Steel structure factory building modeling data processing method based on BIM technology

Abstract

The invention relates to the technical field of BIM model data processing, in particular to a steel structure factory building modeling data processing method based on a BIM technology. The method comprises the following steps: constructing a daily field 3D model; obtaining the design volume and edge of each component in the building information model of the steel structure factory building; surface complexity characteristic values of the components are obtained, and then the components are clustered to obtain clustering clusters; obtaining the construction difficulty level of each component in the clustering cluster and the sum of the construction difficulty levels of all the components; the completion degree coefficient of the component in the field 3D model of one day is obtained, and then the construction progress value of the day is calculated by combining the construction difficulty degree of the component and the sum of the construction difficulty degrees; the construction efficiency of the day is obtained according to the difference value of the construction progress values of the day and the previous day and the construction number and the construction time of the day; and predicting the number of days required by construction completion by using the construction efficiency and the construction time of each day until the current day. The method can accurately predict the construction completion days.

Description

Technical Field

[0001] This invention relates to the field of BIM model data processing technology, and specifically to a method for processing modeling data of steel structure factory buildings based on BIM technology. Background Technology

[0002] Steel structure factory buildings, as a common form of industrial building, have advantages such as fast construction speed, high structural strength, and reusability, and are widely used in manufacturing, logistics warehousing, and other fields. With the rapid development of Building Information Modeling (BIM) technology, its application in building design, construction, and operation and maintenance phases is becoming increasingly widespread. Through parametric modeling and intelligent data processing using BIM technology, automated design of steel structure components can be achieved, construction processes optimized, material waste reduced, and project quality improved. However, in the application of BIM technology to steel structure factory buildings, some key technical issues still exist, requiring in-depth research and optimization.

[0003] Currently, construction progress is mainly monitored through manual collection and reporting, resulting in low integration between the construction progress and the BIM model. This manual approach fails to accurately track progress, and the varying difficulty and time required at different construction stages hinders accurate estimation and monitoring of the overall project duration. This can lead to delays and economic losses. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a data processing method for modeling steel structure factory buildings based on BIM technology. The specific technical solution adopted is as follows:

[0005] One embodiment of the present invention provides a data processing method for modeling steel structure factory buildings based on BIM technology, the method comprising:

[0006] After each day's construction is completed, point cloud data and image data of the construction site are acquired to build a 3D model of the site for that day; the design volume and edges of each component in the building information model of the steel structure factory are also acquired.

[0007] Obtain the surface complexity feature value of the component based on its edges; use the design volume and surface complexity feature value of any two components to perform clustering to obtain clusters;

[0008] The construction difficulty of components within a cluster is evaluated to obtain the construction difficulty of each component within the cluster; the total construction difficulty of all components is obtained.

[0009] The actual completed volume of the components is obtained from the 3D model of the site over a day, and the completion coefficient of the components is obtained by comparing it with the design volume. The construction progress value of the day is obtained from the construction difficulty and completion coefficient of each component in the 3D model of the site over a day, as well as the sum of the construction difficulty.

[0010] The construction efficiency for a given day is obtained by taking into account the difference between the construction progress value of the previous day and the number of workers and the construction time for that day. The number of days required to complete the construction is then predicted by using the construction efficiency and construction time of each day up to the present.

[0011] Preferably, obtaining the surface complexity feature value of the component based on its edges includes:

[0012] Treat one edge of the component as a curve, use the curvature calculation formula to obtain the curvature of the curve, and use it as the curvature of the edge; calculate the surface complexity feature value of the component based on the curvature of all edges.

[0013] Preferably, calculating the surface complexity characteristic value of a component based on the curvature of all its edges includes:

[0014] The curvature average is obtained by averaging the curvature of all edges of a component; the absolute value of the difference between the curvature of each edge of the component and the curvature average is calculated and averaged to obtain the curvature consistency; the curvature consistency is mapped using an exponential function with the natural constant as the base and multiplied by the curvature average to obtain the surface complexity characteristic value of the component.

[0015] Preferably, clustering is performed using the design volume and surface complexity feature values ​​of any two components to obtain clusters, including:

[0016] The surface difference is obtained by normalizing the absolute value of the difference between the surface complexity feature values ​​of the two components; the edge number difference is obtained by normalizing the absolute value of the difference between the number of edges of the two components; the surface difference and the edge number difference are added together to obtain the shape feature difference value of the two components; and clustering is performed on any two components based on their design volume and shape feature difference values ​​to obtain a cluster.

[0017] Preferably, clusters are obtained by clustering based on the differences in design volume and shape characteristics between any two components, including:

[0018] The distance between two components is calculated based on the difference in their design volume and the difference in their shape characteristics; clusters are then formed by clustering the components based on the distance between any two components.

[0019] Preferably, the construction difficulty evaluation of components within a cluster is performed to obtain the degree of construction difficulty for each component within the cluster, including:

[0020] The construction difficulty of the components of the cluster center of a cluster is evaluated and scored by various experts, and the average score of each expert is used as the construction difficulty of the cluster.

[0021] Preferably, the sum of the construction difficulty of all components is obtained, including:

[0022] Multiply the construction difficulty of a component in a cluster by the construction difficulty of the corresponding cluster to obtain the sum of the construction difficulty of that cluster; sum the construction difficulty of all clusters to obtain the total construction difficulty of all components.

[0023] Preferably, the construction progress value for a given day is obtained based on the construction difficulty and completion coefficient of each component in the 3D model of the site, and the sum of the construction difficulty levels, including:

[0024] Multiply the completion coefficient of each component in the 3D model of the site for that day by the construction difficulty level and sum them up. The ratio of the sum to the total construction difficulty level is the construction progress value for that day.

[0025] Preferably, the construction efficiency for a given day is obtained based on the difference between the construction progress value of one day and the previous day, as well as the number of workers and the construction time for that day, including:

[0026] The difference between the construction progress value of the current day and the previous day is compared with the number of construction workers on the current day to obtain the average construction efficiency of the construction workers; the average construction efficiency of the construction workers is compared with the construction time on the current day to obtain the construction efficiency of the construction workers per unit time, which is the construction efficiency of the current day.

[0027] Preferably, the number of days required to complete the construction is predicted using the daily construction efficiency and daily construction time up to the present, including:

[0028] The average construction efficiency is obtained by averaging the daily construction efficiency up to the present; the average construction time is obtained by averaging the daily construction time up to the present; the inverse of the product of the average construction efficiency and the set number of construction workers is compared with the average construction time to obtain the number of days required to complete the construction.

[0029] The embodiments of the present invention have at least the following beneficial effects: In steel plant buildings, since the difficulty of construction of different components varies, the time cost spent on construction also varies. Therefore, in order to achieve more accurate supervision of the time required for construction and the construction efficiency during the actual construction process of steel plant buildings. This application collects daily point cloud data and image data to construct daily on-site 3D models. Then, it analyzes the edges of each component in the building information model (BIM) of the steel structure factory to obtain surface complexity feature values ​​between components. Combined with the design volume, it constructs a metric distance between two components, thereby obtaining more accurate clustering results. Furthermore, it evaluates the construction difficulty of components within a cluster to obtain the degree of construction difficulty for each component. Based on the model constructed during actual construction and the components in the BIM model, it performs association matching to obtain the completion coefficient of the matched components. Then, it combines the construction difficulty of the components and the sum of construction difficulty to obtain the daily construction efficiency. Combining the construction difficulty of the components improves the rationality of the construction efficiency. Finally, using the daily construction efficiency and daily construction time up to the current date, it predicts the number of days required to complete the construction, enabling prediction of the actual subsequent construction period and supervision of construction to ensure the efficiency of actual construction. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart illustrating a BIM-based data processing method for modeling steel structure factory buildings is provided in this embodiment of the invention. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a BIM-based steel structure factory building modeling data processing method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] The following description, in conjunction with the accompanying drawings, details a specific scheme for a BIM-based steel structure factory building modeling data processing method provided by the present invention.

[0035] The main application scenario of this invention is: during the construction of steel structure factory buildings, it is necessary to monitor the construction process in real time, predict the time required for the factory building to be completed, and make timely adjustments to prevent delays in the construction period.

[0036] Please see Figure 1 The diagram illustrates a method flowchart for data processing of steel structure factory building modeling based on BIM technology, provided by an embodiment of the present invention. The method includes the following steps:

[0037] Step S1: After the construction is completed each day, obtain the point cloud data and image data of the construction site to build a 3D model of the site for each day; obtain the design volume and edge of each component in the building information model of the steel structure factory.

[0038] To obtain daily construction progress data, it is necessary to model the construction site in real time after each day's construction is completed. Specifically, after each day's construction is completed, high-density point cloud data and image data of the steel structure factory construction site are captured using laser scanners and drones. Further data preprocessing (filtering, registration, stitching, etc.) is then performed on the acquired point cloud data and image data to obtain preprocessed point cloud data and image data.

[0039] By using structured light, stereo vision and other algorithms, combined with preprocessed point cloud data and image data acquired daily, a 3D model of the site is constructed for each day. The generated model is then subjected to noise filtering, edge extraction and refinement to ensure that it matches the actual structure, providing an accurate basis for subsequent comparisons. At the same time, the number of workers and the construction time are recorded each day during actual construction to facilitate subsequent analysis.

[0040] Before construction begins, a Building Information Model (BIM) of the steel structure factory building is designed. Using this BIM model, parameters such as the dimensions and processing flow of the components required during actual construction can be obtained. While the actual steel structure factory building is constructed based on the BIM model, the construction time may vary depending on the complexity of processing different components.

[0041] Therefore, based on the building information model of the steel structure factory building, the design volume of each component, the edges of the components, and the number of edges are obtained.

[0042] Step S2: Obtain the surface complexity feature value of the component based on its edge; use the design volume and surface complexity feature value of any two components to perform clustering to obtain a cluster.

[0043] In order to combine the relevant parameters and data of the components in the BIM model of the steel plant with relevant data in the actual construction process, so as to monitor the construction progress and predict the construction period required in the actual steel plant construction process, it is necessary to analyze the complex structure of the component surface.

[0044] Therefore, each edge of the component is treated as a curve, and the curvature is obtained by using the curvature calculation formula. This curvature is the curvature of the edge, which represents the degree of change of the edge.

[0045] Further, the surface complexity feature value of a component is calculated based on the curvature of all its edges. Specifically, the curvature of all its edges is averaged to obtain the average curvature; the absolute value of the difference between the curvature of each edge in the component and the average curvature is calculated and averaged to obtain the curvature consistency; the curvature consistency is mapped using an exponential function with the natural constant as the base and multiplied by the average curvature to obtain the surface complexity feature value of the component.

[0046] The specific calculation formula is as follows:

[0047]

[0048] Where G represents the surface complexity feature value of a component. Let $\frac{ ... i This represents the curvature of the i-th edge of the component. This represents the average of the absolute values ​​of the differences between the curvature of the i-th edge in the currently analyzed component and the average curvature of the corresponding edge of the current component. The larger this value is, the worse the consistency of the edge curvature of the current component is, and the greater the complexity of the surface may be. exp is used to map the value in the parentheses to a value greater than 0 to avoid errors caused by the value being 0.

[0049] Furthermore, clusters are obtained by using the design volume and surface complexity feature values ​​of any two components.

[0050] The surface complexity characteristic value of any component can be obtained through the above calculations. Therefore, for components with similar shape complexity and volume, the difficulty and time required for processing them are likely to be similar.

[0051] Therefore, in order to accurately estimate and classify the difficulty of subsequent component construction, it is necessary to analyze the differences in shape characteristics for any two components. Specifically, the absolute value of the difference between the surface complexity characteristic values ​​of the two components is normalized to obtain the surface difference; the absolute value of the difference between the number of edges of the two components is normalized to obtain the edge number difference; and the surface difference and edge number difference are added together to obtain the shape characteristic difference value of the two components.

[0052] The formula for calculating the difference in external features is:

[0053] DG(a,b)=norm(|G a -G b |)+norm(|g a -g b |),

[0054] Where DG(a, b) represents the difference in shape features between the a-th and b-th components, norm represents the normalization operation, and G... a and G b Let g represent the surface complexity feature values ​​of the a-th and b-th components, respectively. a and g b Let norm(|G) represent the number of edges of the a-th and b-th components, respectively. a -G b |) represents surface difference. The larger the value, the greater the difference in surface complexity between the two components, and the greater the potential difference in their shapes. norm(|g) a -g b |) represents the difference in the number of edges, indicating the difference in the overall shape of their surfaces. The larger this value is, the greater the difference in the shape of the two components may be.

[0055] Finally, clusters are obtained by clustering based on the differences in design volume and shape characteristics between any two components. Specifically, the distance between two components is calculated based on the difference in design volume and shape characteristics between the two components; clusters are obtained by clustering the components based on the distance between any two components.

[0056] The formula for calculating the distance between two components is:

[0057]

[0058] d(a, b) represents the distance used for classification between the a-th and b-th components, V a and V bLet A and B represent the design volumes of the a-th and b-th components, respectively. DG(a,b) represents the difference in shape characteristics between the a-th and b-th components. The closer the differences in volume and shape characteristics between these two components, i.e., the smaller their classification distance, the more similar their construction difficulty will be when building the steel plant, and the more likely they are to be classified into the same category. Thus, all components in the steel structure plant are classified, resulting in multiple clusters. Preferably, the clustering algorithm used in this embodiment is Mean Shift, a well-known technique that will not be elaborated upon here.

[0059] Step S3: Evaluate the construction difficulty of the components in a cluster to obtain the construction difficulty of each component in the cluster; obtain the sum of the construction difficulty of all components.

[0060] After clustering all components in the BIM model of the steel structure factory building, it is necessary to assess the construction difficulty of the components in each cluster.

[0061] Specifically, experts evaluate and score the ease of construction of the components at the cluster center of a cluster, and the average score is used as the construction difficulty level of that cluster. The score ranges from 0 to 1, with an increment of 0.1.

[0062] For example, after clustering, clusters such as H-shaped steel columns and steel corridor trusses are obtained. Then, experts analyze any central component of these clusters as a representative example. For instance, the central component of the H-shaped steel column cluster is selected. Experts comprehensively evaluate its surface complexity, processing steps, processing time, and installation difficulty, assigning scores. The average score from multiple experts represents the construction difficulty level of that cluster. For example, the H-shaped steel column cluster has the lowest construction difficulty level compared to other clusters, perhaps 0.1, while the steel corridor truss cluster has a higher construction difficulty level, perhaps 0.3.

[0063] Furthermore, the construction difficulty of all components in the Building Information Model (BIM) is calculated. Specifically, the construction difficulty of a component within a cluster is multiplied by the corresponding construction difficulty of the entire cluster to obtain the sum of the difficulty levels for that cluster. The sums of the difficulty levels for all clusters are then added together to obtain the total construction difficulty of all components. The specific calculation formula is as follows:

[0064]

[0065] Where D represents the total difficulty of construction of all components, M represents the number of clusters, and N represents the total difficulty of construction of all components. s DI represents the number of components in the s-th cluster.s N represents the difficulty of constructing the s-th cluster. s ×DI s Let represent the sum of the difficulty levels corresponding to the s-th cluster. From this, we can obtain the construction difficulty level of each component and the sum of the construction difficulty levels of all components.

[0066] Step S4: Obtain the actual completed volume of the component based on the 3D model of the site for one day, and compare it with the design volume to obtain the completion coefficient of the component; obtain the construction progress value of the day based on the construction difficulty and completion coefficient of each component in the 3D model of the site for one day, and the sum of construction difficulty.

[0067] During actual construction, component processing may be incomplete. Therefore, it is necessary to compare the daily on-site 3D model with the building information model of the steel structure workshop to identify the components in the daily on-site 3D model. Components in the daily on-site 3D model represent those under construction or already completed, reflecting the construction progress. Components not yet under construction are not shown in the corresponding 3D model for each day. Furthermore, the actual completed volume of the components is obtained from the daily on-site 3D model, and compared with the design volume to obtain the component's completion coefficient. The ratio of a component's actual completed volume in the on-site 3D model to its design volume is the component's completion coefficient.

[0068] For components located in the 3D model on site, the formula for calculating their completion coefficient is as follows:

[0069]

[0070] Among them, P i This represents the completion coefficient of the i-th component in the on-site 3D model. This represents the actual completed volume of the i-th component in the on-site 3D model. This represents the design volume of the i-th component in the site 3D model within the BIM model. This represents the ratio of the volume of the i-th component in the on-site 3D model to the design volume of the i-th component in the BIM model, obtained through point cloud data during the actual construction process. If this value equals 1, then the component has been completed.

[0071] Traditional construction progress calculations divide completed work into total work volume. However, this method doesn't consider the complexity of components, leading to overly optimistic progress assessments when some challenging key components are incomplete. For example, if 10 simple components are completed in a day, traditional calculations might consider 10% completion, but the core construction work may not have truly begun. Conversely, for more complex components, the completion rate might be lower. However, during construction, the staff remained diligent, weighting the progress based on the difficulty of each component during construction. This method of calculating progress using difficulty weights more accurately reflects the actual progress and avoids misjudging the actual construction progress due to the perceived difficulty of components at different times.

[0072] For any given day, the construction progress value for that day is obtained based on the construction difficulty of each component in the 3D model of the site, the sum of the construction difficulty levels, and the completion coefficient of each component. Specifically, the completion coefficient and construction difficulty of each component in the 3D model of the site for that day are multiplied and summed to obtain the summation result. The ratio of the summation result to the sum of the construction difficulty levels is the construction progress value for that day.

[0073] The specific calculation formula is as follows:

[0074]

[0075] Where F represents the construction progress value per day, P i DI represents the completion coefficient of the i-th component in the 3D model of the site on that day. i This represents the construction difficulty of the i-th component in the 3D model of the site on that day. The construction difficulty of the constructed component is the construction difficulty of its respective cluster. i This indicates the number of components in the on-site 3D model for that day, which is the number of components in the on-site 3D model that can correspond to the components in the BIM model. D represents the total difficulty of construction.

[0076] This represents the construction progress coefficient as of that day, calculated by multiplying the construction completion coefficient of the i-th component in the model by its corresponding construction difficulty level, and then dividing this coefficient by the sum of the construction difficulty levels of all components. The closer this value is to 1, the closer the construction is to completion; the closer it is to 0, the less complete it is. A value of 1 indicates completion, and a value of 0 indicates that construction has not even started, meaning the construction progress is not yet zero. Therefore, the construction progress value for each day can be obtained after construction is completed.

[0077] Step S5: Obtain the construction efficiency for the day based on the difference between the construction progress value of the day and the previous day, as well as the number of construction workers and the construction time for the day; predict the number of days required to complete the construction by using the construction efficiency and construction time of each day up to the present.

[0078] After obtaining the daily construction progress value in step S4, the construction speed of the current day can be analyzed based on the difference between the current day's and the previous day's construction progress.

[0079] The construction efficiency for a given day is obtained by taking the difference between the construction progress of the current day and the previous day, as well as the number of workers and the construction time for that day. Specifically, the average construction efficiency of the workers is obtained by comparing the difference between the construction progress of the current day and the previous day with the number of workers on that day; the average construction efficiency of the workers is then compared with the construction time for that day to obtain the construction efficiency of the workers per unit time, which is the construction efficiency for that day.

[0080] The specific calculation formula is as follows:

[0081]

[0082] Among them, X j F represents the construction efficiency of the construction workers per unit time on day j. j and F j-1 P represents the construction progress values ​​on day j and day j-1, respectively. j and T j These represent the number of workers and the construction time on day j, respectively. This represents the average construction efficiency of the construction workers on day j. This represents the completion progress of construction on day j divided by the construction time and number of workers, then divided by the construction time of the current day, thus obtaining the efficiency of construction workers per unit time.

[0083] The above calculations can yield the daily construction efficiency per unit time for each construction worker. However, since the daily construction efficiency per unit time may vary, further analysis of the current daily construction efficiency and construction time is necessary for more accurate predictions.

[0084] Furthermore, the number of days required to complete the construction is predicted using the daily construction efficiency and daily construction time up to the present. Specifically, the daily construction efficiency up to the present is averaged to obtain the average construction efficiency; the daily construction time up to the present is averaged to obtain the average construction time; the inverse of the product of the average construction efficiency and the set number of construction workers is compared with the average construction time to obtain the number of days required to complete the construction.

[0085] Its specific prediction model is as follows:

[0086]

[0087] Where Y(P) represents the number of days required to complete the construction when the number of construction workers is set to P. denoted as average construction efficiency, P represents the set number of construction workers (since the number of construction workers is uncertain and may need to be adjusted later), and W represents the average construction time per day. This represents the total construction time required when the number of workers is P, under average construction efficiency. This represents the total construction time required when the number of workers is P, divided by the average daily construction time, to obtain the estimated number of days for construction. From this, the predicted number of days to complete the construction can be obtained.

[0088] Theoretical construction period forecasts typically only provide preliminary planning and often lack feedback on uncertainties during construction and actual implementation. Therefore, a construction period forecast based on actual construction efficiency allows for the rational allocation of construction personnel according to the actual construction situation, balancing construction time and financial costs. Furthermore, it enables the monitoring of construction efficiency based on the estimated number of days required for completion. For example, as construction progresses, an increase in the total number of days needed may indicate a decrease in actual construction efficiency, facilitating the monitoring of construction progress and ensuring both efficiency and minimal delays.

[0089] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0090] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data processing method for modeling steel structure factory buildings based on BIM technology, characterized in that, The method includes: After each day's construction is completed, point cloud data and image data of the construction site are acquired to build a 3D model of the site for that day; the design volume and edges of each component in the building information model of the steel structure factory are also acquired. Obtain the surface complexity feature value of the component based on its edges; use the design volume and surface complexity feature value of any two components to perform clustering to obtain clusters; The construction difficulty of components within a cluster is evaluated to obtain the construction difficulty of each component within that cluster; the total construction difficulty of all components is then obtained. The actual completed volume of the components is obtained from the 3D model of the site over a day, and the completion coefficient of the components is obtained by comparing it with the design volume. The construction progress value of the day is obtained from the construction difficulty and completion coefficient of each component in the 3D model of the site over a day, as well as the sum of the construction difficulty. The construction efficiency for a given day is obtained by taking into account the difference between the construction progress value of the previous day and the number of workers and the construction time for that day. The number of days required to complete the construction is then predicted by using the construction efficiency and construction time of each day up to the present.

2. The data processing method for steel structure factory building modeling based on BIM technology according to claim 1, characterized in that, The step of obtaining the surface complexity feature value of a component based on its edges includes: Treat one edge of the component as a curve, use the curvature calculation formula to obtain the curvature of the curve, and use it as the curvature of the edge; calculate the surface complexity feature value of the component based on the curvature of all edges.

3. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 2, characterized in that, The calculation of the surface complexity feature value of a component based on the curvature of all its edges includes: The curvature average is obtained by averaging the curvature of all edges of a component; the absolute value of the difference between the curvature of each edge of the component and the curvature average is calculated and averaged to obtain the curvature consistency; the curvature consistency is mapped using an exponential function with the natural constant as the base and multiplied by the curvature average to obtain the surface complexity characteristic value of the component.

4. The data processing method for steel structure factory building modeling based on BIM technology according to claim 1, characterized in that, The method of obtaining clusters by clustering using the design volume and surface complexity feature values ​​of any two components includes: The surface difference is obtained by normalizing the absolute value of the difference between the surface complexity feature values ​​of the two components; the edge number difference is obtained by normalizing the absolute value of the difference between the number of edges of the two components; the surface difference and the edge number difference are added together to obtain the shape feature difference value of the two components; and clustering is performed on any two components based on their design volume and shape feature difference values ​​to obtain a cluster.

5. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 4, characterized in that, The method of clustering based on the differences in design volume and shape characteristics of any two components to obtain clusters includes: The distance between two components is calculated based on the difference in their design volume and the difference in their shape characteristics; clusters are then formed by clustering the components based on the distance between any two components.

6. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 1, characterized in that, The method of evaluating the construction difficulty of components within a cluster to obtain the degree of construction difficulty for each component within that cluster includes: The construction difficulty of the components of the cluster center of a cluster is evaluated and scored by various experts, and the average score of each expert is used as the construction difficulty of the cluster.

7. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 1, characterized in that, The process of obtaining the total difficulty level of construction for all components includes: Multiply the construction difficulty of a component in a cluster by the construction difficulty of the corresponding cluster to obtain the sum of the construction difficulty of that cluster; sum the construction difficulty of all clusters to obtain the total construction difficulty of all components.

8. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 1, characterized in that, The process of obtaining the construction progress value for a given day based on the construction difficulty and completion coefficient of each component in the 3D model of the site, and the sum of the construction difficulty levels, includes: Multiply the completion coefficient of each component in the 3D model of the site for that day by the construction difficulty level and sum them up. The ratio of the sum to the total construction difficulty level is the construction progress value for that day.

9. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 1, characterized in that, The method of obtaining the construction efficiency for a given day based on the difference between the construction progress value of one day and the previous day, as well as the number of workers and the construction time for that day, includes: The average construction efficiency of the construction workers is obtained by comparing the difference between the construction progress value of the current day and the previous day with the number of construction workers on the current day; the construction efficiency of the construction workers is obtained by comparing the average construction efficiency of the construction workers with the construction time on the current day, which is the construction efficiency of the construction workers per unit time.

10. The data processing method for modeling steel structure factory buildings based on BIM technology according to claim 1, characterized in that, The method of predicting the number of days required to complete construction by utilizing the daily construction efficiency and daily construction time up to the present includes: The average construction efficiency is obtained by averaging the daily construction efficiency up to the present; the average construction time is obtained by averaging the daily construction time up to the present; the inverse of the product of the average construction efficiency and the set number of construction workers is compared with the average construction time to obtain the number of days required to complete the construction.

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