Demand simulation method and system for engineering project

Abstract

The invention discloses a demand simulation method and system for an engineering project, and relates to the technical field of engineering project simulation. According to the method, the actual execution time Treal of a current project task is calculated by constructing a task execution time formula, the actual execution condition of the task is reflected, and the actual execution condition can be compared with planned time; therefore, the deviation of the task progress is revealed. According to the accurate construction period prediction, a project manager can take measures in time to carry out optimal calculation on the total construction period so as to obtain the total construction period Ttotal. Through the time analysis of each task, the project can reduce the unnecessary construction period delay, and then in combination with the total construction period time Ttotal and the actual and planned resource usage amount of the task, the balance optimization of the construction period demand and resources is carried out, and the comprehensive project optimization simulation result PSSR is obtained. Through preliminary comparison and evaluation with a preset simulation result threshold value Y, the method can effectively analyze the progress demand and the resource utilization rate of the current engineering project.

Description

Technical Field

[0001] This invention relates to the field of engineering project simulation technology, specifically to a method and system for demand simulation in engineering projects. Background Technology

[0002] As project size and complexity continue to increase, the importance of requirements simulation methods in optimizing project schedules and resource allocation is becoming increasingly prominent. Engineering project management encompasses all phases from planning and design to implementation, in which requirements simulation technology plays a crucial role. By dynamically simulating project requirements, resources, and external environmental factors, engineers can identify potential risks at an early stage and develop more scientific project plans. Therefore, developing an efficient requirements simulation method is of great significance for improving the success rate and resource utilization of engineering projects.

[0003] Many engineering projects currently face numerous challenges during implementation, including time delays, resource waste, and uncertainties in the external environment. These problems often stem from the inability of traditional project management methods to adapt to changing needs and complex external environments in real time. Existing simulation methods largely rely on historical data and static analysis, lacking the integration and dynamic adjustment of real-time data, resulting in inaccurate predictions and an inability to effectively reflect project progress. Therefore, the limitations of traditional methods make it difficult for project managers to make timely and accurate decisions at critical moments.

[0004] This has led to unsatisfactory implementation results in multiple engineering projects, resulting in a series of negative impacts. For example, project durations often exceed the original plan, causing significant cost increases and resource waste. Furthermore, project managers lack effective response strategies when facing unforeseen events, failing to adjust resource allocation and schedules in a timely manner, further exacerbating project delays and risks. This situation not only affects the economic benefits of projects but also negatively impacts the company's reputation and market competitiveness. Therefore, an innovative demand simulation method is urgently needed to address the various shortcomings in current engineering project management. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a demand simulation method and system for engineering projects, solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps:

[0007] S1. By integrating with relevant project engineering platforms, project impact data is collected in real time, and the project impact data is preprocessed to obtain a project requirement impact dataset.

[0008] S2. Build a simulation model, input the obtained project demand impact dataset into the simulation model, generate project progress predictions under different scenarios through dynamic adjustments, output a visual progress analysis report, and optimize the project management process;

[0009] S3. Construct a task execution time formula, extract the dataset that affects project requirements and input it into the task execution time formula, calculate and output the actual execution time Treal of the current project task, and then perform total project duration optimization calculation based on the obtained task execution time Treal to obtain the total project duration Ttotal.

[0010] S4. Based on the obtained optimized total project duration Ttotal, optimize the project duration requirements and resource balance, obtain the comprehensive project optimization simulation result PSSR, and conduct a preliminary comparison and evaluation between the preset simulation result threshold Y and the obtained comprehensive project optimization simulation result PSSR to analyze the current project duration requirements and resource balance status.

[0011] S5. Based on the initial comparison and evaluation results, execute the secondary optimization formula to calculate and output the optimized total project duration value Ttotal. opt The process iteratively executes step S4 to output the optimized second comprehensive project optimization simulation result PSSR2 and compares it with the simulation result threshold Y for a second evaluation, and generates an iterative mechanism based on the evaluation result.

[0012] Preferably, S1 includes S11 and S12;

[0013] S11. By setting up an API application interface to integrate with relevant project engineering platforms, the relevant project engineering platforms include project management software, meteorological platform, supply chain management system and project market management library, and extract project impact data of the same historical projects in real time. The project impact data includes planned time (Tplanned), actual execution time of historical projects (Tactual), weather impact factor (Yweather), supply chain impact factor (Ysupply) and economic impact factor (Yeconomy).

[0014] The planned time Tplanned and the actual execution time of historical project tasks Tactual are obtained by using project management software to collect and summarize historical project data and extracting relevant progress times of similar tasks from the historical project dataset.

[0015] The weather impact factor Yweather, supply chain impact factor Ysupply, and economic impact factor Yeconomy are obtained through the meteorological platform, supply chain management system, and project market management database, acquiring historical and real-time weather data, supply chain data, and project market economic data.

[0016] The ARIMA model is used to predict the delay and acceleration effects of weather and project market economy on project accuracy using historical and real-time weather data and project market economy data, and to calculate and obtain the weather impact factor Yweather and the economic impact factor Yeconomy.

[0017] Use supply chain analysis tools to analyze supply chain disruption risks, predict the impact of material supply delays or logistical problems on construction progress, and output the supply chain impact factor Ysupply;

[0018] The weather data includes, but is not limited to, rainfall, snowfall, temperature, and wind speed;

[0019] The supply chain dataset includes, but is not limited to, material delivery delays, logistics disruptions, and project material shortages.

[0020] The market economic data for the project includes, but is not limited to, fluctuations in the market prices of project materials and labor market prices.

[0021] Preferably, in step S12, based on the acquired project impact data, preprocessing is performed to obtain a project demand impact dataset, wherein the project demand impact dataset includes task fluctuation coefficient Bds, process coordination efficiency coefficient X, and external environment impact factor Y.

[0022] The task fluctuation coefficient Bds is calculated using the planned time Tplanned and the actual execution time Tactual of the project task. The specific algorithm formula is as follows: In the formula Bds i Tplanned represents the task variability coefficient of the i-th task. i Tactual represents the planned time of the i-th task. i This represents the actual execution time of the i-th task;

[0023] The process collaboration efficiency coefficient X is calculated by defining and extracting tasks j with dependencies from historical projects, and extracting the planned time Tplanned and actual execution time Tactual of task j. The specific algorithm formula is as follows: In the formula, Xi,j represents the collaboration efficiency coefficient between task i and task j, and Tplanned j Tactual represents the planned time of the j-th task. j This represents the actual execution time of the j-th task;

[0024] The external environmental impact factor Y is obtained by comprehensively calculating based on the acquired weather impact factor Yweather, supply chain impact factor Ysupply, and economic impact factor Yeconomy. The specific algorithm formula is: Y k= w1·Yweather+w2·Ysupply+w3·Yeconomy, where w1, w2 and w3 represent the preset weights of the weather impact factor Yweather, the supply chain impact factor Ysupply and the economic impact factor Yeconomy, respectively, and w1+w2+w3=1, the specific values ​​of which are set by the user.

[0025] Preferably, S2 includes S21;

[0026] S21. Select Discrete Event Simulation (DES) and System Dynamics Simulation (SD) as the simulation model framework. Construct the simulation model using AnyLogic simulation software. Input the obtained project requirement impact dataset into the simulation model. Use the Critical Path Method (CPM) to analyze the sequence, parallel execution, and resource sharing between task i and task j. In the simulation model, introduce the sequence and dependencies of tasks through a task network diagram.

[0027] Then, resource allocation is managed through project management software, resource optimization algorithms are used to balance resource usage for tasks, and the impact of resource constraints on project schedule is simulated. The actual time resource usage of task i is output. i The planned resource usage of task i (Rplanned) i ;

[0028] Monte Carlo simulation is used to simulate the uncertainty in task execution, and various project progress scenarios are simulated by combining random number generation and probability distribution models.

[0029] Using the simulation engine built into AnyLogic simulation software, the simulation model is dynamically adjusted according to the impact dataset of real-time project requirements, and the future progress prediction is optimized through feedback loop to output simulation results.

[0030] Then, using simulation tools and data visualization software Tableau, progress curves, critical path diagrams, and Gantt charts are generated to further dynamically display the simulation results and generate various analysis reports.

[0031] Preferably, S3 includes S31 and S32;

[0032] S31. Construct a task execution time formula, input the obtained project requirement impact dataset into the task execution time formula, and calculate and output the actual execution time Treal of the current project task;

[0033] The actual execution time Treal is calculated and output using the following task execution time formula;

[0034]

[0035] In the formula, n represents the total number of external environmental factors, k represents each specific external environmental influencing factor, Treali represents the actual execution time of the i-th current project task, dep(i) represents the set of related dependent tasks, and j∈dep(i) means that task j is a dependent task of task i. Task j can be a preceding task of task i or a task sharing resources.

[0036] Preferably, in step S32, based on the actual execution time Treal of the current project task and the number of tasks N in the current project, the total project duration is optimized and the total project duration Ttotal is obtained.

[0037] The total project duration Ttotal is calculated using the following algorithm formula;

[0038]

[0039] Preferably, S4 includes S41 and S42;

[0040] S41. Based on the obtained total project duration Ttotal, and combined with the time and resource usage Ractual of task i output by the simulation model... i The planned resource usage of task i (Rplanned) i To optimize the balance between project schedule requirements and resources, obtain the comprehensive project optimization simulation result PSSR, and analyze the simulation results of project schedule requirements and resource utilization.

[0041] The comprehensive project optimization simulation result PSSR is calculated using the following algorithm formula;

[0042]

[0043] In the formula, This represents the resource utilization rate, and α represents the preset weight value between the optimized total project duration and the resource utilization rate. The specific value is set by the user.

[0044] S42. Based on the user's needs and resource utilization of the engineering project, a preset simulation result threshold Y is set, and then a preliminary comparison and evaluation is conducted with the obtained comprehensive project optimization simulation result PSSR. The current engineering project schedule requirements and resource utilization rate are comprehensively evaluated. The specific evaluation content is as follows.

[0045] When the overall project optimization simulation result PSSR > simulation result threshold Y, it indicates that the simulation result does not meet the expected standard. This usually indicates that the project's schedule requirements are not performing well, and the project may have an excessively long duration or low resource utilization efficiency. In this case, the project administrator is prompted to optimize the current project schedule requirements and resource utilization. After optimization, a second optimization formula is executed to further optimize the schedule requirements.

[0046] When the overall project optimization simulation result PSSR ≤ simulation result threshold Y, it means that the simulation result meets the expected standard, the project's planned schedule requirements and resource utilization are consistent with the objectives, the project management plan is reasonable and effective, and no optimization is needed.

[0047] Preferably, S5 includes S51 and S52;

[0048] S51. When the preliminary evaluation results indicate that the simulation results do not meet the expected standards, the optimized total project duration value Ttotal is calculated and output based on the further execution of the secondary optimization formula. opt Secondary optimization and simulation optimization are performed.

[0049] The optimized total project duration value Ttotal opt The output is calculated using the following quadratic optimization formula;

[0050] Ttotal opt =Ttotal - △Timproved;

[0051] In the formula, △Timproved represents the reduction in project schedule requirements after the second optimization, indicating the optimization of schedule requirements achieved through adjustments and optimizations to the project during the second optimization process.

[0052] Preferably, S52, based on the obtained optimized total project duration value Ttotal opt The simulation optimization is performed iteratively by executing S4, outputting the second comprehensive project optimization simulation result PSSR2, and comparing it with the simulation result threshold Y for a second evaluation. An iterative mechanism is then generated based on the evaluation results. The specific evaluation content is as follows:

[0053] When the second comprehensive project optimization simulation result PSSR2 > simulation result threshold Y, it means that the optimized simulation result does not meet the expected standard. At this time, S4 and S5 are executed iteratively until the expected standard is met and the iteration stops.

[0054] When the second comprehensive project optimizes the simulation results PSSR 2≤ When the simulation result reaches the threshold Y, it means that the optimized simulation result meets the expected standard, and there is no need to iterate.

[0055] A demand simulation system for engineering projects includes a data acquisition and processing module, a simulation model visualization module, an engineering task progress analysis module, a demand progress simulation optimization module, and a secondary optimization and iteration module.

[0056] The data acquisition and processing module integrates with the relevant project engineering platform to collect project impact data in real time and preprocess the project impact data to obtain a project requirement impact dataset.

[0057] The simulation model visualization module constructs a simulation model, inputs the acquired project demand impact dataset into the simulation model, dynamically adjusts and generates project progress predictions under different scenarios, outputs a visualized progress analysis report, and optimizes the project management process.

[0058] The project task progress analysis module extracts the project requirement impact dataset and inputs it into the task execution time formula to calculate and output the actual execution time Treal of the current project task. Then, based on the obtained task execution time Treal, it performs total project duration optimization calculation to obtain the total project duration Ttotal.

[0059] The demand schedule simulation optimization module optimizes the schedule demand and resource balance based on the obtained optimized total project duration Ttotal, obtains the comprehensive project optimization simulation result PSSR, and performs a preliminary comparison and evaluation with the obtained comprehensive project optimization simulation result PSSR based on the preset simulation result threshold Y, and analyzes the current status of the project schedule demand and resource balance.

[0060] The secondary optimization and iteration module, based on the initial comparison and evaluation results, executes the secondary optimization formula to calculate and output the optimized total project duration value Ttotal. opt The process iteratively executes step S4 to output the optimized second comprehensive project optimization simulation result PSSR2 and compares it with the simulation result threshold Y for a second evaluation, and generates an iterative mechanism based on the evaluation result.

[0061] This invention provides a method and system for demand simulation in engineering projects. It offers the following advantages:

[0062] (1) This method integrates relevant project engineering platforms to collect project impact data in real time. After preprocessing, this data forms a project demand impact dataset, enabling project managers to obtain comprehensive and real-time information support. In step S2, the simulation model dynamically inputs this data to generate project schedule predictions under different scenarios. This flexibility allows managers to quickly identify potential risks and bottlenecks, such as adjusting project plans in a timely manner to optimize resource allocation and time scheduling in the face of sudden weather or supply chain disruptions, significantly improving the overall efficiency and response speed of project management.

[0063] (2) This method, based on the task execution time formula constructed in step S3, inputs the project requirement impact dataset and calculates the actual execution time Treal of the current project task. This real-time data not only reflects the actual execution status of the task but also allows for comparison with the planned time, thereby revealing deviations in task progress. This accurate schedule prediction enables project managers to take timely measures to optimize the total project duration and obtain the total project duration Ttotal. Through time analysis of each task, the project can reduce unnecessary schedule delays, lower additional costs, ensure on-time project delivery, and improve the project success rate.

[0064] (3) In step S4, this method combines the total project duration Ttotal with the actual and planned resource usage of each task to optimize the balance between project schedule requirements and resources, obtaining the comprehensive project optimization simulation result PSSR. Through preliminary comparison and evaluation with a preset simulation result threshold Y, this method can effectively analyze the current project schedule requirements and resource utilization. This process can identify imbalances in resource allocation; for example, some tasks may face resource shortages, while others may have idle resources. Through this analysis, project managers can take effective measures to optimize resource allocation, ensure the rational use of resources, thereby maximizing resource utilization while keeping project schedule within expected standards and improving overall project performance. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of a requirement simulation method for engineering projects according to the present invention;

[0066] Figure 2 This is a schematic diagram of the steps of a requirements simulation system for engineering projects according to the present invention. Detailed Implementation

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 making creative efforts are within the scope of protection of the present invention.

[0068] Example 1

[0069] Please see Figure 1 This invention provides a requirement simulation method for engineering projects. To achieve the above objectives, this invention is implemented through the following technical solution, including the following steps:

[0070] S1. By integrating with relevant project engineering platforms, project impact data is collected in real time, and the project impact data is preprocessed to obtain a project requirement impact dataset.

[0071] S2. Build a simulation model, input the obtained project demand impact dataset into the simulation model, generate project progress predictions under different scenarios through dynamic adjustments, output a visual progress analysis report, and optimize the project management process;

[0072] S3. Construct a task execution time formula, extract the dataset that affects project requirements and input it into the task execution time formula, calculate and output the actual execution time Treal of the current project task, and then perform total project duration optimization calculation based on the obtained task execution time Treal to obtain the total project duration Ttotal.

[0073] S4. Based on the obtained optimized total project duration Ttotal, optimize the project duration requirements and resource balance, obtain the comprehensive project optimization simulation result PSSR, and conduct a preliminary comparison and evaluation between the preset simulation result threshold Y and the obtained comprehensive project optimization simulation result PSSR to analyze the current project duration requirements and resource balance status.

[0074] S5. Based on the initial comparison and evaluation results, execute the secondary optimization formula to calculate and output the optimized total project duration value Ttotal. opt The process iteratively executes step S4 to output the optimized second comprehensive project optimization simulation result PSSR2 and compares it with the simulation result threshold Y for a second evaluation, and generates an iterative mechanism based on the evaluation result.

[0075] In this embodiment, method S1 integrates with relevant project engineering platforms to collect and preprocess project impact data in real time, forming a project demand impact dataset, ensuring the timeliness and accuracy of information. Next, S2 constructs a simulation model, dynamically inputting this data to generate project progress predictions under different scenarios and outputting a visual analysis report, optimizing the project management process and enabling managers to intuitively identify potential risks and adjust strategies. In S3, by constructing a task execution time formula, the actual execution time Treal is accurately calculated, and based on this, the total project duration is optimized to obtain the total project duration Ttotal. This process not only improves the accuracy of the project duration but also effectively reduces costs caused by delays. Simultaneously, S4, based on the optimized total project duration, performs a comprehensive assessment of project duration requirements and resource balance, obtaining the simulation result PSSR, clarifying the current resource utilization of the project, and thus providing data support for resource allocation. Finally, S5 performs a secondary optimization to achieve the optimized total project duration value Ttotal. optThe calculations are performed, and iterative optimizations are made based on the preliminary assessment results to ensure the effectiveness of project schedule and resource utilization. Compared with traditional technical methods, this method not only improves the scientific and rational nature of project management, but also significantly improves the project execution efficiency and success rate through a data-driven decision-making process, ultimately optimizing and improving the overall project performance.

[0076] Example 2

[0077] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S1 includes S11 and S12;

[0078] S11. By setting up an API application interface to integrate with relevant project engineering platforms, including project management software, meteorological platform, supply chain management system and project market management library, project impact data of the same historical projects can be extracted in real time. Project impact data includes planned time (Tplanned), actual execution time of historical projects (Tactual), weather impact factor (Yweather), supply chain impact factor (Ysupply) and economic impact factor (Yeconomy).

[0079] The planned time (Tplanned) and the actual execution time of historical project tasks (Tactual) are obtained by using project management software to collect and summarize historical project data and extracting relevant progress times for similar tasks from the historical project dataset.

[0080] The weather impact factor Yweather, supply chain impact factor Ysupply, and economic impact factor Yeconomy are obtained through meteorological platforms, supply chain management systems, and project market management databases, acquiring historical and real-time weather data, supply chain data, and project market economic data.

[0081] The ARIMA model is used to predict the delay and acceleration effects of weather and project market economy on project accuracy using historical and real-time weather data and project market economy data, and to calculate and obtain the weather impact factor Yweather and the economic impact factor Yeconomy.

[0082] Use supply chain analysis tools to analyze supply chain disruption risks, predict the impact of material supply delays or logistical problems on construction progress, and output the supply chain impact factor Ysupply;

[0083] Weather data includes, but is not limited to, rainfall, snowfall, temperature, and wind speed;

[0084] Supply chain datasets include, but are not limited to, material delivery delays, logistics disruptions, and project material shortages.

[0085] Project market economic data includes, but is not limited to, fluctuations in project material market prices and labor market prices.

[0086] S12. Based on the acquired project impact data, perform preprocessing to obtain the project demand impact dataset, which includes the task fluctuation coefficient Bds, the process coordination efficiency coefficient X, and the external environment impact factor Y.

[0087] The task fluctuation coefficient Bds is calculated using the planned time (Tplanned) and actual execution time (Tactual) of the project task. The specific algorithm formula is as follows: In the formula Bds i Tplanned represents the task variability coefficient of the i-th task. i Tactual represents the planned time of the i-th task. i This represents the actual execution time of the i-th task;

[0088] The process collaboration efficiency coefficient X is calculated by defining and extracting tasks j with dependencies from historical projects, and extracting the planned time Tplanned and actual execution time Tactual of task j. The specific algorithm formula is as follows: In the formula, Xi,j represents the collaboration efficiency coefficient between task i and task j, and Tplanned j Tactual represents the planned time of the j-th task. j This represents the actual execution time of the j-th task;

[0089] The external environmental impact factor Y is obtained by comprehensively calculating based on the acquired weather impact factor Yweather, supply chain impact factor Ysupply, and economic impact factor Yeconomy. The specific algorithm formula is as follows: Y k = w1·Yweather+w2·Ysupply+w3·Yeconomy, where w1, w2 and w3 represent the preset weights of the weather impact factor Yweather, the supply chain impact factor Ysupply and the economic impact factor Yeconomy, respectively, and w1+w2+w3=1, the specific values ​​of which are set by the user.

[0090] In this embodiment, through the detailed implementation of S1 above, this demand simulation method significantly enhances the accuracy and adaptability of project management. First, S11 integrates the API with relevant project engineering platforms to extract and process project impact data in real time. This integration not only improves data timeliness but also lays a solid foundation for subsequent analysis. Using the ARIMA model to predict weather and economic data allows for a more accurate assessment of their impact on project schedule, thus providing targeted decision support for project managers. Next, in S12, the preprocessing of project impact data generates the task fluctuation coefficient Bds, the process synergy efficiency coefficient X, and the external environmental impact factor Y. These quantitative indicators provide data support for dynamic adjustments to the project, better reflecting the dependencies and synergistic effects between various tasks. Simultaneously, the comprehensive calculation of the external environmental impact factor Y enables project managers to make more flexible adjustments in complex and ever-changing external environments.

[0091] Example 3

[0092] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: S2 includes S21;

[0093] S21. Select Discrete Event Simulation (DES) and System Dynamics Simulation (SD) as the simulation model framework. Construct the simulation model using AnyLogic simulation software. Input the obtained project requirement impact dataset into the simulation model. Use the Critical Path Method (CPM) to analyze the sequence, parallel execution, and resource sharing between task i and task j. In the simulation model, introduce the sequence and dependencies of tasks through a task network diagram.

[0094] Then, resource allocation is managed through project management software, resource optimization algorithms are used to balance resource usage for tasks, and the impact of resource constraints on project schedule is simulated. The actual time resource usage of task i is output. i The planned resource usage of task i (Rplanned) i ;

[0095] Monte Carlo simulation is used to simulate the uncertainty in task execution, and various project progress scenarios are simulated by combining random number generation and probability distribution models.

[0096] Using the simulation engine built into AnyLogic simulation software, the simulation model is dynamically adjusted according to the impact dataset of real-time project requirements, and the future progress prediction is optimized through feedback loop to output simulation results.

[0097] Then, using simulation tools and data visualization software Tableau, progress curves, critical path diagrams, and Gantt charts are generated to further dynamically display the simulation results and generate various analysis reports.

[0098] In this embodiment, the method significantly improves the accuracy and flexibility of project management through Discrete Event Simulation (DES) and System Dynamics (SD) techniques employed in S21. First, a simulation model is built using AnyLogic simulation software, inputting the project requirement impact dataset into the model. This clearly visualizes the sequence, parallelism, and resource sharing among tasks, greatly improving project task management efficiency. Critical Path Method (CPM) effectively analyzes the dependencies between tasks, helping managers optimize resource allocation. This allows for timely adjustments to strategies to minimize negative impacts on project schedules when facing resource constraints. Furthermore, Monte Carlo simulation introduces effective assessment of uncertainty, enabling the prediction of various project schedule scenarios and enhancing risk management capabilities. Through dynamic adjustment of the simulation model and feedback mechanism, project schedule prediction is optimized, ensuring that real-time data reflects the latest project status. The use of data visualization tools such as Tableau to generate schedule curves, critical path diagrams, and Gantt charts makes the simulation results easier to understand, facilitating effective communication and analysis by decision-makers. Compared to traditional project management methods, this approach offers a higher level of flexibility and responsiveness, especially in complex project environments, enabling faster adjustments and ultimately improving project execution efficiency and success rates. This data-driven decision support model injects new vitality into modern project management and promotes an overall improvement in management standards.

[0099] Example 4

[0100] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: S3 includes S31 and S32;

[0101] S31. Construct a task execution time formula, input the obtained project requirement impact dataset into the task execution time formula, and calculate and output the actual execution time Treal of the current project task;

[0102] The actual execution time Treal calculates and outputs using the following task execution time formula;

[0103]

[0104] In the formula, n represents the total number of external environmental factors, k represents each specific external environmental influencing factor, Treali represents the actual execution time of the i-th current project task, dep(i) represents the set of related dependent tasks, and j∈dep(i) means that task j is a dependent task of task i. Task j can be a preceding task of task i or a task sharing resources.

[0105] S32. Based on the actual execution time Treal of the current project task, combined with the number of tasks N of the current project, perform total duration optimization calculation to obtain the total duration Ttotal.

[0106] The total project duration, Ttotal, is calculated using the following formula;

[0107]

[0108] In this embodiment, the method constructs a task execution time formula, enabling the project requirement impact dataset to reflect the actual impact of external environmental factors on task execution in real time, and accurately calculates the actual execution time Treal for each project task. This dependency-based calculation method considers not only direct impacts but also the interdependencies of related tasks, ensuring the comprehensiveness and accuracy of the results. After obtaining Treal, the total project duration Ttotal is optimized by combining it with the number of tasks N, allowing project managers to effectively assess project progress in a multi-task environment. This accurate total duration calculation significantly reduces the risk of schedule deviations compared to traditional estimation methods based on experience or static models. Through these steps, the method achieves higher predictability and flexibility, enabling project managers to better cope with unforeseen delays and changing requirements.

[0109] Example 5

[0110] This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 1 Specifically: S4 includes S41 and S42;

[0111] S41. Based on the obtained total project duration Ttotal, and combined with the time and resource usage Ractual of task i output by the simulation model... i The planned resource usage of task i (Rplanned) i To optimize the balance between project schedule requirements and resources, obtain the comprehensive project optimization simulation result PSSR, and analyze the simulation results of project schedule requirements and resource utilization.

[0112] The comprehensive project optimization simulation result PSSR is calculated using the following algorithm formula;

[0113]

[0114] In the formula, This represents the resource utilization rate, and α represents the preset weight value between the optimized total project duration and the resource utilization rate. The specific value is set by the user.

[0115] S42. Based on the user's needs and resource utilization of the engineering project, a preset simulation result threshold Y is set, and then a preliminary comparison and evaluation is conducted with the obtained comprehensive project optimization simulation result PSSR. The current engineering project schedule requirements and resource utilization rate are comprehensively evaluated. The specific evaluation content is as follows.

[0116] When the overall project optimization simulation result PSSR > simulation result threshold Y, it indicates that the simulation result does not meet the expected standard. This usually indicates that the project's schedule requirements are not performing well, and the project may have an excessively long duration or low resource utilization efficiency. In this case, the project administrator is prompted to optimize the current project schedule requirements and resource utilization. After optimization, a second optimization formula is executed to further optimize the schedule requirements.

[0117] When the overall project optimization simulation result PSSR ≤ simulation result threshold Y, it means that the simulation result meets the expected standard, the project's planned schedule requirements and resource utilization are consistent with the objectives, the project management plan is reasonable and effective, and no optimization is needed.

[0118] In this embodiment, the method utilizes the total project duration Ttotal and the actual and planned resource usage of the task Ractual. i and Rplanned i This system calculates the Project Schedule Response (PSSR) results of a comprehensive project optimization simulation. These results provide project managers with a comprehensive analysis of project schedule requirements and resource utilization, helping to identify shortcomings in resource allocation and potential schedule risks. By setting a preset simulation result threshold Y, the performance of the PSSR results can be effectively compared and analyzed. This comparative evaluation allows project managers to quickly identify whether project schedule requirements and resource utilization meet expected standards. If discrepancies are found, the system will prompt optimization, avoiding the blind adjustments and delays common in traditional methods. This method improves the accuracy of resource utilization and schedule through real-time data feedback and dynamic evaluation, making project management more scientific and rational. Ultimately, project managers can more effectively control project progress, improve resource allocation efficiency, and ensure timely and high-quality project completion. This improvement provides a strong guarantee for the successful implementation of engineering projects.

[0119] Example 6

[0120] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: S5 includes S51 and S52;

[0121] S51. When the preliminary evaluation results indicate that the simulation results do not meet the expected standards, the optimized total project duration value Ttotal is calculated and output based on the further execution of the secondary optimization formula. opt Secondary optimization and simulation optimization are performed.

[0122] Total project duration optimization value Ttotal opt The output is calculated using the following quadratic optimization formula;

[0123] Ttotal opt =Ttotal - △Timproved;

[0124] In the formula, △Timproved represents the reduction in project schedule requirements after the second optimization, indicating the optimization of schedule requirements achieved through adjustments and optimizations to the project during the second optimization process.

[0125] S52. Based on the obtained total project duration optimization value Ttotal opt The simulation optimization is performed iteratively by executing S4, outputting the second comprehensive project optimization simulation result PSSR2, and comparing it with the simulation result threshold Y for a second evaluation. An iterative mechanism is then generated based on the evaluation results. The specific evaluation content is as follows:

[0126] When the second comprehensive project optimization simulation result PSSR2 > simulation result threshold Y, it means that the optimized simulation result does not meet the expected standard. At this time, S4 and S5 are executed iteratively until the expected standard is met and the iteration stops.

[0127] When the second comprehensive project optimizes the simulation results PSSR 2≤ When the simulation result reaches the threshold Y, it means that the optimized simulation result meets the expected standard, and there is no need to iterate.

[0128] In this embodiment, the method calculates the optimized total project duration value Ttotal. opt This further shortened the project schedule requirements, making project management more flexible. This optimization process allows the project team to make in-depth adjustments to address shortcomings in the initial assessment results, ensuring the project can be completed within a tighter timeframe. Then, based on the newly acquired optimized value Ttotal... optThe iterative execution of S4 enabled the calculation and evaluation of the second comprehensive project optimization simulation result, PSSR2. By comparing this result with the set simulation result threshold Y, the project team could quickly identify whether the optimization effect met the expected standards, thus effectively adjusting the optimization strategy. This iterative mechanism provides project management with continuous feedback and adjustment capabilities, ensuring the project maintains high efficiency amidst dynamic changes. This systematic optimization process provides more accurate and real-time data support, significantly improving the management capabilities and response speed for complex projects. This improvement not only enhances the controllability of engineering projects but also improves the flexibility and efficiency of resource allocation, creating more favorable conditions for achieving project goals.

[0129] Example 7

[0130] Please see Figure 1 and Figure 2 A demand simulation system for engineering projects includes a data acquisition and processing module, a simulation model visualization module, an engineering task progress analysis module, a demand progress simulation optimization module, and a secondary optimization and iteration module.

[0131] The data acquisition and processing module integrates with relevant project engineering platforms to collect project impact data in real time and preprocess the project impact data to obtain a project requirement impact dataset.

[0132] The simulation model visualization module constructs a simulation model, inputs the acquired project requirement impact dataset into the simulation model, dynamically adjusts it to generate project progress predictions under different scenarios, outputs a visualized progress analysis report, and optimizes the project management process.

[0133] The project task progress analysis module extracts the dataset of project requirements impact and inputs it into the task execution time formula to calculate and output the actual execution time Treal of the current project task. Then, based on the obtained task execution time Treal, it performs total project duration optimization calculation to obtain the total project duration Ttotal.

[0134] The demand schedule simulation optimization module optimizes the schedule demand and resource balance based on the obtained optimized total project duration Ttotal, obtains the comprehensive project optimization simulation result PSSR, and performs a preliminary comparison and evaluation with the obtained comprehensive project optimization simulation result PSSR based on the preset simulation result threshold Y, and analyzes the current status of project schedule demand and resource balance.

[0135] The secondary optimization and iteration module, based on the initial comparison and evaluation results, executes the secondary optimization formula to calculate and output the optimized total project duration value Ttotal. optThe process iteratively executes step S4 to output the optimized second comprehensive project optimization simulation result PSSR2 and compares it with the simulation result threshold Y for a second evaluation, and generates an iterative mechanism based on the evaluation result.

[0136] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A demand simulation method for an engineering project, characterized in that: The following steps are involved: S1. By integrating with relevant project engineering platforms, real-time collection of project impact data is performed, and project impact data is pre-processed to obtain project demand impact data sets; S2. Build a simulation model, input the acquired project demand impact data set into the simulation model, generate project progress forecasts under different scenarios through dynamic adjustment, output a visual progress analysis report, and optimize the project management process; S3. Construct a task execution time formula, extract the project demand impact data set and input it into the task execution time formula, calculate and output the actual execution time Treal of the current project task, and then perform total construction period optimization calculation based on the obtained task execution time Treal to obtain the total construction period Ttotal; S4. Based on the optimized total construction period Ttotal, optimize the construction period demand and resource balance, obtain the comprehensive project optimization simulation result PSSR, and perform preliminary comparative evaluation with the preset simulation result threshold Y and the obtained comprehensive project optimization simulation result PSSR to analyze the status of the current project construction period demand and resource balance; S5. Based on the initial comparative evaluation results, execute the secondary optimization formula to calculate and output the total construction period time optimization value Ttotal opt , and iteratively execute step S4 to output the optimized second comprehensive project optimization simulation result PSSR2 and the simulation result threshold Y for secondary comparative evaluation, and generate an iterative mechanism based on the evaluation result.

2. A method for simulating engineering project requirements according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, by setting an API application program interface and integrating with a related project engineering platform, the related project engineering platform includes project management software, a meteorological platform, a supply chain management system and a project market management library, and extracting in real time project impact data of the same historical engineering project, the project impact data includes planned time Tplanned, actual execution time of historical projects Tactual, weather impact factor Yweather, supply chain impact factor Ysupply and economic impact factor Yeconomy; The planned time Tplanned and the actual execution time Tactual of the historical project tasks are obtained by collecting and summarizing the historical project data using the project management software and extracting the relevant progress time of the same task from the historical data set of the same project; The weather influencing factor Yweather, the supply chain influencing factor Ysupply and the economic influencing factor Yeconomy obtain historical and real-time weather data, supply chain data and project market economic data through the meteorological platform, the supply chain management system and the project market management library; Use the ARIMA model to predict the delay and acceleration effects of weather and project market economy on project accuracy based on historical and real-time weather data and project market economic data, and calculate the weather impact factor Yweather and economic impact factor Yeconomy; Use supply chain analysis tools to analyze supply chain disruption risks, predict the impact of material supply delays or logistics problems on construction progress, and output the supply chain impact factor Ysupply.

3. A method for simulating engineering project requirements according to claim 2, characterized in that: S12, based on the acquired project impact data, preprocessing is performed to obtain a project demand impact data set, where the project demand impact data set includes a task fluctuation coefficient Bds, a process coordination efficiency coefficient X, and an external environment impact factor Y; The task fluctuation coefficient Bds is calculated by the planned time Tplanned and the actual execution time Tactual of the engineering project task. The specific algorithm formula is: Where Bds i represents the task fluctuation coefficient of the i-th task, Tplanned i represents the planned time of the ith task, Tactual i represents the actual execution time of the i-th task; The process coordination efficiency coefficient X is calculated by defining and extracting tasks j with dependencies in historical projects, extracting the planned time Tplanned and the actual execution time Tactual of task j, and the specific algorithm formula is: Where Xi,j represents the synergy efficiency coefficient between task i and task j, Tplanned j represents the planned time of the jth task, Tactual j represents the actual execution time of the jth task; The external environment impact factor Y is obtained by comprehensive calculation based on the obtained weather impact factor Yweather, supply chain impact factor Ysupply and economic impact factor Yeconomy. The specific algorithm formula is: Y k =w1·Yweather+w2·Ysupply+w3·Yeconomy, where w1, w2 and w3 represent the preset weights of weather influencing factor Yweather, supply chain influencing factor Ysupply and economic influencing factor Yeconomy respectively, and w1+w2+w3=1, and the specific values ​​are set by the user.

4. A method for simulating engineering project requirements according to claim 3, characterized in that: Said S2 includes S21; S21. Discrete event simulation (DES) and system dynamics (SD) simulation technologies were selected as the simulation model framework. The simulation model was constructed using AnyLogic simulation software. The acquired project demand impact dataset was input into the simulation model. The critical path method (CPM) was used to analyze the sequence, parallel execution, and resource sharing between task i and task j. In the simulation model, the sequence and dependency of tasks were introduced through the task network diagram. Then manage resource allocation through project management software, use resource optimization algorithms to balance the resource usage of tasks, simulate the impact of resource constraints on project progress, and output the time resource usage Ractual of task i i and the planned resource usage Rplanned of task i i ; Use Monte Carlo simulation to simulate uncertainty in task execution, and combine random number generation and probability distribution models to simulate multiple project schedule scenarios; The simulation engine of AnyLogic simulation software was used to dynamically adjust the simulation model based on the real-time project demand affecting the data set, and to optimize future schedule forecasts through feedback loops and output simulation results; Then, simulation tools and data visualization software Tableau are used to generate progress curves, critical path diagrams, and Gantt charts to further dynamically display the simulation results and generate various analysis reports.

5. A method for simulating engineering project requirements according to claim 4, characterized in that: The S3 includes S31 and S32; S31, construct a task execution time formula, input the acquired project demand impact data set into the task execution time formula, and calculate and output the actual execution time Treal of the current project task; The actual execution time Treal is calculated and output by the following task execution time formula; Where n represents the total number of external environmental factors, k represents each specific external environmental influencing factor, Treali represents the actual execution time of the i-th current project task, dep(i) represents the set of related dependent tasks, and j∈dep(i) means that task j is a dependent task of task i.

6. A method for simulating engineering project requirements according to claim 5, characterized in that: S32, based on the actual execution time Treal of the current project task obtained, combined with the number of tasks N of the current engineering project, perform total construction period optimization calculation to obtain the total construction period time Ttotal; The total construction period Ttotal is calculated by the following algorithm formula:

7. A method for simulating engineering project requirements according to claim 6, characterized in that: The S4 includes S41 and S42; S41, the total duration Ttotal obtained is combined with the time resource usage Ractual of task i output by the simulation model i and the planned resource usage Rplanned of task i i , optimize the construction period demand and resource balance, obtain the comprehensive project optimization simulation results PSSR, and analyze the project schedule demand and resource utilization simulation results; The comprehensive project optimization simulation result PSSR is calculated and obtained by the following algorithm formula; In the formula, represents resource utilization, α represents the preset weight value between the total construction period optimization value and resource utilization, and the specific value is set by the user; S42, based on the user's needs and resource utilization of the engineering project, a preset simulation result threshold Y is performed, and then a preliminary comparative evaluation is performed with the obtained comprehensive project optimization simulation result PSSR, and a comprehensive evaluation is performed on the current engineering project schedule requirements and resource utilization. The specific evaluation contents are as follows; When the comprehensive project optimization simulation result PSSR> simulation result threshold Y, it means that the simulation result does not meet the expected standard. At this time, the project manager is prompted to optimize the current project schedule requirements and resource utilization, and then further execute the secondary optimization formula after optimization to optimize the construction schedule requirements; When the comprehensive project optimization simulation result PSSR ≤ the simulation result threshold Y, it means that the simulation result meets the expected standard, the planned schedule requirements and resource utilization of the engineering project are consistent with the target, and the project management plan is reasonable and effective, and no optimization is required.

8. A method for simulating engineering project requirements according to claim 6, characterized in that: The S5 includes S51 and S52; S51. When the preliminary evaluation result shows that the simulation result does not meet the expected standard, the total construction period time optimization value Ttotal is calculated and output based on the further execution of the secondary optimization formula. opt , perform secondary optimization simulation optimization; The total construction period time optimization value Ttotal opt The output is calculated by the following quadratic optimization formula; Total opt =Ttotal-△Timproved; Where △Timproved represents the shortening of the project schedule after the secondary optimization, which means the optimization of the project schedule achieved through the adjustment and optimization of the project during the secondary optimization process.

9. A method for simulating engineering project requirements according to claim 8, characterized in that: S52, based on the obtained total construction period time optimization value Ttotal opt , iteratively execute S4 to perform simulation optimization and output the second comprehensive project optimization simulation result PSSR2 and conduct a secondary comparative evaluation with the simulation result threshold Y, and generate an iterative mechanism based on the evaluation result. The specific evaluation content is as follows; When the second comprehensive project optimization simulation result PSSR2>simulation result threshold Y, it means that the optimized simulation result does not meet the expected standard, and S4 and S5 are iteratively executed until the expected standard is met and the iteration is stopped; When the second comprehensive project optimizes the simulation results PSSR 2≤ When the simulation result threshold is Y, it means that the simulation result after optimization meets the expected standard and no iteration is needed.

10. A method for simulating engineering project requirements, applied to a system for simulating engineering project requirements as claimed in any one of claims 1 to 9, characterized in that: It includes data acquisition and processing module, simulation model visualization module, engineering task progress analysis module, demand progress simulation optimization module and secondary optimization and iteration module; The data collection and processing module collects project impact data in real time by integrating with relevant project engineering platforms, and pre-processes the project impact data to obtain a project demand impact data set; The simulation model visualization module constructs a simulation model, inputs the acquired project demand impact data set into the simulation model, generates project progress forecasts under different scenarios through dynamic adjustment, outputs a visualized progress analysis report, and optimizes the project management process; The engineering task progress analysis module extracts the project demand impact data set and inputs it into the task execution time formula to calculate and output the actual execution time Treal of the current project task, and then performs total construction period optimization calculation based on the obtained task execution time Treal to obtain the total construction period time Ttotal; The demand schedule simulation optimization module optimizes the construction period demand and resource balance based on the optimized total construction period time Ttotal, obtains the comprehensive project optimization simulation result PSSR, and performs preliminary comparative evaluation with the preset simulation result threshold Y and the obtained comprehensive project optimization simulation result PSSR to analyze the status of the construction period demand and resource balance of the current engineering project; The secondary optimization and iteration module executes the secondary optimization formula based on the initial comparative evaluation results to calculate and output the total construction period time optimization value Ttotal opt , and iteratively execute step S4 to output the optimized second comprehensive project optimization simulation result PSSR2 and the simulation result threshold Y for secondary comparative evaluation, and generate an iterative mechanism based on the evaluation result.

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