Comprehensive project intelligent management system based on BIM technology

Abstract

The invention discloses an integrated project intelligent management system based on a BIM technology. The system comprises a contract management module, a financial analysis module, a budget prediction model, a progress management module, a resource management module, a data analysis module and a BIM integration module. The user interface design of the system is unified, and an interactive instrument board and real-time data visualization are supported. A user can easily access each module through an interface, check real-time data and analysis reports and perform project management operation. Information intercommunication among the modules is realized through data interfaces, and real-time updating and consistency of data are ensured. The modules of the platform are closely integrated to form a complete project management ecosystem, and a comprehensive solution from contract management, financial analysis, budget prediction, progress management and resource management to data analysis and BIM integration is provided. According to the invention, comprehensive management of contracts, finance, progress, resources and data is realized, and the problem of data dispersion is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of building construction project management, and in particular relates to a comprehensive project intelligent management system based on BIM technology. Background Art

[0002] In the current construction project management, traditional methods and tools often face problems such as data dispersion, poor real-time performance, and low management efficiency. Although there are some project management software on the market, most of them lack comprehensive integration and real-time data analysis capabilities, especially in terms of integration with BIM technology. Existing technologies can usually only manage certain aspects of the project, and cannot provide a unified, integrated management platform to cover all aspects of the project.

[0003] The existing technology has the following main shortcomings in construction project management:

[0004] 1. Data is scattered and lacks a unified platform for centralized management:

[0005] Traditional project management tools and methods often fail to achieve centralized management of various project data. Different data are stored in different systems, resulting in data dispersion. This not only increases the difficulty of data synchronization and integration, but may also lead to data inconsistency and redundancy.

[0006] 2. Poor real-time performance, unable to update and obtain project-related data in a timely manner:

[0007] Existing project management systems usually rely on manual data entry and regular data updates, and the real-time and accuracy of data are difficult to guarantee. This will affect the efficiency of project management and the timeliness of decision-making. Especially in large and complex projects, the lack of real-time data acquisition may have serious consequences.

[0008] 3. Low management efficiency. Traditional tools are difficult to meet the complex needs of modern large-scale projects:

[0009] The management of modern large-scale projects involves multi-faceted and multi-level coordination and control. Traditional project management tools lack integration and automation capabilities and are difficult to meet these complex requirements. Project managers and teams need to spend a lot of time and energy on manual operations and coordination, which is inefficient.

[0010] 4. Insufficient application of BIM technology, failure to fully utilize its advantages of 3D visualization and collaborative work:

[0011] Although BIM technology is becoming more and more popular in the construction industry, many project management systems have failed to achieve effective integration with BIM tools and cannot fully utilize BIM's 3D visualization and collaborative work advantages. This limits the potential of BIM technology in project planning, schedule management and resource optimization.

[0012] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0013] The purpose of the present invention is to provide a comprehensive project intelligent management system based on BIM technology to solve the problems existing in the prior art.

[0014] In order to achieve the above object, the present invention adopts the following technical solutions:

[0015] The present invention provides a comprehensive project intelligent management system based on BIM technology, comprising:

[0016] Contract Management Module: used to manage and track project contract information, including contract amount, payment received, contract change records and contract status;

[0017] Financial Analysis Module: used to monitor and analyze the financial health of the project, calculate and display budget variances, profit margins, and cost management financial indicators;

[0018] Budget forecasting model: used to predict future budget situations based on historical budget and expenditure data to help project managers make financial decisions in advance;

[0019] Progress management module: used to track project progress and plan execution, and display progress compliance rate, completion rate and expected completion time;

[0020] Resource management module: used to manage the labor, materials and equipment required for the project, track resource usage in real time, and optimize resource allocation;

[0021] Data analysis module: used to provide various data analysis and forecasting analysis functions for the project, use data analysis technology to conduct in-depth analysis of various indicators of the project, generate visual reports, and help project managers make more informed decisions;

[0022] BIM Integration Module: used to achieve seamless integration with BIM tools, provide 3D visualization and collaborative working environment, and enhance project planning and management efficiency.

[0023] Furthermore, the contract management module provides contract entry, editing, approval and archiving functions, and supports contract version management; input data includes basic contract information, contract change information and payment information entered by the user; output data includes generated contract reports, automatically generated and sent contract change notifications, and real-time updated contract execution status.

[0024] Furthermore, the financial analysis module automatically obtains and processes various financial data of the project, provides dynamic financial statements and charts, and supports multi-dimensional financial analysis; the input data includes the contract amount, payment information and change records obtained from the contract management module, the resource usage cost data obtained from the resource management module, and the budget target and planned cost entered by the user; the output data includes the generated financial statements, budget analysis reports and cost control suggestions.

[0025] Furthermore, the input data of the budget forecasting model includes historical budget data, project phase information, external economic indicators and project change records; the output data includes future budget forecast values, budget trend charts and confidence intervals.

[0026] Furthermore, the specific implementation method of the budget prediction model includes the following steps:

[0027] 1) Data preparation:

[0028] Collect and organize historical budget and expenditure data;

[0029] Handling missing data and outliers;

[0030] Arrange the data in chronological order to ensure the correctness of the time series;

[0031] 2) Data segmentation:

[0032] The data is divided into training and test sets, with 80% of the chronological data as the training set and 20% as the test set;

[0033] 3) Model selection:

[0034] Select the ARIMA model in time series analysis;

[0035] The ARIMA model consists of the following components:

[0036] Autoregressive (AR) part: using past values ​​to predict current values;

[0037] Integration (I) part: The data is differentiated to make it a stationary series.

[0038] Moving Average (MA) part: uses past forecast errors to correct current values.

[0039] The mathematical formula of the ARIMA model is:

[0040] y t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +θ1∈ t-1 +θ2∈t-2 +…+θ q ∈ t-q +∈ t

[0041] Among them, yt is the budget value at time t, φ and θ are the parameters of the AR and MA parts respectively, and ∈t is the error term;

[0042] Model training: Use the maximum likelihood estimation (MLE) method to fit the ARIMA model and determine the optimal parameters p, d, and q;

[0043] Maximum likelihood estimation formula:

[0044] Among them, θ represents the model parameter set, Y represents the observed data, and μ t is the mean of the model predictions;

[0045] 5) Model evaluation: Use test data to evaluate the prediction accuracy of the model and calculate the mean square error (MSE)

[0046] index;

[0047]

[0048] Among them, y t is the actual value, is the predicted value, n is the number of data points;

[0049] 6) Future prediction: Use the trained model to make future budget predictions;

[0050] 7) Results display: Draw a trend chart of the budget forecast and mark the confidence interval; the confidence interval is calculated based on the standard deviation of the forecast error:

[0051] Among them, z α / 2 is the critical value of the standard normal distribution, is the standard error of the prediction.

[0052] Furthermore, the cost control suggestion is implemented by analyzing historical data through a cost control algorithm to find out the cost overrun pattern; the input data of the cost control algorithm includes historical cost data, project progress data, and project phase information; the output data includes cost overrun warning and cost overrun pattern analysis report; the implementation process and algorithm principle of the cost control algorithm are as follows:

[0053] 1) Data preparation:

[0054] Collect and organize historical cost data, project progress data, and resource usage;

[0055] Add a label to each data record to indicate whether the cost is overrun, 1 for overrun and 0 for no overrun;

[0056] 2) Feature engineering: extracting useful features;

[0057] 3) Data segmentation: Divide the data into training set and test set, with 80% as training set and 20% as test set;

[0058] 4) Model selection: Random forest classification model is selected;

[0059] The random forest model consists of multiple decision trees that perform classification by majority voting; each tree is trained on a different sub-dataset;

[0060] The basic principle of decision tree is to maximize information gain by splitting features;

[0061]

[0062] Among them, Entropy(D) is the entropy of data set D, D v is the subset with value v on feature A;

[0063] 5) Model training: Use the training data to train the random forest classification model and determine the optimal parameters;

[0064] 6) Model evaluation: Use test data to evaluate the classification accuracy of the model and calculate the accuracy and recall rate;

[0065]

[0066] Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example;

[0067] 7) Cost overrun pattern analysis: Analyze the importance of the model’s features and identify the key factors that lead to cost overruns.

[0068] Furthermore, the progress management module provides the functions of formulating, adjusting and monitoring project plans, and supports the progress display methods of Gantt charts and schedules; the input data includes the detailed project plan entered by the user, the actual workload and completion date entered by the user or the automatic system, and the contract execution status and change information obtained from the contract management module; the output data includes the generated project progress report, progress deviation analysis and progress adjustment suggestions.

[0069] Furthermore, the resource management module supports resource demand planning, resource allocation, usage monitoring and cost accounting; input data includes the resource demand plan entered by the user, the recorded actual resource usage data and the resource procurement cost and rental fee; output data includes the generated resource usage report, resource allocation optimization plan and resource cost analysis report.

[0070] Furthermore, the input data of the data analysis module includes basic contract information and change records obtained from the contract management module, budget, cost, and profit financial indicators obtained from the financial analysis module, project plans and actual progress data obtained from the progress management module, and resource usage and cost data obtained from the resource management module; the output data includes the generated comprehensive project data analysis report, predictive analysis results, and decision support recommendations.

[0071] Furthermore, the BIM integration module supports the import, viewing and editing functions of BIM models; the input data includes the three-dimensional model data generated by the imported BIM tool, the planning data related to the project and the collaboration records and communication information of the team members; the output data includes the provided three-dimensional visualization display, collaborative working environment and planning optimization suggestions.

[0072] By adopting the above technical solution, the present invention has the following beneficial effects:

[0073] 1. Realize centralized data management:

[0074] By integrating contract management, financial analysis, schedule management, resource management, data analysis and BIM modules, this platform achieves unified management of project data and avoids data dispersion and redundancy problems.

[0075] 2. Provide real-time data updates and analysis:

[0076] The system supports automatic updating and dynamic analysis of real-time data, ensuring the timeliness and accuracy of project-related information, improving management efficiency and timeliness of decision-making.

[0077] 3. Improve management efficiency and meet the needs of complex projects:

[0078] Through highly integrated modular design and automation functions, this platform simplifies the project management process, improves management efficiency, and can meet the complex needs of modern large-scale projects.

[0079] 4. Make full use of the advantages of BIM technology:

[0080] The platform achieves seamless integration with BIM tools, utilizing the three-dimensional visualization and collaborative work advantages of BIM to enhance the intuitiveness and collaboration of project planning and management, and improve the scientificity and effectiveness of project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0082] Figure 1 This is an architecture diagram of a comprehensive project intelligent management platform based on BIM technology described in the present invention.

[0083] Figure 2 This is an architecture diagram of the contract management module described in the present invention.

[0084] Figure 3 This is an architecture diagram of the financial analysis module described in the present invention.

[0085] Figure 4 This is an architecture diagram of the progress management module described in the present invention.

[0086] Figure 5 This is a diagram of the resource management module architecture described in the present invention.

[0087] Figure 6 This is an architecture diagram of the data analysis module described in the present invention.

[0088] Figure 7 This is the BIM integrated module architecture diagram described in the present invention. DETAILED DESCRIPTION

[0089] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0091] Combination Figure 1-7 As shown, this embodiment provides a comprehensive project intelligent management system based on BIM technology, which specifically includes the following modules:

[0092] Contract management module: The main function of the contract management module is to manage and track the contract information of the project, including the contract amount, the amount received, the contract change record and the contract status. This module provides the functions of contract entry, editing, approval and archiving, and supports the version management of the contract. The input data includes the basic contract information entered by the user (such as contract number, contract amount, signing date, payment terms, etc.), contract change information (such as change reason, change amount, change date, etc.) and payment information (such as payment amount received, payment date, payment method, etc.). The output data includes the generated contract report (including the basic contract information, change record and detailed report of payment status), the automatically generated and sent contract change notice, and the real-time updated contract execution status (such as "in progress", "completed", "terminated", etc.). The system can automatically update the contract information and generate a detailed contract report for reference by the project manager and financial personnel. The contract management module is closely related to the financial analysis module and the progress management module. The contract amount and payment information will be passed to the financial analysis module for budget and cost management, and the execution of the contract affects the planning and adjustment of the project schedule.

[0093] Financial Analysis Module: The function of the financial analysis module is to monitor and analyze the financial health of the project, calculate and display key financial indicators such as budget variance, profit margin, and cost management. This module automatically obtains and processes various financial data of the project, provides dynamic financial statements and charts, and supports multi-dimensional financial analysis. The input data includes the contract amount, payment information and change records obtained from the contract management module, the resource usage cost data obtained from the resource management module, and the budget target and planned cost entered by the user. The output data includes the generated financial statements (dynamic reports containing financial indicators such as income, expenditure, budget variance, and profit margin), budget analysis reports (analysis reports showing budget execution, showing details of budget overruns or savings), and cost control suggestions (cost control suggestions generated based on financial data to help project managers optimize cost management). The financial analysis module is closely related to the contract management module, the schedule management module, and the data analysis module. It receives contract amounts and payment information for financial analysis, adjusts project schedules and resource allocations based on financial conditions, and passes financial data to the data analysis module for in-depth analysis and forecasting.

[0094] Budget forecasting model: The main function of the budget forecasting model is to predict future budget situations based on historical budget and expenditure data to help project managers make financial decisions in advance. Input: historical budget data (for example, budgets and actual expenditures in the past few months or years); project phase information; external economic indicators (such as inflation rate, raw material price index, etc.); project change records (such as contract changes, additional budgets, etc.). Output: future budget forecast values ​​(budgets for the next few quarters or years); budget trend charts; confidence intervals (confidence intervals of forecast values, used to express the uncertainty of forecasts)

[0095] The implementation process and algorithm principles are as follows:

[0096] 1. Data preparation:

[0097] Collect and organize historical budget and expenditure data.

[0098] Handling missing data and outliers.

[0099] Arrange the data in chronological order to ensure the correctness of the time series.

[0100] 2. Data segmentation:

[0101] Split the data into training and test sets, usually 80% of the chronological data as training and 20% as test.

[0102] 3. Model selection:

[0103] The ARIMA model is used in time series analysis.

[0104] The ARIMA model consists of the following components:

[0105] Autoregressive (AR) part: using past values ​​to predict current values.

[0106] Integration (I) part: The data is differentiated to make it a stationary series.

[0107] Moving Average (MA) part: uses past forecast errors to correct current values.

[0108] The mathematical formula of the ARIMA model is:

[0109] y t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q +∈ t

[0110] Among them, yt is the budget value at time t, φ and θ are the parameters of the AR and MA parts respectively, and ∈t is the error term.

[0111] Model training: Use the maximum likelihood estimation (MLE) method to fit the ARIMA model and determine the optimal parameters p, d, and q.

[0112] Maximum likelihood estimation formula:

[0113] Among them, θ represents the model parameter set, Y represents the observed data, and μ t is the mean of the model predictions.

[0114] 5. Model evaluation: Use test data to evaluate the prediction accuracy of the model and calculate indicators such as mean square error (MSE).

[0115]

[0116] Among them, y t is the actual value, is the predicted value, and n is the number of data points.

[0117] 6. Future Forecasting: Use the trained model to make future budget forecasts.

[0118] 7. Results display: Draw a trend chart of the budget forecast and mark the confidence interval. The confidence interval is calculated based on the standard deviation of the forecast error:

[0119] Among them, z α / 2 is the critical value of the standard normal distribution, is the standard error of the prediction.

[0120] Cost control algorithm: The main function of the cost control algorithm is to provide accurate cost control suggestions to project managers by analyzing historical data to find out the pattern of cost overruns.

[0121] Input: historical cost data (including budgeted cost and actual cost); project schedule data; project phase information

[0122] Output: Early warning of cost overruns; Cost overrun pattern analysis report

[0123] Implementation process and algorithm principle:

[0124] 1. Data preparation:

[0125] Collect and organize historical cost data, project progress data, resource usage, etc.

[0126] A label is added to each data record to indicate whether the cost is overrun (1 for overrun and 0 for not overrun).

[0127] 2. Feature engineering: Extract useful features, such as project stage, resource usage, historical cost-to-budget ratio, etc.

[0128] 3. Data segmentation: Divide the data into training set and test set, with 80% as the training set and 20% as the test set.

[0129] 4. Model selection: Select the random forest classification model.

[0130] The random forest model consists of multiple decision trees that perform classification by majority voting. Each tree is trained on a different sub-dataset.

[0131] The basic principle of decision tree is to maximize information gain by splitting features.

[0132]

[0133] Among them, Entropy(D) is the entropy of data set D, D v is the subset whose value is v on feature A.

[0134] 5. Model training: Use the training data to train the random forest classification model and determine the optimal parameters (such as the number of trees, tree depth, etc.).

[0135] 6. Model evaluation: Use test data to evaluate the classification accuracy of the model and calculate indicators such as accuracy and recall.

[0136]

[0137]

[0138] Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example.

[0139] 7. Cost overrun pattern analysis: Analyze the importance of the model’s features and identify the key factors that lead to cost overruns.

[0140] Schedule management module: The function of the schedule management module is to track the project progress and plan execution, and display the progress compliance rate, completion rate and expected completion time. This module provides the functions of project plan formulation, adjustment and monitoring, and supports various progress display methods such as Gantt chart and schedule. Input data includes the detailed project plan entered by the user (such as start date, end date, key nodes, task decomposition, etc.), the actual workload and completion date entered by the user or the automatic system, and the contract execution status and change information obtained from the contract management module. Output data includes the generated project progress report (displaying the comparison between the planned progress and the actual progress), progress deviation analysis (providing a detailed analysis of the progress deviation, identifying delayed or early completed tasks) and progress adjustment suggestions (adjustment suggestions generated based on progress data to optimize the project plan). The schedule management module is closely related to the contract management module, resource management module and BIM integration module. The contract execution affects the project progress. The schedule plan is adjusted according to the actual resource usage, and the project progress is displayed in three dimensions through the BIM integration module to improve intuitiveness.

[0141] Resource management module: The function of the resource management module is to manage the labor, materials and equipment required for the project, track resource usage in real time, and optimize resource allocation. This module supports functions such as resource demand planning, resource allocation, usage monitoring and cost accounting. The input data includes the resource demand plan entered by the user (the type and quantity of resources required for each stage of the project), the actual resource usage data recorded (the quantity, time and cost of labor, materials and equipment), and detailed cost information such as resource procurement cost and rental cost. The output data includes the generated resource usage report (showing the allocation and consumption of resources), the resource allocation optimization plan (providing optimization suggestions for resource allocation to ensure the reasonable use and allocation of resources) and the resource cost analysis report (helping to control resource expenditure). The resource management module is closely related to the schedule management module and the financial analysis module. The actual resource usage affects the project schedule, and the resource usage cost is passed to the financial analysis module for cost accounting.

[0142] Data analysis module: The function of the data analysis module is to provide various data analysis and forecasting analysis functions for the project. It uses advanced data analysis technology to conduct in-depth analysis of various indicators of the project, generate visual reports, and help project managers make more informed decisions. Input data includes basic contract information and change records obtained from the contract management module, financial indicators such as budget, cost, and profit obtained from the financial analysis module, project plans and actual progress data obtained from the schedule management module, and resource usage and cost data obtained from the resource management module. Output data includes the generated comprehensive project data analysis report (covering contracts, finance, schedule, and resources), forecasting analysis results (forecasting analysis using data analysis models to provide predictions of future project risks and opportunities), and decision support suggestions (decision support suggestions provided based on the analysis results to help project managers formulate scientific management strategies). The data analysis module is closely related to all other modules, integrating data from each module for comprehensive analysis and providing decision support.

[0143] Cost and profit forecast:

[0144] enter:

[0145] Historical financial data, including costs and profits over time.

[0146] Time series data (such as months, quarters, etc.).

[0147] Other characteristic variables (such as project scale, market environment, input resources, etc.).

[0148] Output:

[0149] Cost forecasts for future time periods.

[0150] Profit forecasts for future time periods.

[0151] Principle description:

[0152] 1. Data preparation

[0153] First, we need to prepare historical financial data. This data usually contains time series (such as months or quarters), actual costs and profits for the corresponding time period, etc. In order to be used in the model, time features usually need to be converted into numerical form (converted to timestamps or other numerical representations).

[0154] 2. Model training

[0155] Using the linear regression model, time needs to be used as the independent variable, and cost and profit need to be modeled separately as the target variables. The basic formula of the linear regression model is as follows:

[0156]

[0157] in:

[0158] is the predicted value (cost or profit).

[0159] β + is the intercept, which is the value predicted by the model when the independent variable is 0.

[0160] β - is the regression coefficient, which indicates the change in the target variable when the independent variable increases by one unit.

[0161] X is the independent variable (time in this case).

[0162] 3. Prediction

[0163] After the model is trained, you can use data from future time points (i.e. future time features) to make predictions. Input these time points into the model, and the model will output the cost and profit forecasts for the corresponding time period.

[0164] Through linear regression, we can predict future costs and profits based on historical data. The core of the model is to predict future values ​​by fitting linear relationships. Budget deviation analysis helps us understand and manage financial deviations by comparing actual costs with budgeted costs and calculating the difference. Combining these two methods can effectively perform financial forecasting and analysis, thereby optimizing cost control and profit management.

[0165] BIM integration module: The function of the BIM integration module is to achieve seamless connection with BIM tools, provide three-dimensional visualization and collaborative working environment, and enhance project planning and management efficiency. This module supports the import, viewing and editing functions of BIM models. The input data includes the three-dimensional model data generated by the imported BIM tool, the planning data related to the project (such as architectural design drawings, construction processes, etc.) and the collaboration records and communication information of team members. The output data includes the provided three-dimensional visualization display (helping to intuitively understand the project progress and resource allocation), the collaborative working environment (supporting multiple users to view and edit BIM models at the same time, improving team collaboration efficiency) and planning optimization suggestions (project planning optimization suggestions provided based on BIM models and project data). The BIM integration module is closely related to the progress management module and the resource management module, and displays the project progress and resource allocation in three dimensions, which improves the intuitiveness and efficiency of project management.

[0166] Overall description of the platform: The user interface is designed in a unified way, supporting interactive dashboards and real-time data visualization. Users can easily access each module through the interface, view real-time data and analysis reports, and perform project management operations. The modules communicate with each other through data interfaces to ensure real-time update and consistency of data. The modules of the platform are tightly integrated to form a complete project management ecosystem, providing comprehensive solutions from contract management, financial analysis, schedule management, resource management to data analysis and BIM integration.

[0167] Compared with the prior art, the project intelligent management system based on BIM integration created by the present invention has the following technical advantages and beneficial effects:

[0168] Integration: Provides a unified platform to achieve comprehensive management of contracts, finances, schedules, resources and data, avoiding the problem of data fragmentation.

[0169] Real-time: Supports real-time data update and analysis to ensure the timeliness and accuracy of project-related information and improve management efficiency.

[0170] Efficient management: Through dynamic financial analysis, progress management and resource management, efficient project management is achieved, reducing manual intervention and errors.

[0171] BIM technology application: Make full use of the three-dimensional visualization and collaborative work advantages of BIM technology to enhance the intuitiveness and collaboration of project planning and management.

[0172] Intelligent decision support: Through data analysis and predictive analysis functions, it provides decision support to help project teams make more informed decisions and improve the scientific nature of project management.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive project intelligent management system based on BIM technology, characterized in that: include: Contract Management Module: used to manage and track project contract information, including contract amount, payment received, contract change records and contract status; Financial Analysis Module: used to monitor and analyze the financial health of the project, calculate and display budget variances, profit margins, and cost management financial indicators; Budget forecasting model: used to predict future budget situations based on historical budget and expenditure data to help project managers make financial decisions in advance; Progress management module: used to track project progress and plan execution, and display progress compliance rate, completion rate and expected completion time; Resource management module: used to manage the labor, materials and equipment required for the project, track resource usage in real time, and optimize resource allocation; Data analysis module: used to provide various data analysis and forecasting analysis functions for the project, use data analysis technology to conduct in-depth analysis of various indicators of the project, generate visual reports, and help project managers make more informed decisions; BIM Integration Module: used to achieve seamless integration with BIM tools, provide 3D visualization and collaborative working environment, and enhance project planning and management efficiency.

2. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The contract management module provides contract entry, editing, approval and archiving functions, and supports contract version management; input data includes basic contract information, contract change information and payment information entered by the user; output data includes generated contract reports, automatically generated and sent contract change notifications, and real-time updated contract execution status.

3. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The financial analysis module automatically obtains and processes various financial data of the project, provides dynamic financial statements and charts, and supports multi-dimensional financial analysis; the input data includes the contract amount, payment information and change records obtained from the contract management module, the resource usage cost data obtained from the resource management module, and the budget target and planned cost entered by the user; the output data includes the generated financial statements, budget analysis reports and cost control suggestions.

4. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The input data of the budget forecasting model include historical budget data, project phase information, external economic indicators and project change records; the output data include future budget forecast values, budget trend charts and confidence intervals.

5. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The specific implementation method of the budget prediction model includes the following steps: 1) Data preparation: Collect and organize historical budget and expenditure data; Handling missing data and outliers; Arrange the data in chronological order to ensure the correctness of the time series; 2) Data segmentation: The data is divided into training and test sets, with 80% of the chronological data as the training set and 20% as the test set; 3) Model selection: Select the ARIMA model in time series analysis; The ARIMA model consists of the following components: Autoregressive (AR) part: using past values ​​to predict current values; Integration (I) part: The data is differentiated to make it a stationary series. Moving Average (MA) part: uses past forecast errors to correct current values. The mathematical formula of the ARIMA model is: y t =c+φ1y t-1 +φ2y t-2 +…+φ p y t-p +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q +∈ t Among them, yt is the budget value at time t, φ and θ are the parameters of the AR and MA parts respectively, and ∈t is the error term; Model training: Use the maximum likelihood estimation (MLE) method to fit the ARIMA model and determine the optimal parameters p, d, and q; Maximum likelihood estimation formula: Among them, θ represents the model parameter set, Y represents the observed data, and μ t is the mean of the model predictions; 5) Model evaluation: Use test data to evaluate the prediction accuracy of the model and calculate the mean square error (MSE) indicator; Among them, y t is the actual value, is the predicted value, n is the number of data points; 6) Future prediction: Use the trained model to make future budget predictions; 7) Results display: Draw a trend chart of the budget forecast and mark the confidence interval; the confidence interval is calculated based on the standard deviation of the forecast error: Among them, z α / 2 is the critical value of the standard normal distribution, is the standard error of the prediction.

6. The integrated project intelligent management system based on BIM technology according to claim 3 is characterized in that: The cost control suggestion is realized by analyzing historical data through the cost control algorithm to find out the cost overrun pattern; the input data of the cost control algorithm includes historical cost data, project progress data, and project phase information; the output data includes cost overrun warning and cost overrun pattern analysis report; the implementation process and algorithm principle of the cost control algorithm are as follows: 1) Data preparation: Collect and organize historical cost data, project progress data, and resource usage; Add a label to each data record to indicate whether the cost is overrun, 1 for overrun and 0 for no overrun; 2) Feature engineering: extracting useful features; 3) Data segmentation: Divide the data into training set and test set, with 80% as training set and 20% as test set; 4) Model selection: Random forest classification model is selected; The random forest model consists of multiple decision trees that perform classification by majority voting; each tree is trained on a different sub-dataset; The basic principle of decision tree is to maximize information gain by splitting features; Among them, Entropy(D) is the entropy of data set D, D " is the subset with value v on feature A; 5) Model training: Use the training data to train the random forest classification model and determine the optimal parameters; 6) Model evaluation: Use test data to evaluate the classification accuracy of the model and calculate the accuracy and recall rate; Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example; 7) Cost overrun pattern analysis: Analyze the importance of the model’s features and identify the key factors that lead to cost overruns.

7. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The progress management module provides the functions of formulating, adjusting and monitoring project plans, and supports the progress display methods of Gantt charts and schedules; the input data includes the detailed project plan entered by the user, the actual workload and completion date entered by the user or the automatic system, and the contract execution status and change information obtained from the contract management module; the output data includes the generated project progress report, progress deviation analysis and progress adjustment suggestions.

8. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The resource management module supports resource demand planning, resource allocation, usage monitoring and cost accounting; input data includes the resource demand plan entered by the user, the recorded actual resource usage data and the resource procurement cost and rental fee; output data includes the generated resource usage report, resource allocation optimization plan and resource cost analysis report.

9. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The input data of the data analysis module includes the basic contract information and change records obtained from the contract management module, the budget, cost, and profit financial indicators obtained from the financial analysis module, the project plan and actual progress data obtained from the progress management module, and the resource usage and cost data obtained from the resource management module; the output data includes the generated comprehensive project data analysis report, forecast analysis results, and decision support recommendations.

10. The integrated project intelligent management system based on BIM technology according to claim 1 is characterized in that: The BIM integration module supports the import, viewing and editing functions of BIM models; the input data includes the three-dimensional model data generated by the imported BIM tool, the planning data related to the project and the collaboration records and communication information of the team members; the output data includes the provided three-dimensional visualization display, collaborative working environment and planning optimization suggestions.

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