10152 results about "Building construction" patented technology

The invention provides an underwater tunnel shield construction excavation face stability evaluation method, system and equipment, and the method comprises the steps: determining an excavation face stability evaluation index based on a shield construction excavation face instability mechanism; dividing the stability of the excavation surface into a plurality of grades, establishing each grade space, and determining the quantitative interval of each evaluation index in each grade; calculating a combined weight of each stability evaluation index by adopting a combined weighting method, and taking the combined weight as a judgment basis of the influence of the evaluation indexes on an evaluation result; and constructing an ideal point evaluation function by adopting an ideal point method so as to represent the membership degree of the to-be-evaluated object to each grade, calculating the grade membership degree of the to-be-evaluated section in each grade of the evaluation system, and determining the stability grade of the excavation surface in the construction process of the evaluation section.

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

The invention discloses an AR home experience method in a large scene. On the basis of combination of a natural feature identification-based three-dimensional registration method and a binocular tracking positioning and local mapping method, the camera attitude is estimated by using feature points of a real-time scene and corresponding three-dimensional points thereof under the binocular trackingpositioning and local map construction technology. According to the mode, on-site environment features shot in real time are used as recognition tracking objects, a virtual home model can still be normally positioned and tracked under the condition that no identification graph exists, the problems that an existing AR home experience application is small in use range and poor in stability are solved, and therefore the AR home experience of virtual and real fusion can be met in a wider range and more truly.

The invention discloses a building robot multi-machine collaborative operation optimizing and monitoring method and system based on digital twinning, and belongs to the technical field of building construction intellectualization. The method comprises the following steps: constructing a digital twinborn body comprising an environment perception model and a kinematics / dynamics model based on a building information model and robot physical parameters; performing multi-machine task allocation and path planning containing static / dynamic obstacle avoidance in the virtual environment; synchronizing physical environment data in real time through a multi-mode sensor; dynamically optimizing operation parameters by adopting a genetic algorithm or a particle swarm algorithm and realizing closed-loop control; and generating a safety early warning and emergency scheme based on machine learning. According to the method, Markov decision path planning of reinforcement learning and multi-agent game task allocation are creatively fused, laser radar-vision-inertial navigation multi-source data fusion is adopted, the technical problems that in a traditional method, digital twinning precision is insufficient, and dynamic cooperation efficiency is low are solved, and the method is suitable for large-scale popularization and application. And the construction efficiency, the safety and the man-machine interaction experience are remarkably improved.

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 an anti-ant large-core-number fiber ribbon cable comprising a cable core and an outer sheath. The cable core includes loose tubes; and fiber ribbons are sleeved inside the loose tubes. The outer sheath is an outer nylon sheath; and a non-metallic reinforcement layer is arranged between the outer sheath and the cable core. According to the anti-ant large-core-number fiber ribbon cable provided by the invention, with the outer nylon sheath, ants can not bite the sheath, so that the damages on the optical cable by ant biting in a pipe at an area with a serious anti problem are prevented, the normal state of the cable is ensured, and the application region of the cable is extended. The cable based on a simple and reasonable structure has advantages of small cable diameter, high fiber density, large fiber capacity, and low duty ratio and can be laid in a pipe conveniently; and the bending performance is good. During branched connection, 4 to 24 fibers can be melted once, so that the construction time is saved and the manpower cost is lowered, and the construction efficiency is improved. On the basis of the full drying type dielectric structure, branched connection is realized conveniently and usage becomes convenient.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention relates to the field of constructional engineering, and discloses an intelligent construction management and control method for constructional engineering, which comprises the steps of collecting multi-dimensional data of a building construction site, including construction progress data, personnel positioning information, an equipment operation state and site environment parameters, and constructing a construction behavior feature data set in combination with a multi-source data fusion algorithm; performing automatic structured analysis on the construction behavior feature data set, constructing a construction process modeling framework based on a hierarchical feature clustering method, and introducing a dynamic feedback learning mechanism to perform adaptive optimization on a modeling result; judging whether the optimization process is stable or not according to the evolution trend of the process model, and if so, recording the stage state of the current construction process feature; and based on the corrected construction plan path, performing disturbance factor correction on the prediction progress by applying a multi-scale construction simulation algorithm, extracting corresponding control parameters, and performing intelligent adjustment on prediction nodes. The method has the advantage of improving intelligent construction management.

A bilateral expanding excavation construction method for a middle pilot tunnel of a tunnel is provided. Section or excavation width of the middle pilot tunnel is generally not larger than that of a common double-line or double-lane tunnel; a step construction method is adopted, and the construction method is the same as that of a common double-line or double-lane tunnel; and the construction method is familiar to constructors, and the construction difficulty is avoided. A primary support section of the middle pilot tunnel can be expanded and excavated after being closed by at least 3 meters; if a side tunnel face is stable, and the problem of the operation height of manual installation of a steel frame and an anchor rod is solved, one-time excavation and support can be achieved, and the construction is more convenient; or otherwise, a side tunnel is excavated according to a two-step or three-step method. The primary support of the middle pilot tunnel can be timely closed; the primary support of the side tunnel and the primary support of the middle pilot tunnel can be completely connected in sequence, and various errors and deformation influences of surrounding rocks and the supportare left at an inverted arch part for treatment; and the construction progress is basically the same as that of the common double-line or double-lane tunnel.

The invention discloses a building construction intelligent safety monitoring method based on the Internet of Things, and the method comprises the following steps: obtaining construction environment data, structure state data and operation behavior image data, and carrying out the preprocessing; environment state modeling, local structure strain and behavior recognition and operation scene segmentation are carried out through the edge intelligent processing unit; feature fusion is carried out, and a fusion situation vector is constructed; performing high-frequency anomaly identification and emergency preliminary screening, and generating an edge preliminary early warning result and a high-risk data fragment; constructing a safety evolution trajectory crossing a time window, and fusing historical data to generate a risk semantic map; cloud semantic reasoning operation is executed, and a final risk level judgment result and a corresponding trigger source identifier are generated; and automatically triggering a safety response instruction according to a risk level judgment result. According to the method, the Internet of Things and intelligent semantic analysis are fused, multi-source safety monitoring and self-adaptive response are realized, and the method has the advantages of high real-time performance, global perception and continuous optimization.

The invention discloses a digital twinning-based building full life periodicity performance evaluation method and device, and belongs to the field of building construction.The evaluation method comprises the following specific steps that I, in the building planning and design stage, a building foundation digital model is constructed, meanwhile, various construction data are collected in real time through a multi-modal sensor, and block chain storage is established; iI, performing real-time sensor data preprocessing and updating based on the building basic digital model so as to perform multi-physics field coupling simulation, and dynamically updating and predicting a building state; the method overcomes the limitation that traditional simulation is large in calculation amount and difficult to update in real time, improves evaluation reliability, achieves dynamic calibration and precision enhancement, enables material aging and repair prediction to be more accurate, provides a solid basis for durability evaluation, improves the economy and reliability of the whole life cycle of a building, and has a wide application prospect. And a complete closed loop of coverage planning-construction-use-maintenance is formed, so that the performance evaluation can be continuous, dynamic and traceable.

The invention discloses a construction emergency early warning method and system, and the method comprises the following steps: collecting the three-dimensional coordinates of a constructor, combining a sliding time window with a wavelet packet energy entropy and other indexes, and generating a multi-scale movement disorder index through principal component analysis and fusion; continuously unstable persons are recognized according to the disorder index, after the trajectory of the persons is segmented, a weighted graph is constructed in combination with hidden Markov and building information model environment parameters, and the cognitive mismatch degree is calculated; mapping the cognitive mismatch degree to a space grid, calculating a local Moran index, fusing a density gradient and a mechanical operation sequence resonance result, and constructing a propagation weight matrix; constructing a heterogeneous graph based on weight matrix guidance, calculating risk influence propagation potential energy, combining historical disorder sequence analysis and relative entropy, and coupling to obtain a group-level instability pre-judgment value; and outputting a comprehensive critical level for the pre-judgment value over-limit individuals through motion trend prediction, spatial intersection and shortest path algorithms. The safety during building construction operation is improved.

The invention discloses a building construction quality on-line monitoring system based on digital twinning, particularly relates to the field of building engineering informatization, is used for solving the problem of quality judgment deviation caused by dislocation breakpoints of multi-source evidences on a construction site, and is characterized in that a process event label is triggered through an event anchoring module to limit a window boundary and record an event identifier; the object binding module is used for immediately binding and generating object event table alignment records according to a component identity list and a positioning line, and the sequential judgment module is used for extracting a video key point frame and a sensing track, calculating an action synchronization difference and a form coincidence difference, constructing a deviation sequence and inputting the deviation sequence into a single-side CUSUM to obtain a judgment signal and fragment positioning; the judgment issuing module reads a signal calling rule base to generate quality judgment disposal guidance and recheck script issuing execution, and the recheck write-back module receives result write-back, lightly updates a fragment boundary and responds to a neighborhood record version to realize compact transmission from evidence obtaining to judgment execution to write-back so as to form a stable and traceable quality closed loop.

The invention discloses a large-span truss hoisting and splitting system and method based on digital twinning, and belongs to the technical field of building construction, and the system comprises a physical entity unit, a data acquisition and transmission unit, a digital twinning model unit, a function service and data analysis unit and an application and interaction unit. According to the invention, through real-time sensing of a physical entity state, multi-source data fusion transmission, high-precision digital twinning modeling, intelligent simulation analysis and decision optimization, and fusion of multi-modal man-machine interaction, a virtual control system which is in whole-course synchronous mapping and bidirectional interaction with a physical entity is constructed; according to the method, real-time visual monitoring, trend prediction and risk early warning of the hoisting and splitting process can be achieved, scheme preview and dynamic optimization are supported, virtual-real fusion operation guidance is achieved through the AR technology, and the safety, precision and efficiency of hoisting and splitting operation of the large-span truss are improved.

The embodiment of the invention discloses a safety monitoring method and system for building construction, and the method comprises the steps: collecting the original video data of a construction site, carrying out the denoising, illumination correction and frame rate adjustment, and outputting a standardized video data stream; extracting attitude features of the constructors and representing the attitude features as a feature matrix to form an attitude feature matrix set containing spatio-temporal information; the sensitivity and correlation of the feature matrix are analyzed, calibration noise is added after dimension reduction, and feature data conforming to differential privacy are generated; and extracting data advanced representation by utilizing a pre-training model, and completing behavior classification, dangerous area judgment and safety violation detection. And the violation risk is evaluated in combination with the risk level of the construction area, graded early warning is generated, and meanwhile violation information is recorded to form a traceable management mechanism. According to the embodiment of the invention, accurate and efficient safety violation behavior detection is realized, and intelligent technical support is provided for safety management of building construction.

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention discloses an automatic hoisting and positioning system and method for prefabricated parts, and relates to the technical field of building construction. The system comprises a tower crane, a frequency conversion trolley, a three-axis pan-tilt stabilizer, a receiver positioning device and an infrared emitter which are coaxial, an intelligent terminal control system with a pre-stored BIM model, and an integrated inertial sensing unit and a dome camera monitoring system. The intelligent terminal generates target coordinates through the BIM positioning reference module, the real-time deviation calculation module quantifies three-dimensional positioning and axis perpendicularity deviation, and the compensation instruction generation module drives targeted adjustment; through combination of infrared dual-mode switching, frame line deviation correction and inertia swing dynamic suppression, precise positioning and obstacle avoidance cooperation is realized. According to the method, through the closed-loop process of benchmark setting, obstacle avoidance lifting, fine compensation and standard confirmation, it is guaranteed that the positioning deviation is not larger than 0.8 mm, the axis perpendicularity is not larger than 0.3 degree, and the inertia swing is not larger than 10 cm, the problems that traditional hoisting is poor in precision and large in inertia interference are solved, and the installation quality and the operation efficiency are improved.

The invention provides a construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning, relates to the technical field of building construction carbon emission management and control, and solves the problem of limitation of an existing management and control process in the aspects of accounting precision, response speed, emission reduction effect and economical efficiency. The method comprises the following steps: firstly, establishing a multi-dimensional BIM model, constructing a digital twinborn body linked with a physical construction site in real time based on the model, and mapping real-time energy consumption and material data; and then monitoring and calculating the real-time carbon emission intensity of the whole construction process in combination with a dynamic carbon emission factor library, further performing multi-target optimization solution by adopting a mixed integer programming algorithm based on the intensity value and an optimization decision, outputting a low-carbon construction optimization scheme, and guiding field execution. Through BIM, digital twinning and dynamic optimization full-chain technology integration, real-time monitoring, accurate accounting, intelligent optimization and closed-loop management and control of building construction carbon emission are smoothly realized.

The invention provides a construction progress dynamic regulation and control system and method for a building machine, relates to the technical field of special equipment for building construction, and solves the problem of limitation of an existing building machine regulation and control system in the aspects of dynamics, collaboration and the like. In the system, a sensing execution layer is used for collecting construction data in real time and correspondingly executing a regulation and control instruction, and transmitting the data to an edge calculation layer for processing; the edge computing layer is used for carrying out localized real-time preprocessing and decision generation on the data, and synchronizing key information to the cloud analysis layer to obtain a regulation and control strategy; the cloud analysis layer is used for constructing virtual mapping of a construction process, optimizing a regulation and control strategy and driving a dynamic regulation and control module to perform multi-dimensional decision making; and the dynamic regulation and control module is used for comprehensively predicting the construction progress, coordinating and optimizing the target, handling sudden anomalies, generating regulation and control instructions and feeding back the regulation and control instructions to the sensing execution layer to form a closed loop. According to the method, the construction resource utilization rate and the progress prediction precision of the building machine can be effectively improved.

The invention relates to the technical field of construction intelligent monitoring, in particular to a building construction remote intelligent monitoring system based on 5G telephone communication and edge computing. Comprising an on-site sensing unit; the edge processing unit is used for realizing accurate integration of heterogeneous data through an improved self-adaptive weighted fusion algorithm and generating a real-time control strategy in combination with a working condition dynamic model; the 5G communication unit is combined with predictive slice scheduling and a multi-mode anti-interference protocol; and a remote monitoring unit. The industrial-grade sensor array is adopted to collect related data, a monitoring data stream with a timestamp and marked with an abnormal identifier is generated, meanwhile, an edge processing unit complements abnormal data through a data preprocessing module, heterogeneous data integration is achieved through an improved self-adaptive weighted fusion algorithm, and the data processing efficiency is improved. According to the method, the problems that the data processing architecture and the construction multi-source data characteristics are insufficient in adaptation and data validity tracing is missing are effectively solved, and effective tracing and accurate integration of the construction scene multi-dimensional monitoring data are achieved.

The invention relates to a house building construction stage carbon emission dynamic estimation method and system based on full life cycle data, and the method comprises the steps: dividing a construction region based on a BIM model, laying a multi-source sensor network, fusing static BIM data and a dynamic Internet of Things monitoring flow, building a four-dimensional space-time label data set, and carrying out the construction stage carbon emission dynamic estimation based on a double-layer dynamic correction model. Kalman filtering is utilized to eliminate instantaneous interference, LSTM transfer learning is combined to predict mechanical efficiency attenuation, a dynamic correction coefficient matrix of a mechanical aging rate, operator skills and environmental factors is embedded, and a carbon emission value is calibrated in real time; when abnormal emission is detected, a key pollution source is traced and positioned through a graph neural network, a three-dimensional thermodynamic diagram is generated in the digital twinborn model, and component-level carbon flow analysis and early warning are performed, so that the carbon emission estimation precision is remarkably improved, the abnormal traceability time consumption is shortened, and the timeliness is remarkably improved.

The invention relates to the technical field of data processing, and discloses a building construction quality real-time monitoring method and system based on a sensor network. The method comprises the steps of constructing a sensor grid through hydration heat gradient mapping, recognizing welding defects based on acoustic emission spectrum texture and wavelet packet decomposition, obtaining a quality situation by adopting maintenance age weight time-varying fusion, performing multi-scale anomaly detection by applying a residual attention mechanism, and dynamically adjusting a threshold value to generate an intervention strategy in combination with a working condition switching trigger. And intelligent construction quality monitoring is realized. Through the multi-scale feature extraction and cross-modal data fusion technology, the accuracy and real-time performance of construction quality monitoring are remarkably improved.

The invention relates to the technical field of building construction management, and discloses an assembly type building construction whole process management system based on artificial intelligence. The system comprises a construction element sensing layer, a construction state mapping layer, a strategy synthesis layer and an execution coordination layer which are connected in sequence. The construction element sensing layer continuously captures on-site multi-modal environment data and component state data; the construction state mapping layer fuses multi-source data into a construction scene digital image with space-time relevance, and key path nodes and potential conflict domains in a construction process are analyzed and identified through a depth mode; the strategy synthesis layer generates a dynamic optimization strategy set for hoisting, transportation and installation processes based on the identification result; and the execution coordination layer drives on-site construction equipment to execute the corresponding instruction sequence. By constructing the digital image and deeply analyzing the construction mode, accurate insight and dynamic optimization of the construction process are realized, the construction efficiency and the automation level are improved, and process conflicts and resource waste are reduced.

The invention relates to the technical field of intelligent building AI construction monitoring, and discloses a high-rise building construction monitoring method and system based on intelligent AI. The method comprises the following steps: acquiring construction physical signals, and fusing to generate multi-dimensional state sensing information; constructing a digital twinborn body comprising a structure topology and a material parameter evolution path; deducing a construction process by using an AI agent, and outputting a risk situation map and a behavior intervention instruction set; and acquiring a field real-time resource state, carrying out matching verification on the instruction, and starting AI reconstruction on an unmatched instruction to generate a final executable instruction set. Through dynamic evolution modeling of material parameters, the structural state simulation and risk prediction precision is improved; through the real-time verification and reconstruction mechanism of the instruction, the performability of intervention measures is ensured, and closed-loop intelligent control from risk perception to accurate intervention is realized.

The invention provides an intelligent positioning system for precise butt joint of fabricated building components, and belongs to the technical field of fabricated building construction. The system comprises a multi-source heterogeneous sensing module, an intelligent positioning host, an execution adjustment module, a BIM cooperation module and an environment adaptive calibration module. The multi-source heterogeneous sensing module is integrated with a laser radar, a visual sense, a UWB and an attitude sensing unit, and data synchronous acquisition is realized through a self-created time synchronization mechanism; the intelligent positioning host adopts a three-layer architecture of preprocessing, feature fusion and decision output, and processes data in combination with improved Kalman filtering and a dynamic deviation correction model; the execution adjustment module realizes six-degree-of-freedom posture adjustment based on a cross type hydraulic driving structure, and is matched with force feedback compliant control; the BIM collaboration module constructs twin mapping and generates a deviation thermodynamic diagram; and the environment self-adaptive calibration module realizes quick calibration of environment abrupt change. The problems that traditional positioning is low in precision, poor in efficiency, weak in environment adaptability and the like are solved, and the method is suitable for precise butt joint of various assembly type components.

The invention discloses a building pipeline node auxiliary positioning method applying three-dimensional digital management, and relates to the technical field of building construction, and the method comprises the steps: 1, collecting multi-source data of a multi-device terminal, fusing the multi-source data through adaptive federated filtering, and outputting the three-dimensional coordinates and component parameters of a node; 2, a BIM attribute graph of a building pipeline is obtained in advance, an actual measurement attribute graph is constructed based on the three-dimensional coordinates and component parameters of the nodes, deviation nodes are screened out by matching spatial positions and semantic attributes of the BIM attribute graph and the actual measurement attribute graph through a graph convolutional network, the deviation nodes are corrected through reinforcement learning, and a corrected calibration model is output; 3, automatically highlighting corresponding pipeline components in the BIM based on the three-dimensional coordinates of the nodes and the calibration model, and displaying design parameters and historical maintenance records in an overlapping manner; a correction strategy is automatically decided by using the deep Q network according to the deviation type and grade, intelligent and iterative correction of deviation nodes is realized, and manual intervention is greatly reduced.

The invention provides a risk assessment method, system and device for a building construction site and a medium, and the method comprises the steps: collecting an original image of a current construction site, analyzing the image features of the original image in real time, and generating an analysis result; when it is determined that the current construction site is a simple scene according to the analysis result, performing image preprocessing on the original image according to a preset detection area to generate an ROI image; performing preliminary violation identification on the ROI image by using a visual small model, and outputting a detection result, a detection category and risk confidence; and when it is detected that the risk confidence is higher than a preset threshold, transmitting the ROI image and the corresponding detection result, the detection category and the risk confidence to a cloud. Through deep fusion of the large model and the small model, a detection process of'small model preliminary judgment + large model recheck confirmation 'is formed, the potential safety hazard detection requirements in different scenes can be effectively met, and the accuracy and the reliability of a detection result are remarkably improved.

The invention discloses a construction site-oriented multi-mode inspection data intelligent analysis and closed-loop management system, and belongs to the technical field of building construction management. The system comprises a multi-modal data acquisition module, a fusion and feature extraction module, an intelligent analysis module, a decision distribution module and a closed loop verification module. The system performs space-time alignment on the collected visual, sensing and positioning heterogeneous data, and generates unified representation by using cross-modal attention fusion; modal contradictions are eliminated through joint reasoning, risks are evaluated, and a disposal task is automatically generated; after the task is completed, a data rechecking process is automatically started, the improvement degree is calculated by comparing the data before and after rectification so as to verify the effect, and automatic rollback is performed if the result is unqualified. The problem of heterogeneous data integration is effectively solved, a whole-process closed loop from hidden danger recognition to automatic verification is realized, and the risk response speed and management efficiency of a construction site are remarkably improved.

The invention discloses a construction management system and method based on multi-source data analysis, and relates to the technical field of building construction informatization and intelligent management and control, and the method comprises the steps: capturing a front path task completion signal and a subsequent task starting instruction; obtaining preposed task completion time, a dynamic condition type and a threshold parameter; executing time logic verification based on the dynamic condition type, and judging a construction dynamic context ready state; bIM design coordinates, field positioning coordinates and space tolerance parameters of the construction machinery are obtained, and space logic verification is executed by calculating space deviation; when both the time verification and the space verification pass, subsequent task state conversion is allowed, and otherwise, blocking is carried out; and finally generating a structured report containing all verification parameters and results.

The invention provides a building main body construction quality tracing method and system, and relates to the technical field of building construction management, and the method comprises the steps: dividing region blocks based on a pixel matrix of a construction site image, carrying out the matching of each region block with a preset control node coordinate in a building information model, recognizing and positioning a target node, generating node image data; based on a target node associated with the node image data, collecting construction environment parameters and physical state parameters of the target node, and generating a multi-dimensional parameter set based on the timestamp index; performing parameter fitting calculation on the multi-dimensional parameter set to generate a parameterized label sequence for representing the construction progress and the quality state; encrypting the parameterized tag sequence and storing the parameterized tag sequence into a distributed account book to form encrypted evidence storage data; and based on the encrypted evidence storage data, quality responsibility tracing and construction record verification are carried out, and a quality tracing verification result is generated. And comprehensive and accurate real-time monitoring on the construction progress and the quality state can be realized.