Building engineering intelligent progress management and optimization method based on dynamic data analysis

Abstract

The application discloses a building engineering intelligent progress management and optimization method based on dynamic data analysis, and belongs to the technical field of building engineering management. The method comprises the following steps: step 1, collecting engineering progress, resource use and environmental factor data in real time; step 2, continuously monitoring progress deviation, and simultaneously identifying risks in combination with historical data and current trends; step 3, evaluating the influence of foreseeable and unforeseeable factors on engineering progress through quantitative analysis, and automatically generating an influence factor analysis report; step 4, performing mode recognition on accumulated progress deviation, resource use and environmental factor data, identifying potential delay modes, simultaneously predicting future progress trends in combination with historical data and current trends, and generating more accurate progress prediction; step 5, generating resource allocation suggestions and formulating targeted overall optimization strategies; and step 6, implementing the optimization strategies through dynamic resource scheduling and task priority adjustment.

Description

Technical Field

[0001] This application relates to the field of construction project management technology, and more specifically, to a method for intelligent progress management and optimization of construction projects based on dynamic data analysis. Background Technology

[0002] In modern construction engineering, project schedule management is a key factor in ensuring the smooth progress and successful delivery of projects. However, traditional schedule management methods often rely on static planning and experience-based judgment, making it difficult to adapt to real-time changes in working conditions and environmental factors. This leads to a series of problems such as schedule deviations, resource waste, and cost overruns, posing significant challenges to project management.

[0003] In recent years, with the development of information technology, especially the widespread adoption of sensors, the Internet of Things, and big data analytics, construction project schedule management has gradually moved towards intelligent and data-driven approaches. Through real-time data collection and analysis, managers can better understand the current status of a project, identify potential delay risks, and adjust resource allocation in a timely manner. However, most existing intelligent schedule management systems lack in-depth analysis of dynamic data and cannot effectively combine historical data with real-time monitoring data for comprehensive evaluation, resulting in the inability to generate high-precision schedule predictions and optimization suggestions.

[0004] Furthermore, with the continuous expansion of construction project scale and the increasing complexity of construction environments, effectively integrating multi-dimensional data, constructing a comprehensive influencing factor analysis model, and based on this, achieving dynamic allocation and optimization of resources has become crucial for improving project management efficiency and reducing risks. Therefore, there is an urgent need for an intelligent progress management and optimization method for construction projects based on dynamic data analysis to achieve real-time monitoring, accurate prediction, and scientific decision-making regarding project progress.

[0005] In conclusion, how to effectively utilize dynamic data analysis technology to improve the level of intelligence in construction project progress management has become an urgent technical problem to be solved. Summary of the Invention

[0006] To overcome a series of shortcomings in existing technologies, the purpose of this application is to provide a method for intelligent schedule management and optimization of construction projects based on dynamic data analysis, including the following steps: Step 1: Collect real-time data on project progress, resource usage, and environmental factors, and perform data cleaning and standardization. Step 2: Continuously monitor progress deviations and identify risks by combining historical data and current trends; Step 3: Through quantitative analysis, assess the impact of foreseeable and unforeseeable factors on the project schedule and automatically generate an influencing factor analysis report; Step 4: Perform pattern recognition on the accumulated schedule deviations, resource usage, and environmental factor data to identify potential delay patterns. At the same time, combine historical data and current trends to predict future schedule trends and generate more accurate schedule forecasts. Step 5: Based on the results of schedule deviation analysis, influencing factor analysis, pattern recognition and prediction, generate resource allocation suggestions and formulate targeted overall optimization strategies; Step 6: Implement optimization strategies, ensuring effective resource utilization and progress control through dynamic resource scheduling and task priority adjustment, while continuously tracking optimization effects and updating feedback loops.

[0007] Furthermore, step 1 includes the following steps: Deploy various sensors and data acquisition devices at the project site to capture real-time data on project progress, resource usage, and environmental factors; The collected data undergoes preliminary screening and cleaning, including removing obvious outliers, handling missing data, and correcting data format errors. Convert data from different sources and formats into a unified standard format, including unit conversion, timestamp standardization, and naming standardization; By linking project progress data with corresponding resource usage data and environmental factor data in time and space dimensions, a multidimensional dataset is formed. At the same time, a data dictionary and metadata management system are established to record the source, meaning, unit, and update frequency of the data.

[0008] Furthermore, step 2 includes the following steps: Establish a detailed schedule baseline based on the project plan, including the expected completion time of each work package and milestone; at the same time, define a series of key performance indicators as the basis for monitoring and evaluating project progress. Using the real-time data collected and processed in step 1, the deviation between the actual progress and the predetermined project schedule is continuously calculated, including deviation analysis for each work package, each construction phase, and the overall project schedule. By applying deviation analysis and trend analysis algorithms, we evaluate the current values ​​and trends of each schedule indicator, while also considering the dependencies between tasks to analyze the impact of delays on the overall schedule. Based on the calculated schedule deviation and the predetermined threshold, a multi-level alarm mechanism is established; Build a knowledge base containing historical project data, including progress data, problems encountered and solutions adopted in past projects, and identify common delay patterns and risk factors from historical project data; By combining real-time progress data with historical analysis results, risk factors that could lead to severe delays can be identified, and their probability of occurrence and potential impact can be estimated. Based on progress monitoring, alarm information, historical data analysis, and risk prediction results, a comprehensive risk identification report is automatically generated.

[0009] Furthermore, the schedule deviation for each work package is calculated using the following formula: ΔP i =P actual,i -P planned,i , where ΔP i P represents the schedule deviation for the i-th work package. actual,i P represents the actual progress of the i-th work package; planned,i The scheduled progress for the i-th work package; The schedule deviation for each construction phase is calculated using the following formula: ,in, ΔS is the number of work packages in stage j; j Let be the schedule deviation for the j-th construction phase; Overall project schedule deviation Calculate using the following formula: Where N is the total number of work packages; It is the weight of work package i.

[0010] Furthermore, the impact of delays on the overall schedule. Calculate using the following formula: , where m is the number of work packages affected; Let be the delay time for the k-th dependent work package; is the impact coefficient of the k-th dependent work package on the overall schedule.

[0011] Furthermore, step 3 includes the following steps: By leveraging expert knowledge, historical data analysis, and current project characteristics, we comprehensively identify various factors that may affect the project schedule, categorizing them into foreseeable and unforeseeable factors. For each factor, we establish a detailed factor database, including factor descriptions, potential impact ranges, historical frequency of occurrence, and the network of relationships between factors. For the identified influencing factors, design and implement a comprehensive data collection plan, and clean, standardize and structure the collected data to ensure data quality and consistency; Construct a model of the relationship between various influencing factors and project schedule. For foreseeable factors, focus on analyzing their long-term and periodic impact on schedule; for unforeseeable factors, focus on assessing their probability of occurrence and potential impact. Using correlation analysis and causal inference techniques, identify the key factors that have the most significant impact on the schedule; Based on the established relationship model, multiple possible scenarios were designed to simulate the impact of different combinations of factors on project schedule. Monte Carlo simulation was used to generate a large amount of simulation data to evaluate the range of changes in project schedule under different conditions. Based on the current project status and real-time data of various influencing factors, predict changes in project progress over a future period of time; A structured influencing factor analysis report is generated by integrating various analysis results, including the ranking of key influencing factors, progress forecast results and confidence intervals, high-risk scenario analysis and response recommendations.

[0012] Furthermore, the range of project schedule variation (CI) under different conditions is represented as follows: ,in, is the critical value under a normal distribution, used to calculate the confidence interval; Q is the total number of Monte Carlo simulations performed, representing the number of different scenarios generated; The average project schedule across all simulated scenarios, reflecting the expected project schedule, is expressed by the formula: ,in, The project schedule calculated in the r-th simulation considering different combinations of factors; The standard deviation of project schedule under different scenarios reflects the uncertainty or volatility of schedule, and is expressed by the formula: .

[0013] Furthermore, step 4 includes the following steps: Integrate real-time collected progress deviation data, resource usage records, environmental monitoring data, and relevant historical project data, and perform data cleaning, standardization, and normalization. Time series analysis was performed on the processed data to identify patterns in schedule deviations, resource usage, and environmental factors over time. Based on the identified patterns and historical data, a series of machine learning models are trained for progress prediction, including: a regression model for predicting specific progress indicators; a classification model for predicting the level of delay risk; and a deep learning model for capturing complex time dependencies. By utilizing real-time data streams, machine learning models are continuously updated and adjusted to enable dynamic analysis and short-term prediction of current trends. By combining short-term forecast results, historical patterns, and current project characteristics, a long-term trend forecasting model is constructed to generate long-term progress forecasts. Integrate short-term and long-term forecasts to generate a comprehensive progress forecast report.

[0014] Furthermore, step 5 includes the following steps: A comprehensive analysis of schedule deviation data, influencing factor analysis results, identified delay patterns, and future schedule forecasts is conducted. In addition, multi-dimensional assessment and critical path analysis are combined to determine the links and directions that need to be optimized. Based on the comprehensive analysis results, the key bottlenecks in the current project are identified and classified according to their nature. At the same time, the impact of each type of bottleneck on the project schedule is quantitatively assessed, prioritized, and optimization objectives are clarified. Based on historical data and best practice library, multiple possible optimization solutions are automatically generated, while taking into account various constraints to ensure that the generated solutions are practical. The generated multiple optimization schemes are systematically evaluated, the implementation effect of each scheme is predicted by simulation model, and further optimization and adjustment are carried out through multi-objective optimization algorithm to balance multiple needs and constraints; Based on the evaluation results, the optimal solution is selected, a detailed overall optimization strategy is formulated, and corresponding risk response plans are developed to ensure the robustness and adaptability of the strategy. Transform optimization strategies into implementation plans, clarify timelines, responsible persons, resources, and objectives, develop schedules and resource allocation tables, and establish monitoring and feedback mechanisms.

[0015] Furthermore, step 6 includes the following steps: The overall optimization strategy is broken down into specific execution tasks and action plans. A detailed work breakdown structure is established, responsible persons and specific objectives are assigned to each execution unit, and an intelligent project management system is deployed to achieve comprehensive digital management of the execution of the optimization strategy. Based on real-time project progress data and resource status information, the system automatically generates the optimal resource allocation plan, including dynamic allocation of human resources, real-time optimization of equipment usage plans, and precise coordination of material supply, to ensure that key resources are in place in a timely manner. Data on task execution status is collected in real time through various technical means, including workload completion, resource utilization efficiency and construction quality indicators. At the same time, a multi-level early warning mechanism is set up to ensure that deviations are identified and dealt with in a timely manner. Regularly conduct a comprehensive evaluation of the implementation effect of optimization strategies, establish a multi-dimensional evaluation system that includes progress indicators, resource utilization rate, cost control and quality compliance rate, evaluate the actual effect of optimization measures by comparing the changes of various indicators before and after optimization, and analyze existing problems and potential improvement space. Based on the results of the effectiveness evaluation and analysis, and in combination with the actual situation of the project, the optimization strategy is dynamically adjusted to ensure that the optimization strategy can adapt to the dynamic changes of the project and maintain its effectiveness and applicability. Timely summarize the experiences and lessons learned during the implementation of optimization strategies, and establish a systematic learning and review mechanism to promote the accumulation and sharing of organizational experience, provide reference and guidance for the optimization of subsequent projects, and achieve continuous improvement of organizational capabilities.

[0016] Compared with the prior art, the beneficial effects of this application are as follows: This application uses dynamic data analysis and machine learning technology to monitor and predict construction project schedule deviations and delay risks in real time. It generates an influencing factor report by combining historical data and trend analysis, and proposes resource allocation suggestions and optimization strategies to ensure efficient use of project resources. Through feedback loops, it continuously optimizes schedule management and achieves intelligent and refined project schedule control. Attached Figure Description

[0017] Figure 1 This is a flowchart of the intelligent progress management and optimization method for building engineering based on dynamic data analysis disclosed in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0019] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] like Figure 1 As shown, the intelligent schedule management and optimization method for construction projects based on dynamic data analysis includes the following steps: Step 1: Collect real-time data on project progress, resource usage, and environmental factors, and perform data cleaning and standardization. Step 2: Continuously monitor progress deviations and identify risks by combining historical data and current trends; Step 3: Through quantitative analysis, assess the impact of foreseeable and unforeseeable factors on the project schedule and automatically generate an influencing factor analysis report; Step 4: Perform pattern recognition on the accumulated schedule deviations, resource usage, and environmental factor data to identify potential delay patterns. At the same time, combine historical data and current trends to predict future schedule trends and generate more accurate schedule forecasts. Step 5: Based on the results of schedule deviation analysis, influencing factor analysis, pattern recognition and prediction, generate resource allocation suggestions and formulate targeted overall optimization strategies; Step 6: Implement optimization strategies, ensuring effective resource utilization and progress control through dynamic resource scheduling and task priority adjustment, while continuously tracking optimization effects and updating feedback loops.

[0022] In this embodiment, step 1 involves real-time data collection, cleaning, and standardization of project progress, resource usage, and environmental factors. This process utilizes high-precision sensors and data acquisition equipment to monitor the construction site's progress, equipment usage, and environmental factors (such as weather, temperature, and humidity) in real time. The data cleaning and standardization process ensures data accuracy and consistency, thereby improving the reliability of subsequent analysis. The innovation of this stage lies in providing a solid foundation for subsequent analysis through precise data acquisition. After standardization, various types of data can be compared and analyzed on the same platform, laying a good foundation for overall progress management. Simultaneously, this process also reduces data redundancy and improves data processing efficiency.

[0023] In this embodiment, step 2 continuously monitors schedule deviations and identifies risks by combining historical data and current trends. Analyzing the real-time collected data allows for the rapid identification of deviations from the planned schedule. This innovation significantly enhances the project manager's responsiveness, enabling them to take timely measures to avoid further delays. Furthermore, combining historical data analysis can identify potential risk factors, such as insufficient construction personnel and material delays, providing managers with timely early warning information. Through these methods, project managers can take preventative measures early in the project, effectively reducing risks.

[0024] In this embodiment, step 3 quantitatively analyzes and assesses the impact of foreseeable and unforeseeable factors on project progress, and automatically generates an influencing factor analysis report. This process utilizes statistical analysis and machine learning algorithms to quantitatively assess the degree of impact of various factors on project progress. The innovation of this stage lies in providing a scientific basis, enabling managers to understand the impact of each factor and conduct targeted management. The influencing factor analysis report not only provides managers with a clear basis for decision-making but also provides data support for subsequent adjustments and optimizations of the project. Furthermore, by automatically generating the report, manual intervention is reduced, the timeliness and accuracy of the report are improved, and management efficiency is further enhanced.

[0025] In this embodiment, step 4 performs pattern recognition on the accumulated schedule deviations, resource usage, and environmental factor data to identify potential delay patterns and predict future schedule trends by combining historical data and current trends. The innovation of this process lies in its ability to identify key factors affecting schedule and their interrelationships through in-depth analysis of large amounts of data. This not only provides project managers with a deeper understanding of delay patterns but also helps them make more informed decisions regarding schedule control by predicting future schedule trends. The implementation of this method relies on advanced algorithmic techniques, such as deep learning and time series analysis, making the prediction results more accurate and reliable.

[0026] In this embodiment, resource allocation suggestions and an overall optimization strategy are generated based on schedule deviations, influencing factor analysis, pattern recognition, and prediction results. Through comprehensive analysis, inconsistencies in resource allocation can be identified, and effective allocation suggestions can be proposed to ensure optimal resource allocation. Step 5 provides managers with a basis for strategy formulation and significantly improves resource utilization efficiency, thereby reducing project costs. Simultaneously, the formulation of the overall optimization strategy helps the project maintain schedule while also considering quality and safety, forming an effective closed-loop management system.

[0027] In this embodiment, step 6 involves implementing an optimization strategy. Through dynamic resource scheduling and task priority adjustment, effective resource utilization and progress control are ensured, while the optimization effect is continuously tracked and updated with feedback. The innovation of this process lies in ensuring the flexibility and adaptability of the strategy. As the project progresses, managers can adjust resource allocation and task priorities in real time, ensuring the project remains within a controllable range. The established feedback mechanism also allows the system to continuously learn and adjust, gradually improving management effectiveness.

[0028] In summary, the intelligent progress management and optimization method for construction projects based on dynamic data analysis disclosed in this embodiment covers the entire process from data collection, progress monitoring, and risk identification to influencing factor analysis, pattern recognition, resource allocation suggestions, and optimization strategy implementation. Through real-time data monitoring and intelligent analysis, this method can effectively identify and address progress deviations, providing scientific decision support for project management and ultimately achieving efficient project management and optimization. The implementation of this method not only improves the controllability of project progress but also provides an important technological foundation for the digital transformation of the construction industry, demonstrating enormous application potential and development prospects.

[0029] Furthermore, step 1 includes the following steps: Step 1.1: Deploy various sensors and data acquisition devices at the project site to capture real-time data on project progress, resource usage, and environmental factors; Step 1.2 involves preliminary screening and cleaning of the collected data, including removing obvious outliers, handling missing data, and correcting data format errors. Step 1.3 converts data from different sources and formats into a unified standard format, including unit conversion, timestamp standardization, and naming standardization; Step 1.4: Link the project progress data with the corresponding resource usage data and environmental factor data in time and space dimensions to form a multidimensional dataset; Step 1.5: Establish a data dictionary and metadata management system to record the source, meaning, unit, and update frequency of the data.

[0030] In the intelligent progress management and optimization method for construction projects based on dynamic data analysis, step 1 is fundamental and directly affects the effectiveness and accuracy of subsequent analyses. Step 1 mainly includes deploying various sensors and data acquisition devices on the project site, data cleaning and standardization, unifying data formats, and establishing a data dictionary and metadata management system, as detailed below.

[0031] The innovation of step 1.1 lies in achieving comprehensive monitoring of the project site through the collaborative work of multiple sensors. This not only accurately records progress but also collects data on environmental factors affecting construction, such as changes in temperature and humidity. This data provides a rich information foundation for subsequent analysis, enabling project managers to fully understand the dynamic changes at the construction site.

[0032] The innovation of step 1.2 lies in improving data quality and ensuring the accuracy of subsequent analysis. Outlier removal effectively avoids misjudgments caused by noisy data, while handling missing data ensures the integrity of the dataset. Correcting data format errors contributes to data consistency, making subsequent processing smoother. These measures ensure the reliability of the data used, thus improving the effectiveness of the analytical model.

[0033] The key to step 1.3 lies in making data easier to analyze through standardization. Different sensors may use different units and time formats, and standardization can eliminate these differences, making the data comparable during analysis. Unified timestamps not only facilitate time-series data analysis but also accurately integrate data from different time points when performing multi-dimensional data correlation. Furthermore, standardized naming ensures data readability, making it easier for managers to understand the data content.

[0034] The innovation of step 1.4 lies in its ability to deeply analyze the relationships between various factors through a multidimensional dataset. For example, the relationship between resource usage and schedule can be analyzed through the time dimension, thereby identifying key factors that may affect schedule. Simultaneously, spatial correlations help managers understand schedule differences and resource usage across different regions, facilitating the development of more targeted management strategies. The establishment of this multidimensional dataset lays a solid foundation for subsequent analysis, making subsequent pattern recognition and trend prediction more accurate.

[0035] The innovation of step 1.5 lies in providing systematic data governance capabilities. A data dictionary helps managers quickly understand the specific meaning and source of each data item, avoiding decision-making errors caused by misunderstandings of data. The establishment of a metadata management system provides a standardized process for data maintenance and updates, ensuring consistency and reliability during updates. This systematic data management approach not only improves data utilization efficiency but also lays the foundation for long-term data accumulation and analysis for the project.

[0036] In summary, the complete implementation of step 1 provides a reliable data foundation for intelligent progress management of construction projects. By deploying various sensors and data acquisition devices on the project site, combined with data cleaning, format standardization, multidimensional dataset formation, and metadata management, key data can be effectively captured and processed. This process not only ensures the accuracy and integrity of the data but also lays a solid foundation for subsequent dynamic data analysis. With improved data quality, subsequent progress monitoring, risk identification, pattern recognition, and optimization strategy formulation will become more efficient and accurate, making intelligent construction project management possible.

[0037] Furthermore, step 2 includes the following steps: Establish a detailed schedule baseline based on the project plan, including the expected completion time of each work package and milestone; at the same time, define a series of key performance indicators as the basis for monitoring and evaluating project progress. Using the real-time data collected and processed in step 1, the deviation between the actual progress and the predetermined project schedule is continuously calculated, including deviation analysis for each work package, each construction phase, and the overall project schedule. By applying deviation analysis and trend analysis algorithms, we evaluate the current values ​​and trends of each schedule indicator, while also considering the dependencies between tasks to analyze the impact of delays on the overall schedule. Based on the calculated schedule deviation and the predetermined threshold, a multi-level alarm mechanism is established; Build a knowledge base containing historical project data, including progress data, problems encountered and solutions adopted in past projects, and identify common delay patterns and risk factors from historical project data; By combining real-time progress data with historical analysis results, risk factors that could lead to severe delays can be identified, and their probability of occurrence and potential impact can be estimated. Based on progress monitoring, alarm information, historical data analysis, and risk prediction results, a comprehensive risk identification report is automatically generated.

[0038] In the intelligent schedule management and optimization method for construction projects based on dynamic data analysis, step 2 is a crucial step. Its main task is to monitor the deviation between the actual progress and the planned schedule, and to identify potential risk factors. The key to this process lies in providing accurate decision support for project managers through a systematic approach that integrates real-time data, historical data, and predetermined indicators. The following will analyze each sub-step of step 2 in detail.

[0039] First, establish a detailed schedule baseline based on the project plan, including the expected completion times for each work package and milestone, and define a series of Key Performance Indicators (KPIs) as the basis for monitoring and evaluating project progress. The schedule baseline is the core of project management, providing standards and a basis for subsequent schedule monitoring. By setting the expected completion times for each work package, the project team can clearly understand the goals and requirements of each phase. Simultaneously, defining KPIs helps quantify schedule performance, enabling managers to assess the project's operational status in real time through these metrics. Common KPIs include percentage of progress completed, resource utilization efficiency, and interdependencies between work packages. These indicators not only provide quantitative standards for schedule monitoring but also lay the foundation for subsequent variance analysis.

[0040] Secondly, using the real-time data collected and processed in step 1, the deviation between the actual progress and the planned project schedule is continuously calculated. This process involves deviation analysis for each work package, each construction phase, and the overall project schedule. Through real-time calculations, project managers can promptly identify deviations from the planned schedule, facilitating rapid corrective action. Schedule deviation analysis provides data support for identifying potential problems, enabling managers to gain a deeper understanding of the project's progress from both a holistic and local perspective. For example, if the actual completion time of a work package exceeds expectations, managers can analyze the reasons and develop corresponding solutions.

[0041] Next, variance analysis and trend analysis algorithms are applied to evaluate the current values ​​and trends of each schedule indicator, while also considering inter-task dependencies to analyze the impact of delays on the overall schedule. The innovation of this analysis process lies in its ability to deeply understand the dynamic changes in project schedule. Through trend analysis, not only can current deviations be identified, but also potential future trends can be predicted. For example, if the completion rate of a work package has significantly decreased in the past few days, the potential impact of this trend on subsequent work can be predicted. Such analysis not only helps to adjust resource allocation in a timely manner but also improves the foresight of project management.

[0042] Next, based on the calculated schedule deviations and predetermined thresholds, a multi-level alert mechanism is established. This mechanism is designed to ensure that managers can respond promptly to potential risks through different levels of alerts. For example, an alert can be issued when the schedule deviation reaches a predetermined minor threshold; and once the deviation reaches a serious level, a higher-level alert can be automatically triggered to allow senior management intervention. Through this multi-level alert mechanism, the project team can better grasp the dynamic changes of the project and reduce the risk of delays.

[0043] Next, a knowledge base containing historical project data will be built, including past project progress data, encountered problems, and solutions implemented. This knowledge base not only provides a reference for current projects but also helps project teams identify common delay patterns and risk factors. For example, analyzing historical data can reveal that certain types of projects are prone to delays under specific conditions, thus providing a warning for risk management in current projects. By combining historical experience, managers can develop more scientific response strategies.

[0044] Then, by combining real-time progress data with historical analysis results, risk factors leading to severe delays are identified, and their probability of occurrence and potential impact are estimated. The innovation of this process lies in enhancing the proactive analysis of project risks by combining real-time and historical data. Once high-risk factors are identified, managers can proactively take measures to reduce the probability of these risks occurring. For example, teams can proactively allocate resources and plan timelines in advance for common causes of delays in previous projects to minimize their impact on project schedule.

[0045] Finally, based on progress monitoring, alert information, historical data analysis, and risk prediction results, a comprehensive risk identification report is automatically generated. This report helps provide a clear understanding of the project status and potential risks, offering decision support to management. By aggregating and analyzing various types of information, the report provides project managers with a more intuitive perspective, enabling them to quickly understand the current progress status and existing risks, and subsequently formulate corresponding adjustment and optimization strategies.

[0046] In summary, the implementation of step 2 provides intelligent schedule management for construction projects with dynamic monitoring and risk identification capabilities. By establishing detailed schedule benchmarks and KPIs, continuously calculating schedule deviations, applying trend analysis, establishing alarm mechanisms, building a historical knowledge base, and automatically generating risk identification reports, project managers can promptly grasp project dynamics and identify potential risks. This systematic schedule monitoring and analysis method not only improves project transparency but also provides data support for decision-making, ensuring that projects can be effectively managed and optimized in a dynamically changing environment.

[0047] Furthermore, the schedule deviation for each work package is calculated using the following formula: ΔP i =P actual,i -P planned,i , where ΔP i P represents the schedule deviation for the i-th work package. actual,i P represents the actual progress of the i-th work package; planned,i The scheduled progress for the i-th work package; The schedule deviation for each construction phase is calculated using the following formula: ,in, ΔS is the number of work packages in stage j; j Let be the schedule deviation for the j-th construction phase; Overall project schedule deviation Calculate using the following formula: Where N is the total number of work packages; It is the weight of work package i.

[0048] In this embodiment, by quantitatively analyzing the progress of work packages, managers can quickly identify which work packages are lagging behind. This precise data analysis allows the project team to implement targeted interventions for specific problems. Furthermore, calculating schedule deviations helps project managers understand the dependencies between work packages, thereby optimizing resource allocation and ensuring that the overall schedule is not affected by individual work packages. By continuously tracking these deviation data, managers can establish an effective schedule monitoring system to ensure the project progresses smoothly.

[0049] In this embodiment, schedule deviations for each construction phase can assess the performance of that phase over a broad range. By integrating deviation data from multiple work packages, it's possible to understand not only the progress of individual work packages but also the overall operation of the entire construction phase. This comprehensive perspective allows project managers to analyze potential risks to the phased schedule more thoroughly, facilitating proactive adjustments. Furthermore, schedule deviation analysis for each construction phase can provide a reference for resource allocation and task scheduling in subsequent phases, enhancing the flexibility and adaptability of project management.

[0050] In this embodiment, a weighted calculation method can more accurately reflect the overall health of the project. For example, certain work packages that are crucial to the success of the project (such as work packages on the critical path) can be given higher weights. In this way, even if some low-weight work packages are delayed, the overall deviation can still reflect the key factors that truly affect the project schedule.

[0051] In summary, by accurately calculating schedule deviations for each work package, construction phase, and the overall project, comprehensive monitoring and optimization of project progress can be achieved. Using specific calculation formulas not only makes schedule deviation monitoring more scientific and systematic but also provides project managers with data support, enabling timely adjustments to resource allocation, optimization of schedule plans, and reduction of delay risks. This dynamic data analysis method helps improve the management efficiency of construction projects and ensures the smooth progress of the project.

[0052] Furthermore, the impact of delays on the overall schedule. Calculate using the following formula: , where m is the number of work packages affected; Let be the delay time for the k-th dependent work package; Let be the impact coefficient of the k-th dependent work package on the overall schedule. Precise calculation of the impact of delays on the overall schedule allows for a better understanding of the project's health. Using a formulaic approach to quantitatively analyze the impact of delays on the overall schedule helps identify critical work packages, optimize resource allocation, and enhance real-time monitoring and feedback capabilities. Furthermore, the results of delay impact analysis not only promote collaboration among teams but also provide valuable lessons for future project improvements. This systematic approach, within the context of dynamic data analysis, significantly improves the scientific rigor and efficiency of construction project management, laying a solid foundation for achieving project objectives.

[0053] Furthermore, step 3 includes the following steps: By leveraging expert knowledge, historical data analysis, and current project characteristics, we comprehensively identify various factors that may affect the project schedule, categorizing them into foreseeable and unforeseeable factors. For each factor, we establish a detailed factor database, including factor descriptions, potential impact ranges, historical frequency of occurrence, and the network of relationships between factors. For the identified influencing factors, design and implement a comprehensive data collection plan, and clean, standardize and structure the collected data to ensure data quality and consistency; Construct a model of the relationship between various influencing factors and project schedule. For foreseeable factors, focus on analyzing their long-term and periodic impact on schedule; for unforeseeable factors, focus on assessing their probability of occurrence and potential impact. Using correlation analysis and causal inference techniques, identify the key factors that have the most significant impact on the schedule; Based on the established relationship model, multiple possible scenarios were designed to simulate the impact of different combinations of factors on project schedule. Monte Carlo simulation was used to generate a large amount of simulation data to evaluate the range of changes in project schedule under different conditions. Based on the current project status and real-time data of various influencing factors, predict changes in project progress over a future period of time; A structured influencing factor analysis report is generated by integrating various analysis results, including the ranking of key influencing factors, progress forecast results and confidence intervals, high-risk scenario analysis and response recommendations.

[0054] In this embodiment, various factors that may affect project progress are categorized into foreseeable and unforeseeable factors. A detailed factor database is established for each factor, including factor description, potential scope of impact, historical frequency of occurrence, and the relationship network between factors, providing basic information for project management. This systematic factor identification and classification helps managers gain a more comprehensive understanding of the complexity affecting progress, laying the foundation for subsequent analysis.

[0055] In this embodiment, the collected data is cleaned, standardized, and structured to ensure data quality and consistency. This process not only improves data usability but also ensures the reliability and accuracy of subsequent analysis results. For example, by removing outliers and handling missing data, misleading conclusions can be avoided during the analysis process, thereby improving the effectiveness of influencing factor analysis.

[0056] In this embodiment, for foreseeable factors, the focus is on analyzing their long-term and cyclical impacts on schedule. This helps project managers better consider the potential impact of these factors when developing project plans. For unforeseen factors, the focus is on assessing their probability of occurrence and the degree of their potential impact. This hierarchical analysis allows for a more comprehensive capture of the driving factors behind changes in project schedule.

[0057] In this embodiment, identifying the key factors that have the most significant impact on schedule using correlation analysis and causal inference techniques is the core of step 3. Statistical analysis can determine which factors are the main drivers affecting schedule. This identification process not only helps managers prioritize resource allocation but also guides the development of project risk management strategies, thereby enhancing project controllability.

[0058] In this embodiment, designing multiple possible scenarios based on the established relationship model and simulating the impact of different combinations of factors on project schedule is another important step. Generating a large amount of simulation data through Monte Carlo simulation allows for the assessment of the range of project schedule variations under different conditions. This simulation technology allows managers to make more effective decisions in uncertain environments, helping to identify potential risk scenarios and coping strategies, thereby enhancing the project's resilience and adaptability.

[0059] In this embodiment, predicting project progress changes over a future period based on the current project status and real-time data of various influencing factors is a natural extension of the previous analysis. Successful implementation of this process provides project managers with a forward-looking perspective, enabling them to adjust plans promptly to address potential schedule deviations. The accuracy of the prediction model directly impacts the success of the project; therefore, the proper design and continuous optimization of this model are crucial.

[0060] In this embodiment, a structured influencing factor analysis report is generated by integrating various analysis results. This report includes a ranking of key influencing factors, schedule forecast results and confidence intervals, high-risk scenario analysis, and corresponding response recommendations, providing crucial information for decision-making. This type of report not only offers managers a comprehensive perspective but also helps project teams understand the complexities of schedule management and promotes cross-departmental communication and collaboration.

[0061] In summary, step 3, through the systematic identification, analysis, and modeling of various factors affecting project progress, provides a solid data foundation and decision support for the management of construction projects. By combining expert knowledge, standardizing data collection, and constructing relational models, project managers can clearly identify key factors and conduct effective predictive analysis. Furthermore, through scenario simulation and Monte Carlo simulation, the project team can better cope with risks in complex and ever-changing environments, ensuring the project progresses as planned. Ultimately, the generated structured influencing factor analysis report provides a reliable basis for project decision-making, making project management more scientific, accurate, and efficient.

[0062] Furthermore, the range of project schedule variation (CI) under different conditions is represented as follows: ,in, is the critical value under a normal distribution, used to calculate the confidence interval; Q is the total number of Monte Carlo simulations performed, representing the number of different scenarios generated; The average project schedule across all simulated scenarios, reflecting the expected project schedule, is expressed by the formula: ,in, The project schedule calculated in the r-th simulation considering different combinations of factors; The standard deviation of project schedule under different scenarios reflects the uncertainty or volatility of schedule, and is expressed by the formula: By calculating the range of project schedule variations (confidence intervals), project managers can gain a comprehensive understanding of the expected value and uncertainty of project schedule under different conditions. This process relies on accurate calculations of average schedule and standard deviation, as well as the appropriate selection of confidence levels. The rich scenario data generated through Monte Carlo simulations can effectively identify potential risks to project schedules and provide decision support for managers. Ultimately, these analytical results not only enhance project controllability but also lay a solid foundation for successful project implementation.

[0063] Furthermore, step 4 includes the following steps: Integrate real-time collected progress deviation data, resource usage records, environmental monitoring data, and relevant historical project data, and perform data cleaning, standardization, and normalization. Time series analysis was performed on the processed data to identify patterns in schedule deviations, resource usage, and environmental factors over time. Based on the identified patterns and historical data, a series of machine learning models are trained for progress prediction, including: a regression model for predicting specific progress indicators; a classification model for predicting the level of delay risk; and a deep learning model for capturing complex time dependencies. By utilizing real-time data streams, machine learning models are continuously updated and adjusted to enable dynamic analysis and short-term prediction of current trends. By combining short-term forecast results, historical patterns, and current project characteristics, a long-term trend forecasting model is constructed to generate long-term progress forecasts. Integrate short-term and long-term forecasts to generate a comprehensive progress forecast report.

[0064] Step 4 plays a crucial role in intelligent schedule management and optimization methods through in-depth analysis and prediction of schedule deviations, resource usage, and environmental factors. The application of data integration, time series analysis, and machine learning models, combined with dynamic updates and long-term trend forecasting, enables project managers to fully understand the project's current status and future development. This scientific analysis and forecasting approach not only improves the efficiency and accuracy of project management but also provides a solid foundation for addressing uncertainties and risks. Ultimately, a comprehensive schedule forecast report provides vital information for decision-making, ensuring the smooth implementation of construction projects and the effective utilization of resources.

[0065] Furthermore, step 5 includes the following steps: A comprehensive analysis of schedule deviation data, influencing factor analysis results, identified delay patterns, and future schedule forecasts is conducted. In addition, multi-dimensional assessment and critical path analysis are combined to determine the links and directions that need to be optimized. Based on the comprehensive analysis results, the key bottlenecks in the current project are identified and classified according to their nature. At the same time, the impact of each type of bottleneck on the project schedule is quantitatively assessed, prioritized, and optimization objectives are clarified. Based on historical data and best practice library, multiple possible optimization solutions are automatically generated, while taking into account various constraints to ensure that the generated solutions are practical. The generated multiple optimization schemes are systematically evaluated, the implementation effect of each scheme is predicted by simulation model, and further optimization and adjustment are carried out through multi-objective optimization algorithm to balance multiple needs and constraints; Based on the evaluation results, the optimal solution is selected, a detailed overall optimization strategy is formulated, and corresponding risk response plans are developed to ensure the robustness and adaptability of the strategy. Transform optimization strategies into implementation plans, clarify timelines, responsible persons, resources, and objectives, develop schedules and resource allocation tables, and establish monitoring and feedback mechanisms.

[0066] Step 5 plays a crucial role in intelligent schedule management and optimization methods. Through comprehensive analysis and critical path identification, bottleneck problem identification and prioritization, generation of optimization solutions, systematic evaluation and multi-objective optimization, selection of the optimal solution and formulation of optimization strategies, and implementation of plans and monitoring and feedback mechanisms, project managers can gain a comprehensive understanding of the project's current status, identify key issues, and develop practical optimization solutions. This series of optimization strategies aims to improve construction efficiency, shorten project duration, and achieve effective risk management in a dynamic environment, thereby ensuring the smooth progress of the construction project.

[0067] Furthermore, step 6 includes the following steps: The overall optimization strategy is broken down into specific execution tasks and action plans. A detailed work breakdown structure is established, responsible persons and specific objectives are assigned to each execution unit, and an intelligent project management system is deployed to achieve comprehensive digital management of the execution of the optimization strategy. Based on real-time project progress data and resource status information, the system automatically generates the optimal resource allocation plan, including dynamic allocation of human resources, real-time optimization of equipment usage plans, and precise coordination of material supply, to ensure that key resources are in place in a timely manner. Data on task execution status is collected in real time through various technical means, including workload completion, resource utilization efficiency and construction quality indicators. At the same time, a multi-level early warning mechanism is set up to ensure that deviations are identified and dealt with in a timely manner. Regularly conduct a comprehensive evaluation of the implementation effect of optimization strategies, establish a multi-dimensional evaluation system that includes progress indicators, resource utilization rate, cost control and quality compliance rate, evaluate the actual effect of optimization measures by comparing the changes of various indicators before and after optimization, and analyze existing problems and potential improvement space. Based on the results of the effectiveness evaluation and analysis, and in combination with the actual situation of the project, the optimization strategy is dynamically adjusted to ensure that the optimization strategy can adapt to the dynamic changes of the project and maintain its effectiveness and applicability. Timely summarize the experiences and lessons learned during the implementation of optimization strategies, and establish a systematic learning and review mechanism to promote the accumulation and sharing of organizational experience, provide reference and guidance for the optimization of subsequent projects, and achieve continuous improvement of organizational capabilities.

[0068] Step 6 is crucial in intelligent progress management and optimization methods. Through task decomposition and digital management, automatic generation of resource allocation plans, real-time data collection and multi-level early warning mechanisms, regular evaluation of implementation effectiveness, dynamic adjustment of optimization strategies, and experience summarization and learning review mechanisms, it ensures that optimization measures are effectively implemented. This series of implementation processes not only improves project efficiency but also enhances team collaboration and learning capabilities, laying the foundation for the success of subsequent projects.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent progress management and optimization of construction projects based on dynamic data analysis, characterized in that: Includes the following steps: Step 1: Collect real-time data on project progress, resource usage, and environmental factors, and perform data cleaning and standardization. Step 2: Continuously monitor progress deviations and identify risks by combining historical data and current trends; Step 3: Through quantitative analysis, assess the impact of foreseeable and unforeseeable factors on the project schedule and automatically generate an influencing factor analysis report; Step 4: Perform pattern recognition on the accumulated schedule deviations, resource usage, and environmental factor data to identify potential delay patterns. At the same time, combine historical data and current trends to predict future schedule trends and generate more accurate schedule forecasts. Step 5: Based on the results of schedule deviation analysis, influencing factor analysis, pattern recognition and prediction, generate resource allocation suggestions and formulate targeted overall optimization strategies; Step 6: Implement optimization strategies, ensuring effective resource utilization and progress control through dynamic resource scheduling and task priority adjustment, while continuously tracking optimization effects and updating feedback loops.

2. The intelligent progress management and optimization method for construction projects based on dynamic data analysis according to claim 1, characterized in that, Step 1 includes the following steps: Deploy various sensors and data acquisition devices at the project site to capture real-time data on project progress, resource usage, and environmental factors; The collected data undergoes preliminary screening and cleaning, including removing obvious outliers, handling missing data, and correcting data format errors. Convert data from different sources and formats into a unified standard format, including unit conversion, timestamp standardization, and naming standardization; By linking project progress data with corresponding resource usage data and environmental factor data in time and space dimensions, a multidimensional dataset is formed. At the same time, a data dictionary and metadata management system are established to record the source, meaning, unit, and update frequency of the data.

3. The intelligent progress management and optimization method for construction projects based on dynamic data analysis according to claim 2, characterized in that, Step 2 includes the following steps: Establish a detailed schedule baseline based on the project plan, including the expected completion time of each work package and milestone; at the same time, define a series of key performance indicators as the basis for monitoring and evaluating project progress. Using the real-time data collected and processed in step 1, the deviation between the actual progress and the predetermined project schedule is continuously calculated, including deviation analysis for each work package, each construction phase, and the overall project schedule. By applying deviation analysis and trend analysis algorithms, we evaluate the current values ​​and trends of each schedule indicator, while also considering the dependencies between tasks to analyze the impact of delays on the overall schedule. Based on the calculated schedule deviation and the predetermined threshold, a multi-level alarm mechanism is established; Build a knowledge base containing historical project data, including progress data, problems encountered and solutions adopted in past projects, and identify common delay patterns and risk factors from historical project data; By combining real-time progress data with historical analysis results, risk factors that could lead to severe delays can be identified, and their probability of occurrence and potential impact can be estimated. Based on progress monitoring, alarm information, historical data analysis, and risk prediction results, a comprehensive risk identification report is automatically generated.

4. The intelligent progress management and optimization method for construction projects based on dynamic data analysis according to claim 1, characterized in that, Step 3 includes the following steps: By leveraging expert knowledge, historical data analysis, and current project characteristics, we comprehensively identify various factors that may affect the project schedule, categorizing them into foreseeable and unforeseeable factors. For each factor, we establish a detailed factor database, including factor descriptions, potential impact ranges, historical frequency of occurrence, and the network of relationships between factors. For the identified influencing factors, design and implement a comprehensive data collection plan, and clean, standardize and structure the collected data to ensure data quality and consistency; Construct a model of the relationship between various influencing factors and project schedule. For foreseeable factors, focus on analyzing their long-term and periodic impact on schedule; for unforeseeable factors, focus on assessing their probability of occurrence and potential impact. Using correlation analysis and causal inference techniques, identify the key factors that have the most significant impact on the schedule; Based on the established relationship model, multiple possible scenarios were designed to simulate the impact of different combinations of factors on project schedule. Monte Carlo simulation was used to generate a large amount of simulation data to evaluate the range of changes in project schedule under different conditions. Based on the current project status and real-time data of various influencing factors, predict changes in project progress over a future period of time; A structured influencing factor analysis report is generated by integrating various analysis results, including the ranking of key influencing factors, progress forecast results and confidence intervals, high-risk scenario analysis and response recommendations.

5. The intelligent progress management and optimization method for construction projects based on dynamic data analysis according to claim 1, characterized in that, Step 4 includes the following steps: Integrate real-time collected progress deviation data, resource usage records, environmental monitoring data, and relevant historical project data, and perform data cleaning, standardization, and normalization. Time series analysis was performed on the processed data to identify patterns in schedule deviations, resource usage, and environmental factors over time. Based on the identified patterns and historical data, a series of machine learning models are trained for progress prediction, including: a regression model for predicting specific progress indicators; a classification model for predicting the level of delay risk; and a deep learning model for capturing complex time dependencies. By utilizing real-time data streams, machine learning models are continuously updated and adjusted to enable dynamic analysis and short-term prediction of current trends. By combining short-term forecast results, historical patterns, and current project characteristics, a long-term trend forecasting model is constructed to generate long-term progress forecasts. Integrate short-term and long-term forecasts to generate a comprehensive progress forecast report.

6. The intelligent progress management and optimization method for construction projects based on dynamic data analysis according to claim 1, characterized in that, Step 5 includes the following steps: A comprehensive analysis of schedule deviation data, influencing factor analysis results, identified delay patterns, and future schedule forecasts is conducted. In addition, multi-dimensional assessment and critical path analysis are combined to determine the links and directions that need to be optimized. Based on the comprehensive analysis results, the key bottlenecks in the current project are identified and classified according to their nature. At the same time, the impact of each type of bottleneck on the project schedule is quantitatively assessed, prioritized, and optimization objectives are clarified. Based on historical data and best practice library, multiple possible optimization solutions are automatically generated, while taking into account various constraints to ensure that the generated solutions are practical. The generated multiple optimization schemes are systematically evaluated, the implementation effect of each scheme is predicted by simulation model, and further optimization and adjustment are carried out through multi-objective optimization algorithm to balance multiple needs and constraints; Based on the evaluation results, the optimal solution is selected, a detailed overall optimization strategy is formulated, and corresponding risk response plans are developed to ensure the robustness and adaptability of the strategy. Transform optimization strategies into implementation plans, clarify timelines, responsible persons, resources, and objectives, develop schedules and resource allocation tables, and establish monitoring and feedback mechanisms.

7. The intelligent progress management and optimization method for construction projects based on dynamic data analysis according to claim 1, characterized in that, Step 6 includes the following steps: The overall optimization strategy is broken down into specific execution tasks and action plans. A detailed work breakdown structure is established, responsible persons and specific objectives are assigned to each execution unit, and an intelligent project management system is deployed to achieve comprehensive digital management of the execution of the optimization strategy. Based on real-time project progress data and resource status information, the system automatically generates the optimal resource allocation plan, including dynamic allocation of human resources, real-time optimization of equipment usage plans, and precise coordination of material supply, to ensure that key resources are in place in a timely manner. Data on task execution status is collected in real time through various technical means, including workload completion, resource utilization efficiency and construction quality indicators. At the same time, a multi-level early warning mechanism is set up to ensure that deviations are identified and dealt with in a timely manner. Regularly conduct a comprehensive evaluation of the implementation effect of optimization strategies, establish a multi-dimensional evaluation system that includes progress indicators, resource utilization rate, cost control and quality compliance rate, evaluate the actual effect of optimization measures by comparing the changes of various indicators before and after optimization, and analyze existing problems and potential improvement space. Based on the results of the effectiveness evaluation and analysis, and in combination with the actual situation of the project, the optimization strategy is dynamically adjusted to ensure that the optimization strategy can adapt to the dynamic changes of the project and maintain its effectiveness and applicability. Timely summarize the experiences and lessons learned during the implementation of optimization strategies, and establish a systematic learning and review mechanism to promote the accumulation and sharing of organizational experience, provide reference and guidance for the optimization of subsequent projects, and achieve continuous improvement of organizational capabilities.

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