A lean construction management method and system for reservoirs based on BIM technology

Abstract

The present invention relates to the technical field of lean construction management, and in particular to a lean construction management method and system for a reservoir based on BIM technology. The method comprises the following steps: obtaining incoming material message data and real-time sensor data; preprocessing the incoming material message data, and associating the physical data set through a preset BIM model to generate a BIM reservoir construction model data set; using real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model; therefore, the present invention solves the problems of information lag and untimely response in traditional construction management by integrating multi-source data, realizing real-time monitoring and dynamic optimization, and improves the safety and efficiency of the reservoir construction process.

Description

Technical Field

[0001] The present invention relates to the technical field of lean construction management, and in particular to a lean construction management method and system for a reservoir based on BIM technology. Background Art

[0002] In modern construction management, there are many complex challenges. The control and management of construction progress is a key factor in ensuring the timely delivery of engineering projects. However, there are often information islands on the construction site, and various data sources are not unified, which makes it difficult to monitor and adjust the construction progress in real time. In addition, problems such as delayed material supply and high worker mobility will inevitably occur during the construction process, which will lead to construction delays and affect the overall progress of the project. At the same time, traditional construction management methods often rely on static schedules and lack effective use of real-time data, making construction management incapable of responding to emergencies. In addition, insufficient environmental monitoring at the construction site has failed to promptly detect the impact of dust, noise, etc. on the surrounding environment, further increasing the difficulty of management. All these problems have jointly restricted the refined management of the construction process, increased the cost and risk of the project, and reduced construction efficiency and quality. Summary of the invention

[0003] Based on this, it is necessary to provide a lean construction management method and system for reservoirs based on BIM technology to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a lean construction management method and system for a reservoir based on BIM technology is provided, the method comprising the following steps:

[0005] Step S1: Acquire incoming material message data and real-time sensor data; perform data preprocessing on the incoming material message data, and associate the physical data set with the preset BIM model to generate a BIM reservoir construction model data set; use the real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model;

[0006] Step S2: extracting construction site monitoring video data from real-time sensor data; extracting person-vehicle video key frames from the construction site monitoring video data to generate person-vehicle key frame data; performing image recognition on the person-vehicle key frame data to generate construction site identification data; performing GIS integrated construction risk simulation on the construction site identification data and the reservoir construction-plan twin model to generate construction progress simulation data;

[0007] Step S3: acquiring real-time construction progress data and historical engineering construction data; performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; comparing the historical construction progress comparison data of the reservoir according to preset key stage values, and performing construction status analysis with drawing data to generate construction lag item data; generating solutions based on the construction lag item data to generate a reservoir construction progress solution;

[0008] Step S4: Use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model to generate a real-time reservoir construction-planning twin model; perform data visualization on the reservoir real-time construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0009] The beneficial effect of the present invention is that by constructing a reservoir construction-plan twin model, the incoming material message data and sensor data are systematically integrated and monitored in real time, thereby achieving comprehensive control of the reservoir construction process. First, the construction of the BIM model and the real-time data assignment enable the dynamic state of the construction site to be reflected and analyzed in a timely manner. This real-time empowerment not only improves the accuracy and real-time nature of the data, but also helps to identify potential risks in a timely manner, thereby carrying out effective risk management. Secondly, by performing key frame extraction and image recognition on the construction site monitoring video data, the dynamics of personnel and vehicles on the site can be accurately identified. The automation of this process reduces the impact of human factors on construction safety and further enhances the safety of the construction site. In addition, through the combination with GIS technology, construction risk simulation can analyze various influencing factors in the construction process in multiple dimensions, thereby generating more scientific construction progress simulation data and providing a basis for decision-making. In terms of progress management, the application of multiple progress comparison technology enables the real-time construction progress data to be combined with historical data, which can clearly identify construction lag items and generate practical construction progress solutions based on this. This process ensures the dynamic adjustment and optimization of the construction plan, making the construction progress control more refined and scientific. Finally, combined with data visualization processing, the real-time visualization chart of the construction progress can intuitively display the construction status, which is convenient for management decision-making and coordination. Therefore, the present invention solves the problems of information lag and untimely response in traditional construction management by integrating multi-source data, realizing real-time monitoring and dynamic optimization, and improves the safety and efficiency of the reservoir construction process.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire incoming material message data, drawing data and real-time sensor data;

[0012] Step S12: Fill missing values ​​in the incoming material message data to generate material missing value filling data; perform data format standardization on the material missing value filling data to generate incoming material annotation data;

[0013] Step S13: using the preset BIM model to unitize the incoming material annotation data into a model engineering unit to generate a unitized lightweight model of the reservoir project; performing data set engineering coding on the unit data set engineering code to generate a BIM reservoir construction model data set;

[0014] Step S14: Use real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-planning twin model.

[0015] The present invention ensures the diversity and comprehensiveness of information sources through the process of obtaining incoming material message data, drawing data and real-time sensor data, so that subsequent analysis can be carried out based on a rich data foundation. This diversified data fusion provides a comprehensive perspective for subsequent decision-making, which helps to identify potential problems and optimize the construction process. The missing value filling and format standardization of the incoming material message data not only improves the integrity and consistency of the data, but also provides high-quality input data for subsequent analysis. This process significantly reduces the misjudgment caused by missing data, thereby improving the accuracy and reliability of construction material management. The BIM model engineering unitization processing enables the characteristics and requirements of each engineering unit to be fully reflected in the model, and the generated reservoir engineering unitized lightweight model has better operability and visualization effect, which helps the construction team to understand and execute specific tasks. Through the implementation of engineering coding, the effective association between each data set is ensured, and the BIM reservoir construction model data set is formed, which lays the foundation for subsequent real-time monitoring and decision support. The assignment of real-time sensor data to the BIM reservoir construction model data set not only realizes the dynamic update of real-time data, but also forms a reservoir construction-planning twin model. This model can reflect the degree of match between the construction progress and the actual situation in real time, and promote real-time monitoring and adjustment of the construction status.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Obtain dust monitoring sensor data and real-time power consumption monitoring data; extract construction site monitoring video data from the real-time sensor data; perform person-vehicle video key frame extraction on the construction site monitoring video data to generate person-vehicle key frame data; perform image recognition on the person-vehicle key frame data to generate construction site recognition data;

[0018] Step S22: extracting on-site air quality indicators from dust monitoring sensor data to generate on-site monitoring dust concentration change data; performing time series analysis on on-site monitoring dust concentration change data to generate on-site dust monitoring data; performing GIS power consumption device identification on real-time power consumption monitoring data to generate power consumption monitoring equipment data;

[0019] Step S23: Perform GIS integrated construction risk simulation on the construction site identification data, on-site dust monitoring data, power consumption monitoring equipment data and the reservoir construction-plan twin model to generate construction progress simulation data.

[0020] The present invention constructs a multi-dimensional data monitoring framework through the acquired dust monitoring sensor data and real-time power consumption monitoring data, so that the environment and safety status of the construction site can be comprehensively evaluated. This multi-source data fusion not only enhances the richness of information, but also provides a more solid foundation for subsequent analysis. Secondly, by extracting and recognizing the key frames of people and vehicles in the construction site monitoring video data and image recognition, the generated construction site identification data provides an effective means for dynamic monitoring of the construction site. This process enables potential safety hazards to be identified and processed in a timely manner, thereby improving safety during the construction process. The extraction of air quality indicators from dust monitoring sensor data and the time series analysis of on-site dust concentration change data provide a quantitative basis for environmental management of the construction site. This analysis can not only reveal the changing trend of dust, but also provide real-time environmental feedback for construction managers, so as to take effective control measures to reduce the impact of dust on the surrounding environment. At the same time, the GIS equipment identification of real-time power consumption monitoring data and the generated power consumption monitoring equipment data further enhance the resource management capabilities of the construction site. This process ensures the visualization and management of power consumption monitoring, and provides data support for optimizing resource allocation and reducing energy consumption. Finally, by integrating construction site identification data, on-site dust monitoring data, electricity consumption monitoring equipment data and the reservoir construction-plan twin model into GIS integrated construction risk simulation, the generated construction progress simulation data can not only reflect the construction status and progress in real time, but also effectively identify the risk factors in the construction process, thereby realizing dynamic construction optimization and decision support.

[0021] Preferably, step S22 includes the following steps:

[0022] Step S221: extracting on-site air quality indicators from the dust monitoring sensor data to generate on-site monitoring dust concentration change data; performing dust time series analysis on the on-site monitoring dust concentration change data, marking the dust high-incidence period, and generating on-site dust monitoring data;

[0023] Step S222: trace the power consumption pattern of the real-time power consumption monitoring data, and generate a two-dimensional graph to generate a two-dimensional graph of power consumption during the construction phase; extract the peak power consumption period from the two-dimensional graph of power consumption during the construction phase, and mark it to generate peak power consumption mark data; identify the power consumption device on the peak power consumption mark data to generate power consumption monitoring equipment data.

[0024] The present invention can reflect the change of dust concentration at the construction site in real time by extracting air quality indicators from dust monitoring sensor data. This process not only provides a quantitative basis for environmental monitoring, but also lays a data foundation for construction managers to formulate reasonable dust prevention measures. Through the time series analysis of dust concentration change data and the marking of high-incidence periods, the generated on-site dust monitoring data can effectively identify the high-incidence periods of dust during the construction process, and then guide the arrangement and adjustment of construction activities to reduce the negative impact on the environment and improve the environmental friendliness of construction. The power consumption pattern tracing and two-dimensional graph generation of real-time power consumption monitoring data can intuitively present the power consumption status during the construction stage and identify the trend and change of power consumption. This visualized power consumption data not only helps construction managers understand the energy efficiency during the construction process, but also helps managers formulate corresponding power consumption control strategies, optimize power consumption arrangements, and reduce energy consumption during the extraction and marking of peak power consumption. This process enhances the monitoring capability of energy consumption at the construction site and provides a scientific basis for energy conservation and emission reduction.

[0025] Preferably, step S3 comprises the following steps:

[0026] Step S31: Acquire real-time construction progress data;

[0027] Step S32: Perform multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; compare the reservoir construction progress comparison data according to preset key stage values ​​to generate key nodes for real-time progress comparison of the reservoir; perform construction status analysis on the key nodes for real-time progress comparison of the reservoir to generate construction lag item data;

[0028] Step S33: Generate a solution based on the construction delay item data to generate a reservoir construction progress solution, wherein the reservoir construction progress solution includes a first process solution, a second process solution and a third process solution.

[0029] The present invention provides basic data support for subsequent multiple progress comparison by acquiring real-time construction progress data and drawing data, ensuring the timeliness and accuracy of the data. By comprehensively comparing the real-time construction progress data, historical engineering construction data and construction progress simulation data, the generated reservoir historical construction process comparison data not only reflects the current actual construction status of the project, but also reveals the historical trend of the construction progress. This process forms a key node for real-time process comparison of the reservoir by comparing according to the preset key stage values, which provides a key decision-making basis for managers, so that the changes in the construction status can be captured and identified in time, thereby improving the transparency of the construction progress. For the generated construction lag item data, the solution is generated based on these data in step S33, further enhancing the responsiveness and flexibility during the construction process. The reservoir construction progress solution is divided into the first process solution, the second process solution and the third process solution, so that managers can formulate corresponding countermeasures for the lag situation at different stages. This targeted solution not only promotes the recovery and improvement of the construction progress, but also provides a scientific basis for the rational allocation of resources. By implementing these solutions, the risk of construction delays can be effectively reduced, resource use can be optimized, and the overall construction efficiency and quality of the reservoir project can be finally improved.

[0030] Preferably, step S32 includes the following steps:

[0031] Step S321: performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate reservoir historical construction progress comparison data;

[0032] Step S322: comparing the reservoir construction progress comparison data according to the preset key stage values ​​to generate the reservoir real-time progress comparison key nodes;

[0033] Step S323: Performing delayed process analysis on key nodes of reservoir real-time process comparison, marking delayed nodes, and generating delayed nodes for reservoir construction;

[0034] Step S324: Analyze the construction status of the reservoir construction delay nodes and generate construction delay item data.

[0035] The present invention provides a comprehensive perspective for managers by performing multiple comparisons of real-time construction progress data, historical engineering construction data and construction progress simulation data, and the generated reservoir historical construction process comparison data allows real-time monitoring of construction status and insight into potential problems and historical trends in the construction process. The integration of such data effectively improves the transparency of construction information, allowing project managers to identify construction delays and progress deviations in a timely manner, and then take corresponding measures to correct these problems. The construction process comparison data is compared according to preset key stage values ​​to generate reservoir real-time process comparison key nodes, further deepening the data analysis level. The identification of these key nodes not only helps the project team to clarify important milestones and progress indicators, but also provides a solid foundation for subsequent decision-making. By accurately locating key nodes, project managers can implement effective intervention at critical moments, thereby minimizing the risk of construction delays. The real-time process comparison key nodes are subjected to lagging process analysis, and lagging node marking is performed to generate reservoir construction lagging nodes. This process prompts managers to pay attention to and prioritize the key factors affecting the construction progress by marking the lagging nodes. The state analysis of the construction lagging nodes is performed to generate construction lagging item data, so that managers can formulate more scientific response plans on this basis. These lagged item data not only provide a reference for the recovery of construction progress, but also provide data support for the arrangement of subsequent construction activities and the optimal allocation of resources.

[0036] Preferably, step S33 includes the following steps:

[0037] Step S331: Acquire construction schedule data; trace the lag item based on the construction lag item data, generate lag item cause data, and generate solutions to generate a reservoir construction schedule solution, wherein the reservoir construction schedule solution includes a first process solution, a second process solution, and a third process solution; match the lag item cause data with the reservoir construction schedule solution through a feedback loop mechanism to generate lag item-solution data, wherein the lag item-solution data includes pioneer path data, dynamic sequencing data, and final optimization data;

[0038] Step S332: When the lag item-solution data matches the pioneer path data, the first process solution is applied; the resource scheduling analysis is performed on the lag item cause data, and a new resource configuration is performed in combination with the incoming material message data to generate the first process scheduling data of the reservoir construction; the risk point assessment is performed on the first process scheduling data of the reservoir construction to generate the construction risk point assessment data; the pre-construction risk optimization of the first process solution is performed based on the construction risk point assessment data, thereby completing the first process solution;

[0039] Step S333: When the lag item-solution data matches the dynamically sequenced data, the second process solution is applied; the operation sequence of the construction schedule data is adjusted based on the real-time sensor data to generate new construction schedule data; a mid-term work plan is generated for the second process solution based on the new construction schedule data, thereby completing the second process solution;

[0040] Step S334: When the lag item-solution data matches the final optimization data, the third process solution is applied; the lag item cause data is prioritized to generate lag item priority data; the construction delay is completed on the construction plan data based on the lag item priority data to generate reservoir construction completion data; the real-time sensor data is quality reviewed based on the reservoir construction completion ranking, and a post-construction summary report is generated, thereby completing the third process solution.

[0041] The present invention obtains construction schedule data and traces the source of the lag items based on the construction lag item data, thereby generating lag item cause data. This process enables managers to clearly identify the root causes of construction delays, and then formulate corresponding reservoir construction schedule solutions, including first process, second process and third process solutions. The lag item cause data is matched with the reservoir construction schedule solution through a feedback loop mechanism, and the generated lag item-solution data provides a specific basis for subsequent decision-making, making resource allocation more accurate and effective. When the lag item-solution data matches the pioneer path data, the first process solution is applied to perform resource scheduling analysis and combine the incoming material message data to perform a new resource configuration to generate the first process scheduling data of the reservoir construction. This method ensures the optimal configuration of construction resources and minimizes construction delays caused by insufficient resources. At the same time, risk optimization is performed on the generated construction risk point assessment data to ensure the effective control of construction risks during the implementation of the first process solution. Focusing on the response strategy when the lag item-solution data matches the dynamic sequence data, a new construction schedule data is generated by adjusting the job sequence based on real-time sensor data. Such dynamic adjustment capability enables the construction process to adapt to site changes more flexibly, thereby reducing the overall risk and uncertainty of the project. In addition, the generation of a mid-term work plan for the second process solution ensures the continuous advancement and effective management of the construction. When the lagging item-solution data is matched with the final optimization data, the lagging item cause data is classified and managed through priority sorting, and the lagging item priority data is generated, which provides a clear direction for the completion of the construction plan. Based on these priority data, the generation of construction completion data can effectively integrate various construction results and ensure the high-quality completion of the reservoir construction. At the same time, quality review based on real-time sensor data and generation of post-construction summary reports enable project managers to conduct a comprehensive summary and reflection after the completion of the construction, providing an important reference for the implementation of similar projects in the future.

[0042] Preferably, step S4 comprises the following steps:

[0043] Step S41: using the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model, and adjusting the construction sequence of the real-time construction progress to generate the reservoir real-time construction-planning twin model;

[0044] Step S42: extract key parameters of the real-time construction-planning twin model of the reservoir to generate visualized key parameters; perform data visualization processing on the visualized key parameters to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0045] The present invention adjusts the construction parameters in the twin model so that the construction sequence can be optimized according to the actual construction progress, thereby generating a real-time reservoir construction-plan twin model. This process realizes a real-time closed-loop feedback mechanism from planning to implementation, so that the construction plan can be quickly responded and adjusted according to the actual progress of the construction site, avoiding the waste of resources and delays caused by information lag in traditional construction management. The adjustment of the real-time construction progress also ensures the rationality of the construction sequence, so that different construction links can be connected in an orderly manner, reducing the occurrence of potential conflicts and shutdowns. Based on the real-time adjusted reservoir construction-plan twin model, key parameters are extracted. The significance of this step is to extract construction indicators with key influence from massive construction data, ensure that managers can focus on the most critical elements, and simplify the complex construction management process. Through the extracted visualization key parameters, data visualization processing is further performed to generate a real-time construction visualization diagram. This visualization processing not only improves the readability and intuitiveness of the data, but also through graphical presentation, core information such as construction progress and construction status can be grasped immediately, promoting the transparency and efficiency of construction management. In addition, real-time construction visualization provides managers with a dynamic overview of the construction process, enabling them to monitor construction progress and adjust strategies in real time. This visualization tool can also combine different data sources, such as real-time sensors, construction schedules, twin models, etc., to form a multi-dimensional information display platform, providing early warnings for risks that arise during the construction process, and improving the initiative and flexibility of construction management. At the same time, this visualization method greatly improves decision-making efficiency, reduces communication delays and misunderstandings, and enables managers to respond quickly in complex construction environments, thereby improving overall construction efficiency.

[0046] Preferably, step S42 includes the following steps:

[0047] Step S421: extract key parameters of the reservoir real-time construction-planning twin model to generate visualized key parameters, where the visualized key parameters include construction progress parameters, resource allocation parameters and environmental monitoring data;

[0048] Step S422: Generate a Gantt chart of the construction progress parameters to generate a Gantt chart of the construction progress; draw a bar chart of resource allocation to generate a resource allocation percentage table; visualize the environmental monitoring data on a dashboard to generate an environmental impact instrument table;

[0049] Step S423: Visualize and unify the construction progress Gantt chart, resource allocation percentage table and environmental impact instrument table to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0050] The present invention extracts the key parameters of the real-time construction-planning twin model of the reservoir, and focuses on generating core data sets such as construction progress parameters, resource allocation parameters, and environmental monitoring data. These key parameters cover the most important aspects of construction management: construction progress, resource allocation, and construction environment monitoring. By extracting and visualizing these data, managers can monitor the progress in real time, optimize resource allocation, and grasp environmental influencing factors in a timely manner during the construction process. This process transforms construction management from passive response to active regulation, greatly improving information transparency and management efficiency. Various parameters are further processed graphically. A construction progress Gantt chart is generated through construction progress parameters. This chart intuitively shows the progress of each link of the construction, and can quickly identify the links with lagging progress, helping managers to take corrective measures in time. Secondly, a bar chart is drawn for the resource allocation parameters to generate a resource allocation percentage table, thereby showing the use of construction resources. This not only improves the efficiency of resource use, but also effectively avoids waste and uneven distribution of resources. Finally, the environmental monitoring data was visualized using a dashboard scale to generate an environmental impact instrument table, which enables construction managers to monitor the environmental indicators of the construction site (such as air quality, noise, etc.) in real time and take timely measures to reduce negative environmental impacts to ensure the green and sustainable construction. By uniformly visualizing the construction progress Gantt chart, resource allocation percentage table, and environmental impact instrument table, the system generates a real-time construction visualization diagram. This integration of charts presents data from multiple dimensions on one platform, greatly simplifying the information reading and analysis process in construction management. Through unified visualization, managers can monitor construction progress, resource usage, and environmental status on one interface at the same time, achieving multi-dimensional comprehensive management. This real-time construction visualization diagram can also be closely integrated with the twin model, allowing managers to respond quickly to emergencies during construction, thereby ensuring the smooth progress of the construction process and improving the accuracy and efficiency of decision-making.

[0051] In this specification, a reservoir lean construction management system based on BIM technology is provided, which is used to execute the above-mentioned reservoir lean construction management method based on BIM technology. The reservoir lean construction management system based on BIM technology includes:

[0052] Data collection and model building module: used to obtain incoming material message data and real-time sensor data; pre-process the incoming material message data, and associate the physical data set through the preset BIM model to generate a BIM reservoir construction model data set; use real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model;

[0053] Monitoring data extraction and risk simulation module: used to extract construction site monitoring video data from real-time sensor data; extract people-vehicle video key frames from construction site monitoring video data to generate people-vehicle key frame data; perform image recognition on people-vehicle key frame data to generate construction site identification data; perform GIS integrated construction risk simulation on construction site identification data and reservoir construction-plan twin model to generate construction progress simulation data;

[0054] Construction progress comparison and analysis module: used to obtain real-time construction progress data and historical engineering construction data; perform multiple progress comparisons on real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; compare the historical construction progress comparison data of the reservoir according to the preset key stage values, and use the drawing data to analyze the construction status and generate construction lag item data; generate solutions based on the construction lag item data to generate a reservoir construction progress solution;

[0055] Construction parameter adjustment and visualization module: used to use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model, and generate a real-time reservoir construction-planning twin model; perform data visualization on the real-time reservoir construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0056] The beneficial effect of the present invention is that by constructing a reservoir construction-plan twin model, the incoming material message data and sensor data are systematically integrated and monitored in real time, thereby achieving comprehensive control of the reservoir construction process. First, the construction of the BIM model and the real-time data assignment enable the dynamic state of the construction site to be reflected and analyzed in a timely manner. This real-time empowerment not only improves the accuracy and real-time nature of the data, but also helps to identify potential risks in a timely manner, thereby carrying out effective risk management. Secondly, by performing key frame extraction and image recognition on the construction site monitoring video data, the dynamics of personnel and vehicles on the site can be accurately identified. The automation of this process reduces the impact of human factors on construction safety and further enhances the safety of the construction site. In addition, through the combination with GIS technology, construction risk simulation can analyze various influencing factors in the construction process in multiple dimensions, thereby generating more scientific construction progress simulation data and providing a basis for decision-making. In terms of progress management, the application of multiple progress comparison technology enables the real-time construction progress data to be combined with historical data, which can clearly identify construction lag items and generate practical construction progress solutions based on this. This process ensures the dynamic adjustment and optimization of the construction plan, making the construction progress control more refined and scientific. Finally, combined with data visualization processing, the real-time visualization chart of the construction progress can intuitively display the construction status, which is convenient for management decision-making and coordination. Therefore, the present invention solves the problems of information lag and untimely response in traditional construction management by integrating multi-source data, realizing real-time monitoring and dynamic optimization, and improves the safety and efficiency of the reservoir construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the steps of a lean construction management method and system for a reservoir based on BIM technology;

[0058] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0059] Figure 3 for Figure 2 Detailed implementation steps of step S32;

[0060] Figure 4 for Figure 2 Detailed implementation steps of step S33;

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0064] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0065] To achieve this, please refer to Figures 1 to 4 , a lean construction management method for a reservoir based on BIM technology, the method comprising the following steps:

[0066] Step S1: Acquire incoming material message data and real-time sensor data; perform data preprocessing on the incoming material message data, and associate the physical data set with the preset BIM model to generate a BIM reservoir construction model data set; use the real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model;

[0067] Step S2: extracting construction site monitoring video data from real-time sensor data; extracting person-vehicle video key frames from the construction site monitoring video data to generate person-vehicle key frame data; performing image recognition on the person-vehicle key frame data to generate construction site identification data; performing GIS integrated construction risk simulation on the construction site identification data and the reservoir construction-plan twin model to generate construction progress simulation data;

[0068] Step S3: acquiring real-time construction progress data and historical engineering construction data; performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; comparing the historical construction progress comparison data of the reservoir according to preset key stage values, and performing construction status analysis with drawing data to generate construction lag item data; generating solutions based on the construction lag item data to generate a reservoir construction progress solution;

[0069] Step S4: Use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model to generate a real-time reservoir construction-planning twin model; perform data visualization on the reservoir real-time construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0070] The beneficial effect of the present invention is that by constructing a reservoir construction-plan twin model, the incoming material message data and sensor data are systematically integrated and monitored in real time, thereby achieving comprehensive control of the reservoir construction process. First, the construction of the BIM model and the real-time data assignment enable the dynamic state of the construction site to be reflected and analyzed in a timely manner. This real-time empowerment not only improves the accuracy and real-time nature of the data, but also helps to identify potential risks in a timely manner, thereby carrying out effective risk management. Secondly, by performing key frame extraction and image recognition on the construction site monitoring video data, the dynamics of personnel and vehicles on the site can be accurately identified. The automation of this process reduces the impact of human factors on construction safety and further enhances the safety of the construction site. In addition, through the combination with GIS technology, construction risk simulation can analyze various influencing factors in the construction process in multiple dimensions, thereby generating more scientific construction progress simulation data and providing a basis for decision-making. In terms of progress management, the application of multiple progress comparison technology enables the real-time construction progress data to be combined with historical data, which can clearly identify construction lag items and generate practical construction progress solutions based on this. This process ensures the dynamic adjustment and optimization of the construction plan, making the construction progress control more refined and scientific. Finally, combined with data visualization processing, the real-time visualization chart of the construction progress can intuitively display the construction status, which is convenient for management decision-making and coordination. Therefore, the present invention solves the problems of information lag and untimely response in traditional construction management by integrating multi-source data, realizing real-time monitoring and dynamic optimization, and improves the safety and efficiency of the reservoir construction process.

[0071] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a lean construction management method for a reservoir based on BIM technology of the present invention. In this example, the lean construction management method for a reservoir based on BIM technology includes the following steps:

[0072] Step S1: Acquire incoming material message data and real-time sensor data; perform data preprocessing on the incoming material message data, and associate the physical data set with the preset BIM model to generate a BIM reservoir construction model data set; use the real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model;

[0073] In the embodiment of the present invention, the message data of the incoming materials are obtained, which include the type, specification, quantity and supplier information of the materials, etc., to ensure the comprehensiveness and accuracy of the construction materials. By preprocessing the incoming material message data, the missing values, inconsistent formats and redundant information problems in the data are solved, laying the foundation for the standardization of subsequent data. Next, the system uses the preset BIM (Building Information Model) to associate the processed incoming material data with the existing physical data set to generate a BIM reservoir construction model data set. This process matches the material data with the construction nodes in the BIM model by constructing the corresponding relationship between materials and engineering units, realizing seamless connection from data to model. On this basis, real-time sensor data is used to give real-time information to the BIM reservoir construction model data set. These sensor data cover the dynamic parameters of the construction site, such as equipment operation status, material consumption rate, environmental monitoring data, etc. Through the dynamic input of these real-time data, the BIM reservoir construction model is no longer a static engineering model, but is transformed into a twin model that can reflect the real-time status of the construction process.

[0074] Step S2: extracting construction site monitoring video data from real-time sensor data; extracting person-vehicle video key frames from the construction site monitoring video data to generate person-vehicle key frame data; performing image recognition on the person-vehicle key frame data to generate construction site identification data; performing GIS integrated construction risk simulation on the construction site identification data and the reservoir construction-plan twin model to generate construction progress simulation data;

[0075] In the embodiment of the present invention, a series of data processing techniques are used to extract and analyze the construction site monitoring video data, thereby providing a reliable data basis for construction risk management and progress simulation. First, the system extracts the construction site monitoring video data from the real-time sensor data. These video data contain dynamic changes in the construction site, such as the movement of workers and equipment, the operation status, etc. The key frame of the construction site monitoring video data is extracted by video processing technology to obtain representative instantaneous images, and key frame data containing people and vehicles are generated. This process uses motion detection and frame difference method in computer vision to ensure that the extracted key frames can effectively reflect the real-time dynamics of the construction site. After obtaining the key frame data of people and vehicles, the system further uses image recognition technology to analyze these images and generate construction site recognition data. In this stage, deep learning algorithms, especially convolutional neural networks (CNNs), are used to detect and classify objects to identify various types of personnel, construction equipment and vehicles on the site, thereby providing data support for subsequent construction management. The construction site recognition data is integrated with the reservoir construction-plan twin model in GIS (geographic information system) to achieve spatial simulation of construction risks. This integration process combines real-time identification information with construction progress and environmental data to generate construction progress simulation data, allowing managers to intuitively view dynamic changes and potential risks at the construction site in a GIS environment.

[0076] Step S3: acquiring real-time construction progress data and historical engineering construction data; performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; comparing the historical construction progress comparison data of the reservoir according to preset key stage values, and performing construction status analysis with drawing data to generate construction lag item data; generating solutions based on the construction lag item data to generate a reservoir construction progress solution;

[0077] In the embodiment of the present invention, a series of specific technical means are adopted to realize the monitoring and optimization of the construction progress through the integration and analysis of multiple data sources. First, the system obtains real-time construction progress data, drawing data and historical engineering construction data, and these data sources provide the basis for subsequent analysis. In the data integration stage, the data from different sources are cleaned and converted through data warehouse technology to ensure the consistency and integrity of the data, thereby laying a solid foundation for multiple progress comparison. The system performs multiple progress comparisons on real-time construction progress data, historical engineering construction data and construction progress simulation data. This process uses statistical analysis methods, such as time series analysis and regression analysis, to identify the progress deviations of each stage and generate historical construction process comparison data of the reservoir. Through this comparison, managers can clearly understand the progress status of each construction stage and its difference from the preset target, providing intuitive feedback. The system further analyzes the comparison data based on the preset key stage value, and analyzes the construction status in combination with the drawing data to generate construction lag item data. This process involves the application of algorithm models, such as threshold detection and decision tree analysis, to automatically identify the key construction nodes with lags and their causes, and provide data support for subsequent decision-making. Based on the construction delay data, the system generates targeted solutions to form a reservoir construction progress solution. The technical means at this stage include optimization algorithms, such as genetic algorithms or linear programming, which aim to propose reasonable adjustment plans based on the nature and influencing factors of the delays to improve the construction progress. The principles of agile project management are also used to dynamically adjust and optimize the solution to adapt to real-time changes on the construction site.

[0078] Step S4: Use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model to generate a real-time reservoir construction-planning twin model; perform data visualization on the reservoir real-time construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0079] In the embodiment of the present invention, by applying the reservoir construction progress solution, the construction parameters of the reservoir construction-plan twin model are adjusted, and a series of specific technical means are used to optimize and visualize the construction process. First, the implementation of the construction progress solution involves in-depth analysis of real-time construction progress data. This process usually uses data analysis and modeling techniques, including linear regression analysis and time series analysis, to identify key factors affecting the construction progress. By optimizing and adjusting the construction parameters, a higher consistency between the construction plan and the actual progress can be ensured, thereby generating a real-time construction-plan twin model of the reservoir. After generating the real-time construction-plan twin model, the next step is to visualize the model. To this end, the system uses data visualization technology to convert complex construction data into easy-to-understand graphics and charts. This process involves the use of graphical software tools (such as Tableau or Power BI) and programming libraries (such as D3.js or Matplotlib) to create dynamic dashboards and real-time updated visualizations. The visualization of construction parameters includes Gantt charts, bar charts, and line charts, which intuitively display the changing trends of construction progress, resource allocation, and environmental monitoring data. This not only improves the readability of the data, but also provides intuitive decision support for managers. In addition, during the data visualization process, the system uses real-time data stream processing technologies, such as Apache Kafka or Apache Spark Streaming, to ensure real-time updates and feedback of construction data. This real-time processing capability enables managers to keep abreast of the current progress, resource usage, and potential risks during the construction process, so that they can respond quickly. By combining the adjusted real-time construction-planning twin model of the reservoir with the data visualization results, managers can achieve lean decision support in the construction management process. The generation of visualization diagrams not only provides clear guidance for on-site construction and promotes information transparency, but also enhances communication and collaboration among all parties involved, thereby improving overall construction efficiency.

[0080] Preferably, step S1 comprises the following steps:

[0081] Step S11: Acquire incoming material message data, drawing data and real-time sensor data;

[0082] Step S12: Fill missing values ​​in the incoming material message data to generate material missing value filling data; perform data format standardization on the material missing value filling data to generate incoming material annotation data;

[0083] Step S13: using the preset BIM model to unitize the incoming material annotation data into a model engineering unit to generate a unitized lightweight model of the reservoir project; performing data set engineering coding on the unit data set engineering code to generate a BIM reservoir construction model data set;

[0084] Step S14: Use real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-planning twin model.

[0085] In the embodiment of the present invention, by acquiring the incoming material message data, drawing data and real-time sensor data, the system integrates these multi-source data to lay the foundation for subsequent data processing. This integration process adopts data integration technology to ensure that data of different formats and sources can be operated on a unified platform. For the incoming material message data, the system fills in missing values ​​and generates material missing value filling data using methods such as interpolation, mean filling or machine learning algorithms. This process involves data cleaning and missing data processing techniques, aiming to improve data quality. Subsequently, the format of the data after missing value filling is standardized, and a standardization or normalization algorithm is applied to ensure the consistency and comparability of the data, thereby generating the incoming material annotation data. Using the preset building information model (BIM), the incoming material annotation data is processed into a model engineering unit to generate a unitized lightweight model of the reservoir project. The technical means here include model simplification and unitization processing to reduce the complexity of the model while maintaining the key information of the model. This process involves the application of CAD software and BIM platform to ensure that the generated lightweight model can be efficiently applied to subsequent engineering analysis. The engineering codes of the unit data sets were encoded to generate the BIM reservoir construction model data set. Data encoding and classification techniques were used to achieve orderly data management and rapid retrieval. This encoding process ensures the structuring of the data set, making subsequent data processing and analysis more efficient. The BIM reservoir construction model data set was endowed with real-time data using real-time sensor data to generate a reservoir construction-plan twin model. Real-time data processing techniques were applied here, such as stream data processing frameworks (such as Apache Kafka), so that sensor data can be updated to the BIM model in real time, ensuring that the twin model reflects the latest status of the construction site.

[0086] Preferably, step S2 comprises the following steps:

[0087] Step S21: Obtain dust monitoring sensor data and real-time power consumption monitoring data; extract construction site monitoring video data from the real-time sensor data; perform person-vehicle video key frame extraction on the construction site monitoring video data to generate person-vehicle key frame data; perform image recognition on the person-vehicle key frame data to generate construction site recognition data;

[0088] Step S22: extracting on-site air quality indicators from dust monitoring sensor data to generate on-site monitoring dust concentration change data; performing time series analysis on on-site monitoring dust concentration change data to generate on-site dust monitoring data; performing GIS power consumption device identification on real-time power consumption monitoring data to generate power consumption monitoring equipment data;

[0089] Step S23: Perform GIS integrated construction risk simulation on the construction site identification data, on-site dust monitoring data, power consumption monitoring equipment data and the reservoir construction-plan twin model to generate construction progress simulation data.

[0090] In an embodiment of the present invention, the system extracts key environmental and energy consumption data of the construction site from the dust monitoring sensor data and the real-time power consumption monitoring data. In this process, the dust monitoring sensor is responsible for collecting the change data of the dust concentration in the air in real time, while the power consumption monitoring device tracks the energy usage of the construction site in real time through the power sensor. At the same time, the monitoring video data of the construction site is subjected to key frame extraction through advanced video processing algorithms to identify and capture the image data of people and vehicles related to the construction activities. The key frame data of the people and vehicles are then processed by deep learning algorithms or image recognition algorithms to generate construction site recognition data, which includes face detection, vehicle recognition and motion tracking technology. The dust monitoring sensor data is used to extract the on-site air quality indicators. This step applies time series analysis methods, such as moving average method or seasonal decomposition method, to analyze the dynamic changes of dust concentration. Through such technical means, the system generates on-site monitoring dust concentration change data, and then further analyzes the time series based on this data to generate on-site dust monitoring data with more long-term predictive value. This data processing process can not only monitor real-time air quality, but also predict future trends, thereby helping construction site managers make timely decisions. For real-time power consumption monitoring data, Geographic Information System (GIS) technology is applied to spatially identify power consumption equipment to generate power consumption monitoring equipment data. This process combines geographic positioning, equipment identification and power data analysis to determine the spatial distribution of equipment and its energy consumption pattern. Construction site identification data, dust monitoring data and power consumption monitoring equipment data are further integrated with the reservoir construction-plan twin model through GIS. This integration process enables data from different sources to be presented in a unified geographic spatial framework through the fusion and analysis of multi-dimensional data. GIS is not only used to locate equipment or personnel, but also for construction risk simulation - by integrating the above multi-source data with the twin model, the system can dynamically simulate the progress of the construction site and evaluate the construction risks. This simulation process relies on multivariate regression analysis, Monte Carlo simulation or risk prediction algorithms to generate accurate construction progress simulation data.

[0091] Preferably, step S22 includes the following steps:

[0092] Step S221: extracting on-site air quality indicators from the dust monitoring sensor data to generate on-site monitoring dust concentration change data; performing dust time series analysis on the on-site monitoring dust concentration change data, marking the dust high-incidence period, and generating on-site dust monitoring data;

[0093] Step S222: trace the power consumption pattern of the real-time power consumption monitoring data, and generate a two-dimensional graph to generate a two-dimensional graph of power consumption during the construction phase; extract the peak power consumption period from the two-dimensional graph of power consumption during the construction phase, and mark it to generate peak power consumption mark data; identify the power consumption device on the peak power consumption mark data to generate power consumption monitoring equipment data.

[0094] In an embodiment of the present invention, the dust monitoring sensor data is processed to extract key indicators related to the on-site air quality, such as PM2.5 and PM10 concentrations. This process relies on high-precision data acquisition of sensors, and quantifies the monitoring data through air quality evaluation models (such as AQI) to generate real-time monitoring data on dust concentration changes. Next, these concentration data are analyzed in time series, which is a key technical step. The algorithms used include ARIMA model (autoregressive integral moving average model). By performing time series analysis on the data, the system can identify the fluctuation pattern of dust concentration over time, and then analyze the period of high dust concentration. The high-incidence period marking is based on the statistics of historical data, using threshold judgment method or anomaly detection algorithm (such as Z-score) to automatically identify and mark the period when the dust concentration exceeds the standard, and generate on-site dust monitoring data. This data not only provides real-time monitoring function, but also provides a basis for future trend prediction and emergency plans. Real-time electricity consumption monitoring data analyzes the energy consumption pattern during the construction phase by tracing the electricity consumption pattern. This process involves using clustering algorithms (such as K-means clustering or DBSCAN) to classify electricity consumption data in different time periods to identify the characteristics of electricity consumption in different construction stages. Through pattern tracing, the system can clarify the electricity consumption patterns in each construction stage and generate corresponding two-dimensional graphs. In the process of graph generation, two-dimensional data visualization technology is used, such as visualization methods based on tools such as Matplotlib or D3.js, to generate intuitive two-dimensional graphs of electricity consumption in the construction stage. In this graph, the horizontal and vertical axes usually represent time and electricity consumption, thereby showing the temporal trend of electricity consumption during the construction process. The system further analyzes the two-dimensional graph of electricity consumption in the construction stage and uses peak detection algorithms (such as local maximum detection) to extract peak electricity consumption periods. These peak periods are marked to identify the time periods with the highest energy consumption, so as to effectively allocate construction resources. After further processing the marked data of peak electricity consumption periods, the equipment identification technology is applied to determine the type and number of peak electricity consumption equipment by analyzing electricity consumption characteristics such as current and voltage, and generate electricity monitoring equipment data. This process relies on the matching and identification algorithm of power feature data.

[0095] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0096] Step S31: Acquire real-time construction progress data;

[0097] Step S32: Perform multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; compare the reservoir construction progress comparison data according to preset key stage values ​​to generate key nodes for real-time progress comparison of the reservoir; perform construction status analysis on the key nodes for real-time progress comparison of the reservoir to generate construction lag item data;

[0098] Step S33: Generate a solution based on the construction delay item data to generate a reservoir construction progress solution, wherein the reservoir construction progress solution includes a first process solution, a second process solution and a third process solution.

[0099] In the embodiment of the present invention, the process of obtaining real-time construction progress data involves using a data acquisition system, combining sensors and BIM (Building Information Modeling) technology, to collect various types of progress information from the construction site in real time, including process completion, construction personnel and equipment usage, etc. In addition, by combining with engineering drawing data, it can be ensured that all construction activities are consistent with the design intent, thereby laying the foundation for subsequent progress analysis. Multiple comparisons of real-time construction progress data, historical engineering construction data and construction progress simulation data are a core technical link. This process usually applies data fusion technology to integrate data from different sources through algorithms (such as weighted average or Kalman filtering), and on this basis, time series analysis methods are used to compare various data sets. The generated reservoir historical construction process comparison data can reveal the deviation between the actual construction progress and the established plan. Next, in the process of comparing based on the preset key stage values, the system uses methods such as threshold algorithms and logistic regression models to determine key nodes, thereby extracting key nodes for real-time reservoir process comparison, which reflect important turning points and state changes in the construction process. When analyzing the construction status of key nodes of the reservoir real-time process comparison, the state monitoring and fault diagnosis technology was applied. By comparing historical data with real-time data, machine learning algorithms (such as decision trees or random forests) were used to identify the construction lag data. This analysis result provides a basis for the subsequent formulation of corresponding solutions. Based on the process of generating solutions based on the construction lag data, optimization algorithms (such as linear programming or genetic algorithms) are used to evaluate the feasibility and effectiveness of different construction plans, thus forming a multi-level decision-making structure including the first process solution, the second process solution and the third process solution.

[0100] As an example of the present invention, refer to Figure 3 As shown, in this example, step S32 includes:

[0101] Step S321: performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate reservoir historical construction progress comparison data;

[0102] Step S322: comparing the reservoir construction progress comparison data according to the preset key stage values ​​to generate the reservoir real-time progress comparison key nodes;

[0103] Step S323: Performing delayed process analysis on key nodes of reservoir real-time process comparison, marking delayed nodes, and generating delayed nodes for reservoir construction;

[0104] Step S324: Analyze the construction status of the reservoir construction delay nodes and generate construction delay item data.

[0105] In the embodiment of the present invention, the multiple progress comparison of real-time construction progress data, historical engineering construction data and construction progress simulation data is realized based on data fusion and comparative analysis technology. This process usually relies on advanced data acquisition systems, and obtains on-site construction progress information in real time through sensors, BIM models and construction management software, and integrates it with historical data and simulation models to generate reservoir historical construction process comparison data. By applying data association algorithms, such as dynamic time warping (DTW) and weighted matching methods, the difference between the actual progress and the planned progress can be effectively identified, providing a basis for subsequent analysis. Comparing the reservoir construction process comparison data according to preset key stage values ​​involves threshold analysis and data classification technology. This process uses the preset key stage values ​​as the judgment criteria, and compares the data through segmented regression or logistic regression models to classify and process, thereby generating key nodes for reservoir real-time process comparison. These key nodes not only reflect the main progress points of the construction, but also lay the foundation for subsequent state analysis. The hysteresis process analysis of the key nodes of the reservoir real-time process comparison is mainly carried out by state monitoring and fault detection technology. By comparing historical and real-time data, fault diagnosis algorithms (such as rule-based systems or machine learning algorithms) are applied to identify construction delay nodes. The marking of these delay nodes helps decision makers quickly locate problems in the construction process and ensure timely remedial measures. The construction status analysis of reservoir construction delay nodes is usually carried out using data mining and statistical analysis techniques. Through in-depth analysis of the delay node data, cluster analysis and regression analysis methods can be used to generate construction delay item data.

[0106] As an example of the present invention, refer to Figure 4 As shown, in this example, step S33 includes:

[0107] Step S331: Acquire construction schedule data; trace the lag item based on the construction lag item data, generate lag item cause data, and generate solutions to generate a reservoir construction schedule solution, wherein the reservoir construction schedule solution includes a first process solution, a second process solution, and a third process solution; match the lag item cause data with the reservoir construction schedule solution through a feedback loop mechanism to generate lag item-solution data, wherein the lag item-solution data includes pioneer path data, dynamic sequencing data, and final optimization data;

[0108] Step S332: When the lag item-solution data matches the pioneer path data, the first process solution is applied; the resource scheduling analysis is performed on the lag item cause data, and a new resource configuration is performed in combination with the incoming material message data to generate the first process scheduling data of the reservoir construction; the risk point assessment is performed on the first process scheduling data of the reservoir construction to generate the construction risk point assessment data; the pre-construction risk optimization of the first process solution is performed based on the construction risk point assessment data, thereby completing the first process solution;

[0109] Step S333: When the lag item-solution data matches the dynamically sequenced data, the second process solution is applied; the operation sequence of the construction schedule data is adjusted based on the real-time sensor data to generate new construction schedule data; a mid-term work plan is generated for the second process solution based on the new construction schedule data, thereby completing the second process solution;

[0110] Step S334: When the lag item-solution data matches the final optimization data, the third process solution is applied; the lag item cause data is prioritized to generate lag item priority data; the construction delay is completed on the construction plan data based on the lag item priority data to generate reservoir construction completion data; the real-time sensor data is quality reviewed based on the reservoir construction completion ranking, and a post-construction summary report is generated, thereby completing the third process solution.

[0111] In the embodiment of the present invention, the process of obtaining the construction schedule data relies on an integrated information system, which integrates real-time construction data and historical planning information to ensure the comprehensiveness and accuracy of the information. Tracing the lag item based on the construction lag item data uses data mining technology, especially causal analysis methods, to identify the specific reasons affecting the construction progress and generate lag item cause data. Subsequently, through the optimization algorithm, a reservoir construction schedule solution is generated based on the lag item cause data, including a series of strategies for different lag causes. When the lag item-solution data is matched with the pioneer path data, applying the first process solution involves resource scheduling analysis, which usually applies linear programming or integer programming methods to optimize resource allocation, and combines the incoming material message data for a new resource configuration to generate the first process scheduling data of the reservoir construction. In addition, the scheduling data is evaluated for risk points, and a risk assessment model, such as Monte Carlo simulation or decision tree analysis, is used to identify potential risks and generate construction risk point assessment data. Based on these assessment results, the first process solution is optimized for pre-construction risks to ensure that risks are effectively reduced during the implementation phase. Adjusting the work sequence of construction schedule data based on real-time sensor data involves the combination of scheduling algorithms and real-time data processing technology to generate new construction schedule data. This process usually uses dynamic scheduling technology and constraint satisfaction problem (CSP) solving methods to ensure the flexibility and effectiveness of the plan, thereby generating a medium-term work plan suitable for the current actual situation. When the lag item-solution data is matched to the final optimization data, the priority sorting process uses sorting algorithms such as weighted sorting or multi-criteria decision analysis (MCDA) to generate lag item priority data. Based on this priority data, the construction plan data is closed after the construction delay, and process optimization and scheduling algorithms are used to ensure the efficient implementation of the closing work. Finally, by reviewing the quality of the real-time sensor data and combining data analysis technology, a post-construction summary report is generated to evaluate the quality and efficiency of the entire construction process.

[0112] Preferably, step S4 comprises the following steps:

[0113] Step S41: using the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model, and adjusting the construction sequence of the real-time construction progress to generate the reservoir real-time construction-planning twin model;

[0114] Step S42: extract key parameters of the real-time construction-planning twin model of the reservoir to generate visualized key parameters; perform data visualization processing on the visualized key parameters to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0115] In the embodiment of the present invention, the construction parameter model of the reservoir construction-plan twin model is adjusted by using the reservoir construction progress solution, which involves optimizing the model parameters by comparing the real-time construction data with the historical plan data based on advanced model correction technology, such as Bayesian network or machine learning algorithm, to ensure that the model can accurately reflect the current construction status. This process requires the establishment of a data-driven feedback mechanism so that the model can be dynamically adjusted during the construction process to generate a real-time reservoir construction-plan twin model that conforms to the actual situation. In addition, the construction sequence adjustment of the real-time construction progress is based on a scheduling algorithm, such as Gantt chart optimization or priority scheduling method, to achieve reasonable allocation of resources and time management, thereby improving construction efficiency. For the extraction of key parameters of the real-time construction-plan twin model of the reservoir, feature selection and dimensionality reduction techniques are usually used to identify parameters that are critical to the construction progress, resource use and environmental impact from a large amount of construction data through methods such as principal component analysis (PCA) or factor analysis. The generated visualized key parameters can provide intuitive data support for project managers to better understand the construction progress and potential problems. Subsequently, data visualization techniques such as real-time dashboards and dynamic charts are used to present key parameters in an easy-to-understand manner through visualization tools such as Tableau or Power BI to generate real-time construction visualizations.

[0116] Preferably, step S42 includes the following steps:

[0117] Step S421: extract key parameters of the reservoir real-time construction-planning twin model to generate visualized key parameters, where the visualized key parameters include construction progress parameters, resource allocation parameters and environmental monitoring data;

[0118] Step S422: Generate a Gantt chart of the construction progress parameters to generate a Gantt chart of the construction progress; draw a bar chart of resource allocation to generate a resource allocation percentage table; visualize the environmental monitoring data on a dashboard to generate an environmental impact instrument table;

[0119] Step S423: Visualize and unify the construction progress Gantt chart, resource allocation percentage table and environmental impact instrument table to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0120] In an embodiment of the present invention, key parameters of the real-time construction-plan twin model of the reservoir are extracted, and data analysis techniques such as multivariate regression analysis and time series analysis are used to extract visual key parameters with significant influence from a large amount of construction data. These parameters include construction progress parameters (such as the completion percentage of each stage and the deviation between the planned and actual progress), resource allocation parameters (such as the use of manpower, materials and equipment), and environmental monitoring data (such as dust concentration, noise level and water quality indicators). Through the extraction of these key parameters, managers can grasp the overall status of the construction site in a timely manner, thereby providing a scientific basis for subsequent decision-making. Professional data visualization tools (such as Tableau, Power BI or Matplotlib) are used to graphically process construction progress parameters, resource allocation parameters and environmental monitoring data. Specifically, through the Gantt chart generation technology, the construction progress parameters are converted into an easy-to-understand schedule, and the start and end time of each construction stage and its progress are clarified, so as to facilitate monitoring and coordination. In addition, by drawing a bar chart, the percentage of resource allocation is displayed, and the use and allocation strategy of various resources are highlighted, so that managers can identify potential resource bottlenecks or waste. At the same time, for environmental monitoring data, the environmental impact instrument table is generated using the dashboard design principles to display the monitoring data in real time, helping managers to pay attention to the impact of construction on the environment in real time. Through the unified visualization technology, the construction progress Gantt chart, resource allocation percentage table and environmental impact instrument table are integrated to generate a real-time construction visualization diagram. This process involves unified format processing and display design of data to ensure that various charts can be displayed in a coordinated manner on the same interface to form a comprehensive visualization management tool.

[0121] In this specification, a reservoir lean construction management system based on BIM technology is provided, which is used to execute the above-mentioned reservoir lean construction management method based on BIM technology. The reservoir lean construction management system based on BIM technology:

[0122] Data collection and model building module: used to obtain incoming material message data and real-time sensor data; pre-process the incoming material message data, and associate the physical data set through the preset BIM model to generate a BIM reservoir construction model data set; use real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model;

[0123] Monitoring data extraction and risk simulation module: used to extract construction site monitoring video data from real-time sensor data; extract people-vehicle video key frames from construction site monitoring video data to generate people-vehicle key frame data; perform image recognition on people-vehicle key frame data to generate construction site identification data; perform GIS integrated construction risk simulation on construction site identification data and reservoir construction-plan twin model to generate construction progress simulation data;

[0124] Construction progress comparison and analysis module: used to obtain real-time construction progress data, drawing data and historical engineering construction data; perform multiple progress comparisons on real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; compare the historical construction progress comparison data of the reservoir according to the preset key stage values, and use the drawing data to analyze the construction status and generate construction lag item data; generate solutions based on the construction lag item data to generate a reservoir construction progress solution;

[0125] Construction parameter adjustment and visualization module: used to use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model, and generate a real-time reservoir construction-planning twin model; perform data visualization on the real-time reservoir construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

[0126] The beneficial effect of the present invention is that by constructing a reservoir construction-plan twin model, the incoming material message data and sensor data are systematically integrated and monitored in real time, thereby achieving comprehensive control of the reservoir construction process. First, the construction of the BIM model and the real-time data assignment enable the dynamic state of the construction site to be reflected and analyzed in a timely manner. This real-time empowerment not only improves the accuracy and real-time nature of the data, but also helps to identify potential risks in a timely manner, thereby carrying out effective risk management. Secondly, by performing key frame extraction and image recognition on the construction site monitoring video data, the dynamics of personnel and vehicles on the site can be accurately identified. The automation of this process reduces the impact of human factors on construction safety and further enhances the safety of the construction site. In addition, through the combination with GIS technology, construction risk simulation can analyze various influencing factors in the construction process in multiple dimensions, thereby generating more scientific construction progress simulation data and providing a basis for decision-making. In terms of progress management, the application of multiple progress comparison technology enables the real-time construction progress data to be combined with historical data, which can clearly identify construction lag items and generate practical construction progress solutions based on this. This process ensures the dynamic adjustment and optimization of the construction plan, making the construction progress control more refined and scientific. Finally, combined with data visualization processing, the real-time visualization chart of the construction progress can intuitively display the construction status, which is convenient for management decision-making and coordination. Therefore, the present invention solves the problems of information lag and untimely response in traditional construction management by integrating multi-source data, realizing real-time monitoring and dynamic optimization, and improves the safety and efficiency of the reservoir construction process.

[0127] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0128] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A lean construction management method for reservoirs based on BIM technology, characterized in that: The following steps are involved: Step S1: Acquire incoming material message data, drawing data and real-time sensor data; The incoming material message data is preprocessed, and the physical data set is associated through the preset BIM model to generate a BIM reservoir construction model data set; the real-time sensor data is used to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model; Step S2: extracting construction site monitoring video data from real-time sensor data; extracting person-vehicle video key frames from the construction site monitoring video data to generate person-vehicle key frame data; performing image recognition on the person-vehicle key frame data to generate construction site identification data; performing GIS integrated construction risk simulation on the construction site identification data and the reservoir construction-plan twin model to generate construction progress simulation data; wherein step S2 includes the following steps: Step S21: Obtain dust monitoring sensor data and real-time power consumption monitoring data; extract construction site monitoring video data from the real-time sensor data; perform person-vehicle video key frame extraction on the construction site monitoring video data to generate person-vehicle key frame data; perform image recognition on the person-vehicle key frame data to generate construction site recognition data; Step S22: extracting on-site air quality indicators from dust monitoring sensor data to generate on-site monitoring dust concentration change data; performing time series analysis on on-site monitoring dust concentration change data to generate on-site dust monitoring data; performing GIS power equipment identification on real-time power consumption monitoring data to generate power consumption monitoring equipment data; wherein step S22 includes the following steps: Step S221: extracting on-site air quality indicators from the dust monitoring sensor data to generate on-site monitoring dust concentration change data; performing dust time series analysis on the on-site monitoring dust concentration change data, marking the dust high-incidence period, and generating on-site dust monitoring data; Step S222: Tracing the power consumption pattern of the real-time power consumption monitoring data, and generating a two-dimensional graph to generate a two-dimensional graph of power consumption during the construction phase; extracting the peak power consumption period from the two-dimensional graph of power consumption during the construction phase, and marking it to generate peak power consumption marking data; analyzing the current power consumption characteristics of the peak power consumption marking data, and marking the power consumption equipment identification type to generate power consumption monitoring equipment data; Step S23: Perform GIS integrated construction risk simulation on the construction site identification data, on-site dust monitoring data, power consumption monitoring equipment data and reservoir construction-plan twin model to generate construction progress simulation data; Step S3: acquiring real-time construction progress data and historical engineering construction data; performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; comparing the historical construction progress comparison data of the reservoir according to preset key stage values, and performing construction status analysis with drawing data to generate construction lag item data; generating solutions based on the construction lag item data to generate a reservoir construction progress solution; Step S4: Use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model to generate a real-time reservoir construction-planning twin model; perform data visualization on the reservoir real-time construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

2. The lean construction management method for reservoirs based on BIM technology according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire incoming material message data, drawing data and real-time sensor data; Step S12: Fill missing values ​​in the incoming material message data to generate material missing value filling data; perform data format standardization on the material missing value filling data to generate incoming material annotation data; Step S13: using the preset BIM model to unitize the incoming material annotation data into a model engineering unit to generate a unitized lightweight model of the reservoir project; performing data set engineering coding on the unit data set engineering code to generate a BIM reservoir construction model data set; Step S14: Use real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-planning twin model.

3. The lean construction management method for reservoirs based on BIM technology according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Acquire real-time construction progress data; Step S32: Perform multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; compare the reservoir construction progress comparison data according to preset key stage values ​​to generate key nodes for real-time progress comparison of the reservoir; perform construction status analysis on the key nodes for real-time progress comparison of the reservoir to generate construction lag item data; Step S33: Generate a solution based on the construction delay item data to generate a reservoir construction progress solution, wherein the reservoir construction progress solution includes a first process solution, a second process solution and a third process solution.

4. The lean construction management method for reservoirs based on BIM technology according to claim 3 is characterized in that: Step S32 includes the following steps: Step S321: performing multiple progress comparisons on the real-time construction progress data, historical engineering construction data and construction progress simulation data to generate reservoir historical construction progress comparison data; Step S322: comparing the reservoir construction progress comparison data according to the preset key stage values ​​to generate the reservoir real-time progress comparison key nodes; Step S323: Performing delayed process analysis on key nodes of reservoir real-time process comparison, marking delayed nodes, and generating delayed nodes for reservoir construction; Step S324: Analyze the construction status of the reservoir construction delay nodes and generate construction delay item data.

5. The lean construction management method for reservoirs based on BIM technology according to claim 3 is characterized in that: Step S33 includes the following steps: Step S331: Acquire construction schedule data; trace the lag item based on the construction lag item data, generate lag item cause data, and generate solutions to generate a reservoir construction schedule solution, wherein the reservoir construction schedule solution includes a first process solution, a second process solution, and a third process solution; match the lag item cause data with the reservoir construction schedule solution through a feedback loop mechanism to generate lag item-solution data, wherein the lag item-solution data includes pioneer path data, dynamic sequencing data, and final optimization data; Step S332: When the lag item-solution data matches the pioneer path data, the first process solution is applied; the resource scheduling analysis is performed on the lag item cause data, and a new resource configuration is performed in combination with the incoming material message data to generate the first process scheduling data of the reservoir construction; the risk point assessment is performed on the first process scheduling data of the reservoir construction to generate the construction risk point assessment data; the pre-construction risk optimization of the first process solution is performed based on the construction risk point assessment data, thereby completing the first process solution; Step S333: When the lag item-solution data matches the dynamically sequenced data, the second process solution is applied; the operation sequence of the construction schedule data is adjusted based on the real-time sensor data to generate new construction schedule data; a mid-term work plan is generated for the second process solution based on the new construction schedule data, thereby completing the second process solution; Step S334: When the lag item-solution data matches the final optimization data, the third process solution is applied; the lag item cause data is prioritized to generate lag item priority data; the construction plan data is completed based on the lag item priority data to generate reservoir construction completion data; the real-time sensor data is quality reviewed based on the reservoir construction completion ranking, and a post-construction summary report is generated, thereby completing the third process solution.

6. The lean construction management method for reservoirs based on BIM technology according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: using the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model, and adjusting the construction sequence of the real-time construction progress to generate the reservoir real-time construction-planning twin model; Step S42: extract key parameters of the real-time construction-planning twin model of the reservoir to generate visualized key parameters; perform data visualization processing on the visualized key parameters to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

7. The lean construction management method for reservoirs based on BIM technology according to claim 6 is characterized in that: Step S42 includes the following steps: Step S421: extract key parameters of the reservoir real-time construction-planning twin model to generate visualized key parameters, where the visualized key parameters include construction progress parameters, resource allocation parameters and environmental monitoring data; Step S422: Generate a Gantt chart of the construction progress parameters to generate a Gantt chart of the construction progress; draw a bar chart of resource allocation to generate a resource allocation percentage table; visualize the environmental monitoring data on a dashboard to generate an environmental impact instrument table; Step S423: Visualize and unify the construction progress Gantt chart, resource allocation percentage table and environmental impact instrument table to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

8. A lean construction management system for reservoirs based on BIM technology, characterized in that: Used to execute the lean construction management method of a reservoir based on BIM technology as claimed in claim 1, the lean construction management system of a reservoir based on BIM technology comprises: Data collection and model building module: used to obtain incoming material message data and real-time sensor data; pre-process the incoming material message data, and associate the physical data set through the preset BIM model to generate a BIM reservoir construction model data set; use real-time sensor data to assign real-time data to the BIM reservoir construction model data set to generate a reservoir construction-plan twin model; Monitoring data extraction and risk simulation module: used to extract construction site monitoring video data from real-time sensor data; extract people-vehicle video key frames from construction site monitoring video data to generate people-vehicle key frame data; perform image recognition on people-vehicle key frame data to generate construction site identification data; perform GIS integrated construction risk simulation on construction site identification data and reservoir construction-plan twin model to generate construction progress simulation data; Construction progress comparison and analysis module: used to obtain real-time construction progress data and historical engineering construction data; perform multiple progress comparisons on real-time construction progress data, historical engineering construction data and construction progress simulation data to generate historical construction progress comparison data of the reservoir; compare the historical construction progress comparison data of the reservoir according to the preset key stage values, and use the drawing data to analyze the construction status and generate construction lag item data; generate solutions based on the construction lag item data to generate a reservoir construction progress solution; Construction parameter adjustment and visualization module: used to use the reservoir construction progress solution to adjust the construction parameter model of the reservoir construction-planning twin model, and generate a real-time reservoir construction-planning twin model; perform data visualization on the real-time reservoir construction-planning twin model to generate a real-time construction visualization diagram, thereby completing the lean construction management of the reservoir.

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