19225 results about "Steel structures" patented technology

The invention relates to the technical field of data processing, and discloses a BIM-assisted steel structure construction safety management early warning method, device and equipment. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition and space-time alignment processing on a steel structure construction site to obtain an engineering data stream; carrying out dynamic noise reduction and feature extraction on the engineering data flow to obtain a standardized multi-modal feature vector, and carrying out time sequence correlation analysis and mode mining to obtain a potential risk index set; performing multi-level threshold calculation and context sensitivity evaluation on the potential risk index set to obtain an adaptive early warning trigger matrix; performing multi-dimensional cross validation and causal chain analysis on the self-adaptive early warning trigger matrix to obtain a graded and classified risk assessment report; and carrying out scene analysis and response scheme generation on the graded and classified risk assessment report to obtain an executable multi-step emergency plan. According to the invention, the efficiency and accuracy of BIM-assisted steel structure construction safety management early warning are improved.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

The invention relates to the technical field of welding deformation control, and discloses a multi-stage adjusting method and system for steel structure welding deformation control. The method comprises the steps that steel member parameters are measured, a welding deformation prediction model is established, and pre-deformation data are obtained; performing accurate pre-deformation processing according to the data; executing segmented welding and recording a temperature field and a deformation amount; performing local heat treatment on the high-stress area to obtain stress distribution; correcting a deformation area by using multi-point force; and performing precision detection and quality evaluation according to the corrected data to form a control file. According to the method, high-precision control over welding deformation of the complex steel structure is achieved through data transmission and feedback adjustment of all stages.

The invention relates to the technical field of constructional engineering, in particular to a steel structure construction intelligent monitoring method based on unmanned aerial vehicle inspection and point cloud processing, which comprises the following steps: S1, collecting point cloud data of geometric shape, size and position information of each part of a steel structure; s2, registering and fusing the point cloud data from different visual angles to form a three-dimensional point cloud model; s3, geometric features of the steel structure are extracted from the three-dimensional point cloud model, and a steel structure three-dimensional model is established; s4, comparing the steel structure three-dimensional model established in the S3 with a construction drawing, and identifying geometric differences; and S5, analyzing the construction deviation report, and identifying potential quality problems in construction. According to the invention, by combining the unmanned aerial vehicle inspection, the point cloud processing and the graph neural network algorithm, the quality problem in steel structure construction can be efficiently and accurately identified, the safety risk can be evaluated, and the automation and intelligence level of construction quality monitoring can be significantly improved.

The invention relates to a steel structure component virtual pre-assembly method based on three-dimensional laser scanning. Designing a self-adaptive hierarchical scanning strategy, and dynamically adjusting parameters of scanning equipment to obtain data of each region of the component; abnormal points are detected and eliminated by fusing scanning data of different levels; datum points are extracted, coordinate calibration is carried out, data of all levels are spliced, and a full-component three-dimensional point cloud model is formed; classifying the point clouds by using a multi-dimensional data analysis algorithm; establishing a closed-loop data verification system; designing a multi-feature point matching algorithm; performing iterative docking and position calibration through an algorithm; quantitative analysis is carried out on spatial deviation between the components in the assembling process; after the deviation is detected, a fine adjustment scheme is generated, and the position of the component is adjusted; a virtual pre-assembly path is generated according to the shapes, the connection modes and the installation sequence of the components, and the assembly sequence and the layout of spatial positions are controlled; and the assembly state of each component is monitored in real time, and the path is optimized and adjusted through a feedback mechanism.

The invention belongs to the field of steel structure health monitoring, and provides a multi-source data fused large steel structure digital twin model construction method and system, and the method comprises the steps: constructing a geometric model of an in-service steel structure based on multi-view point cloud data and a design drawing; obtaining multi-source heterogeneous data of the steel structure; performing cross-modal fusion on the multi-source heterogeneous data to obtain cross-modal fusion features; constructing a digital twinborn model corresponding to the steel structure geometric model by combining the steel structure geometric model and the cross-modal fusion features; and performing dynamic prediction correction on the constructed digital twinborn model to obtain a corrected digital twinborn model. The current steel structure damage condition is comprehensively predicted by fusing multi-parameter signals including strain, temperature, vibration and the like, and the damage state prediction is more accurate.

The invention relates to the technical field of nondestructive testing, and discloses an ultrasonic nondestructive testing method and system for a welding seam of a steel structure. The method comprises the following steps: adopting a self-adaptive coupling ultrasonic probe to carry out signal acquisition on the surface of a welding seam of a steel structure to obtain original ultrasonic signal data; performing multispectral adaptive wavelet transform processing on the original ultrasonic signal data to obtain an enhanced ultrasonic signal; performing blind source separation on the enhanced ultrasonic signal to obtain a separated weld defect signal; performing triangular constraint average interpolation and adaptive extended Kalman filtering fusion processing on the separated weld defect signals to obtain filtered weld defect feature data; and performing regularization Gaussian field weld contour reconstruction and defect positioning processing based on the filtered weld defect feature data to obtain a weld contour model and defect position data. According to the invention, the stability and reliability of weld defect detection are improved, and high-precision detection of weld internal defects is realized.

Provided is a high-strength steel sheet having excellent member strength, ductility, stretch flangeability, bendability of a shear end surface, and low-temperature toughness. A high-strength steel sheet having a prescribed component composition and a steel structure in which the area ratio of martensite is 10%-80%, the area ratio of bainite is 2%-70%, the area ratio of ferrite is 80% or less, and the area ratio of retained austenite is 15% or less at a position at 1 / 4 of the sheet thickness. The ratio of the number of martensite blocks in which metastable carbides are present relative to the number of martensite blocks is 2% or more, and the standard deviation [sigma] n of the nano-hardness relative to the average value [Hn] ave of the nano-hardness is 0.60 x [Hn] ave or less when the nano-hardness at 225 points or more is measured at a position of 1 / 4 of the sheet thickness.

The invention relates to the technical field of building engineering, in particular to a steel structure construction quality monitoring system based on an unmanned aerial vehicle and three-dimensional scanning, which comprises a data acquisition module, a data preprocessing module, a construction defect identification and risk assessment module and a quality assessment module, the data acquisition module acquires quality monitoring data of a construction site in real time; the data preprocessing module is used for preprocessing the acquired quality monitoring data; the construction defect identification and risk assessment module identifies steel structure defects in the construction process and assesses the severity of the steel structure defects; and the quality evaluation module performs comprehensive quality evaluation on the preprocessed quality monitoring data and provides corresponding repair suggestions. According to the method, key problem areas can be quickly identified, a construction resource allocation strategy is optimized, and comprehensive and accurate data support is provided for steel structure quality evaluation in a complex construction environment.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to a grid steel structure node failure diffusion prediction method, which comprises the following steps of: forming a failure inducement and evolution path model according to a connection form of a bolt-sphere node and a welding node, a stress transfer path, node local yield and fatigue accumulation factors; a diffusion logic of node failure-force flow redistribution-adjacent node stress concentration-chain reaction is constructed; nodes are regarded as middle points of the graph, rod pieces are regarded as edges, and propagation weights between the nodes are defined; the weight parameters are dynamically assigned based on material attributes, node connection types, spatial geometric positions and historical stress states; multi-source monitoring sensors including strain gauges, displacement meters, thermometers and accelerometers are arranged at key nodes of the grid structure, so that node states are sensed; in combination with real-time monitoring data, extracting strain rate change, vibration characteristics, frequency drift and residual deformation key characteristics of each node; a node health state feature vector set is constructed by using time sequence analysis, and the gradual degradation trend of nodes is reflected.

The invention relates to the field of engineering management, and discloses a wharf steel structure construction progress dynamic management and control and resource allocation system, which comprises a real-time data perception and fusion module for collecting field data and an initial plan and generating a fusion data stream; the dynamic construction digital twinborn construction and evolution module is used for updating the digital twinborn and a dynamic empirical risk layer in real time based on the fusion data flow; the multi-agent self-adaptive decision engine is used for generating a self-adaptive construction scheme based on digital twinborn simulation deduction and feeding back a risk signal; and the man-machine cooperation and closed-loop execution module is used for visualizing the scheme for manual decision making and issuing an instruction to a site. According to the method, the physical field data sensed in real time is fused with the digital model, the dynamic construction digital twin capable of being synchronously updated with the physical construction field state is constructed, and the technical effect that digital information and the physical entity state are kept highly consistent is achieved.

The invention discloses a steel structure three-dimensional workpiece welding seam identification and path planning method and system, and relates to the technical field of robot intelligent welding, and the method comprises the steps: building a multi-view fusion point cloud corresponding to a target workpiece; performing welding seam segmentation on the multi-view fusion point cloud by using a welding seam neighborhood point cloud segmentation model to obtain a multi-view fusion welding seam neighborhood point cloud; performing point cloud plane fitting on the multi-view fusion welding seam neighborhood point cloud to obtain a plurality of target welding seam neighborhood planes; and determining a corresponding welding seam straight line based on each pair of intersected target welding seam neighborhood planes, and identifying a welding path and a welding posture according to the welding seam straight line. The method provided by the invention shows good applicability under a complex background, has the advantages of automation, high efficiency, high recognition precision, strong adaptability and the like, and meets the intelligent and flexible operation requirements of the welding robot.

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 a mechanical arm welding detection intelligent diagnosis method and system based on laser ultrasonic guided waves. According to the system, a high-precision multi-degree-of-freedom steering engine, an infrared distance measurement array, a temperature sensor, a laser vibration exciter and a laser vibration meter are integrated at the tail end of a mechanical arm, synchronous operation of welding and online detection is achieved by presetting a welding track, and the system collects an excitation signal, a reflection ultrasonic signal and temperature data through a high-speed data collection card; and the signal is preprocessed through a temperature matching model, phase compensation and baseline correction. Then, wavelet decomposition is adopted to extract time-frequency features, intelligent diagnosis is achieved through a pre-trained one-dimensional convolutional neural network, a dynamic threshold value is set based on the 3 sigma criterion, and recheck feedback is automatically triggered when detection data exceed the limit, the welding detection efficiency and precision are remarkably improved through the scheme, data closed-loop management and intelligent quality tracing are achieved, and the reliability of welding detection is improved. And the automatic production requirement of modern steel structure manufacturing is met.

The invention discloses a cooperative control method for double-crane lifting of a large-span steel structure, which comprises the following steps of: pre-installing sensor modules on a crane hook and a steel structure key node before construction, acquiring load distribution and structural deformation data in real time through a multi-source sensor fusion technology, and establishing a unified coordinate system; and the cooperative control system is started to control the hoisting process. The invention further discloses a safety monitoring system for double-crane lifting of the large-span steel structure. According to the invention, the action matching height precision of the two cranes is ensured, the construction precision and the hoisting efficiency are improved, the safety of hoisting operation is guaranteed in all directions, and the accident rate is reduced.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention relates to the technical field of quality inspection, in particular to a complex steel structure construction quality detection method and system based on three-dimensional laser scanning, and the method comprises the steps: obtaining three-dimensional laser scanning data and corresponding design data of a steel member in a machining quality detection stage, a virtual pre-assembly stage and an on-site actual assembly stage of the steel member; preprocessing the obtained three-dimensional laser scanning data to obtain point cloud data corresponding to a real object, constructing the obtained design data into a BIM model and converting the BIM model into point cloud data corresponding to the model, carrying out point cloud registration on the two parts of data through a registration algorithm, obtaining an error between the two parts of data through comparison, and obtaining the point cloud data of the real object according to the obtained error information. And determining the actual quality of the steel member.

The invention relates to the technical field of intelligent construction, and discloses a construction steel structure fabricated building assembly method which comprises the steps that real-time pose data of a steel structure component is collected through a multi-source sensor array; performing multi-rate fusion processing on the pose data to generate six-dimensional state estimation containing position and pose errors; constructing an error matrix in the Lie group SE (3) space based on the state estimation, wherein the error matrix represents the deviation between the component pose and the target pose; and establishing a matrix differential equation model according to the error matrix, and solving an optimal compensation control quantity in a future time domain in a rolling manner through a model prediction control algorithm. Through combination of Lie group space error modeling and rolling time domain optimization, the system can sense the pose deviation of a component in real time and dynamically adjust a control strategy, limitation of traditional static error compensation and online parameter identification and disturbance suppression under complex working conditions are broken through, and uncertainty caused by dynamic changes of a construction environment is effectively dealt with.

The invention discloses a steel structure support for photovoltaic power generation, relates to the field of photovoltaic power generation supports, and solves the problem that the surface of a photovoltaic panel is easy to damage and the service life is affected due to the fact that an existing steel structure support for photovoltaic power generation is difficult to turn over and fold at multiple angles during use. Comprising a base, a supporting mechanism, an adjusting mechanism, a cleaning mechanism and two sets of photovoltaic panels, the base is rotationally connected with a first electric telescopic rod, the supporting mechanism comprises a guide frame, a connecting plate and a guide piece, the adjusting mechanism comprises a mounting ring and a second electric telescopic rod, and the cleaning mechanism comprises a cleaning roller. A first electric telescopic rod can conveniently drive a connecting plate to ascend and descend, so that the photovoltaic panel is rotated, overturned and contracted, the angle of a guide frame is adjusted through a second electric telescopic rod, the light receiving efficiency is improved, meanwhile, a cleaning roller is linked to clean the surface of the photovoltaic panel, and the cleaning roller is self-cleaned through the connecting plate; and the service life of the photovoltaic panel is prolonged.

The present invention relates to the technical field of fabricated buildings. Disclosed is a fabricated steel structure residence system. The fabricated steel structure residence system of the present invention comprises a frame portion and a shear wall portion, wherein the frame portion comprises a special-shaped column, a steel beam and a floor system; and the special-shaped column comprises a steel pipe and a plurality of T-shaped steels arranged along a peripheral wall of the steel pipe. A combined T-O section form of the special-shaped column provided by the present invention can realize relative separation of a pressure from bending on a combined section, such that the fabricated steel structure residence system has good bending bearing capacity and rigidity. The characteristic of an outer T-shaped steel is used such that a full-bolt connection form can be used between the steel beam and the special-shaped column. Moreover, a reinforcement member is provided at a beam-column joint, such that the rigidity and strength of the joint region are improved. In addition, specific structures of a floor system and a shear wall are further optimized, and a corresponding joint connection method is provided, such that the fabricated steel structure residence system that can be quickly assembled and is stable in terms of connection is formed.

The invention relates to the technical field of machine vision structure health monitoring, and discloses a steel structure weld fatigue cracking real-time monitoring system based on machine vision. The system comprises a space-time registration and fusion module, a multi-scale feature analysis module, a health monitoring module, a crack deduction calculation module and a regulation and control strategy generation module. Performing space-time registration and pixel-level fusion through the visual data of the plurality of image sensors to generate a synchronous multi-source image stream; a multi-level welding seam characteristic spectrum is constructed through multi-scale characteristic analysis, and a welding seam structure knowledge base is dynamically updated; the knowledge base and the real-time characteristic spectrum are used for monitoring the welding seam health state, and abnormity is recognized; deducing a crack initiation position and an evolution path in combination with historical damage data; and real-time load information is fused to pre-estimate the remaining service life, and a structural integrity regulation and control strategy is generated online. According to the invention, high-precision fusion of the multi-source visual data and active prediction of the crack trend are realized, and the monitoring accuracy and the early warning capability are improved.

The invention discloses a steel structure quality defect tracing and analysis method based on deep learning, and the method comprises the following steps: collecting image data, sensor data and construction log information of a steel structure member, and generating a tracing identifier; performing alignment based on the traceability identifier to generate an alignment data sequence; based on the aligned data sequence, outputting a defect segmentation result by using an SE (3) isovariant graph neural network; performing continuous coherence topology analysis on a defect segmentation result, and outputting topologically continuous defect areas and severity scores; extracting process parameters of the defect area, calculating statistical dependency by utilizing an independence criterion, and screening out a paired sample set; based on the sample set, performing stability screening on the causal edges to form a causal graph for output; calculating a causal contribution score output by the causal graph, and outputting a liability sorting list; and filing the responsibility sorting list, and visually outputting a defect traceability analysis atlas and report at the same time. According to the invention, steel structure quality defect tracing and analysis are realized.

The invention discloses a robot system for steel structure welding, relates to the field of steel structure machining equipment, and aims at solving the problem that a steel structure can be welded by collecting robot track data and a welding seam image in the welding process, constructing a track continuity evaluation model, identifying a path change trend and fusing an image structure and track change information. The position and confidence of a track interruption section are recognized, so that the high-precision recognition and automatic recovery capability after path interruption is improved; a smooth compensation track is generated by adopting third-order Bessel interpolation based on starting and ending points of an interruption section, curvature and posture consistency registration with an original track is completed, and continuity and stability of a welding path are ensured; multi-modal data such as current, voltage, temperature, acoustics and welding seam images are collected through multiple channels, welding abnormity is recognized, state feature vectors are formed, and the problems such as splashing abnormity and welding seam deviation can be found in real time; through multi-dimensional analysis and dynamic adjustment of the whole welding process, the stability, intelligence and adaptability of the welding system are remarkably improved.

The invention discloses a BIM technology fused fabricated steel structure assembly optimization method and system, and relates to the technical field of data processing.The method comprises the steps that fabricated steel structure design information of a target assembly project is obtained, and a fabricated steel structure digital twinborn model is constructed; collecting a multi-angle image of a to-be-assembled node, extracting surface defects and connection quality features, and determining a node quality factor; and carrying out assemblability authentication based on the quality factor, if the authentication is passed, carrying out analogue simulation through an assembly type steel structure digital twinborn model, and optimizing an assembly scheme in combination with real-time environmental data. The technical problems that in traditional steel structure assembling in the prior art, node quality evaluation is not accurate, an assembling scheme lacks intelligent optimization and dynamic adjustment, and the assembling precision and the construction efficiency are affected are solved, the node quality is accurately evaluated by fusing the BIM technology, the assembling scheme is optimized in real time, and the construction efficiency is improved. The steel structure assembling precision and the construction efficiency are improved, and the construction risk is reduced.

The invention relates to the field of constructional engineering, and discloses an assembly type reinforced concrete-steel structure foundation pit supporting system convenient to disassemble and a construction technology, and the assembly type reinforced concrete-steel structure foundation pit supporting system comprises a top beam, a waist beam, a supporting pile and an inner supporting system; the inner supporting system is formed by assembling a standard steel-concrete beam section main support and a steel member auxiliary support in cooperation with a jack adjusting system. The invention further provides a new system on-site assembly construction process, namely detailed steps of assembly construction from the uppermost layer to the lower layer step by step. The fabricated structure integrates the advantages of two fabricated structures, is suitable for foundation pit supporting with large stress and strict deformation control, and has the advantages of being high in construction speed, short in construction period, low in construction cost, convenient to dismantle and capable of completely recycling most of inner supporting components compared with a traditional cast-in-place steel-concrete foundation pit supporting system.

The invention discloses a steel structure welding positioning device for constructional engineering, and relates to the technical field of welding equipment, the steel structure welding positioning device comprises a welding table, the top of the welding table is provided with a rotating mechanism, and the rotating mechanism is provided with a welding fixing assembly. The upper surface and the lower surface of the steel structure pipe fitting are clamped through the welding clamping plate, the auxiliary clamping plate makes contact with the side face of the steel structure pipe fitting, the steel structure pipe fitting is pushed to the center position of the upper surface of the welding clamping plate, and the steel structure pipe fitting can be centralized and positioned; meanwhile, the side face of the steel structure pipe fitting can be fixed, the four faces of the steel structure pipe fitting can be supported and limited in cooperation with welding clamping plates, a transmission screw can drive a moving block to move along the transmission screw, and at the moment, the moving block can drive an inner clamping block to move; and the inner clamping blocks move to make contact with the inner wall of the steel pipe and extrude the inner wall of the steel pipe, so that the inner wall of the pipe fitting is fixed, and the stability in the steel structure welding process is improved.

The invention provides a mechanical correction optimization method and device for steel structure welding and electronic equipment, and relates to the technical field of steel structure welding.The mechanical correction optimization method for steel structure welding comprises the steps that a steel structure deformation model is obtained through preset simulation software according to obtained design data and welding parameters of a steel structure; based on the steel structure deformation model, a mechanical correction model of the steel structure is established according to the obtained mechanical correction process data; and the mechanical correction model is optimized through mechanical correction simulation, optimized mechanical correction process data are obtained, and the optimized mechanical correction process data are used for conducting mechanical correction on the welded steel structure. According to the method, the mechanical correction process data are optimized through modeling, then the correction process is controlled according to the optimized mechanical correction process data, welding residual stress and deformation are effectively reduced, the alignment precision and the overall performance of a welded joint are improved, and therefore the welding quality is improved.

According to the offshore booster station steel structure safety monitoring method and system based on artificial intelligence, the stress, vibration, corrosion, crack and environmental data of the steel structure are collected through the multi-modal sensor arranged at the offshore booster station steel structure part, and time alignment is performed on the multi-modal data through the time synchronization protocol; the multi-mode sensor comprises a stress sensor, a vibration sensor, a corrosion sensor, an ultrasonic sensor and an environment sensor. On the basis of finite element analysis and real-time sensor data, a dynamically-updated digital twinborn model is constructed, the limitation of traditional static monitoring is broken through, the stress distribution and damage state of the steel structure can be reflected in real time, a scientific basis is provided for simulating structural response under an extreme load, and the application prospect is wide. And the accuracy and predictability of steel structure safety evaluation in the environment are obviously improved.