BIM model loading and processing method and system for construction progress record

Abstract

The present application proposes a BIM model loading and processing method and system for construction progress records, which belongs to the field of BIM model technology. Among them, the method includes: receiving the latest uploaded construction progress information, combining the historical construction progress information sequence, analyzing and obtaining the rationality coefficient; screening and obtaining abnormal construction progress information according to the theoretical historical construction progress information sequence of the construction progress information index; analyzing the omission rate of abnormal construction progress information, performing correction calculation on the rationality coefficient, and obtaining the corrected rationality coefficient; obtaining the node status parameters of multiple nodes, configuring the loading accuracy information in combination with the corrected rationality coefficient, loading the BIM model and marking the loaded BIM module. By intelligently analyzing the rationality of the construction progress and dynamically correcting the loading accuracy, the actual construction progress can be accurately reflected, and the visualization accuracy of the BIM model and the ability to identify construction management anomalies can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of BIM models, and in particular to a BIM model loading and processing method and system for construction progress records. Background Art

[0002] Building Information Modeling (BIM) technology is widely used in progress management during the construction phase. It displays the construction progress in a visual way. For example, completed components are marked in green and unconstructed components are marked in gray, providing an intuitive monitoring method for project management.

[0003] However, in actual construction, there is often a time lag in uploading progress information for each construction node. Construction workers may fail to upload progress in a timely manner due to busy work or equipment limitations. Progress information for some construction steps may be omitted from uploading, resulting in a discrepancy between the status displayed in the BIM model and the actual site. These issues prevent the BIM model from accurately reflecting the actual construction progress, resulting in low visual reference value of the BIM model and insufficient ability to identify construction management anomalies. Summary of the Invention

[0004] The present invention aims to solve the technical problems in the prior art where BIM model loading cannot accurately reflect the actual construction progress and the visual reference value is low due to untimely uploading or omission of construction progress information. A BIM model loading and processing method and system for construction progress records are provided to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In the first aspect, the present invention provides a BIM model loading and processing method for construction progress records, including: receiving the latest uploaded construction progress information, combining the historical construction progress information sequence within the most recent preset time range, and analyzing to obtain the rationality coefficient of the construction progress information; according to the construction progress information, indexing to obtain a theoretical historical construction progress information sequence, and when it is inconsistent with the historical construction progress information sequence, screening to obtain at least one abnormal construction progress information; analyzing at least one omission rate of the at least one abnormal construction progress information, performing correction calculation on the rationality coefficient, and obtaining a corrected rationality coefficient; obtaining multiple node status parameters of multiple nodes, combining the corrected rationality coefficient, configuring multiple loading accuracy information, loading the BIM model according to the construction progress information, and marking the loaded BIM module corresponding to the construction progress information by the corrected rationality coefficient.

[0007] In the second aspect, the present invention provides a BIM model loading and processing system for construction progress records, including: a rationality analysis module for receiving the latest uploaded construction progress information, combining the historical construction progress information sequence within the most recent preset time range, and analyzing to obtain the rationality coefficient of the construction progress information; an abnormal progress screening module for indexing to obtain a theoretical historical construction progress information sequence according to the construction progress information, and screening to obtain at least one abnormal construction progress information when it is inconsistent with the historical construction progress information sequence; a correction coefficient calculation module for analyzing at least one omission rate of the at least one abnormal construction progress information, performing correction calculation on the rationality coefficient, and obtaining a corrected rationality coefficient; a BIM module labeling module for obtaining multiple node status parameters of multiple nodes, combining the correction rationality coefficient, configuring multiple loading accuracy information, loading the BIM model according to the construction progress information, and labeling the loading BIM module corresponding to the construction progress information through the correction rationality coefficient.

[0008] The beneficial effects of the present invention are:

[0009] Receive the latest uploaded construction progress information, combine it with the historical construction progress information sequence within the recent preset time range, analyze and obtain the rationality coefficient of the construction progress information, and judge whether the currently uploaded construction progress is reasonable based on historical data, providing a basic basis for subsequent processing; according to the construction progress information, index to obtain the theoretical historical construction progress information sequence, and when it is inconsistent with the historical construction progress information sequence, filter and obtain at least one abnormal construction progress information, so as to identify the progress information that may be missed or abnormal in sequence by comparing the theoretical progress and the actual progress; analyze at least one omission rate of at least one abnormal construction progress information, and calibrate the rationality coefficient. Positive calculation is performed to obtain the correction rationality coefficient, and the rationality assessment is dynamically adjusted through quantitative analysis of omissions. The larger the omission rate, the higher the degree of construction management abnormality, and the rationality coefficient is reduced accordingly; multiple node status parameters of multiple nodes are obtained, combined with the correction rationality coefficient, multiple loading accuracy information is configured, and the BIM model is loaded according to the construction progress information. The loading BIM module corresponding to the construction progress information is marked by the correction rationality coefficient. This can not only configure the appropriate loading accuracy according to the differences in equipment computing power of different nodes, but also provide more accurate detailed analysis when the degree of construction management abnormality is large, thereby realizing intelligent BIM model loading and visualization.

[0010] Through the above technical solution, this application can effectively solve the problem of untimely uploading of construction progress information and omissions in the existing technology, which leads to distortion of BIM model visualization, realize intelligent analysis and dynamic correction of construction progress, improve the accuracy and practicality of BIM model loading, thereby improving the ability to identify construction management anomalies and providing more reliable decision-making support for construction management. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flow chart of a BIM model loading and processing method for construction progress records provided by the present invention;

[0012] Figure 2 This is a structural schematic diagram of a BIM model loading and processing system for construction progress records provided by the present invention.

[0013] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0014] Rationality analysis module 11, abnormal progress screening module 12, correction coefficient calculation module 13, BIM module annotation module 14. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0016] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0017] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0018] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a BIM model loading and processing method for construction progress records, including:

[0019] S1. Receive the latest uploaded construction progress information, combine it with the historical construction progress information sequence within a recent preset time range, and analyze and obtain the rationality coefficient of the construction progress information.

[0020] Specifically, the system first receives the latest uploaded construction progress information from the construction site. This progress information includes specific construction steps and corresponding construction progress timestamps. The construction progress steps identify the currently completed construction content, such as column casting, beam and slab installation, or stair construction on a specific floor. The construction progress timestamp records the completion time of the construction step.

[0021] After receiving the latest construction progress information, the system automatically retrieves and obtains a sequence of historical construction progress information within the most recent preset timeframe. This sequence includes all uploaded construction progress records within the preset timeframe, forming a chronologically ordered set of construction progress data. The preset timeframe can be set based on the specific characteristics of the project, for example, the last 7, 15, or 30 days.

[0022] Based on the acquired historical construction progress information sequence, a rationality analysis is performed on the currently received construction progress information. This analysis process evaluates the time rationality of the current construction progress by comparing it with the historical construction progress pattern, that is, determining whether the current construction progress is consistent with the expected construction time schedule. Specifically, it analyzes whether the current construction progress is completed on time, whether there are any delays, or whether it is completed ahead of schedule, and quantifies the degree of this time deviation. Through the above analysis process, a rationality coefficient is output, which is used to quantify the rationality of the current construction progress information. The rationality coefficient ranges from 0 to 1. The closer the value is to 1, the more consistent the current construction progress is with the expected construction schedule and the higher the rationality. The closer the value is to 0, the greater the deviation between the current construction progress and the expected schedule, and the more obvious anomalies exist.

[0023] By analyzing and obtaining the rationality coefficient of the latest uploaded construction progress information, a reliable data basis is provided for subsequent BIM model loading, avoiding BIM model loading deviations caused by erroneous or unreasonable construction progress information, thereby improving the accuracy of BIM model loading.

[0024] S2. According to the construction progress information, obtain a theoretical historical construction progress information sequence by indexing, and when the theoretical historical construction progress information sequence is inconsistent with the historical construction progress information sequence, obtain at least one abnormal construction progress information by screening.

[0025] Specifically, the system first automatically indexes and retrieves the corresponding theoretical historical construction progress information based on the currently received construction progress information. This theoretical historical construction progress information sequence is an idealized construction progress sequence based on the standard construction process and time schedule preset in the BIM model. It reflects the standard time nodes and execution order that each construction step should follow under normal construction conditions. By matching the construction progress steps in the current construction progress information, the corresponding theoretical historical construction progress information sequence is retrieved from the preset theoretical construction progress database.

[0026] Subsequently, the theoretical historical construction progress information sequence obtained is compared and analyzed with the actual historical construction progress information sequence obtained in step S1. The comparison process performs a consistency check on the construction progress step sequence and the corresponding time schedule in the two sequences to determine whether the actual construction progress is strictly executed in accordance with the theoretically designed construction process and time nodes. When an inconsistency is detected between the theoretical historical construction progress information sequence and the actual historical construction progress information sequence, it indicates that abnormal situations such as omission of progress upload, construction sequence adjustment or time node deviation may have occurred in the actual construction process. For the detected inconsistencies, further screen and identify specific abnormal points to obtain at least one abnormal construction progress information. These abnormal construction progress information include the specific construction steps and their related time information that deviate from the theoretical schedule during the actual construction process, providing an accurate data source for subsequent abnormal analysis and correction processing.

[0027] By comparing and analyzing theoretical and historical construction progress information sequences, it is possible to accurately identify abnormal situations in the construction process, including progress omissions, sequence disorders or time deviations, providing anomaly detection functions for construction progress management and improving the accuracy and reliability of construction progress monitoring.

[0028] S3. Analyze at least one omission rate of the at least one abnormal construction progress information, perform correction calculation on the rationality coefficient, and obtain a corrected rationality coefficient.

[0029] Specifically, at least one identified abnormal construction progress information is first analyzed in depth to calculate at least one corresponding omission rate. This omission rate is calculated by analyzing the construction progress record data of multiple similar buildings, extracting the historical records of abnormal construction progress steps contained in the abnormal construction progress information, and forming an abnormal construction progress step dataset. Within this dataset, the proportion of missed records for abnormal construction progress steps is statistically analyzed. Specifically, the frequency of such abnormal construction progress steps failing to be uploaded in a timely manner under similar construction conditions is calculated, thereby obtaining the corresponding omission rate. If multiple abnormal construction progress information items exist, the omission rates for each are calculated separately, and the combined omission rate is obtained by calculating the average.

[0030] Subsequently, the obtained rationality coefficient is corrected based on the calculated omission rate. During the correction calculation process, the omission correction coefficient is first calculated based on the omission rate. The omission correction coefficient is obtained by subtracting the omission rate from 1, that is, omission correction coefficient = 1-omission rate. The larger the omission rate, the smaller the omission correction coefficient, reflecting a higher degree of incompleteness in the uploaded construction progress information. The omission correction coefficient is used to correct the original rationality coefficient, and the corrected rationality coefficient is obtained through corresponding mathematical operations. For example, the corrected rationality coefficient is obtained by multiplying the rationality coefficient by the omission correction coefficient, that is, the corrected rationality coefficient = rationality coefficient × omission correction coefficient; for example, when the rationality coefficient is 0.8 and the omission rate is 0.3, the omission correction coefficient is 0.7, and the corrected rationality coefficient is 0.8×0.7=0.56. The corrected rationality coefficient can more accurately reflect the credibility of the actual construction progress information than the original rationality coefficient. The greater the omission rate, the smaller the omission correction coefficient. The smaller the corrected rationality coefficient, the more unreasonable the construction progress upload and loading, and the greater the degree of construction management abnormality.

[0031] By analyzing the omission rate of abnormal construction progress information and correcting the rationality coefficient, it is possible to effectively compensate for the assessment deviation caused by the omission of uploading construction progress information, improve the accuracy of the credibility assessment of construction progress information, and provide a more reliable reference basis for the subsequent BIM model loading accuracy configuration.

[0032] S4. Acquire multiple node status parameters of multiple nodes, configure multiple loading accuracy information in combination with the correction rationality coefficient, load the BIM model according to the construction progress information, and mark the loading BIM module corresponding to the construction progress information through the correction rationality coefficient.

[0033] Specifically, we first obtain multiple node status parameters for multiple nodes, where each node corresponds to a different operator on the construction site, such as a foreman or construction manager with different divisions of labor. The node status parameters primarily include the computing power parameters of the equipment used by each node, which reflects the computing and processing capabilities of the node device. For example, different models of computers, mobile phones, or tablet devices have different processor performance, memory capacity, and graphics processing capabilities. By comparing the computing power parameters of each node with the standard node status parameters, we calculate the ratios to obtain multiple state loading coefficients, which are used to quantify the relative computing power of each node device.

[0034] Subsequently, the updated loading coefficient is calculated based on the obtained correction rationality coefficient. The updated loading coefficient is obtained by subtracting the correction rationality coefficient from 1, that is, the updated loading coefficient = 1-correction rationality coefficient. The smaller the correction rationality coefficient, the larger the updated loading coefficient, indicating that higher loading accuracy is required to facilitate the analysis of abnormal details in construction progress management. The state loading coefficient of each node is comprehensively calculated with the updated loading coefficient to obtain multiple final loading coefficients. According to the preset loading coefficient interval that each loading coefficient falls into, the corresponding multiple loading accuracy information is classified and obtained, where different loading coefficient intervals correspond to different levels of loading accuracy configuration.

[0035] Based on the configured multiple loading accuracy information, differentiated BIM model loading is performed at each node according to the construction progress information. For nodes with lower computing power, when the correction rationality coefficient is large, a lower loading accuracy is configured, and only major BIM components such as columns and beams are loaded, without loading small components such as bolts and connectors, in order to reduce the equipment's computing burden; for cases where the correction rationality coefficient is small, a higher loading accuracy is configured, and a complete BIM model including small components is loaded, which facilitates detailed analysis of abnormal construction progress management situations. At the same time, the loading BIM modules corresponding to the construction progress information are marked by the correction rationality coefficient. For example, BIM modules such as columns, beams, and plates are color-coded or status-marked according to their construction completion status and credibility.

[0036] Through comprehensive consideration of node status parameters and correction rationality coefficients, intelligent configuration of BIM model loading accuracy is achieved, which not only ensures the smooth operation of different computing power equipment, but also provides sufficient detail analysis capabilities in the event of abnormal construction progress management, accurately reflects the actual construction progress, and improves the visualization accuracy of the BIM model and the ability to identify construction management anomalies.

[0037] Furthermore, the latest uploaded construction progress information is received, and combined with the historical construction progress information sequence within the recent preset time range, a rationality coefficient of the construction progress information is obtained through analysis, including:

[0038] S11. Receive the latest uploaded construction progress information, wherein the construction progress information is uploaded through any one of the multiple secondary nodes and the multiple nodes, and includes a construction progress step and a construction progress timestamp;

[0039] S12. Acquire a historical construction progress information sequence recorded during a recent preset time range, wherein the historical construction progress information sequence includes a timestamp;

[0040] S13. Analyze and obtain a rationality coefficient of the construction progress information based on the historical construction progress information sequence.

[0041] In a feasible implementation, first, the construction progress information of the latest uploaded record is received. The construction progress information can be uploaded through multiple secondary nodes and any one of the multiple nodes at the construction site, where the secondary node can be a mobile device used by on-site workers, and the node is a management terminal used by project managers or foremen. The uploaded construction progress information mainly includes two elements, namely the construction progress step and the construction progress timestamp. The construction progress step describes in detail the specific construction work content currently completed, such as the specific processes such as steel bar binding, concrete pouring, and formwork removal on a certain floor; the construction progress timestamp accurately records the completion time of the construction step, providing an accurate time benchmark for subsequent time rationality analysis.

[0042] Next, the system automatically retrieves a sequence of historical construction progress information uploaded within the most recent preset timeframe. This sequence is an ordered collection of multiple historical construction progress records. Each record in the sequence includes a corresponding timestamp, identifying the time of occurrence of each historical construction progress. The preset timeframe takes into account the construction project's cycle characteristics and progress management requirements, and is typically set to a reasonable time window that reflects the regularity of construction progress, such as the last week, two weeks, or a month.

[0043] Subsequently, a rationality analysis of the current construction progress information is performed based on the acquired historical construction progress information sequence. This analysis process is achieved by constructing a rationality analyzer. Using machine learning techniques, the system uses BIM model loading data from similar buildings as training samples, including a set of sample historical construction progress information sequences, a set of sample construction progress information, and a corresponding set of sample rationality coefficients. The trained rationality analyzer receives the current construction progress information and historical construction progress information sequences as input, and outputs the corresponding rationality coefficient through analysis. This coefficient quantitatively reflects the rationality of the current construction progress information relative to historical construction patterns.

[0044] Through the above steps, an accurate quantitative assessment of the rationality of construction progress information can be achieved, providing a reliable data basis for subsequent anomaly detection and BIM model loading accuracy configuration, and improving the intelligent level of construction progress management.

[0045] Furthermore, according to the historical construction progress information sequence, the rationality coefficient of the construction progress information is obtained by analysis, including:

[0046] S131. Load data based on the BIM model of similar buildings, collect a sample historical construction progress information sequence set and a sample construction progress information set, and mark and obtain a sample rationality coefficient set. The rationality coefficient is greater than or equal to 0 and less than or equal to 1.

[0047] S132. Use machine learning to build a rationality analyzer;

[0048] S133, using the sample historical construction progress information sequence set, the sample construction progress information set, and the sample rationality coefficient set, training the rationality analyzer until convergence;

[0049] S134. Input the construction progress information and the historical construction progress information sequence into the rationality analyzer, identify and output to obtain a rationality coefficient.

[0050] In a preferred embodiment, first, sample data is collected and labeled based on the BIM model loading data of similar buildings. Specifically, first, a large amount of historical construction progress data is collected from multiple completed or ongoing construction projects of the same type to form a sample historical construction progress information sequence set and a sample construction progress information set. These sample data cover real construction progress records in different construction stages and under different construction conditions, providing a rich training data source for the machine learning model; then, the collected sample data are professionally labeled, and construction management experts assign a corresponding rationality coefficient to each sample based on the actual rationality of the construction progress to form a sample rationality coefficient set. The value range of all rationality coefficients is strictly limited to between 0 and 1, where 0 represents completely unreasonable and 1 represents completely reasonable.

[0051] Then, a rationality analyzer is constructed using machine learning techniques. This rationality analyzer utilizes a deep learning network architecture capable of processing complex time series data and multidimensional feature information. The rationality analyzer's network structure design considers the time series characteristics and multivariate correlations of construction progress information, utilizing a multi-layer neural network to achieve intelligent judgment and quantitative assessment of construction progress rationality. The rationality analyzer can employ a hybrid architecture combining a long short-term memory (LSTM) network with fully connected layers. For example, the input layer receives a multidimensional feature vector containing construction progress step codes and timestamps. The embedding layer converts the discrete construction steps into continuous feature representations. Time series features are then extracted using a two-layer LSTM network (128 neurons per layer). Feature fusion and regression output are then performed using a three-layer fully connected network (containing 64, 32, and 1 neurons, respectively). The output layer uses a sigmoid activation function to ensure that the rationality coefficient output ranges between 0 and 1.

[0052] Subsequently, the rationality analyzer is trained using the obtained set of sample historical construction progress information sequences, sample construction progress information sets, and sample rationality coefficient sets. The training process utilizes a supervised learning approach, using sample historical construction progress information sequences and sample construction progress information as input features and sample rationality coefficients as target outputs. The network parameters are continuously adjusted using a backpropagation algorithm. Training continues until the model converges, defined as when the loss function reaches a preset threshold or when the loss function changes less than a set value over multiple consecutive training cycles. The current construction progress information and historical construction progress information sequences are then used as input data for the trained rationality analyzer to perform inference calculations. Based on the learned rules for determining construction progress rationality, the rationality analyzer intelligently analyzes and extracts features from the input data, ultimately identifying and outputting the corresponding rationality coefficient, which accurately quantifies the rationality of the current construction progress information.

[0053] Through the construction and application of a rationality analyzer based on machine learning, an intelligent and automated evaluation of the rationality of construction progress information is achieved. Compared with traditional manual judgment methods, it has higher accuracy, consistency and processing efficiency, providing reliable technical support for construction progress management.

[0054] Furthermore, according to the construction progress information, a theoretical historical construction progress information sequence is obtained by indexing, and when the theoretical historical construction progress information sequence is inconsistent with the historical construction progress information sequence, at least one abnormal construction progress information is obtained by screening, including:

[0055] S21. Obtain a theoretical construction progress information sequence corresponding to the BIM model, and index a theoretical historical construction progress information sequence within a preset historical time range before a construction progress step in the construction progress information;

[0056] S22. Compare the theoretical historical construction progress information sequence and the theoretical historical construction progress step sequence and the historical construction progress step sequence in the historical construction progress information sequence. If there is any inconsistency, filter out at least one abnormal construction progress information corresponding to at least one inconsistent abnormal construction progress step.

[0057] In a preferred embodiment, first, a theoretical construction progress information sequence corresponding to the BIM model of the current construction project is obtained. The theoretical construction progress information sequence is a benchmark schedule determined based on the standard construction process and time schedule preset by the BIM model, reflecting the standard execution order and time nodes that each construction step should follow under ideal construction conditions. Subsequently, based on the construction progress step in the currently received construction progress information, an index search is performed in the theoretical construction progress information sequence to obtain the theoretical historical construction progress information sequence within the preset historical time range before the construction progress step. The setting of the preset historical time range takes into account the correlation and dependency of the construction processes, and usually covers the previous construction operation time period related to the current construction step, ensuring that the logical relationship of the construction progress can be fully reflected.

[0058] The obtained theoretical historical construction progress information sequence is then compared and analyzed in detail with the actual historical construction progress information sequence. This comparison process focuses on two key aspects: first, consistency verification between the theoretical historical construction progress step sequence and the historical construction progress step sequence, verifying whether the execution sequence of the actual historical construction steps matches the theoretically designed process flow; and second, matching analysis of corresponding time nodes, checking whether the actual historical construction schedule is consistent with the theoretical schedule. Detecting inconsistencies between the theoretical and actual historical construction progress indicates that the actual historical construction process deviated from the standard process, such as omitted construction steps, reversed process sequence, or time node deviations. For each identified inconsistency, the specific abnormal construction progress step is further screened and located, and at least one abnormal construction progress information corresponding to each abnormal step is obtained, including detailed information such as the specific content of the abnormal step, the time of occurrence, and the degree of deviation.

[0059] Through the systematic comparison and analysis of theoretical construction progress information sequences and historical construction progress information sequences, various abnormal situations in the construction process can be accurately identified, providing an accurate abnormal data basis for subsequent omission rate analysis and rationality coefficient correction, thereby improving the comprehensiveness and accuracy of construction progress anomaly detection.

[0060] Further, analyzing at least one omission rate of the at least one abnormal construction progress information, performing correction calculation on the rationality coefficient, and obtaining the corrected rationality coefficient, includes:

[0061] S31. Extracting, based on construction progress record data of multiple buildings of the same type, record data of at least one abnormal construction progress step within the at least one abnormal construction progress information to obtain at least one abnormal construction progress step data set;

[0062] S32, analyzing and screening the proportion of missed records of at least one abnormal construction progress step in the at least one abnormal construction progress step data set to obtain at least one abnormal missed rate, and calculating the mean to obtain the missed rate;

[0063] S33. Perform correction calculation on the rationality coefficient according to the omission rate to obtain a corrected rationality coefficient.

[0064] In a preferred embodiment, historical data extraction of abnormal construction progress steps is performed based on the construction progress records of multiple similar buildings. First, multiple similar buildings with similar characteristics to the current project are retrieved from the construction database. These projects are consistent with the current project in terms of building type, scale, structural form, etc., to ensure data comparability and reference value. Subsequently, for the at least one abnormal construction progress information identified, detailed record data of at least one abnormal construction progress step contained therein is extracted. By searching for identical or similar construction progress steps in similar building projects, information such as the execution records, upload status, and management status of these steps in historical projects is collected to form at least one abnormal construction progress step dataset, providing sufficient sample data for subsequent omission rate statistical analysis.

[0065] Then, an in-depth statistical analysis is performed on at least one abnormal construction progress step data set obtained. For each abnormal construction progress step in the abnormal construction progress step data set, the cases where the step was omitted in the historical projects are analyzed and screened out, that is, the frequency of such construction steps failing to upload progress information in a timely manner or missing records is counted. By calculating the ratio of the number of omitted records to the total number of records, the abnormal omission rate corresponding to each abnormal construction progress step is obtained. When there are multiple abnormal construction progress steps, the abnormal omission rate of each is calculated separately, and then the mean of all abnormal omission rates is calculated by arithmetic averaging, and finally a comprehensive omission rate is obtained. This omission rate reflects the typical omission degree of the current abnormal construction progress information in similar projects.

[0066] The obtained rationality coefficient is then corrected based on the calculated omission rate. The correction process first calculates the omission correction coefficient based on the omission rate, using the formula omission correction coefficient = 1 - omission rate. This ensures that the correction coefficient decreases with higher omission rates. The original rationality coefficient is then multiplied by the omission correction coefficient to obtain the corrected rationality coefficient: Corrected rationality coefficient = rationality coefficient × omission correction coefficient. This corrected rationality coefficient more accurately reflects the actual credibility of construction progress information and effectively compensates for assessment bias caused by historical omissions.

[0067] Through systematic analysis of the omission rate of abnormal construction progress information and precise correction of the rationality coefficient, a dynamic optimization evaluation of the credibility of construction progress information is achieved, which improves the accuracy and reliability of subsequent BIM model loading decisions.

[0068] Furthermore, the rationality coefficient is corrected and calculated based on the omission rate to obtain the corrected rationality coefficient, including:

[0069] S331, calculating an omission correction coefficient according to the omission rate;

[0070] S332. Use the omission correction coefficient to perform correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

[0071] In a preferred embodiment, an omission correction coefficient is calculated based on the calculated omission rate. The omission correction coefficient is calculated using a simple and effective linear relationship: omission correction coefficient = 1-omission rate. This calculation method ensures an inverse relationship between the omission correction coefficient and the omission rate: when the omission rate is 0, the omission correction coefficient is 1, indicating no omissions and no correction is required; when the omission rate is higher, the omission correction coefficient is lower, reflecting a higher degree of incompleteness in the uploaded construction progress information, requiring a larger downward correction of the rationality coefficient.

[0072] Subsequently, the original rationality coefficient was corrected using the calculated omission correction coefficient. This correction calculation utilizes a multiplication method: Corrected rationality coefficient = rationality coefficient × omission correction coefficient. This correction method results in a corresponding decrease in the corrected rationality coefficient as the omission rate increases, accurately reflecting the decrease in the credibility of the construction progress information. For example, when the original rationality coefficient is 0.8 and the omission rate is 0.2, the omission correction coefficient is 0.8, resulting in a corrected rationality coefficient of 0.8 × 0.8 = 0.64, which is lower than the original value and reflects the negative impact of omissions on the credibility of progress information.

[0073] By calculating and applying the omission correction coefficient, dynamic optimization of the rationality assessment of construction progress information is achieved, effectively compensating for the assessment deviation caused by historical omissions, so that the corrected rationality coefficient can more truly reflect the actual credibility of the construction progress information, and provide a more accurate reference basis for the subsequent BIM model loading accuracy configuration.

[0074] Further, multiple node status parameters of multiple nodes are obtained, multiple loading accuracy information is configured in combination with the correction rationality coefficient, the BIM model is loaded according to the construction progress information, and the loading BIM module corresponding to the construction progress information is marked by the correction rationality coefficient, including:

[0075] S41. Acquire multiple node status parameters of multiple nodes, wherein each node status parameter includes a computing power parameter;

[0076] S42. Calculate and obtain multiple state loading coefficients based on the multiple node state parameters and standard node state parameters;

[0077] S43. Calculate and obtain an updated loading coefficient based on the correction rationality coefficient;

[0078] S44, calculating and obtaining multiple loading coefficients based on the multiple state loading coefficients and the updated loading coefficients, and classifying and obtaining multiple loading accuracy information based on the loading coefficient intervals into which the multiple sample loading coefficient intervals fall, wherein the multiple sample loading accuracy information corresponds to the multiple sample loading accuracy information;

[0079] S45. Load the BIM model at multiple nodes according to the multiple loading accuracy information and the construction progress information, and mark the loaded BIM module corresponding to the construction progress information using the correction rationality coefficient.

[0080] In a preferred embodiment, first, multiple node status parameters of multiple nodes at the construction site are obtained. Each node corresponds to a different construction management personnel or operation terminal, such as different devices used by project managers, foremen, quality inspectors, etc. The node status parameters mainly include computing power parameters, which cover key technical indicators such as processor performance, memory capacity, graphics processing capabilities, and network bandwidth of the node device. The hardware configuration information of each node device is automatically obtained through the device detection module and quantified into a standardized computing power parameter value, providing a device capability foundation for subsequent loading accuracy configuration.

[0081] Then, a comparative calculation is performed based on the obtained multiple node status parameters combined with the preset standard node status parameters. The standard node status parameters represent the recommended baseline equipment configuration level. The ratio of each node status parameter to the standard node status parameter is calculated respectively to obtain multiple state loading coefficients. For example, when the computing power parameter of a node is 0.6 times the standard value, its corresponding state loading coefficient is 0.6, which reflects the processing capacity level of the node relative to the standard configuration. Subsequently, the updated loading coefficient is calculated based on the obtained correction rationality coefficient. The updated loading coefficient is calculated using the updated loading coefficient = 1-correction rationality coefficient. When the correction rationality coefficient is smaller, it means that the degree of abnormality in construction progress management is greater. At this time, the larger the update loading coefficient is, the higher the loading accuracy needs to be configured to facilitate detailed analysis and troubleshooting of abnormal situations.

[0082] Subsequently, a comprehensive calculation is performed based on multiple state loading coefficients and updated loading coefficients to obtain multiple final loading coefficients. The calculation method is Load Coefficient = State Load Coefficient × Update Load Coefficient, achieving a harmonious balance between equipment capacity and management requirements. Next, based on the preset loading coefficient intervals within which the calculated loading coefficient values ​​fall, multiple corresponding loading accuracy information is classified. For example, multiple sample loading coefficient intervals such as [0, 0.3), [0.3, 0.6), and [0.6, 1.0] are preset, corresponding to low-precision, medium-precision, and high-precision sample loading accuracy information, respectively. Subsequently, based on the configured multiple loading accuracy information, differentiated BIM model loading is performed at each node based on construction progress information. For low-precision loading, only major structural components such as columns, beams, and slabs, such as large BIM modules, are loaded, omitting smaller components such as bolts and connectors. For high-precision loading, the complete BIM model, including all detailed components, is loaded. At the same time, the loaded BIM module corresponding to the construction progress information is visually marked by correcting the rationality coefficient. For example, different colors are used to indicate the construction completion status and information credibility. Green represents the completed part with high credibility, orange represents the ongoing part with medium credibility, and red represents the low credibility or abnormal part.

[0083] Through the intelligent coordinated configuration of node equipment capabilities and construction progress management requirements, adaptive optimization of BIM model loading is achieved, which not only ensures the smooth operation of equipment with different performance, but also provides sufficient detail analysis capabilities under abnormal circumstances, significantly improving the practicality and effectiveness of construction progress visualization management, and improving the accuracy and effectiveness of construction progress management.

[0084] Example 2, as Figure 2 As shown, based on the same inventive concept as the method for loading and processing a BIM model of a construction progress record provided in the first embodiment, an embodiment of the present invention further provides a BIM model loading and processing system for a construction progress record, comprising:

[0085] The rationality analysis module 11 is used to receive the latest uploaded construction progress information and analyze and obtain the rationality coefficient of the construction progress information in combination with the historical construction progress information sequence within the recent preset time range;

[0086] An abnormal progress screening module 12 is configured to obtain a theoretical historical construction progress information sequence based on the construction progress information, and to screen and obtain at least one abnormal construction progress information when the theoretical historical construction progress information sequence is inconsistent with the construction progress information sequence;

[0087] A correction coefficient calculation module 13 is configured to analyze at least one omission rate of the at least one abnormal construction progress information, perform correction calculation on the rationality coefficient, and obtain a corrected rationality coefficient;

[0088] The BIM module annotation module 14 is used to obtain multiple node status parameters of multiple nodes, configure multiple loading accuracy information in combination with the correction rationality coefficient, load the BIM model according to the construction progress information, and annotate the loaded BIM module corresponding to the construction progress information through the correction rationality coefficient.

[0089] Furthermore, the rationality analysis module 11 includes the following execution steps:

[0090] receiving construction progress information of a latest uploaded record, wherein the construction progress information is uploaded through any one of the plurality of secondary nodes and the plurality of nodes and includes a construction progress step and a construction progress timestamp;

[0091] Obtaining a historical construction progress information sequence recorded during a recent preset time range, wherein the historical construction progress information sequence includes a timestamp;

[0092] According to the historical construction progress information sequence, the rationality coefficient of the construction progress information is obtained by analysis.

[0093] Furthermore, the rationality analysis module 11 further includes the following execution steps:

[0094] Load data based on the BIM model of similar buildings, collect sample historical construction progress information sequence sets and sample construction progress information sets, and mark and obtain sample rationality coefficient sets. The rationality coefficient is greater than or equal to 0 and less than or equal to 1.

[0095] Use machine learning to build a rationality analyzer;

[0096] Using the sample historical construction progress information sequence set, the sample construction progress information set, and the sample rationality coefficient set, the rationality analyzer is trained until convergence;

[0097] The construction progress information and the historical construction progress information sequence are input into the rationality analyzer, and the rationality coefficient is obtained by identification output.

[0098] Furthermore, the abnormal progress screening module 12 includes the following execution steps:

[0099] Obtain a theoretical construction progress information sequence corresponding to the BIM model, and index a theoretical historical construction progress information sequence within a preset historical time range before the construction progress step in the construction progress information;

[0100] The theoretical historical construction progress information sequence and the theoretical historical construction progress step sequence and the historical construction progress step sequence in the historical construction progress information sequence are compared, and when there is inconsistency, at least one abnormal construction progress information corresponding to at least one inconsistent abnormal construction progress step is filtered.

[0101] Furthermore, the correction coefficient calculation module 13 includes the following execution steps:

[0102] Extracting, based on construction progress record data of a plurality of buildings of the same type, record data of at least one abnormal construction progress step within the at least one abnormal construction progress information to obtain at least one abnormal construction progress step data set;

[0103] Analyzing and screening the proportion of missed records of at least one abnormal construction progress step in the at least one abnormal construction progress step data set to obtain at least one abnormal missed rate, and calculating the mean to obtain the missed rate;

[0104] According to the omission rate, the rationality coefficient is corrected and calculated to obtain a corrected rationality coefficient.

[0105] Furthermore, the correction coefficient calculation module 13 further includes the following execution steps:

[0106] Calculating an omission correction coefficient according to the omission rate;

[0107] The omission correction coefficient is used to perform correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

[0108] Furthermore, the BIM module annotation module 14 includes the following execution steps:

[0109] Obtain multiple node status parameters of multiple nodes, wherein each node status parameter includes a computing power parameter;

[0110] Calculating and obtaining a plurality of state loading coefficients according to the plurality of node state parameters in combination with standard node state parameters;

[0111] Calculating and obtaining an updated loading coefficient based on the correction rationality coefficient;

[0112] Calculating and obtaining multiple loading coefficients according to the multiple state loading coefficients and the updated loading coefficients, and classifying and obtaining multiple loading accuracy information according to the loading coefficient intervals into which they fall, wherein the multiple sample loading coefficient intervals correspond to the multiple sample loading accuracy information;

[0113] According to the multiple loading accuracy information and the construction progress information, BIM models are loaded at multiple nodes, and the loaded BIM modules corresponding to the construction progress information are marked using the correction rationality coefficient.

[0114] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0120] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A BIM model loading and processing method for construction progress records, characterized in that: The method comprises: Receive the latest uploaded construction progress information, combine it with the historical construction progress information sequence within the recent preset time range, and analyze and obtain the rationality coefficient of the construction progress information; According to the construction progress information, indexing to obtain a theoretical historical construction progress information sequence, and when the theoretical historical construction progress information sequence is inconsistent with the historical construction progress information sequence, screening to obtain at least one abnormal construction progress information; Analyzing at least one omission rate of the at least one abnormal construction progress information, performing correction calculation on the rationality coefficient, and obtaining the corrected rationality coefficient, including: Extracting, based on construction progress record data of a plurality of buildings of the same type, record data of at least one abnormal construction progress step within the at least one abnormal construction progress information to obtain at least one abnormal construction progress step data set; Analyzing and screening the proportion of missed records of at least one abnormal construction progress step in the at least one abnormal construction progress step data set to obtain at least one abnormal missed rate, and calculating the mean to obtain the missed rate; According to the omission rate, the rationality coefficient is corrected and calculated to obtain a corrected rationality coefficient; The rationality coefficient is corrected and calculated based on the omission rate to obtain the corrected rationality coefficient, including: Calculating an omission correction coefficient according to the omission rate; Using the omission correction coefficient, performing correction calculation on the rationality coefficient to obtain a corrected rationality coefficient; Acquire multiple node status parameters of multiple nodes, configure multiple loading accuracy information in combination with the correction rationality coefficient, load the BIM model according to the construction progress information, and mark the loading BIM module corresponding to the construction progress information through the correction rationality coefficient.

2. The BIM model loading and processing method for construction progress record according to claim 1, characterized in that: Receive the latest uploaded construction progress information, combine it with the historical construction progress information sequence within the recent preset time range, and analyze and obtain the rationality coefficient of the construction progress information, including: receiving construction progress information of a latest uploaded record, wherein the construction progress information is uploaded through any one of the plurality of secondary nodes and the plurality of nodes and includes a construction progress step and a construction progress timestamp; Obtaining a historical construction progress information sequence recorded during a recent preset time range, wherein the historical construction progress information sequence includes a timestamp; According to the historical construction progress information sequence, the rationality coefficient of the construction progress information is obtained by analysis.

3. The BIM model loading and processing method for construction progress record according to claim 2, characterized in that: Analyzing and obtaining a rationality coefficient of the construction progress information based on the historical construction progress information sequence includes: Load data based on the BIM model of similar buildings, collect sample historical construction progress information sequence sets and sample construction progress information sets, and mark and obtain sample rationality coefficient sets. The rationality coefficient is greater than or equal to 0 and less than or equal to 1. Use machine learning to build a rationality analyzer; Using the sample historical construction progress information sequence set, the sample construction progress information set, and the sample rationality coefficient set, the rationality analyzer is trained until convergence; The construction progress information and the historical construction progress information sequence are input into the rationality analyzer, and the rationality coefficient is obtained by identification output.

4. The BIM model loading and processing method for construction progress record according to claim 1, characterized in that: According to the construction progress information, a theoretical historical construction progress information sequence is obtained by indexing, and when the theoretical historical construction progress information sequence is inconsistent with the historical construction progress information sequence, at least one abnormal construction progress information is obtained by screening, including: Obtain a theoretical construction progress information sequence corresponding to the BIM model, and index a theoretical historical construction progress information sequence within a preset historical time range before the construction progress step in the construction progress information; The theoretical historical construction progress information sequence and the theoretical historical construction progress step sequence and the historical construction progress step sequence in the historical construction progress information sequence are compared, and when there is inconsistency, at least one abnormal construction progress information corresponding to at least one inconsistent abnormal construction progress step is filtered.

5. The BIM model loading and processing method for construction progress record according to claim 1, characterized in that: Acquire multiple node status parameters of multiple nodes, configure multiple loading accuracy information in combination with the correction rationality coefficient, load the BIM model according to the construction progress information, and mark the loaded BIM module corresponding to the construction progress information by the correction rationality coefficient, including: Obtain multiple node status parameters of multiple nodes, wherein each node status parameter includes a computing power parameter; Calculating and obtaining a plurality of state loading coefficients according to the plurality of node state parameters in combination with standard node state parameters; Calculating and obtaining an updated loading coefficient based on the correction rationality coefficient; Calculating and obtaining multiple loading coefficients according to the multiple state loading coefficients and the updated loading coefficients, and classifying and obtaining multiple loading accuracy information according to the loading coefficient intervals into which they fall, wherein the multiple sample loading coefficient intervals correspond to the multiple sample loading accuracy information; According to the multiple loading accuracy information and the construction progress information, BIM models are loaded at multiple nodes, and the loaded BIM modules corresponding to the construction progress information are marked using the correction rationality coefficient.

6. A BIM model loading and processing system for construction progress records, characterized in that: A BIM model loading and processing method for implementing a construction progress record according to any one of claims 1 to 5, the system comprising: A rationality analysis module is used to receive the latest uploaded construction progress information and analyze and obtain the rationality coefficient of the construction progress information based on the historical construction progress information sequence within the recent preset time range; an abnormal progress screening module, configured to obtain, based on the construction progress information, an index of a theoretical historical construction progress information sequence, and screen and obtain at least one abnormal construction progress information when the sequence is inconsistent with the historical construction progress information sequence; a correction coefficient calculation module, configured to analyze at least one omission rate of the at least one abnormal construction progress information, perform correction calculation on the rationality coefficient, and obtain a corrected rationality coefficient; The BIM module annotation module is used to obtain multiple node status parameters of multiple nodes, configure multiple loading accuracy information in combination with the correction rationality coefficient, load the BIM model according to the construction progress information, and annotate the loaded BIM module corresponding to the construction progress information through the correction rationality coefficient.

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