Project process management progress monitoring method and system

Abstract

The invention relates to the technical field of project management, in particular to a progress monitoring method and system for project process management, which can improve the accuracy, timeliness and intelligent level of project progress monitoring and improve the efficiency of project management. The method comprises the steps of collecting a project progress composition factor set according to an original project progress monitoring plan; performing serialization adjustment on the project progress composition factor set collected in different planned monitoring time periods according to a time sequence to obtain a project progress feature matrix; obtaining a project type, and extracting the latest completion time allowed by each component factor in each planned monitoring time period according to the project type; according to the allowed latest completion time of each component factor in each planned monitoring time period, carrying out column-by-column trend analysis on the project progress characteristic matrix to obtain progress fluctuation characteristics of the component factors; and inputting the progress fluctuation characteristics of the composition factors into a pre-constructed project progress prediction model to obtain project predicted completion time.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and in particular to a progress monitoring method and system for project process management. Background Art

[0002] In the process of project process management, monitoring and forecasting of project progress is crucial. However, existing project progress monitoring methods are usually based on static plans, which are difficult to cope with dynamic changes in project execution. There are often problems such as data dispersion and lack of intelligence, making it difficult to achieve accurate progress forecasting and timely progress adjustments, which can easily lead to project delays and waste of resources. Therefore, there is an urgent need for a progress monitoring method for project process management. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a progress monitoring method for project process management which can improve the accuracy, timeliness and intelligence level of project progress monitoring and improve the efficiency of project management.

[0004] In a first aspect, the present invention provides a progress monitoring method for project process management, the method comprising:

[0005] According to the original project progress monitoring plan, collect the project progress component factors;

[0006] The project progress component factor sets collected in different planned monitoring time periods are serialized and adjusted in chronological order to obtain the project progress feature matrix;

[0007] Obtain the project type, and extract the latest completion time allowed for each component factor within each planned monitoring period according to the project type;

[0008] According to the latest completion time allowed for each component factor in each planned monitoring period, the project schedule characteristic matrix is ​​analyzed column by column to obtain the component factor schedule fluctuation characteristics;

[0009] Input the progress fluctuation characteristics of the component factors into the pre-built project progress prediction model to obtain the estimated completion time of the project;

[0010] Automatically adjust the original project progress monitoring plan based on the estimated project completion time.

[0011] Furthermore, each column in the project progress feature matrix represents the completion progress of the same component factor in different time periods, and each row represents the completion progress of different component factors in the same time period. The project progress feature matrix is:

[0012]

[0013] in, It represents the completion progress of the j-th project schedule component in the i-th time period.

[0014] Furthermore, the project progress feature matrix acquisition method includes:

[0015] The data collected during each planned monitoring period are sorted in chronological order;

[0016] According to the time sequence, the project progress component factors collected in different planned monitoring time periods are serialized and adjusted;

[0017] After serialization adjustment, the project schedule components are organized into a feature matrix in chronological order.

[0018] Furthermore, the method for obtaining the project schedule component factor set includes:

[0019] Collect project schedule, including project milestones, critical paths, work packages and tasks;

[0020] Collect actual data during project execution, including task completion, workload, and milestone achievement;

[0021] Extract project progress components from project management systems, log files, and work records;

[0022] Verify and check the accuracy of the collected data;

[0023] Archive and store the collected project progress data.

[0024] Furthermore, the method for extracting the component factor progress fluctuation characteristics includes:

[0025] Perform data processing on the project schedule feature matrix, including data cleaning, processing missing values, and smoothing to remove noise;

[0026] Conduct trend analysis on the project progress feature matrix column by column, including moving average and exponential smoothing, and then smoothly observe the change trend of the progress and obtain trend analysis results;

[0027] Based on the results of trend analysis, identify the progress fluctuations of each component, including identifying stable progress factors and delay acceleration factors;

[0028] Evaluate the impact of schedule fluctuations of each factor on the overall project schedule;

[0029] Use machine learning methods to predict future progress fluctuations and obtain the progress fluctuation characteristics of component factors.

[0030] Furthermore, the method for constructing the project progress prediction model includes:

[0031] Collect historical data of the project, including project progress, progress of component elements and project-related data;

[0032] Based on project requirements and domain knowledge, select key factors that affect project progress, including task completion time, resource usage, and team performance indicators; perform feature engineering by analyzing and exploring data;

[0033] Select a prediction model based on the project's characteristics and data properties, including a linear regression model, a decision tree model, a support vector regression model, and a neural network model;

[0034] The collected historical data set is divided into a training set and a test set. The training set is used for model training and parameter adjustment, and the test set is used to evaluate model performance.

[0035] Use the training set to train the selected model and adjust the model parameters to fit the training data;

[0036] Use the test set to evaluate and validate the trained model, and evaluate the accuracy and reliability of the model by comparing the model's prediction results with the actual project progress data;

[0037] Optimize and improve the model based on the results of model evaluation;

[0038] Use the optimized model to predict the project progress, input the progress fluctuation characteristics of the component factors into the model, and generate the estimated completion time of the project.

[0039] Furthermore, the adjustment methods of the original project progress monitoring plan include:

[0040] Determine the current progress according to the estimated completion time of the project;

[0041] For adjustments to the estimated completion time, check the progress of each task, resource utilization, and team performance indicators by comparing the actual completion status with the original plan;

[0042] Based on the analysis results, identify tasks that need to be adjusted, re-plan task priorities, resource allocation, and schedule adjustments;

[0043] After identifying the tasks that need to be adjusted, formulate adjustment plans, including reallocating resources, increasing the number of staff, and re-prioritizing tasks;

[0044] Communicate with relevant personnel based on the formulated adjustment plan;

[0045] After implementing the adjustment plan, continue to monitor the progress of the project.

[0046] On the other hand, the present application also provides a progress monitoring system for project process management, the system comprising:

[0047] The data collection module collects and sends the set of project progress components according to the original project progress monitoring plan;

[0048] A data serialization adjustment module is used to receive a set of project progress component factors, serialize and adjust the set of project progress component factors collected in different planned monitoring time periods in chronological order, obtain a project progress feature matrix, and send it;

[0049] The latest completion time extraction module is used to obtain the project type, and extract the latest completion time allowed for each component factor in each planned monitoring period according to the project type, and send it;

[0050] The trend analysis module is used to receive the project schedule feature matrix and the latest completion time allowed for each component factor in each planned monitoring period, and perform column-by-column trend analysis on the project schedule feature matrix according to the latest completion time allowed for each component factor in each planned monitoring period, obtain the component factor schedule fluctuation characteristics, and send them;

[0051] A project progress prediction module is used to receive the progress fluctuation characteristics of the component factors, input the progress fluctuation characteristics of the component factors into a pre-built project progress prediction model, obtain the estimated completion time of the project, and send it;

[0052] The monitoring plan adjustment module is used to receive the estimated completion time of the project and automatically adjust the original project progress monitoring plan based on the estimated completion time of the project.

[0053] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0054] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0055] Compared with the prior art, the present invention has the following beneficial effects: the method can better adapt to the dynamic changes in project execution by performing column-by-column trend analysis on the project progress feature matrix, and realize intelligent monitoring of the dynamically changing project progress; the real-time nature of the data collection and analysis steps can timely obtain the latest project progress data, and ensure the timeliness of project progress monitoring and adjustment; the method is flexible by considering the project type and extracting the latest completion time, so that it can better adapt to the special needs of different types of projects and improve its responsiveness; the pre-built project progress prediction model is adopted, and the progress fluctuation characteristics of the component factors are input to realize the accurate estimation of the expected completion time of the project, and improve the accuracy of the prediction;

[0056] Through the automatic adjustment function of the monitoring plan adjustment module, the monitoring plan can be automatically updated according to the prediction results, reducing the manual operation burden of project managers and improving efficiency; through the orderly project progress feature matrix, the problem of data dispersion is solved, the project progress data is centrally managed, and the consistency and manageability of the data are improved; through real-time progress monitoring and intelligent prediction, it can help optimize resource allocation, avoid unnecessary waste of resources, and improve the overall benefits of the project; through the organic combination of various steps, this method can provide comprehensive performance improvement, effectively solve the problems of existing methods, and reduce the risk of project progress delays and resource waste;

[0057] In summary, this method can improve the accuracy, timeliness and intelligence of project progress monitoring and improve the efficiency of project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the present invention;

[0059] Figure 2 It is a flow chart of the construction method of the project progress prediction model;

[0060] Figure 3 It is the structural diagram of the progress monitoring system of project process management. DETAILED DESCRIPTION

[0061] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0062] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0063] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0064] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0065] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0066] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0067] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0068] The present application is described below in conjunction with the drawings in the present application.

[0069] Embodiment 1: Figure 1 to Figure 2 As shown, a progress monitoring method for project process management of the present invention specifically includes the following steps:

[0070] S1. Collect the project progress component factors according to the original project progress monitoring plan;

[0071] The method for obtaining the project schedule component factor set includes:

[0072] S11. Collect the project schedule, including project milestones, critical paths, work packages and tasks. These plans serve as the basis for project progress monitoring.

[0073] S12. Collect actual data during project execution, including task completion, workload, and milestone achievement;

[0074] S13. Use data analysis and mining techniques to extract project progress components from project management systems, log files, and work records;

[0075] S14. Verify and check the accuracy of the collected data to ensure the credibility and accuracy of the data;

[0076] S15. Archive and store the collected project progress data for subsequent analysis and use.

[0077] In this step, by collecting the project schedule and actual execution data, various factors that have a direct impact on the project progress can be obtained, which helps to fully understand the progress of the project;

[0078] By collecting the critical paths and milestones in the project plan, we can identify the tasks and nodes that play a key role in the project progress, which is convenient for focusing on monitoring and managing the project progress and avoiding delays in key tasks and missed milestones. By verifying and checking the accuracy of the collected data, we can ensure the credibility and accuracy of the data, avoid monitoring and predicting the project progress based on inaccurate or unreliable data, and improve the accuracy and reliability of decision-making.

[0079] By using data analysis and mining technology, the components of project progress can be extracted from project management systems, log files, and work records, which helps to discover hidden correlations, trends, and rules, and further optimize the methods and models of project progress management and prediction; the collected project progress data can be archived and stored to establish a data warehouse for subsequent analysis and use, which is convenient for reviewing and comparing historical data, as well as summarizing and improving the project management process;

[0080] In summary, step S1 can obtain a set of project progress component factors and provide basic data for project process management, thereby achieving accurate progress monitoring and prediction and reducing the risk of project progress delays and resource waste.

[0081] S2. Serialize and adjust the project progress component factor sets collected in different planned monitoring time periods in chronological order to obtain a project progress feature matrix;

[0082] The project progress characteristic matrix acquisition method comprises:

[0083] S21. For the data collected during each planned monitoring period, first sort them in chronological order, from early to late;

[0084] S22. According to the time sequence, the project progress component factor sets collected in different planned monitoring time periods are serially adjusted;

[0085] S23. After serialization adjustment, the project schedule components are sorted into the feature matrix in chronological order;

[0086] Each column in the project progress feature matrix represents the completion progress of the same component in different time periods, and each row represents the completion progress of different components in the same time period. The project progress feature matrix is:

[0087]

[0088] in, It represents the completion progress of the j-th project schedule component in the i-th time period.

[0089] In this step, by arranging the progress components in different planned monitoring time periods in chronological order, the changes in the completion progress of the components can be intuitively displayed in the project progress feature matrix, so as to better observe and understand the trends and changes in the project progress and have a more comprehensive understanding of the progress of the project; by arranging the progress components in different time periods into the feature matrix, it is easier to find progress deviations and abnormal situations;

[0090] The project schedule feature matrix provides a structured view of the schedule data, which is helpful for schedule analysis and decision-making. By analyzing the feature matrix, the key factors affecting the project schedule and bottlenecks can be identified, providing a basis for formulating corresponding adjustment measures and decisions;

[0091] In summary, the project schedule characteristic matrix obtained through step S2 can provide a comprehensive understanding of the project schedule trend, help discover schedule deviations and abnormal situations, and support schedule analysis and decision-making, improve the accuracy and timeliness of project schedule monitoring, and reduce the risk of project delays and resource waste.

[0092] S3. Obtain the project type, and extract the latest completion time allowed for each component factor within each planned monitoring period according to the project type;

[0093] The extraction methods for the latest completion time allowed for each component factor in each planned monitoring period include:

[0094] S31. Standardize the definition of project types and determine the components of each project type;

[0095] S32. Review the data of similar projects in the past, analyze the data of similar projects in the past, and determine the latest completion time allowed for each component factor within each planned monitoring period according to different project types;

[0096] S33, a schedule model based on a network diagram and a prediction algorithm based on statistical analysis, extracts the latest completion time allowed for each component factor in each time period.

[0097] In this step, by extracting the latest completion time of each component in each planned monitoring period, the project management team can more accurately grasp the project progress. According to these time limits, the completion status of each component is evaluated and compared, so as to timely discover potential progress delays and take corresponding measures to adjust and optimize.

[0098] By extracting the latest completion time of each component in each time period, the project schedule can be reasonably arranged according to the requirements of the project type, ensuring that the project is completed within the specified time frame and avoiding project delays and waste of resources;

[0099] Extracting the latest completion time of each component in each time period can be used as the basis for project progress warning, which can timely discover potential risks and problems and take corresponding adjustment measures, so that personnel can respond to changes and adjust resources in time to ensure that the project proceeds as planned and avoids progress delays;

[0100] In summary, the S3 step can help the project team better grasp the project progress, improve the efficiency and accuracy of project execution, and ultimately achieve the goal of successful project delivery.

[0101] S4. According to the latest completion time allowed for each component factor in each planned monitoring period, the project schedule characteristic matrix is ​​trend analyzed column by column to obtain the component factor schedule fluctuation characteristics;

[0102] Step S4 performs a column-by-column trend analysis on the project schedule feature matrix to obtain the schedule fluctuation characteristics of the component factors, which can effectively analyze and understand the changing trend of the project schedule data and predict the future progress of the project accordingly;

[0103] The component factor progress fluctuation feature extraction method comprises:

[0104] S41. Perform data processing on the project schedule feature matrix, including data cleaning, processing missing values, and smoothing to remove noise;

[0105] S42. Use statistical methods to perform column-by-column trend analysis on the project progress feature matrix, including moving average and exponential smoothing, to smoothly observe the change trend of the progress and obtain trend analysis results;

[0106] S43. Based on the results of trend analysis, identify the progress fluctuations of each component factor, including identifying stable progress factors and delayed acceleration factors;

[0107] S44. Evaluate the impact of the progress fluctuation of each factor on the progress of the entire project;

[0108] S45. Use machine learning methods to predict future progress fluctuations and obtain the progress fluctuation characteristics of component factors.

[0109] In this step, by performing a column-by-column trend analysis on the project schedule characteristic matrix, the schedule change trend of each component factor can be revealed, which helps the project management team to fully understand the project progress and identify potential delay or acceleration factors in a timely manner;

[0110] Through the results of trend analysis, it is possible to identify the progress fluctuations of each component, and then determine which factors may be delayed or accelerated during project execution, and identify risks and opportunities;

[0111] With the help of machine learning methods, future schedule fluctuations are predicted, allowing the project management team to identify potential problems or risks in advance and take appropriate measures to deal with them to avoid project delays;

[0112] In summary, the S4 step can help the project management team better understand the changing trends of the project progress, and accurately predict and adjust the project progress, thereby reducing the risk of project delays and avoiding waste of resources.

[0113] S5. Input the progress fluctuation characteristics of the component factors into the pre-built project progress prediction model to obtain the estimated completion time of the project;

[0114] The project schedule prediction model is based on regression analysis. In this model, the component schedule fluctuation characteristics are regarded as independent variables, and the project estimated completion time is regarded as the dependent variable. By analyzing historical project data and using appropriate regression algorithms, a prediction model can be established to estimate the estimated completion time of the project given the component schedule fluctuation characteristics.

[0115] The method for constructing the project progress prediction model includes:

[0116] S51. Collect historical data of the project, including project progress, progress of component factors and project-related data;

[0117] S52. Based on project requirements and domain knowledge, select key factors that affect project progress, including task completion time, resource usage, and team performance indicators; perform feature engineering by analyzing and exploring data to extract features suitable for predictive models;

[0118] S53. Select a prediction model according to the characteristics of the project and the nature of the data, wherein the model includes a linear regression model, a decision tree model, a support vector regression model, and a neural network model;

[0119] S54, dividing the collected historical data set into a training set and a test set, the training set is used for model training and parameter adjustment, and the test set is used for evaluating model performance;

[0120] S55. Use the training set to train the selected model, adjust the parameters of the model to fit the training data to the greatest extent, and capture the relationship between the component factors and the project progress through the process of model training;

[0121] S56. Use the test set to evaluate and verify the trained model, and evaluate the accuracy and reliability of the model by comparing the model's prediction results with the actual project progress data;

[0122] S57. Optimize and improve the model based on the results of model evaluation;

[0123] S58. Use the optimized model to predict the project progress, input the progress fluctuation characteristics of the component factors into the model, and generate the estimated completion time of the project.

[0124] In this step, by inputting the progress fluctuation characteristics of the component factors into the prediction model, the estimated completion time of the project can be obtained, which helps project managers and related personnel understand the progress of the project and make corresponding decisions and plans; by predicting the completion time of the project, resources can be better allocated, work progress can be planned, and resource conflicts and time delays can be avoided; the estimated completion time of the project is an important basis for project management decisions. Based on the prediction results, managers can identify potential problems and challenges and take corresponding measures;

[0125] By establishing a project progress prediction model, the project progress can be monitored and predicted in real time, which makes it easier for managers to find deviations and risks in time and take appropriate measures to adjust project execution to ensure that the project is completed on time. By analyzing the prediction results of the prediction model and comparing them with actual data, the project management method can be evaluated and improved. According to the results of the model evaluation, the model can be continuously optimized to improve the accuracy and reliability of the prediction. The project progress prediction model provides a quantitative tool and method for project management. By predicting the completion time of the project, it can enhance the control and supervision of the project, reduce uncertainty, and improve the controllability of project management.

[0126] In summary, by inputting the progress fluctuation characteristics of the component factors into the project progress prediction model, the estimated completion time of the project can be obtained, the project execution efficiency can be improved, the project management method can be improved, and the project controllability can be enhanced.

[0127] S6. Automatically adjust the original project progress monitoring plan according to the estimated completion time of the project;

[0128] The methods for adjusting the original project progress monitoring plan include:

[0129] S61. Determine the current progress status based on the estimated completion time of the project, and compare the estimated completion time of the project with the current date. If the estimated completion time of the project is earlier than or equal to the current date, it means that the project has been delayed or is about to be delayed. If the estimated completion time is still in the future, further analysis of the current progress status is required.

[0130] S62. For adjustments to the estimated completion time, it is necessary to analyze the specific factors that lead to potential delays, make judgments by comparing the actual completion status with the original plan, and check the progress of each task, resource utilization, and team performance indicators;

[0131] S63. Based on the analysis results, identify the tasks that need to be adjusted, re-plan the task priorities, resource allocation, and schedule adjustments;

[0132] S64. After determining the tasks that need to be adjusted, formulate corresponding adjustment plans, including reallocating resources, increasing the number of staff, and re-prioritizing tasks;

[0133] S65. Communicate with the project team and stakeholders based on the prepared adjustment plan and ensure that they understand and accept the adjustments made;

[0134] S66. After implementing the adjustment plan, continue to monitor the progress of the project and evaluate the effectiveness of the adjustment. By comparing it with the expected completion time, determine whether the adjustment can effectively shorten the project delay, and take further measures in a timely manner to ensure that the project can return to normal progress as soon as possible.

[0135] In this step, by analyzing and adjusting according to the estimated completion time of the project, the project management team can promptly identify the risk of project delay or the trend of impending delay, so that the team can take necessary actions in time to avoid potential project delays and correct deviations from the original plan in a timely manner;

[0136] By analyzing the difference between the actual completion of the project and the original plan, the specific factors that cause project delays can be identified, which helps the project management team understand the bottlenecks and obstacles of the project and provides an accurate basis for formulating effective adjustment plans;

[0137] By re-planning the priority of tasks, the allocation of resources, and the adjustment of schedules, we can optimize the utilization of resources and the arrangement of tasks, increase investment in tasks with high risk of delay, increase the number of staff in a timely manner, or rearrange the order of tasks, thereby improving the overall efficiency and progress of the project;

[0138] In the process of adjusting the project progress monitoring plan, communicate and coordinate with the project team and stakeholders, and communicate the purpose, reasons and impact of the adjustment in a timely manner to ensure that the project team and stakeholders have a clear understanding of the adjustment plan and are able to cooperate and support the implementation of the adjustment;

[0139] By continuously monitoring the progress of the project and evaluating the effect of adjustments, we can achieve accurate control of the project time. By comparing with the expected completion time, we can timely understand the progress of the project and judge whether the adjustment plan is effective, so as to take further measures in time to ensure that the project can return to normal progress as soon as possible and ensure that it is finally completed on time;

[0140] In summary, the S6 step can timely adjust the project progress monitoring plan, identify delay factors, optimize resource utilization and task scheduling, enhance communication and cooperation, achieve accurate project time control, and ultimately ensure that the project can be completed on time.

[0141] Embodiment 2: Figure 3 As shown, a progress monitoring system for project process management of the present invention specifically includes the following modules:

[0142] The data collection module collects and sends the set of project progress components according to the original project progress monitoring plan;

[0143] A data serialization adjustment module is used to receive a set of project progress component factors, serialize and adjust the set of project progress component factors collected in different planned monitoring time periods in chronological order, obtain a project progress feature matrix, and send it;

[0144] The latest completion time extraction module is used to obtain the project type, and extract the latest completion time allowed for each component factor in each planned monitoring period according to the project type, and send it;

[0145] The trend analysis module is used to receive the project schedule feature matrix and the latest completion time allowed for each component factor in each planned monitoring period, and perform column-by-column trend analysis on the project schedule feature matrix according to the latest completion time allowed for each component factor in each planned monitoring period, obtain the component factor schedule fluctuation characteristics, and send them;

[0146] A project progress prediction module is used to receive the progress fluctuation characteristics of the component factors, input the progress fluctuation characteristics of the component factors into a pre-built project progress prediction model, obtain the estimated completion time of the project, and send it;

[0147] The monitoring plan adjustment module is used to receive the estimated completion time of the project and automatically adjust the original project progress monitoring plan based on the estimated completion time of the project.

[0148] Through trend analysis and prediction models, the system can dynamically adapt to changes in project execution and realize intelligent monitoring of dynamically changing project progress; the real-time nature of the data collection and analysis module enables the system to obtain the latest project progress data in a timely manner, ensuring the timeliness of project progress monitoring and adjustment; using the prediction model, the system can provide intelligent decision support and provide accurate predictions based on data, helping project managers make timely decisions;

[0149] Through the integrated data collection and serialization adjustment module, the system can solve the problem of data dispersion, centrally manage project progress data, and improve data consistency and manageability; the latest completion time extraction module considers the project type, so that the system can better adapt to the special needs of different types of projects and increase the flexibility of the system; the automatic adjustment function of the monitoring plan adjustment module can automatically update the monitoring plan according to the prediction results, reducing the manual operation burden of project managers and improving efficiency;

[0150] Through real-time progress monitoring and intelligent prediction, the system can help optimize resource allocation, avoid unnecessary waste of resources, and improve the overall benefits of the project; through the organic combination of various modules, the system can provide comprehensive performance improvement in project process management, effectively deal with problems that are difficult to solve with static plans, and reduce the risks of project progress delays and resource waste;

[0151] In summary, the system can improve the accuracy, timeliness and intelligence of project progress monitoring and improve the efficiency of project management.

[0152] The various variations and specific embodiments of the progress monitoring method for project process management in the aforementioned embodiment 1 are also applicable to the progress monitoring system for project process management in this embodiment. Through the aforementioned detailed description of the progress monitoring method for project process management, those skilled in the art can clearly understand the implementation method of the progress monitoring system for project process management in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0153] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0154] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A progress monitoring method for project process management, characterized in that: The method comprises: According to the original project progress monitoring plan, collect the project progress component factors; The project progress component factor sets collected in different planned monitoring time periods are serialized and adjusted in chronological order to obtain the project progress feature matrix; Obtain the project type, and extract the latest completion time allowed for each component factor within each planned monitoring period according to the project type; According to the latest completion time allowed for each component factor in each planned monitoring period, the project schedule characteristic matrix is ​​analyzed column by column to obtain the component factor schedule fluctuation characteristics; Input the progress fluctuation characteristics of the component factors into the pre-built project progress prediction model to obtain the estimated completion time of the project; Automatically adjust the original project progress monitoring plan based on the estimated project completion time.

2. A method for monitoring the progress of project process management according to claim 1, characterized in that: Each column in the project progress feature matrix represents the completion progress of the same component in different time periods, and each row represents the completion progress of different components in the same time period. The project progress feature matrix is: in, It represents the completion progress of the j-th project schedule component in the i-th time period.

3. A method for monitoring the progress of project process management as claimed in claim 2, characterized in that: The project progress characteristic matrix acquisition method comprises: The data collected during each planned monitoring period are sorted in chronological order; According to the time sequence, the project progress component factors collected in different planned monitoring time periods are serialized and adjusted; After serialization adjustment, the project schedule components are organized into a feature matrix in chronological order.

4. A progress monitoring method for project process management as claimed in claim 1, characterized in that: The method for obtaining the project schedule component factor set includes: Collect project schedule, including project milestones, critical paths, work packages and tasks; Collect actual data during project execution, including task completion, workload, and milestone achievement; Extract project progress components from project management systems, log files, and work records; Verify and check the accuracy of the collected data; Archive and store the collected project progress data.

5. A method for monitoring the progress of project process management according to claim 1, characterized in that: The component factor progress fluctuation feature extraction method comprises: Perform data processing on the project schedule feature matrix, including data cleaning, processing missing values, and smoothing to remove noise; Conduct trend analysis on the project progress feature matrix column by column, including moving average and exponential smoothing, and then smoothly observe the change trend of the progress and obtain trend analysis results; Based on the results of trend analysis, identify the progress fluctuations of each component, including identifying stable progress factors and delay acceleration factors; Evaluate the impact of schedule fluctuations of each factor on the overall project schedule; Use machine learning methods to predict future progress fluctuations and obtain the progress fluctuation characteristics of component factors.

6. A progress monitoring method for project process management as claimed in claim 1, characterized in that: The method for constructing the project progress prediction model includes: Collect historical data of the project, including project progress, progress of component elements and project-related data; Based on project requirements and domain knowledge, select key factors that affect project progress, including task completion time, resource usage, and team performance indicators; perform feature engineering by analyzing and exploring data; Select a prediction model based on the project's characteristics and data properties, including a linear regression model, a decision tree model, a support vector regression model, and a neural network model; The collected historical data set is divided into a training set and a test set. The training set is used for model training and parameter adjustment, and the test set is used to evaluate model performance. Use the training set to train the selected model and adjust the model parameters to fit the training data; Use the test set to evaluate and validate the trained model, and evaluate the accuracy and reliability of the model by comparing the model's prediction results with the actual project progress data; Optimize and improve the model based on the results of model evaluation; Use the optimized model to predict the project progress, input the progress fluctuation characteristics of the component factors into the model, and generate the estimated completion time of the project.

7. A progress monitoring method for project process management as claimed in claim 1, characterized in that: The methods for adjusting the original project progress monitoring plan include: Determine the current progress according to the estimated completion time of the project; For adjustments to the estimated completion time, check the progress of each task, resource utilization, and team performance indicators by comparing the actual completion status with the original plan; Based on the analysis results, identify tasks that need to be adjusted, re-plan task priorities, resource allocation, and schedule adjustments; After identifying the tasks that need to be adjusted, formulate adjustment plans, including reallocating resources, increasing the number of staff, and re-prioritizing tasks; Communicate with relevant personnel based on the formulated adjustment plan; After implementing the adjustment plan, continue to monitor the progress of the project.

8. A progress monitoring system for project process management, characterized in that: The system comprises: The data collection module collects and sends the set of project progress components according to the original project progress monitoring plan; A data serialization adjustment module is used to receive a set of project progress component factors, serialize and adjust the set of project progress component factors collected in different planned monitoring time periods in chronological order, obtain a project progress feature matrix, and send it; The latest completion time extraction module is used to obtain the project type, and extract the latest completion time allowed for each component factor in each planned monitoring period according to the project type, and send it; The trend analysis module is used to receive the project schedule feature matrix and the latest completion time allowed for each component factor in each planned monitoring period, and perform column-by-column trend analysis on the project schedule feature matrix according to the latest completion time allowed for each component factor in each planned monitoring period, obtain the component factor schedule fluctuation characteristics, and send them; A project progress prediction module is used to receive the progress fluctuation characteristics of the component factors, input the progress fluctuation characteristics of the component factors into a pre-built project progress prediction model, obtain the estimated completion time of the project, and send it; The monitoring plan adjustment module is used to receive the estimated completion time of the project and automatically adjust the original project progress monitoring plan based on the estimated completion time of the project.

9. An electronic device for monitoring progress of project process management, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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