Bridge cable vibration excitation method and system based on combination of unmanned aerial vehicle and bumping test

Abstract

The invention relates to the technical field of bridge safety detection, in particular to a bridge cable vibration excitation method and system based on the combination of an unmanned aerial vehicle and a bumping test, and the method comprises the steps: constructing a multi-modal sensing network through employing the unmanned aerial vehicle to carry calibration equipment, recognizing and calibrating the measurement points on the surface of a bridge cable, and building a bridge geometric model; acquiring bridge cable tension distribution, and planning a vehicle excitation path of a bumping test by combining a bridge geometric model; guiding the bumping test vehicle through the vehicle excitation path, and synchronously recording excitation impact force and an excitation timestamp; establishing an empirical mode model, decomposing multi-mode vibration data acquired from the multi-mode sensing network, and extracting dynamic characteristic parameters of the bridge cable; and analyzing and identifying a performance degradation area of the bridge cable based on the empirical mode decomposition model and the dynamic characteristic parameters, and evaluating the comprehensive health state of the bridge cable. According to the invention, the problems of non-synchronization of excitation and measurement, low test precision, low analysis efficiency, insufficient health state evaluation and the like in traditional excitation are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge safety detection, and in particular to a bridge cable vibration excitation method and system based on a combination of an unmanned aerial vehicle and a vehicle jumping test. Background Art

[0002] Bridges are an important part of the transportation system, and the safety and stability of bridge cables directly affect the operation of bridges. However, bridge cables are exposed to complex environments for a long time and are susceptible to damage such as corrosion and fatigue, so regular structural monitoring and health assessment are required. Among the existing bridge cable testing methods, the vibration frequency method is the most commonly used, which extracts modal information by exciting the bridge cables and recording their vibration responses. However, due to factors such as the high stiffness of short cables and environmental noise interference, the accuracy and reliability of the vibration frequency method are limited in practical applications.

[0003] Traditional excitation methods include manual tapping and car jumping tests, but manual tapping has limitations and safety hazards. Car jumping tests are difficult to synchronize excitation and measurement, which affects test efficiency and accuracy.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] The present invention provides a bridge cable vibration excitation method and system based on the combination of unmanned aerial vehicle and vehicle jumping test, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: A bridge cable vibration excitation method based on a combination of an unmanned aerial vehicle and a vehicle jumping test, the method comprising: Use drones equipped with calibration equipment to build a multimodal perception network, identify and calibrate the surface measurement points of the bridge cables, and establish a geometric model of the bridge; Obtaining the tension distribution of the bridge cables, and planning the vehicle excitation path of the vehicle jumping test in combination with the bridge geometric model; guiding the jumping test vehicle through the vehicle excitation path and synchronously recording the excitation impact force and the excitation timestamp; Establishing an empirical modal model, decomposing the multimodal vibration data obtained from the multimodal sensing network, and extracting dynamic characteristic parameters of the bridge cable; Based on the empirical mode decomposition model and the dynamic characteristic parameter analysis, the performance degradation area of ​​the bridge cable is identified, and the comprehensive health status of the bridge cable is evaluated.

[0007] Furthermore, the comprehensive health of the bridge cables is assessed, including: Comparing the dynamic characteristic parameters with the designed operating parameters to identify parameter deviations and abnormal change trends; Based on the dynamic characteristic parameters, a modal sensitivity analysis method is used to quantify the parameter deviation to generate a sensitivity coefficient, and the performance degradation area is located according to the sensitivity coefficient; For the performance degradation area, the vibration energy density distribution is calculated to evaluate the local health status and bearing capacity of the bridge cables in the area; According to the local health status and the sensitivity coefficient, the comprehensive health index of the bridge cable is calculated based on the empirical modal model, and the comprehensive health status is classified and evaluated.

[0008] Further, quantifying the parameter deviation to generate a sensitivity coefficient includes: Obtaining the design operation parameters of the bridge cables and comparing them based on the dynamic characteristic parameters to construct a parameter change matrix; Calculating the parameter deviation of each of the dynamic characteristic parameters to the cable tension distribution through the parameter change matrix to generate a parameter deviation set; Grouping the parameter deviation set according to the cable tension distribution, analyzing the contribution of local deviations to different tension areas, and generating a regional weight distribution; A sensitivity coefficient is generated according to the regional weight distribution, and the sensitivity coefficient quantifies the contribution of each dynamic parameter deviation to the degradation of bridge cable performance.

[0009] Furthermore, a regional weight distribution is generated, including: Based on the bridge geometric model and the cable tension distribution, the cable is divided into a plurality of capture areas, wherein the capture area division is based on tension gradient, cable length and vibration modal characteristics; The dynamic characteristic parameters in the parameter deviation set are grouped according to the capture area, and the contribution value of the dynamic parameter deviation in each partition to the overall performance of the bridge cable is calculated; Generating a regional importance factor reflecting the sensitivity of performance changes in the capture area according to the contribution value of each capture area and the performance of the bridge cable; Based on the regional importance factor, the weights of the capture regions are sorted to generate a regional weight distribution; During multiple vehicle jumping excitation tests, the regional weight distribution is updated in combination with the newly acquired cable tension distribution and dynamic parameter deviation.

[0010] Furthermore, planning a vehicle excitation path for a vehicle jumping test in combination with the bridge geometric model includes: Based on the multimodal sensing network, geometric data of the bridge structure is collected to generate a bridge geometric model; Based on the bridge geometric model and the cable tension distribution, a preliminary analysis is performed on the dynamic response of the cables to identify the cable blocks that are sensitive to vehicle excitation; The vehicle jumping test target is set according to the bridge cable block, and a vehicle excitation path is generated by using a multi-objective optimization algorithm in combination with the bridge geometric model and the layout constraints of the vehicle jumping pads; Performing feasibility verification on the vehicle excitation path, and adjusting the vehicle excitation path in combination with bridge construction conditions and vehicle performance parameters; According to the vehicle excitation path, an execution instruction is sent to the vehicle for jumping test through a path guidance device carried by the UAV, so as to guide the vehicle to complete the jumping excitation according to the preset path.

[0011] Furthermore, the path guidance device carried by the UAV sends an execution instruction to the vehicle for the jump test, including: The UAV is equipped with the path guiding device, receives the vehicle excitation path, and transmits it to the vehicle for jumping test; The drone locates and tracks the current position of the vehicle for the jump test, obtains vehicle position information and driving status data, and compares the information with the vehicle excitation path; The path guidance device generates a path adjustment instruction based on the deviation information between the vehicle position information and the vehicle excitation path; When multiple drones are required to perform collaboratively, the information collection of multiple drones is managed simultaneously through the time series one-control-multiple-drones function.

[0012] Furthermore, a geometric model of the bridge is established, including: Collecting bridge structure geometric data through the multimodal perception network and performing three-dimensional spatial positioning of key parts of the bridge; Integrate and compare the bridge structure geometry data with bridge design parameters to correct errors in the bridge structure geometry data; The bridge geometric data is processed uniformly to generate the bridge geometric model including the overall structure of the bridge and the layout characteristics of the bridge cables; The bridge design parameters and the bridge cable arrangement characteristics are annotated in the bridge geometric model to provide a geometric reference for the planning and performance analysis of the vehicle excitation path.

[0013] Furthermore, the dynamic characteristic parameters of the bridge cable are extracted, including: Acquiring bridge cable vibration response data from the multimodal sensing network and performing preprocessing; Based on the empirical mode decomposition method, the bridge cable vibration response data is decomposed into multiple eigenmode components; Extracting the dynamic characteristic parameters of the bridge cable from the eigenmode components to characterize the dynamic response characteristics of the bridge cable; The consistency of the dynamic characteristic parameters is verified, and the parameter consistency between the different intrinsic mode components and the surface measurement points is analyzed.

[0014] A bridge cable vibration excitation system based on a combination of an unmanned aerial vehicle and a vehicle jumping test, the system comprising: The information perception module uses a drone equipped with calibration equipment to build a multimodal perception network, identify and calibrate the surface measurement points of the bridge cables, and establish a geometric model of the bridge; The path planning module obtains the cable tension distribution and plans the vehicle excitation path for the vehicle jumping test in combination with the bridge geometry model; The vehicle guidance module guides the vehicle for the jump test through the vehicle excitation path and simultaneously records the excitation impact force and excitation timestamp; Parameter decomposition module, which establishes an empirical modal model, decomposes the multimodal vibration data obtained from the multimodal sensing network, and extracts the dynamic characteristic parameters of the bridge cable; The condition assessment module identifies the performance degradation areas of the bridge cables based on the empirical mode decomposition model and dynamic characteristic parameter analysis, and evaluates the comprehensive health status of the bridge cables.

[0015] Furthermore, the status assessment module includes: Deviation identification unit, which compares dynamic characteristic parameters with design operation parameters to identify parameter deviations and abnormal change trends; The coefficient calculation unit, based on the dynamic characteristic parameters, adopts the modal sensitivity analysis method to quantify the parameter deviation to generate the sensitivity coefficient, and locates the performance degradation area according to the sensitivity coefficient; The performance evaluation unit calculates the vibration energy density distribution in the performance degradation area and evaluates the local health status and bearing capacity of the bridge cables in the area; The comprehensive assessment unit calculates the comprehensive health index of the bridge cable according to the local health status and sensitivity coefficient based on the empirical modal model, and classifies and assesses the comprehensive health status.

[0016] The technical solution of the present invention can achieve the following technical effects: The problems of asynchrony between excitation and measurement, low test accuracy, high risk of artificial excitation, low analysis efficiency, and insufficient health status assessment in traditional bridge cable vibration excitation and monitoring have been solved. By combining drones with car jumping tests, precise synchronization of excitation and measurement has been achieved, and high-precision geometric modeling, empirical mode decomposition, and sensitivity analysis have been used to improve test accuracy, replacing artificial excitation to enhance safety, and improving detection efficiency and coverage through automated path optimization and dynamic updates.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of a bridge cable vibration excitation method based on the combination of UAV and car jumping test; Figure 2 Schematic diagram of the process for obtaining comprehensive health status; Figure 3 A schematic diagram of the structure for generating a vehicle excitation path; Figure 4 This is a structural diagram of the bridge geometric model. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0022] Embodiment 1; like Figure 1 As shown, the present application provides a bridge cable vibration excitation method based on a combination of a drone and a car jump test, the method comprising: S10: Use the calibration equipment carried by the drone to build a multimodal perception network, identify and calibrate the measurement points on the surface of the bridge cable, and establish the bridge geometry model; S20: Obtain the cable tension distribution and plan the vehicle excitation path for the vehicle jumping test in combination with the bridge geometry model; S30: guiding the jumping test vehicle through the vehicle excitation path, and synchronously recording the excitation impact force and the excitation timestamp; S40: Establish an empirical modal model, decompose the multimodal vibration data obtained from the multimodal sensing network, and extract the dynamic characteristic parameters of the bridge cable; S50: Identify the performance degradation areas of the bridge cables based on the empirical mode decomposition model and dynamic characteristic parameter analysis, and evaluate the comprehensive health status of the bridge cables.

[0023] Specifically, drones are equipped with high-precision calibration equipment (such as laser scanners or high-definition cameras) to collect geometric data of bridge structures, including the length, angle, layout characteristics and key support point positions of the cables. Through a multimodal perception network, the collected geometric data is combined with the bridge design parameters to correct errors in the geometric data and generate a complete bridge geometric model. At the same time, the cable surface measurement points are calibrated. The measurement points are specifically the characteristic points and marking points on the cable surface. The characteristic points on the cable surface include damaged points or stains on the cable surface, sleeve indirect rings, water guide lines, etc. If the above characteristic points are not available, a spraying drone can be used to spray on the cables to obtain measurement points for subsequent Extraction of dynamic characteristic parameters; clarify the target bridge cable position, arrange the jump pad near the target bridge cable, determine the shooting position of the drone according to the measurement point of the target bridge cable, calculate the bridge cable tension distribution through the multimodal perception network combined with the bridge geometric model, identify the key area with large tension gradient, and plan the vehicle excitation path of the jump test according to the bridge cable tension distribution and the bridge geometric model using the multi-objective optimization algorithm, including the jump position, vehicle speed and acceleration requirements. According to the optimized vehicle excitation path, guide the test vehicle to complete the jump excitation through the drone-mounted path guidance device. The drone and vehicle are ready near the determined target bridge cable, and the rear of the test vehicle is The wheel suddenly falls from a pad at an appropriate height, which has an impact on the bridge. At the same time, a hovering shooting drone is used to shoot a video of the measuring point of the bridge cable to conduct a multi-position continuous jump test. The multi-position continuous jump test refers to a drone guiding the test vehicle to cross the jump pad multiple times, and other shooting drones shoot at multiple measuring points of the target bridge cable to achieve synchronization between the continuous jump test and multi-point continuous measurement. After the shooting of the first target bridge cable is completed, the guiding drone is used to guide the vehicle to the next target bridge cable. At the same time, the shooting drone arrives at the measuring point of the bridge cable to ensure that the test process is carried out continuously. The drone collects the excitation impact force and excitation timestamp in real time and compares it with the bridge The vibration data of the cable is recorded synchronously to provide a complete data set for subsequent data analysis; the vibration response data recorded by the multimodal sensing network is input into the empirical mode decomposition algorithm and decomposed into multiple intrinsic mode components. The dynamic characteristic parameters of the cable are extracted from the decomposition results, including natural frequency, modal vibration shape, damping ratio and vibration energy density, etc., to characterize the dynamic response characteristics of the cable; based on the extracted dynamic characteristic parameters and combined with the empirical mode decomposition model, the performance degradation area of ​​the cable is identified, and through modal sensitivity analysis, the influence of each dynamic parameter deviation on the performance of the cable is quantified, the sensitivity coefficient is generated, and the vibration energy density distribution in the performance degradation area is further calculated to evaluate the health status of the local cable.Finally, based on the sensitivity coefficient and health status analysis results, a comprehensive health index of the bridge cables is generated, and the health status of the bridge cables is classified and assessed; according to the health assessment results, the vehicle excitation path or the layout of the drone measurement points is adjusted, and the dynamic characteristic parameter extraction algorithm is optimized to improve the accuracy and efficiency of subsequent detection and form a closed detection loop.

[0024] The technical solution of the present invention solves the problems of asynchronous excitation and measurement, low test accuracy, high risk of artificial excitation, low analysis efficiency and insufficient health status assessment in traditional bridge cable vibration excitation and monitoring.

[0025] Further, if Figure 2 Assesses the overall health of the bridge cables, including: Compare dynamic characteristic parameters with design operating parameters to identify parameter deviations and abnormal change trends; Based on the dynamic characteristic parameters, the modal sensitivity analysis method is used to quantify the parameter deviation to generate the sensitivity coefficient, and the performance degradation area is located according to the sensitivity coefficient; For the performance degradation area, calculate the vibration energy density distribution and evaluate the local health status and bearing capacity of the bridge cables in the area; According to the local health status and sensitivity coefficient, the comprehensive health index of the bridge cable is calculated based on the empirical modal model, and the comprehensive health status is evaluated by classification.

[0026] As a preferred embodiment of the above embodiment, key parameters (including natural frequency, modal vibration shape and damping ratio) are selected from the extracted dynamic characteristic parameters, and compared one by one with the design operation parameters of the bridge cable. The change trend of the dynamic parameters is identified through deviation analysis, and it is determined whether there is an abnormal change area, and the possible performance degradation area is preliminarily screened; the modal sensitivity analysis method is used for the deviation value of the dynamic characteristic parameter to quantify the specific influence of the parameter deviation on the bridge cable performance, and the sensitivity coefficient is generated according to the analysis result. The sensitivity coefficient is used to measure the contribution of each dynamic characteristic parameter to the performance degradation in different bridge cable areas. Areas with higher sensitivity coefficients are marked as key monitoring areas. For the marked performance degradation areas, the vibration energy density distribution of the area is calculated based on the extracted vibration response data. The vibration energy density reflects the vibration intensity and distribution characteristics of the bridge cable. The local health status of the bridge cable and the impact of tension changes on the bearing capacity are evaluated through the change trend of the regional energy density. According to the local health status and sensitivity coefficient, combined with the empirical modal model, the comprehensive health index of the bridge cable is calculated through a weighted algorithm. The comprehensive health index is used to quantitatively evaluate the overall performance health level of the bridge cable, and the health status of the bridge cable is classified and evaluated according to the value range of the health index (for example, healthy, slightly damaged, significantly damaged, etc.). The evaluation results are output in the form of a health report, which includes the change trend of dynamic characteristic parameters, sensitivity coefficient distribution, location of performance degradation area and health status evaluation results. According to the health status evaluation results, the bridge cable excitation path and measurement point distribution are adjusted, and the dynamic characteristic parameter extraction method and modal sensitivity analysis algorithm are further optimized to improve the subsequent evaluation accuracy and coverage.

[0027] Specifically, the quantization parameter deviation generates sensitivity coefficients including: Obtain the design and operation parameters of the bridge cables and compare them based on the dynamic characteristic parameters to construct a parameter change matrix; Through the parameter variation matrix, the parameter deviation of each dynamic characteristic parameter to the cable tension distribution is calculated to generate a parameter deviation set; The parameter deviation set is grouped according to the cable tension distribution, the contribution of local deviations to different tension areas is analyzed, and regional weight distribution is generated; According to the regional weight distribution, the sensitivity coefficient is generated, which quantifies the contribution of each dynamic parameter deviation to the degradation of bridge cable performance.

[0028] As a preferred embodiment of the above, the dynamic characteristic parameters of the bridge cable, including natural frequency, modal vibration shape and damping ratio, etc., are collected, and the design operation parameters of the bridge cable are obtained at the same time. The dynamic characteristic parameters are compared and analyzed with the design operation parameters, the deviation values ​​are extracted, and a parameter change matrix is ​​constructed. Each element of the parameter change matrix represents the degree of deviation of a certain dynamic characteristic parameter in a specific bridge cable area. Based on the parameter change matrix and in combination with the bridge cable tension distribution data, the deviation influence of each dynamic characteristic parameter on the bridge cable tension distribution is calculated to generate a parameter deviation set. The parameter deviation set includes the deviation values ​​of each key dynamic parameter in different areas, reflecting the dynamic characteristic changes of the bridge cable in each tension area. The parameter deviation sets are grouped according to the bridge cable tension distribution, and the influence of each local deviation on different tensions is analyzed according to the grouping results. The contribution of the force area is analyzed. For areas with large changes in tension distribution, the influence of the deviation value on the regional vibration modal characteristics is further calculated, and the contribution value of the parameters in the area to the overall performance degradation is extracted; according to the results of local contribution analysis, the importance factor of each tension area is calculated, and the regional weight distribution is generated. The regional weight distribution is used to reflect the relative influence of different tension areas on the overall performance of the bridge cable, and provide a weight basis for the generation of the sensitivity coefficient; based on the regional weight distribution, the deviation value of each dynamic characteristic parameter is combined with the regional weight to quantify the contribution of each dynamic parameter deviation to the performance degradation of the bridge cable, and generate the sensitivity coefficient. The size of the sensitivity coefficient directly reflects the sensitivity of the dynamic parameter deviation to the overall performance of the bridge cable. Parameters and areas with higher sensitivity coefficients are marked as key monitoring objects.

[0029] Specifically, the regional weight distribution is generated, including: Based on the bridge geometry model and cable tension distribution, the cables are divided into multiple capture areas. The capture areas are divided based on tension gradient, cable length and vibration modal characteristics. The dynamic characteristic parameters in the parameter deviation set are grouped according to the capture area, and the contribution value of the dynamic parameter deviation in each partition to the overall performance of the bridge cable is calculated; Based on the contribution value of each capture area and the performance of the bridge cables, a regional importance factor is generated to reflect the sensitivity of performance changes within the capture area. Based on the regional importance factor, the weights of each capture area are sorted to generate the regional weight distribution; During multiple vehicle jumping excitation tests, the regional weight distribution is updated by combining the newly acquired cable tension distribution and dynamic parameter deviation.

[0030] As a preferred embodiment of the above embodiment, the cables are divided into multiple capture areas based on the bridge geometric model and the cable tension distribution. The capture area division is based on determining the tension gradient of the area with large tension difference according to the variation amplitude of the cable tension. Based on the physical length of the cable and combined with the tension gradient segmentation, the dynamic response sensitive area is divided into independent capture areas with reference to the vibration modal information of the cable. According to the divided capture areas, the dynamic characteristic parameters in the parameter deviation set (such as natural frequency, modal vibration shape, damping ratio) are grouped according to the area. The contribution value of the dynamic parameter deviation to the overall performance of the bridge cable is calculated in each partition, and the influence of the parameter deviation in the region on the tension distribution and vibration characteristics of the bridge cable is comprehensively evaluated; the regional importance factor is generated according to the contribution value of the capture area and the overall performance index of the bridge cable. The regional importance factor is used to reflect the sensitivity of the performance change in each capture area to the overall health status of the bridge cable; the importance factor of each capture area is normalized, and the weights of different areas are sorted to generate a regional weight distribution. The regional weight distribution reflects the priority of different capture areas in the performance evaluation of the bridge cable and provides a basis for the further calculation of the sensitivity coefficient; during multiple vehicle jumping excitation tests, the regional weight distribution is dynamically updated in combination with the newly acquired cable tension distribution and dynamic parameter deviation data to ensure that the regional weight distribution always reflects the actual changes in the current state of the bridge cable.

[0031] Further, if Figure 3 As shown in the figure, the vehicle excitation path of the vehicle jumping test is planned in combination with the bridge geometry model, including: Based on the multimodal perception network, the geometric data of the bridge structure is collected to generate the bridge geometric model; Based on the bridge geometry model and combined with the cable tension distribution, a preliminary analysis of the cable dynamic response is conducted to identify the cable blocks that are sensitive to vehicle excitation. The jumping test target is set according to the bridge cable block, and the vehicle excitation path is generated by using a multi-objective optimization algorithm combined with the bridge geometry model and the jumping pad layout constraints; Verify the feasibility of the vehicle excitation path and adjust the vehicle excitation path based on bridge construction conditions and vehicle performance parameters; According to the vehicle excitation path, the path guidance device carried by the UAV sends execution instructions to the jumping test vehicle to guide the vehicle to complete the jumping excitation according to the preset path.

[0032] As a preferred embodiment of the above-mentioned embodiment, a multimodal sensing device (such as a laser scanner or a high-precision camera device) carried by an unmanned aerial vehicle is used to collect geometric data of the bridge structure, including structural information such as bridge span, cable length, cable clamp position, tension distribution and bridge tower height. The collected data is processed and corrected to generate a high-precision bridge geometric model to provide basic data for path planning. According to the bridge geometric model and in combination with the cable tension distribution data, the dynamic response characteristics of the cables are analyzed, the vibration modal characteristics and tension change sensitivity of each area are calculated, and the cable blocks that are most sensitive to vehicle jumping excitation are identified. The identification of sensitive blocks is mainly based on the vibration modal energy concentration, dynamic parameter change amplitude and tension gradient. According to the identified sensitive blocks, the excitation targets of the vehicle jumping test are set, including maximizing the vibration amplitude of the cables and the uniformity of the excitation area coverage. The optimization algorithm combines the bridge geometric model and the jump pad layout constraints to generate a vehicle excitation path that meets the excitation target. The path content includes the vehicle jump position, driving speed, acceleration and jump pad layout plan; the feasibility of the generated vehicle excitation path is verified, and the feasibility and safety of the path in actual operation are evaluated in combination with the bridge construction environment and vehicle performance parameters. The path that does not meet the construction conditions or vehicle performance limitations is adjusted to ensure that the excitation path can be safely executed under actual conditions; the optimized vehicle excitation path is input into the path guidance device of the UAV. The UAV transmits the jump path instruction to the jump test vehicle through real-time tracking and communication functions, and guides the vehicle to complete the jump excitation according to the preset path. The UAV also records the vehicle excitation impact force and jump timestamp, and stores them synchronously with the bridge cable vibration data to provide complete data for subsequent analysis.

[0033] Furthermore, the path guidance device carried by the UAV sends execution instructions to the jump test vehicle, including: The UAV is equipped with a path guidance device, which receives the vehicle excitation path and transmits it to the jump test vehicle; The drone locates and tracks the current position of the vehicle in the jump test, obtains the vehicle position information and driving status data, and compares it with the vehicle excitation path; The path guidance device generates a path adjustment instruction based on the deviation information between the vehicle position information and the vehicle excitation path; When multiple drones need to work together, the time series multi-machine control function can be used to manage the information collection of multiple drones at the same time.

[0034] As a preferred embodiment of the above, the UAV is equipped with a path guidance device, establishes communication to receive vehicle excitation path data, including path parameters such as vehicle jumping position, driving speed and acceleration, and the UAV transmits the received path data to the vehicle jumping test in real time for path execution; the UAV uses a high-precision positioning module (such as RTK-GPS or visual SLAM technology) to locate the vehicle jumping test in real time, obtains the vehicle's current position, speed, acceleration and other driving status data, and the UAV dynamically compares the vehicle's actual driving status with the preset excitation path; based on the deviation information between the vehicle's actual position information and the preset vehicle excitation path, the path guidance device generates a path adjustment instruction, and the adjustment instruction includes fine-tuning the vehicle jumping position, correcting the driving speed and adjusting the acceleration, to ensure that the vehicle accurately completes the vehicle jumping excitation according to the optimized path; when the test requires multiple UAVs to execute in coordination The master control drone coordinates and manages multiple drones through the time series one-control-multiple-drones function. The drone's "one-control-multiple-drones" function specifically refers to the use of drone route planning to enable it to fly to the specified location according to the predetermined route, and assign route tasks to multiple drones, so that one drone guides the excitation vehicle forward, and other drones take local photos of the bridge cables and perform subsequent continuous measurements. Each drone collects vehicle information according to the assigned tasks, and the master control drone summarizes all collected data and sends a unified path adjustment instruction to ensure the consistency of data collection and vehicle jumping excitation path between multiple drones; during the path guidance process, the drone collects the execution status information of the test vehicle (such as vehicle impact force, vehicle jumping timestamp) in real time and uploads the data. The path guidance device verifies the execution effect of the excitation path based on the feedback data, providing complete data support for subsequent bridge cable vibration analysis.

[0035] Further, if Figure 4 As shown, a bridge geometric model is established, including: The geometric data of bridge structures is collected through a multimodal perception network, and the key parts of the bridge are located in three-dimensional space; Integrate and compare bridge structure geometry data with bridge design parameters to correct errors in bridge structure geometry data; The bridge geometry data is processed uniformly to generate a bridge geometry model including the overall structure of the bridge and the layout characteristics of the bridge cables; Bridge design parameters and cable layout characteristics are annotated in the bridge geometry model to provide a geometric reference for vehicle excitation path planning and performance analysis.

[0036] As a preferred embodiment of the above embodiment, a multimodal sensing device (such as a laser radar, a high-definition camera, and a panoramic camera) carried by an unmanned aerial vehicle is used to collect geometric data of the bridge structure. The collected content includes key parameters such as the length and angle of the bridge cable, the position of the anchor point, the distribution of the support points, the bridge span, and the height of the bridge tower. At the same time, the key parts of the bridge (such as the connection nodes and the cable clamp area) are positioned in three-dimensional space with high precision to form an initial geometric data set. The collected bridge geometric data are integrated and compared with the bridge design parameters (such as the design drawings, the structural model, etc.). The key point positions in the geometric data are calibrated by the feature point matching algorithm, and the geometric deviations generated during the collection process are corrected by the error analysis method to ensure the accuracy of the data. The corrected bridge geometric data are uniformly processed and combined with the data of the multimodal sensing network. According to the source, a complete bridge geometry model is generated. The bridge geometry model contains the spatial distribution information of the overall bridge structure and the layout characteristics of the bridge cables, such as the cable clamp spacing, tension gradient and bridge cable angle distribution. In the generated bridge geometry model, the bridge design parameters and the key layout characteristics of the bridge cables are marked, including the starting and ending point coordinates of the bridge cables, the tension distribution area, the position of the key support points and the geometric relationship of the connection nodes. The purpose of marking the characteristics of the bridge geometry model is to provide an intuitive geometric reference for the subsequent vehicle excitation path planning and bridge cable performance analysis of the jumping test. The generated bridge geometry model is stored in a digital form and presented through a three-dimensional visualization tool to facilitate inspection and adjustment by engineering personnel. The bridge geometry model can be directly used for jumping test path planning, bridge cable vibration dynamic analysis and health assessment tasks.

[0037] Furthermore, the dynamic characteristic parameters of the bridge cables are extracted, including: Obtain bridge cable vibration response data from the multimodal sensing network and perform preprocessing; Based on the empirical mode decomposition method, the vibration response data of the bridge cable is decomposed into multiple eigenmode components; Extract the dynamic characteristic parameters of the bridge cable from the eigenmode components to characterize the dynamic response characteristics of the bridge cable; The consistency of dynamic characteristic parameters is verified, and the parameter consistency between different eigenmode components and surface measurement points is analyzed.

[0038] As a preferred embodiment of the above embodiment, the vibration response data of the bridge cable is obtained from the multimodal sensing network, and the data includes the displacement, acceleration and frequency characteristics of the measurement points on the surface of the bridge cable, etc. The collected vibration response data is preprocessed, including denoising, smoothing and signal enhancement, to ensure that the data quality meets the subsequent analysis requirements; the preprocessed vibration response data is input into the empirical mode decomposition (EMD) method to decompose it into multiple eigenmode components, each of which represents a vibration mode with different frequency and time characteristics, capturing the multi-scale dynamic characteristics of the bridge cable vibration; the dynamic characteristic parameters of the bridge cable are extracted from the eigenmode components, including but not limited to the natural frequency: the main frequency of each modal component is identified by spectrum analysis; the modal vibration shape: Analyze the spatial distribution characteristics of each modal component; Damping ratio: calculate the energy dissipation characteristics of the bridge cable through the attenuation characteristics; Vibration energy density: calculate the energy contribution of each modal component and evaluate the vibration intensity of the bridge cable; verify the consistency of the extracted dynamic characteristic parameters, analyze the parameter consistency between different intrinsic modal components, and the spatial consistency of the dynamic parameters corresponding to different measurement points. Through consistency analysis, ensure the reliability and accuracy of the extracted parameters, and mark the bridge cable areas where abnormalities may exist; output the extracted dynamic characteristic parameters in tabular or graphical form to provide data support for subsequent bridge cable performance analysis, decay area identification and health status assessment. According to the analysis results, adjust the test plan to optimize the parameter extraction accuracy.

[0039] Embodiment 2: Based on the same inventive concept as the bridge cable vibration excitation method based on the combination of drone and car jumping test in the aforementioned embodiment, the present invention also provides a bridge cable vibration excitation system based on the combination of drone and car jumping test, the system comprising: The information perception module uses a drone equipped with calibration equipment to build a multimodal perception network, identify and calibrate the surface measurement points of the bridge cables, and establish a geometric model of the bridge; The path planning module obtains the cable tension distribution and plans the vehicle excitation path for the vehicle jumping test in combination with the bridge geometry model; The vehicle guidance module guides the vehicle for the jump test through the vehicle excitation path and simultaneously records the excitation impact force and excitation timestamp; Parameter decomposition module, which establishes an empirical modal model, decomposes the multimodal vibration data obtained from the multimodal sensing network, and extracts the dynamic characteristic parameters of the bridge cable; The condition assessment module identifies the performance degradation areas of the bridge cables based on the empirical mode decomposition model and dynamic characteristic parameter analysis, and evaluates the comprehensive health status of the bridge cables.

[0040] The above-mentioned adjustment system in the present invention can effectively realize a bridge cable vibration excitation method based on the combination of drone and vehicle jumping test. The technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0041] More specifically, the status assessment module includes: Deviation identification unit, which compares dynamic characteristic parameters with design operation parameters to identify parameter deviations and abnormal change trends; The coefficient calculation unit, based on the dynamic characteristic parameters, adopts the modal sensitivity analysis method to quantify the parameter deviation to generate the sensitivity coefficient, and locates the performance degradation area according to the sensitivity coefficient; The performance evaluation unit calculates the vibration energy density distribution in the performance degradation area and evaluates the local health status and bearing capacity of the bridge cables in the area; The comprehensive assessment unit calculates the comprehensive health index of the bridge cable according to the local health status and sensitivity coefficient based on the empirical modal model, and classifies and assesses the comprehensive health status.

[0042] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0043] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A bridge cable vibration excitation method based on a combination of UAV and car jumping test, characterized in that: The method comprises: Use drones equipped with calibration equipment to build a multimodal perception network, identify and calibrate the surface measurement points of the bridge cables, and establish a geometric model of the bridge; Obtaining the tension distribution of the bridge cables, and planning the vehicle excitation path of the vehicle jumping test in combination with the bridge geometric model; guiding the jumping test vehicle through the vehicle excitation path and synchronously recording the excitation impact force and the excitation timestamp; Establishing an empirical modal model, decomposing the multimodal vibration data obtained from the multimodal sensing network, and extracting dynamic characteristic parameters of the bridge cable; Based on the empirical mode decomposition model and the dynamic characteristic parameter analysis, the performance degradation area of ​​the bridge cable is identified, and the comprehensive health status of the bridge cable is evaluated.

2. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 1 is characterized in that: Assess the overall health of the cables, including: Comparing the dynamic characteristic parameters with the designed operating parameters to identify parameter deviations and abnormal change trends; Based on the dynamic characteristic parameters, a modal sensitivity analysis method is used to quantify the parameter deviation to generate a sensitivity coefficient, and the performance degradation area is located according to the sensitivity coefficient; For the performance degradation area, the vibration energy density distribution is calculated to evaluate the local health status and bearing capacity of the bridge cables in the area; According to the local health status and the sensitivity coefficient, the comprehensive health index of the bridge cable is calculated based on the empirical modal model, and the comprehensive health status is classified and evaluated.

3. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 2 is characterized in that: Quantifying the parameter deviation to generate a sensitivity coefficient includes: Obtaining the design operation parameters of the bridge cables and comparing them based on the dynamic characteristic parameters to construct a parameter change matrix; Calculating the parameter deviation of each of the dynamic characteristic parameters to the cable tension distribution through the parameter change matrix to generate a parameter deviation set; Grouping the parameter deviation set according to the cable tension distribution, analyzing the contribution of local deviations to different tension areas, and generating a regional weight distribution; A sensitivity coefficient is generated according to the regional weight distribution, and the sensitivity coefficient quantifies the contribution of each dynamic parameter deviation to the degradation of bridge cable performance.

4. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 3 is characterized in that: Generates regional weight distribution, including: Based on the bridge geometric model and the cable tension distribution, the cable is divided into a plurality of capture areas, wherein the capture area division is based on tension gradient, cable length and vibration modal characteristics; The dynamic characteristic parameters in the parameter deviation set are grouped according to the capture area, and the contribution value of the dynamic parameter deviation in each partition to the overall performance of the bridge cable is calculated; Generating a regional importance factor reflecting the sensitivity of performance changes in the capture area according to the contribution value of each capture area and the performance of the bridge cable; Based on the regional importance factor, the weights of the capture regions are sorted to generate a regional weight distribution; During multiple vehicle jumping excitation tests, the regional weight distribution is updated in combination with the newly acquired cable tension distribution and dynamic parameter deviation.

5. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 1 is characterized in that: The vehicle excitation path of the vehicle jumping test is planned in combination with the bridge geometric model, including: Based on the multimodal sensing network, geometric data of the bridge structure is collected to generate a bridge geometric model; Based on the bridge geometric model and the cable tension distribution, a preliminary analysis is performed on the dynamic response of the cables to identify the cable blocks that are sensitive to vehicle excitation; The vehicle jumping test target is set according to the bridge cable block, and a vehicle excitation path is generated by using a multi-objective optimization algorithm in combination with the bridge geometric model and the layout constraints of the vehicle jumping pads; Performing feasibility verification on the vehicle excitation path, and adjusting the vehicle excitation path in combination with bridge construction conditions and vehicle performance parameters; According to the vehicle excitation path, an execution instruction is sent to the vehicle for jumping test through a path guidance device carried by the UAV, so as to guide the vehicle to complete the jumping excitation according to the preset path.

6. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 5 is characterized in that: The path guidance device carried by the drone sends execution instructions to the jump test vehicle, including: The UAV is equipped with the path guiding device, receives the vehicle excitation path, and transmits it to the vehicle for jumping test; The drone locates and tracks the current position of the vehicle for the jump test, obtains vehicle position information and driving status data, and compares the information with the vehicle excitation path; The path guidance device generates a path adjustment instruction based on the deviation information between the vehicle position information and the vehicle excitation path; When multiple drones are required to perform collaboratively, the information collection of multiple drones is managed simultaneously through the time series one-control-multiple-drones function.

7. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 1 is characterized in that: Build the bridge geometry model, including: Collecting bridge structure geometric data through the multimodal perception network and performing three-dimensional spatial positioning of key parts of the bridge; Integrate and compare the bridge structure geometry data with bridge design parameters to correct errors in the bridge structure geometry data; The bridge geometric data is processed uniformly to generate the bridge geometric model including the overall structure of the bridge and the layout characteristics of the bridge cables; The bridge design parameters and the bridge cable arrangement characteristics are annotated in the bridge geometric model to provide a geometric reference for the planning and performance analysis of the vehicle excitation path.

8. The bridge cable vibration excitation method based on the combination of UAV and vehicle jumping test according to claim 1 is characterized in that: Extract the dynamic characteristic parameters of the bridge cable, including: Acquiring bridge cable vibration response data from the multimodal sensing network and performing preprocessing; Based on the empirical mode decomposition method, the bridge cable vibration response data is decomposed into multiple eigenmode components; Extracting the dynamic characteristic parameters of the bridge cable from the eigenmode components to characterize the dynamic response characteristics of the bridge cable; The consistency of the dynamic characteristic parameters is verified, and the parameter consistency between the different intrinsic mode components and the surface measurement points is analyzed.

9. A bridge cable vibration excitation system based on a combination of drone and car jumping test, characterized in that: The system comprises: The information perception module uses a drone equipped with calibration equipment to build a multimodal perception network, identify and calibrate the surface measurement points of the bridge cables, and establish a geometric model of the bridge; The path planning module obtains the cable tension distribution and plans the vehicle excitation path for the vehicle jumping test in combination with the bridge geometry model; The vehicle guidance module guides the vehicle for the jump test through the vehicle excitation path and simultaneously records the excitation impact force and excitation timestamp; Parameter decomposition module, which establishes an empirical modal model, decomposes the multimodal vibration data obtained from the multimodal sensing network, and extracts the dynamic characteristic parameters of the bridge cable; The condition assessment module identifies the performance degradation areas of the bridge cables based on the empirical mode decomposition model and dynamic characteristic parameter analysis, and evaluates the comprehensive health status of the bridge cables.

10. The bridge cable vibration excitation system based on the combination of UAV and vehicle jumping test according to claim 9 is characterized in that: The status assessment module includes: Deviation identification unit, which compares dynamic characteristic parameters with design operation parameters to identify parameter deviations and abnormal change trends; The coefficient calculation unit, based on the dynamic characteristic parameters, adopts the modal sensitivity analysis method to quantify the parameter deviation to generate the sensitivity coefficient, and locates the performance degradation area according to the sensitivity coefficient; The performance evaluation unit calculates the vibration energy density distribution in the performance degradation area and evaluates the local health status and bearing capacity of the bridge cables in the area; The comprehensive assessment unit calculates the comprehensive health index of the bridge cable according to the local health status and sensitivity coefficient based on the empirical modal model, and classifies and assesses the comprehensive health status.

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