14434 results about "Steel structures" patented technology

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 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 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 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 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 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 relates to a steel structure engineering welding quality defect analysis method based on voiceprint monitoring, and the method comprises the steps: carrying out the multi-channel voiceprint synchronous collection, time-frequency feature fusion, wavelet packet analysis and Mel-frequency cepstral coefficient extraction for a plurality of defect features fused in voiceprint data in a welding process; a hierarchical semantic concept space and a dynamic causal relationship generation model are established in combination with a welding physical knowledge base, a causal knowledge graph is constructed, causal association between semantic concepts is deduced through a gating circulation unit and a graph neural network, anti-fact disturbance and path aggregation analysis is carried out on a causal graph structure, and a result is obtained. And finally, defect category probability output and causal traceability graph visual display are realized. According to the scheme, the accuracy, traceability and result interpretability of welding defect recognition are effectively improved, and data support is provided for intelligent diagnosis and continuous model optimization in the welding process.

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 discloses an intelligent monitoring and adjusting method and system for a steel structure, and the method comprises the steps: deploying a plurality of types of sensors at key parts of the steel structure, continuously collecting various types of physical quantity data, recognizing potential abnormal events in real time through a dynamic threshold detection mechanism, and responding to the abnormality of a certain sensor. The system automatically associates and extracts the time sequence data of other sensors associated with the spatial position of the sensor before and after the exception to form a multi-dimensional and time-space coupled data slice, and then performs time-space feature extraction on the data to extract the mode representation of the complex exception state; and based on this, accurate matching and diagnosis of the causes of the abnormal events are realized. Through the mode, the intelligent sensing and analysis capability of the health state of the complex, multi-source and multi-dimensional steel structure is improved, solid data support and decision basis are provided for subsequent scientific adjustment measures, and the operation safety and management intelligence level of large-scale steel structure engineering are better guaranteed.

The invention provides a steel structure welding quality detection method and a storage medium, and the method comprises the steps: generating a composite image # imgabs1 # marked with the type and position of a welding defect based on an unmarked real welding region image # imgabs0 # of a steel structure; the real steel structure welding image # imgabs2 # and the composite image # imgabs3 # marked with the welding defect type and position are combined into a steel structure welding image set # imgabs4 #; performing datamation processing on the steel structure welding image set # imgabs5 # to generate a deformable grid # imgabs6 # matched with the type of the welding defect and a preprocessing image set # imgabs7 #; and inputting the preprocessed image set # imgabs8 # into a pre-trained building engineering steel structure welding defect detection model so as to output welding defect characteristics of a welding seam area. The steel structure welding quality detection method is small in data dependence, high in welding defect recognition robustness and high in intelligent degree, and the intelligent level of constructional engineering steel structure welding quality detection can be improved.

The invention discloses a digital steel structure embedded part accurate positioning system, and belongs to the technical field of computer aided design and digital twinning, and the system comprises a data acquisition module which is used for obtaining a correction vector of a digital twinning model, the currently measured local positioning precision and the current global attitude consistency deviation; the model entropy calculation module is used for calculating a model authority entropy representing the accumulated deviation degree of the digital twin model based on the correction vector and a preset reference correction scale; the model authority calculation module is used for determining the model authority of the digital twin model according to the model authority entropy and a preset attenuation sensitivity coefficient; and the correction decision module is used for generating a correction acceptance probability in combination with the model authority and the local positioning precision, and performing probabilistic screening on the execution of the correction vector based on the correction acceptance probability, thereby providing a solid quantitative basis for subsequent judgment of the health condition of the model.

The invention relates to the field of building structure monitoring, and discloses a large-span space steel structure roof enclosure system wind uplift resistance monitoring method and system.The method comprises the steps that a sensing layer is established based on a sensor to monitor roof sensing data, and a transmission layer is established through cooperative work of LoRa and an optical fiber composite cable to transmit the sensing data; an analysis layer is established through a dynamic wind-structure coupling model, a multi-source data fusion algorithm and early warning grading, and dynamic evaluation and risk early warning are carried out; according to the method, historical data is subjected to data preprocessing and feature extraction, Bayesian parameters are updated and deep learning compensation is constructed, sensor errors are adaptively compensated through drift modeling and multi-sensor cross validation compensation, and an early warning threshold value is dynamically adjusted based on the number of samples exceeding a threshold value.

The invention relates to the technical field of data processing, and discloses a large steel structure hoisting precision real-time monitoring method and system. The method comprises the following steps: scanning and collecting the edge of a steel structure through a distributed sensor network, and obtaining structure data to obtain an original monitoring data set; extracting and analyzing structure transient response features based on the original monitoring data set, and generating a state feature matrix; analyzing a coupling relationship between local deformation and overall torsion to obtain a dynamic response prediction matrix; performing space-time registration and data fusion on the monitoring data, and constructing a state evaluation data set; carrying out optimization calculation on the hoisting parameters to generate a hoisting control data packet; and performing intelligent reasoning analysis in combination with historical case data, and outputting a control instruction sequence. Real-time monitoring of the coupling relation between local deformation and overall torsional deformation in the hoisting process of the large steel structure is achieved, and the technical defect that in the prior art, the structural deformation development trend cannot be accurately recognized and predicted is overcome.

The invention relates to the technical field of structural stress precision measurement, in particular to a distributed optical fiber sensing-based steel structure stress monitoring method, which comprises the following steps of: 1, constructing a sensing optical fiber array in a steel structure node area; 2, injecting optical pulses into the sensing optical fiber array, and synchronously collecting Brillouin frequency shift signals and Raman scattering signals of all points; 3, temperature and mechanical strain are separated based on the reference optical fiber signals, global temperature compensation is adopted for the first optical fiber set, and temperature gradient compensation along the optical fiber path is adopted for the second optical fiber set; 4, decomposing the compensated mechanical strain into multidirectional strain components according to the spatial orientation of the optical fiber, and reconstructing a three-dimensional stress field through strain component fusion; and 5, dynamically judging the damage risk according to the local stress concentration coefficient and the change rate thereof in the stress field. And through the temperature compensation technology, the influence of the environment temperature change on the strain data is eliminated, and the reliability of the monitoring result is improved.

The invention discloses an intelligent welding control system and method based on multiple sensors and electronic equipment, and belongs to the technical field of automatic welding, the system comprises a sensing layer, a decision-making layer and an execution layer, through cooperative work of a front laser vision sensor, a rear laser vision sensor and a molten pool image sensor, a groove three-dimensional model can be accurately established before welding, and the welding precision is improved. And dynamic information of the molten pool is continuously collected in the welding process, a dual monitoring mechanism for the geometric characteristics of the welding line and the state of the molten pool is formed, and multi-source data collection and fusion in the whole welding process are achieved. The control method comprises the steps of pre-scanning modeling, feedforward parameter planning, real-time feedback adjustment of a molten pool, data fusion optimization and the like, and high-precision self-adaptive control over welding parameters is achieved by combining deep learning and the Kalman filtering technology. According to the method, the welding adaptability, the control precision and the intelligent level can be remarkably improved, and the method is suitable for high-precision welding scenes such as pipelines, pressure containers and steel structures and has remarkable engineering application value and popularization prospects.

The invention discloses a steel structure power machine room construction quality whole process management and control method based on digital twinning, and belongs to the technical field of industrial intelligent manufacturing. The method comprises the following steps: obtaining construction object information, carrying out hierarchical division, and constructing and training a three-layer digital twinborn model; collecting and processing construction data; performing hierarchical quality analysis on the processed construction data through a three-layer digital twinborn model, and starting cross-layer correlation analysis to position a problem root when quality abnormality is detected; according to an analysis result of the layered quality analysis, triggering graded early warning, and generating a quantitative construction adjustment instruction based on analog simulation of a three-layer digital twin model; according to the method, a foundation-structure-construction three-layer digital twinborn model is constructed, and a cross-layer parameter association rule is established, so that high-fidelity virtual mapping of construction total elements is realized.

The invention relates to the technical field of steel structure welding assembly, and discloses a dynamic cooperative control steel structure welding assembly production line and an operation method thereof, which are used for automatic welding and deformation correction of H-shaped steel components. The production line comprises a feeding area, an assembling area, a welding station and a correction detection area. Global cooperative control is achieved through a distributed supporting system, a multi-dimensional sensor monitoring system and a cooperative robot system. The distributed supporting system is composed of dynamic roller bed units, the supporting height is adjusted in real time through a pressure sensor array and a micro hydraulic actuator, and workpiece droop is counteracted. The multi-dimensional sensor monitoring system collects temperature, strain and morphology data in real time; the welding robot and the correction robot synchronously apply electromagnetic stirring to optimize a welding seam during welding, and ultrasonic peening and laser heating are combined to reduce residual stress during correction. The industrial control computer dynamically adjusts welding parameters, supporting force and a correction strategy through sensor data, and high-precision straightness and low-residual-stress control is achieved.

The invention belongs to the technical field of BIM (Building Information Modeling), and particularly relates to a curved surface composite truss design and construction integrated method and device based on the BIM technology. The method comprises the following steps: considering four structural schemes for a building facade, generating a corresponding parameterized structural topology and a three-dimensional structural line model, adding a load, calculating, analyzing and optimizing a cross section, and generating four structural calculation models and building scheme models; the building space effects and the structure force transmission efficiency of the four structure schemes are compared, and the optimal one is selected as an implementation scheme; and forming a full-professional BIM model according to the selected implementation scheme. Building datamation is achieved through the BIM technology, digital delivery of design results is achieved, the link of two-dimensional plotting is reduced, steel structure deepening is further conducted through the BIM model in the construction stage, visual deduction is conducted on key procedures such as steel structure machining, assembling and hoisting, and digital construction is achieved.

The invention discloses a steel structure butt welding adjusting device and method, and relates to the technical field of steel structure welding, the steel structure butt welding adjusting device comprises a welding table, fixing seats are symmetrically and fixedly connected to the upper end face of the welding table, steel pipe bodies are placed above the fixing seats, and the steel pipe butt welding adjusting device further comprises a steel pipe alignment detection mechanism and a welding path pretreatment mechanism; the steel pipe alignment detection mechanism comprises symmetrically-arranged circular rings, annular grooves are formed in the two sides of each circular ring, supporting frames are arranged on the two sides of each circular ring, the supporting frames are fixedly connected to the upper end face of the welding table, the circular rings are rotationally connected between the two supporting frames through the annular grooves, and annular racks are fixedly connected to the outer surfaces of the circular rings. The annular racks are in meshed connection with transmission gears, and a driving shaft is fixedly connected between the two transmission gears in a penetrating mode, so that the alignment condition can be rapidly detected after the steel pipe body is placed, a worker can rapidly conduct adjustment according to the detection result, the time for manual repeated inspection and adjustment is shortened, and the steel pipe body adjusting efficiency is improved.

The invention discloses a steel structure butt joint device for steel structure machining, and relates to the technical field of steel structure butt joint welding. Comprising a base, an electric control translation sliding rail is installed on the base, an electric control translation block is installed in the electric control translation sliding rail, an installation frame is fixed to the electric control translation block, a welding mechanism used for welding two to-be-machined steel structures is installed on the installation frame, and an annular rotating base is rotatably installed on the base. By arranging the cutting assembly and the welding mechanism, two to-be-machined steel structures needing to be in butt joint can be cut and machined, the tangent planes of the two to-be-machined steel structures are matched, the two clamping mechanisms are stirred oppositely based on movement of the stirring mechanism, and the end faces of the two cut to-be-machined steel structures are attached; then welding treatment is conducted based on the welding mechanism; due to the arrangement of structures such as the annular rotating seat, the angle of the to-be-machined steel structure can be adjusted based on rotation of the annular rotating seat according to requirements, so that the cutting and welding angles are changed, and the flexibility is improved.

The invention relates to the technical field of monitoring, in particular to a method and system for detecting and evaluating the corrosion risk of an offshore wind power steel structure. The method comprises the following steps: acquiring corrosion environment parameters and corrosion response parameters of a corrosion area in the offshore wind power steel structure; according to the corrosion environment parameters and the corrosion response parameters, the real-time corrosion rate of the corrosion area is determined; determining a local stress concentration factor of the corrosion area according to the three-dimensional scanning characteristics of corrosion deposits in the corrosion area; the dynamic pressure parameters of the marine environment serve as dynamic boundary conditions, the real-time corrosion rate and the local stress concentration factor serve as the basis, and the structural rigidity degradation amount of the steel structure in the corrosion area is determined through finite element iteration; according to the structural rigidity degradation amount, the structural safety coefficient of the steel structure in the corrosion area is determined; and determining the corrosion risk of the steel structure in the corrosion area according to the dynamic pressure fluctuation range of the marine environment and the structure safety coefficient of the steel structure in the corrosion area.