BIM-based intelligent prediction method and equipment for building construction progress

Abstract

This application provides a BIM-based intelligent prediction method and device for building construction progress, relating to the field of building construction progress prediction technology. The method includes: transmitting a set of real-time construction progress correlation data of the target building to a BIM building model; performing multi-dimensional analysis of the correlation relationships between construction sub-projects; conducting horizontal construction progress interference analysis on the real-time BIM building model based on the construction sub-project correlation graph network; conducting vertical construction progress interference analysis based on the historical construction progress correlation data sets of each construction sub-project; and predicting the building construction progress of the real-time BIM building model based on the horizontal and vertical construction progress interference coefficients of the construction sub-projects, thereby obtaining the prediction result for the target building's construction progress. This application can solve the technical problem of inaccurate construction progress prediction in existing technologies, achieving the technical effect of improving the accuracy of construction progress prediction.

Description

Technical Field

[0001] This application relates to the field of construction progress prediction technology, and in particular to a BIM-based intelligent prediction method and equipment for construction progress. Background Technology

[0002] In modern construction management, with the continuous expansion of project scale and the increasing complexity of construction processes, construction progress prediction and control have become key aspects to ensure the timely completion of projects.

[0003] Currently, existing methods struggle to reflect the dynamic changes at the construction site in real time. Factors such as resource allocation conflicts, weather changes, or equipment failures that arise during construction often fail to be promptly reflected in the plan, leading to delayed forecasts and hindering effective on-site decision-making. Secondly, existing technologies lack systematic analysis when dealing with horizontal relationships between sub-projects. They cannot quantify the mutual interference between different sub-projects caused by resource sharing, spatial conflicts, or technological dependencies, easily overlooking the chain reaction of construction milestones, resulting in local delays propagating to the overall project schedule without being identified in a timely manner.

[0004] In summary, existing technologies suffer from technical problems such as the inability to reflect dynamic changes at the construction site in real time and the lack of systematic analysis of the horizontal relationships between sub-projects, resulting in delayed construction progress forecasting, difficulty in quantifying interference effects, and further impacting the timely completion of projects and the optimal allocation of resources. Summary of the Invention

[0005] The purpose of this application is to provide a BIM-based intelligent prediction method and equipment for building construction progress, in order to solve the technical problems in the existing technology that, due to the inability to reflect the dynamic changes of the construction site in real time and the lack of systematic analysis of the horizontal relationships of sub-projects, the construction progress prediction is lagging behind, the interference is difficult to quantify, and this further affects the timely completion of the project and the optimal allocation of resources.

[0006] In view of the above problems, this application provides a BIM-based intelligent prediction method and equipment for building construction progress.

[0007] Firstly, this application provides a BIM-based intelligent prediction method for building construction progress, implemented through a BIM-based intelligent prediction device for building construction progress. The method includes: collecting a set of real-time construction progress correlation data for a target building and transmitting it to various construction sub-projects within the BIM building model to obtain a real-time BIM building model; obtaining a set of construction sub-projects for the target building, traversing the design information of the set of construction sub-projects to perform multi-dimensional analysis of the correlation relationships between the construction sub-projects, and constructing a network of construction sub-project correlation graphs; performing horizontal construction progress interference analysis on the real-time BIM building model based on the network of construction sub-project correlation graphs to obtain horizontal construction progress interference coefficients for the construction sub-projects; performing vertical construction progress interference analysis based on the historical construction progress correlation data set of each construction sub-project to obtain vertical construction progress interference coefficients for the construction sub-projects; and predicting the construction progress of the real-time BIM building model based on the horizontal and vertical construction progress interference coefficients for the construction sub-projects to obtain the prediction result for the construction progress of the target building.

[0008] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: extracting the relationships between each construction sub-project in the construction sub-project set and other construction sub-projects according to the preset association relationships and the design information of the construction sub-project set, to obtain a set of construction sub-project association relationship groups; taking each construction sub-project in the construction sub-project set as a node, connecting the nodes according to the set of construction sub-project association relationship groups, and constructing the construction sub-project association relationship graph network.

[0009] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: the preset association relationships include time association relationships, resource sharing relationships, spatial conflict relationships, and technical association relationships.

[0010] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: extracting a set of construction sub-project pairs with overlapping progress in the network of construction sub-project relationship graphs; identifying conflicts in the set of construction sub-project pairs from two dimensions—resource sharing conflict and spatial conflict—in conjunction with the real-time BIM building model, to obtain a set of resource sharing conflict coefficients and a set of spatial conflict coefficients for construction sub-project pairs; and performing a mapping weighted analysis on the set of resource sharing conflict coefficients and the set of spatial conflict coefficients for construction sub-project pairs to obtain the horizontal construction progress interference coefficient of the construction sub-projects.

[0011] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: performing a one-to-one mapping and weighting on the set of resource sharing conflict coefficients and the set of spatial conflict coefficients of the construction sub-projects respectively to obtain a set of interference coefficients for horizontal construction progress of the construction sub-projects; extracting the set of interference coefficients for horizontal construction progress of the construction sub-projects using each construction sub-project as an index to obtain a set of interference coefficients for horizontal construction progress of multiple construction sub-projects; traversing the set of interference coefficients for horizontal construction progress of multiple construction sub-projects and calculating the mean to obtain the mean of interference coefficients for horizontal construction progress of multiple construction sub-projects; obtaining a set of importance weights for construction sub-projects, and weighting the mean of interference coefficients for horizontal construction progress of multiple construction sub-projects based on the set of importance weights to obtain the interference coefficient for horizontal construction progress of the construction sub-projects.

[0012] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: extracting the number of associations of the set of construction sub-projects based on the network of associations of the construction sub-projects, and obtaining a set of associations of construction sub-projects; and dividing the number of associations of each construction sub-project in the set of associations of construction sub-projects by the total number of the set of associations of construction sub-projects to obtain a set of importance weights for construction sub-projects.

[0013] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: traversing the historical construction progress association data set to extract the historical construction progress lag time and the historical construction progress advance time, to obtain a set of historical construction progress lag time and a set of historical construction progress advance time; and performing historical influence coefficient analysis based on the set of historical construction progress lag time and the set of historical construction progress advance time to obtain the longitudinal construction progress interference coefficient of the construction sub-project.

[0014] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: obtaining the expected building construction progress prediction result; determining whether the difference between the expected building construction progress prediction result and the target building construction progress prediction result exceeds a preset deviation threshold; if so, automatically triggering an alarm message.

[0015] Preferably, the BIM-based intelligent prediction method for building construction progress further includes: sending the prediction results of the target building construction progress to a visualization component for visual display of the construction progress prediction results.

[0016] Secondly, this application also provides a BIM-based intelligent prediction device for building construction progress, used to execute the BIM-based intelligent prediction method for building construction progress as described in the first aspect, including: a model acquisition module, used to collect a set of real-time construction progress correlation data of the target building, transmit it to each construction sub-project in the BIM building model, and obtain a real-time BIM building model; a network construction module, used to obtain a set of construction sub-projects of the target building, traverse the design information of the set of construction sub-projects to perform multi-dimensional analysis of the correlation relationships between the construction sub-projects, and construct a network of correlation graphs of construction sub-projects; a horizontal coefficient acquisition module, used to perform horizontal construction progress interference analysis on the real-time BIM building model based on the network of correlation graphs of construction sub-projects, and obtain horizontal construction progress interference coefficients of construction sub-projects; a vertical coefficient acquisition module, used to perform vertical construction progress interference analysis based on the historical construction progress correlation data set of each construction sub-project, and obtain vertical construction progress interference coefficients of construction sub-projects; and a progress prediction module, used to predict the construction progress of the real-time BIM building model based on the horizontal construction progress interference coefficients and the vertical construction progress interference coefficients of construction sub-projects, and obtain the construction progress prediction result of the target building.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by realizing the technical goal of intelligent prediction and dynamic management of construction progress, it achieves the technical effects of real-time monitoring of construction progress, quantification of interference impact, early warning of key node risks, and significant improvement in resource allocation efficiency.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the intelligent prediction method for building construction progress based on BIM proposed in this application.

[0021] Figure 2 This is a structural schematic diagram of the BIM-based intelligent prediction device for building construction progress in this application.

[0022] Figure labeling: Model acquisition module 1, network construction module 2, horizontal coefficient acquisition module 3, vertical coefficient acquisition module 4, progress prediction module 5. Detailed Implementation

[0023] This application provides a BIM-based intelligent prediction method and equipment for building construction progress, solving the technical problems in existing technologies where the inability to reflect dynamic changes at the construction site in real time and the lack of systematic analysis of horizontal relationships between sub-projects lead to delayed construction progress predictions, difficulty in quantifying interference impacts, and further affect on-time project completion and optimal resource allocation. It achieves the technical goals of intelligent prediction and dynamic management of building construction progress, resulting in real-time monitoring of construction progress, quantifiable interference impacts, early warning of risks at key nodes, and a significant improvement in resource allocation efficiency.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a BIM-based intelligent prediction method for building construction progress, which is applied to a BIM-based intelligent prediction device for building construction progress, and specifically includes the following steps:

[0026] S1: Collect the real-time construction progress data set of the target building and transmit it to each construction sub-project in the BIM building model to obtain the real-time BIM building model.

[0027] Specifically, real-time construction progress data of the target building is collected through sensors, construction logs, progress reports, and other means. This data, directly or indirectly related to the current construction progress, may include the completion percentage of each construction unit, the man-hours used, the quantity of materials consumed, and the equipment usage status. The real-time construction progress data is then categorized and matched according to the corresponding construction sub-projects and transmitted to each construction sub-project in the BIM building model. This data is then imported into the digital building information model to obtain a real-time BIM building model, ensuring that each sub-project in the real-time BIM building model has status information corresponding to the actual progress.

[0028] S2: Obtain the set of construction sub-projects of the target building, traverse the design information of the set of construction sub-projects to perform multi-dimensional analysis of the relationship between the construction sub-projects, and construct a network of relationship graphs of construction sub-projects.

[0029] Specifically, the process involves obtaining a set of construction sub-projects for the target building. The entire construction project is divided into construction tasks, and all independent construction units are extracted as sub-projects, such as foundation construction, main structure construction, roofing, exterior wall decoration, and electromechanical installation. Each sub-project has a clearly defined scope, schedule, and resource requirements. The design information of the sub-project set is then traversed, meaning the design data for each sub-project is read and analyzed sequentially, including construction drawings, technical specifications, construction process descriptions, structural parameters, and material lists, to determine dependencies or conflicts between sub-projects. Multi-dimensional analysis of the relationships between construction sub-projects involves analyzing the relationships between them across multiple dimensions, such as time, resources, space, and technology. For example, it determines whether two sub-projects must be constructed sequentially, whether they compete for the same resources, whether they interfere with each other physically, and whether their technical conditions affect each other. The resulting multi-dimensional relationships are then expressed as a graph network, constructing a graph network of construction sub-project relationships, where edges represent the relationships between different nodes.

[0030] S3: Based on the network of the relationship diagram of the construction sub-projects, perform a horizontal construction progress interference analysis on the real-time BIM building model to obtain the horizontal construction progress interference coefficient of the construction sub-projects.

[0031] Specifically, a network of construction sub-project relationship graphs, containing nodes and edges to represent the connections between various construction sub-projects, is used to perform lateral construction progress interference analysis on the real-time BIM building model. This analysis examines the potential impact of progress changes in one sub-project on another. Lateral interference refers to the mutual constraints between different sub-projects within the same time period. For example, two sub-projects under construction simultaneously may affect each other's progress due to sharing a crane. A lateral construction progress interference coefficient is obtained to quantify the degree to which a sub-project is affected by other sub-projects in the current time period. A larger lateral construction progress interference coefficient indicates a more significant interference.

[0032] S4: Perform longitudinal construction progress interference analysis based on the historical construction progress correlation data set of each construction sub-project to obtain the longitudinal construction progress interference coefficient of the construction sub-project.

[0033] Specifically, we collect and organize all data related to the progress of each sub-project during the past construction process, including the difference between the planned and actual completion time, as well as the dependencies between related construction stages. We conduct longitudinal construction progress interference analysis to study the progress impact of the same sub-project at different construction stages in the time dimension. For example, we analyze whether early delays will be passed on to mid- or late-stage construction, thus causing changes in the overall project schedule. We obtain the longitudinal construction progress interference coefficient of the construction sub-project. The larger the value of the longitudinal construction progress interference coefficient of the construction sub-project, the more significant the impact of historical delays or early completion on the subsequent progress.

[0034] S5: Based on the horizontal construction progress interference coefficient and the vertical construction progress interference coefficient of the construction sub-project, predict the construction progress of the real-time BIM building model to obtain the target building construction progress prediction result.

[0035] Specifically, the degree of schedule impact caused by horizontal relationships such as resource sharing and spatial conflicts between sub-projects reflects the mutual constraints between different sub-projects. For example, two sub-projects requiring the same large crane may delay each other due to resource queuing. The vertical construction schedule interference coefficient of a construction sub-project reflects the degree of schedule impact between successive stages of the same sub-project in terms of time sequence. For example, early delays directly lead to the probability and magnitude of later delays. Based on the horizontal and vertical construction schedule interference coefficients of construction sub-projects, the construction schedule of the real-time BIM building model is predicted. That is, the two types of interference coefficients are input into the real-time updated building information model, and the model calculation and simulation functions are used to predict the future construction time of the entire project, obtain the target building construction schedule prediction results, and output the expected completion time, critical path, and possible delays of the entire project after comprehensive calculation. For example, when obtaining the horizontal construction schedule interference coefficient of a construction sub-project... Interference coefficient of longitudinal construction progress of construction sub-projects Then, these factors are input as weighting factors into the schedule prediction module of the real-time BIM building model. The baseline construction period for each construction sub-project is then calculated. The construction period is then adjusted by weighting the corresponding interference coefficients to obtain the corrected construction period. . .in, and These are the weighting factors for horizontal and vertical interference, respectively, which can be determined based on project type or historical data, such as... =0.6, =0.4. Then, the real-time BIM building model integrates a schedule prediction engine based on the critical path method or Monte Carlo simulation. After inputting the corrected construction period for each sub-project, the real-time BIM building model automatically recalculates the critical path and total duration of the entire project, generating the target building construction schedule prediction result. Furthermore, the real-time BIM building model supports visual comparisons of the schedule before and after correction, facilitating user identification of high-risk sub-projects. Next, the horizontal and vertical construction schedule interference coefficients of the construction sub-projects are passed to the model schedule management module through the API interface of the real-time BIM building model. The real-time BIM building model dynamically updates the duration attributes of the corresponding sub-projects based on the horizontal and vertical construction schedule interference coefficients, and highlights the affected areas in the 3D view, achieving a visual warning of schedule risks.

[0036] Furthermore, this application also includes: extracting the relationship between each construction sub-project in the construction sub-project set and other construction sub-projects according to the preset association relationship and the design information of the construction sub-project set, to obtain a construction sub-project association relationship group set; taking each construction sub-project in the construction sub-project set as a node, connecting the nodes according to the construction sub-project association relationship group set, and constructing the construction sub-project association relationship graph network.

[0037] Specifically, based on the pre-defined relationships and design information of the construction sub-project set, and according to the pre-determined relationship types between sub-projects in the construction plan, such as temporal sequence, resource sharing, spatial conflicts, and technical dependencies, and combined with the design information of each construction sub-project, such as construction technology, structural dimensions, and equipment layout, the potential connections between each sub-project and other sub-projects are analyzed. The relationships between each construction sub-project and other construction sub-projects in the construction sub-project set are extracted separately. For each sub-project, all dependencies or constraints with other sub-projects are identified individually; for example, the main structure construction must begin after the foundation is completed, or multiple electromechanical installation sub-projects share the same pipeline. These relationships are extracted to form relationship data. The relationship data of each sub-project with other sub-projects are grouped to obtain a set of construction sub-project relationship groups, forming a set that facilitates analysis and modeling.

[0038] Each construction sub-project in the set of construction sub-projects is treated as a node. In the network graph model, each sub-project is considered an independent element or node, representing its position within the entire construction system. Connecting nodes based on the set of relationships between construction sub-projects utilizes these relationships to link them according to dependency, sharing, or conflict, forming a complete network structure that visually reflects the horizontal and vertical connections between sub-projects. For example, the main structure node and the foundation node are connected by a time-related line, and the electromechanical installation nodes are connected by a resource-sharing line. Combining all nodes and related lines into a visual graph network constructs the construction sub-project relationship graph network, thus forming a structured data model that can be used for interference analysis and schedule prediction. Table 1 shows the construction sub-project relationship data.

[0039] Table 1: Data Table of Relationships Between Construction Sub-projects

[0040]

[0041] Furthermore, this application also includes: the preset association relationships include time association relationships, resource sharing relationships, spatial conflict relationships, and technical association relationships.

[0042] Specifically, pre-defined relationships include temporal relationships, resource-sharing relationships, spatial conflict relationships, and technical relationships. Temporal relationships refer to the sequential or parallel requirements of different construction sub-projects during construction, determined by the construction plan or process flow. For example, foundation construction must be completed before the main structure construction, and roof construction can only proceed after the main structure is completed. There may also be situations where two sub-projects are carried out simultaneously within the same time period. Resource-sharing relationships refer to multiple construction sub-projects sharing the same resources during construction, such as manpower, machinery, materials, or construction sites. When the total amount of resources is limited, resource-sharing relationships can lead to mutual impacts on construction progress. For example, if two sub-projects need the same tower crane, their usage time must be scheduled, thus affecting their respective progress. Spatial conflict relationships indicate the overlap or mutual interference of different construction sub-projects in the construction space. When two sub-projects are operating in the same physical space or adjacent areas, conflicts may arise due to operating range, construction safety, or equipment layout. For example, when indoor electrical installation and interior decoration are carried out simultaneously, the workers and material stacking may obstruct each other, affecting construction efficiency. Technical interdependence refers to the dependence between construction sub-projects in terms of construction technology. The completion quality, process parameters or technical standards of one sub-project may directly affect the construction conditions and quality of another sub-project. For example, the quality of concrete pouring will affect the accuracy of subsequent steel structure installation. If the technical conditions of the previous process are not up to standard, the subsequent construction must be adjusted or delayed.

[0043] Furthermore, this application also includes: extracting a set of construction sub-project pairs with overlapping schedules from the network of construction sub-project relationship graphs; identifying conflicts in the set of construction sub-project pairs from two dimensions—resource sharing conflict and spatial conflict—in conjunction with the real-time BIM building model, to obtain a set of resource sharing conflict coefficients and a set of spatial conflict coefficients for construction sub-project pairs; and performing a mapping weighted analysis on the set of resource sharing conflict coefficients and the set of spatial conflict coefficients for construction sub-project pairs to obtain the horizontal construction progress interference coefficient of the construction sub-projects.

[0044] Specifically, the set of construction sub-project pairs with overlapping schedules is extracted from the construction sub-project relationship graph network. In the already constructed relationship graph network, the combination of sub-projects that have partial or complete overlap in construction time is identified, which is called a construction sub-project pair. For example, the main structure construction and electromechanical installation may be carried out in the same time period, and the roof construction and exterior wall decoration may also have overlapping schedules. Project pairs with overlapping schedules are often prone to interference and need to be analyzed in detail.

[0045] This study identifies conflicts between construction sub-project pairs from two dimensions: resource sharing conflicts and spatial conflicts, using real-time BIM building models. Specifically, it assesses the existence of conflicts among identified sub-project pairs with overlapping schedules, considering both resource usage and space occupancy. Resource sharing conflicts arise when two sub-projects require the same limited resources at the same time, such as the same tower crane, the same group of construction workers, or the same type of machinery. Spatial conflicts occur when two sub-projects have overlapping work areas or interfere with each other in adjacent areas, such as hoisting operations affecting nearby scaffolding construction. By utilizing real-time BIM building models, the work areas and resource allocation of sub-projects can be accurately located in three-dimensional space, leading to more precise conflict identification.

[0046] A weighted analysis is performed on the sets of resource-sharing conflict coefficients and spatial conflict coefficients of construction sub-projects to obtain the lateral construction progress interference coefficients of the construction sub-projects. This means that the coefficients of each pair of sub-projects in terms of resource conflict and spatial conflict are mapped and weighted, and the degree of conflict in the two dimensions is combined into a value representing the intensity of lateral interference. The weighted analysis can set weights according to the importance of resource conflict and spatial conflict on the progress in actual construction. For example, the weight of resource conflict is 0.6 and the weight of spatial conflict is 0.4. Then, the weighted average is calculated to obtain the lateral interference coefficient of each sub-project. The larger the lateral construction progress interference coefficient of a construction sub-project, the more severe the lateral interference impact on that sub-project.

[0047] Furthermore, this application also includes: performing a one-to-one mapping and weighting on the resource sharing conflict coefficient set and the spatial conflict coefficient set of the construction sub-projects respectively to obtain a set of horizontal construction progress interference coefficients of the construction sub-projects; extracting the set of horizontal construction progress interference coefficients of the construction sub-projects using each construction sub-project as an index to obtain a set of horizontal construction progress interference coefficients associated with multiple construction sub-projects; traversing the set of horizontal construction progress interference coefficients associated with multiple construction sub-projects and calculating the mean to obtain the mean of the horizontal construction progress interference coefficients associated with multiple construction sub-projects; obtaining a set of importance weights for construction sub-projects, and weighting the mean of the horizontal construction progress interference coefficients associated with multiple construction sub-projects based on the set of importance weights of construction sub-projects to obtain the horizontal construction progress interference coefficient of the construction sub-projects.

[0048] Specifically, a one-to-one mapping and weighting is performed on the resource sharing conflict coefficient set and the spatial conflict coefficient set of the construction sub-projects, respectively. The coefficients of each pair of sub-projects in terms of resource sharing conflict and spatial conflict are matched one-to-one, and then weighted calculation is performed according to the set weights. The influence of the two conflict dimensions is integrated into a comprehensive value, which is the horizontal construction progress interference value of the sub-project pair.

[0049] Using each construction sub-project as an index, the set of interference coefficients for horizontal construction progress of each sub-project is extracted. The interference coefficients of each pair of sub-projects are then categorized and summarized according to the individual sub-projects participating in that pair, so that each sub-project can be associated with all interference coefficients. For example, if sub-project A participates in 3 pairs of projects, namely AB, AC, and AD, then the interference coefficients of all 3 pairs will be extracted and attributed to A.

[0050] The average value of the set of horizontal construction progress interference coefficients associated with multiple construction sub-projects is calculated. The average value of all interference coefficients associated with each sub-project is then obtained to obtain the average value of the horizontal interference intensity experienced by the sub-project as a whole.

[0051] The process involves obtaining a set of importance weights for each construction sub-project. Based on this set, the average horizontal construction progress interference coefficients of multiple sub-projects are weighted. This means that each sub-project's average interference coefficient is multiplied by a weight according to its importance in the overall project, resulting in the final horizontal construction progress interference coefficient. Importance weights can be determined by the proportion of connections a sub-project has in the relationship graph. For example, the more connections a sub-project has with other projects, the greater its impact on the overall progress, and the higher its weight.

[0052] Furthermore, this application also includes: based on the network of associations of the construction sub-projects, extracting the number of associations of the set of construction sub-projects to obtain a set of associations of construction sub-projects; and dividing the number of associations of each construction sub-project in the set of associations of construction sub-projects by the total number of the set of associations of construction sub-projects to obtain a set of importance weights of construction sub-projects.

[0053] Specifically, based on the network of relationships between construction sub-projects, the number of relationships within the set of construction sub-projects is extracted. The number of direct connections between each sub-project and other sub-projects is counted; this number of connections is the number of relationships. The relationship graph is a structure composed of nodes and edges. Nodes represent construction sub-projects, and edges represent relationships between two sub-projects, such as time, resource, or spatial conflicts. The number of relationships reflects the connection density of a sub-project within the entire construction network. For example, if a sub-project has direct relationships with four other sub-projects, then its number of relationships is four.

[0054] The importance weight set of a sub-project is calculated by dividing the number of associations of each sub-project in the set of associations by the total number of associations of all sub-projects. This yields a ratio value that represents the proportion of a sub-project in the entire project network. For example, if the total number of associations is 40 and a sub-project has 4 associations, its weight is 0.1, reflecting its influence on the overall construction progress management.

[0055] Furthermore, this application also includes: traversing the historical construction progress associated data set to extract the historical construction progress lag time and the historical construction progress advance time, to obtain a historical construction progress lag time set and a historical construction progress advance time set; and performing historical impact coefficient analysis based on the historical construction progress lag time set and the historical construction progress advance time set to obtain the vertical construction progress interference coefficient of the construction sub-project.

[0056] Specifically, the process involves sequentially reviewing and analyzing all historical data related to construction progress, including comparisons between the planned and actual progress of each construction sub-project. This includes extracting historical construction progress lag times to determine how much later each sub-project was completed than planned. For example, if a sub-project was originally scheduled to be completed in 20 days but actually took 25 days, the lag time is 5 days. Similarly, extracting historical construction progress advance times identifies the difference in time between completion and actual completion. For instance, if the project was originally planned for 20 days but was completed in 15 days, the advance time is 5 days. Through these two processes, a set of historical construction progress lag times and a set of historical construction progress advance times are obtained, representing complete records of all lag and advance times, respectively.

[0057] Next, based on the historical construction progress lag duration set and the historical construction progress advance duration set, a historical impact coefficient analysis is conducted to calculate the potential impact on subsequent construction and obtain the longitudinal construction progress interference coefficient of the construction sub-project. This coefficient reflects the positive or negative effects of past advances or delays on future progress at different construction stages of the same sub-project. The reliability of the overall construction progress is analyzed based on the advance and lag duration, and this coefficient is used as the longitudinal construction progress interference coefficient of the construction sub-project.

[0058] Furthermore, this application also includes: obtaining the expected construction progress prediction result; determining whether the difference between the expected construction progress prediction result and the target construction progress prediction result exceeds a preset deviation threshold, and if so, automatically triggering an alarm message.

[0059] Specifically, based on the original project plan, historical experience, or ideal construction period prediction obtained through simulation calculations, the expected construction progress prediction results are obtained. These expected construction progress prediction results are calculated under the condition of no sudden interference or major resource conflicts and serve as a benchmark reference.

[0060] The system determines the difference between the expected and target construction progress forecasts by comparing the ideal forecast with the latest construction progress forecast calculated based on the real-time BIM model. The time difference is calculated; for example, if the ideal forecast is 200 days and the latest forecast is 210 days, the difference is 10 days. This difference is then compared to a pre-set tolerance threshold, which may be 5 days, 10 days, or a percentage. If the difference exceeds the pre-set threshold, it is considered a serious deviation from the schedule. In this case, an alarm is automatically triggered, and a notification or warning is sent to remind project managers to take measures to adjust construction arrangements or resource allocation.

[0061] Furthermore, this application also includes: sending the construction progress prediction results of the target building to a visualization component for visual display of the construction progress prediction results.

[0062] Specifically, the construction progress forecast results for the target building are sent to the visualization component. This involves transmitting the construction progress forecast data, calculated based on the BIM model and interference coefficients, to a system module for graphical display. The visualization component refers to an interface or software module capable of displaying data in the form of charts, curves, Gantt charts, or 3D models. Visualizing the construction progress forecast results presents numerical values ​​and timelines to project managers in an intuitive way, enabling them to quickly understand the progress, critical path, and potential delays of each sub-project.

[0063] In summary, the BIM-based intelligent prediction method for building construction progress provided in this application has the following technical effects: by achieving the technical goal of intelligent prediction and dynamic management of building construction progress, it achieves the technical effects of real-time monitoring of construction progress, quantifiable interference impact, early warning of key node risks, and significant improvement in resource allocation efficiency.

[0064] Example 2: Based on the same inventive concept as the BIM-based intelligent prediction method for building construction progress in the foregoing examples, this application also provides a BIM-based intelligent prediction device for building construction progress. Please refer to the appendix. Figure 2 The system includes: a model acquisition module 1, used to collect a set of real-time construction progress correlation data of the target building and transmit it to each construction sub-project in the BIM building model to obtain a real-time BIM building model; a network construction module 2, used to obtain a set of construction sub-projects of the target building, traverse the design information of the set of construction sub-projects to perform multi-dimensional analysis of the correlation between construction sub-projects, and construct a network of correlation graphs of construction sub-projects; a horizontal coefficient acquisition module 3, used to perform horizontal construction progress interference analysis on the real-time BIM building model based on the network of correlation graphs of construction sub-projects to obtain the horizontal construction progress interference coefficients of construction sub-projects; a vertical coefficient acquisition module 4, used to perform vertical construction progress interference analysis based on the historical construction progress correlation data set of each construction sub-project to obtain the vertical construction progress interference coefficients of construction sub-projects; and a progress prediction module 5, used to predict the construction progress of the real-time BIM building model based on the horizontal construction progress interference coefficients and the vertical construction progress interference coefficients of construction sub-projects to obtain the construction progress prediction result of the target building.

[0065] Furthermore, the BIM-based intelligent prediction device for building construction progress is also used to: extract the relationship between each construction sub-project in the construction sub-project set and other construction sub-projects according to the design information of the preset relationship and the construction sub-project set, and obtain a set of construction sub-project relationship groups; take each construction sub-project in the construction sub-project set as a node, connect the nodes according to the set of construction sub-project relationship groups, and construct the construction sub-project relationship graph network.

[0066] Furthermore, the BIM-based intelligent prediction device for building construction progress is also used for: the preset association relationships include time association relationships, resource sharing relationships, spatial conflict relationships, and technical association relationships.

[0067] Furthermore, the BIM-based intelligent prediction device for building construction progress is also used to: extract the set of construction sub-project pairs with overlapping progress in the network of construction sub-project relationship graphs; identify conflicts in the set of construction sub-project pairs from two dimensions—resource sharing conflict and spatial conflict—in conjunction with the real-time BIM building model, to obtain the set of resource sharing conflict coefficients and the set of spatial conflict coefficients for construction sub-project pairs; and perform mapping weighted analysis on the set of resource sharing conflict coefficients and the set of spatial conflict coefficients for construction sub-project pairs to obtain the horizontal construction progress interference coefficient of the construction sub-projects.

[0068] Furthermore, the BIM-based intelligent prediction device for construction progress is also used to: perform one-to-one mapping and weighting on the resource sharing conflict coefficient set and the spatial conflict coefficient set of the construction sub-projects respectively, to obtain the set of interference coefficients for horizontal construction progress of the construction sub-projects; extract the set of interference coefficients for horizontal construction progress of the construction sub-projects using each construction sub-project as an index, to obtain a set of interference coefficients for horizontal construction progress associated with multiple construction sub-projects; traverse the set of interference coefficients for horizontal construction progress associated with multiple construction sub-projects and calculate the mean, to obtain the mean of the interference coefficients for horizontal construction progress associated with multiple construction sub-projects; obtain the set of importance weights for construction sub-projects, and weight the mean of the interference coefficients for horizontal construction progress associated with multiple construction sub-projects based on the set of importance weights for construction sub-projects, to obtain the interference coefficient for horizontal construction progress of the construction sub-projects.

[0069] Furthermore, the BIM-based intelligent prediction device for construction progress is also used to: extract the number of associations of the set of construction sub-projects based on the network of associations of the construction sub-projects, and obtain a set of associations of construction sub-projects; and divide the number of associations of each construction sub-project in the set of associations of construction sub-projects by the total number of the set of associations of construction sub-projects to obtain a set of importance weights of construction sub-projects.

[0070] Furthermore, the BIM-based intelligent prediction device for construction progress is also used to: traverse the historical construction progress associated data set to extract the historical construction progress lag time and the historical construction progress advance time, to obtain a set of historical construction progress lag time and a set of historical construction progress advance time; and perform historical influence coefficient analysis based on the set of historical construction progress lag time and the set of historical construction progress advance time to obtain the longitudinal construction progress interference coefficient of the construction sub-project.

[0071] Furthermore, the BIM-based intelligent prediction device for building construction progress is also used to: obtain the expected prediction result of building construction progress; determine whether the difference between the expected prediction result of building construction progress and the target prediction result of building construction progress exceeds a preset deviation threshold, and if so, automatically trigger an alarm message.

[0072] Furthermore, the BIM-based intelligent prediction device for building construction progress is also used to: send the prediction results of the target building construction progress to the visualization component for visualization display of the construction progress prediction results.

[0073] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The BIM-based intelligent prediction method and specific examples of building construction progress in the aforementioned embodiment 1 are also applicable to the BIM-based intelligent prediction device of building construction progress in this embodiment. Through the foregoing detailed description of the BIM-based intelligent prediction method of building construction progress, those skilled in the art can clearly understand the BIM-based intelligent prediction device of building construction progress in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0074] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0075] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A BIM-based intelligent construction progress prediction method, characterized in that, The method comprises: Collecting real-time construction progress correlation data sets of the target building, transmitting them to each construction sub-project in the BIM building model, and obtaining a real-time BIM building model; Obtaining a construction sub-project set of the target building, traversing the design information of the construction sub-project set to perform multi-dimensional analysis of the correlation between construction sub-projects, and constructing a construction sub-project correlation graph network; Based on the construction sub-project correlation graph network, the real-time BIM building model is subjected to horizontal construction progress interference analysis to obtain a construction sub-project horizontal construction progress interference coefficient; According to the historical construction progress correlation data sets of each construction sub-project, vertical construction progress interference analysis is performed to obtain a construction sub-project vertical construction progress interference coefficient; Based on the construction sub-project horizontal construction progress interference coefficient and the construction sub-project vertical construction progress interference coefficient, the real-time BIM building model is subjected to building construction progress prediction to obtain a target building construction progress prediction result; Based on the construction sub-project correlation graph network, the real-time BIM building model is subjected to horizontal construction progress interference analysis to obtain a construction sub-project horizontal construction progress interference coefficient, comprising: Extracting a set of construction sub-project pairs with overlapping progress from the construction sub-project correlation graph network; From the dimensions of resource sharing conflict and space conflict, combining the real-time BIM building model, the set of construction sub-project pairs is subjected to conflict identification to obtain a set of construction sub-project pair resource sharing conflict coefficients and a set of construction sub-project pair space conflict coefficients; Mapping and weighting analysis is performed on the set of construction sub-project pair resource sharing conflict coefficients and the set of construction sub-project pair space conflict coefficients to obtain the construction sub-project horizontal construction progress interference coefficient; Mapping and weighting analysis is performed on the set of construction sub-project pair resource sharing conflict coefficients and the set of construction sub-project pair space conflict coefficients to obtain the construction sub-project horizontal construction progress interference coefficient, comprising: Respectively performing one-to-one mapping and weighting on the set of construction sub-project pair resource sharing conflict coefficients and the set of construction sub-project pair space conflict coefficients to obtain a set of construction sub-project pair horizontal construction progress interference coefficients; Taking each construction sub-project as an index, the set of construction sub-project pair horizontal construction progress interference coefficients is extracted to obtain a plurality of construction sub-project associated horizontal construction progress interference coefficient sets; Traversing the plurality of construction sub-project associated horizontal construction progress interference coefficient sets to perform mean value calculation to obtain a plurality of construction sub-project associated horizontal construction progress interference coefficient means; Obtaining a set of construction sub-project importance weights, and weighting the plurality of construction sub-project associated horizontal construction progress interference coefficient means based on the set of construction sub-project importance weights to obtain the construction sub-project horizontal construction progress interference coefficient; The set of construction sub-project importance weights is obtained, comprising: Based on the construction sub-project correlation graph network, the correlation quantity of the construction sub-project set is extracted to obtain a set of construction sub-project correlation quantities; obtaining a construction sub-project importance weight set by dividing each construction sub-project correlation quantity in the construction sub-project correlation quantity set by a total quantity of the construction sub-project correlation quantity set; obtaining a construction sub-project longitudinal construction progress interference coefficient by performing longitudinal construction progress interference analysis according to a historical construction progress correlation data set of each construction sub-project, including: obtaining a historical construction progress lag time set and a historical construction progress advance time set by performing historical construction progress lag time extraction and historical construction progress advance time extraction on the historical construction progress correlation data set; obtaining a construction sub-project longitudinal construction progress interference coefficient by performing historical influence coefficient analysis based on the historical construction progress lag time set and the historical construction progress advance time set. 2.The BIM-based construction progress intelligent prediction method of claim 1, wherein, obtaining a construction sub-project set of a target building, performing multi-dimensional analysis on the correlation relationship between construction sub-projects by traversing design information of the construction sub-project set, and constructing a construction sub-project correlation relationship graph network, including: extracting the correlation relationship between each construction sub-project and other construction sub-projects in the construction sub-project set according to a preset correlation relationship and the design information of the construction sub-project set, and obtaining a construction sub-project correlation relationship group set; constructing the construction sub-project correlation relationship graph network by taking each construction sub-project in the construction sub-project set as a node and connecting the nodes according to the construction sub-project correlation relationship group set. 3.The BIM-based construction progress intelligent prediction method of claim 2, wherein, The preset correlation relationship includes a time correlation relationship, a resource sharing relationship, a space conflict relationship, and a technology correlation relationship. 4.The BIM-based construction progress intelligent prediction method of claim 1, wherein, obtaining a target building construction progress prediction result, and then further including: obtaining an expected building construction progress prediction result; judging whether a difference between the expected building construction progress prediction result and the target building construction progress prediction result exceeds a preset deviation threshold, and if so, automatically triggering an alarm information. 5.The BIM-based construction progress intelligent prediction method of claim 1, wherein, sending the target building construction progress prediction result to a visualization component for visualized display of the construction progress prediction result.

6. A BIM-based construction progress intelligent prediction device, characterized by, Steps for implementing the BIM-based building construction progress intelligent prediction method in any one of claims 1 to 5, including: a model obtaining module for collecting a real-time construction progress correlation data set of a target building, and transmitting the real-time construction progress correlation data set to each construction sub-project in a BIM building model to obtain a real-time BIM building model; a network constructing module for obtaining a construction sub-project set of a target building, performing multi-dimensional analysis on the correlation relationship between construction sub-projects by traversing design information of the construction sub-project set, and constructing a construction sub-project correlation relationship graph network; a horizontal coefficient obtaining module for performing horizontal construction progress interference analysis on the real-time BIM building model based on the construction sub-project correlation relationship graph network to obtain a construction sub-project horizontal construction progress interference coefficient; a longitudinal coefficient obtaining module for obtaining a construction sub-project longitudinal construction progress interference coefficient by performing longitudinal construction progress interference analysis according to a historical construction progress correlation data set of each construction sub-project; and a longitudinal coefficient obtaining module for obtaining a construction sub-project longitudinal construction progress interference coefficient by performing longitudinal construction progress interference analysis according to a historical construction progress correlation data set of each construction sub-project. A progress prediction module is configured to predict the construction progress of the real-time BIM building model based on the horizontal construction progress interference coefficient of the construction sub-project and the vertical construction progress interference coefficient of the construction sub-project, and obtain a target construction progress prediction result.

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