701 results about "Bridge maintenance" patented technology

The invention relates to the technical field of bridge structure safety monitoring and maintenance, and discloses a road bridge real-time monitoring and maintenance system based on intelligent perception, which comprises a data acquisition module used for acquiring multi-dimensional data of a bridge operation state through a deployed multi-source sensor, the multi-source sensor comprises a vibration sensor, a strain sensor, a temperature and humidity sensor and image acquisition equipment; the data preprocessing module is used for carrying out cleaning, synchronization and feature extraction on the collected multi-dimensional data to obtain dynamic features, environment features and visual features; and a data fusion module. According to the method, the comprehensiveness and accuracy of bridge monitoring are improved through multi-source data fusion, the reliability of an evaluation result is ensured in combination with physical constraint optimization, health trend prediction and early warning functions are realized based on a time sequence model, scientific decision support is provided for bridge maintenance, and the monitoring and management efficiency is improved.

The invention relates to the technical field of bridge digital twinborn platform construction, and particularly discloses a Unity 3D bridge digital twinborn platform construction method integrated with a Midaas-Civil calculation function, and the method carries out the real-time monitoring and evaluation of a bridge through a digital twinborn technology, thereby improving the structural safety and maintenance efficiency of the bridge. The method comprises the following steps: acquiring stress distribution data of a bridge in real time through bridge monitoring equipment, and analyzing a stress concentration area in a digital twin platform; a crack propagation rate and a stress gradient change value are extracted, a comprehensive feature vector is constructed, the current state of the bridge is evaluated by using a machine learning model, and a high-risk area is identified according to an evaluation result; the maintenance strategy of the bridge is dynamically optimized by predicting the future crack expansion trend and stress distribution change of the low-risk area; and on the basis of a risk assessment result, a resource optimization allocation model is constructed, optimal allocation and dynamic adjustment of bridge maintenance resources are realized, and accurate implementation of a maintenance strategy is ensured.

The invention relates to the technical field of suspension bridges, in particular to a suspension bridge stress simulation analysis system, which comprises a vibration monitoring module, a cable force prediction and calibration module, an environmental influence analysis module and a structural performance evaluation module, and extracts frequency characteristic values and amplitude information based on vibration frequency data of a suspension bridge steel cable. According to the method, the vibration frequency data is used for deep analysis, potential structure defects are recognized in an early stage, the early warning capacity is remarkably improved, the potential maintenance cost is reduced, the service life of the bridge is prolonged, and the stability and safety of the bridge under variable loads are guaranteed through prediction and adjustment of the real-time cable force state; by integrating environmental data to update the fluid-solid coupling model, the system can accurately simulate the influence of environmental change on the bridge, optimize the structural design, enhance the structural adaptability, improve the economical efficiency and practicability of the bridge design, and optimize decision support, so that the bridge maintenance is more efficient, and the long-term structural safety of the bridge is ensured.

The invention relates to the technical field of bridge maintenance, in particular to a beam bridge dynamic anti-seismic performance evaluation method and system, and the method comprises the steps: model preparation, model establishment, boundary condition and load application, analysis and calculation, and result analysis and evaluation. According to the method, through detailed data collection and model preparation, a detailed finite element model of the bridge is accurately constructed, actual characteristics of a main beam, a pier, a support and other key components are fully considered, boundary conditions are reasonably set, seismic loads are applied, calculation is performed by adopting a proper anti-seismic analysis method, and the seismic response of the bridge is effectively calculated; the method has the advantages that the method is simple, the anti-seismic performance of the bridge under the earthquake action is evaluated, potential weak parts of the bridge under the earthquake action are accurately identified, reliable basis is provided for formulating targeted anti-seismic reinforcement measures, and the efficiency and reliability of anti-seismic analysis and performance evaluation of the bridge structure are remarkably improved.

The invention discloses a hollow slab girder bridge maintenance decision-oriented hierarchical knowledge graph construction method, which comprises the following steps of: constructing a hollow slab girder bridge disease classification and maintenance decision general knowledge graph, combing disease types and evaluation indexes of a hollow slab girder bridge through cross-standard feature alignment and rule fusion, and defining each disease and the index thereof in a variable manner so as to construct a hollow slab girder bridge disease classification and maintenance decision-oriented hierarchical knowledge graph. The weight of each disease index is determined based on a fuzzy-Bayesian hybrid reasoning algorithm, and a complete general knowledge graph structure is formed; extracting disease information based on a detection report of a single bridge, endowing each disease with a maintenance suggestion by using a general knowledge graph, and forming a maintenance decision knowledge graph special for the bridge; and dynamically updating the special knowledge graph, and adjusting disease information and maintenance suggestions according to a subsequent detection report of the bridge to ensure timeliness and accuracy of the graph. According to the method, the problems that knowledge is dispersed and systematization is difficult in traditional hollow slab girder bridge maintenance decision making are solved, and scientificity and efficiency of bridge maintenance management are improved.

The invention relates to the technical field of data processing, in particular to a vehicle load deflection integrated intelligent analysis method, system and device and a medium, and the method comprises the steps: obtaining a vehicle basic data set and a vehicle load data set corresponding to all vehicles on a to-be-detected bridge within a preset time period; acquiring a bridge state data set and an environment data set of the to-be-detected bridge in a preset time period; determining a target vehicle distribution load data set according to the vehicle basic data set and the vehicle load data set; determining a bridge load deflection parameter set according to the bridge state data set, the environment data set and the target vehicle distribution load data set; and performing bridge damage evaluation on each bridge load deflection parameter in the bridge load deflection parameter set to obtain a bridge damage evaluation result. According to the method, a more accurate and more comprehensive bridge damage assessment result can be obtained, and the effectiveness of bridge maintenance management and the accuracy of bridge structure optimization can be improved.

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 invention relates to a bridge technical condition prediction method based on category clustering and trend modeling fusion, belongs to the technical field of bridge state prediction and maintenance management, and solves the problems of limited generalization ability and low prediction accuracy of an existing prediction model. The method comprises the following steps: firstly, collecting regular detection data of a plurality of bridges and preprocessing; after feature coding and dimension reduction are carried out, a K-means clustering algorithm is adopted to group bridges, and an intra-class degradation trend model is established by using a Huber regression method; an XGBoost regression method is adopted to establish an intra-class technical condition prediction model; according to the intra-class technical condition prediction model and the intra-class degradation trend model, bridge technical condition prediction results of the target bridge in multiple prediction years in the future are generated through a dynamic weighted fusion formula; and finally performing model performance evaluation. According to the method, the accuracy of bridge technical condition prediction can be effectively improved, reliable long-term degradation trend prediction is provided, and the maintenance decision of the bridge is optimized.

The invention provides a bridge group maintenance priority dynamic decision-making method and device based on reinforcement learning, and relates to the technical field of bridge intelligent maintenance. The method comprises the following steps: constructing a topological structure of a bridge network and a road; defining a state space, a maintenance action space and a state transition matrix of the bridge; defining a reliability index corresponding to the state of the bridge, and designing a comprehensive reward function based on maintenance cost, asset risk and traffic network capacity loss risk based on the topological structure; constructing a bridge maintenance decision problem; the method comprises the following steps of: describing a bridge maintenance decision problem as a Markov decision process, establishing a pointer network strategy model by adopting a pointer network, and training the pointer network strategy model by adopting an Actor-Critic algorithm to obtain a maintenance decision model based on reinforcement learning; and training the maintenance decision model based on reinforcement learning until convergence, and outputting a bridge maintenance action sequence under limited constraints. By adopting the method, the limitation problem of traditional single bridge assessment can be solved.

The invention discloses a method and a system for measuring a load force at a bridge steel structure connection position, and belongs to the field of bridge maintenance, the method comprises the following steps: constructing a bridge overall digital twin model, and carrying out refined modeling on a steel structure at the bridge steel structure connection position; a sensor is arranged at the steel structure joint of the bridge for real-time data acquisition, and it is ensured that the steel structure digital twinborn model can be dynamically updated; when new data collected by the sensor detection network are received, the digital twinborn platform adjusts parameters of the corresponding steel structure digital twinborn model according to the unique identifier of the sensor; carrying out load force analysis and dynamic prediction according to parameters in the steel structure digital twinborn model in combination with comprehensive data; and integrating to form a real-time visual platform, and displaying the stress condition of the connecting part through the real-time visual platform. According to the invention, the load force change at the joint of the bridge steel structure can be monitored in real time, and the safety of the bridge structure is improved.

The invention provides a municipal bridge damage defect detection method and a municipal bridge damage defect detection system, which are used for solving the problem that the quality and the accuracy of acquired data are difficult to guarantee due to the fact that vibration signals are easily interfered by environmental noise, such as vehicle load, wind load and the like. A bridge key part vibration signal data set is obtained, and a final damage area range is determined based on the vibration signal data set; whether the bridge is damaged or not is judged based on recognition of the vibration signals, so that the signal-to-noise ratio of monitoring data and the damage positioning precision are improved, a more accurate and reliable decision basis is provided for a bridge management department, and finally maintenance and reinforcement work of the bridge is effectively guided.

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 intelligent operation and maintenance, in particular to a bridge maintenance system based on data analysis, which comprises a multi-source data acquisition module, a fatigue degradation monitoring module, a wave velocity anomaly positioning module, a risk collaborative judgment module and a maintenance task planning module. According to the method, structural fatigue characteristics are subjected to cross validation by integrating two-dimensional data of a strain sequence and a stress wave propagation parameter, and a dynamic window range deviation degree is adopted to replace a static threshold value to establish a datum reference linked with a bridge bearing period; a same-path propagation difference value is extracted by using a high-frequency and low-frequency stress wave frequency band energy separation technology to detect continuous overrun, and a degradation trend weight and a priority list of damage space distribution are generated in combination with multi-index coexistence verification and a hierarchical sorting strategy; resource allocation is optimized by integrating a multi-level response mechanism of emergency repair coordinate positioning, directional detection and regular inspection, accurate crack positioning is achieved, the early degradation trend capturing capacity is enhanced, the misjudgment risk is reduced, and an operation and maintenance response chain is shortened.

The invention relates to the technical field of bridge detection, in particular to a multi-type bridge substructure crack depth detection system. The system comprises a data acquisition module, a three-dimensional reconstruction module and a crack detection module. The data acquisition module acquires a multi-view image of a crack area at the lower part of the bridge through a camera and a checkerboard calibration plate, and generates a multi-view crack feature image set; the three-dimensional reconstruction module generates a high-precision three-dimensional point cloud by using the multi-view crack image, and constructs a depth map of a crack region; and the crack detection module realizes classification of crack types and regression prediction of crack depths based on a deep learning model. According to the method, accurate calculation of the crack depth and automatic identification of multiple types of cracks can be efficiently realized, danger early warning is given out in combination with the depth threshold value, and reliable data support is provided for bridge maintenance.

The invention discloses a control method for axle coupling vibration of a continuous steel box girder ramp bridge. According to the control method, the anti-overturning stability of the continuous steel box girder ramp bridge is analyzed by analyzing the dynamic characteristics of the axle of the steel box girder ramp bridge and performing special inspection / monitoring on the axle of the steel box girder ramp bridge; the method comprises the following steps: carrying out coupling vibration analysis and driving comfort evaluation on a steel box girder ramp bridge axle, carrying out vibration reduction on a target ramp bridge by adopting a tuning type damper and inertial damper combined vibration reduction method, and carrying out vibration response analysis and driving comfort evaluation on the bridge. And the safety of the bridge structure and the driving comfort are improved.

The invention relates to the technical field of bridge maintenance equipment, and particularly discloses a prefabricated box girder maintenance device which comprises a device body, the device body comprises a plurality of bearing mechanisms and a spraying mechanism, the bearing mechanisms are arranged in an array mode in the length direction of a box girder, and each bearing mechanism comprises a first supporting frame and a second supporting frame; the bearing mechanisms are provided with first driving assemblies, the first driving assemblies are used for driving the multiple bearing mechanisms to move in the length direction of the box girder, the multiple bearing mechanisms are arranged at equal intervals, the bearing mechanisms have the folded state and the unfolded state, the multiple spraying mechanisms are arranged, and each spraying mechanism comprises a water pipe and a second driving assembly. A plurality of nozzles are arranged on the water pipe, and the second driving assembly is used for driving the water pipe to rotate around the axis of the water pipe so as to increase the spraying area of the nozzles; the device body can adapt to maintenance of box girders with different lengths through the unfolding degree of the bearing mechanism, and the maintenance efficiency can be improved.

The invention discloses a bridge technical performance prediction method and device, equipment, a storage medium and a product, and relates to the technical field of network-level bridge maintenance, and the method comprises the steps: obtaining the state parameters of a network-level bridge, and determining the current bridge performance distribution of the network-level bridge through the state parameters; a Markov degradation matrix of the network-level bridge is constructed based on historical bridge technical performance degradation data, and the historical bridge technical performance degradation data comprises historical state degradation data of the network-level bridge and historical state degradation data of related bridges; the related bridge is a bridge of which the similarity with the network-level bridge is greater than a preset threshold value; and through the current bridge performance distribution and the Markov degradation matrix, predicting the future technical performance of the network-level bridge after a preset duration. Therefore, the method achieves the prediction of the future technical performance of the bridge, and the prediction result can provide a decision basis for the maintenance of the bridge.

The invention relates to a bridge technical condition evaluation method and system. The method comprises the steps that bridge basic information input by a user is acquired; generating bridge structure information according to the bridge basic information; determining a disease type and a disease index of each part according to a bridge disease library; according to the disease type and the disease index of each part, calculating the score of each part deducted according to the disease; calculating the score of each part type in each structure according to the score of each part deducted according to the disease; redistributing the weight of each part type in each structure, and obtaining the technical condition score of each structure based on the score of each part type in each structure; performing weighted calculation on the technical condition score of each structure to obtain an overall technical condition score of the bridge; and determining a technical condition grade according to the overall technical condition score of the bridge. According to the invention, automatic evaluation of the bridge state is realized, and the efficiency and accuracy of bridge maintenance and management are greatly improved.

The invention relates to a large model-assisted bridge knowledge graph generative construction method, which comprises the following steps of S1, extracting bridge related entities in a document based on a pre-trained large language model, and obtaining a structure ontology model and structure ontology attributes; the method comprises the following steps: performing hierarchical design on a bridge management and maintenance knowledge graph, performing knowledge graph construction through more than two pre-trained large language models, dividing the knowledge graph into a generator and a discriminator, and performing entity and attribute extraction by using the large language models of the generator; whether entities and attributes are reasonable or not is judged through a large language model of a discriminator; s2, a disambiguation method based on a graph neural classification network; and S3, a knowledge graph reasoning method based on a component relationship. Compared with the prior art, the method is innovatively combined with a large model, so that the entity extraction efficiency and accuracy are improved; the availability of the knowledge graph and the reasoning accuracy are improved; the knowledge graph reasoning method based on the component relation is provided according to the actual requirements of bridge maintenance decision making.

The invention relates to the technical field of engineering structure health monitoring, and discloses a method for evaluating the fatigue life of a main beam based on sample continuous increment, which comprises the following steps: acquiring multi-modal monitoring data including physical parameters of strain, displacement and acceleration and external factor data of environmental temperature and humidity and traffic flow information; performing multi-source data fusion by using an adaptive feature extraction network; constructing a dual-knowledge consolidation incremental learning framework, and keeping the memory of historical knowledge when newly added data is processed; performing uncertainty quantification on a prediction result based on Bayesian deep learning, and establishing a risk early warning mechanism; according to the method, the problem of disastrous forgetting in the model updating process is effectively solved, and a relatively high historical knowledge retention rate is kept; a scientific basis is provided for bridge maintenance and maintenance decision making, and maintenance resource allocation is optimized; the method has excellent adaptability and generalization ability, and realizes continuous monitoring and evaluation in the whole life cycle.

The invention discloses a big data-based bridge maintenance decision-making method, which comprises the following steps of: firstly, acquiring and analyzing basic data of bridge maintenance, determining a bridge maintenance decision-making standard, constructing a corresponding bridge maintenance countermeasure library, and determining a bridge maintenance demand based on the bridge maintenance countermeasure library; and based on the maintenance grading decision tree and the priority ranking model, maintenance priority ranking is carried out on the to-be-maintained bridge, a bridge maintenance decision is optimized, and finally a bridge maintenance scheme and required cost are generated. According to the scheme, in decision making, the current situation of a bridge structure is considered, various factors such as bridge maintenance historical information are also considered, the factors include the technical situation, the current situation of structure diseases and the development trend, the decision-making scheme is optimized from multiple aspects through the maintenance grading decision-making tree and the optimization sequencing model, and the bridge maintenance scheme is output according to the final decision-making result. The decision making speed is greatly improved, the actual maintenance work requirement is met, effective configuration of maintenance funds is achieved, and scientificity, high efficiency and reliability of bridge maintenance decision making are improved.

The invention discloses a bridge pier column strength detection device and method, and relates to the field of bridge maintenance. The device comprises a climbing robot unit, a strength detection module, a camera shooting module and a terminal, the climbing robot unit carries the strength detection module and the camera shooting module, and a first retracting and releasing pre-tightening system and a second retracting and releasing pre-tightening system in the climbing robot unit surround a bridge pier column. The terminal controls a second climbing unit in the climbing robot unit to vertically move along a guide rail plate, and controls a first climbing unit to synchronously move along a first retracting and releasing pre-tightening system and the second climbing unit to synchronously move along a second retracting and releasing pre-tightening system; and the vertical driving assembly is controlled to drive the whole climbing robot unit to vertically move along the bridge pier column, so that the climbing robot unit reaches each preset position, and annular visual detection and strength detection are carried out on the bridge pier column at each preset position. According to the invention, the strength of the bridge pier column can be automatically and comprehensively detected.

The invention belongs to the technical field of bridge maintenance, and discloses a machine vision and digital twinning combined remote repair welding repair method and system for a cracked steel bridge structure. A transmission steel bridge maintenance method mainly depends on manual detection and field repair, and has many limitations in practical application and is difficult to meet bridge maintenance requirements. The method comprises the following steps: acquiring steel bridge data through a detection robot to identify a steel bridge crack, generating a three-dimensional model of the steel bridge crack based on the steel bridge crack data, performing finite element stress analysis, generating a crack welding strategy, and issuing the crack welding strategy to a welding robot to execute a welding action; further dynamically mapping the welding information to the three-dimensional model of the crack, fusing the three-dimensional dynamic model of crack repair into a VR operation interface, and monitoring the welding process of the welding robot through the VR operation interface; and finally, the welding robot detects the repair integrity of the steel bridge crack, and the repair integrity information is updated to the three-dimensional dynamic model of steel bridge crack repair.

The invention discloses a bridge maintenance operation supervision method, and belongs to construction site management, and the method comprises the steps: building a sensor model, a personnel model and a bridge BIM model; registering the sensor model and the bridge BIM model by using a geographic coordinate system; integrating a personnel model, the registered sensor model and the bridge BIM model through a GIS platform to obtain a digital twin scene based on BIM + GIS; the method comprises the following steps: acquiring GPS coordinate data of an operator through an intelligent safety helmet, and converting the GPS coordinate data into coordinate data under a Cartesian coordinate system; establishing a multi-source data correction mechanism, and correcting the coordinate data under the Cartesian coordinate system; and according to the corrected coordinate data, the position of the operator is displayed in real time in the digital twinborn scene through the operator model. In order to solve the problem that the positioning precision of operators is low due to GPS signal drifting in bridge maintenance, the monitoring and positioning precision is improved.

The utility model belongs to the technical field of bridge maintenance, and particularly relates to a climbing device convenient for cable-stayed bridge steel cable detection, which comprises a rope climbing mechanism and a positioning component connected to one end of the rope climbing mechanism, a plurality of rotating wheels are arranged in the rope climbing mechanism, the rotating wheels are connected with a motor I, and the motor I drives the rotating wheels to rotate and drives the device to climb on a steel cable. The positioning assembly comprises a sliding plate, a positioning piece and a connecting plate, an axially-arranged groove is formed in the connecting plate, the sliding plate is arranged in the groove in a sliding mode, a cavity for containing the positioning piece is formed between the connecting plate and the sliding plate, the sliding plate slides along the groove to open the cavity, and therefore the positioning piece in the cavity is released. According to the steel rope marking device, the positioning piece has elasticity, the positioning piece is placed in the cavity, the top plate and the cavity are compressed and stretched through the shapes of the positioning piece and the top plate, and when the sliding plate slides out, the positioning piece rebounds and pops out of the connecting plate to the surface of the steel rope for marking.

The invention discloses a road and bridge expansion joint defect detection system based on image recognition. Real-time image data of a road and a bridge are acquired based on a cruise image acquisition sensor; the method comprises the following steps: performing coordinate system matching on real-time image data and a standard image, eliminating a visual angle difference by adopting an SIFT (Scale Invariant Feature Transform) feature point extraction algorithm and homography matrix transformation, comparing and aligning the real-time image data and the standard image through a double-encoder-decoder network, and judging whether a defect exists in a suspicious defect area or not by using a MobileNetV3 classification model. If the defects exist, a ResNet-50 model is adopted to classify the types and severity of the defects, and cross validation is carried out on defect detection results and vibration characteristics of the bridge and the road. The method can have high classification precision, provides an accurate decision basis for maintenance and management of roads and bridges, guarantees the detection efficiency, and improves the detection accuracy.

The invention relates to the technical field of bridge maintenance, in particular to a method, a device, equipment, a system and a medium for determining cable force of a bridge stay cable, by collecting current parameter information of the stay cable under the circumferential constraint effect and the self-weight extrusion effect, the cable force of the stay cable is determined according to the current parameter information, and the cable force of the stay cable is determined. Establishing an unconstrained cable force solving functional equation of the stay cable, establishing a correction framework for obtaining the cable force of the stay cable according to the current parameter information, correcting the cable force solving functional equation by using the correction framework to obtain a target function, and performing solving operation by using the target function to obtain the cable force of the stay cable. Therefore, when the stay cable force calculation method is used, the defect of inaccurate cable force calculation caused by the annular constraint effect and the self-weight extrusion effect can be avoided, the calculation precision of the cable force is improved, and the calculation error of the cable force is reduced.

The invention relates to a dismantling method for a large-span steel strand stay cable, and belongs to the technical field of bridge maintenance. The dismantling method for the large-span steel strand stay cable comprises the steps that a steel strand is tensioned through a first traction mechanism, so that the steel strand at a fixed end anchorage device is subjected to force unloading, the steel strand subjected to force unloading is cut, and the large-span steel strand stay cable is obtained; the second traction mechanism is used for tensioning the hoisted steel strand so as to unload the steel strand at the tensioning end anchorage device, the unloaded steel strand is cut, the steel strand connected with the second traction mechanism is separated from the tensioning end anchorage device, the remaining steel strand is detached, and all the detached steel strands are carried; by means of the first traction mechanism and the second traction mechanism, the situation that a complex large hanging bracket needs to be installed on the tower top, consequently, installation is inconvenient and complex, and the working efficiency is reduced is avoided, the whole steel strand is broken up into parts by cutting the steel strands at the anchorage device at the fixed end one by one and cutting the steel strands at the anchorage device at the tensioning end in a grouping mode, and the construction cost is reduced. And the hoisting risk and the hoisting cost are reduced.

The invention relates to a bridge degradation identification and maintenance decision support method and system based on a knowledge graph, belongs to the technical field of traffic infrastructure intelligent operation and maintenance and engineering information processing, and solves the defects of weak data fusion capability, opaque causal modeling and non-traceable decision support in the prior art. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data to generate a standardized data set; semantic alignment is carried out, and candidate entities and relations are extracted; constructing a knowledge graph body and loading the knowledge graph body into a knowledge graph; a self-adaptive quantile threshold value is adopted to binarize the factor intensity and the disease severity sequence, and a smooth point mutual information and a Spearman correlation coefficient are fused to learn a causal weight; complementing the influence relation of factors on the bridge through graph reasoning and calculating the weight; and finally, calculating a bridge degradation risk score, and outputting a high-confidence influence path based on a shortest path algorithm. According to the method, multi-source data fusion and degradation causal chain explicit modeling are realized, and a scientific basis is provided for a bridge maintenance decision.

The invention relates to the technical field of bridge engineering maintenance decision, and discloses a bridge maintenance decision method based on big data. The method comprises the steps of collecting bridge monitoring data through a sensor network, performing hierarchical processing on an original data stream, identifying and packaging abnormal fluctuation, trend change and a periodic mode, and forming a structured health event sequence. And dynamically constructing and updating the digital sign profile of the bridge in combination with the design parameters and the material degradation model. And when a decision is triggered, simulating a performance evolution path under multi-factor coupling based on the profile, generating a future maintenance scene set, and matching and assembling a preliminary plan from historical cases and the knowledge graph. And outputting a final maintenance instruction list sorted according to priorities by performing resource and time coupling analysis and multi-target tradeoff on all plans. According to the method, deep analysis and prospective simulation of the bridge state are realized, and the intelligence, accuracy and performability of maintenance decision making are improved.