A BIM-based construction project construction optimization system

Abstract

The application relates to the field of construction optimization, and discloses a building engineering construction optimization system based on BIM, which is used for meeting the demand of high quality and rapid delivery of engineering projects. The system comprises the following steps: constructing a perfect construction task topology network, accurately identifying high-load hub nodes, scientifically deploying personnel, defining different strategies as topology structure adjustment simulation of bottleneck nodes and fragile path list items, quantifying the influence on the structure entropy value set, calculating the expected promotion rate and improvement value through an entropy change efficiency correlation model, and providing a scientific basis for strategy making. The work efficiency is significantly improved, and local problems are avoided to cause global delay. The application realizes effective management and optimization of the construction process, meets the demand of high quality and rapid delivery, and solves the problem of efficient construction management in the field.

Description

Technical Field

[0001] This invention relates to the field of construction optimization, and in particular to a BIM-based construction optimization system for building engineering. Background Technology

[0002] In the field of construction engineering, with the continuous expansion of project scale and increasing complexity, construction management faces numerous severe challenges. Construction involves many interconnected and interdependent stages, forming a vast and complex system. Traditional construction management methods, relying primarily on manual experience and simple project management tools, have gradually revealed their limitations in dealing with such a complex system, failing to meet the demands of efficient and precise management in modern construction engineering.

[0003] Traditional management methods often fail to accurately identify critical task milestones and bottlenecks in personnel allocation. This frequently leads to unreasonable personnel allocation during actual construction, with some critical tasks lagging behind due to insufficient personnel, affecting the overall project schedule; while some non-critical tasks may result in wasted resources due to redundant personnel.

[0004] In existing technologies, the formulation of administrative optimization strategies is mostly based on experience and judgment, lacking in-depth analysis of the overall structure of the construction system. This makes the optimization strategies often blind and difficult to accurately target the actual problems in the construction process;

[0005] In commercial construction projects, such as large commercial complexes and high-end office buildings, the requirements for construction management are even more stringent. These projects are typically characterized by tight schedules, high quality requirements, and the involvement of multiple professional disciplines, necessitating more refined and efficient construction management methods. Existing technologies struggle to achieve precise control and optimization of the construction process, failing to meet the projects' demands for high quality and rapid delivery.

[0006] Therefore, we propose a BIM-based construction optimization system to address the aforementioned issues. Summary of the Invention

[0007] This invention provides a BIM-based construction optimization system for building engineering projects, designed to meet the demands of engineering projects for high quality and rapid delivery.

[0008] The first aspect of this invention provides a BIM-based construction optimization system for building projects, comprising: an extraction module for extracting all construction tasks and their logical dependencies based on a three-dimensional building information model of the building project, abstracting each task as a node and dependencies as directed edges, and constructing a complete construction task topology network; an entropy module for calculating the global structural entropy and the critical path local entropy based on the complete construction task topology network using information entropy theory, jointly forming a structural entropy value set; and a processing module for fusing the node degree centrality, edge weight strength, and the structural entropy value set of the complete construction task topology network, performing coupling analysis, and identifying... The system identifies high-load hub nodes that cause resource congestion and highly vulnerable task chains that are prone to failure due to complex dependencies, generating a list of bottleneck nodes and vulnerable paths. A strategy module defines different administrative optimization strategies as topological adjustments to the items listed in the bottleneck node and vulnerable path list, quantifies the impact of these adjustments on the set of structural entropy values, and calculates the expected improvement rate of overall work efficiency and the improvement value of personnel allocation utility after implementing each optimization strategy using a preset entropy change efficiency correlation model. A reporting module generates a management decision prediction report based on the bottleneck node and vulnerable path list and the expected improvement rates and values ​​under various strategies.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the method includes: extracting the geometric attribute sequence of components, a predefined list of construction procedures, a set of logical constraint rules between procedures, and an associated historical work time consumption dataset from the three-dimensional building information model; defining each independent procedure as a network node based on the list of construction procedures, with all nodes forming a node set; identifying pairs of procedures with sequential dependencies based on the set of logical constraint rules, creating a directed edge between each pair of nodes, pointing from the preceding procedure node to the subsequent procedure node, with all directed edges forming a directed edge set; constructing a directed acyclic graph as an initial topology network using elements in the node set as vertices and elements in the directed edge set as connections; calculating the number of direct predecessor nodes and direct successor nodes for each node on the initial topology network, as the basic degree information of that node; calculating the basic dependency strength value of the preceding and following node pairs connected by each edge, to obtain a complete construction task topology network.

[0010] Optionally, in a second implementation of the first aspect of the present invention, the method includes: based on the improved construction task topology network, extracting the connection relationships between its nodes to generate a network adjacency matrix; extracting the basic values ​​of the dependency strength of each edge in the network and the baseline parameters of the task workload of each node; generating an edge weight matrix through composite calculation; calculating the eigenvector centrality of each node in the network based on the network adjacency matrix and the edge weight matrix; processing the eigenvector centrality of all nodes to form a global node influence probability distribution; calculating the global structure entropy by applying the definition of information entropy according to the global node influence probability distribution; and based on the improved construction task topology network... The critical path method is applied to calculate the critical path that determines the shortest total project time, based on the nodes, directed edges, and baseline parameters of the task workload. All nodes on this path are extracted to form a critical path node subset. Within this critical path node subset, the conditional transition probability from the preceding node to the subsequent node is calculated based on the actual connection relationships between nodes and the corresponding edge weight matrix values, generating a local node transition probability sequence on the critical path. Based on this local node transition probability sequence, the definition of information entropy is applied to calculate the entropy value, which is the local entropy of the critical path. The global structural entropy and the local entropy of the critical path are combined to form a structural entropy value set.

[0011] Optionally, in the third implementation of the first aspect of the present invention, the method includes: based on the improved construction task topology network, extracting the number of direct predecessor nodes and direct successor nodes of each node, and calculating its node degree centrality; fusing the node degree centrality with the dependency strength base value of the associated edges, and calculating a quantified node congestion pressure index for each network node; traversing the directed paths in the improved construction task topology network, and extracting the dependency strength base value of all edges on the path; calculating a path dependency vulnerability index for each directed path according to the distribution and accumulation of dependency strength in the path; reading the set of structural entropy values, using the global structural entropy to perform global standardization correction on all node congestion pressure indices, and utilizing... The path dependency vulnerability index is risk-weighted and corrected using the local entropy of the critical path, generating a corrected set of node congestion pressure indicators and a set of path dependency vulnerability indices. Statistical analysis is performed on the corrected set of node congestion pressure indicators, and a threshold is set to filter out nodes with congestion pressure significantly higher than the network average, marking them as a candidate set of high-load hub nodes. Statistical analysis is also performed on the corrected set of path dependency vulnerability indices, and another threshold is set to filter out paths with vulnerability indices significantly higher than the average, marking them as a candidate set of high-vulnerability task chains. The candidate sets of high-load hub nodes and high-vulnerability task chains are integrated and cross-validated, and sorted according to their indicator values ​​to generate a list of bottleneck nodes and vulnerable paths.

[0012] Optionally, in the fourth implementation of the first aspect of the present invention, based on the construction organization design, a set of key resource types required for construction is defined, and the total amount constraint, phased supply ceiling, and flow cost coefficient of each resource type within the project cycle are determined, which together constitute a global resource allocation constraint table; for each high-load hub node and high-vulnerability task chain identified in the bottleneck node and vulnerable path list, its resource demand characteristics are analyzed, and under the premise of satisfying the global resource allocation constraint table, multiple allocation logics that prioritize the allocation of additional resources to these bottlenecks are designed to generate a trial resource dynamic allocation scheme; each trial resource dynamic allocation scheme is mapped to the attribute adjustment of a specific node or edge in the improved construction task topology network, and based on the adjusted network, the network is re-evaluated. Calculate the congestion pressure and path vulnerability index of relevant nodes to obtain a set of key network indicators corresponding to each allocation scheme after simulation. Compare each set of key network indicators after simulation with the set of node congestion pressure indicators and path dependency vulnerability indexes after the original correction before implementing the allocation scheme. Calculate the node congestion pressure reduction rate, path vulnerability attenuation rate, and equivalent resource cost consumed by each allocation scheme, which together constitute the predicted value of resource allocation optimization effect. Based on the predicted value of resource allocation optimization effect, rank and evaluate the cost-effectiveness of all exploratory dynamic resource allocation schemes, select the optimal schemes, and provide a structured description of their specific measures, expected effects, and implementation conditions to generate dynamic resource pre-allocation strategy recommendations.

[0013] Optionally, in the fifth implementation of the first aspect of the present invention, the method includes: for each item recorded in the list of bottleneck nodes and vulnerable paths, designing at least one corresponding administrative optimization strategy, specifying each strategy as an adjustment instruction for nodes and edges in the improved construction task topology network, and generating a set of optimization strategy simulation schemes; sequentially applying each scheme in the optimization strategy simulation scheme set to a copy of the improved construction task topology network, executing node addition / deletion, edge connection modification, and attribute update operations defined in the scheme, and generating a corresponding simulated adjusted network for each optimization strategy; for each simulated adjusted network, recalculating the node influence distribution and critical path based on the network adjacency and weight matrix, and applying information entropy theory to calculate, obtaining a set of newly effective structural entropy values ​​corresponding to each simulated adjusted network; comparing the entropy values ​​in each newly effective set of structural entropy values ​​with the corresponding entropy values ​​in the original set of structural entropy values ​​before the simulation adjustment, calculating their difference, and obtaining a set of structural entropy change values ​​corresponding one-to-one with each optimization strategy; inputting each set of structural entropy change values ​​into the entropy change efficiency correlation model, and outputting the expected improvement rate of overall work efficiency and the improvement value of personnel allocation utility after implementing the corresponding optimization strategy.

[0014] Optionally, in the sixth implementation of the first aspect of the present invention, the method includes: reading the improved construction task topology network and the list of bottleneck nodes and vulnerable paths; mapping the high-load hub nodes and high-vulnerability task chains identified therein back to the corresponding components or processes in the original three-dimensional building information model through unique task identifiers, thereby generating a visualized association data of risk elements; using each record in the list of bottleneck nodes and vulnerable paths as a row and each strategy in the set of optimization strategy simulation schemes as a column, filling the expected improvement rate and improvement value into the corresponding rows and columns to construct a two-dimensional matrix of efficiency risk and optimization potential; based on the two-dimensional matrix of efficiency risk and optimization potential, performing a comprehensive analysis of all optimization strategies according to their expected improvement rate and improvement value. The benefits are ranked, and the dependencies and mutual exclusions between strategies are considered. Strategies that can be implemented collaboratively are combined to generate a recommended sequence of optimized strategies and a strategy combination package. Several independent strategies or their combination packages with the highest ranking in the recommended sequence of optimized strategies are selected as typical management scenarios. Based on these scenarios and their corresponding expected improvement rates and improvement values, their potential impact on the overall project duration and peak resources is deduced, generating multiple sets of multi-scenario prediction comparison charts of future work efficiency and staffing under different management decisions. The visualized correlation data of the risk factors, the two-dimensional matrix of efficiency risk and optimization potential, the recommended sequence of optimized strategies and the strategy combination package with priority, and the multi-scenario prediction comparison charts are integrated to compile and generate a management decision prediction report.

[0015] Optionally, in the seventh implementation of the first aspect of the present invention, based on the two-dimensional matrix of efficiency risk and optimization potential, all optimization strategies are ranked according to the comprehensive benefits of their expected improvement rate and improvement value, and a comprehensive benefit index, NBI, is defined for ranking:

[0016] ,

[0017] in, This is the ratio of the efficiency improvement rate to the baseline efficiency. The ratio of the improvement in personnel utility to the baseline utility; This is the ratio of resource cost equivalent to benchmark cost. , , As weight.

[0018] Optionally, in the eighth implementation of the first aspect of the present invention, a supplementary module is further included for supplementing the energy consumption impact analysis of the construction task topology network: extracting the material thermal property sequence of the enclosure structure components, the functional zoning information of the building space, and the spatial adjacency relationship data between components from the three-dimensional building information model; based on the material thermal property sequence and spatial adjacency relationship data, and according to the preset building energy consumption simulation rules, calculating the theoretical energy consumption impact value of each component involved in the construction task under a unit working condition, as the basic value of the node's energy consumption impact; and assigning the basic value of the node's energy consumption impact as a new attribute to the improved... For the nodes in the construction task topology network, an extended construction task topology network is generated. Based on the extended construction task topology network, the impact of construction sequence on the overall energy consumption of the building is analyzed. Continuous task sequences that have a significant impact on the final energy consumption of the building are identified and marked as energy-sensitive paths. The node with the highest basic energy consumption impact value in the path is marked as a key energy consumption node. The energy-sensitive paths are compared with highly vulnerable task chains to find overlapping parts, which are marked as collaborative management critical paths. For this path and the key energy consumption nodes, collaborative optimization strategy suggestions are generated by combining the optimization strategy simulation scheme set.

[0019] Optionally, in the ninth implementation of the first aspect of the present invention, environmental attribute information of major building materials, simulated transportation distance data of large components or equipment, and potential environmental impact category identifiers involved in construction processes are extracted from the three-dimensional building information model; based on the environmental attribute information, simulated transportation distance data, and potential environmental impact category identifiers, and according to preset quantitative conversion rules for different environmental impact categories, the environmental load value per unit of engineering work for each construction task node is calculated as the environmental impact intensity value of the node; the environmental impact intensity value of the node is assigned as another new attribute, along with the basic energy consumption impact value of the node, to the corresponding node in the improved construction task topology network, generating a dual-attribute extended construction task topology network; in the dual-attribute extended construction task topology network, node clusters or paths with both environmental impact intensity values ​​and basic energy consumption impact values ​​at high levels are identified, and key areas of superimposed impact are located; for the key areas of superimposed impact, cross-analysis is performed in combination with the analysis results of highly vulnerable task chains, composite management measures are designed, and a multi-objective collaborative optimization strategy package is generated.

[0020] The mechanism of this invention is as follows: by converting BIM data into a topology network and calculating its information entropy, a quantitative diagnosis of structural deficiencies in personnel, chaotic administrative coordination, and loss of work efficiency in construction projects is achieved.

[0021] Beneficial Effects: By constructing and analyzing a comprehensive construction task topology network, high-load hub nodes can be accurately identified. These nodes represent critical construction tasks with high personnel demand. The resulting management decision prediction report allows for the scientific and rational allocation of personnel, placing suitable personnel on key tasks, improving personnel-task matching, maximizing personnel effectiveness, effectively addressing the problem of unreasonable personnel allocation, and enhancing personnel utilization.

[0022] Administrative optimization strategies are defined as topological adjustments to bottleneck nodes and vulnerable path items. By quantifying the impact of these adjustments on the set of structural entropy values ​​and using a pre-defined entropy change efficiency correlation model, the expected improvement rate of overall work efficiency and the improvement value of personnel allocation utility after implementing each optimization strategy are calculated. This provides a scientific basis for the formulation of administrative optimization strategies, making the optimization strategies more precise and effective, and enhancing the scientific nature of construction management.

[0023] By constructing a comprehensive construction task topology network and applying information entropy theory, we can accurately identify high-load hub nodes that cause resource congestion and highly vulnerable task chains that are prone to failure due to complex dependencies. Developing and implementing scientific optimization strategies to address these issues can effectively avoid congestion and delays during construction, improve the smoothness of the construction process, significantly improve overall work efficiency, and ensure on-time project delivery and quality assurance. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of an embodiment of the BIM-based building construction optimization system of the present invention;

[0025] Figure 2 A schematic diagram illustrating the extraction of construction tasks from a BIM model and the construction of a topology network;

[0026] Figure 3 This is a schematic diagram of one embodiment of the BIM-based construction optimization equipment for building engineering. Detailed Implementation

[0027] This invention provides a BIM-based construction optimization system for building engineering projects, designed to meet the demands of high quality and rapid delivery. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0028] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the BIM-based construction optimization system for building engineering in this invention includes:

[0029] 101. Extraction module, used to construct construction task topology network: Based on the three-dimensional building information model of the building project, extract all construction tasks and their logical dependencies; abstract each task as a node, and the dependency relationship as a directed edge, to construct a complete construction task topology network that represents the task sequence and process structure.

[0030] It is understood that the executing entity of this invention can be a BIM-based building construction optimization device, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0031] It should be noted that the topology network construction for the steel structure roof hoisting task of the large convention center is based on the 3D BIM design model of the convention center, from which all components (main trusses, secondary trusses, edge arches, welding nodes, etc.) and their engineering attributes (geometric dimensions, weight, installation elevation) related to the steel structure roof construction are extracted. Then, the complete roof hoisting work is broken down into a series of specific construction tasks: "Ground assembly of the first main truss in Area A", "Segmented hoisting of the first main truss in Area A", "High-altitude connection and closure of the main truss in Area A", "Welding of main and secondary trusses", and "Unloading of the support frame", etc.

[0032] The logical dependencies between these tasks are analyzed. In terms of timing, the "main truss high-altitude docking" must be carried out after the "main truss segment hoisting" is in place, and the "support frame unloading" can only begin after all main and secondary truss welding is completed and accepted. In terms of spatial dependency, considering construction safety and efficiency, truss hoisting in different zones (Zone A and Zone B) can be carried out in parallel, but they may share the same large crawler crane, thus creating resource dependency. In terms of process dependency, the "welding" task can only begin after the "truss hoisting" tasks preceding and following it are completed.

[0033] Based on the above analysis, each construction task is abstracted as a node in the topological network, and each node is assigned multi-dimensional attributes, including the planned construction period ("Ground assembly of the first main truss in Area A" is planned to take 2 days), required resources (one 350-ton crawler crane and four welders are needed), associated BIM component IDs (directly associated with specific truss components in the model), and task type. The dependencies between tasks are abstracted as directed edges, with the direction of the edge representing the order in which the tasks are executed. Some edges are assigned weights to represent the minimum technical interval time (the time required for concrete curing) between two tasks.

[0034] By connecting nodes and directed edges, a complete construction task topology network is formed, representing the task sequence and process structure. This network can clearly display all tasks in the entire steel structure roof hoisting project, their execution order, logical relationships, and the rich attribute information contained in each task.

[0035] 102. Entropy module, used to calculate network structure entropy to quantify administrative complexity: Based on the improved construction task topology network, information entropy theory is applied to calculate its global structure entropy and critical path local entropy; global structure entropy is used to quantify the disorder and coordination difficulty of overall project management, and critical path local entropy is used to identify the inherent delay risk points in the administrative information flow, together constituting the set of structure entropy values.

[0036] It should be noted that global structural entropy is used to measure the orderliness of overall project management. In the topology network of this project, each task node has different influences based on its number of connections (node ​​degree) and resource allocation weight. During the calculation, the connection distribution of all 42 task nodes in the network was first analyzed. The "Area A Main Truss High-Altitude Docking" node has direct dependencies on as many as 8 tasks, making it a network hub; while "Weld Non-Destructive Inspection" is only associated with 1 task, placing it on the edge. By calculating the dispersion of node degree distribution, it was found that the node degree distribution of this network is relatively dispersed, with significant differences between key hub nodes and ordinary nodes, indicating a high concentration of resource and instruction transmission paths. The calculated global structural entropy value is 2.85 (the entropy value range is set from 0-5, with a larger value indicating higher disorder). This indicates that the overall coordination difficulty of the project is at a medium-to-high level, and management needs to focus on a few high-connectivity tasks to avoid information congestion.

[0037] The local entropy of the critical path focuses on the critical task chain that determines the total project duration (in this example, the critical path contains 15 tasks) to identify inherent delay risks in administrative information flow. During calculation, the "dependency strength uncertainty" and "resource competition" of each task node on the critical path are analyzed. The "Synchronous Unloading of Support Frames" task on the critical path depends on the successful acceptance of all truss welding in the preceding three areas; this strong dependency has significant uncertainty. Simultaneously, this task requires the cooperation of two large crawler cranes, resulting in intense resource competition. By quantifying the inherent volatility of such tasks in information transmission and resource coordination, its local entropy reaches 0.92 (assuming a sub-entropy range of 0-1), and it is marked as a high-risk delay point. In contrast, the "Secondary Truss Hoisting in Area B" task on the critical path has a simpler dependency and dedicated resources; its local entropy is only 0.31, indicating lower risk.

[0038] The calculated global structural entropy (2.85) is combined with the local entropy of each task on the critical path (a total of 15 values, including 0.92 and 0.31 mentioned above) to form the set of structural entropy values ​​for this project. This set objectively quantifies the administrative complexity: the global entropy indicates that overall management needs to strengthen system coordination, while high local entropy (points > 0.7) clearly points out inherent delay risks such as "synchronous unloading of the support frame" and "closure of the main truss in area A".

[0039] 103. Processing module, used to identify efficiency bottlenecks and configuration risks: integrates the node degree centrality, edge weight strength, and structural entropy value set of the complete construction task topology network for coupled analysis; identifies high-load hub nodes that cause resource congestion and highly vulnerable task chains that are prone to failure due to complex dependencies, and generates a list of bottleneck nodes and vulnerable paths containing specific locations and risk levels.

[0040] It should be noted that multi-dimensional data coupling analysis was performed. The system superimposed and calculated the node degree centrality (number of connections), critical path local entropy (information flow uncertainty), and edge weight strength (strength of dependencies and degree of resource competition) of each task in the topology network. The task "High-altitude docking of the main truss in Area A" has the highest node degree centrality (directly dependent on 8 tasks), its critical path local entropy is 0.76 (high risk), and the 350-ton crawler crane resources required for it have strong resource competition with the lifting task in Area B (high edge weight strength). After coupling analysis, this node was identified as a high-load hub node, meaning that any delay in resource allocation or coordination error to this node will quickly spread through its numerous connections, causing large-scale process waiting, which is expected to cause a delay of 3-5 days in the core lifting route.

[0041] The analysis focuses on a task sequence with complex dependencies and high local entropy, specifically the "Support Frame Synchronous Unloading" task chain. The "Support Frame Unloading" task in this chain requires the completion of welding and flaw detection in areas A, B, and C. This complex multi-dependency relationship results in a local entropy as high as 0.92. Coupling analysis reveals that the vulnerability of this task chain stems from its strong temporal coupling and concentrated resource requirements. Even a minor delay in a preceding task (such as the need for re-inspection of welds in a certain area) will be amplified in this chain, preventing the unloading operation from starting as planned and directly impacting the overall project duration. Therefore, this path is assessed as extremely vulnerable.

[0042] Based on the above analysis, a detailed management checklist is generated. This checklist not only lists the problems and their locations but also assigns risk levels based on the results of the coupling analysis (a comprehensive score of node degree, entropy value, and resource competition degree).

[0043] Bottleneck Node List: Node No. T09 - High-altitude connection of main truss in Area A: Risk level "High". Specific location: In the area where axes 3-5 intersect with axes BC in Area A. The main risk is resource congestion; it is the most critical hub in this topology network. Node No. T23 - Large crane relocation and scheduling: Risk level "Medium". Specific location: Involves the boundary passage between Area A and Area B. The main risks are equipment scheduling conflicts and path planning uncertainties.

[0044] Vulnerable Path List: Path No. P05 - Support Frame Synchronous Unloading Chain: Risk Level "Extremely High". The path includes the task (T35 Welding Acceptance Area A -> T36 Welding Acceptance Area B -> T37 Welding Acceptance Area C -> T38 Support Frame Unloading). The inherent risk is that the asynchronous completion progress of multiple areas will lead to delays in unloading instructions, which is a high-risk point of inherent delay in administrative information flow.

[0045] 104. Strategy Module, used to predict efficiency gains after administrative optimization: Different administrative optimization strategies are defined as topological adjustments to the items listed in the bottleneck node and vulnerable path list; the impact of the simulation adjustment on the set of structural entropy values ​​is quantified; and the expected improvement rate of overall work efficiency and the improvement value of personnel allocation utility are calculated after implementing each optimization strategy through a preset entropy change efficiency correlation model.

[0046] It should be noted that, based on the bottleneck node and vulnerable path list generated in the previous step (high-load hub node "Area A main truss high-altitude docking" and extremely vulnerable task chain "support frame synchronous unloading chain"), targeted administrative optimization strategies are designed and their efficiency gains are predicted.

[0047] Three different administrative optimization strategies were simulated, each corresponding to specific adjustments to the task topology network, and their impact on the set of structural entropy values ​​was quantified. Table 1 below presents the core results of the simulation predictions.

[0048] Table 1

[0049]

[0050] By using a pre-defined entropy change-efficiency correlation model (a quantitative model trained on historical project data), the structural entropy changes of the topological network are transformed into intuitive management efficiency indicators. Strategy C (Comprehensive Optimization), through systematic adjustments, minimizes the project's disorder and the uncertainty of the critical path, thus predicting a 7.5% overall work efficiency improvement, equivalent to saving approximately 4 days of time for this demanding critical path process. The improved personnel allocation utility value indicates that the efficiency of resource allocation and coordination will be significantly improved, helping to reduce idle time and making human resource input more productive.

[0051] 105. Reporting module, used to generate structured efficiency prediction reports: integrate the list of bottleneck nodes and vulnerable paths with the expected improvement rate and improvement value under various strategies to generate a structured management decision prediction report; the report shall include at least the administrative optimization priority suggestions, the personnel reconfiguration focus plan and the corresponding quantitative efficiency prediction results.

[0052] It should be noted that, based on the aforementioned analysis (including the identified bottleneck nodes, vulnerable path list, and simulation prediction results of different administrative optimization strategies), the system automatically generated a structured document entitled "Administrative Optimization Decision Prediction Report for the Steel Structure Roof Erection Project of the Convention and Exhibition Center." This report aims to provide project managers with quantitative action guidelines.

[0053] The report clearly defines the objectives of this optimization analysis: to reduce project administrative complexity by adjusting the task topology, thereby improving overall work efficiency and personnel allocation effectiveness. The main body of the report integrates the risk list with simulation data of optimization strategies and provides priority recommendations.

[0054] Table 2 below summarizes the simulation prediction results of the three optimization strategies, providing an intuitive basis for decision-making:

[0055] Table 2

[0056]

[0057] Administrative optimization priority recommendations: Based on the principle of "maximizing benefits," the report explicitly recommends Strategy C (Comprehensive Optimization) as the preferred solution. This is because it systematically solves the problem, has the highest expected improvement rate of 7.5%, equivalent to saving approximately 4 days of time on the critical path. It is recommended that Strategy A and Strategy B be implemented concurrently as sub-items of Strategy C.

[0058] Personnel Reconfiguration Focus Plan: The report outlines the optimized core personnel configuration adjustments: Hoisting Team: Prioritize the dedicated use of resources for hoisting the main truss in Area A to avoid downtime caused by equipment scheduling conflicts. Construction Coordinator: Shift the focus from passively handling the progress synchronization issues of the "support frame unloading" chain to proactively coordinating information and providing early warnings using the BIM platform to improve management efficiency.

[0059] Risk Warning and Continuous Monitoring: The report concludes by emphasizing that despite the significant improvements in the optimized simulation, the residual risks associated with the "Support Frame Synchronous Unloading Chain" (P05) still require close monitoring. It is recommended that this path be designated as a red high-risk warning in the BIM model, and that daily reporting of the completion status of pre-tasks in zones A, B, and C be required to ensure that any delays are detected and addressed promptly.

[0060] Please see Figure 2 Another embodiment of the BIM-based construction optimization system for building engineering in this invention includes:

[0061] 101. Extraction module, used to construct construction task topology network: Based on the three-dimensional building information model of the building project, extract all construction tasks and their logical dependencies; abstract each task as a node, and the dependency relationship as a directed edge, to construct a complete construction task topology network that represents the task sequence and process structure.

[0062] Specifically, the process involves: multi-dimensional construction information extraction: extracting the geometric attribute sequence of components, a predefined list of construction procedures, a set of fixed logical constraints between procedures, and associated historical work time consumption datasets from the 3D building information model; definition and generation of network nodes and edges: based on the list of construction procedures, defining each independent procedure as a network node, with all nodes forming a node set; based on the set of logical constraints, identifying pairs of procedures with sequential dependencies, creating a directed edge between each pair of nodes, pointing from the preceding procedure node to the following procedure node, with all directed edges forming a directed edge set; initial topology network graph construction: using elements in the node set as vertices and elements in the directed edge set as connections, constructing a directed acyclic graph representing the basic flow of the construction task as the initial topology network; calculation of basic network structure attributes: on the initial topology network, calculating the number of direct predecessor nodes and direct successor nodes for each node as the basic degree information of that node; calculating the basic dependency strength of the preceding and following node pairs connected by each edge to obtain the complete construction task topology network.

[0063] It should be noted that, taking a subway station foundation pit support project as an example, the following multi-dimensional information was extracted from the 3D BIM model of the subway station project: Geometric attribute sequence: including the diameter (1.2 meters), length (18 meters), and horizontal spacing (1.5 meters) of the support piles; the cross-sectional dimensions and length of the steel supports; the layer height of earthwork excavation (3 meters / layer), etc. Construction procedure list: the procedure determined according to the construction organization design: surveying and setting out → support pile construction → first layer earthwork excavation → first layer steel support installation → second layer earthwork excavation → second layer steel support installation → base slab construction. Logical constraint rule set: hard constraints based on process and safety: "first layer earthwork excavation" can only begin after "support pile construction" has passed acceptance; "first layer steel support installation" must be completed within 24 hours after "first layer earthwork excavation" reaches the design elevation. Historical work time dataset: referring to similar projects, the standard duration of each procedure was obtained, such as support pile construction (15 days), each layer of earthwork excavation (10 days), and each layer of support installation (5 days), etc.

[0064] Based on the above information, the basic elements of a network are defined as shown in Table 3 below:

[0065] Table 3

[0066]

[0067] Node set: Define the above 6 independent processes as network nodes, forming a node set {N01, N02, N03, N04, N05, N06}.

[0068] Directed edge set: Based on logical constraints, a directed edge is created. The constraint "N02 support pile construction → N03 first layer earthwork excavation" generates a directed edge from N02 to N03. The final edge set is {N01→N02, N02→N03, N03→N04, N04→N05, N05→N06}.

[0069] Using nodes as vertices and directed edges as connections, a directed acyclic graph is constructed, which is the initial construction task topology network. This network clearly represents the sequential process that the tasks must follow: surveying and setting out → support pile construction → first layer of earthwork excavation → first layer of steel support installation → second layer of earthwork excavation → second layer of steel support installation.

[0070] Calculate basic properties on the initial topology network: Node fundamentality information: Calculate the number of direct predecessors and successors of each node. The direct predecessor of node N03 (first layer of earthwork excavation) is N02, with a quantity of 1; the direct successor is N04, also with a quantity of 1. Edge dependency strength base value: Assign a value to each edge based on historical data or expert evaluation to quantify the strength of the dependency relationship. The dependency strength of the edge "N03→N04" is set to 0.9 (out of 1.0) because the support must be installed in time after earthwork excavation to ensure the safety of the foundation pit, and this constraint is extremely strong.

[0071] 102. Entropy module, used to calculate network structure entropy to quantify administrative complexity: Based on the improved construction task topology network, information entropy theory is applied to calculate its global structure entropy and critical path local entropy; global structure entropy is used to quantify the disorder and coordination difficulty of overall project management, and critical path local entropy is used to identify the inherent delay risk points in the administrative information flow, together constituting the set of structure entropy values.

[0072] Specifically, the network adjacency and weight matrix generation is as follows: Based on the complete construction task topology network, the connection relationships between its nodes are extracted to generate a network adjacency matrix; simultaneously, the dependency strength baseline values ​​of each edge in the network and the task workload baseline parameters of each node are extracted, and through composite calculation, an edge weight matrix representing the difficulty of information transmission and coordination between tasks is generated; Global node influence distribution calculation is performed: Based on the network adjacency matrix and the edge weight matrix, the eigenvector centrality of each node in the network is calculated; the eigenvector centrality of all nodes is normalized to form a global node influence probability distribution representing the relative influence weight of each construction task in the entire management network; Global structure entropy calculation is performed: Using the global node influence probability distribution as input, the definition of information entropy is applied for calculation, and the resulting entropy value is the global structure entropy; this global structure entropy serves as the core indicator for quantifying the disorder of overall project management and coordination; Critical path node subset identification is performed: Based on the complete construction task... The critical path method is applied to calculate the critical path that determines the shortest total project time, based on the nodes, directed edges, and task workload baseline parameters in the task topology network. All nodes on this path are extracted to form a critical path node subset. Critical path local transition probability calculation: Within the critical path node subset, based on the actual connection relationships between nodes and the corresponding edge weight matrix values, the conditional transition probability from the preceding node to the subsequent node is calculated, generating a local node transition probability sequence on the critical path. Critical path local entropy calculation: Using the local node transition probability sequence on the critical path as input, the entropy is calculated using the definition of information entropy; the resulting entropy value is the critical path local entropy. This critical path local entropy serves as a core indicator for quantifying the uncertainty and delay risk of administrative instructions being transmitted on this critical link. Structural entropy value set composition: The global structural entropy and the critical path local entropy are combined to form a structural entropy value set for subsequent risk identification and predictive analysis.

[0073] It should be noted that, based on the topology network containing 6 nodes (N01-N06) obtained from the aforementioned steps, its adjacency matrix (reflecting task dependencies) and edge weight matrix (reflecting coordination difficulty) are generated. The weights are calculated by combining the dependency strength (based on safety and technical constraint assessment) and the task workload (15 days for support pile construction). The edge "N03 (first layer of earthwork excavation) → N04 (first steel support installation)" has a dependency strength of 0.9 due to its immediate precedence relationship and its involvement in foundation pit safety. Combined with the 5-day construction period for N04, the final weight is calculated to be 0.86.

[0074] Based on the adjacency matrix and weight matrix, the eigenvector centrality of each node is calculated to measure its global influence in the task flow: Node N02 (support pile construction): As a prerequisite for most subsequent tasks, its centrality is high, set to 0.28. Node N01 (surveying and setting out): Although it is the starting point, its continuous influence on subsequent processes is weak, and its centrality is low, set to 0.05. The centrality values ​​of all nodes are normalized (so that the sum is 1) to obtain the global node influence probability distribution, which represents the relative weight of each task in the management network.

[0075] Using the aforementioned global node influence probability distribution as input, the global structural entropy is calculated using the information entropy formula. This entropy value quantifies the disorder or coordination difficulty of the overall task network of the project. If the influence distribution among tasks is uniform (all tasks have similar centrality), the entropy value is high, indicating high management complexity and dispersed information flow. In this example, the calculated global structural entropy is 1.72, reflecting that the overall management of the project presents certain coordination challenges.

[0076] Critical Path Identification: Based on the task duration (15 days for support pile construction, 10 days for each layer of earthwork excavation) and dependencies, the Critical Path Method (CPM) is applied. In this example, the critical path is N01→N02→N03→N04→N05→N06, and the total duration determines the shortest project duration. Local Transition Probability Calculation: Within the critical path node subset, based on the edge weight matrix, the conditional transition probability from the preceding node to the subsequent node is calculated. On the critical path, the probability of transitioning from N03 to N04 is extremely high (close to 1.0) because the two are closely connected and have high weights.

[0077] Critical path local entropy calculation: The transition probability sequence of nodes on the critical path is input into the information entropy formula to obtain the critical path local entropy (in this example, the calculated value is set to 0.45). This entropy value quantifies the uncertainty and delay risk when administrative instructions are transmitted along the critical link. The lower the entropy value, the more certain and smooth the information flow on the critical path; if the entropy value is high, it indicates that there are high information transmission delay or blockage risk points on the critical link.

[0078] The global structural entropy (1.72) and the critical path local entropy (0.45) are combined to form the set of structural entropy values ​​for this project.

[0079] 103. Processing module, used to identify efficiency bottlenecks and configuration risks: integrates the node degree centrality, edge weight strength, and structural entropy value set of the complete construction task topology network for coupled analysis; identifies high-load hub nodes that cause resource congestion and highly vulnerable task chains that are prone to failure due to complex dependencies, and generates a list of bottleneck nodes and vulnerable paths containing specific locations and risk levels.

[0080] Specifically, the node congestion pressure index is calculated as follows: Based on the improved construction task topology network, the number of direct predecessor nodes and direct successor nodes of each node is extracted, and its node degree centrality is calculated; the node degree centrality is fused with the basic value of the dependency strength of the associated edges to calculate a quantitative node congestion pressure index for each network node; path dependency vulnerability assessment: the directed paths in the improved construction task topology network are traversed, and the basic value of the dependency strength of all edges on the path is extracted; based on the distribution and accumulation of dependency strength in the path, a path dependency vulnerability index reflecting the possibility of overall failure due to task dependency is calculated for each directed path; key attribute correction of entropy values ​​is performed: the set of structural entropy values ​​is read, in which the global structural entropy is used to correct the perception of the overall network complexity, and the local entropy of the critical path is used to identify high uncertainty segments; the global structural entropy is used to perform global standardization correction on all node congestion pressure indices, and the local entropy of the critical path is used to correct the path dependency vulnerability index. The path dependence vulnerability index is used for risk-weighted correction, generating a corrected set of node congestion pressure indicators and a set of path dependence vulnerability indices. Threshold determination for hub nodes and vulnerable paths: Statistical analysis is performed on the corrected set of node congestion pressure indicators, and thresholds are set to filter out nodes with congestion pressure significantly higher than the network average, marking them as a high-load hub node candidate set. Statistical analysis is also performed on the corrected set of path dependence vulnerability indices, and another threshold is set to filter out paths with vulnerability indices significantly higher than the average, marking them as a high-vulnerability task chain candidate set. List generation and risk level labeling: The high-load hub node candidate set and the high-vulnerability task chain candidate set are integrated and cross-validated, and sorted according to their indicator values. A structured list of bottleneck nodes and vulnerable paths is generated, clearly recording the unique identifier of each bottleneck node and vulnerable path, its location in the topology network, its corresponding quantitative indicator value, and the risk level defined based on the numerical range.

[0081] Furthermore, after generating the bottleneck node and vulnerable path list, the process also includes steps for simulating and predicting dynamic resource allocation based on this list. Specifically, this includes: defining resource types and global constraints: based on the construction organization design, defining the set of key resource types required for construction, and determining the total quantity constraints, phased supply limits, and flow cost coefficients for each resource type within the project cycle, collectively forming a global resource allocation constraint table; generating tentative resource allocation schemes for bottlenecks: analyzing the resource demand characteristics of each high-load hub node and high-vulnerability task chain identified in the bottleneck node and vulnerable path list; under the premise of satisfying the global resource allocation constraint table, designing multiple allocation logics that prioritize additional resources (personnel, equipment, coordination attention) to these bottlenecks, generating a set of distinct tentative dynamic resource allocation schemes; simulating the impact of allocation schemes on topology network indicators: mapping each set of tentative dynamic resource allocation schemes to attribute adjustments of specific nodes or edges in the improved construction task topology network (reducing the baseline parameters of key node task workload, lowering...). (Based on the low critical path dependency strength value); Based on the adjusted network, recalculate the congestion pressure of relevant nodes and the vulnerability index of paths to obtain a set of key network indicators corresponding to each allocation scheme after simulation; calculate and compare the resource allocation optimization effect: compare each set of key network indicators after simulation with the set of node congestion pressure indicators and path dependency vulnerability indices before implementing the allocation scheme based on the original correction; calculate the node congestion pressure reduction rate, path vulnerability decay rate, and the equivalent resource cost consumed by each allocation scheme, which together constitute the predicted value of resource allocation optimization effect; generate resource pre-allocation strategy suggestions: based on the predicted value of resource allocation optimization effect, rank and evaluate the cost-effectiveness of all exploratory dynamic resource allocation schemes; select the best several schemes, and describe their specific measures, expected effects, and implementation conditions in a structured manner to generate a dynamic resource pre-allocation strategy suggestion that can be directly used to guide resource preparation before construction, and integrate this suggestion as an important input into the structured efficiency diagnosis and optimization prediction report.

[0082] It should be noted that, based on the topology network (nodes N01-N06) and structural entropy values ​​(global entropy 1.72, critical path local entropy 0.45) obtained from the aforementioned steps, the node congestion pressure index and path dependency vulnerability index are calculated. Node congestion pressure index: This index combines the degree centrality of nodes (number of connected tasks) and the dependency strength of associated edges. Node N03 (first layer of earthwork excavation) directly connects N02 and N04, exhibiting high degree centrality; and the dependency strength of edge N03→N04 reaches 0.9 (due to foundation pit safety requirements), therefore its basic congestion pressure value is calculated as 0.75. Path dependency vulnerability index: This index is calculated by traversing all directed paths and summing the dependency strength of each edge on the path. In this example, the critical path is N01→N02→N03→N04→N05→N06, and its basic vulnerability index is the sum of the dependency strengths of each edge on the path, set to 4.2.

[0083] The structural entropy value is used to correct the basic indicators to more accurately reflect the risks brought about by network complexity and uncertainty. Entropy correction: The global structural entropy (1.72) is used to globally standardize and correct the node congestion pressure indicator, and the pressure indicator of N03 increases to 0.81 after correction. The path vulnerability index is corrected using the local entropy of the critical path (0.45) with risk weighting, and the vulnerability index of the critical path increases to 4.5 after correction.

[0084] Threshold Determination and List Generation: A node congestion pressure threshold is set (0.5 above the average), identifying high-load hub nodes: N03 (first layer of earthwork excavation, correction value 0.81) and N04 (first steel support installation, correction value 0.63). A path vulnerability threshold is set (3.0 above the average), identifying highly vulnerable task chains: the critical path (N01→N02→N03→N04→N05→N06, correction value 4.5). A list of bottleneck nodes and vulnerable paths is generated, clearly defining the risk location and level.

[0085] Based on the above list, a dynamic resource allocation simulation is conducted to seek an optimization solution. Resources and constraints are defined: key resources (piling rigs, cranes, support materials) and their total quantity, supply ceiling, and cost are determined. Tentative allocation schemes are generated: for bottleneck nodes N03 and N04, two schemes are designed: Scheme A (Reinforce N03): Add one excavator to N03, with its task workload baseline parameter expected to decrease by 15%. Scheme B (Buffer N04): Set up a dedicated material storage yard for N04, with its dependency strength base value expected to decrease by 0.1.

[0086] Simulation Results and Comparison: After mapping Schemes A and B to network attributes and adjusting them, the indicators were recalculated. Simulation results show that Scheme A reduces congestion pressure on N03 by 22%, while Scheme B reduces the critical path vulnerability index by 8%. Comparing resource consumption, Scheme A is more costly but significantly more effective, while Scheme B is less costly and mitigates critical path risk.

[0087] Pre-allocation strategy recommendations: Considering cost-effectiveness, it is recommended to prioritize Option B, as it effectively reduces critical path risk at a lower cost; meanwhile, Option A, as an alternative, should be prioritized to alleviate N03 pressure when resources are sufficient. This recommendation will be integrated into the final efficiency forecast report to guide resource preparation before construction.

[0088] 104. Strategy Module, used to predict efficiency gains after administrative optimization: Different administrative optimization strategies are defined as topological adjustments to the items listed in the bottleneck node and vulnerable path list; the impact of the simulation adjustment on the set of structural entropy values ​​is quantified; and the expected improvement rate of overall work efficiency and the improvement value of personnel allocation utility are calculated after implementing each optimization strategy through a preset entropy change efficiency correlation model.

[0089] Specifically, the optimization strategy definition and scheme generation are as follows: For each item recorded in the bottleneck node and vulnerable path list, at least one corresponding administrative optimization strategy is designed. Strategy types include task decomposition for high-load nodes and adding parallel coordination channels or resource buffers for highly vulnerable paths. Each strategy is concretized into adjustment instructions for nodes and edges in the improved construction task topology network, generating a set of optimization strategy simulation schemes. Network topology simulation adjustment: Each scheme in the optimization strategy simulation scheme set is applied sequentially to a copy of the improved construction task topology network. The node addition / deletion, edge connection modification, and attribute update operations defined in the scheme are executed to generate a corresponding simulated adjusted network for each optimization strategy. Simulated network structure entropy recalculation: For each simulated adjusted network, the node influence distribution is recalculated based on the network adjacency and weight matrix. The critical path is calculated using information entropy theory; a new set of effective structural entropy values ​​is obtained for each simulated adjusted network, including the new global structural entropy and the new critical path local entropy; entropy change value calculation and analysis: the entropy value in each new effective set of structural entropy values ​​is compared with the corresponding entropy value in the original set of structural entropy values ​​before the simulation adjustment, and the difference is calculated; a set of structural entropy change values ​​corresponding to each optimization strategy is obtained, which includes the global structural entropy change value and the critical path local entropy change value; efficiency gain is mapped through a correlation model: each set of structural entropy change values ​​is used as input to a pre-set entropy change efficiency correlation model that describes the quantitative relationship between entropy value change and construction efficiency and personnel utility; through the calculation of this model, the expected improvement rate of overall work efficiency and the improvement value of personnel allocation utility after implementing the corresponding optimization strategy are output.

[0090] It should be noted that, based on the bottleneck node and vulnerable path list obtained from the aforementioned steps (high-load hub node: N03 first layer of earthwork excavation; high-vulnerability task chain: critical path N01→N02→N03→N04→N05→N06), three typical administrative optimization strategies are designed and defined as specific adjustment instructions for the topology network as shown in Table 4 below:

[0091] Table 4

[0092]

[0093] Each optimization strategy is applied sequentially to a copy of the construction task topology network, performing the aforementioned adjustments. Simulated adjusted networks: After applying S-01, the number of network nodes increases, and edge relationships change; after applying S-02, the weights of specific edges are optimized; after applying S-03, the time parameters of critical nodes are reduced. Structural entropy recalculation: For each simulated adjusted network, its global structural entropy and critical path local entropy are recalculated. Entropy change analysis: The new entropy value is compared with the original entropy value (global entropy 1.72, critical path local entropy 0.45) to obtain the entropy change value. A decrease in entropy value usually signifies a reduction in system complexity and uncertainty, indicating effective optimization.

[0094] The calculated entropy change value is input into a preset entropy change efficiency correlation model (this model is based on historical project data and describes the quantitative relationship between entropy change and construction efficiency and personnel utility), which will output a quantified expected improvement value. The simulation calculation results are shown in Table 5 below:

[0095] Table 5

[0096]

[0097] Note: Efficiency improvement rate refers to shortening the construction period or increasing output; personnel utility improvement value refers to optimizing personnel workload or improving coordination efficiency.

[0098] Through the above steps, abstract administrative strategies are transformed into quantifiable expected benefits. The table shows that although "task decomposition" (S-01) has the most significant effect on improving overall efficiency, "increasing parallel coordination channels" (S-02) has the greatest effect on improving personnel coordination efficiency.

[0099] 105. Reporting module, used to generate structured efficiency prediction reports: integrate the list of bottleneck nodes and vulnerable paths with the expected improvement rate and improvement value under various strategies to generate a structured management decision prediction report; the report shall include at least the administrative optimization priority suggestions, the personnel reconfiguration focus plan and the corresponding quantitative efficiency prediction results.

[0100] Specifically, the topology analysis results are linked with the BIM model visualization: A complete construction task topology network and a list of bottleneck nodes and vulnerable paths are read, and the high-load hub nodes and high-vulnerability task chains identified are mapped back to their corresponding components or processes in the original 3D building information model using unique task identifiers; a visualization of risk factors with spatial locations is generated to highlight the physical location and process steps of efficiency bottlenecks in the BIM model environment; an efficiency risk and optimization potential matrix is ​​generated: each record in the bottleneck node and vulnerable path list is used as a row, and each optimization strategy simulation scheme is used as a column; the expected improvement rate and improvement value are filled into the corresponding rows and columns to construct a quantitative two-dimensional matrix of efficiency risk and optimization potential. This matrix intuitively displays the expected efficiency gains and personnel utility improvements that different management strategies can bring to each specific risk point; comprehensive optimization effect ranking and strategy package generation: based on efficiency risk and optimization potential... The system employs a two-dimensional matrix to rank all optimization strategies based on their combined benefits of expected improvement rates and values. Simultaneously, considering the dependencies and mutual exclusions between strategies, it combines synergistically implementable strategies to generate a priority-ranked recommended sequence and strategy combination package. Multi-scenario prediction and comparative analysis selects the highest-ranking independent strategies or their combination packages from the recommended sequence as typical management scenarios. Based on these scenarios and their corresponding expected improvement rates and values, it extrapolates their potential impact on the overall project duration and resource peak, generating multiple sets of multi-scenario prediction and comparison charts for future work efficiency and staffing under different management decisions. Structured report compilation and output integrates visualized correlation data of risk factors, a two-dimensional matrix of efficiency risk and optimization potential, priority-ranked recommended sequences and strategy combination packages, and multi-scenario prediction and comparison charts. Following a preset management report format, it compiles and generates a management decision prediction report.

[0101] It should be noted that, based on the bottleneck nodes (high-load hub node N03 "first layer of earthwork excavation"), the highly vulnerable task chains (critical path N01→N06) identified in the previous steps, and the simulation results of the optimization strategies (strategy S-01 task decomposition, S-02 adding parallel coordination channels), this step will integrate this information to generate a structured efficiency prediction report to guide management decisions.

[0102] Risk points in the topology network are mapped back to the BIM model using task IDs. Node N03 is associated with earthwork components in the excavation area of ​​the foundation pit in the BIM model, and the critical path is associated with the installation sequence of components such as support piles and steel supports. Visual data is generated in the BIM platform (Revit or Navisworks), highlighting the excavation area where N03 is located as a red warning area, and marking the construction process of components on the critical path with yellow arrows, forming a spatial distribution map of risk elements.

[0103] A quantitative matrix is ​​constructed using the bottleneck list as rows and optimization strategies as columns. Table 6 below is an example (data based on simulation results):

[0104] Table 6

[0105]

[0106] The matrix provides a clear comparison of the optimization effects of different strategies on specific risk points, with S-02 showing the most significant improvement in personnel utility on the critical path.

[0107] Define the Normalized Overall Benefit Index (NBI) for ranking:

[0108] ,

[0109] in: : The ratio of efficiency improvement rate to baseline efficiency (dimensionless). : The ratio of the improvement in personnel utility to the baseline utility (dimensionless); : The ratio of resource cost equivalent to benchmark cost (dimensionless). , , For the weights (in this example, let them be weights) =0.4, =0.4, =0.2, emphasizing efficiency and utility).

[0110] Ranking results: After calculation, strategy S-02 has the highest NBI (assumed value 1.25), followed by S-03 (1.10) and S-01 (0.95). Strategy package generation: Prioritize the implementation of S-02, and combine it with S-03 to form a collaborative scheme of parallel coordination and resource buffering.

[0111] Multi-scenario predictive comparative analysis. Scenario settings: Scenario A: Implement only S-02 (adding parallel channels); Scenario B: Implement the S-02+S-03 combination package. Output charts: Generate a dual-axis comparison chart, showing that Scenario A is expected to shorten the total project duration by 7 days and reduce peak resource costs by 15%; Scenario B further optimizes the project duration to 10 days, but increases resource costs by 5%. The chart highlights the long-term efficiency advantages of Scenario B.

[0112] Integrating the above content, the report includes the following sections: Risk Visualization Summary: with screenshots of BIM model highlighting risk areas; Optimization Potential Matrix Table; Strategy Priority List (based on NBI sorting); Multi-Scenario Comparison Charts; Action Recommendations: It is recommended to deploy the S-02 strategy before construction and prepare contingency resource buffers for the N03 node. Through this report, project managers can directly identify optimization focuses, quantify decision-making basis, and reduce construction risks.

[0113] 106. Supplementary Module: Used to supplement the energy consumption impact analysis for the construction task topology network: Extracting thermal properties and spatial relationships from the BIM model: Extracting the material thermal property sequence of the building envelope components, functional zoning information of the building space, and spatial adjacency data between components from the 3D building information model; Calculating the basic energy consumption impact value of network nodes: Based on the material thermal property sequence and spatial adjacency data, and according to the preset building energy consumption simulation rules, calculating the theoretical energy consumption impact value of each component involved in the construction task under unit operating conditions, as the basic energy consumption impact value of the node; Constructing an extended network with embedded energy consumption attributes: Assigning the basic energy consumption impact value of the node as a new attribute to the corresponding node in the improved construction task topology network; Generating an extended network with embedded energy consumption impact attributes. Expand the construction task topology network; identify energy-sensitive paths and key nodes: Based on the expanded construction task topology network, analyze the impact of construction sequence on the overall energy consumption performance of the building; identify continuous task sequences that have a significant impact on the final operating energy consumption of the building due to construction timing or process selection, mark them as energy-sensitive paths, and mark the nodes with the highest basic energy consumption impact value in the path as energy-critical nodes; generate collaborative energy consumption optimization strategies: compare energy-sensitive paths with highly vulnerable task chains, find overlapping parts, and mark them as collaborative management critical paths; for this path and energy-critical nodes, combine the optimization strategy simulation scheme set to generate a collaborative optimization strategy recommendation aimed at simultaneously improving construction efficiency and the future operating energy efficiency of the building, and integrate this recommendation into the structured efficiency diagnosis and optimization prediction report.

[0114] Furthermore, a collaborative optimization strategy recommendation aimed at simultaneously improving construction efficiency and the future operational energy efficiency of buildings is generated. This includes a comprehensive consideration of the environmental impact dimension, specifically: extracting environmental attributes and construction parameters from the BIM model: extracting environmental attribute information of major building materials, simulated transportation distance data of large components or equipment, and potential environmental impact category identifiers related to construction processes from the 3D building information model; quantifying the environmental impact value of construction nodes: based on environmental attribute information, simulated transportation distance data, and potential environmental impact category identifiers, and according to preset quantification conversion rules for different environmental impact categories (carbon emissions, dust, noise), calculating the environmental load value per unit of project volume for each construction task node, as the node's environmental impact intensity value; constructing a dual attribute network embedding environmental and energy consumption: adding the node's environmental impact intensity value as another new attribute, along with the node's energy consumption impact base value, to improve the construction task topology. The network is divided into several parts: 1. Nodes corresponding to the network are identified; 2. A dual-attribute extended construction task topology network is generated, embedding both energy consumption and environmental impact attributes; 3. Key areas of superimposed environmental-energy consumption impact are identified: In the dual-attribute extended construction task topology network, node clusters or paths with high environmental impact intensity and high energy consumption base values ​​are identified; 4. Key areas of superimposed impact that have significant environmental impact during the construction phase and also have a major impact on the building's long-term energy consumption are located; 5. A multi-objective collaborative optimization strategy package is generated: For key areas of superimposed impact, cross-analysis is performed based on the analysis results of highly vulnerable task chains; 6. Composite management measures that can simultaneously alleviate process vulnerability, reduce environmental load during construction, and improve the building's long-term energy efficiency are designed; 7. A multi-objective collaborative optimization strategy package with priority and expected comprehensive benefit assessment is generated, and this strategy package is updated and integrated into the final structured efficiency diagnosis and optimization prediction report as a core component.

[0115] It should be noted that, taking a subway station foundation pit support project as an example, based on the construction task topology network already constructed in the previous steps (including node N01 surveying and setting out, N02 support pile construction, N03 first layer earthwork excavation, etc.), this step achieves multi-objective collaborative optimization by expanding energy consumption and environmental attributes. The specific implementation is as follows:

[0116] Extract material thermal parameters of the retaining structure components (diaphragm wall, retaining piles) from the subway station BIM model: thermal conductivity (concrete 1.8 W / m·K), specific heat capacity, density, etc. Obtain building space zoning information (excavation pit area, equipment area) and component spatial adjacency relationships (contact area between retaining piles and soil). Simultaneously extract environmental impact data: carbon emission factors (kgCO2 / t) and transportation distances (average steel transportation distance 150km) for major materials (reinforcing steel, concrete), as well as noise (dB) and dust (mg / m³) from the construction process. 3 Indicator labeling.

[0117] Energy Consumption Impact Baseline Values: Based on thermal parameters and spatial relationships, the energy consumption of each task under unit working conditions is simulated: Node N02 (Support Pile Construction): Due to the heat release from concrete curing and mechanical energy consumption, the energy consumption per unit project volume is 85 kWh / m². 3 Node N04 (Steel Support Installation): Due to the high thermal conductivity of steel and the energy consumption of the equipment, the unit energy consumption is 120 kWh / t. Environmental Impact Intensity Value: Calculated based on material carbon emissions, transportation distance, and process pollution: N02 Support Pile Construction: Concrete production carbon emissions + transportation emissions, totaling 320 kgCO2 / m² 3 N03 Earthwork Excavation: Diesel fuel consumption and dust emissions from machinery and equipment, carbon emissions 45 kgCO2 / m³ 3 Dust load 0.8 mg / m³ 3 .

[0118] Energy consumption and environmental impact values ​​are added as new attributes to the topology network nodes, forming a dual-attribute extended network. Key node data is shown in Table 7 below:

[0119] Table 7

[0120]

[0121] Energy-Sensitive Path: Analysis of the construction sequence revealed that the sequence "N02→N03→N04" had the highest cumulative energy consumption (265 kWh / unit), marking it as a sensitive path. Among these, N04 (steel support installation) was designated as a critical energy-consuming node due to its high energy consumption and carbon emissions. Environmental-Energy Overlap Area: The dust load and carbon emissions of both N03 (earthwork excavation) and N04 (steel support) exceeded the network average, forming a critical area with overlapping impacts. The risk is particularly significant when N03 overlaps with a highly vulnerable task chain (critical path).

[0122] Strategy Design: For the overlapping area (N03-N04 section), combined with the bottleneck list (high vulnerability task chain), the following composite measures were formulated: For N03 (earthwork excavation): a mist cannon dust suppression process was introduced to reduce the dust load to 0.3 mg / m³. 3 Optimize transportation routes and reduce idle machinery, expected to reduce carbon emissions by 15%. For N04 (steel supports): adopt prefabricated assembled supports (permanent and temporary combination) to reduce on-site welding energy consumption, lowering it to 100 kWh / t; simultaneously use high-strength steel to reduce carbon emissions by 20%. Benefit assessment: This strategy package is expected to simultaneously reduce critical path vulnerability by 12%, construction-period carbon emissions by 18%, and improve long-term energy efficiency (improved thermal performance of the support structure). After prioritizing the strategies, they are integrated into the final optimization report to guide construction decisions.

[0123] Figure 3This is a schematic diagram of a BIM-based construction optimization device according to an embodiment of the present invention. The device 300 may include: a processor 301, a receiver 302, a transmitter 303, and a memory 304. The receiver 302, transmitter 303, and memory 304 are respectively connected to the processor 301 via a bus. It should be noted that in some possible implementations, the processor 301 and the memory 304 may be integrated together.

[0124] The processor 301 includes one or more processing cores. The processor 301 executes the methods performed by the base station in the random access method provided in this application embodiment by running software programs and modules. The memory 304 can be used to store software programs and modules. Specifically, the memory 304 can store an operating system 3041 and at least one application module 3042 required for a function. The receiver 302 is used to receive communication data sent by other devices, and the transmitter 303 is used to send communication data to other devices.

[0125] The present invention also provides a BIM-based construction optimization device, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the BIM-based construction optimization system in the above embodiments.

[0126] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the BIM-based building construction optimization system.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A BIM-based construction project construction optimization system, characterized by, Comprise: An extraction module for extracting all construction tasks and their logical dependencies based on a three-dimensional building information model of a construction project, abstracting each task as a node and the dependencies as directed edges, and constructing a complete construction task topology network; An entropy value module for calculating the global structural entropy and the local entropy of the critical path of the complete construction task topology network based on information entropy theory, and forming a structural entropy value set; A processing module for coupling analysis by fusing the node degree centrality, edge weight strength of the complete construction task topology network, and the structural entropy value set, identifying high-load hub nodes causing resource congestion and high-vulnerability task chains prone to failure due to complex dependencies, and generating a list of bottleneck nodes and fragile paths, comprising: Based on the complete construction task topology network, extracting the number of direct predecessor nodes and direct successor nodes of each node, and calculating its node degree centrality; fusing the node degree centrality and the dependency strength base value of the associated edge to calculate a quantitative node congestion pressure indicator for each network node; Traverse the directed paths in the complete construction task topology network, extract the dependency strength base value of all edges in the path; according to the distribution and accumulation of dependency strength in the path, calculate the path dependency vulnerability index for each directed path; Read the structural entropy value set, use the global structural entropy to globally standardize and correct the node congestion pressure indicators of all nodes, and use the local entropy of the critical path to risk-weightedly correct the path dependency vulnerability index, to generate a set of corrected node congestion pressure indicators and a set of path dependency vulnerability indexes; Statistical analysis of the corrected node congestion pressure indicator set, set a threshold, filter out nodes with significantly higher congestion pressure than the average level of the network, and mark them as a high-load hub node candidate set; statistical analysis of the corrected path dependency vulnerability index set, set another threshold, filter out paths with significantly higher vulnerability index than the average level, and mark them as a high-vulnerability task chain candidate set; Integrate and cross-verify the high-load hub node candidate set and the high-vulnerability task chain candidate set, sort them according to their index values, and generate a list of bottleneck nodes and fragile paths; A strategy module for defining different administrative optimization strategies as topology structure adjustment simulations for the items listed in the list of bottleneck nodes and fragile paths, quantifying the impact of the simulation adjustments on the structural entropy value set, and calculating the expected improvement rate of overall work efficiency and the personnel configuration utility improvement value after implementing each optimization strategy through a pre-set entropy change efficiency correlation model; A report module for generating a management decision prediction report based on the list of bottleneck nodes and fragile paths and the expected improvement rate and improvement value under each strategy.

2. The BIM-based construction project optimization system of claim 1, wherein, Comprise: From the three-dimensional building information model, extract the geometric attribute sequence of the component, the pre-defined construction process list, the set of logical constraint rules between processes, and the associated historical man-hour consumption data set; Based on the construction procedure list, each independent procedure is defined as a network node, and all nodes form a node set; based on the logical constraint rule set, a procedure pair with a sequential dependency relationship is identified, and a directed edge is created between each pair of nodes, with the predecessor procedure node pointing to the successor procedure node, and all directed edges form a directed edge set; With the elements in the node set as vertices and the elements in the directed edge set as connections, a directed acyclic graph is constructed as an initial topology network; On the initial topology network, the number of direct predecessors and direct successors of each node is calculated as the basic degree information of the node; The dependency strength basic value of each edge connected to the predecessor and successor node pair is calculated to obtain a refined construction task topology network.

3. The BIM-based construction project optimization system of claim 2, wherein, It includes: Based on the refined construction task topology network, the connection relationship between its nodes is extracted to generate a network adjacency matrix, and the dependency strength basic value of each edge in the network and the task workload benchmark parameter of each node are extracted, and the edge weight matrix is generated through composite calculation; Based on the network adjacency matrix and the edge weight matrix, the characteristic vector centrality of each node in the network is calculated; the characteristic vector centrality of all nodes is processed to form a global node influence probability distribution; According to the global node influence probability distribution, the entropy value obtained by applying the information entropy definition for calculation is the global structure entropy; Based on the nodes, directed edges and task workload benchmark parameters in the refined construction task topology network, the critical path method is applied for calculation to identify the critical path that determines the shortest total working hours of the project, and all nodes on the path form a critical path node subset; In the critical path node subset, based on the actual connection relationship between nodes and the corresponding edge weight matrix value, the conditional transition probability from the predecessor node to the successor node is calculated to generate a local node transition probability sequence on the critical path; According to the local node transition probability sequence on the critical path, the entropy value obtained by applying the information entropy definition for calculation is the local entropy of the critical path; The global structure entropy and the local entropy of the critical path are collected to form a structure entropy value set.

4. The BIM-based construction project optimization system of claim 3, wherein, According to the construction organization design, define the key resource type set required for construction, and determine the total quantity constraint, stage supply upper limit and flow cost coefficient of each resource type in the project cycle to form a resource allocation global constraint table; For each high-load hub node and high-vulnerability task chain identified in the list of bottleneck nodes and fragile paths, analyze its resource demand characteristics, and on the premise of meeting the resource allocation global constraint table, design multiple allocation logics that preferentially tilt additional resources to these bottlenecks to generate tentative resource dynamic allocation schemes; Each set of tentative resource dynamic allocation scheme is mapped to the attribute adjustment of a specific node or edge in the refined construction task topology network, and based on the adjusted network, the congestion pressure of the related nodes and the vulnerability index of the path are recalculated to obtain a set of simulated network key indicators corresponding to each allocation scheme. The node congestion pressure reduction rate, the path vulnerability attenuation rate, and the consumed resource amount of each allocation scheme are calculated by comparing each set of key indicators of the simulated post-network with the original modified node congestion pressure indicator set and the path dependence vulnerability index set before the allocation scheme is implemented, to form a resource allocation optimization effect prediction value; According to the resource allocation optimization effect prediction value, all the exploratory resource dynamic allocation schemes are ranked in terms of performance and cost, and their feasibility is evaluated, a number of optimal schemes are selected, and their specific measures, expected effects, and implementation conditions are structurally described to generate a dynamic resource pre-allocation strategy suggestion.

5. The BIM-based construction project optimization system of claim 1, wherein, It includes: For each item recorded in the bottleneck node and fragile path list, at least one corresponding administrative optimization strategy is designed, each strategy is specified as an adjustment instruction for the nodes and edges in the improved construction task topology network, and a set of optimization strategy simulation scheme set is generated; Each scheme in the optimization strategy simulation scheme set is applied to the copy of the improved construction task topology network in turn, and the node addition and deletion, edge connection modification, and attribute update operations defined in the scheme are executed, to generate a corresponding simulated adjusted network for each optimization strategy; For each simulated adjusted network, the node influence distribution and critical path are recalculated based on the network adjacency and weight matrix, and the information entropy theory is applied to calculate the new effective structure entropy value set corresponding to each simulated adjusted network; The entropy values in each new effective structure entropy value set are compared with the corresponding entropy values in the original structure entropy value set before simulation adjustment, and the difference is calculated to obtain a structure entropy change value set corresponding to each optimization strategy; According to each structure entropy change value set, the expected improvement rate of overall work efficiency and the personnel configuration utility improvement value after implementing the corresponding optimization strategy are output by inputting into the entropy change efficiency correlation model.

6. The BIM-based construction project optimization system of claim 5, wherein, It includes: Read the improved construction task topology network and the bottleneck node and fragile path list, and map the high-load hub nodes and high-vulnerability task chains identified therein back to the corresponding components or processes in the original three-dimensional building information model through task unique identification to generate a risk factor visualization correlation data; Each record in the bottleneck node and fragile path list is taken as a row, and each strategy in the optimization strategy simulation scheme set is taken as a column, and the expected improvement rate and improvement value are filled in the corresponding row and column to construct an efficiency risk and optimization potential two-dimensional matrix; Based on the efficiency risk and optimization potential two-dimensional matrix, all optimization strategies are sorted according to the comprehensive benefits of the expected improvement rate and improvement value they produce, the dependent and mutually exclusive relationships between strategies are considered, strategies that can be implemented together are combined, and an optimization strategy recommendation sequence and strategy combination package are generated; Select a number of independent strategies or their strategy combination packages ranked highest in the optimization strategy recommendation sequence as typical management scenarios, and based on these scenarios and their corresponding expected improvement rate and improvement value, deduce their potential impact on the overall project duration and resource peak to generate a multi-scenario prediction comparison chart of future work efficiency and personnel configuration under different management decisions; The risk factor visualization correlation data, the efficiency risk and optimization potential two-dimensional matrix, the optimization strategy recommendation sequence with priority, the strategy combination package, and the multi-scenario prediction comparison chart are integrated to compile a management decision prediction report.

7. The BIM-based construction project optimization system of claim 6, wherein, Based on the efficiency risk and optimization potential two-dimensional matrix, all optimization strategies are sorted according to the comprehensive benefits of expected improvement rate and improvement value, and the comprehensive benefit index NBI is defined for sorting: wherein, is the ratio of the efficiency improvement rate to the baseline efficiency; is the ratio of the personnel utility improvement value to the baseline utility; is the ratio of the resource cost improvement amount to the baseline cost; , , is the weight.

8. The BIM-based construction project optimization system of claim 1, wherein, The supplementary module is also included for supplementing energy consumption impact analysis for the construction task topology network: From the three-dimensional building information model, the material thermal property sequence of the envelope component, the functional partition information of the building space, and the spatial adjacency relationship data between components are extracted; Based on the material thermal property sequence and the spatial adjacency relationship data, the theoretical energy consumption impact value of each component involved in the construction task under a unit working condition is calculated as the energy consumption impact base value of the node according to the preset building energy consumption simulation rules; The energy consumption impact base value of the node is taken as a new attribute, and the corresponding node in the perfect construction task topology network is given to generate an extended construction task topology network; Based on the extended construction task topology network, the influence of the construction sequence on the overall energy consumption performance of the building is analyzed, and those continuous task sequences that have a significant influence on the final operation energy consumption of the building are identified as energy-sensitive paths, and the node with the highest energy consumption impact base value in the path is marked as the energy key node; By comparing the energy-sensitive path and the high-vulnerability task chain, the overlapping part is marked as the collaborative management key path, and the collaborative optimization strategy suggestion is generated for the path and the energy key node in combination with the optimization strategy simulation scheme set.

9. The BIM-based construction project optimization system of claim 8, wherein, From the three-dimensional building information model, the environmental attribute information of the main building materials, the transportation distance simulation data of large components or equipment, and the potential environmental impact category identification involved in the construction process are extracted; Based on the environmental attribute information, the transportation distance simulation data, and the potential environmental impact category identification, the environmental load value of each construction task node per engineering quantity is calculated as the environmental impact intensity value of the node according to the preset quantitative conversion rules for different environmental impact categories; The environmental impact intensity value of the node is taken as another new attribute, and the node's energy consumption impact base value is given to the corresponding node in the perfect construction task topology network to generate a dual-attribute extended construction task topology network; In the dual-attribute extended construction task topology network, the node cluster or path with high environmental impact intensity value and energy consumption impact base value is identified, and the superimposed impact key area is located; For the superimposed impact key area, cross analysis is performed in combination with the analysis results of the high-vulnerability task chain to design composite management measures and generate a multi-target collaborative optimization strategy package.

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