900 results about "Construction scheduling" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

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 relates to a subway construction progress dynamic regulation and control method and system based on digital twinning. According to the method, a digital twin model is constructed by collecting multi-source heterogeneous data of a construction site, and a multi-dimensional construction progress characterization model is generated. Based on the model, progress deviation calculation and risk prediction are carried out to obtain a current progress deviation value. And once the value exceeds a preset threshold value, generating a regulation and control scheme set by using a multi-objective optimization algorithm. Then, the schemes are simulated and verified in a digital twinning environment, and a feasible regulation and control scheme set is formed to guide actual construction progress regulation and control. According to the method, by means of the digital twinborn technology and multi-source data fusion, the construction state is presented in real time, progress deviation and risks are rapidly recognized, an effective regulation and control strategy is formulated through an optimization algorithm, the construction progress management efficiency and accuracy are improved, the capability of responding to environment changes and emergencies is enhanced, construction delay and resource waste are reduced, and the construction progress management efficiency and accuracy are improved. And the subway construction management level and quality are optimized.

The invention relates to the technical field of civil construction scheduling, and discloses a BIM-based civil construction intelligent scheduling method and system. The method comprises various resource parameters, construction procedures and site arrangement information. Performing construction progress dynamic simulation on the BIM model, generating a multi-dimensional progress plan in combination with a resource database, identifying a process logic relationship and potential resource conflict points, and forming a conflict early warning list with priorities; establishing an intelligent scheduling model according to the progress plan and the conflict early warning list, optimizing a scheduling algorithm by using a conflict early warning result, and dynamically adjusting resource allocation parameters; comparing the real-time construction data with the intelligent scheduling model, identifying resource supply and demand deviations and process delay risks, marking key conflict events and feeding the key conflict events back to the BIM model for visual analysis, and obtaining a conflict influence range; and performing construction process deduction on the BIM model based on a conflict identification result, identifying a potential optimization path and a deployment scheme, and determining an optimal scheduling strategy and implementation steps through feasibility verification.

The invention relates to the technical field of project cost progress management control methods, in particular to a project cost progress management control method and system. Comprising a multi-dimensional data integration and simulation deduction module, an intelligent early warning and dynamic decision module and a block chain collaboration and knowledge evolution module. Through the unique coding rule, the engineering quantity list, the material price, the labor cost and the model component are dynamically associated, the construction progress plan is embedded in the BIM model, the 4D construction animation is automatically generated, the resource demand peak value of each time period is counted, the resource conflict is identified in advance, and the component coding integrity and the cost parameter logicality are automatically verified through the attribute checking tool. And the manual auditing time is reduced.

The invention relates to the technical field of BIM, in particular to a BIM-based construction progress intelligent management and control method and system, and the method comprises the steps: constructing a BIM digital twin cloud model, fusing a panoramic image and LiDAR point cloud data, dynamically selecting a data source update model through an algorithm, extracting engineering topology, building a construction network diagram, and generating a state evolution trajectory in combination with environment and resource data. Predicting and visualizing milestone time, comparing field data with a BIM model to generate progress deviation information, performing stability analysis, triggering resource allocation when a threshold value is exceeded, synchronizing a material supplier and a field manager, dynamically adjusting personnel, equipment and materials, dynamically adjusting a milestone plan based on multiple data, and presenting and guiding allocation through the BIM model. The construction progress is ensured to be consistent with the plan, and the complex interaction relationship and dynamic characteristics in the construction process are captured through nonlinear dynamic system modeling in combination with LiDAR point cloud and other high-precision data.

The invention relates to the technical field of electric power engineering purchase demand prediction, in particular to an electric power engineering purchase demand prediction system based on machine learning, and the system comprises the steps: obtaining historical purchase data, construction progress information and electric power engineering design parameters, carrying out the standard stage division and time alignment, and constructing a stage sequence model reflecting the material use rhythm; and a coupling factor matrix is generated based on the material co-occurrence frequency and the stage position relationship, and the modeling capability of the model for the material cooperation relationship is enhanced. And the stage time sequence features, the coupling information and the structured engineering parameter vectors are fused and input into a regression prediction model, so that accurate mapping of material demands and multi-dimensional engineering features is realized, and the purchase prediction precision in a target period is improved. A deviation sequence is constructed based on historical prediction errors, and error correction is performed through a feedforward neural network, so that prediction accuracy and response capability are effectively improved, and resource waste and construction delay are reduced.

The invention discloses a project cost risk monitoring method and a project cost management platform, and the method comprises the steps: S1, carrying out the real-time collection and standardization processing of multi-source data: collecting dynamic data in real time through an Internet of Things device at a construction site, carrying out the butt joint of a design drawing, a financial system and the like, obtaining static data, and employing a data cleaning, conversion and integration technology; s2, carrying out risk factor identification and correlation analysis based on a knowledge graph; s3, implementing risk quantitative prediction and early warning driven by artificial intelligence; s4, real-time monitoring and automatic verification of contract performance of blockchain enabling are realized; s5, providing dynamic cost adjustment and optimization decision support; through real-time collection and standardization processing of multi-source data, data islands of all parties participating in a project are broken, and accuracy, integrity and timeliness of key data such as construction progress and cost expenditure are ensured. Based on big data analysis and artificial intelligence prediction, more accurate risk assessment and cost prediction are provided for project managers.

The invention relates to the technical field of project cost management, and discloses a project cost dynamic management method. The method comprises the following steps: marking a timestamp of a data updating node in a historical cost database, extracting a consumption index of each subentry project in a specified interval before and after the timestamp, and constructing a consumption feature set dynamically associated with the cost; inputting the consumption feature set into an optimized neural network, calculating the deviation degree of the same sub-project in the construction stage and the design stage through time sequence comparative analysis, and generating the difference representation of the actual cost and the plan cost; a material price fluctuation coefficient is used as an adjusting variable, a fuzzy comprehensive evaluation model is adopted to carry out multi-dimensional reasoning on the difference representation, and the influence weight value of each cost component on the cost deviation is obtained; key parameters related to the market supply and demand index and the construction progress deviation rate are mined, the key parameters are input into a pre-trained evaluation model, a screening threshold value is dynamically adjusted according to a model output numerical value, and core parameters with influence weight values higher than the adjusted threshold value are screened out.

The invention discloses a construction environment sudden change risk field prediction method fusing geological and meteorological data, and belongs to the technical field of construction engineering risk prediction, and the method comprises the steps: obtaining geological data, meteorological data and construction progress data of a construction area, carrying out the structural processing of the multi-modal heterogeneous data, and carrying out the construction engineering risk prediction. And generating graph data containing the multi-modal features. According to the method, a space-time adaptive graph neural network prediction model based on an attention mechanism is adopted, weights of different features can be dynamically calculated and allocated according to real-time environment and construction data, and a space-time propagation relationship of risks among geographic nodes is captured by using a message passing mechanism of a graph. According to the method, data of different modals can be deeply fused, accurate prediction of the construction environment sudden change risk is realized, corresponding early warning is triggered according to the risk level, the accuracy and timeliness of risk prediction are improved, and scientific support is provided for intelligent decision making of constructional engineering.

The invention relates to the technical field of big data analysis and mining, in particular to a construction progress deviation early warning method and system based on data mining, and the method comprises the following steps: integrating a construction task list, the spatial position of a component in a BIM, the number of operators in a construction log, and the operation state of a tower crane, and generating a multi-dimensional constraint construction map node set; according to the method, a multi-dimensional node set with time, space and resource attributes is constructed by integrating a construction task list, the spatial position of a BIM component, the number of operators in a construction log and the operation state of a tower crane, and structural expression of a construction site is achieved; on the basis, a causal association relationship among construction logic, resource occupation and space proximity is identified, and a time sequence causal conduction map with a deviation conduction coefficient value is further established, so that a risk conduction path between construction events has quantitative calculation capability.

The invention discloses an intelligent monitoring method and system for the construction progress of a building decoration curtain wall. The method comprises the steps that multi-source construction data of a construction site are collected through Internet of Things equipment and a high-definition camera; constructing a multi-dimensional tensor model and extracting data association features by applying a local reversible mapping algorithm to generate a standardized feature data set; performing parallel computing through shared memory hierarchical process mapping by utilizing the standardized feature data set, and outputting a project progress state evaluation result; constructing a decision diagram based on the project progress state evaluation result, and generating a construction progress deviation report; predicting a construction progress trend by adopting a sample optimal private regression algorithm and generating an optimization scheme; and visually displaying the construction progress prediction report and carrying out early warning. According to the invention, the problems of low data integration efficiency, delayed decision response and weak multi-dimensional data association analysis capability of traditional curtain wall construction monitoring are solved, and intelligent and efficient monitoring of the construction progress of the building decoration curtain wall is realized.

The invention discloses a bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence, relates to the technical field of bridge construction safety and intelligent scheduling, and discloses the bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence. Through three-dimensional wind field modeling, dynamic safety operation domain calculation, multi-dimensional risk fusion and intelligent optimization scheduling, the problem that a traditional method cannot dynamically evaluate wind load risks and equipment conflicts is solved, the risks can be accurately evaluated in real time, the conflicts can be dynamically predicted, the effectiveness of a bridge construction scheduling scheme is improved, and the construction efficiency is improved. And the construction safety and efficiency are improved.

The invention provides a BIM construction progress optimization method and system based on multi-agent autonomous decision, and belongs to the technical field of building information. The BIM construction progress optimization method comprises the steps that a BIM construction progress model is constructed based on multiple construction agents, and the BIM construction progress model comprises a multi-agent construction simulation algorithm model; in the multi-agent construction simulation algorithm model, a plurality of construction agents determine the current construction action according to the real-time field state of the construction progress; wherein each construction agent represents one construction resource in the construction progress and autonomously decides to execute a construction task; the multiple construction intelligent agents independently execute construction actions and output simulation result data corresponding to execution of the construction actions; and updating the model state of the BIM construction progress model according to the simulation result data. The problem that in the prior art, a one-way static deduction mode is difficult to respond to field changes in the construction progress can be solved.

The invention discloses a tunnel construction progress digital twinborn prediction and resource scheduling system and method, relates to the technical field of tunnel construction, and provides the following scheme that the method comprises the steps that real-time monitoring data of a tunnel construction area are collected, the real-time monitoring data comprise equipment position information and process progress quantitative data, and a BIM model of the tunnel construction area is constructed; dividing the equipment position information into a plurality of continuous time windows according to time nodes of the BIM model, and obtaining equipment operation parameters and corresponding energy consumption values in each time window; when the effective energy consumption is calculated, the geological condition grade weight and the construction process reference value are introduced to dynamically correct the initial efficiency coefficient, and meanwhile, the geological condition fluctuation, the equipment performance attenuation and the process connection change in the construction conditions are reflected through the time attenuation coefficient, so that the nonlinear influence of the geological difference and the process adjustment on the energy consumption efficiency is effectively eliminated; the strong relevance between the energy consumption and the progress is established, and a reliable data association basis is provided for progress prediction.

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.

The invention discloses a BIM-based hoisting construction supervision optimization management system. The system comprises a sensing layer which collects multi-source heterogeneous data in real time; according to the decision-making layer, a bottom layer utilizes an incremental RRT # algorithm to generate candidate paths meeting crane kinematics constraints, a distributed Q-learning framework is embedded in an upper layer, all cranes serve as independent agents, collaborative learning is carried out through a shared experience pool, a path planning strategy is dynamically optimized according to environment sensing data and construction progress requirements, and a path planning strategy is established. Meanwhile, a fuzzy sliding mode control algorithm is developed to be combined with an LSTM-Transformer crane cart and trolley walking speed, a hook crane cart and trolley walking speed, a hook lifting speed and a steel wire rope disturbance prediction model to calculate crane motion compensation parameters, and an optimal control instruction is generated in advance based on predicted crane moving walking and lifting data; the execution layer is used for issuing the instruction generated by the decision-making layer to construction equipment; and the optimization layer is used for constructing a BIM model and feeding back an equipment execution result and structure safety monitoring data to the decision-making layer.

The invention relates to the technical field of progress management, in particular to a BIM (Building Information Modeling)-based construction progress control system, which comprises a component list analysis module, a node task sorting module, a connection sequence conflict verification module, a process compression allocation module and a component path intensive control module. According to the method, unified expression of the construction tasks under multi-dimensional parameters can be realized by analyzing the component labeling information and performing weighted merging on the volumes and the working hours of the node tasks, and the matching degree of task sorting priority adjustment is improved by comparing standardized vector sorting with a construction logic sequence; the consistency verification of the connection direction and the scheduling sequence of the adjacent components in the construction path ensures that the component connection logic is not damaged, and by setting the compression ratio and resource utilization rate parameters, the time window fine compression of the tasks in the conflict path is realized, the dynamic coordination capability of the construction resources is improved, and the process stacking risk caused by scheduling congestion is reduced.

The invention relates to the technical field of building construction monitoring, and discloses a construction progress real-time monitoring system based on BIM and Internet of Things, and the system comprises an Internet of Things data collection module which obtains real-time construction data through a sensor network and divides the real-time construction data into a spatial position flow and an equipment state flow; the BIM data integration module processes the two types of data streams to generate a first progress index and a second progress index; the progress deviation analysis module is combined with a preset progress reference model, and the progress indexes are fused and then mapped into a progress deviation value; the response decision module calls a preset scheme based on the deviation value to generate a target response strategy; the central processing unit is responsible for data transmission and logic verification. In addition, the system is further provided with a construction linkage module, an instruction output module and a progress storage module which are respectively used for optimizing resource scheduling, sending control commands and archiving data. According to the system, real-time monitoring, deviation analysis and dynamic adjustment of the construction progress are realized, and the intelligence and accuracy of construction management are improved.

The invention discloses an airport hub construction intelligent regulation and control method and system based on BIM + 5G, and relates to the technical field of construction scheduling optimization, and the method comprises the steps: collecting on-site real-time data to construct a path construction sequence model, constructing a dynamic construction state set with consistent space and time sequence through component coding and semantic attribute mapping, and constructing a dynamic construction state set with consistent time sequence; and constructing a minimum disturbance optimization algorithm of four-dimensional disturbance cost based on the construction disturbance mapping graph, outputting procedure sequence adjustment, resource rearrangement, path decoupling and environment avoidance intervention suggestions, and tracking a construction response state in real time based on an intervention execution feedback mechanism. According to the method, unified modeling of construction plans, resource allocation and green indexes is achieved by constructing a ternary structure and a construction disturbance mapping graph, the field state is dynamically collected in combination with a 5G and edge sensing system, process abnormity and resource conflicts are accurately recognized, an efficient and executable regulation and control strategy is generated based on a multi-target disturbance optimization algorithm, and the efficiency and the reliability of the system are improved. And the intelligence, responsiveness and energy-saving level of the construction process are obviously improved.

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 water conservancy projects, in particular to a construction scheduling method for collaborative linkage of cross-basin water conservancy projects, which comprises the following steps of: constructing a space-time water conservancy topological network to realize multi-source data fusion and spatial association and provide a basis for accurate scheduling; potential contradictions between construction and hydrological regulation and control are found in advance by recognizing conflict areas and performing quantitative analysis; a space-time safety window strategy set is formulated, the construction period is optimized in combination with risk prediction, and operation safety and efficiency are guaranteed; through task decomposition and collaborative factor allocation, an atomic operation unit and a dependency relationship are defined, and the cross-project linkage capability is improved; by generating a preliminary scheduling scheme, and through a resource-collaborative double-constraint graph and a genetic algorithm, efficient resource configuration and conflict minimization are realized; through dynamic risk monitoring and scheduling scheme reconstruction, hydrological abrupt change and construction deviation are quickly responded, cooperative factors are triggered for rebinding, and scheme adaptability and robustness are ensured.

The invention relates to the technical field of intelligent monitoring, in particular to a municipal building engineering construction progress monitoring method based on an unmanned aerial vehicle and laser scanning, and the method comprises the following steps: obtaining point cloud data through carrying laser scanning by the unmanned aerial vehicle, calibrating a space coordinate, binding a design coordinate system, carrying out Gaussian filtering denoising processing, and analyzing point cloud through coordinate transformation. The method comprises the following steps: extracting a main axis vector by adopting a principal component analysis method, dividing a sectioning region, calculating a cosine value of a normal included angle, classifying and identifying a difference region by Euclidean distance, carrying out secondary scanning clustering segmentation, and calculating an offset judgment state label set. According to the method, a three-dimensional reference is established by adopting laser scanning and coordinate calibration, Gaussian filtering is combined to eliminate noise, PCA is used to extract a geometric main axis, a normal included angle matching degree is calculated, Euclidean distance classification detection deviation is carried out, geometric difference is quantitatively identified, graph-model matching precision is improved, millimeter-level capture is realized, an automatic monitoring system is constructed, and manual errors are reduced. And controlling the power-assisted progress.

The invention discloses a water conservancy and hydropower construction resource management scheduling method and system based on artificial intelligence. The method comprises the following steps: collecting construction multi-source data in real time; based on an ST-GNN space-time diagram neural network, predicting the resource demand of each construction area in a future T time period, and combining with an LSTM long short-term memory network to perform joint modeling on the construction progress and environmental disturbance to obtain a demand prediction model; adjusting an inertia weight and a cognitive factor of the demand prediction model by using an IPSO improved particle swarm optimization algorithm, and learning an optimal scheduling strategy in a simulation environment in combination with an RL reinforcement learning training agent to obtain a target demand prediction model; and inputting the construction multi-source data into the target demand prediction model to output a scheduling scheme, and performing digital twin simulation verification and adjustment on the scheduling scheme to obtain a target scheduling scheme. And the prediction accuracy of future resource demands and the management scheduling efficiency are improved.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention provides a construction site pollution source analysis method and system, and the method comprises the steps: determining the atmosphere pollution source intensity information of each monitoring region of a construction site based on a preset Gaussian diffusion model, environment monitoring data and meteorological data, and carrying out the detection and recognition of a video stream through a pre-trained target detection model, the comprehensive score of the construction site in a preset time period is determined based on a preset multi-dimensional scoring model, the environment monitoring data, the detection and recognition result and the atmospheric pollution source intensity information, and the key pollution information of the construction site is determined based on the atmospheric pollution source intensity information and the satellite image data and the construction progress information of the construction site; and performing pollution cause analysis based on the environmental monitoring data and historical construction data of the construction site, and determining a pollution source analysis result of the construction site based on the comprehensive score, the key pollution information and the pollution cause analysis result. With the adoption of the method, the high-real-time pollution source analysis requirement caused by quick change of the actual site construction condition of the construction site can be met.

The invention relates to the technical field of engineering construction progress intelligent control, in particular to an engineering construction progress intelligent control method and system based on digital twinning, and the method comprises the steps: obtaining a preset plan logic relation set; acquiring a real-time process state set of the construction site; determining a plan failure index through real-time logic deviation calculation; in response to the plan failure index being greater than a preset failure determination threshold, determining that the plan fails and switching to a logic emergence mode; in response to the plan failure index being less than or equal to the failure determination threshold, maintaining the plan driving mode; deducing and generating an emergence logic set through emergence logic confidence calculation; determining a failure logic set; reconstructing to generate a hybrid logic model; the prediction completion time is deduced again; outputting a decision support signal; according to the method, the defect that a traditional system cannot judge the failure is overcome, the prediction accuracy is improved, and invalid warning out of reality is avoided.

The invention relates to the technical field of building engineering, and provides a building construction progress digital monitoring method and system. The method comprises the following steps: collecting three-dimensional point location data of a construction area, and comparing the three-dimensional point location data with a BIM model to extract a progress deviation; associating the construction log to a BIM component node and setting a first evaluation index; associating the material records to BIM component nodes, and setting second evaluation indexes; drawing up a resource map of construction process logic in combination with the deviation and the two indexes; and based on the atlas, according to a weight attenuation coefficient, multi-scale assessment of cumulative influence is carried out by taking efficiency as a feature, and resource compensation reminding is carried out in combination with dynamic threshold adjustment. The technical problem that progress deviation is difficult to accurately recognize due to data updating lagging and construction process logic disjunction in traditional building construction progress monitoring is solved, real-time accurate analysis of the construction progress is achieved through intelligent comparison of multi-source data fusion and the BIM model, resource allocation is dynamically optimized in combination with multi-dimensional evaluation indexes, and the construction progress monitoring efficiency is improved. And the construction management efficiency is improved.

The invention provides a method for monitoring and controlling cable-stayed buckling construction of a concrete-filled steel tube arch bridge based on a digital twin system, and the method comprises the steps: collecting the monitoring data, such as wind speed and wind direction, arch rib stress displacement, tower bottom stress, tower deviation and cable force, through a sensor; establishing a concrete filled steel tube arch bridge construction digital twinning system based on a three-dimensional model, real-time monitoring, data analysis and a visual platform; time sequence data is decomposed into different frequency components through variational mode decomposition (VMD), a bridge construction stage prediction model is established through a long short-term memory network (LSTM), and real-time accurate prediction of the construction progress and state is achieved. And secondly, based on the prediction model and on-site real-time monitoring conditions, an intelligent load adjustment construction control method based on a digital twin system is provided, and high-precision control over automatic load adjustment of cable hoisting and real-time rectification of tower displacement is achieved. The problems that in a traditional monitoring and control method, early warning is not timely, the assembling precision is insufficient, and the arch axis shape control difficulty is large are solved.