Task risk early warning method and system based on project management data

Abstract

The invention provides a task risk early warning method and system based on project management data, and relates to the technical field of project management early warning. According to the method, the task risk index weight is automatically adjusted to adapt to process states and external environment changes in different stages of a project, the accuracy and flexibility of risk assessment are remarkably improved, and good dynamic adaptability is achieved; performing grading evaluation on tasks in each stage of the project by means of a quantitative comprehensive risk value, determining a management priority, and assisting a project team to focus on key risks; a task cooperation relationship is visually presented by constructing a task cooperation network, a center node and a fragile link are accurately identified, and a risk propagation path is effectively grasped; and real-time monitoring and graded early warning are realized in combination with a dynamic risk early warning rule, so that the risk can be found and dealt with in time.

Description

Technical Field

[0001] The present invention relates to the technical field of project management early warning, and in particular to a task risk early warning method and system based on project management data. Background Art

[0002] In today's complex and ever-changing business environment, project management faces numerous challenges. As projects continue to expand in scale and complexity, project risks are becoming more diverse and dynamic. Traditional project management methods have significant limitations in risk early warning and are difficult to adapt to the needs of modern project management. The following problems exist: On the one hand, traditional risk assessment methods often use fixed risk indicator weights, making it difficult to flexibly adjust them based on the progress status of different project stages and changes in the external environment. This leads to deviations between risk assessment results and actual conditions, reducing the accuracy and effectiveness of the assessment and making it difficult for project teams to accurately grasp the true extent of project risks. On the other hand, traditional risk assessment methods lack in-depth analysis of the collaborative relationships between tasks, making it difficult to visually present inter-task dependencies and collaboration patterns. This makes it difficult for project teams to identify risk propagation paths and key risk points, making it difficult to effectively focus on management priorities, leading to irrational resource allocation and inefficient risk management. Furthermore, traditional risk early warning mechanisms often rely on manual experience and periodic inspections, lacking real-time and dynamic capabilities. During project execution, risks can arise at any time and spread rapidly, and traditional methods are unable to capture these changes in a timely manner.

[0003] Therefore, it is necessary to provide a task risk warning method and system based on project management data to solve the above technical problems. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a task risk warning method and system based on project management data to solve the problems of traditional task risk warning methods or systems, such as the lack of dynamic adaptability in risk assessment, low efficiency in risk management, and lack of real-time and accuracy in risk warning.

[0005] The present invention provides a task risk early warning method based on project management data, comprising the following steps: S1. Obtain project management data and automatically adjust the weight coefficients of task risk indicators by analyzing the project's stage progress status and external environment changes in the project management data. The risk indicators include task delay rate, resource overrun rate, and quality defect rate. S2. Based on the weight coefficients of the risk indicators of the automatically adjusted tasks, perform a hierarchical assessment of the tasks at each stage of the project to obtain a hierarchical assessment result, wherein the hierarchical assessment specifically includes a high risk level, a medium risk level, and a low risk level; S3. Based on the hierarchical assessment results, analyze the task collaboration relationships in the project management data and construct the task collaboration relationships into a task collaboration network to identify central nodes and vulnerable links; S4. After identifying the central nodes and vulnerable links in the task collaboration network, the influence propagation model is used to simulate the diffusion process of negative risks, and the key nodes and vulnerable links are re-evaluated and updated. S5. Based on the updated key nodes and vulnerable links of the task collaboration network and in conjunction with pre-set dynamic risk warning rules, real-time assessment and warning of task risks are carried out.

[0006] Preferably, the specific steps of step S1 include: S101. Collect project management data from the project management system, including task progress, resource consumption, quality inspection results, and external environment data; S102. Calculate three risk indicators for each task in the current phase. The risk indicators include task delay rate, resource overrun rate, and quality defect rate. The formulas for calculating the risk indicators are: task delay rate = (actual duration - planned duration) / planned duration; resource overrun rate = (actual resource consumption - budgeted resources) / budgeted resources; quality defect rate = number of tasks with quality defects / total number of tasks. S103. Analyze the project's phase progress status and external environment changes, where the analyzed phase progress status includes the start phase, execution phase, and closing phase; external environment changes include sudden policy requirements to shorten the project period or market resource shortages; S104. Preset the weight coefficient of the risk indicator of the task, and automatically adjust the weight coefficient of the risk indicator of the task based on the analysis results of the stage process status and external environment changes. Specifically, the weight of the quality defect rate is increased in the execution stage, and the weight of the task delay rate is increased in the closing stage; if the emergency policy requires the construction period to be shortened, the weight of the task delay rate is increased; if there is a shortage of market resources, the weight of the resource overspending rate is increased.

[0007] Preferably, the specific steps of step S2 include: S201, integrating the automatically adjusted weight coefficient of the risk indicator of the task with the task delay rate, resource overspending rate, and quality defect rate data in the project management data; S202. For each task, calculate a comprehensive risk value based on its corresponding weight coefficient and risk index value. The calculation formula is: Comprehensive risk value = Task delay rate × Task delay rate weight coefficient + Resource overrun rate × Resource overrun rate weight coefficient + Quality defect rate × Quality defect rate weight coefficient; S203. Pre-set grading standards for high risk, medium risk, and low risk, and conduct a graded assessment of tasks at each stage of the project based on the calculated comprehensive risk value and the pre-set grading standards to determine the risk level of each task; S204. Summarize the hierarchical assessment results of tasks at each stage to form a hierarchical assessment result including each task name, stage to which it belongs, and risk level.

[0008] Preferably, the specific steps of step S3 include: S301. Analyze the task collaboration relationships in the project management data, including the dependencies between tasks, resource allocation relevance, and team collaboration records; S302: Build a task collaboration network with tasks as nodes and dependencies as edges, and mark high-risk tasks as central nodes; S303: Analyze the links connecting the central node in the task collaboration network and evaluate the links with the probability of abnormal situations occurring as vulnerable links, wherein the abnormal situations include both situations that cause risk propagation and task interruption.

[0009] Preferably, the specific steps of step S4 include: S401. Use the influence propagation model to simulate the diffusion of negative risks from the central node in the network, estimate the propagation path and impact range of different risk events, and obtain simulation prediction results; S402: Re-evaluate and update the list of vulnerable links and key nodes based on the estimated results of the simulation.

[0010] Preferably, the specific steps of step S5 include: The specific steps of step S5 include: S501. Pre-set dynamic risk warning rules, including central node delay thresholds and fragile link conflict rules. Specifically, a warning is triggered when the number of high-risk tasks exceeds the set threshold, or a warning signal is issued when an abnormal situation occurs in a task on a fragile link; S502: Monitor the task collaboration network in real time, match dynamic risk warning rules to trigger graded warnings, and conduct real-time assessment and warning of task risks.

[0011] A task risk early warning system based on project management data, the early warning system comprising: The early warning system includes: The data analysis module is used to obtain project management data and automatically adjust the weight coefficients of task risk indicators by analyzing the project's stage progress status and external environment changes in the project management data. The risk indicators include task delay rate, resource overrun rate, and quality defect rate; A hierarchical assessment module is used to perform hierarchical assessment of tasks at each stage of the project based on the weight coefficients of the risk indicators of the automatically adjusted tasks, and obtain hierarchical assessment results, wherein the hierarchical assessment specifically includes high risk level, medium risk level and low risk level; The link association module is used to analyze the task collaboration relationships in the project management data based on the hierarchical evaluation results, construct the task collaboration relationships into a task collaboration network, and identify the central nodes and vulnerable links; The node update module is used to identify the central nodes and vulnerable links in the task collaboration network, simulate the diffusion process of negative risks using the influence propagation model, and re-evaluate and update the key nodes and vulnerable links; The assessment and warning module is used to conduct real-time assessment and warning of mission risks based on the key nodes and vulnerable links updated in the task collaboration network, in conjunction with pre-set dynamic risk warning rules.

[0012] Compared with related technologies, the task risk early warning method and system based on project management data provided by the present invention has the following beneficial effects: The present invention significantly improves the accuracy and flexibility of risk assessment by automatically adjusting the weights of task risk indicators to adapt to the process status of different stages of the project and changes in the external environment, and has good dynamic adaptability; it uses quantitative comprehensive risk values ​​to conduct hierarchical assessments of tasks at each stage of the project, clarifies management priorities, and helps project teams focus on key risks; it intuitively presents task collaboration relationships by constructing a task collaboration network, accurately identifies central nodes and vulnerable links, and effectively grasps risk propagation paths; it combines dynamic risk warning rules to achieve real-time monitoring and hierarchical warnings, ensuring that risks can be discovered and responded to in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of a task risk early warning method based on project management data of the present invention; Figure 2 This is a system block diagram of a task risk early warning system based on project management data of the present invention. DETAILED DESCRIPTION

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Example 1 like Figure 1 As shown, a task risk early warning method based on project management data includes the following steps: S1. Obtain project management data and automatically adjust the weight coefficients of task risk indicators by analyzing the project's stage progress status and external environment changes in the project management data. The risk indicators include task delay rate, resource overrun rate, and quality defect rate. S2. Based on the weight coefficients of the risk indicators of the automatically adjusted tasks, perform a hierarchical assessment of the tasks at each stage of the project to obtain a hierarchical assessment result, wherein the hierarchical assessment specifically includes a high risk level, a medium risk level, and a low risk level; S3. Based on the hierarchical assessment results, analyze the task collaboration relationships in the project management data and construct the task collaboration relationships into a task collaboration network to identify central nodes and vulnerable links; S4. After identifying the central nodes and vulnerable links in the task collaboration network, the influence propagation model is used to simulate the diffusion process of negative risks, and the key nodes and vulnerable links are re-evaluated and updated. S5. Based on the updated key nodes and vulnerable links of the task collaboration network and in conjunction with pre-set dynamic risk warning rules, real-time assessment and warning of task risks are carried out.

[0016] In the specific implementation process, the specific steps of step S1 include: S101. Collect project management data from the project management system, including task progress, resource consumption, quality inspection results and external environment data.

[0017] Specifically, by connecting to the interface of the project management system or directly accessing the system database, task progress, resource consumption, quality inspection results and external environment data are collected. Task progress data includes information such as the planned start time, planned end time, actual start time and actual end time of each task; resource consumption data includes the actual usage and budget of human resources, material resources and financial resources; quality inspection results record the quality inspection status after each task is completed, such as whether it passes the quality inspection, existing quality defects, etc.; external environment data includes changes in policies and regulations.

[0018] S102. Calculate the three risk indicators for each task in the current stage. The risk indicators include task delay rate, resource overrun rate, and quality defect rate. The calculation formulas for the risk indicators are: task delay rate = (actual duration - planned duration) / planned duration, resource overrun rate = (actual resource consumption - budgeted resources) / budgeted resources, and quality defect rate = number of tasks with quality defects / total number of tasks.

[0019] In this example, a software development project includes the following tasks and data: Task A (Requirements Analysis) Planned Duration: 5 days, Actual Duration: 7 days, Budgeted Human Resource Cost: 10,000 yuan, Actual Human Resource Cost: 14,000 yuan, Quality Inspection: Passed (no defects). Task B (Code Writing) Planned Duration: 10 days, Actual Duration: 12 days, Budgeted Server Resource Cost: 15,000 yuan, Actual Server Resource Cost: 18,750 yuan, Quality Inspection: 2 defects found (total number of tasks: 5 modules, this task covers 3 modules). Task C (Testing) Planned Duration: 8 days, Actual Duration: 8 days, Budgeted Test Tool Cost: 8,000 yuan, Actual Test Tool Cost: 8,000 yuan, Quality Inspection: 1 defect found (total number of tasks: 5 modules, this task covers 2 modules). Based on the calculation formulas for task delay rate, resource overrun rate, and quality defect rate in step S102, we can calculate that: Task A has a delay rate of 40%, Task B has a delay rate of 20%, and Task C has no delay; Task A has a resource overrun rate of 40%, Task B has a resource overrun rate of 25%, and Task C has no resource overrun; in this embodiment, the total number of tasks is 5 modules, of which Task B is responsible for 3 and Task C is responsible for 2. Therefore, Task B has 2 defective modules, of which 2 of the 3 modules are defective, and the calculated quality defect rate of Task B is 40%. Task C has 1 defective module, of which 1 of the 2 modules is defective, and its calculated quality defect rate is 20%.

[0020] S103. Analyze the stage progress status and external environment changes of the project, wherein the analyzed stage progress status includes the starting stage, the execution stage and the closing stage; the external environment changes include sudden policy requirements to shorten the construction period or market resource shortages.

[0021] Specifically, based on the actual progress of the project, the project is divided into the initiation phase, the execution phase, and the closing phase. The initiation phase involves project launch, demand research, and planning. The execution phase is the main implementation phase, with all tasks carried out according to plan. The closing phase involves project acceptance, delivery, and summary. Pay attention to changes in various external factors of the project, such as sudden policy requirements to shorten the construction period or market resource shortages.

[0022] S104. Preset the weight coefficient of the risk indicator of the task, and automatically adjust the weight coefficient of the risk indicator of the task based on the analysis results of the stage process status and external environment changes. Specifically, the weight of the quality defect rate is increased in the execution stage, and the weight of the task delay rate is increased in the closing stage; if the emergency policy requires the construction period to be shortened, the weight of the task delay rate is increased; if there is a shortage of market resources, the weight of the resource overspending rate is increased.

[0023] Specifically, based on project characteristics and past experience, weight coefficients for task risk indicators (task delay rate, resource overrun rate, and quality defect rate) are preset. In this embodiment, the default weight coefficient for task delay rate is 0.3, the default weight coefficient for resource overrun rate is 0.3, and the default weight coefficient for quality defect rate is 0.4. When the adjustment mechanism is triggered, the weight coefficients of the task risk indicators are automatically adjusted. This includes identifying trigger conditions, which include project status, receipt of a notification requiring a shortened construction period due to unexpected policies, or identification of market resource shortages. When the project status is identified as "starting phase," the weight coefficients of the task risk indicators are automatically adjusted to: delay rate 0.25, resource overrun rate 0.25, and quality defect rate 0.5. When the project status is identified as "executing phase," the weight coefficients of the task risk indicators are automatically adjusted to: task delay rate 0.25, resource overrun rate 0.25, and quality defect rate 0.5. When the project status is identified as "closing phase," the weight coefficients of the task risk indicators are automatically adjusted to: task delay rate 0.5, resource overrun rate 0.25, and quality defect rate 0.25. When receiving a notification regarding a sudden policy requiring a shortened construction period, the system automatically adjusts the risk index weights for the task: task delay rate 0.4, resource overrun rate 0.2, and quality defect rate 0.4. When identifying a market resource shortage, the system automatically adjusts the task delay rate 0.2, resource overrun rate 0.4, and quality defect rate 0.4.

[0024] In the specific implementation process, the specific steps of step S2 include: S201. Integrate the automatically adjusted weight coefficient of the risk indicator of the task with the task delay rate, resource overspending rate, and quality defect rate data in the project management data.

[0025] Specifically, after completing step S1 to automatically adjust the weight coefficient of the task risk indicator, it is necessary to integrate these adjusted weight coefficients with the risk indicator values ​​of each task calculated in the project management data, the task delay rate, the resource overrun rate, and the quality defect rate. Specifically, it includes establishing a data association mechanism to match and store each task with its corresponding task delay rate, resource overrun rate, quality defect rate and the automatically adjusted weight coefficient.

[0026] S202. For each task, calculate the comprehensive risk value according to its corresponding weight coefficient and risk index value. The calculation formula is: Comprehensive risk value = task delay rate × task delay rate weight coefficient + resource overrun rate × resource overrun rate weight coefficient + quality defect rate × quality defect rate weight coefficient.

[0027] Specifically, based on the data integrated in step S201, the comprehensive risk value of each task is calculated using the formula: "Comprehensive Risk Value = Task Delay Rate × Task Delay Rate Weight Coefficient + Resource Overrun Rate × Resource Overrun Rate Weight Coefficient + Quality Defect Rate × Quality Defect Rate Weight Coefficient." During the calculation process, ensure that each risk indicator value is multiplied by its corresponding weight coefficient, and then add the three products to obtain the comprehensive risk value for the task. Specifically, batch calculations are performed using the formula function of spreadsheet software such as Excel.

[0028] In this embodiment, the current project is in the execution phase, so the weight coefficients are adjusted to: Task Delay Rate: 0.25, Resource Overrun Rate: 0.25, and Quality Defect Rate: 0.5. Based on the embodiment of step S102, the comprehensive risk value of Task A is: 0.4×0.25+0.4×0.25+0×0.5=0.1+0.1+0=0.2; the comprehensive risk value of Task B is: 0.2×0.25+0.25×0.25+0.4×0.5=0.05+0.0625+0.2=0.3125; and the comprehensive risk value of Task C is: 0×0.25+0×0.25+0.2×0.5=0.1.

[0029] S203. Pre-set grading standards for high risk, medium risk, and low risk levels, and conduct graded assessments on tasks at each stage of the project based on the calculated comprehensive risk value and the pre-set grading standards to determine the risk level of each task.

[0030] Specifically, grading standards for high risk, medium risk and low risk levels are set in advance. In this embodiment, a comprehensive risk value of 0.15 and above is set as a high risk level, 0.08-0.14 is set as a medium risk level, and below 0.08 is set as a low risk level; the comprehensive risk value calculated for each task is compared with the pre-set grading standards to determine the risk level of each task. If the comprehensive risk value is greater than or equal to the lower limit of the high risk level, the task is at a high risk level; if the comprehensive risk value is between the lower limit of the high risk level and the lower limit of the medium risk level, the task is at a medium risk level; if the comprehensive risk value is less than the lower limit of the medium risk level, the task is at a low risk level.

[0031] S204. Summarize the hierarchical assessment results of tasks at each stage to form a hierarchical assessment result including each task name, stage to which it belongs, and risk level.

[0032] In the specific implementation process, the specific steps of step S3 include: S301. Analyze the task collaboration relationships in the project management data, including the dependencies between tasks, resource allocation relevance, and team collaboration records.

[0033] Specifically, search and review the task schedules in project management data, identify the predecessor and successor tasks for each task, and determine the dependencies between tasks. For example, in a software development project, the code writing task may depend on the completion of the requirements analysis task. Code writing can only be carried out after the requirements are clear; while the testing task depends on the completion of the code writing task. Testing can only be carried out after the code is written. Count the resources allocated to each task, including human resources, material resources, and financial resources, and analyze the overlap and correlation in resource usage between different tasks. For example, if multiple tasks share the same group of developers or need to use the same server at the same time, the resource allocation correlation is determined. Collect communication records between project team members, work handover records, and other team collaboration related information.

[0034] S302: Build a task collaboration network with tasks as nodes and dependencies as edges, and mark high-risk tasks as central nodes.

[0035] Specifically, use graphics drawing tools such as Visio and Graphviz to construct a task collaboration network, represent each task as a node, and connect the corresponding nodes with directed edges based on the inter-task dependencies analyzed in step S301. The direction of the edge represents the dependency order between tasks. For example, if task A is a predecessor task of task B, draw a directed edge from node A to node B; from the hierarchical assessment results obtained in step S2, determine high-risk tasks, and mark these high-risk task nodes with special colors such as red or shapes such as circles in the task collaboration network to highlight their status as central nodes. It should be noted that central nodes usually have high connectivity and importance in the network, and may have a greater impact on the risk propagation of the entire project.

[0036] S303: Analyze the links connecting the central node in the task collaboration network and evaluate the links with the probability of abnormal situations occurring as vulnerable links, wherein the abnormal situations include both situations that cause risk propagation and task interruption.

[0037] Specifically, all links connecting the central node are identified, that is, the paths formed by the edges starting from the central node and the edges pointing to the central node, and the length of each link, that is, the number of tasks included, the dependency strength between the tasks on the link, such as whether it is a strong dependency, and the resource allocation situation, such as whether the tasks on the link share key resources, are analyzed; then, based on the results of the link analysis, the probability of abnormal situations occurring in each link is evaluated. In this embodiment, the evaluation method includes evaluating the probability of abnormal situations occurring in a link if a link contains multiple high-risk tasks that exceed a preset number threshold, or if there is resource competition between tasks on the link that exceeds forty percent.

[0038] In the specific implementation process, the specific steps of step S4 include: S401. Use the influence propagation model to simulate the diffusion process of negative risks starting from the central node in the network, estimate the propagation path and impact range of different risk events, and obtain the simulation prediction results.

[0039] Specifically, an initial influence value is assigned to each task node based on the hierarchical assessment results. In this embodiment, high-risk tasks are assigned a high initial influence value of 0.85, medium-risk tasks are assigned an initial influence value of 0.45, and low-risk tasks are assigned a low initial influence value of 0.15. Using the Independent Cascade Model (ICM) of influence propagation models, in this embodiment, tasks A, B, and C are connected. When a problem occurs in task A, it is activated and attempts to influence all directly connected tasks, such as tasks B and C. For each connected task, a random number between 0 and 1 is generated. If this random number is less than a preset probability of 0.7, the task is also activated and affected. Once task B or task C is activated, they will continue to attempt to influence their neighboring task nodes according to the same rules until no new task nodes can be activated. Throughout this process, we record which tasks are ultimately affected and how they are activated in sequence. The results are then averaged. Based on this statistical average, we obtain data on the propagation paths and impact ranges of different risk events, which serve as the simulation's estimated results.

[0040] S402: Re-evaluate and update the list of vulnerable links and key nodes based on the estimated results of the simulation.

[0041] Specifically, based on the data information of the simulation prediction results, the original list of key nodes and fragile links is updated, the size of the chain reaction of each task node on the subsequent nodes is counted, and the original list of key nodes and fragile links is updated.

[0042] In the specific implementation process, the specific steps of step S5 include: S501. Pre-set dynamic risk warning rules, including central node delay threshold and fragile link conflict rules. Specifically, when the number of high-risk tasks exceeds the set threshold, a warning is triggered, or when an abnormal situation occurs in the task on the fragile link, a warning signal is issued.

[0043] Specifically, regarding the central node delay threshold, in this embodiment, when a high-risk task is delayed by more than 10% of the planned duration, the overall project progress will be significantly affected, so the central node delay threshold for the current project is set to 10%. For different types of abnormal situations, corresponding triggering conditions are set. In this embodiment, for resource conflicts, an alert is triggered when the demand for the same resource by multiple tasks exceeds 80% of the available amount within a certain time period. For task dependency conflicts, an alert is issued when the delay time of a task's predecessor exceeds 5% of its planned duration.

[0044] S502: Monitor the task collaboration network in real time, match dynamic risk warning rules to trigger graded warnings, and conduct real-time assessment and warning of task risks.

[0045] Specifically, project management tools or custom-developed monitoring systems are used to obtain real-time status information of each task in the task collaboration network, including task progress, resource usage, and dependency execution. The task status information obtained from real-time monitoring is matched with pre-set dynamic risk warning rules. Based on the matching results, the corresponding graded warning is triggered. It should be noted that different levels of warning signals are set, such as yellow warnings indicating that there is a certain risk, but it has not yet caused a serious impact on the project, orange warnings indicating that the risk is high and may have a certain impact on the project progress or quality, and red warnings indicating that the risk is extremely high and has already caused a serious impact on the project. After the warning, the specific warning level information of the project graded warning is notified via email, instant messaging tools, or telephone conferences, so that staff can make corresponding strategies.

[0046] Example 2 like Figure 2 As shown, a task risk early warning system based on project management data is applied to a task risk early warning method based on project management data, specifically including: The early warning system includes: The data analysis module is used to obtain project management data and automatically adjust the weight coefficients of task risk indicators by analyzing the project's stage progress status and external environment changes in the project management data. The risk indicators include task delay rate, resource overrun rate, and quality defect rate; A hierarchical assessment module is used to perform hierarchical assessment of tasks at each stage of the project based on the weight coefficients of the risk indicators of the automatically adjusted tasks, and obtain hierarchical assessment results, wherein the hierarchical assessment specifically includes high risk level, medium risk level and low risk level; The link association module is used to analyze the task collaboration relationships in the project management data based on the hierarchical evaluation results, construct the task collaboration relationships into a task collaboration network, and identify the central nodes and vulnerable links; The node update module is used to identify the central nodes and vulnerable links in the task collaboration network, simulate the diffusion process of negative risks using the influence propagation model, and re-evaluate and update the key nodes and vulnerable links; The assessment and warning module is used to conduct real-time assessment and warning of mission risks based on the key nodes and vulnerable links updated in the task collaboration network, in conjunction with pre-set dynamic risk warning rules.

[0047] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0049] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A task risk early warning method based on project management data, characterized in that: The following steps are involved: S1. Obtain project management data and automatically adjust the weight coefficients of task risk indicators by analyzing the project's stage progress status and external environment changes in the project management data. The risk indicators include task delay rate, resource overrun rate, and quality defect rate. S2. Based on the weight coefficients of the risk indicators of the automatically adjusted tasks, perform a hierarchical assessment of the tasks at each stage of the project to obtain a hierarchical assessment result, wherein the hierarchical assessment specifically includes a high risk level, a medium risk level, and a low risk level; S3. Based on the hierarchical assessment results, analyze the task collaboration relationships in the project management data and construct the task collaboration relationships into a task collaboration network to identify central nodes and vulnerable links; S4. After identifying the central nodes and vulnerable links in the task collaboration network, the influence propagation model is used to simulate the diffusion process of negative risks, and the key nodes and vulnerable links are re-evaluated and updated. S5. Based on the updated key nodes and vulnerable links of the task collaboration network and in conjunction with pre-set dynamic risk warning rules, real-time assessment and warning of task risks are carried out.

2. The task risk early warning method based on project management data according to claim 1 is characterized in that: The specific steps of step S1 include: S101. Collect project management data from the project management system, including task progress, resource consumption, quality inspection results, and external environment data; S102. Calculate three risk indicators for each task in the current phase. The risk indicators include task delay rate, resource overrun rate, and quality defect rate. The formulas for calculating the risk indicators are: task delay rate = (actual duration - planned duration) / planned duration; resource overrun rate = (actual resource consumption - budgeted resources) / budgeted resources; quality defect rate = number of tasks with quality defects / total number of tasks. S103. Analyze the project's phase progress status and external environment changes, where the analyzed phase progress status includes the start phase, execution phase, and closing phase; external environment changes include sudden policy requirements to shorten the project period or market resource shortages; S104. Preset the weight coefficient of the risk indicator of the task, and automatically adjust the weight coefficient of the risk indicator of the task based on the analysis results of the stage process status and external environment changes. Specifically, the weight of the quality defect rate is increased in the execution stage, and the weight of the task delay rate is increased in the closing stage; if the emergency policy requires the construction period to be shortened, the weight of the task delay rate is increased; if there is a shortage of market resources, the weight of the resource overspending rate is increased.

3. The task risk early warning method based on project management data according to claim 1 is characterized in that: The specific steps of step S2 include: S201, integrating the automatically adjusted weight coefficient of the risk indicator of the task with the task delay rate, resource overspending rate, and quality defect rate data in the project management data; S202. For each task, calculate a comprehensive risk value based on its corresponding weight coefficient and risk index value. The calculation formula is: Comprehensive risk value = Task delay rate × Task delay rate weight coefficient + Resource overrun rate × Resource overrun rate weight coefficient + Quality defect rate × Quality defect rate weight coefficient; S203. Pre-set grading standards for high risk, medium risk, and low risk, and conduct a graded assessment of tasks at each stage of the project based on the calculated comprehensive risk value and the pre-set grading standards to determine the risk level of each task; S204. Summarize the hierarchical assessment results of tasks at each stage to form a hierarchical assessment result including each task name, stage to which it belongs, and risk level.

4. The task risk early warning method based on project management data according to claim 1 is characterized in that: The specific steps of step S3 include: S301. Analyze the task collaboration relationships in the project management data, including the dependencies between tasks, resource allocation relevance, and team collaboration records; S302: Build a task collaboration network with tasks as nodes and dependencies as edges, and mark high-risk tasks as central nodes; S303: Analyze the links connecting the central node in the task collaboration network and evaluate the links with the probability of abnormal situations occurring as vulnerable links, wherein the abnormal situations include both situations that cause risk propagation and task interruption.

5. The task risk early warning method based on project management data according to claim 1 is characterized in that: The specific steps of step S4 include: S401. Use the influence propagation model to simulate the diffusion of negative risks from the central node in the network, estimate the propagation path and impact range of different risk events, and obtain simulation prediction results; S402: Re-evaluate and update the list of vulnerable links and key nodes based on the estimated results of the simulation.

6. The task risk early warning method based on project management data according to claim 1 is characterized in that: The specific steps of step S5 include: S501. Pre-set dynamic risk warning rules, including central node delay thresholds and fragile link conflict rules. Specifically, a warning is triggered when the number of high-risk tasks exceeds the set threshold, or a warning signal is issued when an abnormal situation occurs in a task on a fragile link; S502: Monitor the task collaboration network in real time, match dynamic risk warning rules to trigger graded warnings, and conduct real-time assessment and warning of task risks.

7. A task risk early warning system based on project management data, applied to a task risk early warning method based on project management data according to any one of claims 1 to 6, characterized in that: The early warning system includes: The data analysis module is used to obtain project management data and automatically adjust the weight coefficients of task risk indicators by analyzing the project's stage progress status and external environment changes in the project management data. The risk indicators include task delay rate, resource overrun rate, and quality defect rate; A hierarchical assessment module is used to perform hierarchical assessment of tasks at each stage of the project based on the weight coefficients of the risk indicators of the automatically adjusted tasks, and obtain hierarchical assessment results, wherein the hierarchical assessment specifically includes high risk level, medium risk level and low risk level; The link association module is used to analyze the task collaboration relationships in the project management data based on the hierarchical evaluation results, construct the task collaboration relationships into a task collaboration network, and identify the central nodes and vulnerable links; The node update module is used to identify the central nodes and vulnerable links in the task collaboration network, simulate the diffusion process of negative risks using the influence propagation model, and re-evaluate and update the key nodes and vulnerable links; The assessment and warning module is used to conduct real-time assessment and warning of mission risks based on the key nodes and vulnerable links updated in the task collaboration network, in conjunction with pre-set dynamic risk warning rules.

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