Construction operation management method and system based on Beidou digital twinning

Abstract

The invention discloses a construction operation management method and system based on Beidou digital twinning, and relates to the related field of intelligent management, and the method comprises the steps: carrying out the three-dimensional scanning of a target construction region, constructing the construction space data, carrying out the digital twinning based on the Beidou high-precision positioning technology in combination with the construction space data, constructing a digital twinning model, and carrying out the calculation of the digital twinning model. The method comprises the following steps: performing sensing acquisition on a target construction area by using a sensor group to obtain a construction sensing data set, synchronizing to a digital twin model based on the construction sensing data set to perform data matching, obtaining a data matching result, and combining with the digital twin model to formulate a construction operation management scheme to perform intelligent operation management on the target construction area. The technical problems that the construction efficiency is low and the construction progress is slow due to the fact that traditional construction operation depends on manual recording and reporting are solved, and the technical effects that the digital twinborn model is accurately constructed through Beidou digital acquisition to monitor and manage the construction operation, and the construction efficiency and progress are improved are achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent management, and in particular to a construction operation management method and system based on BeiDou digital twins. Background Technology

[0002] With the rapid development and transformation of the construction industry, especially the emergence of large-scale construction projects and the application of new technologies and materials, construction management faces greater challenges. Meanwhile, existing construction operation management often relies on manual recording and reporting, making it difficult to comprehensively and accurately assess safety risks at construction sites, and hindering the timely detection and prevention of potential safety hazards. This results in a lack of specific operational guidelines and lax enforcement of effective quality control and safety supervision mechanisms, leading to chaotic construction site management, serious resource waste, and severely impacting construction efficiency and schedule. Summary of the Invention

[0003] This application provides a construction operation management method and system based on BeiDou digital twins, which solves the technical problems of low construction efficiency and slow construction progress caused by the reliance on manual recording and reporting in traditional construction operations. It achieves the technical effect of improving construction efficiency and progress by accurately constructing a digital twin model through BeiDou digital data collection to monitor and manage construction operations.

[0004] This application provides a construction operation management method based on BeiDou digital twins. The method is applied to a construction operation management system based on BeiDou digital twins, including: constructing construction space data by performing three-dimensional scanning of the target construction area; constructing a digital twin model by combining the construction space data with BeiDou high-precision positioning technology; collecting data from the target construction area using a sensor array to obtain a construction sensor dataset; synchronizing the construction sensor dataset to the digital twin model for data matching to obtain a data matching result; analyzing the data matching result and combining it with the digital twin model to formulate a construction operation management plan; and executing the construction operation management plan to perform intelligent operation management of the target construction area.

[0005] In one possible implementation, construction space data is constructed by performing a 3D scan of the target construction area, and the following processing is performed: the target construction area is scanned using a 3D scanner to obtain multiple 3D point cloud data of the target construction area; the multiple 3D point cloud data are denoised to extract the geometric information set of the target construction area; the geometric information set is registered and stitched according to the structural diagram of the target construction area to determine the construction space data.

[0006] In one possible implementation, a digital twin model is constructed by combining BeiDou high-precision positioning technology with the construction space data, and the following processing is performed: using BeiDou high-precision positioning technology with the construction space data to determine multiple construction space location information, which includes multiple construction equipment location information and multiple construction personnel location information; mapping the multiple construction equipment location information and the multiple construction personnel location information to the construction space data to obtain dynamic construction scene information; and constructing the digital twin model based on the dynamic construction scene information.

[0007] In a possible implementation, the digital twin model is constructed based on the construction dynamic scene information, and the following processes are performed: historical construction logs are retrieved, and the construction dynamic scene information is matched with the historical construction logs to determine the construction progress dynamic dataset; the construction progress dynamic dataset is normalized, and the construction progress dynamic dataset is integrated with the construction space data according to the processing result to generate an integration result; the integration result is validated, and if the validation is successful, the digital twin model of the target construction area is output.

[0008] In a possible implementation, the construction sensor dataset is synchronized to the digital twin model for data matching to obtain data matching results. The following processing is then performed: the construction sensor dataset is clustered according to the location information of the multiple construction equipment and the location information of the multiple construction personnel to generate clustering analysis results; multiple construction operation features of the target construction area are extracted based on the clustering analysis results; the multiple construction operation features are synchronized to the digital twin model for comparison and matching to obtain the construction operation change trend; and the construction operation change trend is added to the data matching results.

[0009] In a possible implementation, the construction operation management plan is formulated by analyzing the data matching results and combining them with the digital twin model, and the following processes are performed: a construction quality assessment is conducted based on the data matching results to generate a construction quality assessment result; data mining is performed on the digital twin model based on the construction quality assessment result to determine multiple construction indicators; the target construction area is predicted according to the construction operation cycle using the digital twin model to generate a construction operation prediction dataset; the multiple construction indicators are used as index information to traverse the construction operation prediction dataset and generate construction optimization suggestions; the construction optimization suggestions are added to the construction operation management plan.

[0010] In a possible implementation, a construction quality assessment is performed based on the data matching results to generate a construction quality assessment result. The following processes are then performed: construction expected quality data is extracted based on the construction quality assessment result to construct a construction time-varying model; multiple construction operation nodes are divided based on the construction operation cycle; data is processed according to the multiple construction operation nodes using the construction time-varying model to generate operation instructions; construction behavior analysis is performed based on the construction trajectory information using the operation instructions to generate construction behavior analysis results; and the construction quality assessment result is generated based on the construction behavior analysis results.

[0011] This application also provides a construction operation management system based on BeiDou digital twin, comprising: a first construction module, used to construct construction space data by performing 3D scanning of the target construction area; a digital twin module, used to construct a digital twin model by combining the construction space data with BeiDou high-precision positioning technology; a data acquisition module, used to use a sensor group to perform sensing and data acquisition on the target construction area to obtain a construction sensing dataset; a data matching module, used to perform data matching based on the construction sensing dataset synchronized to the digital twin model to obtain a data matching result; and an operation management module, used to analyze the data matching result and, in conjunction with the digital twin model, formulate a construction operation management plan, and execute the construction operation management plan to perform intelligent operation management of the target construction area.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages: The construction operation management method and system based on BeiDou digital twin provided in this application belong to the field of intelligent management technology. It solves the technical problems of low construction efficiency and slow construction progress caused by the reliance on manual recording and reporting in traditional construction operations. It achieves the technical effect of improving construction efficiency and progress by accurately constructing a digital twin model through BeiDou digital data collection to monitor and manage construction operations. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings of the embodiments of this application will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0014] Figure 1 A flowchart illustrating the construction operation management method based on BeiDou digital twin provided in this application embodiment; Figure 2 A schematic diagram of the structure of the construction operation management system based on BeiDou digital twin provided in the embodiments of this application. Detailed Implementation

[0015] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0018] This application provides a construction operation management method based on BeiDou digital twins. The method is applied to a construction operation management system based on BeiDou digital twins, such as... Figure 1 As shown, the method includes: Step A100 involves constructing construction space data by performing a 3D scan of the target construction area. In one possible implementation, step A100 further includes step A110, which involves scanning the target construction area using a 3D scanner to obtain multiple 3D point cloud data of the target construction area; step A120, which involves denoising the multiple 3D point cloud data to extract the geometric information set of the target construction area; and step A130, which involves registering and stitching the geometric information set according to the structural diagram of the target construction area to determine the construction space data.

[0019] First, a suitable 3D scanner, such as a laser scanner or structured light scanner, is selected based on the size, complexity, and environmental conditions of the construction area. Then, multiple local areas within the target construction area are scanned sequentially using the 3D scanner, generating point cloud data containing a large number of 3D points. This multiple 3D point cloud data can be used to characterize the 3D morphology of the construction area. However, due to potential influences from environmental noise and equipment errors during the scanning process, the point cloud data needs to be denoised to eliminate outliers and noise. Further, statistical filtering, radius filtering, and voxel filtering can be used to denoise the multiple 3D point cloud data. Statistical filtering refers to filtering based on the spatial distribution density of points to remove isolated points or points with abnormal density. Radius filtering involves setting a radius threshold and deleting points whose average distance from their neighbors exceeds that threshold. Voxel filtering involves dividing the point cloud data into small 3D voxels (similar to 3D pixels) and replacing these points with the centroids of all points within each voxel to reduce the number of points.

[0020] Furthermore, extracting geometric information of the target construction area, such as planes, surfaces, and boundaries, from the denoised point cloud data involves using algorithms (such as RANSAC and Hough transform) to extract features like lines, circles, and planes from the point cloud. Then, algorithms such as Poisson surface reconstruction and implicit surface reconstruction are used to convert the multiple denoised 3D point cloud data into a continuous 3D surface model. Based on this, the length, area, volume, and other parameters of the extracted geometric features are calculated. Finally, the geometric information sets of multiple extracted local regions are registered and stitched together according to the structural diagram of the target construction area to form a complete construction space data model. This process involves identifying features in different point cloud data... The matching is performed based on the same features (such as corners, boundaries, etc.). Then, the transformation relationship between the two point clouds is estimated by iteratively finding the nearest point pair between them. The transformation relationship may include rotation and translation. The cumulative error in the registration process is eliminated by using global optimization algorithms such as bundle adjustment and graph optimization. After registration and stitching, a construction space data containing complete three-dimensional geometric information of the target construction area is obtained. The construction space data can be used for pre-construction planning and simulation to help determine the construction sequence, material requirements, etc. Collision detection is performed before construction to avoid collisions and conflicts during construction. It can also be compared with the actual construction progress to monitor the construction progress and quality.

[0021] Step A200 involves constructing a digital twin model based on BeiDou high-precision positioning technology combined with the construction space data. In one possible implementation, step A200 further includes step A210, using BeiDou high-precision positioning technology combined with the construction space data to determine multiple construction space location information, including multiple construction equipment location information and multiple construction personnel location information. Step A220 involves mapping the multiple construction equipment location information and the multiple construction personnel location information to the construction space data to obtain dynamic construction scene information. Because the positioning data provided by the BeiDou system has high accuracy, it can meet the needs of construction management. Therefore, by utilizing BeiDou high-precision positioning technology, multiple construction equipment and personnel within the construction area can be located in real time. During the positioning process, the BeiDou system can receive and process satellite signals in real time, calculate the precise location information of the construction equipment and personnel, and integrate the acquired location information of multiple construction equipment and personnel to form a complete construction spatial location information dataset, namely, multiple construction spatial location information. The multiple construction spatial location information includes multiple construction equipment location information and multiple construction personnel location information. The multiple construction spatial location information not only includes specific coordinate data, but may also include additional information such as the operating status of the equipment and the activity trajectory of the personnel.

[0022] Furthermore, mapping the integrated construction space location information onto the previously collected construction space data refers to matching and corresponding the actual location information with data such as the layout and design drawings of the construction site. Through mapping, the specific locations of construction equipment and personnel on the construction site, as well as their relationship with the surrounding environment, can be obtained more clearly. Based on the mapped construction space location information, dynamic construction scene information is constructed, which means transforming static construction space data into dynamic, real-time construction scenes. The dynamic construction scene information can include information such as the operating trajectory of construction equipment, the activity range of construction personnel, and construction progress, which can comprehensively reflect the actual situation of the construction site. By using the dynamic construction scene information, combined with relevant digital technologies and software tools, a digital twin model is constructed.

[0023] Perform step A230 to construct the digital twin model based on the construction dynamic scene information.

[0024] In one possible implementation, step A200 further includes step A231, retrieving historical construction record logs, matching the construction dynamic scene information with the historical construction record logs, and determining the construction progress dynamic dataset; executing step A232, normalizing the construction progress dynamic dataset, and integrating the construction progress dynamic dataset with the construction space data according to the processing result to generate an integration result; executing step A233, validating the integration result, and outputting the digital twin model of the target construction area when the validation is successful.

[0025] By retrieving historical construction logs related to the current construction project from the construction management system—logs that record detailed information about each construction stage, such as progress, quality, and safety data—the system then iterates through the currently collected dynamic construction scenario information, including the real-time location and status of construction equipment and personnel, and matches it with the historical construction logs. Through comparison and analysis, it identifies historical construction records corresponding to the current dynamic construction scenario to obtain key information such as construction progress and critical nodes. Based on the matching results, a dynamic construction progress dataset is then compiled. This dynamic dataset includes real-time progress information for the current construction stage, comparison results with historical construction records, and any possible deviations or anomalies. The dataset is then normalized to ensure data consistency and comparability. Normalization may include data standardization and scaling to better utilize this data in subsequent data integration and analysis. Finally, the normalized dynamic construction progress dataset is integrated with construction spatial data—that is, by combining dynamic data with static geospatial data, building models, etc.—to form a complete construction scenario containing real-time information.

[0026] Finally, the integration results are validated for effectiveness. This validation process may include checking the completeness, accuracy, and consistency of the data to ensure that the generated digital twin model accurately reflects the actual conditions of the construction site. Validation methods may include data quality checks and model simulation tests to ensure the model's accuracy and reliability in prediction and optimization. Once validation is successful, the generated integration results are output as the digital twin model of the target construction area. The digital twin model contains detailed information such as the real-time progress of the construction site, the location of personnel and equipment, and the status of key nodes. It supports further analysis, simulation, and optimization operations, providing strong data support and decision-making basis for construction project management.

[0027] Step A300 involves using a sensor array to collect data from the target construction area, obtaining a construction sensor dataset. First, a suitable sensor array is selected based on the specific needs and characteristics of the target construction area. These sensors may include temperature sensors, humidity sensors, pressure sensors, displacement sensors, vibration sensors, etc., used to monitor different types of construction parameters. Then, the sensor array is deployed at key locations within the target construction area. These key locations may include critical structural points of buildings, operating parts of construction equipment, and monitoring points in the soil or foundation. This ensures that the sensor deployment comprehensively covers the construction area and accurately reflects the actual situation of construction activities.

[0028] Furthermore, the sensor array is connected to the data acquisition system to ensure that the sensors can transmit data to the data processing center in real time. Based on the type and function of the sensor array, necessary configuration and calibration are performed to ensure data accuracy and reliability. The sensor array then collects data from the target construction area. It can monitor various parameters during the construction process in real time, such as temperature, humidity, pressure, displacement, and vibration, and convert these parameters into digital signals for transmission.

[0029] The system can also receive digital signals from the sensor array via its embedded data acquisition unit and convert them into a readable construction sensor dataset. This dataset can contain real-time data from the construction process, reflecting the dynamic changes and status of construction activities. The construction sensor dataset provides real-time and accurate data support for the construction of a digital twin model, facilitating real-time monitoring and management of the construction process.

[0030] Step A400 involves synchronizing the construction sensor dataset to the digital twin model for data matching to obtain a data matching result. In one possible implementation, step A400 further includes step A410, performing cluster analysis on the construction sensor dataset according to the location information of the multiple construction equipment and the location information of the multiple construction personnel to generate a cluster analysis result. Step A420 involves extracting multiple construction operation features of the target construction area based on the cluster analysis result. Step A430 involves synchronizing the multiple construction operation features to the digital twin model for comparison and matching to obtain a construction operation change trend. Step A440 involves adding the construction operation change trend to the data matching result.

[0031] Various clustering algorithms, such as K-means, hierarchical clustering, and DBSCAN, are used to perform cluster analysis on the construction sensor dataset based on the location information of construction equipment and personnel. The goal of cluster analysis is to group similar data points, i.e., equipment or personnel in close proximity, into different clusters or groups. After the cluster analysis is completed, cluster analysis results are generated. These results may include the number of clusters, the centroid of each cluster, and the number of data points in each cluster. Based on the cluster analysis results, multiple construction operation characteristics of the target construction area are extracted, i.e., these characteristics are determined by analyzing the number, location, and changes of data points in the clusters. These multiple construction operation characteristics may include the construction intensity, construction efficiency, equipment usage, and personnel distribution of different work areas or work surfaces.

[0032] Furthermore, the extracted construction operation features are synchronized into a digital twin model to simulate the actual construction scenario, and then further analyzed and predicted. In the digital twin model, the synchronized construction operation features are compared and matched with expected or historical data in the model to identify differences or deviations between actual construction and expectations. Through the comparison and matching results, the changing trends of construction operations are analyzed and obtained. These trends may include faster or slower construction progress, increased or decreased resource demand, and improved or decreased construction quality. Finally, the obtained construction operation changing trends are added to the data matching results. The data matching results can be a set of multiple indicators and trends, used to comprehensively reflect the construction status of the target construction area. Based on the construction operation changing trends in the data matching results, further decisions and plans can be made. For example, construction plans can be adjusted, resource allocation optimized, and quality control strengthened, leading to a better understanding and control of the construction process and improved efficiency and quality of construction management.

[0033] Next, step A500 is executed, where the data matching results are analyzed and the digital twin model is used to formulate a construction operation management plan. The construction operation management plan is then executed to perform intelligent operation management on the target construction area.

[0034] In one possible implementation, step A500 further includes step A510, which involves performing a construction quality assessment based on the data matching results to generate a construction quality assessment result; in another possible implementation, step A510 further includes step A511, which involves extracting expected construction quality data based on the construction quality assessment results and constructing a construction time-varying model; step A512, which involves dividing the construction operation cycle into multiple construction operation nodes; step A513, which involves processing data according to the multiple construction operation nodes using the construction time-varying model to generate operation instructions; step A514, which involves performing construction behavior analysis based on the construction trajectory information and executing the operation instructions to generate construction behavior analysis results; and step A515, which involves generating the construction quality assessment result based on the construction behavior analysis results.

[0035] First, based on historical construction data, project requirements, and design standards, expected construction quality data is extracted. This expected quality data defines the quality standards and parameter ranges for each stage of the construction process and serves as the foundation for construction quality control and evaluation. It also provides an important reference for subsequently constructing a time-varying construction model. The model is built using mathematical models and algorithms such as time series analysis and machine learning. This time-varying model should accurately capture key quality factors during construction and predict their changing trends. It can be used to characterize the dynamic changes in quality over time during construction. Based on the expected construction quality data, combined with factors such as construction progress and environmental conditions, the dynamic changes in construction quality can be predicted and simulated.

[0036] Furthermore, based on the construction cycle and progress, the construction process is divided into multiple construction work nodes. Each node represents a specific construction stage or task, with clear timeframes and quality requirements. Simultaneously, a pre-constructed time-varying construction model is used to process and analyze data from these work nodes. The model predicts the construction quality and potential quality risks of the next work node based on current construction progress, environmental conditions, and other factors. Based on the prediction results of the time-varying construction model, specific work instructions are generated. These work instructions may include adjustments to construction parameters, optimization of construction methods, and quality control measures, aiming to ensure that construction quality and progress meet expectations.

[0037] Further collection of trajectory information during the construction process, such as the movement trajectories of construction equipment and personnel, and operation records, is conducted. This information reflects the actual situation and changes in construction activities. Based on this trajectory information, work instructions are executed, and the construction activities are analyzed. This involves comparing the actual construction activities with the expected activities generated based on a time-varying construction model to assess the rationality and effectiveness of the construction activities. Finally, a construction activity analysis report is generated based on the results of the analysis. The report includes the actual situation of the construction activities, a comparison with the expected activities, existing problems, and improvement suggestions. The analysis results help identify problems in the construction process, providing guidance for subsequent construction quality improvement and optimization. Combined with the analysis results and other relevant information (such as quality inspection data and acceptance results), a comprehensive assessment of construction quality is conducted. The assessment results reflect the actual level and compliance of construction quality. Ultimately, the construction quality assessment results will be used as feedback to adjust and optimize the time-varying construction model to improve the accuracy and effectiveness of subsequent construction operations.

[0038] Step A520: Based on the construction quality assessment results, perform data mining on the digital twin model to determine multiple construction indicators; Step A530: Use the digital twin model to predict the target construction area according to the construction operation cycle to generate a construction operation prediction dataset; Step A540: Use the multiple construction indicators as index information to traverse the construction operation prediction dataset and generate construction optimization suggestions; Step A550: Add the construction optimization suggestions to the construction operation management scheme.

[0039] First, the constructed digital twin model is used to perform data mining on historical construction data to analyze key parameters and patterns during the construction process. Through data mining, multiple construction indicators are identified, including construction efficiency, resource utilization, and safety risks. Then, the digital twin model is used to predict the target construction area according to the construction cycle. This includes predicting the progress, resource requirements, and potential quality issues for each construction stage. The prediction results are compiled into a construction operation prediction dataset for subsequent analysis and decision-making.

[0040] Furthermore, using defined construction indicators as index information to traverse the construction operation prediction dataset involves analyzing potential problems and areas for improvement in the current construction plan based on the prediction results and construction indicators, and generating specific construction optimization suggestions. These suggestions may include adjusting the construction schedule, optimizing resource allocation, and improving construction methods. These optimization suggestions are then added to the construction operation management plan. This addition process may involve updating the construction plan, adjusting resource allocation, modifying construction methods, or introducing new quality control measures, ensuring that all relevant personnel understand and accept these changes to guarantee the smooth progress of construction activities. During construction, the difference between the actual execution and the prediction results is continuously monitored, and adjustments are made as needed. Actual construction data is collected to update the digital twin model and the time-varying construction model to improve the accuracy of predictions and the applicability of the models.

[0041] By implementing intelligent operation management plans for target construction areas, construction efficiency can be significantly improved, construction costs reduced, and construction quality and safety enhanced. Furthermore, intelligent operation management facilitates visualization and traceability of the construction process, providing strong support for project management and decision-making.

[0042] This application's embodiments solve the technical problem that traditional construction operations rely on manual recording and reporting, resulting in low construction efficiency and slow construction progress. It achieves the technical effect of using BeiDou digital data collection to accurately construct a digital twin model for monitoring and management of construction operations, thereby improving construction efficiency and progress.

[0043] In the above text, refer to Figure 1 This paper describes in detail a construction operation management method based on BeiDou digital twin according to embodiments of this application. Next, we will refer to... Figure 2 This application describes a construction operation management system based on BeiDou digital twins according to embodiments of the present application.

[0044] The BeiDou-based digital twin-based construction operation management system, according to an embodiment of this application, solves the technical problems of low construction efficiency and slow progress caused by the reliance on manual recording and reporting in traditional construction operations. It achieves the technical effect of improving construction efficiency and progress by accurately constructing a digital twin model through BeiDou digital data acquisition to monitor and manage construction operations. The BeiDou-based digital twin-based construction operation management system includes: a first construction module 10, a digital twin module 20, a data acquisition module 30, a data matching module 40, and an operation management module 50.

[0045] The first construction module 10 is used to construct construction space data by performing a three-dimensional scan of the target construction area. Digital twin module 20, which is used to construct a digital twin model by combining the construction space data with Beidou high-precision positioning technology; Data acquisition module 30 is used to use a sensor group to perform sensing and data acquisition on the target construction area to obtain a construction sensing dataset. Data matching module 40, the data matching module 40 is used to perform data matching based on the construction sensor dataset synchronized to the digital twin model, and obtain data matching results; The operation management module 50 is used to analyze the data matching results and combine them with the digital twin model to formulate a construction operation management plan, and execute the construction operation management plan to carry out intelligent operation management of the target construction area.

[0046] The specific configuration of the first construction module 10 will be described in detail below. As mentioned above, the first construction module 10 can further include: a scanning unit for scanning the target construction area using a 3D scanner to obtain multiple 3D point cloud data of the target construction area; a denoising unit for denoising the multiple 3D point cloud data and extracting the geometric information set of the target construction area; and a registration and stitching unit for registering and stitching the geometric information set according to the structural diagram of the target construction area to determine the construction space data.

[0047] The specific configuration of the digital twin module 20 will be described in detail below. As mentioned above, based on BeiDou high-precision positioning technology combined with the construction space data, a digital twin model is constructed. The digital twin module 20 may further include: a positioning unit used to perform positioning using BeiDou high-precision positioning technology combined with the construction space data to determine multiple construction space location information, which includes multiple construction equipment location information and multiple construction personnel location information; a mapping unit used to map the multiple construction equipment location information and the multiple construction personnel location information to the construction space data to obtain construction dynamic scene information; and a second construction unit used to construct the digital twin model based on the construction dynamic scene information.

[0048] The specific configuration of the digital twin module 20 will be described in detail below. As mentioned above, the digital twin module 20, which constructs the digital twin model based on the construction dynamic scene information, may further include: a first matching unit for retrieving historical construction record logs, traversing the construction dynamic scene information and matching it with the historical construction record logs to determine the construction progress dynamic dataset; a normalization processing unit for normalizing the construction progress dynamic dataset, integrating the construction progress dynamic dataset with the construction space data according to the processing result, and generating an integration result; and a verification unit for validating the integration result, outputting the digital twin model of the target construction area when the verification is successful.

[0049] The specific configuration of the data matching module 40 will be described in detail below. As mentioned above, based on the construction sensor dataset synchronized to the digital twin model for data matching to obtain data matching results, the data matching module 40 may further include: a first analysis unit for performing cluster analysis on the construction sensor dataset according to the location information of the multiple construction equipment and the location information of the multiple construction personnel, and generating cluster analysis results; a feature extraction unit for extracting multiple construction operation features of the target construction area according to the cluster analysis results; a second matching unit for synchronizing the multiple construction operation features to the digital twin model for comparison and matching to obtain the construction operation change trend; and a first adding unit for adding the construction operation change trend to the data matching result.

[0050] The specific configuration of the operation management module 50 will be described in detail below. As mentioned above, the operation management module 50, which analyzes the data matching results and combines them with the digital twin model to formulate a construction operation management plan, may further include: a quality assessment unit for performing construction quality assessment based on the data matching results and generating construction quality assessment results; a data mining unit for performing data mining on the digital twin model based on the construction quality assessment results and determining multiple construction indicators; a prediction unit for predicting the target construction area according to the construction operation cycle using the digital twin model and generating a construction operation prediction dataset; a traversal unit for using the multiple construction indicators as index information to traverse the construction operation prediction dataset and generate construction optimization suggestions; and a second adding unit for adding the construction optimization suggestions to the construction operation management plan.

[0051] The specific configuration of the operation management module 50 will be described in detail below. As mentioned above, based on the data matching results, a construction quality assessment is performed, and a construction quality assessment result is generated. The operation management module 50 may further include: a data extraction unit for extracting expected construction quality data based on the construction quality assessment result and constructing a construction time-varying model; a node division unit for dividing multiple construction operation nodes based on the construction operation cycle; a second analysis unit for processing data according to the multiple construction operation nodes through the construction time-varying model and generating operation instructions; a third analysis unit for performing construction behavior analysis based on the construction trajectory information and executing the operation instructions, and generating construction behavior analysis results; and a result generation unit for generating the construction quality assessment result based on the construction behavior analysis results.

[0052] The construction operation management system based on BeiDou digital twin provided in this application can execute the construction operation management method based on BeiDou digital twin provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this application.

[0054] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A construction operation management method based on BeiDou digital twins, characterized in that, The method includes: Construction space data is constructed by performing 3D scanning of the target construction area; A digital twin model is constructed by combining the construction space data with BeiDou high-precision positioning technology. The target construction area is sensor-collected using a sensor array to obtain a construction sensor dataset; The construction sensor dataset is synchronized to the digital twin model for data matching to obtain the data matching result; The data matching results are analyzed and combined with the digital twin model to formulate a construction operation management plan, and the construction operation management plan is executed to carry out intelligent operation management of the target construction area.

2. The construction operation management method based on BeiDou digital twin as described in claim 1, characterized in that, Construction space data is constructed by performing 3D scanning of the target construction area. The methods include: The target construction area is scanned using a 3D scanner to obtain multiple 3D point cloud data of the target construction area; The multiple 3D point cloud data are denoised to extract the geometric information set of the target construction area; The geometric information set is registered and stitched together according to the structural diagram of the target construction area to determine the construction space data.

3. The construction operation management method based on BeiDou digital twin as described in claim 1, characterized in that, A digital twin model is constructed based on BeiDou high-precision positioning technology combined with the aforementioned construction space data. The method includes: Using BeiDou high-precision positioning technology in conjunction with the construction space data, multiple construction space location information is determined, including multiple construction equipment location information and multiple construction personnel location information. The location information of the multiple construction equipment and the location information of the multiple construction personnel are mapped to the construction space data to obtain dynamic construction scene information. The digital twin model is constructed based on the construction dynamic scene information.

4. The construction operation management method based on BeiDou digital twin as described in claim 3, characterized in that, The method for constructing the digital twin model based on the aforementioned construction dynamic scene information includes: Retrieve historical construction log records, iterate through the construction dynamic scene information and match it with the historical construction log records to determine the construction progress dynamic dataset; The dynamic dataset of construction progress is normalized, and the dynamic dataset of construction progress is integrated with the construction space data based on the processing result to generate an integration result. The integration results are validated. If the validation passes, the digital twin model of the target construction area is output.

5. The construction operation management method based on BeiDou digital twin as described in claim 3, characterized in that, The method involves synchronizing the construction sensor dataset to the digital twin model for data matching to obtain the data matching result, and includes: The construction sensor dataset is clustered according to the location information of the multiple construction equipment and the location information of the multiple construction personnel to generate clustering analysis results; Based on the cluster analysis results, multiple construction operation features of the target construction area are extracted; The multiple construction operation features are synchronized to the digital twin model for comparison and matching to obtain the trend of construction operation changes; The trend of construction operations is added to the data matching results.

6. The construction operation management method based on BeiDou digital twin as described in claim 1, characterized in that, The method for developing a construction operation management plan by analyzing the data matching results and combining them with the digital twin model includes: Based on the data matching results, a construction quality assessment is performed, and a construction quality assessment result is generated. Data mining is performed on the digital twin model based on the construction quality assessment results to determine multiple construction indicators; The digital twin model is used to predict the target construction area according to the construction operation cycle, generating a construction operation prediction dataset. Using the multiple construction indicators as index information, the construction operation prediction dataset is traversed to generate construction optimization suggestions. Add the construction optimization suggestions to the construction operation management plan.

7. The construction operation management method based on BeiDou digital twin as described in claim 6, characterized in that, Based on the data matching results, a construction quality assessment is performed to generate a construction quality assessment result. The method includes: Based on the construction quality assessment results, expected construction quality data are extracted, and a construction time-varying model is constructed. The construction operation cycle is divided into multiple construction operation nodes; The construction time-varying model is used to process data according to the multiple construction operation nodes to generate operation instructions. Based on the construction trajectory information, the operation instructions are executed to perform construction behavior analysis and generate construction behavior analysis results. The construction quality assessment results are generated based on the analysis results of the construction behavior.

8. A construction operation management system based on BeiDou digital twin, characterized in that: The system includes: The first construction module is used to construct construction space data by performing a three-dimensional scan of the target construction area; A digital twin module, which is used to create a digital twin model based on the construction space data using BeiDou high-precision positioning technology; The data acquisition module is used to use a sensor group to sense and collect data on the target construction area to obtain a construction sensor dataset. The data matching module is used to perform data matching based on the construction sensor dataset synchronized to the digital twin model, and obtain data matching results; The operation management module is used to analyze the data matching results, combine them with the digital twin model to formulate a construction operation management plan, and execute the construction operation management plan to carry out intelligent operation management of the target construction area.

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