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

Abstract

The present application relates to the technical field of bridge safety detection, and more particularly to a bridge cable vibration excitation method and system based on a combination of unmanned aerial vehicles and jump test, the method comprising: using an unmanned aerial vehicle to carry a calibration device to construct a multi-modal perception network, identify and calibrate bridge cable surface measurement points, and establish a bridge geometric model; obtaining bridge cable tension distribution, planning a vehicle excitation path for the jump test in combination with the bridge geometric model; guiding the jump test vehicle through the vehicle excitation path and synchronously recording the excitation impact force and the excitation timestamp; establishing an empirical mode model, decomposing the multi-modal vibration data obtained from the multi-modal perception network, and extracting the dynamic characteristic parameters of the bridge cable; and based on the empirical mode decomposition model and the dynamic characteristic parameters, analyzing and identifying the performance degradation area of the bridge cable and evaluating the comprehensive health status of the bridge cable. Through the present application, the problems of asynchronization between excitation and measurement, low test precision, low analysis efficiency, and insufficient health status evaluation in traditional excitation are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge safety detection, and particularly relates to a bridge cable vibration excitation method and system based on combination of an unmanned aerial vehicle and a jump test. BACKGROUND

[0002] As an important part of the transportation system, 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 prone to be affected by corrosion, fatigue and other damages, so it is necessary to regularly conduct structural monitoring and health assessment. The vibration frequency method is the most commonly used method for testing bridge cables. The vibration response of the bridge cable is recorded by exciting the bridge cable, and the modal information is extracted. However, due to the large stiffness of the short cable, environmental noise interference and other factors, the accuracy and reliability of the vibration frequency method are limited in practical application.

[0003] Traditional excitation methods include manual knocking and jump tests, but manual knocking has limitations and safety hazards, and jump tests are difficult to synchronize excitation and measurement, affecting test efficiency and accuracy.

[0004] The information disclosed in this BACKGROUND section is only intended to enhance the understanding of the general background of the present disclosure and should not be construed as recognition or any form of suggestion that this information constitutes prior art. SUMMARY

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

[0006] In order to achieve the above purpose, the technical solution adopted by the present application is:

[0007] A bridge cable vibration excitation method based on combination of an unmanned aerial vehicle and a jump test, the method comprising:

[0008] Using an unmanned aerial vehicle to carry a calibration device to construct a multi-modal perception network, identifying and calibrating bridge cable surface measurement points, and establishing a bridge geometric model;

[0009] Obtaining bridge cable tension distribution, and planning a vehicle excitation path for the jump test in combination with the bridge geometric model;

[0010] Guiding the jump test vehicle through the vehicle excitation path, and synchronously recording the excitation impact force and the excitation timestamp;

[0011] Establishing an empirical mode model, decomposing the multi-modal vibration data obtained from the multi-modal perception network, and extracting the dynamic characteristic parameters of the bridge cable;

[0012] Based on the empirical mode decomposition model and the dynamic characteristic parameter analysis, a performance degradation area of the bridge cable is identified, and a comprehensive health state of the bridge cable is evaluated.

[0013] Further, the comprehensive health state of the bridge cable is evaluated, including:

[0014] The dynamic characteristic parameters are compared with design operation parameters to identify parameter deviations and abnormal change trends;

[0015] Based on the dynamic characteristic parameters, a modal sensitivity analysis method is used to quantify the parameter deviations to generate sensitivity coefficients, and the performance degradation area is located according to the sensitivity coefficients;

[0016] For the performance degradation area, the vibration energy density distribution is calculated to evaluate the local health state and carrying capacity of the bridge cable in the area;

[0017] According to the local health state and the sensitivity coefficients, a comprehensive health index of the bridge cable is calculated based on the empirical mode model, and the comprehensive health state is classified and evaluated.

[0018] Further, the sensitivity coefficients are quantified by generating parameter deviations, including:

[0019] Obtain the bridge cable design operation parameters and compare them based on the dynamic characteristic parameters to construct a parameter change matrix;

[0020] Through the parameter change matrix, the parameter deviations of each dynamic characteristic parameter to the bridge cable tension distribution are calculated to generate a parameter deviation set;

[0021] The parameter deviation set is grouped according to the bridge cable tension distribution, the contribution of local deviations to different tension areas is analyzed, and a regional weight distribution is generated;

[0022] According to the regional weight distribution, a sensitivity coefficient is generated, which quantifies the contribution of each dynamic parameter deviation to the performance degradation of the bridge cable.

[0023] Further, the regional weight distribution is generated, including:

[0024] Based on the bridge geometric model and the bridge cable tension distribution, the bridge cable is divided into multiple capture areas, and the capture area division includes tension gradient, bridge cable length and vibration modal characteristics;

[0025] 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;

[0026] According to the contribution value of each capture area and the bridge cable performance, a region importance factor reflecting the performance change sensitivity in the capture area is generated;

[0027] Based on the region importance factor, the weight of each capture area is adjusted to generate a region weight distribution;

[0028] During multiple jump test excitation tests, the region weight distribution is updated in combination with newly acquired bridge cable tension distribution and dynamic parameter deviation.

[0029] Further, the vehicle excitation path of the jump test is planned in combination with the bridge geometric model, including:

[0030] Based on the multi-modal perception network, geometric data of the bridge structure is collected to generate a bridge geometric model;

[0031] According to the bridge geometric model, in combination with the bridge cable tension distribution, a preliminary analysis of the bridge cable dynamic response is performed to identify the bridge cable block sensitive to vehicle excitation;

[0032] According to the bridge cable block, the jump test target is set, and a multi-objective optimization algorithm is used to generate a vehicle excitation path in combination with the bridge geometric model and jump pad arrangement constraints;

[0033] The feasibility of the vehicle excitation path is verified, and the vehicle excitation path is adjusted in combination with the bridge construction conditions and vehicle performance parameters;

[0034] According to the vehicle excitation path, the path guiding device carried by the unmanned aerial vehicle sends execution instructions to the jump test vehicle to guide the vehicle to complete the jump excitation according to the preset path.

[0035] Further, the path guiding device carried by the unmanned aerial vehicle sends execution instructions to the jump test vehicle, including:

[0036] The unmanned aerial vehicle carries the path guiding device, receives the vehicle excitation path, and transmits it to the jump test vehicle;

[0037] The unmanned aerial vehicle locates and tracks the current position of the jump test vehicle, obtains vehicle position information and driving state data, and compares them with the vehicle excitation path;

[0038] Based on the deviation information of the vehicle position information and the vehicle excitation path, the path guiding device generates path adjustment instructions;

[0039] When multiple unmanned aerial vehicles need to be cooperatively executed, time series one-control multi-machine function is used to simultaneously manage information collection of multiple unmanned aerial vehicles.

[0040] Further, a bridge geometric model is established, including:

[0041] Bridge structure geometric data is collected by the multi-modal perception network, and three-dimensional spatial positioning is performed on key parts of the bridge;

[0042] The bridge structure geometric data is integrated and compared with the bridge design parameters, and errors in the bridge structure geometric data are corrected;

[0043] The bridge geometric data is uniformly processed to generate the bridge geometric model containing the overall structure of the bridge and the bridge cable arrangement characteristics;

[0044] The bridge design parameters and the bridge cable arrangement characteristics are labeled in the bridge geometric model, providing geometric reference for planning and performance analysis of the vehicle excitation path.

[0045] Further, dynamic characteristic parameters of the bridge cable are extracted, including:

[0046] Bridge cable vibration response data is obtained from the multi-modal perception network and preprocessed;

[0047] Based on the empirical mode decomposition method, the bridge cable vibration response data is decomposed into multiple intrinsic mode components;

[0048] The dynamic characteristic parameters of the bridge cable are extracted from the intrinsic mode components to represent the dynamic response characteristics of the bridge cable;

[0049] The dynamic characteristic parameters are verified for consistency, and the parameter consistency between different intrinsic mode components and surface measurement points is analyzed.

[0050] A bridge cable vibration excitation system based on the combination of unmanned aerial vehicles and jump test, the system comprising:

[0051] An information perception module uses an unmanned aerial vehicle to carry a calibration device to construct a multi-modal perception network, identifies and calibrates bridge cable surface measurement points, and establishes a bridge geometric model;

[0052] A path planning module obtains bridge cable tension distribution, and plans a vehicle excitation path for the jump test in combination with the bridge geometric model;

[0053] A vehicle guidance module guides the jump test vehicle through the vehicle excitation path, and synchronously records the excitation impact force and the excitation timestamp;

[0054] A parameter decomposition module establishes an empirical mode model, decomposes multi-modal vibration data obtained from the multi-modal perception network, and extracts dynamic characteristic parameters of the bridge cable;

[0055] The state evaluation module identifies the performance degradation area of the bridge cable based on the empirical mode decomposition model and dynamic characteristic parameter analysis, and evaluates the comprehensive health state of the bridge cable.

[0056] Further, the state evaluation module comprises:

[0057] The deviation identification unit compares the dynamic characteristic parameters with the design operation parameters, identifies the parameter deviation and abnormal change trend;

[0058] The coefficient calculation unit quantifies the parameter deviation to generate a sensitivity coefficient based on the dynamic characteristic parameters using a modal sensitivity analysis method, and locates the performance degradation area according to the sensitivity coefficient;

[0059] The performance evaluation unit calculates the vibration energy density distribution in the performance degradation area, and evaluates the local health state and carrying capacity of the bridge cable in the area;

[0060] The comprehensive evaluation unit calculates the comprehensive health index of the bridge cable based on the local health state and the sensitivity coefficient according to the empirical mode model, and classifies and evaluates the comprehensive health state.

[0061] The technical scheme of the present application can achieve the following technical effects:

[0062] The problems of asynchronization between excitation and measurement, low test precision, high risk of manual excitation, low analysis efficiency and insufficient health state evaluation in traditional bridge cable vibration excitation and monitoring are solved. Through the combination of unmanned aerial vehicle and car jumping test, precise synchronization of excitation and measurement is realized, high-precision geometric modeling, empirical mode decomposition and sensitivity analysis are used to improve the test precision, manual excitation is replaced to enhance safety, and the detection efficiency and coverage are improved through automatic path optimization and dynamic updating.

[0063] The above description is only a summary of the technical scheme of the present application, in order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following will describe the specific embodiments of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical scheme of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0065] Figure 1 It is a flowchart of a bridge cable vibration excitation method based on the combination of unmanned aerial vehicle and car jumping test.

[0066] Figure 2 Flowchart for obtaining comprehensive health status

[0067] Figure 3 Structural diagram for generating vehicle excitation path

[0068] Figure 4 Structural diagram of bridge geometric model DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0071] Embodiment one

[0072] As shown in the drawings, the present application provides a bridge cable vibration excitation method based on unmanned aerial vehicle and jump test combination, the method comprises: Figure 1

[0073] S10: using unmanned aerial vehicle to carry calibration equipment to construct multi-modal perception network, identifying and calibrating bridge cable surface measurement points, and establishing bridge geometric model;

[0074] S20: obtaining bridge cable tension distribution, combining bridge geometric model to plan vehicle excitation path of jump test;

[0075] S30: guiding jump test vehicle through vehicle excitation path, and synchronously recording excitation impact force and excitation timestamp;

[0076] S40: establishing empirical mode model, decomposing multi-modal vibration data obtained from multi-modal perception network, and extracting dynamic characteristic parameters of bridge cable;

[0077] S50: identifying performance degradation area of bridge cable based on empirical mode decomposition model and dynamic characteristic parameter analysis, and evaluating comprehensive health status of bridge cable.

[0078] ​Specifically, a high-precision calibration device (such as a laser scanner or a high-definition camera) is carried by a UAV to collect geometric data of the bridge structure, including the length, angle, arrangement characteristics and key support point position of the bridge cable. Through a multi-modal 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 surface measurement points of the bridge cable are calibrated, which are specific feature points and marker points on the surface of the bridge cable. The feature points on the surface of the bridge cable include damaged or stained points, sleeve indirect rings, and water lines. If there are no feature points, a spraying UAV can be used to spray on the bridge cable to obtain measurement points for subsequent extraction of dynamic characteristic parameters. The position of the target bridge cable is determined, and jump blocks are arranged near the target bridge cable. According to the measurement points of the target bridge cable, the shooting position of the UAV is determined. Through a multi-modal perception network combined with the bridge geometric model, the tension distribution of the bridge cable is calculated, the key areas with large tension change gradient are identified, and the vehicle excitation path of the jump test is planned using a multi-objective optimization algorithm based on the tension distribution of the bridge cable and the bridge geometric model. The jump test path includes jump position, vehicle speed and acceleration requirements. According to the optimized vehicle excitation path, the test vehicle is guided to complete the jump excitation by the UAV carrying a path guiding device. When the UAV and the vehicle are ready near the determined target bridge cable, the rear wheel of the test vehicle suddenly falls from the appropriate height of the pad, producing an impact on the bridge. At the same time, a hovering shooting UAV shoots videos of the measurement points of the bridge cable, and a multi-position continuous jump test is performed. The multi-position continuous jump test refers to a UAV guiding a test vehicle to cross multiple jump blocks, and other shooting UAVs shooting at multiple measurement points of the target bridge cable to realize the synchronization of continuous jump test and multi-point continuous measurement. After the shooting of the first target bridge cable is completed, the guiding UAV guides the vehicle to the next target bridge cable, and at the same time, the shooting UAV reaches the measurement point position of the bridge cable to ensure continuous testing. The UAV real-time collects the excitation impact force and excitation timestamp, and synchronously records the vibration data of the bridge cable to provide a complete data set for subsequent data analysis. The vibration response data recorded by the multi-modal perception network is input into an empirical mode decomposition algorithm to decompose into multiple intrinsic mode components. From the decomposition results, the dynamic characteristic parameters of the bridge cable are extracted, including natural frequency, modal shape, damping ratio and vibration energy density, which characterize the dynamic response characteristics of the bridge cable. Based on the extracted dynamic characteristic parameters and the empirical mode decomposition model, the performance degradation area of the bridge cable is identified. Through modal sensitivity analysis, the influence of the deviation of each dynamic parameter on the performance of the bridge cable is quantified to generate a sensitivity coefficient. The vibration energy density distribution of the performance degradation area is further calculated to evaluate the health status of the local bridge cable.Finally, based on the sensitivity coefficient and health state analysis results, a comprehensive health index of the bridge cable is generated, the health state of the bridge cable is classified and evaluated, the vehicle excitation path or the unmanned aerial vehicle measurement point layout is adjusted according to the health evaluation results, and the dynamic characteristic parameter extraction algorithm is optimized to improve the accuracy and efficiency of subsequent detection, thereby forming a detection closed loop.

[0079] By the technical scheme of the present application, the problems of asynchronization between excitation and measurement, low test accuracy, high risk of manual excitation, low analysis efficiency and insufficient health state evaluation in traditional bridge cable vibration excitation and monitoring are solved.

[0080] Further, as shown in Figure 2 The comprehensive health state of the bridge cable is evaluated, including:

[0081] The dynamic characteristic parameters are compared with the design operation parameters to identify parameter deviation and abnormal change trend;

[0082] 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;

[0083] For the performance degradation area, the vibration energy density distribution is calculated to evaluate the local health state and carrying capacity of the bridge cable in the area;

[0084] According to the local health state and the sensitivity coefficient, the comprehensive health index of the bridge cable is calculated based on an empirical modal model to classify and evaluate the comprehensive health state.

[0085] As a preferred embodiment of the above, from the extracted dynamic characteristic parameters, key parameters (including natural frequency, modal shape and damping ratio) are selected and compared with the design and operation parameters of the bridge cable one by one, the change trend of the dynamic parameters is identified through deviation analysis, whether there is an abnormal change region is judged, and the possible performance degradation region is preliminarily screened; the deviation value of the dynamic characteristic parameters is quantified by using the modal sensitivity analysis method, the specific influence degree of the parameter deviation on the performance of the bridge cable is analyzed, 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 regions of the bridge cable. The region with higher sensitivity coefficient is marked as the key monitoring region; for the marked performance degradation region, based on the extracted vibration response data, the vibration energy density distribution of the region is calculated, the vibration energy density reflects the vibration intensity and its distribution characteristics of the bridge cable, and the local health state of the bridge cable and the influence of the tension change on the carrying capacity are evaluated through the change trend of the regional energy density; according to the local health state and the sensitivity coefficient, the comprehensive health index of the bridge cable is calculated through a weighted algorithm based on the empirical modal model, the comprehensive health index is used to quantitatively evaluate the overall performance health level of the bridge cable, and the health state of the bridge cable is classified and evaluated (for example, healthy, slight damage, significant damage, etc.) according to the value range of the health index; the evaluation result is output in the form of a health report, the report content includes the change trend of the dynamic characteristic parameters, the sensitivity coefficient distribution, the location of the performance degradation region and the health state evaluation result; according to the health state evaluation result, the excitation path and the distribution of the measuring points of the bridge cable are adjusted, and the dynamic characteristic parameter extraction method and the modal sensitivity analysis algorithm are further optimized to improve the subsequent evaluation precision and coverage.

[0086] Further, the sensitivity coefficient is generated by quantifying the parameter deviation, including:

[0087] The design and operation parameters of the bridge cable are obtained and compared based on the dynamic characteristic parameters to construct a parameter change matrix;

[0088] The parameter deviation set is generated by calculating the parameter deviation of each dynamic characteristic parameter to the tension distribution of the bridge cable through the parameter change matrix;

[0089] The parameter deviation set is grouped according to the tension distribution of the bridge cable, the contribution of the local deviation to different tension regions is analyzed, and the regional weight distribution is generated;

[0090] The sensitivity coefficient is generated according to the regional weight distribution, the sensitivity coefficient quantifies the contribution of each dynamic parameter deviation to the performance degradation of the bridge cable.

[0091] As a preferred of the above embodiment, the dynamic characteristic parameters of the bridge cable are collected, including the natural frequency, modal shape and damping ratio, etc., and the design operation parameters of the bridge cable are obtained, 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 representing the deviation degree of a certain dynamic characteristic parameter in a specific bridge cable region; based on the parameter change matrix, the deviation influence of each dynamic characteristic parameter on the bridge cable tension distribution is calculated in combination with the bridge cable tension distribution data, a parameter deviation set is generated, the parameter deviation set including the deviation values of each key dynamic parameter in different regions, reflecting the dynamic characteristic changes of the bridge cable in each tension region; the parameter deviation set is grouped according to the bridge cable tension distribution, the contribution of each local deviation to different tension regions is analyzed according to the grouping result, for the regions with larger tension distribution changes, the influence degree of the deviation values on the regional vibration modal characteristics is further calculated, and the contribution value of the parameters in the region to the overall performance degradation is extracted; according to the local contribution analysis result, the importance factor of each tension region is calculated, and a regional weight distribution is generated, the regional weight distribution is used to reflect the relative influence degree of different tension regions on the overall performance of the bridge cable, and provides weight basis for the generation of the sensitivity coefficient; based on the regional weight distribution, the deviation values of each dynamic characteristic parameter are combined with the regional weight to quantify the contribution degree of each dynamic parameter deviation to the performance degradation of the bridge cable, and a sensitivity coefficient is generated, the size of the sensitivity coefficient directly reflects the sensitivity of the dynamic parameter deviation to the overall performance of the bridge cable, and the parameters and regions with higher sensitivity coefficients are marked as key monitoring objects.

[0092] Further, the regional weight distribution is generated, including:

[0093] Based on the bridge geometric model and the bridge cable tension distribution, the bridge cable is divided into a plurality of capture regions, and the capture region division includes tension gradient, bridge cable length and vibration modal characteristics;

[0094] The dynamic characteristic parameters in the parameter deviation set are grouped according to the capture regions, and the contribution value of the dynamic parameter deviation in each partition to the overall performance of the bridge cable is calculated;

[0095] According to the contribution value of each capture region and the performance of the bridge cable, a regional importance factor reflecting the performance change sensitivity in the capture region is generated;

[0096] Based on the regional importance factor, the weight of each capture region is arranged to generate a regional weight distribution;

[0097] During the multiple jump test processes, the regional weight distribution is updated in combination with the newly obtained bridge cable tension distribution and dynamic parameter deviation.

[0098] As a preferred embodiment of the above embodiment, based on the bridge geometric model and the bridge cable tension distribution, the bridge cable is divided into multiple capture regions, the capture region division is based on including determining the tension gradient of the region with larger tension difference according to the change amplitude of the bridge cable tension, combining the tension gradient segmentation division based on the physical length of the bridge cable, referring to the vibration modal information of the bridge cable, and dividing the dynamic response sensitive region into an independent capture region; according to the divided capture region, the dynamic characteristic parameters (such as natural frequency, modal shape, damping ratio) in the parameter deviation set are grouped according to the region. Calculate the contribution value of the dynamic parameter deviation to the overall performance of the bridge cable in each sub-region, and comprehensively evaluate the influence of the parameter deviation in the region on the tension distribution and vibration characteristics of the bridge cable; according to the contribution value of the capture region and the overall performance index of the bridge cable, generate a region importance factor, the region importance factor is used to reflect the sensitivity of the performance change in each capture region to the overall health status of the bridge cable; normalize the importance factor of each capture region, and arrange the weight of different regions to generate a region weight distribution, the region weight distribution reflects the priority of different capture regions in the bridge cable performance evaluation, and provides a basis for further calculation of the sensitivity coefficient; during the multiple jump test excitation tests, combined with the newly acquired bridge cable tension distribution and dynamic parameter deviation data, the region weight distribution is dynamically updated to ensure that the region weight distribution always reflects the actual changes of the current state of the bridge cable.

[0099] Further, as shown in Figure 3 The vehicle excitation path of the jump test is planned in combination with the bridge geometric model, including:

[0100] Based on the multi-modal perception network, the geometric data of the bridge structure is collected to generate a bridge geometric model;

[0101] According to the bridge geometric model, the dynamic response of the bridge cable is preliminarily analyzed in combination with the bridge cable tension distribution, and the bridge cable block sensitive to vehicle excitation is identified;

[0102] According to the bridge cable block, the jump test target is set, and a multi-objective optimization algorithm is used to generate a vehicle excitation path in combination with the bridge geometric model and the jump pad arrangement restriction condition;

[0103] The feasibility of the vehicle excitation path is verified, and the vehicle excitation path is adjusted in combination with the bridge construction conditions and the vehicle performance parameters;

[0104] According to the vehicle excitation path, the path guiding device carried by the unmanned aerial vehicle sends execution instructions to the jump test vehicle to guide the vehicle to complete the jump excitation according to the preset path.

[0105] As a preferred embodiment of the above embodiment, the bridge structure geometry data, including bridge span, bridge cable length, cable clamp position, tension distribution, and bridge tower height, are collected by a multi-modal sensing device (such as a laser scanner or high-precision camera) mounted on a UAV, the collected data is processed and corrected to generate a high-precision bridge geometry model, which provides basic data for path planning; based on the bridge geometry model, combined with the bridge cable tension distribution data, the dynamic response characteristics of the bridge cable are analyzed, the vibration modal characteristics and tension change sensitivity of each region are calculated, and the bridge cable block most sensitive to the bumping excitation is identified, the identification of the sensitive block is mainly based on the vibration modal energy concentration, dynamic parameter change amplitude and tension gradient; according to the identified sensitive block, the excitation target of the bumping test is set, including the maximization of bridge cable vibration amplitude and the uniformity of excitation area coverage, using a multi-objective optimization algorithm, combined with the bridge geometry model and the bumping pad arrangement restriction condition, a vehicle excitation path that meets the excitation target is generated, the path content includes vehicle bumping position, driving speed, acceleration and bumping pad arrangement scheme; the generated vehicle excitation path is verified for feasibility, combined with the bridge construction environment and vehicle performance parameters, the feasibility and safety of the path in actual operation are evaluated, 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 guiding device of the UAV, the UAV transmits the bumping path instructions to the bumping test vehicle through real-time tracking and communication functions, guides the vehicle to complete the bumping excitation according to the preset path, and the UAV records the vehicle excitation impact force and the bumping timestamp at the same time, and synchronously stores the bridge cable vibration data, to provide complete data for subsequent analysis.

[0106] Further, the path guiding device mounted on the UAV sends execution instructions to the bumping test vehicle, including:

[0107] The path guiding device mounted on the UAV receives the vehicle excitation path and transmits it to the bumping test vehicle;

[0108] The UAV locates and tracks the current position of the bumping test vehicle, obtains vehicle position information and driving state data, and compares them with the vehicle excitation path;

[0109] Based on the deviation information of the vehicle position information and the vehicle excitation path, the path guiding device generates path adjustment instructions;

[0110] When multiple UAVs need to be coordinated to execute, the time sequence one controls multiple machines function is used to simultaneously manage the information collection of multiple UAVs.

[0111] As a preferred embodiment of the above, the UAV-mounted path guiding device receives the vehicle excitation path data, including path parameters such as jump position, driving speed and acceleration, and transmits the received path data to the jump test vehicle in real time for path execution; the UAV uses a high-precision positioning module (such as RTK-GPS or visual SLAM technology) to perform real-time positioning on the jump test vehicle, obtains the current position, speed and acceleration of the vehicle, and dynamically compares the actual driving state of the vehicle with the preset excitation path; based on the deviation information between the actual position of the vehicle and the preset vehicle excitation path, the path guiding device generates path adjustment instructions, including jump position fine-tuning, driving speed correction and acceleration adjustment, to ensure that the vehicle accurately completes the jump excitation according to the optimized path; when multiple UAVs are required to perform the test cooperatively, the master UAV coordinates and manages multiple UAVs through the time sequence one-control-multiple-machine function; the one-control-multiple-machine function of the UAV specifically refers to using UAV route planning to enable it to fly to a specified position according to a predetermined route, and assigning the route task to multiple UAVs, so that one UAV guides the excitation vehicle to move forward, and other UAVs locally photograph the bridge cable and continuously measure it subsequently; each UAV collects vehicle information according to the assigned task, the master UAV collects all collected data and sends unified path adjustment instructions, to ensure the consistency of data collection among multiple UAVs and the jump excitation path; during the path guiding process, the UAV collects the execution state information (such as vehicle impact force and jump timestamp) of the test vehicle in real time and uploads the data, and the path guiding device verifies the execution effect of the excitation path according to the feedback data, to provide complete data support for subsequent bridge cable vibration analysis.

[0112] Further, as shown in Figure 4 , a bridge geometric model is established, including:

[0113] Bridge structure geometric data is collected through a multi-modal perception network, and key parts of the bridge are positioned in three-dimensional space;

[0114] The bridge structure geometric data is integrated and compared with the bridge design parameters, and errors in the bridge structure geometric data are corrected;

[0115] The bridge geometric data is uniformly processed to generate a bridge geometric model containing the overall structure of the bridge and the arrangement characteristics of the bridge cable;

[0116] The bridge design parameters and the arrangement characteristics of the bridge cable are labeled in the bridge geometric model, providing geometric reference for the planning and performance analysis of the vehicle excitation path.

[0117] As a preferred embodiment of the above-mentioned embodiment, the bridge structure geometry data is collected by using the multi-modal perception device (such as laser radar, high-definition camera, panoramic camera) carried by the unmanned aerial vehicle, and the collected content includes key parameters such as bridge cable length, angle, anchor point position, support point distribution, bridge span and bridge tower height, and at the same time, high-precision three-dimensional spatial positioning is performed on the key parts of the bridge (such as the connection node and the cable clamp area) to form an initial geometry data set. The collected bridge geometry data is integrated and compared with the bridge design parameters (such as design drawings, structure models, etc.), the key point positions in the geometry data are calibrated through a feature point matching algorithm, and the geometry deviation generated in the collection process is corrected using an error analysis method to ensure the accuracy of the data. The corrected bridge geometry data is uniformly processed, combined with the data sources of the multi-modal perception network, and a complete bridge geometry model is generated. The bridge geometry model includes the spatial distribution information of the overall structure of the bridge and the cable arrangement characteristics, such as cable clamp spacing, tension gradient and cable angle distribution. In the generated bridge geometry model, the bridge design parameters and the key arrangement characteristics of the cable are labeled, including the starting point and ending point coordinates of the cable, the tension distribution area, the position of the key support point and the geometric relationship of the connection node. The purpose of the bridge geometry model characteristic labeling is to provide intuitive geometric reference for subsequent vehicle excitation path planning and cable performance analysis. The generated bridge geometry model is stored in a digital form and presented through a three-dimensional visualization tool for easy checking and adjustment by engineers. The bridge geometry model can be directly used for vehicle jump test path planning, cable vibration dynamic analysis and health assessment tasks.

[0118] Further, the dynamic characteristic parameters of the cable are extracted, including:

[0119] Obtain the cable vibration response data from the multi-modal perception network and preprocess it;

[0120] Based on the empirical mode decomposition method, the cable vibration response data is decomposed into multiple intrinsic mode components;

[0121] The dynamic characteristic parameters of the cable are extracted from the intrinsic mode components to represent the dynamic response characteristics of the cable;

[0122] The dynamic characteristic parameters are verified for consistency, and the parameter consistency between different intrinsic mode components and surface measurement points is analyzed.

[0123] As a preferred embodiment of the above embodiment, the bridge cable vibration response data is obtained from the multi-modal perception network, the data including displacement, acceleration and frequency characteristics of the bridge cable surface measurement points, 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, which is decomposed into multiple intrinsic mode components, each intrinsic mode component representing 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 intrinsic mode components, including but not limited to natural frequency: the main frequency of each modal component is identified through frequency spectrum analysis; modal shape: the spatial distribution characteristics of each modal component are analyzed; damping ratio: the energy dissipation characteristics of the bridge cable are calculated through the attenuation characteristics; vibration energy density: the energy contribution of each modal component is calculated to evaluate the vibration intensity of the bridge cable; the extracted dynamic characteristic parameters are verified for consistency, the parameter consistency between different intrinsic mode components and the spatial consistency of the dynamic parameters corresponding to different measurement points are analyzed, through the consistency analysis, the reliability and accuracy of the extracted parameters are ensured, and the bridge cable regions that may have abnormalities are marked; the extracted dynamic characteristic parameters are output in tabular or graphical form, providing data support for subsequent bridge cable performance analysis, decay area identification and health state evaluation, and according to the analysis results, the test scheme is adjusted to optimize the parameter extraction accuracy.

[0124] Embodiment two;

[0125] Based on the same inventive concept as the bridge cable vibration excitation method combined with the unmanned aerial vehicle and the jump test in the foregoing embodiment, the present application also provides a bridge cable vibration excitation system based on the combination of an unmanned aerial vehicle and a jump test, the system comprising:

[0126] An information perception module uses an unmanned aerial vehicle to carry a calibration device to construct a multi-modal perception network, identifies and calibrates bridge cable surface measurement points, and establishes a bridge geometric model;

[0127] A path planning module obtains the bridge cable tension distribution, and plans the vehicle excitation path of the jump test in combination with the bridge geometric model;

[0128] A vehicle guidance module guides the jump test vehicle through the vehicle excitation path, and synchronously records the excitation impact force and the excitation timestamp;

[0129] A parameter decomposition module establishes an empirical mode decomposition model, decomposes the multi-modal vibration data obtained from the multi-modal perception network, and extracts the dynamic characteristic parameters of the bridge cable;

[0130] A state evaluation module analyzes and identifies the performance decay area of the bridge cable based on the empirical mode decomposition model and the dynamic characteristic parameters, and evaluates the comprehensive health state of the bridge cable.

[0131] The adjustment system in the application can effectively realize a bridge cable vibration excitation method based on the combination of unmanned aerial vehicles and vehicle jumping tests, and can achieve the technical effects as described in the above embodiments, which will not be repeated here.

[0132] Further, the state evaluation module comprises:

[0133] The deviation identification unit compares the dynamic characteristic parameters with the design operation parameters, identifies the parameter deviation and abnormal change trend;

[0134] The coefficient calculation unit quantifies the parameter deviation to generate sensitivity coefficients based on the dynamic characteristic parameters by using a modal sensitivity analysis method, and locates the performance degradation area according to the sensitivity coefficients;

[0135] The performance evaluation unit calculates the vibration energy density distribution of the performance degradation area, and evaluates the local health state and carrying capacity of the bridge cable in the area;

[0136] The comprehensive evaluation unit calculates the comprehensive health index of the bridge cable based on the empirical modal model according to the local health state and the sensitivity coefficients, and classifies and evaluates the comprehensive health state.

[0137] Similarly, the above optimization scheme of the system can also correspondingly achieve the optimization effects of the method in Embodiment 1, which will not be repeated here.

[0138] Although the present application has been described in connection with specific features and embodiments thereof, it will be evident to those of ordinary skill in the art that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are to be regarded as illustrative only and the true scope of the application is to be indicated by the appended claims. Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A bridge cable vibration excitation method based on the combination of unmanned aerial vehicles and car jumping tests, characterized in that, The method comprises: constructing a multi-modal perception network using a drone carrying a calibration device, identifying and calibrating bridge cable surface measurement points, and establishing a bridge geometric model; obtaining a bridge cable tension distribution, combining the bridge geometric model to plan a vehicle excitation path for a jump test; guiding a jump test vehicle through the vehicle excitation path and synchronously recording excitation impact force and excitation timestamp; establishing an empirical mode model, decomposing multi-modal vibration data obtained from the multi-modal perception network, and extracting dynamic characteristic parameters of the bridge cable; based on the empirical mode model and the dynamic characteristic parameters, analyzing and identifying performance degradation areas of the bridge cable, and evaluating the comprehensive health status of the bridge cable; evaluating the comprehensive health status of the bridge cable, including: comparing the dynamic characteristic parameters with design operating parameters to identify parameter deviations and abnormal change trends; based on the dynamic characteristic parameters, using modal sensitivity analysis methods to quantify the parameter deviations to generate sensitivity coefficients, and positioning performance degradation areas according to the sensitivity coefficients; for the performance degradation areas, calculating vibration energy density distribution to evaluate the local health status and carrying capacity of the bridge cable in the area; based on the local health status and the sensitivity coefficients, calculating the comprehensive health index of the bridge cable based on the empirical mode model, and classifying the comprehensive health status; planning a vehicle excitation path for a jump test based on the bridge geometric model, including: based on the multi-modal perception network, collecting geometric data of the bridge structure to generate a bridge geometric model; based on the bridge geometric model, combining the bridge cable tension distribution to preliminarily analyze the dynamic response of the bridge cable and identify bridge cable blocks sensitive to vehicle excitation; based on the bridge cable blocks, setting jump test targets, using multi-objective optimization algorithms, combining the bridge geometric model and jump pad arrangement constraints to generate a vehicle excitation path; feasibility verification of the vehicle excitation path, combining bridge construction conditions and vehicle performance parameters to adjust the vehicle excitation path; based on the vehicle excitation path, sending execution instructions to the jump test vehicle through the path guiding device carried by the drone to guide the vehicle to complete the jump excitation according to the preset path; establishing a bridge geometric model, including: collecting bridge structure geometric data through the multi-modal perception network and positioning key parts of the bridge in three-dimensional space; integrating and comparing the bridge structure geometric data with bridge design parameters to correct errors in the bridge structure geometric data; unifying the bridge geometric data to generate the bridge geometric model containing the overall structure of the bridge and the arrangement characteristics of the bridge cable; labeling the bridge design parameters and the arrangement characteristics of the bridge cable in the bridge geometric model to provide geometric reference for planning and performance analysis of the vehicle excitation path. 2.The bridge cable vibration excitation method based on the combination of the UAV and the jump test according to claim 1, wherein, quantifying the parameter deviations to generate sensitivity coefficients, including: obtaining bridge cable design operating parameters and comparing them based on the dynamic characteristic parameters to construct a parameter variation matrix; through the parameter variation matrix, calculating the parameter deviations of each dynamic characteristic parameter to the bridge cable tension distribution to generate a parameter deviation set; grouping the parameter deviation set according to the bridge cable tension distribution, analyzing the contribution of local deviation to different tension areas, and generating a regional weight distribution; generating a sensitivity coefficient according to the regional weight distribution, the sensitivity coefficient quantifying the contribution of each dynamic parameter deviation to the performance degradation of the bridge cable. 3.The bridge cable vibration excitation method based on the combination of the UAV and the jump test according to claim 2, characterized in that, generating a regional weight distribution, including: dividing the bridge cable into multiple capture regions based on the bridge geometric model and the bridge cable tension distribution, the capture region division being based on tension gradient, cable length, and vibration modal characteristics; grouping the dynamic characteristic parameters in the parameter deviation set according to the capture regions, calculating the contribution value of dynamic parameter deviation in each sub-region to the overall performance of the bridge cable; generating a regional importance factor reflecting the performance change sensitivity in each capture region according to the contribution value of each capture region and the performance of the bridge cable; based on the regional importance factor, arranging the weight of each capture region to generate a regional weight distribution; during multiple jump test excitation tests, updating the regional weight distribution in combination with newly acquired bridge cable tension distribution and dynamic parameter deviation. 4.The bridge cable vibration excitation method based on the combination of UAV and jump test according to claim 1, wherein, sending execution instructions to the jump test vehicle through the path guiding device carried by the unmanned aerial vehicle, including: the unmanned aerial vehicle carries the path guiding device, receives the vehicle excitation path, and transmits it to the jump test vehicle; the unmanned aerial vehicle locates and tracks the current position of the jump test vehicle, obtains vehicle position information and driving state data, and compares them with the vehicle excitation path; based on the deviation information of the vehicle position information and the vehicle excitation path, the path guiding device generates path adjustment instructions; when multiple unmanned aerial vehicles need to be coordinated to execute, time series one-control-multiple-machine function is used to simultaneously manage information collection of multiple unmanned aerial vehicles. 5.The bridge cable vibration excitation method based on the combination of UAV and jump test according to claim 1, wherein, extracting dynamic characteristic parameters of the bridge cable, including: obtaining bridge vibration response data from the multi-modal perception network and preprocessing it; based on the empirical mode decomposition method, the bridge vibration response data is decomposed into multiple intrinsic mode components; extracting the dynamic characteristic parameters of the bridge cable from the intrinsic mode components to represent the dynamic response characteristics of the bridge cable; performing consistency verification on the dynamic characteristic parameters to analyze the parameter consistency between different intrinsic mode components and the surface measurement points.

6. A bridge cable vibration excitation system based on the combination of unmanned aerial vehicles and car jumping tests, characterized in that, using the bridge cable vibration excitation method based on the combination of unmanned aerial vehicles and jump tests as claimed in claim 1, the system includes: an information perception module using an unmanned aerial vehicle carrying a calibration device to construct a multi-modal perception network, identify and calibrate bridge surface measurement points, and establish a bridge geometric model; a path planning module obtaining a bridge cable tension distribution and planning a vehicle excitation path for jump tests in combination with the bridge geometric model; a vehicle guiding module guiding the jump test vehicle through the vehicle excitation path and simultaneously recording the excitation impact force and the excitation timestamp; a parameter decomposition module establishing an empirical mode model and decomposing multi-modal vibration data obtained from the multi-modal perception network to extract dynamic characteristic parameters of the bridge cable; The state evaluation module is used for identifying the performance degradation area of the bridge cable and evaluating the comprehensive health state of the bridge cable based on the empirical mode model and dynamic characteristic parameter analysis; The state evaluation module comprises: A deviation identification unit is configured to compare the dynamic characteristic parameters with the design operation parameters, identify parameter deviation and abnormal change trend; A coefficient calculation unit is configured to quantize the parameter deviation to generate sensitivity coefficients by using a modal sensitivity analysis method based on the dynamic characteristic parameters, and locate the performance degradation area according to the sensitivity coefficients; A performance evaluation unit is configured to calculate the vibration energy density distribution in the performance degradation area, and evaluate the local health state and carrying capacity of the bridge cable in the area; A comprehensive evaluation unit is configured to calculate the comprehensive health index of the bridge cable based on the empirical mode model according to the local health state and the sensitivity coefficients, and classify and evaluate the comprehensive health state.

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