Enterprise project management system and method oriented to multi-project collaboration

Abstract

The invention relates to the technical field of project management, in particular to an enterprise project management system and method oriented to multi-project collaboration. A resource scheduling module; a progress control module; a cost quantification management module; and a risk management module. According to the scheme, through a micro-service architecture and a distributed data storage technology, multi-project full-period collaborative management is realized. And each module can operate and expand independently, so that the flexibility and expandability of the system are remarkably improved. The distributed data storage can ensure the real-time synchronization and quick access of the data even under the condition that a plurality of projects run in parallel. By constructing the incidence relation matrix, the system can clearly present the relation between each task and the resource of the project, and solid data support is provided for subsequent resource allocation and task management. The optimal configuration of resources and the smooth promotion of projects are realized, and the management efficiency of enterprises and the project success rate are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and in particular to an enterprise project management system and method oriented to multi-project collaboration. Background Art

[0002] In modern enterprise management, project management has become a crucial component of business operations. With the continuous expansion of enterprise scale and increasing business complexity, the collaborative management of multiple projects has become a common challenge. Traditional enterprise project management systems typically utilize a centralized architecture. While this architecture excels when managing a single project, it often struggles to efficiently integrate resources, coordinate schedules, and control costs when managing multiple projects. Existing project management systems have numerous shortcomings when handling multiple projects, severely impacting enterprise management effectiveness and project success rates.

[0003] Existing technologies suffer from inefficient resource allocation and are unable to achieve dynamic cross-project resource scheduling, resulting in frequent resource waste and shortages. Secondly, in terms of progress control, it is difficult to monitor and predict the progress of multiple projects in real time, and there is a lack of intelligent means to adjust for progress deviations, which can easily lead to project delays. Furthermore, in terms of cost quantification and management, it is difficult to accurately predict and control costs, which can easily lead to cost overruns. Finally, the ability to identify and respond to project risks is insufficient, and the lack of a systematic risk assessment and early warning mechanism prevents timely and effective risk response, thus affecting the overall success rate of projects. Therefore, we propose a multi-project enterprise project management system and method. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an enterprise project management system and method for multi-project collaboration, thereby solving the technical problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] An enterprise project management system for multi-project collaboration, comprising:

[0007] A project initialization module is used to input basic project information, construct a work breakdown structure, and adapt team resources through semantic parsing technology and a Bayesian network decomposition model. The project initialization module includes a multidimensional information modeling unit, a dynamic WBS decomposition unit, and a team resource adaptation unit.

[0008] The resource scheduling module is used to build a dynamic resource pool by integrating enterprise resources and implement the scheduling and allocation of multi-project resources using the improved NSGA-II algorithm. The resource scheduling module includes a cross-project resource pool construction unit, a multi-objective scheduling optimization unit, and an execution monitoring unit.

[0009] A progress control module is used to achieve real-time monitoring, deviation analysis, and intelligent adjustment of project progress through multi-source data fusion technology and dynamic deviation adjustment algorithms. The progress control module includes a multi-source data fusion acquisition unit, a dynamic deviation adjustment unit, and a progress prediction and early warning unit.

[0010] A cost quantification management module, which is used to implement full-cycle modeling, dynamic monitoring, and intelligent adjustment of project costs based on activity-based costing and real-time cost hedging strategies; the cost quantification management module includes a full-cycle cost modeling unit and a real-time cost hedging unit;

[0011] The risk management module is used to identify, assess and respond to project risks by establishing a three-dimensional risk assessment system and a dynamic risk response mechanism; the risk management module includes a three-dimensional risk assessment unit, a risk warning response unit and a risk plan management unit.

[0012] In a possible implementation, in the project initialization module:

[0013] The multi-dimensional information modeling unit builds a project metadata model through semantic parsing technology, allowing users to input basic project information, work breakdown structure, and team member information. The system will automatically generate a unique identifier (PID) for each project and establish an association relationship matrix.

[0014] The dynamic WBS decomposition unit builds a Bayesian network decomposition model based on historical project data, supports templated decomposition and custom adjustments, and the system automatically generates task dependency diagrams and calculates critical paths and slack times.

[0015] The team resource adaptation unit adapts team members to project tasks based on the WBS decomposition results and resource pool status by calculating the personnel skill matching and task load. The system uses the Hungarian algorithm to solve the optimal allocation plan.

[0016] In a possible implementation, in the resource scheduling module:

[0017] The cross-project resource pool construction unit integrates enterprise human resources, equipment, funds, and other resources to establish a real-time dynamic resource pool. The human resource pool records employee skill matrix and current load, the equipment pool records equipment type, available time period, and maintenance cycle, and the capital pool connects to the financial system to update project budget balances and cash flow in real time.

[0018] The multi-objective scheduling optimization unit uses an improved non-dominated sorting genetic algorithm II to solve the resource scheduling model. Its objective functions include minimizing construction duration, minimizing costs, and maximizing balance. The algorithm uses the relationship matrix and resource pool status as input and calculates the optimal scheduling solution.

[0019] The execution monitoring unit tracks the execution of resource scheduling plans in real time, promptly identifies execution deviations by comparing actual resource consumption with planned allocations, and uses exponential smoothing to predict resource demand trends.

[0020] In a possible implementation, in the progress control module:

[0021] The multi-source data fusion acquisition unit captures project data in real time through the API interface and constructs a progress data set, which includes the actual start time, actual completion time, task status, and resource usage. It uses the DS evidence theory to fuse multi-source data and calculate the credibility of task completion.

[0022] The dynamic deviation adjustment unit, based on earned value management (EVM) theory, quantitatively evaluates and dynamically adjusts project progress by calculating the schedule deviation (SV) and the schedule performance index (SPI). Based on the values ​​of SPI and SV, combined with the impact of critical path tasks on the progress, the unit determines the warning level using pre-set judgment rules and initiates corresponding warning and adjustment measures.

[0023] The progress forecast and warning unit is based on the long short-term memory network (LSTM) model. It combines historical progress data with the current project status to predict the future progress trend of the project. The system sets warning thresholds for key nodes. Once the prediction results show that the project progress will deviate from the plan, a risk warning will be issued immediately.

[0024] In a possible implementation, in the cost quantification management module:

[0025] The full-cycle cost modeling unit uses activity-based costing (ABC) to build a cost model, dividing project costs into direct costs and indirect costs. The system automatically generates a cost breakdown structure (CBS), associates costs with work breakdown structure (WBS) tasks and cost accounts, and sets a budget baseline and flexibility zone.

[0026] The real-time cost hedging unit establishes cost early warning indicators. When the cost deviation rate exceeds the set threshold, it initiates the cost hedging strategy, uses big data analysis to find more cost-effective alternative resources, evaluates the feasibility of deleting non-critical tasks, and uses Monte Carlo simulation technology to predict the cost distribution under different cost hedging schemes.

[0027] In a possible implementation, in the risk management module:

[0028] The three-dimensional risk assessment unit constructs a three-dimensional risk assessment matrix covering the probability of risk occurrence, impact, and diffusion speed. It uses historical project data and combines Bayesian estimation methods to calculate the probability of occurrence of various risks, comprehensively assess the impact of risks on the project after they occur, and analyze the diffusion speed of risks.

[0029] Based on the results of the three-dimensional risk assessment unit, the risk warning response unit quickly triggers an early warning when the risk level reaches the set threshold, and initiates corresponding response measures for different levels of risk;

[0030] The risk plan management unit establishes a comprehensive risk plan library that covers response plans for various risks that the project may face. It regularly organizes drills for the plans and optimizes and improves the plans based on the drill results and the actual situation of the project.

[0031] In one possible implementation, an enterprise project management method for multi-project collaboration includes the following steps:

[0032] S1: Project initialization: Enter basic project information, build a work breakdown structure, and adapt it to team resources. If the information is incomplete or fails verification, the system will highlight the error field and prompt the user to make corrections until all required fields are correctly filled in and the skill match meets the requirements.

[0033] S2: Resource scheduling: Integrate enterprise resources to build a dynamic resource pool and use the improved NSGA-II algorithm for resource scheduling. If the resource scheduling plan does not meet the optimization goal, the system will trigger a re-optimization process until the generated scheduling plan meets the requirements.

[0034] S3: Progress control: This system uses multi-source data fusion technology and a dynamic deviation adjustment algorithm to achieve real-time monitoring and adjustment of project progress. If the data credibility is low, the system will trigger a "manual review process" to manually check and correct the data until the data credibility meets the standard. If the progress lags for three consecutive monitoring cycles, the system will initiate a three-level progressive warning and take appropriate adjustment measures based on the warning level.

[0035] S4: Cost quantification management, based on activity-based costing and real-time cost hedging strategies, enables full-cycle modeling and adjustment of project costs. If the cost model fails verification, the data will be returned to the parameter calibration interface, allowing users to adjust the cost model parameters until they meet the requirements. When the cost deviation rate exceeds 8%, the system will activate the cost hedging strategy to find a cost optimization solution.

[0036] S5: Risk Management: By establishing a three-dimensional risk assessment system and a dynamic risk response mechanism, we can comprehensively identify and address project risks. If the risk level continues to rise or is not effectively controlled, the system will upgrade response measures and allocate more resources to address the situation until the risk is resolved.

[0037] S6: Project Execution and Monitoring: During project execution, we continuously track project progress, resource usage, and cost expenditures to ensure that the project proceeds according to the planned schedule. If any anomalies occur during execution, the system will trigger a rescheduling process to adjust resource allocation and task plans until project execution returns to normal.

[0038] S7: Project evaluation and summary. After the project is completed, a project evaluation is conducted to analyze the successful experiences and shortcomings during the project execution process. If the evaluation results show that the project has not achieved the expected goals, the system will initiate a project review process, conduct an in-depth analysis of the reasons, and formulate improvement measures to provide lessons learned for subsequent projects.

[0039] Beneficial effects compared with existing technologies:

[0040] 1. In this solution, the collaborative management of multiple projects throughout their entire lifecycle is achieved through the microservice architecture and distributed data storage technology. Compared with the traditional centralized architecture, the microservice architecture splits the system into multiple independent service modules. Each module can run and expand independently, which significantly improves the flexibility and scalability of the system. Distributed data storage ensures data consistency and high availability, and guarantees real-time synchronization and fast access to data even when multiple projects are running in parallel. By constructing an association relationship matrix, the system can clearly present the relationship between each project task and resource, providing solid data support for subsequent resource allocation and task management. Achieve optimal resource allocation and smooth project progress, significantly improving the company's management efficiency and project success rate;

[0041] 2. In this solution, a real-time dynamic resource pool is established by integrating the company's human, equipment, and financial resources, and the NSGA-II algorithm is used to solve the resource scheduling model. The objective function of this algorithm covers minimizing construction period, minimizing cost, and maximizing balance. It can comprehensively consider multiple goals and output the optimal resource scheduling solution. During the construction of the resource pool, the system monitors the status of resources in real time. Once a resource conflict or load imbalance is detected, the system will automatically trigger an early warning and adjust the scheduling plan. By dynamically adjusting resource allocation, the system not only improves resource utilization, but also ensures the smooth progress of the project, avoiding project delays or cost increases due to resource issues;

[0042] 3. This solution comprehensively enhances the project's risk management capabilities by building a three-dimensional risk assessment system and a dynamic risk response mechanism. The three-dimensional risk assessment system assesses risks based on three dimensions: probability of occurrence, impact, and diffusion rate. It uses historical project data combined with Bayesian estimation methods to calculate the probability of risk occurrence, comprehensively assesses the impact of risks from multiple dimensions, and analyzes the diffusion rate of risks based on factors such as the nature of the risk, the project's organizational structure, and business processes. The system categorizes risks into low, medium, and high levels. At the same time, the system dynamically adjusts response measures based on the effectiveness of risk responses to ensure that risks are effectively controlled. This allows companies to promptly identify potential risks and implement effective countermeasures to reduce their impact on projects, thereby improving project success rates and enhancing the company's competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings.

[0044] Figure 1 This is a schematic diagram of the enterprise project management system framework for multi-project collaboration of the present invention;

[0045] Figure 2 This is a flowchart of the steps of the enterprise project management method for multi-project collaboration of the present invention. DETAILED DESCRIPTION

[0046] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various forms, and therefore the present invention is not limited to the embodiments described below. In addition, in order to more clearly describe the present invention, components that are not related to the present invention will be omitted from the drawings.

[0047] The technical solutions in the embodiments of the present application are to solve the problems of the above-mentioned background technology, which are generally as follows:

[0048] Example 1:

[0049] This embodiment introduces an enterprise project management system for multi-project collaboration. The system uses a microservice architecture and distributed data storage to achieve full-cycle management of multiple projects, including a project initialization module, a resource scheduling module, a progress control module, a cost quantification management module, and a risk management module.

[0050] 1. Project initialization module

[0051] This module uses semantic parsing technology and Bayesian network decomposition models to implement basic project information entry, work breakdown structure construction, and team resource adaptation, thereby providing data for project management. This module includes a multidimensional information modeling unit, a dynamic WBS decomposition unit, and a team resource adaptation unit.

[0052] 1. Multidimensional Information Modeling Unit

[0053] This unit uses semantic parsing technology to build a project metadata model, which supports users to input basic project information (such as name, cycle, person in charge), work breakdown structure (WBS) and team member information. The system will automatically generate a unique identifier PID for each project and establish a "project-task-resource" relationship matrix R PTR .

[0054] Among them, t ij represents the demand of the i-th task for the j-th type of resources, r mn The actual amount of resources allocated. Through this matrix, the system can clearly present the relationship between each task and resources in the project, providing a data basis for subsequent resource allocation and task management.

[0055] The system checks for missing mandatory fields, including the project's unique identifier (PID), cycle start and end times, and key task leaders. The mandatory field fill rate is set to 100%. Completeness requirements are met only when all mandatory fields are correctly filled.

[0056] Task end time T e Need to meet T e ≤Project cycle end time T p , to ensure that the task is completed within the project cycle. At the same time, the task leader's skill matching degree S k Need to meet S k ≥0.7. Among them, the skill matching degree S k The calculation formula is: Among them, s ki is the skill label set of member k, t ri is the skill requirement set for task r. This formula calculates the ratio of the intersection of member skills and task skill requirements to the task skill requirements to obtain the skill matching degree, which measures whether the person in charge has the ability to complete the task.

[0057] When the information passes the above integrity and logic checks and is judged to be qualified, the data will enter the "resource pre-allocation unit"; if the check fails, the data will return to this unit and the error field will be highlighted to prompt the user to make changes.

[0058] 2. Dynamic WBS decomposition unit

[0059] This unit builds a Bayesian network decomposition model based on historical project data, supporting template decomposition and custom adjustments. The system automatically generates a task dependency graph and calculates the critical path CP and slack time ST. Among them, slack time ST i The calculation formula is: ST i =T LSi -T ESi , where T LSi is the latest start time of task i, T ESi is the earliest start time for task i. This formula calculates the slack time for each task, helping project managers understand the flexibility of task scheduling. The system also visualizes the breakdown results on a Gantt chart, noting task owners and peak resource demand, making project progress and resource usage clear at a glance.

[0060] To avoid excessive concentration of tasks on the critical path, the critical path CP length is required to be ≤ project week × 0.8, ensuring that the project has a certain buffer space in the schedule.

[0061] Specify the duration of a single task D i Must meet 3 days ≤ D i ≤30 days. This time frame can ensure that the tasks are monitorable without affecting the efficiency of project management due to over-detailed or over-coarse task division.

[0062] When the WBS decomposition results pass the above judgment and are confirmed to be qualified, the system will simultaneously update the "project-task-resource" association matrix R PTR and transfer the data to the "Team Resource Adaptation Unit"; if the critical path exceeds the threshold, the system will trigger the "intelligent optimization algorithm" and re-decompose the WBS.

[0063] 3.Team Resource Adaptation Unit

[0064] Based on WBS decomposition results and resource pool status, this unit accurately matches team members to project tasks by calculating personnel skill matching and task load. The system uses the Hungarian algorithm to find the optimal allocation plan to maximize overall team effectiveness.

[0065] The overall team skill matching must be ≥ 0.8, and the member task load balance (measured by standard deviation) must be ≤ 1.2. If these conditions are not met, the team will return to readjust the personnel allocation.

[0066] If the adaptation result is qualified, the data enters the resource dynamic scheduling module; otherwise, it returns to this unit for re-adaptation.

[0067] 2. Resource Scheduling Module

[0068] This module integrates enterprise resources to build a dynamic resource pool and uses the improved NSGA-II algorithm to achieve intelligent scheduling and optimal allocation of multi-project resources, thereby improving resource utilization efficiency. This module includes a cross-project resource pool construction unit, a multi-objective scheduling optimization unit, and an execution monitoring unit.

[0069] 1. Cross-project resource pool construction unit

[0070] This unit integrates enterprise human resources, equipment, funds and other resources to establish a real-time dynamic resource pool RP = {R h ,R e ,R f}.in:

[0071] Human Resource Pool h :Record employee skills matrix S m , current load L k . Current employee load L k By formula It is calculated that w ki The workload assigned to employee k for task i. Through this formula, the system can understand the workload of each employee in real time.

[0072] Device Pool R e :Record device type, available time period [T s ,T e ] and maintenance cycles, making it convenient to arrange and schedule equipment resources reasonably.

[0073] Funding Pool R f :Connect to the financial system and update the project budget balance in real time r and cash flow CF t , providing accurate data for project fund management.

[0074] In order to ensure that the work intensity of employees is reasonable, the load of each employee is required to be L k ≤0.8×8 hours / day, while the team's average load deviation σ L ≤1.5 hours. By controlling these two indicators, we can ensure a balanced distribution of human resources within the team and avoid overwork or underload of employees.

[0075] Using time window overlapping algorithm To detect device usage conflicts. a and T b Respectively represent the usage time windows of the same device for two different projects. For window T a The start and end time, For window T bThe algorithm determines the possible overlapping time periods by taking the maximum value of the start time and the minimum value of the end time of the two time windows.

[0076] When O(T a ,T b ) is an empty set, that is This means that the two time windows do not overlap, and there is no usage conflict for the device in these two projects; otherwise, there is a conflict.

[0077] The real-time status of the resource pool will be synchronized to the "intelligent scheduling engine". If abnormal conditions such as unbalanced manpower load or equipment conflict occur, the system will trigger a "load warning" and push the warning information to the project manager for timely processing.

[0078] 2. Multi-objective scheduling optimization unit

[0079] This unit uses the improved non-dominated sorting genetic algorithm II (NSGA-II) to solve the resource scheduling model. Its objective function includes three aspects:

[0080] Minimize construction period: Among them D i is the duration of task i, δ i Identify the key tasks (key tasks δ i =1, non-critical tasksδ i =0). This formula shortens the overall project duration by summing the durations of key tasks.

[0081] Cost minimization: Among them, c j is the unit price of resource j, r ij is the demand for resource j by task i. This formula is used to calculate the total cost of the resources required for the task and achieve cost control.

[0082] Maximizing balance: σ L is the standard deviation of team member load, L avg is the average workload of team members. This formula aims to improve the balance of workload among team members and avoid situations where some members are overloaded while others are underloaded.

[0083] The algorithm is based on the "project-task-resource" relationship matrix R PTR With the resource pool status as input, the optimal scheduling solution S is output after calculation * ={s1,s2,...,s n}, the plan clearly defines the resource allocation amount and time window, providing a basis for the rational scheduling of resources.

[0084] Feasibility of the plan: All task resource requirements is the available amount of resource j. The scheduling scheme is considered feasible only when the resource demand of each task does not exceed the actual available amount of the resource.

[0085] Optimization target achievement: at least meet the schedule deviation ≤ 5% or cost savings ≥ 3%. If the scheduling plan does not meet the above standards in terms of schedule or cost, it will be judged as unsatisfactory.

[0086] When the scheduling plan passes the feasibility and optimization target judgment and is confirmed to be qualified, it will enter the "execution monitoring unit"; if the plan is unqualified, it will return to the "parameter adjustment interface" for users to manually intervene and adjust.

[0087] 3. Execution monitoring unit

[0088] This unit tracks the execution of resource scheduling plans in real time, identifying deviations by comparing actual resource consumption with planned allocations. It also uses exponential smoothing to predict resource demand trends, providing a basis for dynamic adjustments.

[0089] Set a resource consumption deviation threshold of ±10% and trigger an alert when actual consumption exceeds this range. Simultaneously, monitor the matching of task progress and resource usage, and immediately initiate adjustments if idle resources or shortages occur.

[0090] If the execution is normal, the data will be synchronized to the progress intelligent management and control module; if an exception occurs, it will return to the multi-objective scheduling optimization unit to regenerate the scheduling plan.

[0091] 3. Progress Control Module

[0092] This module uses multi-source data fusion technology and dynamic deviation adjustment algorithms to achieve real-time monitoring of project progress, deviation analysis, and intelligent adjustment to ensure that the project proceeds as planned. This module includes a multi-source data fusion acquisition unit, a dynamic deviation adjustment unit, and a progress prediction and warning unit.

[0093] 1. Multi-source data fusion acquisition unit

[0094] This unit uses the API interface to capture project management tools (such as Jira, Trello), OA system and IoT device data in real time to build a progress data set D p ={T a ,T c ,S t ,R u}, which contains: actual start time T a , actual completion time T c , Task status S t (0=not started, 1=in progress, 2=completed), resource usage R u .

[0095] In order to more accurately determine the task completion status, the system uses DS evidence theory to fuse multi-source data and calculate the task completion credibility C t The calculation formula is as follows: Among them, m i is the data source confidence, w i is the weight coefficient. This formula calculates the confidence level of task completion by taking the weighted sum of the confidence levels and weights of different data sources and dividing it by the weighted sum. This is used to assess the degree of task completion.

[0096] The frequency of data collection for critical tasks must be ≥1 time / day, and for non-critical tasks ≥1 time / 3 days, to ensure that project progress data can be collected in a timely and comprehensive manner.

[0097] When C t When the reliability is ≥0.8, the task status is considered valid. Only when this reliability standard is met will the system recognize the accuracy and reliability of the task status data.

[0098] Valid data will be further processed; if the data credibility is low and does not meet the threshold requirements, the system will trigger the "manual review process" and the data will be checked and corrected manually.

[0099] 2. Dynamic deviation adjustment unit

[0100] This unit is based on the Earned Value Management (EVM) theory and conducts quantitative evaluation and dynamic adjustment of project progress by calculating the Schedule Variances (SV) and Schedule Performance Index (SPI).

[0101] Schedule variance (SV) is used to reflect the difference in cost between the actual project progress and the planned progress at the current time point. The calculation formula is: SV(t) = BCWP(t) - BCWS(t), where the budgeted cost of work performed (BCWP(t)) is the cost of the work completed according to the budgeted price. It is calculated as follows: Among them, D i represents the planned workload of task i, p i is the budget unit price of task i, C i (t) represents the completion percentage of task i at time t; the budgeted cost of planned work BCWS(t) is the cost of the planned work volume calculated according to the budgeted price, which is calculated as follows: S i (t) is the planned completion percentage of task i at time t.

[0102] The Schedule Performance Index (SPI) is used to measure the execution efficiency of the project schedule. The calculation formula is:

[0103] When SPI is greater than 1, it indicates that the actual project progress is ahead of the planned progress; when SPI is equal to 1, it indicates that the project progress is on schedule; when SPI is less than 1, it means that the project progress is lagging behind. When SPI(t) < 0.95 and SV(t) < -0.1×BCWS(t) for three consecutive monitoring cycles, the system will initiate a three-level progressive warning:

[0104] Yellow alert (SPI∈[0.9, 0.95)): Activate the resource elasticity scheduling strategy. The system selects available resources with a skill match of 0.7 or higher from the enterprise resource pool or projects ahead of schedule, requiring a response within 24 hours. After selecting resources, the system optimizes them based on task priority and resource cost, develops a resource allocation plan, executes resource adjustments, and simultaneously updates the project status.

[0105] Orange alert (SPI∈[0.8, 0.9)): The task parallelization and reorganization strategy is initiated. The system first constructs a project task dependency graph, then identifies non-critical path tasks with a total time difference greater than 3 days and treats these tasks as a set of tasks that can be executed in parallel. The system then generates a task parallelization plan, evaluates the plan's effect on project duration, and updates the project schedule based on the plan.

[0106] Red alert (SPI < 0.8): This triggers a project re-prioritization mechanism. The system prioritizes all enterprise projects based on multiple dimensions, including strategic importance, contract breach penalties, expected returns, and resource dependency. Based on these assessments, it generates resource reallocation recommendations, shifting resources from lower-priority projects to higher-priority, critical projects with lagging schedules. Final recommendations are subject to approval by the decision support system before implementation.

[0107] The system determines the warning level based on the SPI and SV values, combined with the impact of critical path tasks on progress, using pre-set judgment rules. In the actual system, these judgment threshold parameters are trained by a machine learning model to ensure the accuracy and rationality of the judgment.

[0108] After implementing the adjustment measures, the system requirements meet And the cost performance index CPI (t + Δt) ≥ 0.98. Among them, the cost performance index Used to measure the efficiency of project cost utilization. Adjustment measures are considered effective only when the schedule performance index growth meets the requirements and the cost performance remains at a reasonable level.

[0109] Once the deviation adjustment plan is finalized, it is synchronized to multiple modules. The plan triggers the resource allocation workflow in the resource scheduling module, causing it to reallocate resources according to the plan. The cost control module updates the cost baseline forecast based on the plan to better monitor project costs. The risk management module adds progress risk monitoring items based on the plan to strengthen the management of related risks. The decision support module stores the plan as a historical case in the knowledge base, providing a reference for subsequent project decisions.

[0110] 3. Progress forecast and early warning unit

[0111] Based on the Long Short-Term Memory (LSTM) model, this unit combines historical progress data with current project status to accurately predict future project progress trends. The LSTM model effectively captures long-term dependencies in time series data. By learning from historical task start times, completion times, resource inputs, and other data, it predicts the time nodes for subsequent tasks and the overall project completion time. The system sets warning thresholds for key nodes, and if the forecast results indicate that the project progress will deviate from the plan, it immediately issues a risk warning. At the same time, it monitors key factors affecting progress, such as the progress of critical path tasks and resource availability, to improve the accuracy of predictions and the timeliness of warnings.

[0112] When the predicted progress deviates from the planned progress by more than 15%, an early warning is triggered. Furthermore, the risk assessment module evaluates factors that could impact progress, such as resource shortages and technical difficulties. If high-risk factors could cause progress deviations to exceed thresholds, an early warning will also be triggered. The system also dynamically adjusts the warning level based on the severity and trend of the deviation, allowing the project team to take appropriate countermeasures.

[0113] Early warning information is synchronized in real time to the risk management module and project decision-makers. The risk management module uses this information to further analyze the causes of the risk and develop targeted response strategies. Based on the early warning and analysis results, the project decision-makers decide whether to adjust the project plan, reallocate resources, or communicate and coordinate with relevant parties. If the predicted progress is normal, the data continues to flow into the next stage of the progress monitoring process to continuously track project progress.

[0114] 4. Cost Quantification Management Module

[0115] Based on activity-based costing and real-time cost hedging strategies, this module implements full-cycle modeling, dynamic monitoring, and intelligent adjustment of project costs to ensure controllable project costs. This module includes a full-cycle cost modeling unit and a real-time cost hedging unit.

[0116] 1. Full-cycle cost modeling unit

[0117] This unit uses activity-based costing (ABC) to build a cost model, dividing the project cost into direct cost C d and indirect costs C i , total project cost C p By adding the two together, we get in, Used to calculate direct cost C d , n represents the number of resource types, r j is the consumption of resource type j, c j is the unit price of the jth type of resource. The resource cost directly related to the project task is obtained by multiplying the resource consumption by the unit price.

[0118] Used to calculate indirect costs C i , m is the number of jobs, f k is the kth indirect cost, such as enterprise management expenses, equipment depreciation expenses, etc. k is the time taken for the kth task, ∑D k It represents the total time taken for all activities. The indirect cost is calculated by allocating the indirect expenses according to the time taken for the activities.

[0119] The system automatically generates a cost breakdown structure (CBS), closely links costs with work breakdown structure (WBS) tasks and cost accounts, and sets a budget baseline B p With elastic area Provide clear goals and scope for cost management, facilitating real-time monitoring of cost changes.

[0120] Strictly control the deviation of labor cost and require the actual labor cost C h Budgeted labor costs The absolute value of the deviation Equipment rental cost C e It must not exceed 1.1 times the market average price to ensure that the project budget is within a reasonable range and avoid cost overruns.

[0121] It is stipulated that the number of associated cost items for each task shall not exceed 3, so as to avoid overly complicated division of cost items, ensure the accuracy of cost accounting and the efficiency of management, and facilitate financial personnel to conduct cost analysis and control.

[0122] When the cost model passes the above judgment and verification, the data will enter the "dynamic monitoring unit" to monitor the project cost in real time; if it is judged to be unqualified, it will return to the "parameter calibration interface" for users to adjust cost model parameters such as resource unit price and indirect cost allocation ratio until the requirements are met.

[0123] 2. Real-time cost hedging unit

[0124] This unit establishes the cost early warning indicator CI, the calculation formula is Among them, ACWP represents the cost of the actual work completed in the project, and CI reflects the deviation rate between the actual cost of the project and the budgeted cost. When CI is a positive value, it means that the actual cost is higher than the budgeted cost; when CI is a negative value, it means that the actual cost is lower than the budgeted cost.

[0125] When CI>0.08, the system will start the cost hedging strategy:

[0126] The system uses big data to analyze the internal and external resource markets of the enterprise and combines it with the supplier resource library to find alternative resources with higher cost performance. s =(c o -c n )×r u To evaluate the feasibility of alternatives, where c o is the original resource cost, c n is the new resource cost, r u is the amount of resource used. The substitution benefit is positive and the larger the value is, the better the alternative is.

[0127] Organize consultations with project teams, customers and other stakeholders to assess the feasibility of deleting non-critical tasks and save costs through calculations To measure the effect of scope optimization, where N is the set of deleted tasks, D i is the duration of task i, p i is the weight of task i, c i is the cost of task i.

[0128] The system uses Monte Carlo simulation technology to predict the cost distribution under different cost hedging schemes. * =argmin(E[C p ]+3σ[C p ]) The optimal hedging solution H is obtained * .in, The expected cost value reflects the average level of project costs under various possible circumstances. The cost standard deviation measures the degree of cost volatility. The formula determines the optimal cost hedging solution by minimizing the sum of the expected value of cost and three times the standard deviation, taking into account the risk of cost volatility.

[0129] The skill matching degree of alternative resources must reach 0.6 or above to ensure that the alternative resources can meet the technical requirements of the task and avoid the decline in task quality or delay in progress due to resource mismatch; at the same time, the impact of deleting tasks on the construction period must be controlled within 3 days to avoid significant delays in the project construction period due to deleting tasks, which will affect project delivery.

[0130] The monthly CI reduction must be 0.05 or greater, and no red alert for progress must be triggered after implementing cost hedging measures. Only when the cost hedging strategy can effectively reduce the cost deviation rate without affecting the project schedule is the strategy considered effective, ensuring a balance between cost and schedule.

[0131] After the hedging plan is implemented, the system will simultaneously update the "cost-schedule" dual baseline to monitor project cost and schedule changes in real time. If any abnormal situation occurs, such as ineffective cost reduction or serious progress impact, the system will trigger a "cross-module coordination meeting" to convene project management, finance, technical personnel, and other relevant personnel to jointly resolve the issue and ensure the smooth progress of the project.

[0132] 5. Risk Management Module

[0133] This module establishes a three-dimensional risk assessment system and a dynamic risk response mechanism to achieve comprehensive identification, accurate assessment, and effective response to project risks, thereby enhancing the project's risk resistance. This module includes a three-dimensional risk assessment unit, a risk early warning and response unit, and a risk plan management unit.

[0134] 1. Three-dimensional risk assessment unit

[0135] This unit constructs a three-dimensional risk assessment matrix, RM = [P, I, D], encompassing the probability of risk occurrence (P), impact (I), and diffusion rate (D). Using historical project data and Bayesian estimation methods, the probability of each risk occurrence (P) is calculated. The impact (I) of a risk on the project after it occurs is comprehensively assessed from multiple perspectives, including cost, schedule, quality, and safety. The diffusion rate (D) of the risk is analyzed based on factors such as the nature of the risk, the project's organizational structure, and business processes. This three-dimensional assessment comprehensively and accurately characterizes project risks, providing a basis for risk early warning and response.

[0136] Risk assessment criteria are set, categorizing risks into low, medium, and high levels based on the numerical combination of the probability of occurrence (P), the degree of impact (I), and the rate of spread (D). For example, when P, I, and D are all high, the risk is considered high; when all three are low, the risk is low; and intermediate risk is medium. The system also regularly reviews risk assessment results and adjusts risk levels based on project progress and environmental changes.

[0137] The assessment results are synchronized to the risk warning response unit. If high risk is found, an early warning will be triggered immediately. At the same time, they are stored in the risk database to provide historical reference data for subsequent project risk assessments, facilitating the continuous optimization of risk assessment models and methods.

[0138] 2. Risk warning response unit

[0139] Based on the results of the three-dimensional risk assessment unit, when the risk level reaches the set threshold, an early warning is quickly triggered. Appropriate response measures are initiated for different risk levels: for low-risk situations, conventional monitoring strategies are implemented, with regular risk status tracking. For medium-risk situations, preliminary response plans are developed, with clear responsibilities and timelines, and preventive and mitigation measures implemented. For high-risk situations, emergency plans are immediately implemented, and resources are mobilized to minimize risk losses. Throughout the risk response process, risk development trends are tracked in real time, and response strategies are dynamically adjusted based on actual conditions to ensure effective risk control.

[0140] The effectiveness of response measures is judged based on changes in risk levels and the effectiveness of risk responses. If the risk level continues to rise or is not effectively controlled, the response measures are upgraded and more resources and effort are invested. If the risk is significantly mitigated, the response level is gradually lowered until the risk is resolved. At the same time, the system records and analyzes various data from the risk response process, providing lessons learned for subsequent risk responses.

[0141] The response process data is fed back to the three-dimensional risk assessment unit to update the risk assessment results; the early warning and response information is synchronized to the project decision-making level and relevant departments to facilitate the coordinated response to risks among various departments and ensure smooth information flow and consistent actions.

[0142] 3. Risk plan management unit

[0143] Establish a comprehensive risk plan library that covers response plans for various risks the project may face, such as market risk plans, technical risk plans, and resource risk plans. Each plan specifies emergency measures, division of responsibilities, resource allocation, communication and coordination, and other content to ensure that response work can be carried out quickly and orderly when risks occur. Regularly organize plan drills to simulate risk scenarios to test the feasibility and effectiveness of the plans and collect feedback from participants. Based on the results of the drills and the actual project situation, optimize and improve the plans to ensure that they remain practical and effective. In addition, support the rapid customization of personalized risk plans based on project characteristics and actual needs, improving the targeted nature of risk response.

[0144] Plans are scored based on the effectiveness of plan drills and feedback from actual applications. Scoring criteria are set to evaluate the plan's completeness, operability, and effectiveness. If the score falls below the set standard or if there are significant changes in the project environment, the plan revision process is initiated, and relevant personnel are organized to conduct a comprehensive revision of the plan.

[0145] The optimized plan is updated to the risk plan library; the newly formulated plan is synchronized to the risk warning response unit to provide support for risk response; the relevant data during the plan drills and revisions are stored in the knowledge base to provide reference for subsequent plan management.

[0146] Example 2:

[0147] An enterprise project management method for multi-project collaboration includes the following steps:

[0148] S1 Project Initialization

[0149] First, basic project information is entered, including project name, duration, and responsible person. A work breakdown structure (WBS) and team member information are also constructed. The system automatically generates a unique identifier (PID) for each project and establishes a "project-task-resource" relationship matrix. Using semantic parsing technology, the system analyzes and processes the input project information to support subsequent project management tasks.

[0150] Next, dynamic WBS decomposition is performed, building a Bayesian network decomposition model based on historical project data. This model supports both templated decomposition and custom adjustments. The system automatically generates a task dependency diagram and calculates critical paths and slack times to help project managers understand the flexibility of task scheduling. Decomposition results are visualized on a Gantt chart, with task owners and resource demand peaks marked, providing a clear overview of project progress and resource usage.

[0151] Next, team resource allocation is performed. Based on the WBS breakdown results and resource pool status, the system calculates the skill match and task load to accurately match team members to project tasks. The system uses the Hungarian algorithm to find the optimal allocation plan to maximize the overall team effectiveness.

[0152] During the project initialization phase, the system will check for missing mandatory fields, including the project's unique identifier (PID), cycle start and end times, and key task leaders. The mandatory field fill rate is set to 100%. Only when all mandatory fields are filled in correctly can the completeness requirements be met. The task end time must meet the project cycle end time to ensure that the task is completed within the project cycle. At the same time, the skill match of the task leader must be met. Only when the match reaches 0.7 or above is the person considered to have sufficient ability to complete the task. If the information is incomplete or the verification fails, the system will highlight the error field and prompt the user to make modifications until all mandatory fields are filled in correctly and the skill match meets the requirements.

[0153] S2 resource scheduling

[0154] Integrate enterprise resources such as human resources, equipment, and funds to establish real-time dynamic resource pools, including human resources, equipment, and funds. The human resources pool records employee skill matrix and current workload; the equipment pool records equipment type, availability, and maintenance cycle; and the funds pool connects to the financial system to update project budget balances and cash flow in real time.

[0155] A modified non-dominated sorting genetic algorithm II (NSGA-II) is used to solve a resource scheduling model. Its objective functions include minimizing duration, minimizing cost, and maximizing balance. The algorithm takes the "project-task-resource" relationship matrix and the resource pool status as input and calculates an optimal scheduling solution. This solution specifies resource allocation amounts and time windows, providing a basis for rational resource scheduling.

[0156] Track resource scheduling execution in real time, identifying deviations by comparing actual resource consumption with planned allocations. Use exponential smoothing to predict resource demand trends, providing a basis for dynamic adjustments.

[0157] To ensure reasonable employee workload, individual employee load is required to be no more than 0.9, and the average team load deviation is required to be no more than 0.1. A time window overlap algorithm is used to detect equipment usage conflicts. If the time windows for the same equipment used by two different projects overlap, the system automatically adjusts the scheduling plan to avoid conflicts. If the resource scheduling plan does not meet the optimization goals, such as if the duration deviation exceeds 5% or the cost savings do not reach 3%, the system triggers a re-optimization process until the resulting schedule meets the requirements.

[0158] S3 progress control

[0159] By using APIs to capture data from project management tools, OA systems, and IoT devices in real time, we construct a progress dataset containing information such as actual start time, actual completion time, task status, and resource usage. We use DS evidence theory to fuse multi-source data and calculate the credibility of task completion to assess the specific extent of task completion.

[0160] Based on Earned Value Management (EVM) theory, project progress is quantitatively assessed and dynamically adjusted by calculating Schedule Variances (SV) and Schedule Performance Indexes (SPI). Based on the SPI and SV values, combined with the impact of critical path tasks on progress, pre-set rules determine the warning level and initiate appropriate warning and adjustment measures.

[0161] Data collection for critical tasks is once a day, and once every three days for non-critical tasks, ensuring timely and comprehensive collection of project progress data. At that time, the task status is deemed valid. Only when this confidence level is met will the system recognize the accuracy and reliability of the task status data. If the data confidence level is low, failing to meet the threshold, the system triggers a manual review process, requiring manual inspection and correction of the data until it meets the required confidence level.

[0162] If progress falls behind schedule for three consecutive monitoring cycles, the system will initiate a three-level progressive alert: Yellow alert triggers flexible resource scheduling; Orange alert initiates task parallelization and reorganization; and Red alert triggers project re-prioritization. If the progress performance index still fails to meet requirements after implementing adjustment measures, the system will escalate the alert level and expand resource allocation until the project returns to normal.

[0163] S4 Cost Quantification Management

[0164] Build a cost model using activity-based costing (ABC), meticulously dividing project costs into direct and indirect costs. The system automatically generates a cost breakdown structure (CBS), links costs to work breakdown structure (WBS) tasks and cost accounts, and sets a budget baseline and flexibility zone, providing clear goals and scope for cost management.

[0165] Establish cost early warning indicators and initiate cost hedging strategies when the cost deviation rate exceeds 8%. Use big data analysis to identify more cost-effective alternative resources, assess the feasibility of eliminating non-critical tasks, and use Monte Carlo simulation technology to predict cost distribution under different cost hedging schemes.

[0166] Labor cost deviations are strictly controlled, requiring the absolute deviation between actual and budgeted labor costs to not exceed 10%. Equipment rental costs must not exceed 1.1 times the market average. Each task's cost account must not be associated with more than three items to avoid overly complex cost account divisions. If the cost model fails validation, the data will be returned to the parameter calibration interface, allowing users to adjust cost model parameters such as resource unit prices and indirect cost allocation ratios until they meet the requirements.

[0167] S5 Risk Management

[0168] Construct a three-dimensional risk assessment matrix covering the probability of risk occurrence, impact, and diffusion speed. Utilize historical project data and combine it with the Bayesian estimation method to calculate the probability of occurrence of various risks, comprehensively assess the impact of risks on the project after they occur, and analyze the diffusion speed of risks.

[0169] Based on the three-dimensional risk assessment results, when the risk level reaches the set threshold, an early warning is triggered. Appropriate response measures are initiated for each risk level: for low risk, conventional monitoring strategies are implemented; for medium risk, preliminary response plans are developed; for high risk, emergency plans are immediately implemented.

[0170] Risk levels are determined using established criteria, with risks categorized as low, medium, and high based on a combination of numerical values ​​for the probability of occurrence, impact, and diffusion rate. For example, when the probability of occurrence (P) is greater than 0.7, the impact (I) is greater than 0.6, and the diffusion rate (D) is greater than 0.5, the risk is considered high; when P < 0.3, I < 0.4, and D < 0.3, the risk is considered low; and intermediate levels are considered medium. If the risk level continues to rise or is not effectively controlled, the system will escalate its response measures and allocate additional resources until the risk is resolved.

[0171] S6 Project Execution and Monitoring

[0172] During project execution, we continuously track project progress, resource usage, and cost expenditures to ensure that projects proceed according to plan. Through real-time monitoring, we promptly identify and resolve issues that arise during project execution, such as resource conflicts, schedule delays, and cost overruns. We utilize project management tools and systems to maintain effective communication between the project team and relevant parties, ensuring the timely transfer and sharing of information.

[0173] A resource consumption deviation threshold of 10% is set, triggering an alert when actual consumption exceeds this range. Simultaneously, the system monitors the alignment between task progress and resource usage, immediately initiating adjustments if idle resources or resource shortages occur. If anomalies occur during execution, such as resources not being allocated as planned or tasks significantly lagging behind schedule, the system triggers a rescheduling process, adjusting resource allocation and task plans until project execution returns to normal.

[0174] S7 project evaluation and summary

[0175] After project completion, a project evaluation is conducted to analyze both successes and shortcomings during project execution. Project-related data, including progress, cost, and quality, is collected to comprehensively assess project performance. Lessons learned during project management are summarized to provide a reference and basis for improvement in subsequent project management. Project evaluation results and summary reports are archived as part of the company's project management knowledge base for reference and application in future projects. If the evaluation results indicate that a project has not achieved its intended goals, the system initiates a project review process to conduct an in-depth analysis of the causes and develop corrective measures, providing lessons learned for subsequent projects.

[0176] Finally, it should be noted that the above embodiments are merely examples for the purpose of illustrating the present invention and are not intended to limit the embodiments. Those skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. It is not necessary and impossible to provide an exhaustive list of all embodiments. However, obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. An enterprise project management system for multi-project collaboration, characterized by: include: A project initialization module is used to input basic project information, construct a work breakdown structure, and adapt team resources through semantic parsing technology and a Bayesian network decomposition model. The project initialization module includes a multidimensional information modeling unit, a dynamic WBS decomposition unit, and a team resource adaptation unit. The resource scheduling module is used to build a dynamic resource pool by integrating enterprise resources and implement the scheduling and allocation of multi-project resources using the improved NSGA-II algorithm. The resource scheduling module includes a cross-project resource pool construction unit, a multi-objective scheduling optimization unit, and an execution monitoring unit. A progress control module is used to achieve real-time monitoring, deviation analysis, and intelligent adjustment of project progress through multi-source data fusion technology and dynamic deviation adjustment algorithms. The progress control module includes a multi-source data fusion acquisition unit, a dynamic deviation adjustment unit, and a progress prediction and early warning unit. A cost quantification management module, which is used to implement full-cycle modeling, dynamic monitoring, and intelligent adjustment of project costs based on activity-based costing and real-time cost hedging strategies; the cost quantification management module includes a full-cycle cost modeling unit and a real-time cost hedging unit; The risk management module is used to identify, assess and respond to project risks by establishing a three-dimensional risk assessment system and a dynamic risk response mechanism; the risk management module includes a three-dimensional risk assessment unit, a risk warning response unit and a risk plan management unit.

2. An enterprise project management system for multi-project collaboration according to claim 1, characterized in that: In the project initialization module: The multi-dimensional information modeling unit builds a project metadata model through semantic parsing technology, allowing users to input basic project information, work breakdown structure, and team member information. The system will automatically generate a unique identifier (PID) for each project and establish an association relationship matrix. The dynamic WBS decomposition unit builds a Bayesian network decomposition model based on historical project data, supports templated decomposition and custom adjustments, and the system automatically generates task dependency diagrams and calculates critical paths and slack times. The team resource adaptation unit adapts team members to project tasks based on the WBS decomposition results and resource pool status by calculating the personnel skill matching and task load. The system uses the Hungarian algorithm to solve the optimal allocation plan.

3. The enterprise project management system for multi-project collaboration according to claim 1, characterized in that: In the resource scheduling module: The cross-project resource pool construction unit integrates enterprise human resources, equipment, funds, and other resources to establish a real-time dynamic resource pool. The human resource pool records employee skill matrix and current load, the equipment pool records equipment type, available time period, and maintenance cycle, and the capital pool connects to the financial system to update project budget balances and cash flow in real time. The multi-objective scheduling optimization unit uses an improved non-dominated sorting genetic algorithm II to solve the resource scheduling model. Its objective functions include minimizing construction duration, minimizing costs, and maximizing balance. The algorithm uses the relationship matrix and resource pool status as input and calculates the optimal scheduling solution. The execution monitoring unit tracks the execution of resource scheduling plans in real time, promptly identifies execution deviations by comparing actual resource consumption with planned allocations, and uses exponential smoothing to predict resource demand trends.

4. The enterprise project management system for multi-project collaboration according to claim 1, characterized in that: In the progress control module: The multi-source data fusion acquisition unit captures project data in real time through the API interface and constructs a progress data set, which includes the actual start time, actual completion time, task status, and resource usage. It uses the DS evidence theory to fuse multi-source data and calculate the credibility of task completion. The dynamic deviation adjustment unit, based on earned value management (EVM) theory, quantitatively evaluates and dynamically adjusts project progress by calculating the schedule deviation (SV) and the schedule performance index (SPI). Based on the values ​​of SPI and SV, combined with the impact of critical path tasks on the progress, the unit determines the warning level using pre-set judgment rules and initiates corresponding warning and adjustment measures. The progress forecast and warning unit is based on the long short-term memory network (LSTM) model. It combines historical progress data with the current project status to predict the future progress trend of the project. The system sets warning thresholds for key nodes. Once the prediction results show that the project progress will deviate from the plan, a risk warning will be issued immediately.

5. The enterprise project management system for multi-project collaboration according to claim 1, characterized in that: In the cost quantification management module: The full-cycle cost modeling unit uses activity-based costing (ABC) to build a cost model, dividing project costs into direct costs and indirect costs. The system automatically generates a cost breakdown structure (CBS), associates costs with work breakdown structure (WBS) tasks and cost accounts, and sets a budget baseline and flexibility zone. The real-time cost hedging unit establishes cost early warning indicators. When the cost deviation rate exceeds the set threshold, it initiates the cost hedging strategy, uses big data analysis to find more cost-effective alternative resources, evaluates the feasibility of deleting non-critical tasks, and uses Monte Carlo simulation technology to predict the cost distribution under different cost hedging schemes.

6. The enterprise project management system for multi-project collaboration according to claim 1, characterized in that: In the risk management module: The three-dimensional risk assessment unit constructs a three-dimensional risk assessment matrix covering the probability of risk occurrence, impact, and diffusion speed. It uses historical project data and combines Bayesian estimation methods to calculate the probability of occurrence of various risks, comprehensively assess the impact of risks on the project after they occur, and analyze the diffusion speed of risks. Based on the results of the three-dimensional risk assessment unit, the risk warning response unit quickly triggers an early warning when the risk level reaches the set threshold, and initiates corresponding response measures for different levels of risk; The risk plan management unit establishes a comprehensive risk plan library that covers response plans for various risks that the project may face. It regularly organizes drills for the plans and optimizes and improves the plans based on the drill results and the actual situation of the project.

7. An enterprise project management method for multi-project collaboration using any one of the management systems of claims 1 to 6, characterized in that: The following steps are involved: S1: Project initialization, inputting basic project information, building a work breakdown structure and adapting it to team resources; If the information is incomplete or fails verification, the system will highlight the error field and prompt the user to make corrections until all required fields are filled in correctly and the skill matching degree meets the requirements; S2: Resource scheduling: Integrate enterprise resources to build a dynamic resource pool and use the improved NSGA-II algorithm for resource scheduling. If the resource scheduling plan does not meet the optimization goal, the system will trigger a re-optimization process until the generated scheduling plan meets the requirements. S3: Progress control: This uses multi-source data fusion technology and a dynamic deviation adjustment algorithm to achieve real-time monitoring and adjustment of project progress. If the data credibility is low, the system will trigger a "manual review process" to manually check and correct the data until the credibility is met. When progress lags for three consecutive monitoring cycles, the system will initiate a three-level progressive warning and take corresponding adjustment measures based on the warning level; S4: Cost quantification management, based on activity-based costing and real-time cost hedging strategies, to achieve full-cycle modeling and adjustment of project costs; If the cost model fails verification, the data will be returned to the parameter calibration interface for the user to adjust the cost model parameters until they meet the requirements; when the cost deviation rate exceeds 8%, the system will activate the cost hedging strategy to find a cost optimization solution; S5: Risk Management: By establishing a three-dimensional risk assessment system and a dynamic risk response mechanism, we can comprehensively identify and address project risks. If the risk level continues to rise or is not effectively controlled, the system will upgrade response measures and allocate more resources to address the situation until the risk is resolved. S6: Project Execution and Monitoring: During project execution, we continuously track project progress, resource usage, and cost expenditures to ensure that the project proceeds according to the planned schedule. If any anomalies occur during execution, the system will trigger a rescheduling process to adjust resource allocation and task plans until project execution returns to normal. S7: Project evaluation and summary. After the project is completed, a project evaluation is conducted to analyze the successful experiences and shortcomings during the project execution process. If the evaluation results show that the project has not achieved the expected goals, the system will initiate a project review process, conduct an in-depth analysis of the reasons, and formulate improvement measures to provide lessons learned for subsequent projects.

Images