Bridge gap measuring equipment and measuring method thereof

Abstract

The invention relates to the technical field of bridge detection, in particular to bridge crack measuring equipment and a measuring method. A bridge gap measuring device comprises a flight platform assembled on an unmanned aerial vehicle; the pod is mounted below the flying platform and comprises an equipment bin, and a positioning module, a sensor scheduling engine, a power supply module, an airborne edge computing host and a data analysis host are mounted in the equipment bin; the two electric holders are assembled at the bottom of the equipment bin; the camera is mounted on one of the electric holders and is used for acquiring an RGB (Red, Green and Blue) image; the multispectral sensor is mounted on the other electric holder and is used for acquiring multispectral data; and the laser radar is fixed at the bottom of the equipment bin and is used for collecting point cloud data.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge detection, in particular to bridge crack measuring equipment and a measuring method. Background Art

[0002] Measuring bridge joints, commonly referred to as expansion joints, cracks, or construction joints, is an important task used to assess the health, maintenance needs, and safety of a bridge. The measurement method depends on the type of joint, its location, size, and the accuracy required.

[0003] A Chinese patent document with publication number CN116294919A discloses a probe-type bridge crack measurement device and method. In this technical solution, a needle-shaped tube is inserted into the bridge crack by holding a handle. Two vertical detection rods are provided inside the needle-shaped tube. These two detection rods can be deployed inside the crack through a rotating mechanism. The rotating mechanism is composed of a circular slider, a rigid spring, a vertical moving column and a rectangular moving block. When the rectangular slider is pulled, the circular slider slides inside the detection tube and compresses the rigid spring, and then drives the rectangular moving block to move through the vertical moving column, so that the two vertical detection rods are deployed to both sides until their bottom ends are against the two sides of the crack. At this time, the moving distance of the rectangular slider can be read through the measuring scale on the outside of the detection tube, so as to calculate the distance between the bottom ends of the two vertical detection rods, that is, the width of the crack.

[0004] However, these devices suffer from the following technical drawbacks: First, they rely heavily on manual operation, making rapid inspection difficult at high altitudes, at the bottom of bridges, or in complex structural areas. This results in low efficiency and poses safety risks associated with high-altitude operations. Furthermore, they can only measure crack widths at a single point, failing to automate large-scale scanning. Second, the accuracy of test results is highly dependent on the operator's skill and experience, and human error can lead to errors. Furthermore, manual inspections at high altitudes or in hazardous environments, such as bridges, pose safety risks. Summary of the Invention

[0005] In order to overcome the above-mentioned defects in the prior art, a bridge gap measurement device and a measurement method thereof are provided.

[0006] The application is implemented by the following technical scheme: a bridge gap measuring device, comprising: a flight platform, mounted on a UAV; a pod, installed below the flight platform, the pod comprising: a device bin, in which a positioning module, a sensor scheduling engine, a power supply module, an airborne edge computing host and a data analysis host are installed; two electric gimbals, mounted at the bottom of the device bin; a camera, mounted on one of the electric gimbals, for collecting RGB images; a multispectral sensor, mounted on the other electric gimbal, for collecting multispectral data; a laser radar, fixed at the bottom of the device bin, for collecting point cloud data; the airborne edge computing host is used for data fusion of the collected bridge surface data; and the data analysis host is used for classification labeling of the crack data.

[0007] As a preferred embodiment of the application, the airborne edge computing host is pre-installed with a crack identification model and a real-time three-dimensional point cloud reconstruction algorithm.

[0008] As a preferred embodiment of the application, the data analysis host is internally provided with a crack classification module and a BIM integration engine; the crack classification module is configured to classify cracks according to crack morphology and risk level; and the BIM integration engine is configured to map crack data to a bridge BIM model and generate a crack evolution space-time heat map.

[0009] As a preferred embodiment of the application, the sensor scheduling engine is in electrical signal connection with the airborne edge computing host, and is used to control the start and stop of data collection and the switching of data collection modes of the camera, the laser radar and the multispectral sensor.

[0010] As a preferred embodiment of the application, a rotary connecting piece is arranged between the electric gimbal and the flight platform, and is used to adjust the shooting direction of the camera and the multispectral sensor.

[0011] A bridge gap measuring method, comprising the following steps:

[0012] S1, a data collection stage, the UAV flies along a preset path, and the camera, the laser radar, the multispectral sensor and the positioning module are started to collect bridge surface data by the sensor scheduling engine, and the bridge surface data is transmitted to the airborne edge computing host;

[0013] S2, a real-time processing stage: the airborne edge computing host performs image preprocessing, crack detection, geometric measurement and multi-modal data fusion on the bridge surface data collected in S1, transmits the data processing result to the data analysis host, generates crack classification labels and crack data corresponding to the crack classification labels through the crack classification module;

[0014] S3, a model labeling stage, the crack data is mapped to a BIM model through the data analysis host, and a crack evolution space-time heat map is generated through the BIM integration engine;

[0015] S4, a decision support stage, generating a detection report including a high-risk crack distribution map, a crack propagation trend analysis and a maintenance priority suggestion.

[0016] As a preferred embodiment of the present application, in step S1, the distance between the unmanned aerial vehicle and the bridge surface is dynamically adjusted according to the bridge surface characteristics.

[0017] As a preferred embodiment of the present application, in step S4, the detection report contains crack ID, spatial coordinates, length, maximum width, risk level and model anchor point information, supporting historical data comparison and expansion rate prediction.

[0018] As a preferred embodiment of the present application, the crack classification module evaluates the crack risk level based on crack width, expansion rate and location sensitivity index.

[0019] As a preferred embodiment of the present application, the priority of the maintenance suggestion is determined based on the crack risk level.

[0020] Compared with the prior art, the present application has the following beneficial effects:

[0021] A bridge gap measuring device is integrated into an unmanned aerial vehicle flight platform, which can realize all-around and multi-dimensional data acquisition of bridge gaps by integrating various sensors in a nacelle, improve measurement efficiency and accuracy, reduce the difficulty and risk of manual detection, and quickly obtain detailed information of the bridge surface.

[0022] Further, the on-board edge computing host preinstalls a crack identification model and a real-time three-dimensional point cloud reconstruction algorithm, which can quickly process and analyze the collected data at the device end, identify cracks in real time and perform three-dimensional reconstruction, greatly shortening the data processing time and improving the real-time performance of the measurement results.

[0023] Further, the data analysis host has a crack classification module and a BIM integrated engine built-in, which can classify and process crack data and map the crack data to a bridge BIM model to generate a crack evolution space-time heat map, providing more intuitive and accurate crack information for bridge maintenance personnel and facilitating the development of a reasonable maintenance plan.

[0024] Further, the sensor scheduling engine is connected to the on-board edge computing host by a telecommunication signal, which can realize accurate control of camera, laser radar and multispectral sensor data acquisition, flexibly adjust data acquisition start and stop and mode switching according to actual needs, and improve the pertinence and efficiency of data acquisition.

[0025] Further, the rotary connecting piece is arranged between the motorized gimbal and the flight platform, which can adjust the shooting direction of the camera and the multispectral sensor, so that the sensor can collect bridge surface data from different angles, improve the comprehensiveness and accuracy of data collection, and reduce the blind area.

[0026] The bridge crack measurement method based on the above measurement device realizes the automation and intelligentization of bridge crack measurement through data acquisition, real-time processing, model labeling and decision support, and can quickly generate detailed detection reports to provide a scientific basis for bridge maintenance.

[0027] Further, the distance between the unmanned aerial vehicle and the bridge surface is dynamically adjusted according to the bridge surface characteristics during data acquisition, which can ensure that the collected data has appropriate resolution and clarity, improve data quality, and avoid collision between the unmanned aerial vehicle and the bridge.

[0028] Further, the detection report contains crack ID, spatial coordinates, length, maximum width, risk level and model anchor point information, and supports historical data comparison and expansion rate prediction, providing comprehensive and detailed crack information for bridge maintenance personnel, facilitating tracking and evaluation of the development trend of the crack, and formulating a more reasonable maintenance plan.

[0029] Further, the crack classification module evaluates the crack risk level based on crack width, expansion rate and position sensitivity index, which can more scientifically and accurately evaluate the influence of the crack on the safety of the bridge structure, and provide a reliable basis for bridge maintenance decision.

[0030] Further, the priority of the repair recommendation is determined based on the crack risk level, which can enable the bridge maintenance personnel to prioritize cracks with high risk, reasonably allocate maintenance resources, and improve the efficiency and safety of bridge maintenance.

[0031] Other features and advantages of the present application will be disclosed in detail in the following specific embodiments and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0032] The present application will be further described below in conjunction with the drawings:

[0033] Figure 1 The structure of a bridge crack measurement device of the present application is shown in the figure.

[0034] Figure 2 The flowchart of a bridge crack measurement method of the present application is shown in the figure.

[0035] The reference signs are explained as follows:

[0036] Flight platform 1, pod 2, device compartment 201, motorized gimbal 202, camera 203, laser radar 204, multispectral sensor 205. DETAILED DESCRIPTION

[0037] The following is an explanation and description of the technical solutions of the embodiments of the present invention in conjunction with the drawings of the embodiments of the present invention. However, the following embodiments are only preferred embodiments of the present invention and are not exhaustive. Based on the embodiments in the implementation manner, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0038] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0039] like Figures 1 to 2 As shown, a bridge gap measurement device includes: a flying platform 1, which is assembled on a drone; a pod 2, which is installed below the flying platform 1, and the pod 2 includes: an equipment compartment 201, in which a positioning module, a sensor scheduling engine, a power supply module, an airborne edge computing host and a data analysis host are installed; two electric pan-tilt platforms 202, which are assembled at the bottom of the equipment compartment 201; a camera 203, which is mounted on one of the electric pan-tilt platforms 202 and is used to collect RGB images; a multispectral sensor 205, which is mounted on the other electric pan-tilt platform 202 and is used to collect multispectral data; a lidar 204, which is fixed to the bottom of the equipment compartment 201 and is used to collect point cloud data; the airborne edge computing host is used to perform data fusion on the collected bridge surface data; and the data analysis host is used to classify and label the crack data.

[0040] Equipment Installation and Debugging: First, install the flight platform 1 on the drone, ensuring a secure connection between the flight platform and the drone, ensuring it can properly receive the drone's flight commands and maintain stable flight. Next, install the pod 2 below the flight platform 1 and check its secure installation to prevent it from shaking or falling off during flight. Inside the equipment compartment 201 of pod 2, install the positioning module, sensor scheduling engine, power supply module, onboard edge computing host, and data analysis host in sequence. Electrical connections and debugging are performed to ensure proper communication between modules and stable power supply. Install two electric pan-tilt platforms 202 at the bottom of the equipment compartment 201, adjusting their positions and angles to ensure flexible rotation. Install the camera 203 on one of the electric pan-tilt platforms 202, the multispectral sensor 205 on the other, and the lidar 204 on the bottom of the equipment compartment 201. Calibrate and debug each sensor to ensure accurate data collection.

[0041] The airborne edge computing host is pre-installed with a crack identification model and a real-time three-dimensional point cloud reconstruction algorithm. Specifically, the crack identification model is based on deep learning technology, etc., and extracts and analyzes the features of the image data collected by the camera to identify the position and shape of the crack. The real-time three-dimensional point cloud reconstruction algorithm processes the point cloud data collected by the laser radar to construct a three-dimensional model of the bridge surface, thereby more intuitively displaying the geometric characteristics of the bridge crack.

[0042] The data analysis host is built-in with a crack classification module and a BIM integration engine; the crack classification module is configured to classify cracks according to their shape and risk level; and the BIM integration engine is configured to map crack data to a bridge BIM model and generate a crack evolution space-time heat map. Specifically, the crack classification module classifies the shape and risk level of the crack according to pre-set rules and algorithms, such as evaluating the width, direction, and other morphological characteristics of the crack and the degree of impact on the safety of the bridge structure. The BIM integration engine associates the classified crack data with the bridge BIM model and generates a crack evolution space-time heat map on the BIM model through color, etc., to visually display the distribution and risk level of the crack.

[0043] The sensor scheduling engine is in electrical signal connection with the airborne edge computing host, and is used to control the start and stop of data collection and the switching of data collection modes of the camera 203, the laser radar 204, and the multi-spectral sensor 205. Specifically, the sensor scheduling engine receives instructions from the airborne edge computing host through electrical signal interaction with the airborne edge computing host, and controls the opening and closing of the camera, laser radar, and multi-spectral sensor according to pre-set collection strategies or real-time requirements, and adjusts their collection parameters, such as the exposure time of the camera, the scanning frequency of the laser radar, etc.

[0044] A rotating connecting piece is provided between the motorized gimbal 202 and the flight platform 1, and the rotating connecting piece is used to adjust the shooting direction of the camera 203 and the multi-spectral sensor 205. Specifically, the rotating connecting piece allows the motorized gimbal to rotate relative to the flight platform, and when the unmanned aerial vehicle flies to different positions, the angle of the motorized gimbal is adjusted through the rotating connecting piece, thereby changing the shooting direction of the camera and the multi-spectral sensor, and ensuring that each area of the bridge surface can be covered.

[0045] As shown in Figure 2 , the specific working principle and use method of the bridge crack measurement equipment are as follows:

[0046] S1, in the data collection stage, the unmanned aerial vehicle flies along the pre-set path, and the camera, laser radar, multi-spectral sensor, and positioning module are started to collect bridge surface data through the sensor scheduling engine, and the bridge surface data is transmitted to the airborne edge computing host.

[0047] The camera 203 is preferably a 4K optical zoom camera for collecting RGB images, LiDAR point cloud data collected by the laser radar 204, and spectral data detected by the multi-spectral sensor 205, and the positioning module is preferably an RTK positioning device for collecting position data of the cracks,

[0048] Further, during the data collection process, the distance between the unmanned aerial vehicle and the bridge surface will be dynamically adjusted according to the characteristics of the bridge surface. Generally, when the unmanned aerial vehicle performs regular scanning, its cruising distance is set to 2-3m from the bridge surface. Within this distance range, the unmanned aerial vehicle can fly safely, and the sensor can collect comprehensive bridge surface data. However, when the unmanned aerial vehicle flies to a crack-dense area, in order to obtain clearer and more detailed crack information and ensure image quality, the unmanned aerial vehicle will adjust to a distance of 0.5m from the bridge surface for scanning. At the same time, in order to ensure flight safety and data collection stability, the flight speed of the unmanned aerial vehicle will be limited to ≤3m / s at a scanning distance of 0.5m. Through this dynamic distance adjustment method, the data collection effect can be flexibly optimized according to the characteristics of different areas of the bridge, providing a high-quality data basis for subsequent crack analysis and processing.

[0049] S2, real-time processing stage: the airborne edge computing host performs image preprocessing, crack detection, geometric measurement, and multi-modal data fusion on the bridge surface data collected in S1, transmits the data processing results to the data analysis host, and generates crack classification labels and crack data corresponding to the crack classification labels through the crack classification module.

[0050] The following is the specific algorithm for processing bridge surface data:

[0051] 1. The image preprocessing is preferably adaptive image preprocessing, and the specific algorithm is as follows:

[0052] def preprocess(img):# 1. Illumination compensation

[0053] lab=cv2.cvtColor(img,cv2.COLOR_BGR2LAB)

[0054] l,a,b=cv2.split(lab)

[0055] clahe=cv2.createCLAHE(clipLimit=3.0,tileGridSize=(8,8))

[0056] l_clahe=clahe.apply(l)# Enhance low-contrast cracks

[0057] #2. Inhibit concrete surface texture

[0058] denoised = cv2.bilateralFilter(l_clahe, d=9, sigmaColor=75, sigmaSpace=75)

[0059] # 3. Edge Enhancement

[0060] sobel_x = cv2.Sobel(denoised, cv2.CV_64F, 1, 0, ksize=3)

[0061] sobel_y = cv2.Sobel(denoised, cv2.CV_64F, 0, 1, ksize=3)

[0062] edge_mag = np.sqrt(sobel_x**2 + sobel_y**2)

[0063] return edge_mag.astype(np.uint8)

[0064] 2. Crack Detection is preferably based on improved YOLOv7 crack detection, and the specific algorithm is:

[0065] class CrackAttention(nn.Module):

[0066] def __init__(self):

[0067] super().__init__()

[0068] self.conv1 = nn.Conv2d(256, 64, kernel_size=3, padding=1)

[0069] self.attn = nn.Sequential(

[0070] nn.AdaptiveAvgPool2d(1),

[0071] nn.Conv2d(64, 16, kernel_size=1),

[0072] nn.ReLU(),

[0073] nn.Conv2d(16, 64, kernel_size=1),

[0074] nn.Sigmoid() # Channel attention focuses on crack features

[0075] defforward(self, x):

[0076] feat = self.conv1(x)

[0077] attn_map = self.attn(feat)

[0078] return feat * attn_map # enhance crack region response

[0079] 3. The geometric measurement is preferably a pixel-level crack segmentation measurement, and the specific algorithm is:

[0080] def crack_segmentation(detection_roi):

[0081] # Input: candidate region detected by YOLO (256x256 patch)

[0082] # Network structure:

[0083] encoder = MobileNetV3_Small(pretrained=True) # lightweight encoder

[0084] decoder = nn.Sequential(

[0085] UpConv(576, 256), # up-sampling module

[0086] CrackAttention(),

[0087] nn.Conv2d(256, 1, kernel_size=1) # output single-channel mask )

[0089] # Hybrid loss function

[0090] loss = α * BCEWithLogitsLoss() + β * DiceLoss() + γ * EdgeLoss()

[0091] # Where EdgeLoss enhances crack boundary learning

[0092] Sub-pixel-level geometric measurement, and the specific sub-pixel-level geometric measurement is:

[0093] def measure_width(mask, scale=0.1): # scale: mm / pixel

[0094] # 1. Skeleton extraction of center line

[0095] skeleton=skeletonize(mask)

[0096] #2. Measure along the center line normal

[0097] widths=[]

[0098] fory,xinnp.argwhere(skeleton):

[0099] normal_angle=get_normal_angle(mask,(x,y))#Find normal direction based on gradient direction

[0100] profile=sample_orthogonal_line(mask,(x,y),normal_angle,length=20)

[0101] #3. Sub-pixel edge positioning

[0102] left_edge=find_subpixel_edge(profile[:10])

[0103] right_edge=find_subpixel_edge(profile[10:])

[0104] widths.append(abs(left_edge-right_edge)*scale

[0105] returnnp.median(widths)#Take the median as the crack width;

[0106] 4. The specific algorithm for multimodal data fusion is:

[0107] deffuse_rgb_lidar(rgb_patch,lidar_points):

[0108] #1. Projecting the point cloud to the image plane

[0109] proj_points=project_lidar_to_camera(lidar_points,calib_matrix)

[0110] #2. Constructing a depth-texture feature map

[0111] depth_map=generate_depth_map(proj_points,rgb_patch.shape)

[0112] texture_map = sobel(rgb_patch) # texture feature

[0113] # 3. Decision level fusion

[0114] if np.mean(depth_map[mask_area]) > 5.0: # structural crack if depth > 5mm

[0115] crack_type = "STRUCTURAL"

[0116] else:

[0117] crack_type = "SURFACE" # surface crack

[0118] 5. Generate classification label and crack data:

[0119] Danger rating criteria implementation as follows:

[0120] I low risk: Indicator Threshold range Typical characteristics Treatment recommendation Width <0.2 mm Hairline crack Review after 12 months Rate of expansion <0.02 mm / month Stable and no development Risk value <15 Away from the key load-bearing area Case Micro-cracks on the surface of the deck pavement layer No rust / leakage characteristics

[0121] II moderate risk: Indicator Threshold range Typical characteristics Treatment recommendation Width 0.2-0.5mm Continuous linear crack Review after 6 months + surface sealing Rate of expansion 0.02-0.1mm / month Slow expansion Risk value 15~35 Located in the non-stress area of the web Case Vertical crack in the box girder web Local slight leakage

[0122] III high risk: Indicator Threshold range Typical characteristics Treatment recommendation Width 0.5-2.0mm Through crack / branch development GROUTING reinforcement within 3 months Rate of expansion 0.1-0.2mm / month Accelerated expansion Risk value 35~70 Pier pile cap / pretension anchorage area Case Pier ring crack + rust 650nm / 1550nm ratio>2.0

[0123] IV critical: Indicator Threshold range Typical characteristics Treatment recommendation Width >2.0mm Network cracking / structure penetration 24-hour traffic control Rate of expansion >0.2mm / month Rapid expansion Emergency support + structure reinforcement Risk value >70 Main beam midspan / pier beam joint Case Main beam bottom plate longitudinal crack With concrete spalling + exposed reinforcement

[0124] When the crack is located in a special sensitive position such as the cable anchorage area, the danger rating is automatically upgraded by one level.

[0125] Specific algorithm as follows:

[0126] BIM annotation code:

[0127] def annotate_bim(model, crack_data):

[0128] bim_model = ifcopenshell.open(model)

[0129] element = bim_model.by_guid(crack_data['element_id'])

[0130] # create crack entity and associate attributes

[0131] crack_entity = bim_model.create_entity('IFC CRACK')

[0132] crack_entity.HasProperties=[

[0133] create_property('Length', crack_data['length']),

[0134] create_property('Width', crack_data['max_width']),

[0135] create_property('RiskLevel', crack_data['risk']) ]

[0137] # Spatial positioning

[0138] placement = bim_model.create_entity('IFC LOCALPLACEMENT',

[0139] crack_data['position'])

[0140] crack_entity.ObjectPlacement = placement

[0141] S3, model labeling stage, crack data is mapped to BIM model through data analysis host, and crack evolution space-time heat map is generated through BIM integration engine;

[0142] The specific algorithm is as follows:

[0143] {

[0144] "crack_id": "B2-P3-20240604-001",

[0145] "position": {"x":23.45,"y":67.89,"z":-5.32},

[0146] "length": 152.3,

[0147] "max_width": 0.78,

[0148] "risk_level": "Ⅱ",

[0149] "model_anchor": "Girder_Section2"

[0150] };

[0151] S4, decision support stage, generates an inspection report, which includes a high-risk crack distribution map, crack expansion trend analysis and repair priority recommendations.

[0152] The inspection report includes the crack ID, spatial coordinates, length, maximum width, risk level, and model anchor information, supporting historical data comparison and growth rate prediction. The crack classification module assesses the crack risk level based on crack width, growth rate, and location sensitivity. Repair recommendations are prioritized based on the crack risk level.

[0153] The following is an example of a high-risk crack distribution map:

[0154] #BridgeCrackRiskAssessmentReport

[0155] ##Top 3 high-risk cracks:

[0156] 1. Crack ID: B2-P3-001;

[0157] Location: northwest of pier 3#;

[0158] Maximum width: 1.85mm;

[0159] Risk level: IV;

[0160] Maximum width: 0.92mm;

[0161] Main causes: steel bar corrosion + load fatigue;

[0162] 2. Crack ID: G5-S2-012;

[0163] Location: 3# main span box girder web;

[0164] Maximum width: 0.92mm;

[0165] Risk level: Level III;

[0166] Main causes: water seepage and freeze-thaw;

[0167] ##Repair priority recommendations:

[0168] 1. Emergency treatment: B2-P3-001 requires grouting within 72 hours

[0169] 2. Repair within 1 month: G5-S2-012;

[0170] The steps for analyzing the crack growth trend by comparing historical data are as follows:

[0171] graph LR

[0172] A[Current detection data] --> B[Call history database]

[0173] B-->C{Same location crack exist}

[0174] C -->|Yes| D[Calculate width change Δw / time Δt]

[0175] C -->|No| E[Mark as new crack]

[0176] D --> F[Exclude temperature deformation effect]

[0177] F --> G[Generate rate curve];

[0178] The history data comparison algorithm is:

[0179] def track_crack_evolution(crack_id):

[0180] # Extract all historical records of this crack

[0181] history = query_database(f"SELECT * FROM cracks WHERE id='{crack_id}'")

[0182] # Calculate the average monthly expansion rate

[0183] growth_rate = (history[-1].width - history[0].width) / len(history) * 30

[0184] # Predict the critical time

[0185] critical_time = (2.0 - history[-1].width) / growth_rate # 2mm is the critical threshold

[0186] return f"Predict {critical_time:.1f} days later to reach dangerous width".

[0187] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Those skilled in the art should understand that the present application includes but is not limited to the contents described in the above specific embodiments and the accompanying drawings. Any modification that does not deviate from the functional and structural principles of the present application will be included in the scope of the claims.

Claims

1. A bridge gap measurement device, characterized in that: include: A flight platform (1), mounted on a UAV; A pod (2) is installed below the flying platform (1), and the pod (2) comprises: an equipment compartment (201), wherein the equipment compartment (201) is equipped with a positioning module, a sensor scheduling engine, a power supply module, an airborne edge computing host, and a data analysis host; two electric pan-tilt platforms (202) are assembled at the bottom of the equipment compartment (201); a camera (203) is installed on one of the electric pan-tilt platforms (202) and is used to collect RGB images; a multispectral sensor (205) is installed on the other electric pan-tilt platform (202) and is used to collect multispectral data; a laser radar (204) is fixed to the bottom of the equipment compartment (201) and is used to collect point cloud data; the airborne edge computing host is used to perform data fusion on the collected bridge surface data; and the data analysis host is used to classify and label the crack data.

2. The bridge gap measurement device according to claim 1, characterized in that: The airborne edge computing host is pre-installed with a crack recognition model and a real-time three-dimensional point cloud reconstruction algorithm.

3. The bridge gap measurement device according to claim 1, characterized in that: The data analysis host has a built-in crack classification module and a BIM integration engine; the crack classification module is configured to classify according to crack morphology and risk level; the BIM integration engine is configured to map crack data to a bridge BIM model and generate a spatiotemporal thermal map of crack evolution.

4. The bridge gap measurement device according to claim 1, characterized in that: The sensor scheduling engine is connected to the airborne edge computing host via electrical signals and is used to control the start and stop of data acquisition and the switching of data acquisition modes of the camera (203), the laser radar (204) and the multispectral sensor (205).

5. The bridge gap measurement device according to claim 1, characterized in that: A rotating connection piece is provided between the electric pan-tilt platform (202) and the flying platform (1), and the rotating connection piece is used to adjust the shooting direction of the camera (203) and the multispectral sensor (205).

6. A bridge gap measurement method, based on the bridge gap measurement device according to any one of claims 1 to 5, characterized in that: The following steps are involved: In the data collection phase, the drone flies along a preset path and uses the sensor scheduling engine to activate the camera, lidar, multispectral sensor, and positioning module to collect bridge surface data, which is then transmitted to the onboard edge computing host. S2, real-time processing stage: The airborne edge computing host performs image preprocessing, crack detection, geometric measurement, and multimodal data fusion on the bridge surface data collected by S1; the data processing results are transmitted to the data analysis host, and the crack classification module generates crack classification labels and crack data corresponding to the crack classification labels; S3, model annotation stage, maps the crack data to the bridge BIM model through the data analysis host, and generates a spatiotemporal heat map of crack evolution through the BIM integration engine; S4, decision support stage, generates a test report including a high-risk crack distribution map, crack expansion trend analysis, and repair priority recommendations.

7. A bridge gap measurement method according to claim 6, characterized in that: In step S1, the distance between the UAV and the bridge surface is dynamically adjusted according to the surface characteristics of the bridge.

8. The bridge gap measurement method according to claim 6, characterized in that: In step S4, the detection report includes crack ID, spatial coordinates, length, maximum width, risk level and model anchor point information, supporting historical data comparison and expansion rate prediction.

9. The bridge gap measurement method according to claim 6, characterized in that: The crack classification module assesses the crack risk level based on crack width, expansion rate and location sensitivity indicators.

10. The bridge gap measurement method according to claim 9, characterized in that: The repair priority recommendations are determined based on the crack risk level.

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