246 results about "Bridge inspection" patented technology

The invention relates to the technical field of bridge detection, in particular to a bridge defect automatic identification and inspection method based on deep learning, which comprises the following steps: S1, multi-modal data acquisition: acquiring multi-modal data of the surface and internal structure of a bridge; s2, multi-modal data preprocessing: generating a space-time aligned enhanced data set; s3, feature fusion and extraction: generating a fusion feature spectrum; s4, adaptively optimizing the characteristic spectrum: adjusting the connection weight between network layers according to the real-time environment parameters; s5, defect positioning and type identification: positioning space coordinates of defects and synchronously outputting probability distribution of defect types; and S6, defect analysis and report generation: generating an inspection report including defect evolution trend analysis based on the result of defect positioning and type identification. According to the invention, the bridge inspection efficiency is improved, the labor cost is reduced, and the intelligent upgrading of the bridge management field is further promoted.

The invention relates to the technical field of real-time image superposition, in particular to a bridge inspection method and system based on AR, and the method comprises the following steps: shooting a bridge on site, capturing a real-time image of a structure, and recording vibration frequency, displacement change and stress data by using acceleration, displacement and strain sensors; and synchronously integrating the structure real-time image, the vibration frequency, the displacement change and the stress data, and naming the data as multi-dimensional dynamic monitoring data. According to the method, the vibration frequency, the displacement change and the stress data are integrated in real time, so that the real-time performance and the analysis depth of the multi-dimensional dynamic monitoring data are remarkably improved, the high fusion of a real-time image and a three-dimensional model is realized, the accuracy of data visualization is enhanced, an abnormal region is directly visible through the change of colors and textures, and the accuracy of data visualization is improved. According to the method, the structure safety monitoring intuitiveness and interactivity are improved, in addition, through time sequence analysis, structure changes under different environment conditions are accurately simulated, and prospective risk prediction is provided.

The invention belongs to the technical field of unmanned aerial vehicle bridge detection, and particularly relates to an unmanned aerial vehicle-based integrated bridge detection system, which comprises an unmanned aerial vehicle, a detection module, a cleaning module and a comprehensive information processing control module, the unmanned aerial vehicle mounted measurement module is used for collecting boundary dimension data and surface image data of a bridge, and the unmanned aerial vehicle mounted cleaning module is used for cleaning the surface of the bridge; the comprehensive information processing control module is used for establishing a bridge coordinate model, planning an inspection route, detecting and marking defects and generating a detection report; the cleaning module comprises a ground high-pressure air source and a cleaning pipe, the unmanned aerial vehicle mounts the cleaning pipe to pass along the surface of the bridge, and surface image data are collected after the surface of the bridge is cleaned; according to the invention, the multiple unmanned aerial vehicles cooperate to inspect the bridge, dirt on the surface of the bridge is cleaned before or during inspection, and the accurate bridge detection effect is ensured.

The invention discloses a bridge image crack identification method and system based on a deep neural network. The method comprises the following steps: acquiring a bridge B-Scan image through ground penetrating radar equipment; performing preprocessing such as normalization, direct wave weakening and automatic gain enhancement on the image; features are extracted on the basis of an improved OfficientDeet-D3 network, a MoHAttention module is introduced into a backbone network to enhance weak crack features, a BiFPN structure is optimized through deep separable convolution in the feature fusion process to improve the detection capacity of a small-scale crack target, and crack position frames, types and confidence information are output; the system comprises a data acquisition module, a preprocessing module, a neural network detection module and an output display module. According to the method, high-precision and high-robustness identification of the internal hidden crack can be realized, and the method is suitable for rapid detection and intelligent monitoring application in actual bridge inspection engineering.

The invention discloses an unmanned aerial vehicle self-adaptive inspection method based on multi-layer characterization learning, which comprises two stages of automatic coarse inspection and self-adaptive fine inspection, and learns the space structure, component characteristics and disease characteristics of a bridge layer by layer. In the automatic rough detection stage, in combination with a spatial clustering post-processing method and a point cloud semantic segmentation network, instance attributes of bridge components are quickly learned. Then, the point cloud space is compressed and converted by using a simulation view field model and a dimension reduction technology, and the unmanned aerial vehicle is guided to carry out efficient and comprehensive space patrol in plane geometry. In the adaptive fine detection stage, a space-channel two-dimensional optimized hybrid convolution structure Light-PVIT is utilized to learn and extract disease features in the automatic coarse detection stage as prior information. The information guides the unmanned aerial vehicle to carry out fine detection on a disease area in a small field of view. According to the unmanned aerial vehicle self-adaptive inspection method, the bridge inspection efficiency and precision are remarkably improved, and reliable technical support is provided for bridge maintenance work.

The invention relates to the technical field of unmanned aerial vehicles and bridge inspection, and discloses a bridge support inspection method based on an unmanned aerial vehicle. The method comprises the following steps: importing a bridge three-dimensional point cloud model and a reference slice elevation, and extracting a bridge pier contour; calculating an acquisition interval according to the load parameters, and determining a bridge pier center line as an inspection baseline; buffer inspection waypoints are generated on the two sides of the center line, and the nearest waypoint is selected as an inspection starting point; generating an environment map comprising the bridge piers and the surrounding preset range of the bridge piers; and planning a path by adopting an A-Star algorithm, and carrying out smooth processing through a B spline algorithm to generate an inspection path. Various technical means are fused, an efficient, safe and accurate bridge inspection solution is provided, the manual operation risk and cost are reduced, the inspection efficiency is improved, the flight stability is optimized, and technical support is provided for future intelligent bridge inspection.

The invention provides a hollow slab bridge disease time sequence prediction method and system based on machine learning, and is applied to the technical field of bridge disease analysis and prediction.The method comprises the steps that hollow slab bridge detection data is acquired, and an original data set is constructed according to the disease data; performing structured preprocessing on the original data; feature engineering is carried out on the structured data, random forest screening is utilized, feature correlation analysis is carried out, and a low-redundancy high-correlation feature set is obtained; dividing the bridge detection data corresponding to the low-redundancy high-correlation features into training samples according to a time sliding window, and constructing a time sequence sample set; and carrying out model training and optimization, training a time sequence sample, and predicting continuous values of future diseases. According to the method, the accuracy, precision and stability of bridge disease prediction are improved through data acquisition, preprocessing, feature optimization, model establishment and output of the dynamic change prediction result of the disease continuous index, the efficiency is higher, and the method has a good application prospect.

The invention discloses an in-service concrete beam bridge dismantling process structure safety dynamic evaluation method, which comprises the following steps: installing a distributed optical fiber sensor and a high-precision series camera on a beam body, measuring beam body stress and temperature data, transmitting the data to a monitoring system, calculating beam body displacement monitoring image data, and transmitting the data to the monitoring system; based on bridge design data, bridge inspection data and static and dynamic load test data, establishing a corrected bridge disassembly finite element model; and building a bridge disassembly process forward and reverse proxy model based on a deep learning algorithm, and constructing a bridge disassembly process structure safety dynamic evaluation system. The data measured by the distributed optical fiber sensor and the high-precision series camera is real-time global, a large amount of data can be conveniently obtained, the forward and reverse models can be better dynamically corrected on the basis of the global data monitored in real time, and the safety state of the bridge dismantling structure can be dynamically evaluated in real time.

The invention provides a bridge inspection method, device and system based on an unmanned aerial vehicle, and the method, device and system are applied to a GNSS-free environment, real-time point cloud data of a bridge and a surrounding environment are obtained through an end-side unmanned aerial vehicle, and a three-dimensional point cloud model of the bridge and the surrounding environment is constructed in combination with the real-time pose information of the end-side unmanned aerial vehicle; based on the three-dimensional point cloud model, in combination with the real-time pose information of the end-side unmanned aerial vehicle, an obstacle avoidance path of the end-side unmanned aerial vehicle is planned, and autonomous cruise is completed; in the autonomous cruise process, acquiring a bridge surface image through an end-side unmanned aerial vehicle, detecting bridge damage in the bridge surface image in real time by using a pre-trained target detection neural network model, and generating a damage detection result; and through a side control terminal, mapping a damage detection result to a world coordinate system of the three-dimensional point cloud model, and generating a bridge three-dimensional digital twin space-time model with damage marks. According to the invention, the whole process of unmanned aerial vehicle bridge inspection, bridge structure three-dimensional modeling and bridge surface damage detection can be integrally realized in a GNSS-free environment.

The invention discloses a universal elbow winding path planning method, which realizes efficient winding design of complex elbows through multiple steps of discretizing the elbows, initializing path points, optimizing path lengths, checking bridges and the like. The method does not depend on a geodesic equation, adopts a'start point-end point 'two-point method to be combined with an iterative optimization algorithm, remarkably reduces calculation complexity, ensures path stability and adaptability, and can be widely applied to the field of composite material pipeline manufacturing.

The invention provides bridge pier surface defect detection equipment and method, and relates to the technical field of bridge pier detection.The equipment comprises a detection mechanism and a hoisting mechanism, the detection mechanism comprises a surrounding device and a first visual collection device, the surrounding device comprises two symmetrically-arranged arc-shaped arm frames, and the arc-shaped arm frames are unfolded or folded through a driving assembly; the periphery of the pier is surrounded; the plurality of first visual acquisition devices are uniformly arranged above the arc-shaped arm frame and are used for acquiring surface images of the bridge pier; the hoisting mechanism comprises a hanging bracket, a lifting assembly, a supporting assembly and a swinging assembly, the supporting assembly is fixedly connected with a maintenance platform of the bridge inspection vehicle, the swinging assembly drives the hanging bracket to horizontally swing, and the lifting assembly is used for adjusting the vertical position of the inspection mechanism; the equipment can efficiently, flexibly and stably detect the surface defects of the bridge pier by cooperating with the bridge inspection vehicle and combining the encircling device, the visual acquisition device and the like, is suitable for detecting the bridge pier under different environmental conditions, and overcomes the limitation of a traditional method.

The utility model provides an underwater robot for bridge detection, which relates to the technical field of bridge detection, and comprises a robot main body, a floating component for providing floating force is arranged at the upper part of the robot main body; the propeller set comprises two side propellers and two tail propellers, the two side propellers are located on the front face of the robot body, and the two tail propellers are located at the same end of the buoyancy assembly and symmetrically arranged front and back; and the two symmetrical walking mechanisms are arranged on the robot main body through mounting structural members. The beneficial effects of the utility model are that through the reasonable layout of the propeller group and the cooperative work of the propellers at the two sides and the tail part, accurate water surface positioning and movement are realized, and the robot is ensured to smoothly approach and fit a pile foundation from the water surface; the posture of the whole machine does not need to be adjusted when the pile foundation is close to the pile foundation, and the pile foundation can be directly adsorbed and attached through the propellers on the two sides; a tensioning mechanism in the walking mechanism has self-adaptability, and an elastic support is matched with a connecting frame connected in a sliding mode.

The utility model discloses a bridge inspection hand-held terminal, and relates to the technical field of mobile terminal equipment. A distance measuring device is arranged at the bottom of the inspection terminal, a shell is arranged at the bottom of the inspection terminal, a driving assembly is arranged in the shell, an installation groove is formed in one side of an inner cavity of the shell, a limiting assembly is arranged in the installation groove, a movable through groove is formed in the bottom of the shell, and a fixing block is arranged on the driving assembly. An elastic belt is arranged at the bottom of the fixing block and located in the moving through groove, a handle is arranged at the bottom of the shell, and the bottom end of the elastic belt is fixedly connected with the handle. The anti-falling assembly is bound to the wrist of an inspector, the elastic band is controlled to move through the driving assembly, the hand of the inspector is fixed through the elastic band and the handle, and therefore the problem that the device slides on the ground due to fatigue and is damaged is solved, the device can be clamped on a belt through the buckle, and the device is convenient to use. And the portability of the device is improved.

The invention discloses a navigation-free autonomous flight bridge bottom inspection control method and unmanned aerial vehicle-mounted control equipment, and the method comprises the steps: responding to the obtained three-dimensional point cloud data of a to-be-inspected bridge, planning and selecting a bridge bottom inspection path through the three-dimensional point cloud data, and enabling the starting point of the bridge bottom inspection path to start from the outside of the bridge; and generating a bridge bottom inspection route based on the bridge bottom inspection path and executing inspection, wherein the inspection comprises bridge bottom navigation-free flight control, autonomous obstacle avoidance, roof-imitating flight and inspection waypoint image acquisition. After the unmanned aerial vehicle flies out of the bridge, when the electric quantity of the unmanned aerial vehicle is too low or the unmanned aerial vehicle is manually triggered to return, the return flight route of the unmanned aerial vehicle is automatically called to execute a return flight task during return flight. The method disclosed by the invention can be efficiently operated on light embedded equipment, so that the unmanned aerial vehicle can realize real-time perception and real-time flight control on the bridge in the flight process, a more efficient and safer bridge bottom inspection flight control solution is provided, and the development of the unmanned aerial vehicle in the bridge inspection field is promoted.

The invention relates to the technical field of bridge detection, in particular to a bridge stay cable appearance detection device which comprises two movable shells which are symmetrically distributed, arc-shaped grooves are formed in the sides, close to each other, of the movable shells, and walking wheels which are symmetrically distributed are arranged in the arc-shaped grooves. A plurality of pressure rods distributed circumferentially penetrate through the inner wall of the movable shell, the ends, located in the movable shell, of the pressure rods are connected with the inner wall of the movable shell through first elastic pieces, a supporting plate is fixed to the interior of the movable shell, a plurality of spraying barrels distributed circumferentially are fixed to the side wall of the supporting plate, and the spraying barrels communicate with spraying pipes. The spray pipe penetrates through the inner wall of the mobile shell; the pressure rod is driven by the moving shell to move outside the stay cable, when the skin of the stay cable cracks, the extrusion degree of the stay cable on the pressure rod can be changed, at the moment, paint in the spraying barrel can be automatically discharged from the spraying pipe, and the stay cable can be protected in time.

The invention relates to the technical field of intelligent control, in particular to an intelligent bridge inspection vehicle laser radar abnormal state monitoring method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining point cloud data of a target bridge during operation of target equipment; based on processing of the point cloud data, acquiring environment feature information in a detection area of the target equipment; according to the environment characteristic information and the motion state information of the target equipment, carrying out abnormal state discrimination; and if the environment feature information and the motion state information meet a preset abnormal state constraint condition, generating and outputting corresponding abnormal state early warning information. By adopting the method, the running state of the intelligent bridge inspection vehicle can be detected, and the safety of bridge inspection work is improved.

The invention discloses an erecting method for a bridge suspension frame construction platform. The erecting method comprises the following steps that construction preparation is conducted; bridge core-pulling drilling: drilling the measured and marked point positions of the bridge surface of the arch bridge; an upper support is installed, a layer of frame pipe is installed on the bridge floor of the arch bridge in the longitudinal drilling direction, a vertical suspension pipe is installed on the bridge floor of the arch bridge in the longitudinal drilling direction, and the vertical suspension pipe and the layer of frame pipe are connected and locked through a fastener. The invention provides a method for erecting a bridge suspension frame construction platform to solve the problems that a traditional construction support is inconvenient to erect due to underbridge navigation requirements during underbridge maintenance and reinforcement construction operation of a two-way curved arch bridge, and meanwhile certain limitation exists during construction operation by using a bridge inspection vehicle. The suspension frame construction platform meeting the construction requirements is obtained by assembling and splicing the fasteners and pin-connecting the pipe fittings in manners of mounting the frame pipe and the suspension pipe, so that the construction cost is reduced, and the efficiency of construction operation is improved at the same time.

The invention belongs to the field of bridge detection and evaluation, and discloses a bridge line shape detection and damage prediction method based on a smart phone and a hybrid neural network, and the method comprises the steps: obtaining a bridge image through the smart phone by employing a distributed vision method, and obtaining vertical acceleration response data and environment temperature when a standard vehicle passes through a bridge through a mobile phone sensor; a YOLOv5s-CECA target detection model is used to obtain the pixel coordinates of the feature points; taking gyroscope attitude parameters during shooting as initial external parameters, converting positioning feature point pixel coordinates into a world coordinate system in combination with a pre-calibrated mobile phone internal parameter matrix, optimizing feature point coordinates by minimizing a re-projection error, drawing an actual bridge line shape, and obtaining a target line shape. And inputting the obtained bridge data into the trained AFTCNN-LSTM model to obtain a damage degree prediction result. The method can be rapidly deployed in a complex engineering field environment, improves the efficiency of bridge line shape detection and damage prediction, and reduces the bridge detection and evaluation cost.

The invention relates to the technical field of bridge detection, and discloses a rail type bridge inspection robot which comprises an inspection unit, a jacking positioning unit and an extrusion positioning unit. When a robot needs to be positioned, a jacking groove in a combined groove drives a push plate to move towards the inspection track, at the moment, a T-shaped cover at the tail end of the push plate drives a horizontal rod to move towards the center, then the horizontal rod and the side wall of the inspection track are extruded, positioning operation is conducted, a swing arm on the horizontal rod drives a vertical rod to slide downwards, and the vertical rod is driven to move downwards. When the robot moves to a certain degree, the two vertical rods can extrude the two sides of the inspection track, positioning in the vertical direction is finally completed, positioning is more stable, and the robot is more convenient to use. Through fixing and positioning operation in multiple directions, it is guaranteed that the robot can stably resist crosswind, and the use safety is higher.

The invention discloses a bridge inspection system and method, the bridge inspection system comprises a control module and an execution module, and the control module is wirelessly connected with the execution module; the control module comprises an information acquisition unit, a crack detection unit, a crack trend analysis unit, a crack width calculation unit, a crack depth calculation unit and an execution control unit. And the width and depth of the crack are automatically calculated according to the identification result, and the trend of the crack is analyzed, so that automatic detection of the bridge pavement crack is realized, the data precision is high, the detection efficiency is high, professional personnel can judge the safety of a bridge according to the data, and a targeted decision is made according to the judgment result.

The invention discloses a magnetic attraction type self-adaptive bridge inspection robot, and relates to the technical field of bridge engineering inspection, the magnetic attraction type self-adaptive bridge inspection robot comprises a frame body and a baffle plate, a detection assembly used for detecting a bridge to be detected is fixed on the top surface of the frame body, and at least two magnetic attraction wheel sets are arranged on the bottom surface of the frame body; a rotating piece used for adjusting the rotating direction of each magnetic attraction wheel set is arranged between the frame body and the corresponding magnetic attraction wheel set, the baffles are symmetrically arranged on the two side edges of the bottom face of the frame body, one ends of the baffles are fixedly connected with the frame body, sliding plates are arranged at the other ends of the baffles, and protruding blocks are arranged on the faces, away from the baffles, of the sliding plates in the length direction of the sliding plates. The two opposite ends of the protruding block are slidably connected with movable sliding blocks, the bottom face of each movable sliding block is fixedly connected with a guide wheel, and the two movable sliding blocks located at the two opposite ends of the protruding block are connected through a first spring. The magnetic type self-adaptive bridge inspection robot provided by the invention can improve the safety of bridge inspection, adapts to a complex environment, and automatically adjusts steering.

The utility model discloses a water-air cross medium wall-climbing robot for bridge inspection, which relates to the technical field of bridge inspection and comprises a bottom frame, buoys are symmetrically fixed at the bottom end of the bottom frame, a base frame is fixed at the upper end of the bottom frame, front vector motors are fixed on two sides of one end of the base frame, and rear vector motors are arranged on two sides of the other end of the base frame. An adsorption chassis is arranged in the center of the upper end of the base frame, a negative pressure fan is arranged on the inner side of the adsorption chassis, gear motors are fixed to the four end corners of the outer side of the adsorption chassis, and a rubber anti-skid wheel is fixed to the output end of each gear motor. According to the water-air cross-medium wall-climbing robot for bridge inspection, through the overall structural matching design, water-air amphibious rapid movement can be achieved to be close to a bridge pier, meanwhile, negative pressure can be generated to adsorb the robot to the wall face of the bridge pier, and then the robot is driven to achieve wall face rapid crawling to execute the inspection task.

The invention provides a bridge support anomaly detection method, and belongs to the technical field of bridge detection based on deep learning. According to the method, a high-precision sensor array is deployed at a key position of a support, and vibration, displacement, strain and environmental parameters are synchronously collected; a one-dimensional convolutional neural network and a self-attention mechanism are innovatively adopted to extract local and global features of a vibration signal, ST-GCN is utilized to capture spatial distribution and time evolution laws of displacement and strain, and an environment long-term trend is modeled through GRU; meanwhile, a multi-modal fusion method based on an adversarial auto-encoder is introduced, heterogeneous features are mapped to a unified and compact potential space, distribution of the heterogeneous features is more regular through adversarial training, and the essential distinguishing capacity of the model for normal and abnormal states is enhanced. According to the invention, comprehensive perception, accurate diagnosis and prospective early warning of the state of the support are realized.

The invention provides a safety protection method, device and equipment for a bridge inspection vehicle and a storage medium, and relates to the technical field of bridge detection.The safety protection method for the bridge inspection vehicle comprises the steps that environment point cloud data and environment parameter data around the bridge inspection vehicle are obtained in real time, and time and space synchronization processing is carried out; the environment point cloud data and the environment parameter data after time-space synchronization processing are preprocessed, the environment point cloud data are analyzed through a clustering algorithm, obstacle feature information on the advancing path of the bridge inspection vehicle is recognized, the environment parameter data are analyzed, and environment condition information of the bridge inspection vehicle on the advancing path is determined; and combining the detected obstacle feature information with the environmental condition analysis information, carrying out comprehensive risk assessment, and adjusting the inspection path and operation parameters of the bridge inspection vehicle in real time according to a comprehensive assessment result. The safety and inspection efficiency of the bridge inspection vehicle in a complex environment can be improved.

The invention discloses a bridge intelligent inspection unmanned aerial vehicle integrated measurement and control system, and relates to the technical field of unmanned aerial vehicle measurement and control, and the system comprises a reference construction module, a pose acquisition module, a pose matching module, a coordinate generation module, and a disease locking module. According to the method, the reference three-dimensional model and the space reference control point are constructed, so that accurate registration of the real-time video stream and the historical model is realized; driving the unmanned aerial vehicle to actively adjust the pose and lock the disease area through visual servo control; the absolute three-dimensional space coordinates of the diseases are calculated through reverse ray tracing, integration of disease recognition, active observation and accurate positioning is achieved, the automation degree of bridge inspection is improved, and the problems that in the prior art, bridge disease recognition and space positioning links are disjointed, cross-cycle data cannot be aligned, and an active observation mechanism is lacked are solved.

The present invention relates to the technical field of bridge construction, and in particular to a bridge inspection robot, the robot comprising: a track, which is used to be arranged on the side web of the bridge; a walking mechanism, which is arranged on the track and can slide relative to the track; a connecting arm, which is connected to the walking mechanism; and a detection device, which is arranged on the connecting arm and is used to detect the flange plate, web plate and bottom plate of the bridge. The robot can solve the problem of the difficulty of using the manned beam bottom inspection vehicle in the prior art when the beam bottom space is narrow and there are both limited height highway lines and limited height railway lines under the beam bottom.

The invention relates to the technical field of bridge inspection, in particular to a road bridge automatic inspection system based on an unmanned aerial vehicle, which comprises the following steps: determining bridge structure information of an inspected bridge, and determining a bridge type according to the bridge structure information; determining a structure inspection grade table of the bridge according to the bridge type; according to the structure inspection grade table and the unmanned aerial vehicle information, the acquisition distance and the acquisition angle of the unmanned aerial vehicle at each position during inspection are determined, so that accurate bridge inspection information can be obtained during inspection, the probability of collision between the unmanned aerial vehicle and the bridge can be reduced, and the flight safety of the unmanned aerial vehicle is improved. By means of the mode, the problems that in the bridge inspection aspect, the control ability of an operator on the unmanned aerial vehicle is relied on, the problem cannot be found in time, and an air crash event is likely to happen are solved.

The invention relates to a bridge inspection method and system and a computer readable storage medium. The inspection method of the bridge comprises the following steps: receiving an inspection task, and driving the inspection device according to the inspection task, so that the inspection device travels along the running guide rail of the bridge; obtaining a sequence image of the bridge surface acquired by the image acquisition assembly; identifying the disease type of the bridge based on the sequence image; and determining geometric feature parameters corresponding to the disease type according to the disease type of the bridge, and determining the severity level of the disease type based on the geometric feature parameters. According to the bridge inspection method, automatic grading evaluation of bridge apparent diseases is facilitated, errors caused by manual judgment are reduced, and the accuracy degree of disease detection is improved.

The utility model relates to the technical field of bridge inspection vehicles, aims to solve the problems that in the prior art, due to arrangement of tracks and portal frames, standardization of trusses is difficult, the efficiency of design, development and large-scale production of conventional beam bottom inspection vehicles is low, and manpower resources are wasted, and provides a suspension type inspection vehicle portal frame with a variable track gauge. Comprising an upper connecting seat, the upper connecting seat is used for being connected with a rotary support of a walking mechanism, the bottom of the upper connecting seat is connected with at least one pair of adjustable pull rods, the length of the adjustable pull rods can be adjusted and changed, and one ends of two of the pair of adjustable pull rods are hinged to the same point of the bottom of the upper connecting seat. The other ends of the two adjustable pull rods are hinged to the two ends of a fixing frame respectively, the fixing frame is used for being fixed to the top face of a truss, and the two ends of the fixing frame are fixed to truss nodes. According to the utility model, under the condition that the truss of the inspection vehicle is reasonably stressed and the portal frame is arranged at the node position of the truss, the installation and arrangement of the inspection vehicle at any gauge can be realized.

The utility model relates to the technical field of parking of bridge inspection vehicles, aims to solve the problem that in the prior art, a bridge inspection vehicle is parked in the mode that a motor brakes to prevent track wheels from rotating, and vehicle sliding is prone to occurring, and provides an inspection vehicle parking device which comprises a rapid parking mechanism of an inspection vehicle. The quick parking mechanism for the inspection vehicle comprises a stop block and a sliding assembly, the sliding assembly comprises a rotating groove body and a sliding block, the rotating groove body and the stop block are rotationally connected to a driving box mounting plate of a driving box through a rotating shaft, and the sliding block is in sliding connection with the rotating groove body. The sliding block can be fixed on the rotating groove body; a pin hole is formed in the driving box mounting plate, a rotating limiting piece is detachably mounted in the pin hole, the rotating limiting piece is located on the lower side of the rotating groove body, and the rotating limiting piece is used for limiting rotation of the rotating groove body; the sliding block is connected with the stopping block, and the rotating groove body is connected with the stopping block in a sliding mode.