19116 results about "Building construction" patented technology

The invention relates to an intelligent management system, in particular to a building construction quality safety risk management system which comprises a data acquisition module, a convolution analysis module, a risk assessment module, an early warning decision module and a feedback optimization module. The data acquisition module generates a sensing signal. And the convolution analysis module receives and adopts a multi-scale feature pyramid and a cross-modal attention mechanism to process the sensing signal to form a four-dimensional fusion signal. And the risk assessment module carries out comparative analysis on the four-dimensional fusion signal under a digital twinborn framework and forms a five-dimensional risk early warning signal. The early warning decision module receives the risk early warning signal and forms a multi-level risk early warning level signal. And the feedback optimization module receives the risk early warning level signal and optimizes the building construction quality safety risk management system through an incremental learning technology. The building construction quality safety risk management system can solve the problems that the dynamic characteristic adaptability is low in the construction process, and misinformation or missing information is likely to be generated.

The invention relates to the technical field of data processing, and discloses a BIM-based building construction quality information intelligent supervision system, which is used for improving the efficiency and accuracy of BIM-based building construction quality information intelligent supervision. The method comprises the following steps: a modeling module performs BIM modeling processing on a building engineering design drawing and building specification data to obtain a building twinborn body; the deployment module carries out field data acquisition system deployment on the building twinborn body to obtain a real-time data acquisition system; the detection module performs anomaly detection processing on the data acquired by the system to obtain a quality anomaly alarm and a priority processing level; the processing module carries out multi-dimensional visualization processing on the quality abnormity alarm and the priority processing level to obtain a visual interface; the decision-making module performs intelligent decision-making analysis on the quality abnormal information in the visual interface to obtain a target processing scheme; and the analysis module performs time sequence analysis and multivariable correlation analysis on the target processing scheme to obtain a dynamic quality evaluation report.

The invention relates to the technical field of building information models, augmented reality, synchronous localization and map construction, and provides a deep learning-based SLAM-BIM augmented reality cooperative localization method and system, and the method comprises the steps: introducing a Transform time sequence feature extractor and a geometric relation graph, evaluating a dynamic distribution weight through combining with the confidence, achieving the cross-modal closed-loop detection, and obtaining an SLAM-BIM augmented reality cooperative localization result. A lightweight semantic segmentation network and a feature fusion module are utilized, a dense map with consistent geometric semantics is constructed, a space-time error propagation equation is constructed, online calibration is realized by means of BIM scale prior, an incremental fusion algorithm is designed, a global pose is optimized in combination with AR interaction, and the system fuses SLAM visual trajectory features and BIM semantic geometric features through a deep learning technology. According to the method, the problems that traditional SLAM accumulative errors are large and the BIM fusion precision is low are solved, robust positioning and map construction in a complex scene are achieved, the cooperation precision and real-time performance of SLAM and BIM are improved, and the method is suitable for AR scenes such as building construction and operation and maintenance.

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 a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention provides a building construction scheme dynamic collaborative design system and method based on real-time surveying and mapping, and belongs to the technical field of building construction management and intelligent construction, and the system comprises a data collection module which is used for collecting surveying and mapping data of a construction site through a multi-source sensor, and generating real-time surveying and mapping data; the data analysis module is used for comparing the real-time surveying and mapping data with a preset BIM model so as to identify a construction deviation area and obtain key deviation parameters; the deviation acquisition module is used for determining a dynamic deviation coefficient based on the key deviation parameter and the construction deviation area; the strategy generation module is used for dividing deviation grades according to the numerical range of the dynamic deviation coefficient and generating a corresponding construction response strategy; and the deviation correction module executes the construction response strategy, verifies a deviation correction result based on the executed surveying and mapping data, and updates the dynamic deviation coefficient until the deviation correction result meets a preset requirement. And the accuracy and efficiency of the construction process are improved.

The invention discloses a precise environment monitoring and early warning system, and belongs to the technical field of environment monitoring. Aiming at the problems that an existing system is fixed in threshold value, poor in adaptability and complex in calibration, the system integrates a temperature sensor, a humidity sensor, a vibration strength sensor and a structural stress sensor through a multi-parameter acquisition module, and environment parameter data are obtained in real time; the central processing module operates a dynamic threshold adjustment algorithm to automatically update the dynamic threshold of each parameter, triggers a multi-sensor calibration mechanism when detecting the sudden change of the parameter, and automatically corrects the reference value of the corresponding parameter after calibration; the early warning execution module responds immediately when any parameter exceeds a dynamic threshold value. According to the system, through the dynamic threshold optimization and automatic calibration technology, the environmental parameter monitoring precision and the system adaptability are remarkably improved, and the system can be widely applied to real-time safety monitoring and early warning in the fields of prefabricated building hoisting operation, building construction, ultra-deep foundation pit supporting and the like.

The invention provides a building construction scheme intelligent auditing system based on a large model, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps that a construction scheme template library and a building construction standard specification library are constructed, a building specification knowledge graph is constructed, a to-be-audited construction scheme document is received and subjected to structural division and preprocessing, semantic vector data are obtained, matching operation is conducted on the semantic vector data and graph nodes in the building specification knowledge graph, and the graph nodes in the building specification knowledge graph are obtained; and outputting audit target data, analyzing and generating an analysis result data structure based on the audit target data, generating an audit report according to an analysis result, and completing recording and tracking of an audit process. The method has the beneficial effects that automatic auditing and whole-process tracking of the construction scheme are realized by constructing the structured template and the standard specification knowledge graph in combination with semantic vector matching and rule work efficiency comparison, the accuracy, consistency and efficiency of auditing are effectively improved, and the informatization and intelligence level of building construction management is remarkably enhanced.

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 provides a building construction site safety monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the steps that a three-base-point dynamic topological structure is established in a target monitoring domain, a first datum point is arranged at the extension end of the axis vertical line of a slewing bearing of a tower crane, and a second datum point is arranged at the extension end of the axis vertical line of the slewing bearing of the tower crane; the second datum point is embedded in the radial section of a force bearing flange plate of the hydraulic jacking module, the third datum point is fixedly connected to the intersection joint of a deep foundation pit top beam and a supporting system, and a non-collinear vector set is constructed through three-point coordinate offset; and based on the non-collinear vector group, deformation compensation correction is performed on the dynamic calibration parameter set through a vector space topological transformation algorithm, a topological reconstruction data stream containing a displacement gradient tensor and strain energy density is generated, and a risk situation feature space is constructed based on the topological reconstruction data stream. According to the invention, the intelligent construction safety management is realized.

The invention relates to the technical field of building management, in particular to a BIM-based building construction management method and system and a medium. The method comprises the following steps: acquiring building structure data; a tower crane full-life-cycle BIM model is constructed based on the building structure data, wherein the tower crane full-life-cycle BIM model comprises three-dimensional parameterized information of a base embedded part, a tower body standard knot, a slewing mechanism and a suspension arm assembly; acquiring building construction environment data; carrying out tower crane installation optimization on the tower crane full-life-cycle BIM model by utilizing building construction environment data to obtain a tower crane installation scheme; and base stress distribution simulation is conducted on the tower crane full-life-cycle BIM model according to the tower crane installation scheme, and tower crane base stress distribution data are generated. According to the invention, by integrating the building structure and the construction environment data and combining real-time sensing monitoring and intelligent collaborative optimization, the accuracy and dispatchability of tower crane management in building construction are improved.

The invention provides a three-dimensional modeling design method based on multi-robot cooperation of building robots. The method comprises the following steps: taking a set of tasks and paths allocated to each robot as task allocation information; a task execution scheme after error correction based on real-time feedback and task distribution information; performing global analysis on execution errors of all robots according to the corrected task execution scheme; based on the optimized task execution scheme, constructing a task-robot cooperation matrix, and performing task scheduling on the task-robot cooperation matrix by using a cooperation scheduling algorithm; and combining the adjusted point cloud data and the optimized three-dimensional model into a complete three-dimensional model. According to the invention, by optimizing a collaborative operation mechanism of the robot cluster and introducing an error correction mechanism of dynamic task allocation and real-time feedback, the defects in the existing building robot technology are overcome, so that the efficiency of building construction and the modeling precision are remarkably improved.

The invention relates to the technical field of BIM building optimization, in particular to a BIM-based prefabricated building design construction optimization management system and method. The method comprises the following steps: acquiring a panoramic monitoring image of a building area and a construction design initial BIM model; carrying out regional space structure evolution and construction scene three-dimensional simulation on the panoramic monitoring image of the building region, and constructing a three-dimensional construction scene graph; identifying assembly type building component information according to the construction design initial BIM model so as to generate building component assembly demand characteristics; performing dynamic construction sequence simulation optimization on the three-dimensional construction scene graph and the construction design initial BIM model based on building component assembly demand characteristics, and constructing a dynamic optimization construction sequence; and carrying out available material transportation path analysis on the three-dimensional construction scene graph so as to generate an optimal operation path of each material. According to the invention, real-time collaborative management based on the BIM and the cloud is realized, the building construction quality is improved, and the construction design decision is optimized.

The invention relates to the field of building construction simulation, in particular to a building construction simulation model construction method and system based on a BIM model. The method comprises the following steps of obtaining a multi-source design material of a building project, and performing BIM format heterogeneous conversion so as to generate multi-source heterogeneous conversion BIM data; generating a plurality of component independent sub-models based on the multi-source heterogeneous conversion BIM data; according to the multiple component independent sub-models, building component space position positioning calculation is carried out one by one, global BIM modeling is carried out, and a building global BIM model is constructed; performing inter-component structure collision identification on the building global BIM model, and performing collision conflict optimization so as to construct a conflict optimization BIM model; building construction behaviors are analyzed one by one based on the multi-source heterogeneous conversion BIM data, then construction sequential sequence fitting is conducted, and a construction process behavior sequence is constructed. Through efficient and refined building construction simulation model construction, the construction efficiency is improved, and construction resource allocation is optimized.

The invention discloses a building construction process risk assessment method and system based on a fault tolerance mechanism, and particularly relates to the field of building construction process risk assessment, and the method comprises the steps: building a measurement point hierarchical index of a building construction process, synchronously arranging time sequence displacement data, and executing difference calculation to obtain a space gradient evolution sequence; mapping a trend change interval set extracted by sliding window processing into a path node set, and constructing a topological connection relation of the path node set to form an initial risk path map; the data state of each path node in the risk path map is subjected to anomaly identification, the access control effect of the abnormal node in the path is analyzed, and whether the path fracture risk is formed or not is judged in combination with the sensitive monitoring area. By constructing a risk map structure based on a trend path and combining a path interruption node identification and alternative path dynamic deduction mechanism, continuous assessment and map self-correction of the risk trend under the condition of measuring point data failure are realized.

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

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 fabricated building construction, in particular to an intelligent hoisting and precise positioning construction method for fabricated building components. The method comprises the following steps: acquiring a geometric parameter set of the special-shaped curved glass component, a parameter set of hoisting equipment, a real-time environment parameter set of a construction site and a reference positioning parameter set of a building; based on the special-shaped curved glass component geometric parameter set, the hoisting equipment parameter set and the construction site real-time environment parameter set, a special-shaped curved glass hoisting simulation information set is integrated, and affected state characteristics of the special-shaped curved glass in the hoisting process are obtained; performing data fusion on the influenced state characteristics and a building reference positioning parameter set to obtain absolute deviation information of the special-shaped curved glass relative to building reference positioning; and according to the absolute deviation information, operation parameters of the hoisting equipment are dynamically adjusted, and an intelligent hoisting precise positioning construction scheme report is output. And assembly type building hoisting construction is powerfully promoted to develop towards the direction of intelligence and standardization.

The invention relates to the technical field of building construction monitoring, in particular to a construction site real-time monitoring system and method based on an unmanned aerial vehicle technology. The method specifically comprises the steps that an unmanned aerial vehicle carries a high-definition camera, an infrared thermal imager and a laser radar and collects multi-modal data of a construction site in real time; a deep learning algorithm is adopted to carry out multi-modal semantic segmentation, RGB images, infrared thermal imaging and laser point cloud projection data are fused, YOLOv8 target detection and time sequence behavior modeling technologies are combined, and abnormal risks are dynamically identified; by constructing a context-aware comprehensive risk scoring model, the risk level of a construction scene is quantified, and a graded early warning mechanism is triggered; and meanwhile, based on a three-dimensional modeling technology, tank vertex cloud data is analyzed by utilizing an LIO-SLAM algorithm, and the inclination degree of the structure is detected and is linked with emergency response. According to the invention, through multi-sensor fusion, dynamic risk assessment and closed-loop control, intelligentization and precision of construction monitoring are realized.

The invention aims to provide a multi-robot cooperative building operation method and system, and belongs to the technical field of intelligent building robot cooperative control, and the method comprises the steps: firstly obtaining a building operation task and a building three-dimensional model, decomposing the task into a plurality of sub-tasks based on the model, and generating an initial task allocation scheme according to the operation type and the spatial position of the subtask, and allocating the initial task allocation scheme to a plurality of robots. In the operation process, the operation state and environment data of each robot are collected in real time, and a dynamic conflict detection matrix is constructed. And when a path conflict or a resource conflict is detected, dynamically adjusting the conflict task according to a preset priority rule, generating an updated task allocation instruction, sending the updated task allocation instruction to the corresponding robot, and controlling the corresponding robot to execute the adjusted sub-task. According to the invention, through a dynamic conflict detection and intelligent adjustment mechanism, the problem of path and resource conflict of multiple robots in a dynamic building construction environment is effectively solved, and the construction efficiency and safety are improved.

The invention provides an instrument panel type building engineering construction progress display method, system and device and a medium, and relates to the field of building construction management.The method comprises the steps that geographic information system map data and building information model data of an area where a building project is located are obtained and fused, and then a multi-dimensional display platform of the building engineering progress is constructed and formed; actual progress data, historical construction data and environment data of a construction site are collected, risk factors of construction tasks are analyzed, the dependency relationship between the construction tasks is analyzed through topological sorting, and a construction risk task tree is constructed; a construction risk task tree is adopted to identify potential risks of a construction task, weighted calculation is carried out on risk factors according to weight coefficients, a comprehensive risk assessment score of the construction task is generated, a construction progress sequence is optimized, and integration is carried out through an instrument panel to form a unified visual display interface. Dynamic prediction, real-time display and automatic adjustment can be realized, and the accuracy of construction progress monitoring is improved.

The invention relates to the technical field of building informatization, and discloses a construction collaborative management method and system based on BIM and Internet of Things. The method comprises the steps of building a BIM model, collecting field real-time data, generating a dynamic construction state through association, performing collaborative analysis and forming a scheduling plan, and finally realizing closed-loop management and control through feedback of the Internet of Things. The system comprises a BIM model construction module, a field data acquisition module and a collaborative analysis and scheduling module which are in collaborative linkage to realize information integration, state perception and dynamic optimization control in the whole process of building construction. The method can effectively improve the precision and efficiency of the engineering construction process, enhances the intelligent management and control capability of the construction site, and has good engineering practicability and popularization value.

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 discloses a construction elevator intelligent dynamic scheduling system and method based on multi-modal deep learning, and relates to the technical field of elevator scheduling management, the system comprises a multi-modal data acquisition module, a multi-modal deep learning processing module, a hierarchical decision module, an adaptive learning module, a hybrid reinforcement learning scheduling module, and a conflict prediction and solution module; time sequence data, image data and text requirements are processed at the same time through a multi-mode deep learning architecture, a three-layer neural network decision architecture is adopted to realize full-process intelligent decision from strategy to execution, and a learning strategy and decision weight are automatically adjusted according to a project progress stage in combination with an adaptive learning system. The optimal scheduling scheme can be automatically generated according to information such as material types, quantity and latest completion time submitted by a team, the use efficiency of the construction elevator is effectively improved, resource waste and scheduling conflicts are reduced, and the method is suitable for scenes such as building construction elevator scheduling and a multi-elevator cooperative working environment.

The invention relates to the technical field of construction risk early warning, in particular to a BIM-based building construction risk early warning method and system, and the method comprises the following steps: constructing an incidence matrix between risk nodes based on time and space dimensions for the risk factor of each node in a construction process, calculating the risk propagation intensity value between the nodes, and calculating the risk propagation intensity value between the nodes; and judging the risk association relationship between the nodes, performing grading processing on the risk coupling degree according to the propagation rate, and outputting a risk coupling matrix value. According to the method, a high-risk area is extracted by combining spatial distribution data, dynamic partition management is achieved, dynamic influences of environmental variables such as temperature, humidity and vibration are quantified, the complex relation between environmental conditions and risk states is revealed, the accuracy and real-time performance of risk assessment are optimized, and the risk assessment accuracy is improved by analyzing the risk transfer trend and propagation path changes. And dynamically adjusting node priorities and resource allocation, updating the influence of newly added risk signals in real time, and realizing full-process dynamic optimization of construction risk early warning.

The invention discloses a building construction robot dynamic scheduling method and system based on artificial intelligence, and particularly relates to the technical field of building construction. Potential schedulable building construction robots are screened by analyzing process requirements and resource allocation of construction tasks; based on the robot task execution record, the operation stability is evaluated; the scheduling adaptability of the robot in different construction technologies is evaluated by analyzing task switching flexibility; and the real-time performance of the scheduling demand of the robot is judged by integrating the operation stability and the scheduling adaptation degree. And the flexibility demand intensity of robot scheduling is evaluated by analyzing the frequency change and the interference range of the path crossing area. Through comprehensive analysis of operation stability, scheduling adaptability and flexibility demand intensity, a robot scheduling strategy is dynamically adjusted, the method is suitable for a complex construction environment, scheduling efficiency can be optimized, construction conflicts can be reduced, and the overall performance of multi-robot cooperation can be improved.

The invention relates to a visual safety risk early warning system for pseudo-classic architecture mixed structure construction, in particular to the field of safety risk early warning of pseudo-classic architecture construction, through multi-source data fusion and intelligent algorithm cooperation, the construction safety risk management and control capability is remarkably improved, the system collects mechanical and environmental data of key nodes of a high-support formwork body in real time, and the construction safety risk early warning capability is improved. Load distribution is dynamically updated in combination with the building information model, and a high-precision space-time label data stream is generated; predicting a short-term instability risk based on a two-channel neural network, constructing a complex network model to quantify a node failure linkage effect, and generating a visual risk propagation thermodynamic diagram; finally, a grading alarm strategy is triggered through a dynamic threshold value, augmented reality visual warning and equipment linkage control is achieved, the system breaks through the limitation of traditional manual monitoring, full-process closed-loop management of risk perception, prediction, positioning and response is achieved, the occurrence rate of safety accidents is effectively reduced, and the safety and reliability of construction of the complex structure of the pseudo-classic architecture are guaranteed.

The invention relates to the technical field of building construction, and particularly discloses a high-efficiency and high-precision cable net lifting control system for an annular cable-supported grid structure and application of the high-efficiency and high-precision cable net lifting control system. A prestress applying and cable net shape control module; the circumferential cable detection module is used for acquiring a real-time 3D tensioning model of the circumferential cable, performing comparative analysis on the real-time 3D tensioning model and a preset real-time 3D model, evaluating a tensioning process state and generating a tensioning early warning signal based on an evaluation result; the tensioning speed adjusting module is used for analyzing the completed tensioning process, evaluating whether the tensioning process is caused by the tensioning speed or not, and adjusting the tensioning speed based on an evaluation result; according to the method, the radial cable prestress tensioning speed and the tool downward pulling force tensioning speed in the follow-up tensioning process are calculated and adjusted through the relation model, it is ensured that the cable net tensioning process is stable and accurate, and the reliability and accuracy of annular cable-supported grid structure cable net lifting construction are further improved.

The invention relates to the technical field of construction management, in particular to a BIM-based building construction management system, which comprises a path compression module, a priority scheduling module, an equipment allocation module, a material arrangement module and a progress coordination module. According to the method, the BIM model construction path node set is obtained, the node compression condition is judged based on the labor resource availability index of the construction scheduling schedule, high coordination of the construction path and resource configuration is achieved, the problems of resource mismatching and construction efficiency reduction are avoided, and the construction efficiency is improved based on the density comparison between the space area to which the component belongs and the construction equipment in the area. Reasonable allocation of construction equipment is achieved, full utilization of mechanical equipment is guaranteed, structural hierarchical indexes, space coordinates and construction logic dependency relationships are associated through component identification numbers, a construction feasible dependency graph and material entering batch sorting are formed, and a material entering time window is accurately determined; coordination conflicts between material approach and storage area arrangement are mitigated.