2802 results about "Bridge engineering" patented technology

The invention relates to a bridge safety early warning method and system based on construction monitoring, and belongs to the technical field of bridge engineering.The early warning method comprises the steps that multi-dimensional monitoring data collected by sensor networks deployed on a bridge girder and a bridge key section in real time are obtained; performing edge calculation processing on the multi-dimensional monitoring data to obtain a cleaned monitoring data set; based on the cleaned monitoring data set, combining a finite element model to construct a three-dimensional dynamic stress field, and generating a dynamic stress nephogram matrix; dynamically correcting a pre-configured stress allowable threshold value according to the environment monitoring data; predicting a stress prediction matrix in a future time window based on historical stress data and the dynamic stress nephogram matrix; the real-time stress value and the stress prediction matrix are compared with a preset stress allowable threshold value, and an early warning score is calculated; and determining a corresponding early warning level according to the early warning score and triggering a maintenance control strategy. According to the invention, the accuracy of bridge structure risk early warning can be improved.

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

The invention relates to the technical field of bridge engineering, in particular to a cable structure bridge design method adopting a BIM (Building Information Modeling) model, which comprises the steps of three-dimensional bridge foundation model construction, cable structure parameterization configuration, nonlinear mechanical simulation, construction error modeling, feedback optimization and the like. Through introduction of structured geological data and NURBS curved surface reconstruction, a geological-structure integrated model is realized. Adopting a genetic algorithm and a particle swarm optimization method to intelligently configure pile foundation and cable parameters; a construction error closed-loop adjustment mechanism is constructed through dynamic simulation and real-time tension feedback control; and finally, parameter correction write-back and BIM delivery model integration is realized. According to the invention, the design precision, the construction stage adaptability and the digital delivery integrity of the cable structure can be improved, and the method is suitable for cable structure bridge engineering with complex geology and high-precision control requirements.

The invention relates to the technical field of bridge engineering, in particular to a system and method for predicting the service life of a steel structure bridge after fatigue crack repair. The method comprises the steps that load data and stress data in the operation process of a bridge structure after fatigue crack repairing are collected; dynamic response characteristic parameters are extracted in combination with stress distribution characteristics of the fatigue crack repair area; performing multi-dimensional data fusion and preprocessing on the dynamic response characteristic parameters, and constructing a data set for training a deep learning algorithm; combining a deep learning algorithm and a fracture mechanics theory to construct a crack propagation rate prediction model and a residual life prediction model; according to the operation state of the bridge and the performance degradation law of the repairing area, the remaining life of the repaired structure is predicted, and multi-stage early warning is conducted. According to the method provided by the invention, bridge structure performance evaluation, life prediction and potential risk early warning can be realized, and the safety and reliability of bridge operation are ensured.

The invention discloses an on-bridge pedestrian abnormal behavior identification and early warning method based on intelligent monitoring, and relates to the technical field of bridge engineering, and the method comprises the steps: carrying out the registration of a current visible light image and a current infrared image, and obtaining a registration dual-mode image based on a current image coordinate system; considering current bridge deformation to establish a current coordinate mapping conversion model for mapping pixel coordinate points in the current image coordinate system to physical coordinate points of a current bridge physical space coordinate system; obtaining the current three-dimensional actual coordinates of the pedestrian; recognizing abnormal behaviors of the pedestrians on the bridge based on the current three-dimensional actual coordinates of the pedestrians and acquiring current dynamic risk score values of the pedestrians; and determining the current risk level of the pedestrian according to the current dynamic risk score value of the pedestrian and a preset risk threshold value to realize multi-level linkage protection response. The method provided by the invention solves the technical problem in the prior art that the current actual coordinate of the pedestrian is difficult to accurately obtain due to environmental interference, and the abnormal recognition of the pedestrian on the bridge is inaccurate.

The invention discloses a bridge welding quality detection method based on a gray scale-relative depth fitting visual technology, and relates to the technical field of bridge engineering quality detection.The bridge welding quality detection method comprises the following steps that S100, multi-dimensional image capturing is carried out, specifically, a high-resolution camera is used, and by means of illumination control and multi-angle shooting, a multi-dimensional image is obtained; according to the method, the image of the bridge welding part is comprehensively collected, rich original data is provided for subsequent detection, the high-resolution camera is used for capturing the multi-dimensional image, fine gray processing is combined, the image contrast and definition are effectively enhanced, key features of the welding part are highlighted, further, relative depth calculation is carried out by using the binocular vision principle, and the accuracy of the relative depth calculation is improved. The relative depth information of the welding part is accurately obtained, the problems that in a traditional method, the image quality is unstable, and the feature point extraction matching difficulty is large are solved, and the three-dimensional curved surface construction precision is remarkably improved.

The invention discloses a bridge safety assessment method based on multi-intelligent algorithm fusion, and belongs to the technical field of bridge engineering and intelligent algorithms. Aiming at the problems of insufficient multi-source data processing, lack of physical constraints and the like of a traditional evaluation technology, the method integrates multi-dimensional indexes through eight steps of multi-source data fusion preprocessing, physical information neural network construction, adaptive deep forest feature learning and the like, and generates a structural vulnerability index (SVI) to guide a maintenance decision. Innovation points comprise a five-algorithm deep coupling architecture, a physically embedded digital twinborn body, a dynamic weight proof theory and the like. The method has the advantages that high-precision recognition, interpretable prediction and visual decision making of bridge damage are achieved, evaluation precision and operation and maintenance efficiency are remarkably improved, and the method is suitable for full-life-cycle management of various bridges.

The invention discloses a monitoring method and device for a cable-supported bridge, a storage medium and a program product, and relates to the technical field of bridge engineering, and the method comprises the steps: obtaining a vibration signal of each bridge cable of a to-be-detected bridge in a current monitoring period; for any target inhaul cable in the bridge inhaul cables, fast Fourier transform is carried out on the target vibration signal to obtain a target spectrogram, and the target vibration signal is the vibration signal of the target inhaul cable; identifying a main frequency peak of each amplitude value in the target spectrogram, determining a respective frequency order of each main frequency peak based on a main peak frequency value of each main frequency peak, and determining an inhaul cable force value of the target inhaul cable based on each frequency order; and performing anomaly detection on the to-be-detected bridge based on the cable force value of each inhaul cable to obtain a monitoring result. The vibration frequency order of the inhaul cable is identified, so that the cable force value of the inhaul cable is calculated based on the frequency order, the accuracy of the cable force value is improved, and the accuracy of bridge vibration monitoring is improved.

The invention discloses a railway beam bridge anti-seismic toughness evaluation method and system, relates to the technical field of bridge engineering structures, and solves the technical problem that the prior art lacks an anti-seismic toughness evaluation method suitable for a railway bridge. The method comprises the following steps: establishing a full-bridge nonlinear finite element model to carry out vulnerability analysis; according to a vulnerability result, weighting and quantifying a component damage state and a passage function damage state by adopting a damage probability; determining post-earthquake repair time and a function recovery value, obtaining a post-earthquake bridge passing function recovery curve, finally obtaining a toughness index of the bridge, and dividing the function state of the bridge into three grades; according to the method, a whole process solution of'bridge design parameter-nonlinear time history analysis-probability earthquake demand model-vulnerability analysis-passage function recovery-toughness index-earthquake resistance toughness three levels' of the railway beam bridge is provided, and reference is provided for pre-earthquake design and post-earthquake emergency repair decision.

The invention relates to the technical field of bridge construction quality detection, in particular to a bridge engineering construction quality detection method and system. The method comprises the following steps: carrying out periodic detection image shooting on a reinforcing steel bar binding site during a bridge construction period to obtain clear bridge construction image data; performing reinforcement binding main direction identification on the clear bridge construction image data to generate optimized bridge reinforcement binding skeleton data; carrying out bridge curved surface effect compensation processing on the optimized bridge steel bar binding skeleton data, and carrying out image pixel proportion scale mapping to generate a steel bar binding spacing estimation value; acquiring bridge steel bar spacing design specification data; and carrying out construction quality scoring on the steel bar binding spacing estimation value by utilizing the bridge steel bar spacing design specification data, and generating a project quality detection report to obtain a bridge steel bar binding project quality detection report. According to the invention, the steel bar binding spacing during the bridge construction period can be automatically detected, and rapid and accurate spacing measurement and engineering quality evaluation are realized.

The invention relates to the technical field of bridge engineering, and discloses a concrete box girder prefabricated segmental girder formwork system and a construction method thereof.The concrete box girder prefabricated segmental girder formwork system comprises a connecting beam frame and fences, the fences are fixedly connected to the upper surface of the connecting beam frame, and an end formwork is detachably installed on the front surface of the connecting beam frame; a side formwork is fixedly connected to the lower portion of the front surface of the connecting beam frame, a side formwork is detachably installed on the upper surface of the side formwork, and an auxiliary formwork is detachably installed on the lower portion of the side formwork. Through the arrangement of the end formworks, the side formworks, the auxiliary formworks, the movable bottom formworks, the walking sliding shoes, the jacks, the reinforcement cages and the side formwork frames, a short line matching method is adopted for prefabrication, a formwork system is matched, reasonable hydraulic jack arrangement is adopted, precise formwork adjustment, supporting and demolding are achieved, the segmental beam prefabrication precision is improved, the bridge splicing quality is guaranteed, the construction process is optimized, and the construction efficiency is improved. All procedures are closely connected, construction time is shortened, and construction efficiency is improved.

The present invention relates to the field of virtual pre-assembly matching of bridge engineering components based on 3D point clouds, and particularly relates to a method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models. An oriented bounding box is computed respectively for 3D point clouds of two prefabricated beams with assembly relationship therebetween, and two point cloud slices and design point cloud are formed; the two point cloud slices are respectively registered with the generated design point cloud by the iterative closest point algorithm; boundary features and corner features of a pre-assembly interface of two components to be assembled are fitted and extracted; and coarse matching and fine matching of the assembly interface are achieved by the Procrustes analysis algorithm and the iterative closest point algorithm in sequence, a matching degree error of the interface is computed, and an assembly result is evaluated. According to the present invention, design point cloud is introduced to achieve the screening of point clouds near features to be extracted, and posture adjustment on components to be assembled is performed by combining a plurality of algorithms, which not only improves the degree of automation and reduces the manual intervention, but also improves the precision of virtual pre-assembly matching and saves the computation time.

The invention relates to the technical field of bridge engineering construction, in particular to a bridge deck concrete flatness detection and correction method, which comprises the following steps: synchronously acquiring bridge deck point cloud, elevation and crack data through a three-dimensional laser scanner, a laser profiler and an ultrasonic sensor, and fusing to generate a three-dimensional digital model; defects such as local protrusions and wave deformation are recognized based on an intelligent analysis algorithm, causes are analyzed, and correction process parameters such as milling, cladding, grouting or ultrathin layer pouring are generated in a targeted mode; operation is executed through equipment such as a finish milling machine, closed-loop control is realized by combining real-time monitoring of a laser profiler, and finally, a traceable digital file is formed through laser detection, inspection and acceptance of an unmanned aerial vehicle and data archiving. By means of the method, the pass percent of the flatness of the bridge floor can be increased to 94% or above, the maximum height difference is controlled within 3 mm, the maintenance period of the bridge is prolonged, and the maintenance cost is reduced.

The utility model belongs to the technical field of bridge engineering, and particularly relates to a steel pipe butt joint device for extra-large bridge steel pipe construction, which comprises a threaded rod, a mounting plate fixedly connected with the other end of the threaded rod, a guide rod fixedly connected with one side of the mounting plate, a sleeve fixed on one side of the mounting plate, an electrode plate mounted on one side in the sleeve, and an electrode slice assembled on one side of a movable rod. An extension spring is fixedly connected between the electrode plate and the sleeve, and an auxiliary roller is mounted in the mounting rack; a threaded rod is rotated through a handle, an auxiliary roller is attached to the outer wall of a butt joint gap of a steel pipe under cooperation of a guide rod, in the rotating process of the steel pipe, if the phenomenon of irregular butt joint exists, the auxiliary roller can push a movable rod to move towards the interior of a sleeve, an extension spring is made to stretch inwards, an electrode plate makes contact with an electrode slice, and a warning lamp is controlled to be turned on; and an operator is reminded of untidy butt joint and need of adjustment, so that the welding quality can be ensured, and the stability of bridge construction is improved.

The invention discloses a nondestructive testing device for a steel profile and a testing method of the nondestructive testing device. According to the method disclosed by the invention, the ultrasonic technology is combined with optical imaging, so that synchronous detection of surface and internal defects can be completed at one time. Ultrasonic waves penetrate through the interior of the steel profile, and hidden defects such as cracks and air holes are accurately recognized; the optical camera module captures visible problems such as surface scratches and corrosion in real time. The integrated detection mode avoids the tedious process of step-by-step operation required by a traditional method, for example, magnetic powder detection of surface defects and ultrasonic scanning of an internal structure are not needed, the detection period is greatly shortened, and the method is especially suitable for steel structure buildings, bridge engineering and other scenes with high requirements for detection efficiency and has good application prospects. The full-process automatic design effectively reduces the influence of human intervention, and ensures the scanning continuity of the detection area; the intelligent algorithm automatically analyzes the ultrasonic echo signal and the optical image data, and judges the defect level according to the preset standard, thereby avoiding subjective misjudgment of manual visual inspection.

The invention relates to a quality risk assessment method for a highway bridge engineering construction stage and a medium, and the method comprises the steps: determining the quality influence factor indexes of the highway bridge engineering construction stage, carrying out the dimension reduction through employing a PCA method, and constructing a quality risk index system, wherein the quality risk index system comprises a risk factor layer and a corresponding index layer; based on the quality risk index system, adopting a DEMATEL method to calculate the subjective influence weight of each index; based on the quality risk index system, an improved CRITIC method is adopted to calculate the objective weight of each index; based on the subjective influence weight and the objective weight, determining a comprehensive weight of each index by adopting a minimum identification information theory; and based on the comprehensive weight, combining the determined grade standard interval, and based on a variable fuzzy set theory, carrying out quality risk assessment, and outputting a quality risk assessment result. Compared with the prior art, the method has the advantages of improving the reliability of risk grade evaluation and the like.

The invention provides a bridge settlement monitoring method and system, and relates to the technical field of bridge engineering monitoring, and the method comprises the steps: firstly selecting a component surface region directly associated with settlement conduction in a bridge structure as a visual clue node, and constructing a bridge structure visual clue network; setting shooting parameters of image acquisition equipment according to spatial distance parameters in the bridge structure visual clue network to form a multi-clue collaborative image set; then, carrying out settlement conduction characteristic modeling on the multi-clue collaborative image set, extracting a visual characteristic variable quantity, establishing a corresponding relation with a settlement conduction path, and generating a settlement conduction characteristic matrix; inputting the settlement conduction characteristic matrix into a preset model, converting the settlement conduction characteristic matrix into settlement displacement parameters of corresponding components, and integrating the settlement displacement parameters to form bridge settlement state information; and finally, based on the settlement state information, generating a monitoring instruction containing a settlement conduction path identifier and settlement parameters of each component, and transmitting the monitoring instruction to a monitoring terminal. The method is comprehensive, accurate, economical and efficient, and can effectively guarantee the safety of the bridge structure.

The invention discloses a bridge engineering-oriented foundation pile quality detection system, which relates to the technical field of engineering quality detection, and comprises a data acquisition module configured to acquire detection data including foundation pile construction parameters, geological environment data, stress wave signals and geometric dimension parameters, the wave velocity correction module is configured to correct a standard wave velocity through a dynamic coupling algorithm, the defect depth calculation module is configured to calculate the defect depth of a foundation pile defect, and the credibility evaluation module is configured to output a credibility correction coefficient and feed back the credibility correction coefficient to the wave velocity correction module by quantifying errors of detection data; according to the method, through multi-physical field wave velocity correction, pile diameter-defect depth double correction and credibility quantitative iteration, a high-precision, objective and traceable integrated solution is systematically provided for bridge engineering foundation pile quality detection.

The invention relates to a method for determining the flexural capacity of a high-strength steel-UHPC composite beam. The method is characterized by comprising the following steps that (1) the corresponding section curvature k when UHPC reaches crushing serves as the section curvature of the steel-concrete composite beam, and the strain distribution rule of the section of the composite beam is determined; 2) aiming at two conditions that a neutralizing shaft is positioned in a UHPC plate and the neutralizing shaft is positioned in a steel beam, simplifying a UHPC stress diagram of a pressed area into a triangle, and by considering the strain hardening characteristic of high-strength steel, simplifying a steel beam web stress diagram into a trapezoid, and determining internal force distribution of the section of the main beam under the two conditions; and (3) according to the bending moment balance equation of the section of the main beam, the method for determining the flexural capacity of the high-strength steel-UHPC composite beam is established. According to the method, the problem that a method for calculating the flexural capacity of the high-strength steel-UHPC composite beam is lacked is solved, and technical support is provided for promoting application of the high-strength steel-UHPC composite beam in bridge engineering.

The invention belongs to the technical field of bridge engineering, and particularly discloses a bridge safety early warning method and system based on fusion of a data model and a physical model. Comprising the following steps: in a first stage, on the basis of a neural network monitoring site excitation-response mapping relation model established by an initial state full-bridge finite element model, calculating response information corresponding to field excitation, and comparing response monitoring signals to determine whether a structure is damaged from a full-bridge angle; in the second stage, through data expansion from a monitoring point position to a non-monitoring point position, a set threshold value is compared to determine a damage position and a damage grade; and in the third stage, adjusting parameters of the model are calculated according to excitation-response signals of a monitoring point position sensor, the finite element structure model is updated, the residual bearing capacity of the structure is calculated by combining state information borne by the structure model, and the service life of the component is determined. The invention provides a safety assessment method fusing monitoring data and a physical model for structural health monitoring, and the method is clear in physical significance, high in real-time performance and reliable in result.

The invention discloses a cable-stayed bridge tension and compression support service life evaluation method and system under random traffic flow load, and belongs to the field of bridge engineering and structure health monitoring, and the method comprises the steps: carrying out the statistical analysis of traffic load data, building a random traffic flow load model, and generating a load spectrum; correcting stay cable force and modal frequency based on bridge monitoring data, and constructing a refined multi-scale finite element model; a unit fatigue vehicle load is applied to the model, a stress influence line is obtained, a rain flow counting method is adopted to calculate stress amplitude, cycle index and average stress, the stress amplitude, the cycle index and the average stress are converted into daily average equivalent stress amplitude and equivalent daily cycle index through a fatigue damage equivalence principle, and the fatigue life of the tension-compression support is evaluated by utilizing a Miner criterion. According to the method, the problem that the influence of three-way alternating stress on bolt fatigue damage cannot be accurately reflected in the prior art is solved, the service life prediction precision of the cable-stayed bridge tension and compression support is improved, the damage of the tension and compression support can be early warned in advance, and data support is provided for safe operation and maintenance of the cable-stayed bridge.

The invention discloses a vibration noise analysis method for a combined box girder bridge with an axle coupling effect, and belongs to the technical field of bridge engineering. The method comprises the following steps: S1, establishing a train-track-bridge coupled vibration model of a high-speed railway steel-concrete combined box girder bridge, and compiling a train-track-bridge coupled vibration simulation analysis program based on a Matlab language and Ansys finite element software; s2, establishing a low-frequency noise prediction model in the steel-concrete combined box girder bridge of the high-speed railway, and obtaining a low-frequency noise radiation rule of a bridge structure; s3, establishing a medium-high noise prediction model of the steel-concrete combined box girder bridge of the high-speed railway, and performing numerical simulation on medium-high frequency radiation noise of a bridge structure; and S4, analyzing the influence parameters of the vibration noise of the steel-concrete combined box girder bridge of the high-speed railway in combination with the models established in the steps S1 to S3. The technical scheme is used for analyzing vibration and noise conditions of the steel-concrete composite beam applied to the field of railroad bridges.

The invention discloses an intelligent auxiliary decision-making method and system for a bridge construction progress based on a multi-modal visual language model, belongs to the technical field of bridge engineering management, and solves the problems that when an existing multi-modal model is used for integrating static scene data and dynamic real-time data of a bridge construction site, the fusion degree is low, and the integration efficiency is high. And therefore, the problems of information incompleteness and cognition deficiency can be solved. The method comprises the following steps: acquiring bridge construction progress data, preprocessing the data, and training a constructed multi-modal bridge construction decision analysis model based on image-text information fusion; preprocessing bridge construction progress data acquired in real time, inputting the preprocessed data into the trained image-text information fused multi-modal bridge construction decision analysis model for analysis, and constructing a unified fusion expression model for feeding back the construction progress and quality in real time according to an instruction normalized visual model required by multi-level user tasks; and obtaining a bridge construction progress scene three-dimensional model. The method is used for intelligent auxiliary decision making of the bridge construction progress.

The invention relates to a large-span bridge anti-seismic performance optimization and evaluation system based on digital twinning, and belongs to the field of computer and bridge engineering. The optimization and evaluation system comprises a data acquisition and pre-processing module, a model updating module and a performance evaluation and optimization module; the optimization and evaluation method comprises the following steps: identifying the modal and frequency of a structure, obtaining the modal shape and natural vibration frequency of a bridge, and establishing a digital twinborn model of an object bridge; and according to the multi-objective optimization design function, carrying out supercomputing driven damping device parameter optimization, outputting an optimal parameter combination, and controlling hierarchical response of the bridge in an earthquake. The artificial waves generated based on the design response spectrum of the bridge can fully consider the dynamic characteristics of the large-span cable-stayed bridge and the seismic oscillation characteristics of the design site; meanwhile, only time history analysis of single seismic oscillation is involved in the anti-seismic optimization design process, the calculation amount of optimization design is greatly reduced, the comprehensive influence of each nonlinear factor on the bridge seismic response is better considered, and the complexity of large-span bridge anti-seismic optimization is remarkably simplified.

The invention relates to the technical field of bridge engineering intelligent construction, discloses a digital twinborn and multi-physics field simulation-based lattice pier assembly error intelligent prediction method, and aims to construct a lattice pier assembly error prediction model. The lattice pier assembly error prediction model is composed of a channel independent feature extraction module of multi-physics field data, a self-adaptive space-time coupling and multi-factor dynamic fusion module and a training lattice pier assembly error prediction model, the multi-physical field data channel independent feature extraction module maps data of each physical field to a high-dimensional feature space through calculation, and according to a state space model, the adaptive space-time coupling and multi-factor dynamic fusion module introduces a plurality of factors to dynamically adjust weights of the physical fields in different time steps. Carrying out adaptive fusion on the processed physical field data through a space-time coupling function to obtain coupled physical field data; and finally, taking the output of the two as the input, and combining a regression model to predict the lattice pier assembly error.

The invention relates to the field of bridge engineering, in particular to a bridge structure toughness dynamic evaluation method and system based on digital twinning, and the system firstly collects the environment, geometry and performance information of a bridge, and then establishes an accurate bridge digital twinning model; a bridge structure toughness prediction model is systematically constructed by using a space-time diagram convolutional network, and the performance degradation process of a bridge in extreme weather can be predicted; meanwhile, toughness evaluation indexes are formulated by the system, and accurate performance evaluation of real-time monitoring data is realized; through multi-source data fusion, the system improves the comprehensiveness and accuracy of data acquisition; the prediction model based on the space-time diagram convolutional network significantly improves the accuracy of performance degradation prediction; a scientific and efficient tool is provided for bridge maintenance and management, and safe operation of the bridge in extreme weather is effectively guaranteed.

The invention relates to the technical field of bridge engineering, and discloses an RGV rail car automatic operation control method and system, and the method comprises the steps: obtaining a target transportation site of an RGV rail car, and carrying out the initial path planning of the target transportation site based on an optimized algorithm; driving according to a preset driving path, performing real-time environment scanning through a sensor, and analyzing scanned data; based on the real-time environment data, utilizing a TEB time elastic band algorithm to perform dynamic path re-planning on a predetermined driving path, driving according to the obstacle avoidance driving path, obtaining the residual electric quantity of the rail car, utilizing a charging demand decision model to judge the residual electric quantity, and if the residual electric quantity cannot support the rail car to drive to the target transportation place, determining that the rail car is not driven to the target transportation place. If yes, acquiring a nearest charging position, and generating a charging driving path according to the charging position. Various people and obstacles in the running process can be effectively avoided, and the safety and smoothness of the transportation process are guaranteed.

The invention belongs to the technical field of bridge engineering, and particularly discloses a proxy model and finite element fusion method and system for bridge safety early warning. Comprising the following steps: simulating a real load of a bridge by adopting a structural multi-scale finite element model to calculate a bridge structural response, constructing a finite element agent model, and training the finite element agent model by adopting a data set to extract a relationship between weight matrix analysis and output items of a neural network; and in the refined grid division part, considering the damage directly observed by the structure, dividing the model parameters extracted from the monitoring point by the model parameters directly corrected by the damage to obtain a final corrected model. According to the method, sensor signal processing, model correction and finite element calculation are closely combined together through neural network analysis, a method basis is provided for dynamic evolution of a digital object fusion model, and technical support is provided for subsequent bridge structure safety evaluation and health early warning.

The invention belongs to the field of bridge engineering construction monitoring and intelligent control, and provides a Beidou digital twinning system for bridge steel member precision hoisting and a hoisting method aiming at the problems of low efficiency and poor real-time performance of on-site hoisting of a segment steel member in a large-span bridge structure. The system comprises a multi-source data acquisition module, a coordinate conversion module, an attitude calculation module, a three-dimensional virtual model construction module, a hoisting motion state monitoring module and a digital twinning module. The attitude resolving module calculates a standard attitude vector by using a comprehensive deep learning model according to the degree-of-freedom data, the attitude information and the movement speed, and performs attitude resolving based on the standard attitude vector to obtain an attitude angle of the bridge steel member; the hoisting motion state monitoring module can perform safety early warning and posture adjustment when the bridge steel member is abnormal; and the digital twin module can synchronously update the attitude information and the three-dimensional virtual model of the bridge steel member to obtain the current space trajectory and historical state of the bridge steel member.

The invention relates to the technical field of bridge engineering detection and repair, in particular to a bridge crack repair effect prediction method and system based on performance simulation, and the method comprises the following steps: obtaining basic data and environmental load data of a target bridge, and matching at least two candidate repair schemes from a preset repair scheme library; constructing an initial performance model before repair, and calculating an initial bearing capacity loss rate and a crack propagation rate; quantifying the interaction influence of the multi-modal data through a coupling factor matrix, and iteratively calculating the repair condition of a set time period after repair to obtain an initial simulation result; and collecting real-time monitoring data in a set time period in the repairing process and after repairing, and inputting the real-time monitoring data into a preset LSTM adaptive correction model to obtain a corrected simulation result. According to the method, the long-term repair condition after repair can be accurately calculated, the accuracy of a prediction result and the environmental adaptability are remarkably improved, and a scientific basis is provided for long-term service performance pre-judgment.