518 results about "Construction scheduling" patented technology

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 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 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.

The invention relates to the technical field of data processing, in particular to a BIM (Building Information Modeling)-based intelligent robot construction progress and quality linkage analysis system, which comprises a data acquisition module for acquiring robot sensor data and environment monitoring data, a semantic fusion module for generating structured data with semantic tags through a railway station building ontology library and outputting abnormal flag bits, the time sequence twinborn module generates a digital twinborn new version and a change vector based on an abnormal flag bit, the space matching module adopts an incremental learning mechanism to optimize a coordinate registration strategy, and the coupling analysis module establishes a quantitative evaluation model of the influence of the quality defect on the construction period and generates a risk early warning signal; the decision optimization module simulates a disposal scheme in a virtual environment to output a Pareto optimal solution set, and the execution tracing module generates a machine instruction set and constructs an auditable operation chain. Dynamic linkage analysis of construction progress and quality is realized through cooperation of multiple modules, and the problem of response delay is solved.

The invention discloses a project quality detection method and system based on green building design, and relates to the technical field of project quality detection. The engineering quality detection method and system based on the green building design comprises the steps that S1, BIM structured data, real-time monitoring data, structure acceptance data and construction event and window data are preprocessed; s2, on the basis of BIM structured data and in combination with a BIM model, the qualification state of the component and the quality grade of the partition are judged; s3, identifying a working condition switching anchor point and an event, and performing segmented division of a working condition process; s4, feature clustering and performance state discrimination are carried out, different working condition types are identified, and tagging management is carried out; s5, identifying dominant factors and performing attribution assignment through multi-dimensional label attribution and traceability judgment; and S6, performing grade judgment and grade response through multi-source time sequence item-by-item difference analysis. The problems of state interval disjunction and difficulty in accurate attribution and stable diagnosis caused by space-time mismatching of construction progress, BIM nodes and energy consumption environment data and data asynchronization are solved.

The invention discloses a water conservancy project construction progress prediction and intelligent management method, and relates to the technical field of water conservancy projects, and the method comprises the following steps: constructing a unique identification code system of construction elements, collecting multi-source data bound with a unique identification code, carrying out the correlation fusion of the multi-source data based on a preset dynamic correlation rule base, and carrying out the prediction of the construction progress of a water conservancy project. Generating a fusion data set and mapping the fusion data set to a corresponding component of the BIM model in real time, visually displaying fusion data in the BIM model, comparing the fusion data set with a preset scene feature library, identifying a complex scene and marking a core influence factor, and matching a collaborative plan from a mapping library of the scene and the plan, and generating a regulation and control instruction set, simulating an execution effect in the BIM model, obtaining execution data according to the collaborative plan, adjusting the regulation and control instruction set, obtaining an execution result, and updating a fusion data set and corresponding data in the BIM model, so that closed-loop management, accurate data association and dynamic management are realized, and the construction progress management efficiency is improved.

The invention relates to the technical field of municipal planning, in particular to a BIM + GIS-based municipal underground pipe network intelligent planning and conflict avoidance system and method, and the system comprises a BIM-GIS data fusion module which is used for integrating a BIM model and GIS topographic data; the construction process association module is used for analyzing the construction plan to generate a dynamic construction progress table, and constructing a directed graph based on the spatial position relationship of each construction unit in the BIM model; the conflict prediction and avoidance module is used for calculating the space conflict probability of the temporary pipe network and the permanent pipe network in real time in combination with the directed graph and GIS topographic data; the land-saving optimization module is used for carrying out iterative optimization on the temporary pipe network layout through a genetic algorithm based on GIS topographic data and construction area boundary constraints so as to minimize the total length and the occupied area of the temporary pipe network; and the dynamic visualization interaction module is used for providing a three-dimensional BIM-GIS fusion view. According to the method, management and control from static design to dynamic construction are achieved, and the scientificity of underground pipe network planning and the land resource utilization efficiency are improved.

The invention discloses a BIM-based electromechanical installation resource management and control system, and the system comprises a data sensing layer which integrates a laser radar, an RFID, a UWB and an environment sensor, collects multi-modal data, and forms a total-factor digital twin base data stream after the multi-modal data is preprocessed by a mobile edge computing node; the model fusion layer is used for binding the construction progress with BIM component attributes, dynamically deducing a component-level progress path through an AI algorithm, identifying key path conflicts, integrating ERP resource data to generate a thermodynamic diagram and a cost deviation curve, and automatically detecting pipeline collision, progress lag and cost hyper-branched risks through a rule engine machine learning dual-mode engine; triggering multi-stage early warning and dynamically adjusting a threshold value; the intelligent decision-making layer is used for predicting the progress deviation probability of the future seven days by adopting LSTM, generating a non-tampering deviation correction scheme in combination with a block chain, and realizing hierarchical response through a yellow-orange-red three-level alarm system; and the user interaction layer is used for realizing multi-terminal collaboration and executing verification data feedback through AR glasses, a mobile terminal and a large-screen monitoring center.

The invention discloses a construction progress intelligent pre-control method based on a large model and related equipment, and the method comprises the steps: carrying out the semantic coding and structuring of construction contract terms, constructing a contract term relation graph, and generating construction review opinions; determining an engineering task according to the structured field set, binding the engineering task and the engineering quantity list to obtain an engineering quantity set corresponding to the engineering task, constructing a task-resource allocation optimization model according to the engineering quantity set and a resource database, and generating an initial task execution plan; calculating a comprehensive deviation index of the construction stage, and further generating early warning and optimization suggestions; and generating an acceptance report in combination with information such as an actual construction state vector and constructing an engineering resume chain. According to the embodiment of the invention, the method can solve the problems of one-sided text understanding, extensive resource allocation, lag in deviation analysis, low acceptance efficiency and the like in a conventional management method, and effectively reduces the project delay rate and compliance risk. The method can be widely applied to the technical field of intelligent management of communication engineering.

The invention provides a construction site steel pipe scaffold stability risk analysis and early warning method, which comprises the following steps: constructing a node mechanical coupling graph based on structure topological information, deploying a non-intrusive sensor to collect pose and stress data of key nodes, and generating standardized feature data after multi-stage filtering and normalization processing; the time sequence features and the node coupling relation are fused and input into a graph neural network model in a graph structure format, and multi-node stress state co-evolution modeling and short-term trend prediction are achieved; the deformation prediction model is dynamically optimized in combination with the construction progress and the environment disturbance parameters, the structural risk is judged according to the theoretical displacement and the stability threshold value, early warning information is generated and uploaded in real time, the accuracy and adaptability of scaffold deformation trend prediction are improved, and scientific support is provided for construction safety management.

The invention relates to the technical field of construction management, in particular to a highway construction scheduling optimization method and system based on big data, and the method comprises the following steps: screening abnormal points and calculating a driving distance based on the coordinate data of a transport vehicle, analyzing the matching condition of a track and route planning, and recognizing progress offset and time period rearrangement signals; and dynamically adjusting material putting and equipment operation nodes to obtain a field operation response beat amount. According to the invention, based on big data real-time processing and sequence analysis, time sequence screening and abnormal point elimination are carried out on vehicle coordinate data, remaining road section features are extracted, progress offset is continuously tracked, rhythm offset features are dynamically generated by comparing arrival time and node demands, a periodic trend monitoring and abnormal state identification mechanism is established, and the abnormal state identification accuracy is improved. And dynamically triggering resource allocation time sequence rearrangement, carrying out integrated linkage on data such as a job node state, a material release rhythm and an equipment job window, and realizing resource release rhythm optimization and task parameter self-adaptive adjustment.

The invention discloses a construction supervision and prediction method and device based on a large language model agent, and the method comprises the steps: collecting multi-modal original information of a construction site, carrying out the cross-modal deep semantic analysis of the multi-modal original information, and recognizing and extracting construction entity information; packaging the analyzed construction entity information into structured construction data in a preset format, aligning and fusing the structured construction data with a pre-constructed construction field knowledge graph, and dynamically updating the construction field knowledge graph; based on the dynamically updated construction field knowledge graph and the structured construction data, performing real-time monitoring and abnormality diagnosis of the construction progress and compliance by using a preset large language model agent; and performing construction trend prediction based on the large language model agent by fusing historical construction data, a current construction state and external environment information. Construction entity information can be extracted, association between the information is established, and potential risks can be found in advance based on a large language model agent.

The invention discloses a water conservancy construction progress prediction system based on multi-source data fusion, and relates to the technical field of artificial intelligence and engineering management, and the system comprises a multi-source sensing collection module which carries out the hierarchical adaptive sampling, quality evaluation and space-time alignment of multiple types of data of a construction site; the data comprises project progress, meteorological environment, geology and hydrology, energy consumption, water consumption and personnel state, and the sampling frequency is dynamically adjusted through an adaptive sampling function according to the prediction error. According to the invention, through hierarchical adaptive sampling and data quality evaluation of the multi-source sensing acquisition module, comprehensive, real-time and high-quality acquisition of construction site data is realized, the problems of data isomerism and uneven quality are effectively solved, the reliability of data input is improved, and the data input efficiency is improved. Through working condition semantic driving and physical consistency constraint fusion of the feature fusion modeling module, internal association among multi-source data can be deeply mined, and fusion features with better interpretation and robustness are generated.

The invention discloses a road and bridge construction progress prediction method and system based on unmanned aerial vehicle vision, and belongs to the technical field of construction monitoring. Image and material approach data are collected through an unmanned aerial vehicle, construction components are recognized by combining target detection and a three-dimensional reconstruction technology, visual engineering quantity estimation and material consumption time sequence data are fused, and multi-source collaborative analysis and anomaly detection are achieved. The construction progress is automatically judged, the completion percentage is calculated, deviation root causes are actively identified through a dynamic risk feature library, and future completion time and risk early warning are predicted in combination with a time sequence model. Compared with a traditional method, the accuracy and real-time performance of construction progress monitoring are improved, full-process automatic management from data collection to intelligent early warning is achieved, the manual checking cost is effectively reduced, and the engineering risk prevention and control capacity is improved.

The invention discloses a water conservancy project construction progress and resource allocation intelligent optimization system, and relates to the technical field of water conservancy projects, and the system comprises a resource identification generation module which employs a Hash algorithm to generate unique identification codes for each batch of construction resources used in a water conservancy project construction process, calculating according to the resource attribute information, the construction task code and the batch identification associated data to obtain a non-repeatable digital resource code; when the same batch of resource information is detected, grouping identification is carried out according to a time sequence, a unified coding system covering the whole process of purchasing, transportation, entering, warehousing and construction is formed, and space coordinate basic data corresponding to each resource is generated; according to the intelligent optimization system for the construction progress and resource allocation of the water conservancy project, accurate control of the construction progress of the water conservancy project, intelligent optimization of resource allocation and comprehensive visualization of operation of a construction site can be realized, and the management efficiency, the resource utilization rate and the overall construction quality of the construction of the water conservancy project are greatly improved.

The invention provides a multi-data combined construction site three-dimensional model construction method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the calling of a preset resolution satellite image, and obtaining a full-region stereo image pair data set; after radiation correction is carried out, a region surface model and a region elevation model are generated; grid-level modeling defect detection is carried out, and distributed blind area blocks are positioned; performing obstacle avoidance path fitting to generate an obstacle avoidance optimized flight path; the unmanned aerial vehicle is driven to execute multi-angle surrounding scanning, and distributed blind area dense point clouds are directionally collected; constructing a distributed blind area model; and carrying out microscopic superposition, and outputting a three-dimensional model of the construction site. The technical problems that a construction site modeling method in the prior art is limited in the aspects of coverage, modeling efficiency and data real-time performance, depends on a single data source, cannot provide a comprehensive and accurate three-dimensional model of a construction site, and affects the construction progress and decision are solved.

The invention discloses a high steep narrow terrain earthwork oblique photography collaborative modeling method, and belongs to the technical field of engineering surveying and mapping and three-dimensional digital modeling. The method comprises the following steps: designing an imitated flight route based on a digital elevation model of a high steep narrow terrain, controlling an unmanned aerial vehicle carrying a laser radar and multiple lenses to synchronously collect data, arranging hierarchical image control points, de-noising a laser radar point cloud to generate a digital elevation surface, and processing an oblique photography image to generate a three-dimensional grid model. And fusing the two types of models to correct the elevation and re-project the texture, generating a collaborative live-action three-dimensional model, and finally calculating the earth-rock volume and planning the construction machinery path based on the model. The method can be used for earthwork surveying and mapping, metering and construction scheduling of high, steep and narrow terrains, and collaborative improvement of modeling geometric accuracy and texture authenticity is achieved.

The invention relates to a BIM-based urban rail transit construction period carbon emission monitoring method and system, and the method mainly comprises the steps: decomposing a construction project system, and associating member carbon emission codes with corresponding construction parts and members in a BIM model; obtaining building materials, construction machinery and energy consumption related data of a transportation link on a construction site; constructing a step-by-step summarized carbon emission calculation model; and adding time and carbon emission dimensions in the BIM model, and dynamically displaying the carbon emission data of each component and construction area along with time and construction progress. According to the method and the system, through multi-source carbon data fusion acquisition during the construction period of the urban rail transit, the carbon emission in the construction process of the urban rail transit can be accurately calculated, deep integration and visual display of carbon emission data and a BIM model are realized, a data basis is provided for formulating emission reduction strategies and suggestions, and the construction efficiency of the urban rail transit is improved. The method is especially suitable for monitoring carbon emission during the construction period of urban rail transit engineering.

The invention discloses an engineering cost real-time evaluation and early warning system based on multi-source data fusion and dynamic learning, and the system comprises a multi-source data collection module which is used for collecting construction process data, historical engineering data, market information data, and design drawing data; the preprocessing module is used for preprocessing and fusing the collected multi-source data to form a multi-dimensional feature vector with a unified timestamp; the hybrid neural network evaluation module is used for inputting the multi-dimensional feature vector into a pre-constructed hybrid neural network model for cost evaluation; the dynamic optimization learning module is used for carrying out online learning and dynamic optimization processing on the hybrid neural network evaluation module based on a transfer learning strategy; the block chain evidence storage module is used for storing key data generated in the evaluation process; and the progress monitoring and early warning module is used for monitoring the construction progress in real time, calculating progress deviation and triggering corresponding early warning. Accurate evaluation, real-time early warning and dynamic optimization of the construction cost are realized.

The invention relates to the technical field of Internet of Things, and particularly discloses an Internet of Things-based engineering construction progress dynamic regulation and control method and system, which automatically collects personnel, machinery and material data of a construction site through Internet of Things sensing equipment, replaces traditional manual recording, solves the problem of data islands, and improves the working efficiency. The method comprises the following steps: calculating a human influence factor based on a personnel state, quantifying configuration rationality and working efficiency, calculating a mechanical influence factor according to mechanical operation efficiency and a safety state, evaluating comprehensive performance of equipment, obtaining a material influence factor through material supply and inventory data, and monitoring supply timeliness and inventory stability. The three are fused to obtain a construction progress comprehensive deviation index, if the construction progress comprehensive deviation index exceeds a preset range, an adjustment instruction is automatically generated, resource allocation is dynamically optimized, real-time diagnosis and intelligent regulation and control of progress management and control are achieved, and the problem of traditional management lag is effectively solved.

The invention provides an electric power project progress identification method based on integration of a live-action three-dimensional GIS (Geographic Information System) and a BIM (Building Information Modeling), which comprises the following steps of: acquiring a multi-view image of a construction site by utilizing oblique photography of an unmanned aerial vehicle, and constructing a live-action three-dimensional GIS model based on the image; mapping an electric power engineering BIM design model of a construction site to a geographic coordinate system which is the same as the real scene three-dimensional GIS model to obtain a fusion model; based on an artificial intelligence image recognition algorithm, engineering components are detected for the images, and detection results are mapped to the fusion model according to image positioning information; correspondingly comparing the actual installation state with a component in a BIM design model, and generating construction progress information of a component level; and displaying the fusion model in a three-dimensional visual platform, and comparing the construction progress information with a preset planned progress. The real-time performance and accuracy of progress monitoring are remarkably improved, personal errors are reduced, and the project management efficiency is improved.

The invention discloses an engineering excavation progress dynamic estimation method, device and equipment, and relates to the technical field of engineering project progress management, and the method comprises the steps: monitoring the position, posture and operation state of intelligent wearing equipment, and combining a BIM model and a dynamic engineering amount calculation model, meanwhile, an engineering quantity calculation model is dynamically adjusted according to the operation mode of the constructor and environmental data, the accuracy of progress estimation is ensured, and a progress situation map is generated to visually display the construction progress; the accuracy and real-time performance of construction progress management are improved, real-time estimation of the excavation progress and dynamic visual simulation of the construction progress are achieved, and a decision basis is provided for project management.

The invention provides an engineering investment construction assessment method and system. Belongs to the technical field of civil engineering and construction evaluation. The method comprises the following steps: accessing multi-source heterogeneous big data in the engineering investment construction field in real time to generate a comprehensive data set; based on the comprehensive data set, utilizing a graph network technology to carry out modeling on a dynamic association relationship among different data nodes, and constructing a project investment construction dynamic association graph network model; multi-dimensional information such as market dynamics, raw material prices, construction progress and quality detection is accessed in real time, and dynamic association modeling among data nodes is performed by using a graph network technology, so that key factors in investment construction of various projects can be tracked and analyzed in real time, and a global perspective is provided.

The invention relates to the technical field of carbon emission processing, in particular to a visual building construction process carbon emission calculation method, which comprises the following steps of: endowing each building component with a component identifier, binding carbon footprint data of a building material corresponding to the building component in a construction stage, and correspondingly generating a plurality of target carbon units; combining the plurality of target carbon units to generate a carbon genome data model; according to a preset carbon emission factor, converting resource consumption data and construction activity data generated in the current building construction process into a carbon emission data stream; fusing the carbon emission data flow with the carbon genome data model, updating the carbon emission state of each target carbon unit in the building construction process, generating carbon emission distribution information mapped with the three-dimensional space position of the building and the construction progress of the building, and performing visual rendering on the carbon emission distribution information, the technical problem that significant deviation exists between the calculation result and the carbon emission actually generated in the construction process is solved.

The invention discloses an optimization method and device for a power transmission and transformation project construction progress deviation correction model, and relates to the technical field of resource allocation and progress deviation correction, and the method comprises the steps: constructing prediction data used for predicting the future construction progress deviation, and multi-target optimization data used for solving an optimal deviation correction scheme when the progress deviation occurs, the initial data set is used for performing closed-loop correction on the prediction and optimization model based on the correction scheme; according to the method, the complex change of the construction environment can be dynamically responded by constructing the self-adaptive prediction model fused with real-time feedback; in combination with a multi-objective optimization algorithm with an elastic resource constraint mechanism, effective balance and dynamic optimization between key objectives such as the construction period and the cost are realized; the defects of a traditional model in the aspects of dynamic adaptability, resource constraint elasticity and multi-target collaborative optimization are effectively overcome, and a scientific and accurate progress control solution is provided for a power transmission and transformation project in a complex and changeable construction environment.

The invention discloses a water conservancy project construction progress and quality management system and method, and belongs to the technical field of water conservancy project construction progress and quality management, and the method comprises the steps: S1, obtaining real-time construction parameters, a design reference value and environment load data; s2, based on the real-time construction parameters and the design reference value, calculating to obtain a comprehensive construction deviation index; s3, combining the building information model data and the comprehensive construction deviation index to construct a digital twinborn model containing an initial defect; s4, based on the digital twinborn model containing the initial defect, coupling the comprehensive construction deviation index and the environmental load data, and calculating to obtain an accumulated damage factor; and S5, determining a long-term health degree score based on the accumulated damage factors, and laying an accurate mathematical foundation for subsequent full life cycle performance prediction.

The invention relates to a construction progress and construction cost collaborative management method, system and device and a medium. The method comprises the following steps: inputting resource demand data, construction period constraint and cost parameters of each key node, construction progress data, resource consumption data and environmental parameters of a current pumped storage project into a pre-trained construction progress and construction cost correlation model; and associating the construction progress and the construction cost of the current pumped storage project through the pre-trained construction progress and construction cost association model. Whether the construction progress and the construction cost of the current pumped storage project have deviation or not is judged, if deviation exists, an early warning mechanism is triggered, and a current construction scheme is optimized based on a multi-target genetic algorithm. Through the method, collaborative management of the construction progress and the construction cost of the pumped storage project can be realized, the problems of unreasonable resource allocation, risk response lag and the like are avoided, and the method has the characteristics of pertinence, flexibility and capability of adapting to the pumped storage project.

The utility model relates to monitoring equipment technical field discloses a movable video monitoring device, including installation ring, the installation plate of rotation setting in the inside of installation ring and set below the monitoring camera of installation plate, including: connecting mechanism, including fixed in the both sides of monitoring camera and the connecting shaft of the connecting plate of tail end rotary connection for driving monitoring camera to rotate, the fixed plate of fixed between two connecting plates and the linkage block for driving monitoring camera to carry out displacement and fixed in fixed plate top side, the utility model discloses reasonable in structure can monitor the construction worker's construction operation and construction progress, can carry out adaptive adjustment to the angle and position of monitoring camera, can conveniently construction worker to monitoring camera carries out dismounting maintenance, and work performance is high, and convenient to use.

The invention discloses a building indoor component construction state automatic evaluation method and system, and relates to the technical field of building construction, and the method comprises the steps: obtaining building indoor point cloud data, carrying out the preprocessing of the point cloud data, and generating a point cloud model; recognizing a column point cloud based on an AI algorithm and extracting a central point, and generating an actual construction axis network; executing axis net alignment and registration based on the actual construction axis net and the design axis net of the BIM model; extracting a three-dimensional parameter of the to-be-monitored component from the registered BIM model, generating a BIM bounding box parameter, calculating a coverage degree, and judging a construction state based on a quantization threshold value; and the proportion of each state component is counted according to subentry projects, the progress deviation rate of the actual progress and the planned progress is calculated in combination with the planned project amount and the planned construction period in the BIM model imported in advance, and a visual evaluation report is generated. According to the invention, high efficiency and standardization of building indoor component construction progress evaluation are realized.

This application belongs to the field of construction progress control technology, and specifically discloses an adaptive control system for building construction progress. In this application, the actual progress status of construction task nodes is obtained through a coupling propagation boundary construction module, and a coupling propagation boundary between construction task nodes is constructed based on the construction coupling relationship between the construction task nodes. Then, a latent instability state determination module performs boundary propagation discrimination based on the coupling propagation boundary, generates boundary instability indicators, and determines the construction task nodes that have entered the latent instability state. Subsequently, a directional mitigation control module determines the disturbance propagation channel, identifies the channel intervention nodes, and performs directional mitigation control. Finally, a coupling propagation boundary update module updates the coupling propagation boundary according to the state changes of the construction task nodes and cyclically triggers discrimination and control. This helps to enhance the correlation between state judgment and the determination process of corresponding control objects, and improves the pertinence and continuity of building construction progress control.