10202 results about "Steel structures" patented technology

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 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 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 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 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 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 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.

The invention discloses a digital modeling steel structure multi-dimensional collaborative optimization design method and system, belongs to the technical field of steel structure design, and aims to solve the problems that multi-dimensional comprehensive consideration and automatic optimization tools are lacked, and complex multi-objective optimization requirements are difficult to quickly respond. Comprising the steps of establishing a full-parameterized digital model containing construction process parameters and environmental protection indexes, constructing a multi-objective evaluation system of structural safety, economical efficiency, construction feasibility and environmental influence, performing automatic optimization by adopting an improved multi-objective evolutionary algorithm, selecting 10 groups of candidate schemes, generating a visual interface, supporting human-computer interaction decision, and generating an optimization report. According to the method, multi-dimensional collaborative optimization is realized by constructing a multi-target evaluation system of structural safety, economy, construction feasibility and environmental influence, construction process parameters and environmental protection indexes are integrated through the digital modeling unit, it is ensured that factors of all parties are comprehensively considered in design decision, and the design efficiency is remarkably improved.

The invention discloses a nondestructive testing method for a welding seam of a steel structure. The method comprises the steps that firstly, morphology data of the surface of a building steel structure are obtained through a high-precision laser scanner and an electromagnetic induction technology; coating thickness distribution and weld surface roughness are analyzed based on the morphology data, and a surface information matrix is constructed; then automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the adaptive parameter regulation and control model and executing detection to obtain a preliminary ultrasonic echo signal; the time-frequency domain difference between the preliminary echo and the reference signal is calculated through an echo signal compensation model, and secondary compensation is completed to obtain an optimized ultrasonic echo signal; fusing the optimized echo signal, the morphology data and the surface information matrix to construct a detection data set, and adopting a pre-trained steel structure detection model to realize defect detection; and finally, evaluating the confidence of a detection result by adopting a three-dimensional confidence evaluation model, performing grading, and performing local re-detection and parameter adjustment on a low-confidence region to form closed-loop optimization. According to the method, the surface state of the steel structure can be adaptively matched, the accuracy of echo signals and the defect detection precision are improved, and the detection reliability is guaranteed through three-dimensional confidence evaluation.

The invention relates to the technical field of building steel structure construction, and provides a closed-loop control method for segmented construction of a special-shaped curtain wall steel structure, and the method comprises the steps: S101, carrying out the three-dimensional scanning of a to-be-installed component and a supporting jig frame, building a coordinate system through a positioning target, and obtaining point cloud data; step S102, extracting component splicing key points, matching point cloud with a design model, performing collision detection and generating pre-splicing positioning data; s103, converting the positioning data into a visual instruction, and guiding the component to be accurately in place in combination with the real-time hoisting data; s104, after installation, a seam area is scanned, and a new entering component is reinspected; s105, the step S102 to the step S104 are repeated until all the components are installed; and S106, integrally scanning to generate a completion model, comparing the completion model with the design model, and outputting a construction quality acceptance report. According to the technical scheme, the construction precision, efficiency and construction quality of the special-shaped curtain wall steel structure are collaboratively optimized by constructing a closed-loop control process.

The invention discloses a steel structure welding guiding and positioning device, which relates to the technical field of steel structure processing, and comprises a main body supporting frame, a workpiece dynamic limiting and conveying unit and a self-centering clamping and welding module, and is matched with a guiding and feeding mechanism, a self-adaptive reducing connecting cabin and a multi-dimensional self-adaptive welding gun executing mechanism to realize integrated welding and positioning. The main body frame serves as a bearing foundation, the guide feeding mechanism drives guide rollers to stably feed through meshing transmission, the stability is improved through a surface pattern sleeve, the dynamic limiting unit forms bidirectional limiting through an adjusting screw, a cross connecting rod and a driving limiting shaft rod and is adaptive to different workpieces, and the self-centering clamping module adjusts the distance through gear transmission. A profiling clamping jaw is matched with a spring to achieve self-adaptive clamping, an anti-vibration pad protects a workpiece, a self-adaptive connecting cabin adjusts the height of a welding gun, a multi-dimensional welding gun mechanism achieves accurate alignment in the X direction and the Z direction, all parts of the device are in cooperative linkage, the welding positioning accuracy and adaptability are improved, and the welding quality of a steel structure is guaranteed.

The invention discloses a multi-target bridge unmanned aerial vehicle inspection path optimization method and system, and relates to the technical field of crossing of bridge operation and maintenance and unmanned aerial vehicle cluster control. The method comprises the following steps: constructing a bridge three-dimensional point cloud model, extracting key inspection points, and establishing a multi-objective optimization function including path length, time consumption, coverage rate, risk coefficient, cluster safety and formation retention; a multi-target reward mechanism is fused through a double-delay depth deterministic strategy gradient network, and a deep learning model is constructed and used for generating an unmanned aerial vehicle cluster obstacle avoidance strategy; training a deep learning model; and based on real-time data, performing multi-target bridge unmanned aerial vehicle routing inspection path optimization by using the trained deep learning model, and adjusting a weight coefficient of a multi-target optimization function through an attention network dynamic weight distribution technology in the optimization process. According to the invention, multi-objective optimization of routing inspection safety, efficiency, quality and cluster cooperation is realized, and the method is suitable for automatic routing inspection of various complex steel structure bridges.

The present invention belongs to the field of steel structure building construction detection. Disclosed are a prefabricated steel structure welding data transmission system based on digital twin, and a method. The prefabricated steel structure welding data real-time transmission system based on digital twin comprises an unmanned aerial vehicle, which is used for flying to a high point of a prefabricated steel structure, wherein an RFID tag positioning identifier is provided at the top of the unmanned aerial vehicle, and is used for identifying RFID tags attached to edges of welding positions. By using an image registration and superposition module, an image, which is processed in real time, is matched and superposed with ultrasonic feedback array points having array point coordinate information; and by means of a result generation module, defective parts are marked on a matched and superposed image which has array point coordinate information, and continuous welding seam length data, height difference data between adjacent array points and a lack-of-welding length value are attached, such that a welding seam quality inspection error is avoided, and the defect situation of each welding point can be effectively uploaded to a computer for checking, thereby avoiding the quality problem of a steel structure building that is caused by an error of a welder.

The invention relates to the field of building construction, and discloses a BIM model dynamic update-driven steel structure construction real-time accurate early warning method, which comprises the following steps: deploying a sensor array at a construction site for collecting various data of a steel structure, the various data including actual size, installation position, stress strain, temperature and humidity data; capturing various data of a construction area through a three-dimensional laser scanner and an image recognition technology; the invention also discloses a BIM model dynamic update-driven steel structure construction real-time accurate early warning system. The system comprises a data acquisition unit, a three-dimensional scanning unit, a data processing unit, a risk assessment unit, an early warning mechanism unit and a feedback correction unit. According to the method, the construction deviation is fed back in real time, the BIM model is dynamically updated, and risk assessment is performed by integrating multiple factors, so that the construction precision and safety are improved, the precise monitoring and management of the construction process are ensured, and manual errors and delayed response are avoided.

The invention relates to the field of welding quality detection, and discloses a steel structure welding quality detection method and system based on machine vision, and the method comprises the steps: obtaining an original welding seam image sequence through high-frequency sampling, and carrying out the preprocessing to generate a clear sequence; extracting features from the sequence to obtain a welding seam static defect index; time sequence data are constructed according to the time arrangement characteristics, and a dynamic time sequence data set is obtained by calculating the welding seam symmetry fluctuation amplitude and the molten pool shape change trend; inputting the data set into a neural network model, analyzing a surface profile curve time sequence evolution mode, and determining potential defect points; extracting a crack period amplitude sequence from the potential defect points, and performing Fourier transform to obtain a defect frequency distribution diagram; extracting a frequency peak value position from the graph, judging process parameter deviation, generating an alarm and updating a database; and determining a sampling frequency deviation value according to the alarm, adjusting the high-frequency sampling frequency through feedback circulation, and optimizing dynamic detection. According to the invention, the detection real-time performance, the accuracy and the parameter adjustment accuracy are improved.

The invention relates to the technical field of intelligent welding systems, in particular to a laser cutting method, system and device suitable for steel structure machining, and the method comprises the steps that a grid map used for planning a cutting path of a to-be-cut steel plate is obtained; acquiring the temperature rise amount of each edge generated by laser cutting; marking the cut edges before each edge is cut as each cut edge, and obtaining the residual heat coefficient and the heat accumulation coefficient of each edge; obtaining the angle influence degree of each edge by comparing the cutting duration of each edge with all previous cut edges and combining the included angle between each edge and the previous cut edge adjacent to each edge with the difference between the included angle and a preset angle threshold value; and heuristic information of each edge of the ant colony optimization algorithm during path planning is obtained, and then the optimal laser cutting path is obtained. According to the method, the cutting path of the steel plate can be optimized by reconstructing the heuristic information in the ant colony optimization algorithm, and then the intelligent welding effect of a subsequent steel structure is improved.

The invention relates to the technical field of intelligent control of heavy equipment, in particular to a hydraulic synchronous intelligent control system for segmented turnover of an overweight steel structure, and discloses the hydraulic synchronous intelligent control system for segmented turnover of the overweight steel structure. According to the system, full-field strain data of a structure surface is acquired by deploying a flexible grid sensor array, and a deformation gradient topological graph is constructed to identify an instability risk area. And a dynamic fluid network model of the hydraulic system is established, fluid-solid coupling simulation is carried out in combination with deformation characteristics, and pressure pulsation transmission paths under different control instructions are predicted. And the system dynamically optimizes a data sampling strategy and network model parameters according to a prediction path, generates a hydraulic cylinder coordination action instruction subjected to pressure balance verification, and finally drives an execution mechanism to complete turnover operation. According to the scheme, full-field real-time sensing of structural deformation and dynamic coupling prediction control of hydraulic fluid are achieved, and the safety and control precision of the overweight component in the turnover process are improved.

The invention relates to the technical field of intelligent installation control systems, in particular to a steel structure installation method, system and equipment based on laser scanning and BIM fusion. According to the steel structure installation method based on laser scanning and BIM fusion, the three-dimensional point cloud data of the component is obtained through laser scanning, the three-dimensional point cloud data and a BIM theoretical model are compared and analyzed, and the actual state of the component is accurately mastered; the construction site is scanned according to the actual deviation value of the component, and spatial data of the installation position are obtained; importing the actually measured data and the field data of the component into a BIM system for virtual pre-assembly, and generating installation guidance parameters; in the installation process, a monitoring system is established based on the guiding parameters, the component position change is tracked in real time, and an adjusting instruction is generated. Precise control over the whole steel structure installation process is achieved, and the construction efficiency and the installation quality are improved.

The invention relates to the technical field of building engineering construction management, and discloses a house steel building construction prediction progress and deployment system based on deep learning. According to the system, periodic stable paragraphs and non-periodic fluctuation paragraphs are recognized by analyzing the time sequence form of construction flow data, and construction links are stripped accordingly. And performing cross mapping on the hoisting event sequence in the link and the environmental monitoring reading of the associated link to generate a link toughness spectrogram representing the construction robustness. And determining a resource demand based on the spectrum graph, and forming a multi-dimensional resource demand vector, so as to drive a graph convolutional network to construct a dynamic construction deduction graph. And carrying out message passing and neighborhood aggregation on the graph, analyzing a key bottleneck path, and iteratively generating a progress prediction and resource allocation scheme. According to the method, the construction dynamic toughness can be evaluated, and accurate positioning and dynamic optimization of resource bottlenecks are realized.

The utility model provides a bending machine for steel structure machining. According to the bending machine for steel structure machining, two supporting plates are fixedly connected to the top of a bottom plate, a bending table is fixedly connected to the tops of the two supporting plates, a fan-shaped movable groove is formed in the top of the bending table, and a sliding rail and an arc-shaped movable opening are formed in the bottom of the inner wall of the movable groove; and a movable plate is rotationally connected to the interior of the movable groove through a rotating buckle, a tooth groove ring is fixedly connected to the position, located on the other side, of the bottom of the movable plate, two rows of fixing rods are installed on the top of the movable plate and the top of the bending table correspondingly, and rotating pipes are rotationally connected to the outer surfaces of the fixing rods correspondingly. According to the bending machine for machining the steel structure, the limiting plate and the mounting frame can assist in bending the steel structure through the structure, the bending stability and precision of the steel structure are improved, meanwhile, the rotating pipe capable of rotating assists in bending, scratches of the fixing rod on the outer surface of the steel structure are reduced, and attractiveness is improved.

The invention discloses a contact force field-based topological optimization design method for a detachable steel structure beam-column joint reinforcing member, which comprises the following steps of: (a) establishing an integral joint finite element model, defining a frictional contact relationship and carrying out nonlinear static analysis; (b) extracting node counter-force components on all contact surfaces and taking the node counter-force components as equivalent loads acting on a reinforcing component; (c) establishing a reinforcing member independent optimization model, applying an equivalent load and defining an inertia release boundary; (d) setting a design variable, a constraint condition and a target function of topological optimization, and solving; and (e) carrying out geometric reconstruction and performance verification on an optimization result. According to the method, a complex contact nonlinear problem is converted into a determined load boundary condition, an inertia release method is utilized to deal with a constraint problem in optimization ingeniously, an optimal topology result of the detachable reinforcing component is obtained finally, and the problem of optimization result distortion caused by the uncertain boundary condition is effectively avoided.

Disclosed in the present invention are a lifting synchronous control system and control method for a high-rise steel structure corridor. The control system comprises a data acquisition module comprising a sensor network, an execution module, and a data-processing and decision-making module comprising a central control unit. The execution module comprises a lifting drive provided with a receiver. The sensor network comprises a coordinate sensor, an inclination angle sensor, a temperature and humidity sensor, and a wind speed sensor. The sensor network continuously acquires coordinate information, inclination angle information, and environment information and sends same to the central control unit. By means of a machine learning or deep learning algorithm, the central control unit performs self-optimization and regulates the lifting drive in real time on the basis of the continuously collected environment data and structural data. In the present invention, by combining dynamic regulation of an intelligent algorithm with the application of an Internet of Things technology, all components in the control system are modularly designed, thereby obviously mitigating the defects of conventional synchronous control systems, and significantly improving the stability of the control system and the capability of resisting the influence of external factors.

The invention discloses a track robot inspection path planning method for a space steel structure, and the method comprises the following steps: S1, calculating the clear height of the space steel structure of each grid of a three-dimensional space, obtaining a passable three-dimensional space region, converting the three-dimensional space into a two-dimensional plane grid, and carrying out the calculation to obtain a key point coordinate set; s2, performing grouping solution according to the key point coordinate set to obtain an optimal path and a total path length; and S3, inputting the key point coordinates corresponding to the optimal path into a three-dimensional space, and verifying the rationality of the planned path. The problem of railway station steel structure complex three-dimensional space track path arrangement is solved, the optimal path is achieved through an algorithm, the optimal target combination with the shortest track length, the minimum robot number and the maximum inspection coverage rate is achieved, meanwhile, cruising power of the robots is saved, the walking time is prolonged, and the service life of the robots is prolonged. The space steel structure is widely applied to the industrial fields of railway stations, airports, rail transit, stadiums and the like.

The invention discloses a high-speed rail canopy steel structure corrosion image recognition and evaluation method based on deep learning, and the method comprises the following steps: obtaining and calibrating a multi-view image, and organizing the multi-view image into an observation sequence; performing normalization and quality checking on the observation sequence, and extracting a state vector and an observation driving vector; constructing an energy function and a structure matrix in the Hamiltonian neural network, and promoting state vector time evolution to obtain an evolved corrosion state vector; inputting the evolution result into a decoder, generating a pixel-level corrosion mask and a boundary position, and obtaining a corrosion segmentation result; the corrosion type is judged based on the segmentation result, and corrosion severity indexes are calculated and graded; and extrapolating and predicting the severity index by using time evolution, outputting a predicted maintenance time window and priority, and finally generating a detection report. According to the method, the Hamiltonian neural network is introduced to realize identification, grading and evolution prediction of steel structure corrosion, and the detection stability and the engineering availability are improved.

According to the large-span steel structure refined construction method based on the simulation model, through whole-process refined simulation and optimization, the construction level of a large-span steel structure is remarkably improved, the construction scheme is determined based on a multi-objective optimization algorithm, accurate design of parameters such as hoisting, supporting and monitoring is achieved, a temperature field model is combined with actual measurement correction, and the construction efficiency is improved. The integration of optimization closure temperature, node semi-rigidity and initial defects enables simulation errors, support stability evaluation and optimization guarantee of safety coefficients, most construction risks can be predicted in advance on the whole, the field adjustment time is shortened, the formal construction period is shortened, the construction efficiency and safety are greatly improved, the obvious engineering value and economic benefits are achieved, and the method is suitable for popularization and application. The problem that an existing large-span steel structure construction method lacks a calculation model for predicting the risk in advance and shortening the construction period by combining multi-aspect data is solved.

The invention relates to the technical field of building construction welding, in particular to steel structure electric arc welding equipment for building construction. The device comprises a welding gun, a nozzle structure is installed at the bottom of a welding head connecting pipe in a threaded mode, the nozzle structure comprises a hollow spraying pipe and a contact tube extending from the interior of the welding head connecting pipe, an isolation inner pipe is installed in the spraying pipe in a clamped mode, and a gas flow channel formed between the spraying pipe and the contact tube is separated through the isolation inner pipe. The external expansion structure at the lower end of the isolation inner pipe is matched with the height difference between the bottom end of the isolation inner pipe and the bottom end of the spray pipe, physical shielding is formed, and high-temperature splashes entering the external sub-runner during welding are reduced; therefore, adhesion on the inner wall of the outer sub-runner is effectively avoided, the outer sub-runner is kept smooth all the time, and stable conveying of the protective gas is guaranteed.