BIM model loading processing method and system for construction progress record

Abstract

The invention provides a BIM model loading processing method and system for construction progress records, and belongs to the technical field of BIM models. The method comprises the following steps: receiving latest uploaded construction progress information, and analyzing and acquiring a rationality coefficient in combination with a historical construction progress information sequence; according to the construction progress information index theory historical construction progress information sequence, abnormal construction progress information is obtained through screening; analyzing the omission rate of the abnormal construction progress information, and performing correction calculation on the rationality coefficient to obtain a corrected rationality coefficient; and acquiring node state parameters of the plurality of nodes, configuring loading precision information in combination with the correction rationality coefficient, carrying out BIM model loading, and labeling the loaded BIM module. By intelligently analyzing the rationality of the construction progress and dynamically correcting the loading precision, the actual construction progress is accurately reflected, and the visualization accuracy of the BIM model and the construction management anomaly recognition capability are improved.

Description

Technical Field

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

[0002] Building Information Modeling (BIM) technology is widely used in progress management during the construction stage, and visualizes the construction progress. For example, the completed components are marked in green, and the unconstructed components are marked in gray, providing an intuitive monitoring means for project management.

[0003] However, in actual construction, the uploading of progress information for each construction node often lags behind. Construction workers may fail to upload the progress in a timely manner due to busy work, equipment limitations, etc.; the progress information for some construction steps is omitted from uploading, resulting in a deviation between the display status of the BIM model and the actual site. These problems cause the BIM model to be unable to accurately reflect the actual construction progress, resulting in a low visual reference value of the BIM model and insufficient ability to identify construction management anomalies. Summary of the Invention

[0004] In view of the technical problem that the BIM model loading in the prior art cannot accurately reflect the actual construction progress and has a low visual reference value due to untimely or omitted uploading of construction progress information, the present invention provides a method and system for loading and processing a BIM model for construction progress recording to solve this problem.

[0005] The technical solution of the present invention for solving the above technical problems is as follows: In a first aspect, the present invention provides a method for loading and processing a BIM model for construction progress recording, including: receiving the latest uploaded construction progress information, combining the historical construction progress information sequence within a recently preset time range, and analyzing to obtain the rationality coefficient of the construction progress information; indexing to obtain the theoretical historical construction progress information sequence according to the construction progress information, 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 a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient; obtaining multiple node state 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 through the corrected rationality coefficient.

[0006] In a second aspect, the present invention provides a BIM model loading and processing system for construction progress records, comprising: a rationality analysis module, which is configured to receive the construction progress information of the latest uploaded record, and analyze and obtain the rationality coefficient of the construction progress information in combination with the historical construction progress information sequence within a recently preset time range; an abnormal progress screening module, which is configured to index and obtain the theoretical historical construction progress information sequence according to the construction progress information, and screen and obtain at least one abnormal construction progress information when it is inconsistent with the historical construction progress information sequence; a correction coefficient calculation module, which is configured to analyze at least one omission rate of the at least one abnormal construction progress information, and perform correction calculation on the rationality coefficient to obtain a corrected rationality coefficient; a BIM module annotation module, which is configured to obtain multiple node state parameters of multiple nodes, configure multiple loading accuracy information in combination with the corrected rationality coefficient, perform BIM model loading according to the construction progress information, and annotate the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient.

[0007] The beneficial effects of the present invention are as follows: Receive the construction progress information of the latest uploaded record, and analyze and obtain the rationality coefficient of the construction progress information in combination with the historical construction progress information sequence within a recently preset time range, so as to judge whether the currently uploaded construction progress is reasonable based on historical data, and provide a basic basis for subsequent processing; index and obtain the theoretical historical construction progress information sequence according to the construction progress information, and screen and obtain at least one abnormal construction progress information when it is inconsistent with the historical construction progress information sequence, so as to identify the progress information that may be missing or have an abnormal order by comparing the theoretical progress and the actual progress; analyze at least one omission rate of the at least one abnormal construction progress information, and perform correction calculation on the rationality coefficient to obtain a corrected rationality coefficient, and dynamically adjust the rationality assessment by quantitatively analyzing the omission situation. The larger the omission rate, the higher the degree of abnormality in construction management, and accordingly the rationality coefficient is reduced; obtain multiple node state parameters of multiple nodes, configure multiple loading accuracy information in combination with the corrected rationality coefficient, perform BIM model loading according to the construction progress information, and annotate the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient. In this way, it is possible to configure appropriate loading accuracy according to the device computing power differences of different nodes, and provide more detailed analysis with higher accuracy when the degree of abnormality in construction management is large, realizing intelligent BIM model loading and visual display.

[0008] Through the above technical solutions, the present application can effectively solve the problems in the prior art such as untimely uploading of construction progress information and omission resulting in visual distortion of the BIM model, realize intelligent analysis and dynamic correction of construction progress, improve the accuracy and practicality of BIM model loading, thereby improving the ability to identify abnormalities in construction management, and providing more reliable decision-making support for construction management. Brief Description of the Drawings

[0009] Figure 1 It is a schematic flowchart of a BIM model loading and processing method for construction progress record provided by the present invention; Figure 2 It is a schematic structural diagram of a BIM model loading and processing system for construction progress record provided by the present invention.

[0010] In the drawings, the components represented by each reference numeral are as follows: Rationality analysis module 11, abnormal progress screening module 12, correction coefficient calculation module 13, BIM module annotation module 14. Detailed Embodiment

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without using these specific details. In other instances, 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 broadest scope that conforms to the principles and features disclosed in the present invention.

[0014] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a BIM model loading and processing method for construction progress record, including: S1. Receive the construction progress information of the latest upload record, and combine it with the historical construction progress information sequence within the recently preset time range to analyze and obtain the rationality coefficient of the construction progress information.

[0015] Specifically, first, receive the construction progress information of the latest upload record from the construction site. The construction progress information includes specific construction progress steps and corresponding construction progress timestamps. The construction progress steps are used to identify the specific construction content completed currently, such as the column pouring, beam-slab installation, or staircase construction of a certain floor; the construction progress timestamp records the completion time of this construction step.

[0016] After receiving the latest construction progress information, automatically retrieve and obtain the historical construction progress information sequence within the recently preset time range. This historical construction progress information sequence contains all the uploaded construction progress records within the preset time range, forming a construction progress data set arranged in chronological order. The preset time range can be set according to the specific characteristics of the engineering project, such as set to the last 7 days, 15 days, or 30 days, etc.

[0017] Based on the obtained historical construction progress information sequence, conduct a rationality analysis on the currently received construction progress information. This analysis process evaluates the time rationality of the current construction progress by comparing the current construction progress with the historical construction progress pattern, that is, determines whether the current construction progress conforms to the expected construction time arrangement. Specifically, analyze whether the current construction progress is completed on time, whether there is a delay or whether it is completed ahead of schedule, and quantify the degree of this time deviation. Through the above analysis process, output the rationality coefficient, which is used to quantify the rational degree of the current construction progress information. The value range of the rationality coefficient is between 0 and 1. The closer the value is to 1, the more the current construction progress conforms to the expected construction arrangement and the higher the rationality; the closer the value is to 0, the greater the deviation between the current construction progress and the expectation, indicating a more obvious abnormal situation.

[0018] By analyzing and obtaining the rationality coefficient of the construction progress information of the latest upload record, it provides a reliable data basis for the subsequent BIM model loading, avoids the BIM model loading deviation caused by incorrect or unreasonable construction progress information, and thus improves the accuracy of the BIM model loading.

[0019] S2. According to the construction progress information, index to obtain the theoretical historical construction progress information sequence. When it is inconsistent with the historical construction progress information sequence, screen to obtain at least one abnormal construction progress information.

[0020] Specifically, first, according to the currently received construction progress information, automatically index and obtain the corresponding theoretical historical construction progress information sequence. This theoretical historical construction progress information sequence is an idealized construction progress sequence determined based on the preset standard construction process and time arrangement in the BIM model, reflecting 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, retrieve the corresponding theoretical historical construction progress information sequence from the preset theoretical construction progress database.

[0021] Subsequently, compare and analyze the obtained theoretical historical construction progress information sequence with the actual historical construction progress information sequence obtained in step S1. This comparison process conducts a consistency check on the construction progress step sequences and corresponding time arrangements in the two sequences to determine whether the actual construction progress strictly follows the construction process and time nodes designed theoretically. When an inconsistency is detected between the theoretical historical construction progress information sequence and the actual historical construction progress information sequence, it indicates that there may be abnormal situations such as progress omission upload, construction sequence adjustment, or time node deviation during 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 that deviate from the theoretical progress arrangement and their related time information during the actual construction process, providing an accurate data source for subsequent abnormal analysis and correction processing.

[0022] Through the comparative analysis of the theoretical and historical construction progress information sequences, abnormal situations during the construction process can be accurately identified, including problems such as progress omission, sequence disorder, or time deviation, providing an abnormal detection function for construction progress management and improving the accuracy and reliability of construction progress monitoring.

[0023] S3. Analyze at least one omission rate of the at least one abnormal construction progress information, and perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

[0024] Specifically, first, conduct an in-depth analysis of the identified at least one abnormal construction progress information and calculate its corresponding at least one omission rate. The calculation process of this omission rate is to analyze the construction progress record data of multiple buildings of the same type, extract the historical record data of the abnormal construction progress steps included in the abnormal construction progress information, and form an abnormal construction progress step data set. Statistically analyze the proportion of abnormal construction progress step omission records in this data set, that is, calculate the frequency of such abnormal construction progress steps not being uploaded in a timely manner under the same type of construction conditions, so as to obtain the corresponding omission rate. When there are multiple abnormal construction progress information, calculate their respective omission rates separately and obtain a comprehensive omission rate by calculating the mean value.

[0025] Subsequently, based on the calculated omission rate, a correction calculation is performed on the obtained rationality coefficient. During the correction calculation process, first, an omission correction coefficient is 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, indicating a higher degree of incompleteness in the upload of construction progress information. The original rationality coefficient is corrected using this omission correction 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, 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 compared to the original rationality coefficient. Among them, the larger the omission rate, the smaller the omission correction coefficient and the corrected rationality coefficient, indicating that the upload and loading of construction progress are more unreasonable and the degree of abnormal construction management is greater.

[0026] Through the analysis of the omission rate of abnormal construction progress information and the correction calculation of the rationality coefficient, the evaluation deviation caused by the omission of construction progress information upload can be effectively compensated, the accuracy of the credibility evaluation of construction progress information can be improved, and a more reliable reference basis can be provided for the subsequent loading accuracy configuration of the BIM model.

[0027] S4. Obtain multiple node state parameters of multiple nodes, combine the corrected rationality coefficient, configure multiple loading accuracy information, perform BIM model loading according to the construction progress information, and label the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient.

[0028] Specifically, first, obtain multiple node state parameters of multiple nodes, where each node corresponds to different operators at the construction site, such as foremen or construction management personnel with different divisions of labor. The node state parameters mainly include the computing power parameters of the equipment used at each node, and this computing power parameter reflects the computing and processing capabilities of the node equipment. For example, different models of computers, mobile phones, or tablet devices have different processor performances, memory capacities, and graphics processing capabilities. By comparing the computing power parameters of each node with the standard node state parameters, multiple state loading coefficients are obtained by calculating ratios to quantify the relative computing capabilities of each node's equipment.

[0029] Subsequently, an updated loading coefficient is calculated based on the obtained calibration rationality coefficient. The updated loading coefficient is obtained by subtracting the calibration rationality coefficient from 1, i.e., updated loading coefficient = 1 - calibration rationality coefficient. When the calibration rationality coefficient is smaller, the updated loading coefficient is larger, indicating that a higher loading accuracy is required to facilitate the analysis of abnormal details in construction progress management. The status loading coefficients of each node are comprehensively calculated with the updated loading coefficient to obtain multiple final loading coefficients. According to the preset loading coefficient intervals into which each loading coefficient falls, multiple corresponding loading accuracy information is classified, where different loading coefficient intervals correspond to different levels of loading accuracy configurations.

[0030] Based on the configured multiple loading accuracy information, differential BIM model loading is performed at each node according to the construction progress information. For nodes with relatively low computing power, when the calibration rationality coefficient is large, a lower loading accuracy is configured, and only the main BIM components such as columns and beams are loaded, while small components such as bolts and connectors are not loaded to reduce the computing burden on the device; for the case where the calibration rationality coefficient is small, a higher loading accuracy is configured to load the complete BIM model including small components to facilitate the detailed analysis of abnormal construction progress management. At the same time, the loading BIM modules corresponding to the construction progress information are marked by the calibration rationality coefficient. For example, BIM modules such as columns, beams, and slabs are color-coded or status-marked according to their construction completion status and credibility.

[0031] Through the comprehensive consideration of the node status parameters and the calibration rationality coefficient, the intelligent configuration of the BIM model loading accuracy is realized, which not only ensures the smooth operation of different computing power devices but also provides sufficient detail analysis ability in case of abnormal construction progress management, accurately reflects the actual construction progress, and improves the visualization accuracy of the BIM model and the abnormal recognition ability of construction management.

[0032] Furthermore, receive the construction progress information of the latest uploaded record, and combine the historical construction progress information sequence within the recently preset time range to analyze and obtain the rationality coefficient of the construction progress information, including: S11. Receive the construction progress information of the latest uploaded record, where the construction progress information is uploaded by any one of multiple secondary nodes and multiple nodes, including the construction progress steps and the construction progress timestamp; S12. Obtain the historical construction progress information sequence of the uploaded records within the recently preset time range, where the historical construction progress information sequence includes timestamps; S13. Analyze and obtain the rationality coefficient of the construction progress information according to the historical construction progress information sequence.

[0033] In a feasible implementation, first, the construction progress information of the latest upload record is received. The construction progress information can be uploaded through any one of multiple secondary nodes and multiple nodes at the construction site. The secondary nodes can be mobile devices used by on-site workers, and the nodes are management terminals 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 details the specific construction operations completed currently, such as specific processes like steel bar binding, concrete pouring, and formwork removal on a certain floor; the construction progress timestamp accurately records the completion time of this construction step, providing an accurate time reference for subsequent time rationality analysis.

[0034] Then, the historical construction progress information sequence of the upload records within the recently preset time range is automatically obtained. The historical construction progress information sequence is an ordered data set containing multiple historical construction progress records. Each record in the sequence includes corresponding timestamp information to identify the occurrence time of each historical construction progress. The setting of the preset time range takes into account the cycle characteristics of the construction project and the progress management requirements, and is usually set to a reasonable time window that can reflect the construction progress law, such as the last week, two weeks, or one month, etc.

[0035] Subsequently, the rationality analysis of the current construction progress information is carried out according to the obtained historical construction progress information sequence. This analysis process is realized by constructing a rationality analyzer. Using machine learning technology, the BIM model loading data of similar buildings is used as training samples, including the sample historical construction progress information sequence set, the sample construction progress information set, and the corresponding sample rationality coefficient set for training. The trained rationality analyzer can receive the current construction progress information and the historical construction progress information sequence as inputs, and output the corresponding rationality coefficient through analysis. This coefficient quantitatively reflects the rationality degree of the current construction progress information relative to the historical construction mode.

[0036] Through the above steps, the accurate quantitative evaluation of the rationality of the construction progress information can be realized, providing a reliable data basis for subsequent anomaly detection and BIM model loading accuracy configuration, and improving the intelligent level of construction progress management.

[0037] Furthermore, according to the historical construction progress information sequence, analyzing and obtaining the rationality coefficient of the construction progress information includes: S131. According to the BIM model loading data of similar buildings, collect the sample historical construction progress information sequence set and the sample construction progress information set, and mark and obtain the sample rationality coefficient set, where the rationality coefficient is greater than or equal to 0 and less than or equal to 1; S132. Use machine learning to construct a rationality analyzer; S133. Use the sample historical construction progress information sequence set, the sample construction progress information set, and the sample rationality coefficient set to train the rationality analyzer until convergence; S134. Input the construction progress information and the historical construction progress information sequence into the rationality analyzer, and identify and output to obtain the rationality coefficient.

[0038] In a preferred implementation manner, first, collect and label sample data according to the BIM model loading data of similar buildings. Specifically, first, collect a large amount of historical construction progress data from multiple completed or ongoing building 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 the true construction progress records under different construction stages and different construction conditions, providing a rich training data source for the machine learning model; subsequently, perform professional annotation on the collected sample data, and assign corresponding rationality coefficients to each sample by construction management experts according to the actual rationality degree of the construction progress to form a sample rationality coefficient set. The value range of all rationality coefficients is strictly limited between 0 and 1, where 0 represents completely unreasonable and 1 represents completely reasonable.

[0039] Then, use machine learning technology to construct a rationality analyzer. The rationality analyzer adopts a deep learning network architecture and can process complex time series data and multi-dimensional feature information. The network structure design of the rationality analyzer takes into account the time series characteristics and multi-variable correlation of the construction progress information, and realizes the intelligent judgment and quantitative evaluation of the construction progress rationality through a multi-layer neural network. Among them, the rationality analyzer can adopt a hybrid architecture combining a long short-term memory network (LSTM) and a fully connected layer. For example, the input layer receives a multi-dimensional feature vector containing the construction progress step encoding and time stamp, converts the discrete construction steps into continuous feature representations through the embedding layer, then extracts time series features through two layers of LSTM networks (128 neurons in each layer), and then performs feature fusion and regression output through three layers of fully connected networks (including 64, 32, and 1 neuron respectively). The output layer uses the Sigmoid activation function to ensure that the output range of the rationality coefficient is between 0 and 1.

[0040] Subsequently, the rationality analyzer is trained using the obtained set of sample historical construction progress information sequences, the set of sample construction progress information, and the set of sample rationality coefficients. The training process adopts a supervised learning method, using the sample historical construction progress information sequence and the sample construction progress information as input features, and the sample rationality coefficient as the target output, and continuously adjusts the network parameters through the backpropagation algorithm. The training process continues until the model converges, that is, the loss function reaches a preset threshold or the change range of the loss function is less than a set value within multiple consecutive training cycles. After that, the current construction progress information and the historical construction progress information sequence are used as input data and input into the trained rationality analyzer for inference calculation. Based on the judgment rules of construction progress rationality it has learned, the rationality analyzer conducts intelligent analysis and feature extraction on the input data, and finally identifies and outputs the corresponding rationality coefficient, which accurately quantifies the rationality degree of the current construction progress information.

[0041] Through the construction and application of the rationality analyzer based on machine learning, the intelligent and automated evaluation of the rationality of construction progress information is realized. Compared with the traditional manual judgment method, it has higher accuracy, consistency, and processing efficiency, providing reliable technical support for construction progress management.

[0042] Furthermore, according to the construction progress information, a theoretical historical construction progress information sequence is indexed. When it is inconsistent with the historical construction progress information sequence, at least one abnormal construction progress information is screened out, including: S21. Obtain the theoretical construction progress information sequence corresponding to the BIM model, and index the theoretical historical construction progress information sequence within a preset historical time range before the construction progress step in the construction progress information; S22. Compare the theoretical historical construction progress step sequence and the historical construction progress step sequence in the theoretical historical construction progress information sequence and the historical construction progress information sequence. When they are inconsistent, screen out at least one abnormal construction progress information corresponding to at least one inconsistent abnormal construction progress step.

[0043] In a preferred embodiment, first, obtain a theoretical construction progress information sequence corresponding to the BIM model of the current construction project. This theoretical construction progress information sequence is a benchmark progress schedule determined based on the standard construction process flow and time arrangement preset in the BIM model, reflecting the standard execution sequence and time nodes that each construction step should follow under ideal construction conditions. Subsequently, according to the construction progress steps in the currently received construction progress information, perform an index search in the theoretical construction progress information sequence to obtain a theoretical historical construction progress information sequence within a preset historical time range before this construction progress step. The setting of the preset historical time range takes into account the relevance and dependency of construction processes, usually covering 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.

[0044] Then, conduct a detailed comparison and analysis between the obtained theoretical historical construction progress information sequence and the obtained actual historical construction progress information sequence. The comparison process mainly focuses on two aspects: one is the consistency test of the theoretical historical construction progress step sequence and the historical construction progress step sequence, that is, verifying whether the execution sequence of the actual historical construction steps conforms to the theoretically designed process flow; the other is the matching analysis of the corresponding time nodes, that is, checking whether the actual historical construction time arrangement is consistent with the theoretical progress plan. When an inconsistency is detected between the theory and the actual history, it indicates that there are abnormal situations deviating from the standard process in the actual historical construction process, such as missing construction steps, reversed process sequences, time node deviations, etc. For the identified inconsistencies, further screen and locate the specific abnormal construction progress steps, and obtain at least one abnormal construction progress information corresponding to these abnormal steps, including detailed information such as the specific content, occurrence time, and deviation degree of the abnormal steps.

[0045] Through the systematic comparison and analysis of the theoretical construction progress information sequence and the historical construction progress information sequence, 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, and improving the comprehensiveness and accuracy of construction progress anomaly detection.

[0046] Furthermore, analyze at least one omission rate of the at least one abnormal construction progress information, and perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient, including: S31: According to the construction progress record data of multiple buildings of the same type, extract the 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; S32: Analyze and screen the proportion of missing records of at least one abnormal construction progress step within the at least one abnormal construction progress step data set to obtain at least one abnormal omission rate, and calculate the mean value to obtain the omission rate; S33. Calculate the correction of the rationality coefficient according to the omission rate to obtain the corrected rationality coefficient.

[0047] In a preferred embodiment, historical data extraction of abnormal construction progress steps is performed based on the construction progress record data of multiple buildings of the same type. First, retrieve multiple buildings of the same type with similar characteristics to the current project from the construction database. These projects are consistent with the current project in terms of building type, scale, structural form, etc., ensuring the comparability and reference value of the data. Subsequently, for at least one identified abnormal construction progress information, extract the detailed record data of at least one abnormal construction progress step contained therein. By searching for the same or similar construction progress steps in building projects of the same type, collect information such as the execution records, upload status, and management status of these steps in historical projects to form at least one dataset of abnormal construction progress steps, providing sufficient sample data for subsequent omission rate statistical analysis.

[0048] Then, conduct in-depth statistical analysis on the obtained at least one dataset of abnormal construction progress steps. For each abnormal construction progress step in the dataset of abnormal construction progress steps, analyze and screen out the situation of missing records of this step in historical projects, that is, count the occurrence frequency of the failure to upload progress information or missing records of this type of construction step in a timely manner. By calculating the ratio of the number of missing records to the total number of records, obtain the abnormal omission rate corresponding to each abnormal construction progress step. When there are multiple abnormal construction progress steps, calculate their respective abnormal omission rates separately, and then calculate the mean value of all abnormal omission rates by arithmetic mean to finally obtain the comprehensive omission rate, which reflects the typical omission degree of the current abnormal construction progress information in similar projects.

[0049] After that, perform correction calculation on the obtained rationality coefficient based on the calculated omission rate. The correction process first calculates the omission correction coefficient according to the omission rate, using the calculation method of omission correction coefficient = 1 - omission rate, ensuring that the correction coefficient is smaller when the omission rate is higher. Then, multiply the original rationality coefficient by the omission correction coefficient to obtain the corrected rationality coefficient, that is, corrected rationality coefficient = rationality coefficient × omission correction coefficient. The corrected rationality coefficient can more accurately reflect the actual credibility of the construction progress information, effectively compensating for the evaluation deviation caused by historical omission situations.

[0050] Through the systematic analysis of the omission rate of abnormal construction progress information and the precise correction of the rationality coefficient, a dynamic optimized evaluation of the credibility of construction progress information is achieved, improving the accuracy and reliability of subsequent BIM model loading decisions.

[0051] Further, according to the omission rate, perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient, including: S331. Calculate and obtain an omission correction coefficient according to the omission rate; S332. Use the omission correction coefficient to perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

[0052] In a preferred embodiment, the omission correction coefficient is calculated according to the calculated omission rate. The calculation of the omission correction coefficient adopts a simple and effective linear relationship, that is, 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 that there is no omission and no correction is required; when the omission rate is larger, the omission correction coefficient is smaller, reflecting a higher degree of incompleteness in the upload of construction progress information and requiring a greater downward correction of the rationality coefficient.

[0053] Subsequently, use the calculated omission correction coefficient to perform a correction calculation on the original rationality coefficient. The correction calculation adopts a multiplication operation method, that is, corrected rationality coefficient = rationality coefficient × omission correction coefficient. Through this correction method, when the omission rate is larger, the omission correction coefficient is smaller, so that the corrected rationality coefficient decreases accordingly, accurately reflecting the reduction 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, and the corrected rationality coefficient is 0.8 × 0.8 = 0.64, which is lower than the original value, reflecting the negative impact of the omission on the credibility of the progress information.

[0054] Through the calculation and application of the omission correction coefficient, the dynamic optimization of the rationality evaluation of the construction progress information is realized, effectively compensating for the evaluation deviation caused by historical omission records, so that the corrected rationality coefficient can more truly reflect the actual credibility of the construction progress information, providing a more accurate reference basis for the subsequent BIM model loading accuracy configuration.

[0055] Further, obtain multiple node state parameters of multiple nodes, combine the corrected rationality coefficient, configure multiple loading accuracy information, perform BIM model loading according to the construction progress information, and label the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient, including: S41. Obtain multiple node state parameters of multiple nodes, where each node state parameter includes a computing power parameter; S42. Calculate and obtain multiple state loading coefficients according to the multiple node state parameters in combination with the standard node state parameters; S43. Calculate and obtain an updated loading coefficient based on the correction rationality coefficient. S44. Calculate and obtain multiple loading coefficients based on the multiple state loading coefficients and the updated loading coefficient, and classify multiple loading accuracy information according to the falling loading coefficient intervals, where multiple sample loading coefficient intervals correspond to multiple sample loading accuracy information. S45. Perform BIM model loading at multiple nodes according to the multiple loading accuracy information and the construction progress information respectively, and label the loaded BIM module corresponding to the construction progress information through the correction rationality coefficient.

[0056] In a preferred implementation, first, obtain multiple node state parameters of multiple nodes at the construction site. Each node corresponds to different construction management personnel or operation terminals, such as different devices used by project managers, foremen, quality inspectors, etc. The node state parameters mainly include computing power parameters, which cover key technical indicators such as the processor performance, memory capacity, graphics processing ability, and network bandwidth of the node devices. Automatically obtain the hardware configuration information of each node device through the device detection module and quantify it into a standardized computing power parameter value to provide a device capability basis for subsequent loading accuracy configuration.

[0057] Then, perform comparison calculations based on the obtained multiple node state parameters in combination with preset standard node state parameters. The standard node state parameters represent the recommended benchmark device configuration level. Calculate the ratio of each node state parameter to the standard node state parameter respectively to obtain multiple state loading coefficients. For example, when the computing power parameter of a certain node is 0.6 times the standard value, its corresponding state loading coefficient is 0.6, reflecting the processing ability level of this node relative to the standard configuration. Subsequently, calculate and obtain an updated loading coefficient according to the obtained correction rationality coefficient. The updated loading coefficient is calculated by the formula: updated loading coefficient = 1 - correction rationality coefficient. When the correction rationality coefficient is smaller, it indicates a greater degree of abnormality in construction progress management. At this time, the updated loading coefficient is larger, and a higher loading accuracy needs to be configured to facilitate detailed analysis and troubleshooting of abnormal situations.

[0058] Subsequently, through comprehensive calculation based on multiple status loading coefficients and updated loading coefficients, multiple final loading coefficients are obtained. The calculation method is: loading coefficient = status loading coefficient × updated loading coefficient, achieving the coordinated balance between equipment capabilities and management requirements. Then, according to the preset loading coefficient intervals into which the calculated loading coefficient values fall, multiple corresponding loading accuracy information is classified. For example, multiple sample loading coefficient intervals are preset, such as [0, 0.3), [0.3, 0.6), [0.6, 1.0], corresponding to multiple sample loading accuracy information such as low accuracy, medium accuracy, and high accuracy respectively. After that, according to the configured multiple loading accuracy information, differential BIM model loading is performed at each node according to the construction progress information. For low-precision loading, only the main structural components such as columns, beams, and slabs, i.e., large BIM modules, are loaded, while small components such as bolts and connectors are omitted; for high-precision loading, a complete BIM model including all detailed components is loaded. At the same time, the loaded BIM modules corresponding to the construction progress information are visually marked through the correction 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 part with low credibility or abnormality.

[0059] Through the intelligent coordinated configuration of node equipment capabilities and construction progress management requirements, the adaptive optimization of BIM model loading is achieved, which not only ensures the smooth operation of different performance equipment but also provides sufficient detailed analysis capabilities in case of anomalies, significantly improving the practicality and effectiveness of construction progress visualization management and enhancing the accuracy and effectiveness of construction progress management.

[0060] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the BIM model loading processing method for construction progress recording provided in Embodiment 1, the present invention embodiment also provides a BIM model loading processing system for construction progress recording, including: A rationality analysis module 11, configured 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 recently preset time range; An abnormal progress screening module 12, configured to index and obtain the theoretical historical construction progress information sequence according to the construction progress information, and when it is inconsistent with the historical construction progress information sequence, screen and obtain at least one abnormal construction progress information; A correction coefficient calculation module 13, configured to analyze at least one omission rate of the at least one abnormal construction progress information, and perform correction calculation on the rationality coefficient to obtain a corrected rationality coefficient; The BIM module annotation module 14 is used to obtain multiple node state parameters of multiple nodes, configure multiple loading accuracy information in combination with the calibration rationality coefficient, perform BIM model loading according to the construction progress information, and annotate the loaded BIM module corresponding to the construction progress information through the calibration rationality coefficient.

[0061] Furthermore, the rationality analysis module 11 includes the following execution steps: Receive the construction progress information of the latest uploaded record, where the construction progress information is uploaded by any one of multiple secondary nodes and multiple nodes, and includes construction progress steps and construction progress timestamps; Obtain the historical construction progress information sequence of the uploaded records within the most recent preset time range, where the historical construction progress information sequence includes timestamps; Analyze and obtain the rationality coefficient of the construction progress information according to the historical construction progress information sequence.

[0062] Furthermore, the rationality analysis module 11 also includes the following execution steps: According to the BIM model loading data of similar buildings, collect the sample historical construction progress information sequence set and the sample construction progress information set, and label and obtain the sample rationality coefficient set, where the rationality coefficient is greater than or equal to 0 and less than or equal to 1; Use machine learning to construct a rationality analyzer; Use the sample historical construction progress information sequence set, the sample construction progress information set, and the sample rationality coefficient set to train the rationality analyzer until convergence; Input the construction progress information and the historical construction progress information sequence into the rationality analyzer, and identify and output to obtain the rationality coefficient.

[0063] Furthermore, the abnormal progress screening module 12 includes the following execution steps: Obtain the theoretical construction progress information sequence corresponding to the BIM model, and index the theoretical historical construction progress information sequence within the preset historical time range before the construction progress step in the construction progress information; Compare the theoretical historical construction progress step sequence and the historical construction progress step sequence in the theoretical historical construction progress information sequence and the historical construction progress information sequence. When they are inconsistent, screen at least one abnormal construction progress information corresponding to at least one inconsistent abnormal construction progress step.

[0064] Furthermore, the correction coefficient calculation module 13 includes the following execution steps: Extract the record data of at least one abnormal construction progress step from the at least one abnormal construction progress information according to the construction progress record data of multiple buildings of the same type, and obtain at least one abnormal construction progress step data set; Analyze and screen the proportion of missing records of at least one abnormal construction progress step in the at least one abnormal construction progress step data set, obtain at least one abnormal omission rate, and calculate the mean value to obtain the omission rate; According to the omission rate, perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

[0065] Further, the correction coefficient calculation module 13 further includes the following execution steps: Calculate a missing correction coefficient according to the omission rate; Use the missing correction coefficient to perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

[0066] Further, the BIM module annotation module 14 includes the following execution steps: Obtain multiple node state parameters of multiple nodes, where each node state parameter includes a computing power parameter; Calculate multiple state loading coefficients according to the multiple node state parameters in combination with the standard node state parameters; Calculate an updated loading coefficient according to the corrected rationality coefficient; Calculate multiple loading coefficients according to the multiple state loading coefficients and the updated loading coefficient, and classify multiple loading accuracy information according to the falling loading coefficient interval, where multiple sample loading coefficient intervals correspond to multiple sample loading accuracy information; Perform BIM model loading at multiple nodes according to the construction progress information respectively according to the multiple loading accuracy information, and annotate the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient.

[0067] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0068] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows 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 the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0070] These computer program instructions can 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, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0072] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic inventive concept.

[0073] Obviously, those skilled in the art can make various changes and variations 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 equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A BIM model loading and processing method for construction progress records, characterized in that, The method includes: Receiving the construction progress information of the latest uploaded record, combining with the historical construction progress information sequence within the recently preset time range, and analyzing to obtain the rationality coefficient of the construction progress information; Indexing to obtain the 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; Analyzing at least one omission rate of the at least one abnormal construction progress information, performing a correction calculation on the rationality coefficient, and obtaining a corrected rationality coefficient; Obtaining multiple node state parameters of multiple nodes, combining with the corrected rationality coefficient, configuring multiple loading accuracy information, performing BIM model loading according to the construction progress information, and marking the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient.

2. The BIM model loading and processing method for construction progress records according to claim 1, wherein Receiving the construction progress information of the latest uploaded record, combining with the historical construction progress information sequence within the recently preset time range, and analyzing to obtain the rationality coefficient of the construction progress information, including: Receiving the construction progress information of the latest uploaded record, where the construction progress information is uploaded by any one of multiple secondary nodes and multiple nodes, and includes construction progress steps and construction progress timestamps; Obtaining the historical construction progress information sequence of the uploaded records within the recently preset time range, where the historical construction progress information sequence includes timestamps; Analyzing to obtain the rationality coefficient of the construction progress information according to the historical construction progress information sequence.

3. The BIM model loading and processing method for construction progress records according to claim 2, characterized in that, Analyzing to obtain the rationality coefficient of the construction progress information according to the historical construction progress information sequence, including: Collecting a sample historical construction progress information sequence set and a sample construction progress information set according to the BIM model loading data of similar buildings, and marking to obtain a sample rationality coefficient set, where the rationality coefficient is greater than or equal to 0 and less than or equal to 1; Using machine learning to construct a rationality analyzer; Training the rationality analyzer with the sample historical construction progress information sequence set, the sample construction progress information set and the sample rationality coefficient set until convergence; Inputting the construction progress information and the historical construction progress information sequence into the rationality analyzer, and identifying and outputting to obtain the rationality coefficient.

4. The BIM model loading and processing method for construction progress records according to claim 1, characterized in that Indexing to obtain the 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, including: Obtaining the theoretical construction progress information sequence corresponding to the BIM model, and indexing the theoretical historical construction progress information sequence within the preset historical time range before the construction progress steps in the construction progress information; Comparing the theoretical historical construction progress step sequence and the historical construction progress step sequence in the theoretical historical construction progress information sequence and the historical construction progress information sequence, and screening at least one abnormal construction progress information corresponding to at least one inconsistent abnormal construction progress step when they are inconsistent.

5. The BIM model loading and processing method for construction progress records according to claim 1, characterized in that Analyzing at least one omission rate of the at least one abnormal construction progress information, performing a correction calculation on the rationality coefficient, and obtaining a corrected rationality coefficient, including: Extract the recorded data of at least one abnormal construction progress step within the at least one abnormal construction progress information according to the construction progress record data of multiple buildings of the same type, and obtain at least one abnormal construction progress step data set; Analyze and screen the proportion of missing records of at least one abnormal construction progress step in the at least one abnormal construction progress step data set, obtain at least one abnormal omission rate, and calculate the mean value to obtain the omission rate; According to the omission rate, perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

6. The BIM model loading and processing method for construction progress records according to claim 5, wherein According to the omission rate, perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient, including: Calculate an omission correction coefficient according to the omission rate; Use the omission correction coefficient to perform a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient.

7. The BIM model loading and processing method for construction progress records according to claim 1, characterized in that Obtain multiple node state parameters of multiple nodes, combine the corrected rationality coefficient, configure multiple loading accuracy information, perform BIM model loading according to the construction progress information, and label the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient, including: Obtain multiple node state parameters of multiple nodes, where each node state parameter includes a computing power parameter; Calculate multiple state loading coefficients according to the multiple node state parameters combined with the standard node state parameters; Calculate an updated loading coefficient according to the corrected rationality coefficient; Calculate multiple loading coefficients according to the multiple state loading coefficients and the updated loading coefficient, and classify to obtain multiple loading accuracy information according to the falling loading coefficient interval, where multiple sample loading coefficient intervals correspond to multiple sample loading accuracy information; Perform BIM model loading at multiple nodes according to the multiple loading accuracy information respectively according to the construction progress information, and label the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient.

8. A BIM model loading and processing system for construction progress record, characterized in that For implementing the BIM model loading processing method for construction progress recording as described in any one of claims 1 to 7, the system includes: A rationality analysis module for receiving the construction progress information of the latest uploaded record, and analyzing and obtaining the rationality coefficient of the construction progress information in combination with the historical construction progress information sequence within the most recent preset time range; An abnormal progress screening module for indexing and obtaining the theoretical historical construction progress information sequence according to the construction progress information, and screening and obtaining 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, and performing a correction calculation on the rationality coefficient to obtain a corrected rationality coefficient; A BIM module labeling module for obtaining multiple node state parameters of multiple nodes, combining the corrected rationality coefficient, configuring multiple loading accuracy information, performing BIM model loading according to the construction progress information, and labeling the loaded BIM module corresponding to the construction progress information through the corrected rationality coefficient.

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