and the rigid transformation matrix in the feature point cluster.

[0036] In this embodiment, first, based on the formula calculate the local relative displacement of each bridge feature point at time t; the local relative displacement is the relative displacement of each bridge feature point relative to the centroid. Among them, represents the local relative lateral displacement, Indicates the local relative longitudinal displacement. The local relative displacement is used to reveal the local deformation mode, and the statistical distribution characteristics (standard deviation, range) are used to quantitatively evaluate the degree of local non-rigid deformation.

[0037] Then, based on the formula Determine the local relative displacement of each bridge feature point.

[0038] Based on the local relative displacement sequence of all bridge feature points of the target bridge at time t , calculate the standard deviation , and the corresponding calculation formula is:

[0039] Among them, Represents the mean value of the local relative displacement, and N represents the number of bridge feature points.

[0040] The range of the local relative displacement distribution The calculation formula is:

[0041] In this embodiment, the specific process of calculating the local rigidity residual in S103 is as follows: All bridge feature points in the current frame are divided into multiple feature point clusters according to the spatial proximity relationship. Each feature point cluster includes M bridge feature points, where 3 < M < 6; each feature point cluster is used to represent the local unit of the target bridge, and these local units can be approximately regarded as small-range bridge deck rigid bodies. Specifically, the feature point clusters can be divided based on the Euclidean distance threshold or the region division rule.

[0042] For each feature point cluster, first fit the rigid transformation matrix (rotation + translation) between the corresponding bridge feature points in the initial frame and the current frame. The rigid transformation matrix is used to describe the rigid body motion of the local feature point cluster, and its calculation process is based on the least squares fitting, aiming to find the rotation matrix R and translation vector T that minimize the fitting residual of the local feature points.

[0043] After determining the rigid transformation matrix, based on the formula Calculate the local fitting residual of each feature point cluster.

[0044] Among them, Represents the position of the i-th bridge feature point at time t; Is the position of the i-th bridge feature point at the initial time; , Are the fitting rotation matrix and translation vector respectively.

[0045] As can be seen from the above embodiments, the larger the RMS, the more serious the local rigidity mismatch. The long-term cumulative growth or short-term sharp jump of the RMS will cause local damage.

[0046] In a possible implementation, when the centroid position information is obtained by weighted averaging of the actual relative displacements, the position information of each bridge feature point needs to be normalized to the position information in the actual coordinate system through the pixel rate. Accordingly, the specific implementation process of S103 is as follows: Update the local relative displacement sequence of the target bridge according to the position information of different bridge feature points in the actual coordinate system and the centroid position information of the current frame; and determine the local deformation parameters of different bridge feature points.

[0047] S104: Perform PCA decomposition on the local relative displacement sequence to extract the principal component contribution rate; the local relative displacement sequence includes the multi-frame local relative displacements of each bridge feature point.

[0048] In this embodiment, the local relative displacement sequence includes the multi-frame local relative displacements of each bridge feature point. In this embodiment, the principal component contribution rate and the energy spectrum diffusion degree can be determined by performing PCA decomposition on the local relative displacement sequence. The energy spectrum diffusion degree measures the dispersion degree of the principal component energy in each order component, and can be quantitatively described by information entropy or energy entropy. The larger the entropy value, the more dispersed the energy distribution and the more complex the structural deformation.

[0049] S105: Perform a collapse warning on the target bridge based on the macroscopic rigid body deformation parameter, the local deformation parameter, and the principal component contribution rate.

[0050] In a possible implementation, the specific implementation process of S105 includes: S301: If the macroscopic rigid body deformation parameter exceeds the preset macroscopic alarm condition, trigger a first-level alarm for the target bridge; S302: If the local deformation parameter exceeds the preset local alarm condition, trigger a second-level alarm for the target bridge; S303: If the local deformation parameter exceeds the preset local alarm condition and the decrease amplitude of the principal component contribution rate exceeds the preset amplitude threshold, trigger a third-level alarm for the target bridge; The level of the first-level alarm is lower than the level of the second-level alarm, and the level of the second-level alarm is lower than the level of the third-level alarm.

[0051] In a possible implementation, the macroscopic rigid body deformation parameter includes the centroid displacement, the centroid tilt angle, and the main vibration frequency; the specific implementation process of S301 includes: If the absolute value of the centroid displacement exceeds the preset centroid displacement threshold, or the absolute value of the centroid tilt angle exceeds the preset angle threshold, or the difference between the main vibration frequency and the baseline frequency is less than the preset frequency difference, trigger a first-level alarm for the target bridge.

[0052] In this embodiment, when a first-level alarm is triggered, it indicates that the overall displacement of the bridge is abnormal and the local area is not damaged. At this time, the first-level alarm can be displayed through the terminal to prompt the user that there is a first-level alarm for the target bridge.

[0053] In a possible implementation manner, the local relative displacement parameters include the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution; The specific implementation process of S302 includes: If the absolute value of the local rigidity residual is greater than a preset residual threshold, or the standard deviation of the local relative displacement distribution is greater than a preset standard deviation threshold, or the range of the local relative displacement distribution is greater than a preset range threshold, then a second-level alarm for the target bridge is triggered.

[0054] In this embodiment, when a second-level early warning occurs, it indicates that damage or local buckling has occurred in the local area of the target bridge at this time. Similarly, the second-level alarm can be displayed through the terminal and the location of the local alarm area is indicated.

[0055] In a possible implementation manner, when the second-level alarm is triggered, and at this time the decrease amplitude of the contribution rate of the first principal component of PCA exceeds a preset amplitude threshold, and the energy diffusion degree exceeds a preset diffusion degree threshold, then a third-level alarm for the target bridge is triggered. Triggering the third-level alarm indicates that the structure of the target bridge is complexly deformed and there is a risk of instability or imminent collapse. At this time, relevant personnel can be notified through multiple alarm methods so that relevant personnel can quickly understand the risk and make corresponding handling in a timely manner.

[0056] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0057] The following is an apparatus embodiment of the present invention. For details not described in detail herein, reference can be made to the corresponding method embodiments above.

[0058] Figure 3 The structural schematic diagram of the bridge displacement monitoring and alarm device provided by the embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows: As Figure 3 shown, the bridge displacement monitoring and alarm device 100 includes: A position information extraction module 110, configured to extract the position information of different bridge feature points in the current frame image of the target bridge; A macroscopic rigid body deformation parameter acquisition module 120, configured to calculate the centroid position information in the current frame image, and determine the macroscopic rigid body deformation parameter of the target bridge based on the centroid position information of the initial frame and the centroid position information of the current frame; The local deformation parameter acquisition module 130 is configured to update the local relative displacement sequence of the target bridge according to the position information of different bridge feature points and the centroid position information of the current frame; and determine the local deformation parameters of different bridge feature points; the local deformation parameters include local rigid residuals and local relative displacement parameters; The principal component extraction module 140 is configured to perform PCA decomposition on the local relative displacement sequence and extract the principal component contribution rate; the local relative displacement sequence includes the multi-frame local relative displacements of each bridge feature point; The collapse warning module 150 is configured to perform collapse warning on the target bridge based on the macroscopic rigid body deformation parameters, the local deformation parameters, and the principal component contribution rate.

[0059] In a possible implementation manner, the macroscopic rigid body deformation parameter acquisition module 120 includes: The relative displacement calculation unit is configured to subtract the position information of each bridge feature point in the current frame image from the initial position information of the corresponding bridge feature point to obtain the relative displacement of each bridge feature point; The actual relative displacement conversion unit is configured to convert the relative displacement of each bridge feature point on the image into an actual relative displacement; The centroid position calculation unit is configured to perform weighted average on the actual relative displacements of all bridge feature points in the current frame image to obtain the centroid position information of the target bridge.

[0060] In a possible implementation manner, the centroid position information includes centroid coordinates; the centroid position calculation unit is specifically configured to: Based on the formula Calculate the centroid coordinates of the target bridge; Where Represents the centroid abscissa at time t, Represents the centroid ordinate at time t; N represents the number of bridge feature points in the current frame image; (t) represents the actual relative lateral displacement of the i-th bridge feature point at time t; (t) represents the actual relative longitudinal displacement of the i-th bridge feature point at time t; Represents the weight of the i-th bridge feature point.

[0061] In a possible implementation manner, the macroscopic rigid body deformation parameters include centroid displacement, centroid tilt angle, and main vibration frequency; The macroscopic rigid body deformation parameter acquisition module 120 includes: Subtract the centroid position information of the current frame from the centroid position information of the initial frame to obtain the centroid displacement of the current frame; the centroid displacement includes centroid lateral displacement and centroid longitudinal displacement; Determine the centroid tilt angle of the current frame based on the lateral displacement and longitudinal displacement of the centroid of the current frame; Perform a fast Fourier transform on the current centroid trajectory sequence to obtain the spectrum of the current centroid trajectory sequence, and extract the main vibration frequency of the spectrum; the current centroid trajectory sequence includes all centroid position information from the initial frame to the current frame.

[0062] In a possible implementation, the local relative displacement parameter includes the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution; The local deformation parameter acquisition module 130 includes: Calculate the local relative displacement of different bridge feature points in the current frame based on the position information of different bridge feature points in the current frame and the centroid position information of the current frame; Calculate the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution of the target bridge in the current frame based on the local relative displacements of all bridge feature points in the current frame; Divide all bridge feature points into multiple feature point clusters according to the spatial proximity relationship; Based on the position information of each bridge feature point in the current frame and the position information of the initial frame, fit the rigid transformation matrix corresponding to the bridge feature point; For each bridge feature point cluster, calculate the local fitting residual of the feature point cluster based on the position information of each bridge feature point in the current frame, the position information of the initial frame, and the rigid transformation matrix in the feature point cluster.

[0063] In a possible implementation, the collapse warning module 150 includes: A first-level alarm unit, configured to trigger a first-level alarm for the target bridge if the macroscopic rigid body deformation parameter exceeds a preset macroscopic alarm condition; A second-level alarm unit, configured to trigger a second-level alarm for the target bridge if the local deformation parameter exceeds a preset local alarm condition; A third-level alarm unit, configured to trigger a third-level alarm for the target bridge if the local deformation parameter exceeds the preset local alarm condition and the decrease amplitude of the principal component contribution rate exceeds a preset amplitude threshold; The level of the first-level alarm is lower than the level of the second-level alarm, and the level of the second-level alarm is lower than the level of the third-level alarm.

[0064] In a possible implementation, the macroscopic rigid body deformation parameter includes centroid displacement, centroid tilt angle, and main vibration frequency; the first-level alarm unit includes: If the absolute value of the centroid displacement exceeds a preset centroid displacement threshold, or the absolute value of the centroid tilt angle exceeds a preset angle threshold, or the difference between the main vibration frequency and the baseline frequency is less than a preset frequency difference, a first-level alarm for the target bridge is triggered.

[0065] In a possible implementation, the local relative displacement parameter includes the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution; The second-level alarm unit includes: If the absolute value of the local rigidity residual is greater than a preset residual threshold, or the standard deviation of the local relative displacement distribution is greater than a preset standard deviation threshold, or the range of the local relative displacement distribution is greater than a preset range threshold, a second-level alarm for the target bridge is triggered.

[0066] Figure 4 is a schematic diagram of the terminal provided by the embodiment of the present invention. As Figure 4 shown, the terminal 4 of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned embodiments of various bridge displacement monitoring and alarm methods are implemented, such as Figure 1 the steps S101 to S105 shown. Alternatively, when the processor 40 executes the computer program 42, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0067] Exemplarily, the computer program 42 can be divided into one or more modules / units, and the one or more modules / units are stored in the memory 41 and executed by the processor 40 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and this instruction segment is used to describe the execution process of the computer program 42 in the terminal 4.

[0068] The terminal 4 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art can understand that Figure 4 merely examples of the terminal 4, which do not constitute a limitation on the terminal 4, and may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the terminal may further include input / output devices, network access devices, a bus, etc.

[0069] The so-called processor 40 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0070] The memory 41 may be an internal storage unit of the terminal 4, such as the hard disk or memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk equipped on the terminal 4, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 41 may also include both the internal storage unit of the terminal 4 and the external storage device. The memory 41 is used to store the computer program and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0071] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0072] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0073] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0074] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0075] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, the functional units in each embodiment of the present invention can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0077] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described embodiments of various bridge displacement monitoring and alarm methods can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0078] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention and should all be included within the protection scope of the present invention.

Claims

1. A method for monitoring and alarming bridge displacement, characterized in that, Including: Extracting the position information of different bridge feature points in the current frame image of the target bridge; Calculating the centroid position information in the current frame image, and determining the macroscopic rigid body deformation parameters of the target bridge based on the centroid position information of the initial frame and the centroid position information of the current frame; Updating the local relative displacement sequence of the target bridge according to the position information of different bridge feature points and the centroid position information of the current frame; and determining the local deformation parameters of different bridge feature points; the local deformation parameters include local rigid residuals and local relative displacement parameters; Performing PCA decomposition on the local relative displacement sequence to extract the principal component contribution rate; the local relative displacement sequence includes the multi-frame local relative displacements of each bridge feature point; Performing a collapse warning on the target bridge based on the macroscopic rigid body deformation parameters, the local deformation parameters, and the principal component contribution rate.

2. The bridge displacement monitoring and alarm method according to claim 1, wherein, The calculating the centroid position information in the current frame image includes: Subtracting the position information of each bridge feature point in the current frame image from the initial position information of the corresponding bridge feature point to obtain the relative displacement of each bridge feature point; Converting the relative displacement of each bridge feature point on the image into an actual relative displacement; Performing a weighted average on the actual relative displacements of all bridge feature points in the current frame image to obtain the centroid position information of the target bridge.

3. The bridge displacement monitoring and alarming method according to claim 2, characterized in that, The centroid position information includes centroid coordinates; the performing a weighted average on the actual relative displacements of all bridge feature points in the current frame image to obtain the centroid position information of the target bridge includes: Based on the formula Calculate the centroid coordinates of the target bridge; Among them, represents the abscissa of the centroid at time t, represents the ordinate of the centroid at time t; N represents the number of bridge feature points in the current frame image; (t) represents the actual relative lateral displacement of the i th bridge feature point at time t; (t) represents the actual relative longitudinal displacement of the i th bridge feature point at time t; represents the weight of the i th bridge feature point.

4. The bridge displacement monitoring and alarming method according to claim 1, characterized in that, The macroscopic rigid body deformation parameters include centroid displacement, centroid tilt angle, and main vibration frequency; The determining the macroscopic rigid body deformation parameters of the target bridge based on the centroid position information of the initial frame and the centroid position information of the current frame includes: Subtracting the centroid position information of the current frame from the centroid position information of the initial frame to obtain the centroid displacement of the current frame; the centroid displacement includes centroid lateral displacement and centroid longitudinal displacement; Determining the centroid tilt angle of the current frame based on the centroid lateral displacement and centroid longitudinal displacement of the current frame; Performing a fast Fourier transform on the current centroid trajectory sequence to obtain the spectrum of the current centroid trajectory sequence, and extracting the main vibration frequency of the spectrum; the current centroid trajectory sequence includes all centroid position information from the initial frame to the current frame.

5. The bridge displacement monitoring and alarming method according to claim 1, characterized in that The local relative displacement parameters include the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution; Determining the local deformation parameters of different bridge feature points according to the position information of different bridge feature points and the centroid position information of the current frame includes: Calculating the local relative displacement of different bridge feature points in the current frame based on the position information of different bridge feature points in the current frame and the centroid position information of the current frame; Calculating the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution of the target bridge in the current frame based on the local relative displacements of all bridge feature points in the current frame; Dividing all bridge feature points into multiple feature point clusters according to the spatial proximity relationship; Fitting the rigid transformation matrix of the corresponding bridge feature point based on the position information of each bridge feature point in the current frame and the position information of the initial frame; For each cluster of bridge feature points, based on the position information of each bridge feature point in the current frame, the position information in the initial frame, and the rigid transformation matrix in the cluster of feature points, calculate the local fitting residuals of the cluster of feature points.

6. The bridge displacement monitoring and alarm method according to claim 1, characterized in that, The collapse warning for the target bridge based on the macro rigid body deformation parameters, the local deformation parameters, and the principal component contribution rate includes: If the macro rigid body deformation parameters exceed the preset macro alarm conditions, trigger a first-level alarm for the target bridge; If the local deformation parameters exceed the preset local alarm conditions, trigger a second-level alarm for the target bridge; If the local deformation parameters exceed the preset local alarm conditions and the decrease amplitude of the principal component contribution rate exceeds the preset amplitude threshold, trigger a third-level alarm for the target bridge; The level of the first-level alarm is lower than the level of the second-level alarm, and the level of the second-level alarm is lower than the level of the third-level alarm.

7. The bridge displacement monitoring and alarm method according to claim 6, characterized in that, The macro rigid body deformation parameters include the centroid displacement, the centroid tilt angle, and the main vibration frequency; The step of triggering a first-level alarm for the target bridge if the macro rigid body deformation parameters exceed the preset macro alarm conditions includes: If the absolute value of the centroid displacement exceeds the preset centroid displacement threshold, or the absolute value of the centroid tilt angle exceeds the preset angle threshold, or the difference between the main vibration frequency and the baseline frequency is less than the preset frequency difference, trigger a first-level alarm for the target bridge.

8. The bridge displacement monitoring and alarming method according to claim 6, characterized in that, The local relative displacement parameters include the standard deviation of the local relative displacement distribution and the range of the local relative displacement distribution; The step of triggering a second-level alarm for the target bridge if the local deformation parameters exceed the preset local alarm conditions includes: If the absolute value of the local rigid residual is greater than the preset residual threshold, or the standard deviation of the local relative displacement distribution is greater than the preset standard deviation threshold, or the range of the local relative displacement distribution is greater than the preset range threshold, trigger a second-level alarm for the target bridge.

9. A terminal, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 above are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 8 above are implemented.

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