An Engineering Progress Management Optimization and Monitoring System in a Design Collaboration Environment

Abstract

An engineering progress management optimization and monitoring system in a design collaboration environment, including a collaborative design integration module, a progress intelligent optimization module, a real-time monitoring and dashboard module, an alarm and early warning module, a data analysis and reporting module, a permissions and access control module, and a notification and communication module. The collaborative design integration module realizes the high integration of collaborative design tools; the progress intelligent optimization module can dynamically adjust the engineering progress plan, optimize resources and manage risks; the real-time monitoring and dashboard module provides an intuitive visualization tool; the alarm and early warning module detects potential problems in real time; the data analysis and reporting module deeply mines project data and generates detailed reports; the permissions and access control module ensures the security of project data; the notification and communication module promotes timely communication among team members. The beneficial effects of the present invention are as follows: the provided system provides comprehensive management support for engineering projects and promotes the team to smoothly push forward the projects.

Description

Technical Field

[0001] The present invention relates to the fields of collaborative technologies and intelligent engineering management, and specifically to an engineering progress management optimization and monitoring system in a design collaboration environment. Background Art

[0002] In the current field of engineering project management, design collaboration and progress management are key challenges. Traditional project management methods have difficulties in adapting to dispersed design teams, multi-site collaborative work, dynamically changing requirements, and complex project structures. First of all, the challenges of design collaboration are reflected in issues such as dispersed team members, the use of different design tools, and inconsistent file formats. To address this issue, an advanced design collaboration system is needed that can provide an integrated platform to achieve real-time data sharing, collaborative editing, and version control to improve the collaboration efficiency among design teams.

[0003] Secondly, engineering progress management also faces the problem that static plans are difficult to adapt to the dynamic changes during project execution. Problems in aspects such as resource allocation, task priorities, and risk management often lead to project delays. In this regard, an intelligent system is needed that can dynamically adjust the progress plan, optimize resource utilization, adapt to project changes, and improve the execution efficiency of the project and the probability of successful delivery.

[0004] Real-time monitoring is a key factor in ensuring the healthy execution of a project. Project managers need to know the status of the project, key performance indicators at any time, and be able to make decisions quickly. Therefore, a system that can provide real-time monitoring and intuitive dashboard display is needed to enhance the real-time perception of project progress and help managers better cope with project challenges.

[0005] In this technical background, it is urgent to design an innovative solution for an integrated engineering progress management optimization and monitoring system in a design collaboration environment. Such a system will effectively alleviate the problems of insufficient collaboration among design teams and static project execution plans, improve team efficiency, reduce risks, and provide more flexible and intelligent management support for complex engineering projects. Summary of the Invention

[0006] The present invention aims to provide an engineering progress management optimization and monitoring system in a design collaboration environment, including a collaborative design integration module, a progress intelligent optimization module, a real-time monitoring and dashboard module, an alarm and early warning module, a data analysis and reporting module, a permission and access control module, and a notification and communication module. The collaborative design integration module realizes the highly integrated collaborative design tools, enabling team members to achieve real-time data sharing, collaborative editing, and version control on the same platform. Its intuitive user interface and multi-format file compatibility provide strong support for design collaboration; the progress intelligent optimization module realizes the dynamic adjustment of the engineering progress plan, resource optimization, and risk management through intelligent algorithms. Through data analysis, adaptive algorithms, and real-time adjustment, the project can flexibly respond to changes and maximize the execution efficiency; the real-time monitoring and dashboard module provides an intuitive visualization tool for the project, displaying the real-time project status, key performance indicators, and milestones, helping project managers and team members to understand the project progress at any time and make decisions quickly; the alarm and early warning module detects potential problems in real time through an intelligent early warning mechanism and sends timely alarm notifications to relevant personnel to prevent the development of problems; the data analysis and reporting module generates detailed reports through in-depth mining of project data, providing in-depth project analysis and decision support for the team; the permission and access control module ensures the security of project data and prevents unauthorized access through a fine-grained permission management mechanism; the notification and communication module promotes timely communication among team members through a built-in notification system and real-time communication tools, ensuring the accuracy and efficiency of information transmission.

[0007] Furthermore, the collaborative design integration module realizes the close combination of collaborative design tools and the engineering progress management system, promoting real-time data sharing, collaborative editing, and version control functions among team members. Through real-time data sharing, team members can view the latest design changes on the same platform, reducing information lag and errors. The collaborative editing function supports multi-user simultaneous editing of design documents, avoiding version conflicts and improving work efficiency. Version control ensures the integrity and traceability of design documents, enabling the team to trace back to previous versions to track design decisions. Real-time communication and collaborative work tools promote instant communication among team members to solve design problems. The permission management function ensures the security of design documents, restricting access rights. File format compatibility, approval processes, automatic synchronization and updates, user interface friendliness, and logging and auditing functions further enhance the comprehensiveness and practicality of the collaborative design integration module.

[0008] Furthermore, the progress intelligent optimization module realizes the dynamic adjustment of the project progress plan to maximize the project execution efficiency. First, the module analyzes the specific conditions of the project, including resource availability, task dependencies, and task priorities, to achieve the dynamic adjustment of the progress plan and ensure that the entire project can be completed in the shortest time. In addition, the module considers various resources required for the project, including personnel, equipment, and materials, and optimizes the allocation and utilization of resources through intelligent scheduling algorithms to prevent resource bottlenecks and delays. The progress intelligent optimization module can assign appropriate priorities according to the urgency and importance indicators of tasks to ensure that critical tasks are processed in a timely manner and minimize the impact on the overall progress. The progress intelligent optimization module also has an intelligent risk management function. By identifying potential problems in real time and providing corresponding solutions, it reduces the risk of project delays. In addition, the module supports real-time adjustment and feedback, can flexibly adjust the progress plan during project execution, and provides instant notifications about progress changes to team members and project managers through a real-time feedback mechanism to ensure that the team has a clear understanding of the project dynamics.

[0009] Furthermore, the progress intelligent optimization module includes an engineering project with N tasks, and each task is described by a construction period t i and a resource requirement r i . The goal is to minimize the completion time of the entire project through appropriate resource allocation and task scheduling. Assume that each task is composed of task elements, and each task element contains n elements, denoted as G = {G1, G2, …, G n}, where G1, G2, …, G n represent the first, second, and nth elements respectively. The weighted sum of task time and resource requirement is denoted as:

[0010]

[0011] where f1 represents the first fitness function, and α is a parameter that weighs task time and resource requirements. Considering the dependencies between tasks, the progress intelligent optimization module has:

[0012]

[0013] f2 represents the second fitness function, β is a weighing factor for task dependencies, D ij is the dependency matrix, and G i , G j represent the ith and jth elements respectively. The present invention introduces adaptive temperature adjustment to make the temperature T change dynamically with the search process, and has:

[0014]

[0015] Among them, T0 is the initial temperature, t is the number of iterations. The adjustment is such that the temperature is relatively high initially, which is helpful for global search. As the number of iterations increases, the temperature gradually decreases, which is helpful for local optimization. To further apply the adaptive temperature to the field applicable to the system provided by the present invention, further, a temperature-sensitive factor for the relationship between tasks is established. After adding the temperature-sensitive factor, the first fitness function and the second fitness function are integrated as: f({G1, G2, …, G n}, t)

[0016] =(1 - γ(t))f1({G1, G2, …, G n}) + γ(t)f2({G1, G2, …, G n})

[0017] Among them, f is the fitness function. To prevent the system from falling into a local optimal solution during the iteration process, elements are exchanged according to probability. Assume that in a task element, the probability of exchanging the i-th element and the j-th element is P a , there is:

[0018]

[0019] Among them, fit i is the fitness of the i-th element, fit j is the fitness of the j-th element. Denote the probability of the i-th element mutating as P b , there is:

[0020]

[0021] Among them, fit best is the optimal fitness. For the probability P i that element i is selected, there is: The difference in energy ΔE in simulated annealing is expressed as:

[0022] ΔE = f({G1, G2, …, G n}) - f({G`1, G`2, …, G` n})

[0023] f({G`1, G`2, …, G` n}) is the fitness function value of the new task arrangement obtained after exchange and mutation. Considering the gradient change of the temperature drop The change in temperature should satisfy:

[0024]

[0025] Furthermore, the real-time monitoring and dashboard module provides real-time monitoring of project progress and visual display, enabling project managers and team members to understand the project status, key performance indicators, and milestones at any time. Through real-time monitoring, the team can quickly identify potential problems, track project progress, and take timely actions. By presenting the project status, key performance indicators, and milestones on the dashboard, it provides an intuitive overall view for project managers, including task completion, resource utilization, and the status of the critical path. The dashboard is designed to be user-friendly, enabling team members to quickly understand and analyze the data, facilitating rapid decision-making. In addition, the real-time monitoring and dashboard module provides a customized alert mechanism that can detect potential problems or schedule delays in a timely manner and notify relevant personnel through alerts, ensuring that the team can quickly respond to problems and take corrective measures to keep the project progress within control.

[0026] Furthermore, the alarm and early warning module, through an intelligent alarm mechanism, monitors the key indicators of the project in real time and sends alarm and early warning notifications to relevant personnel in the event of potential problems or schedule delays, providing a mechanism for rapid response and preventing problem escalation to ensure the overall smooth progress of the project. The alarm and early warning module first monitors the key performance indicators of the project by setting predefined thresholds and conditions. Once the system detects signs of potential problems or schedule delays, it will immediately trigger an alarm in the form of an email, text message, or system notification. By analyzing the data, it identifies possible risk factors and sends early warning notifications in advance, enabling project managers and the team to take corresponding measures before the problem develops to an uncontrollable level. This preventive alarm mechanism helps reduce project risks and improve the controllability of overall project execution.

[0027] Furthermore, the data analysis and reporting module collects, analyzes, and presents key performance indicators and statistical data related to project progress. Through customizable report generation tools, the system can automatically generate detailed project reports, support comparison of historical data and trend analysis, helping the team understand the changes and improvements during project execution. By communicating the information to project stakeholders, the team can better evaluate the success factors of the project and adjust the plan in a timely manner to cope with changes.

[0028] Furthermore, in the data analysis and reporting module, the data analysis function consists of three stages: the data preprocessing stage, the feature engineering stage, and the model training stage. In the data preprocessing stage, missing values and outliers are processed to ensure the reliability of data quality. For missing values, the method of filling with the mean value is used, that is, for missing elements, the mean value of the column is used for filling. For outliers, the Hampel filter is introduced to detect the median absolute deviation of the data, thereby effectively identifying and processing outliers. For the X ij th data, there are the following situations:

[0029]

[0030] Among them, X ij is the element in the i-th row and j-th column of the data matrix, X j is the data in the j-th column, n is the total number of data, Hampel(X j , k) represents the Hampel filter with a window size of k. To improve the performance and robustness of the model, the model input is optimized through feature scaling and feature selection. In the feature scaling stage, the data features are scaled to the standard normal distribution:

[0031]

[0032] Among them, is the element in the i-th row and j-th column after feature scaling. In the feature selection stage, information gain is introduced as the criterion for feature selection. By evaluating the information gain of each feature, the feature that contributes the most to the model is selected. There is:

[0033]

[0034]

[0035] Among them, RANK(X j ) represents the ranking of the data in the j-th column, Gain(X j ) is the information gain of the data in the j-th column, E(X j ) is the entropy of the data in the j-th column, P(X ij ) is the probability of the data in the j-th column X j taking the value in the i-th row. Construct the training model Y:

[0036]

[0037] Among them, Y is the predicted output of the system, sign is the sign function, a and b are index numbers, satisfying a + b = n, Tree i (X) is the output of the i-th tree, w1, w2 are the decision tree weights and the ensemble learning weights, is the weight corresponding to the output of the i-th tree, g i (X) is the output of the i-th base learner, is the weight corresponding to the output of the i-th base learner, β0, β1, β2, β n are the coefficients of the logistic regression, X1, X2, X n are the data in the 1st column, 2nd column, and nth column. Construct the data mining optimization objective function Obj:

[0038]

[0039] Among them, Yi is the predicted output corresponding to the i-th data in the system, y i is the actual output corresponding to the i-th data in the system, λ is the regularization term weight, and there is: β i is the regression coefficient, Regularization is the regularization term of logistic regression, is the weight corresponding to the output of the j-th tree, is the weight corresponding to the output of the j-th base learner. For and there is:

[0040]

[0041]

[0042] Among them, μ is a parameter used to adjust the trade-off of base learners in ensemble learning.

[0043] Furthermore, the permission and access control module is used to reasonably control the access to information and functions in the system, protect the security and integrity of project data. The permission and access control module allows system administrators and project managers to define user roles and corresponding permission levels. Through fine-grained permission management, team members are assigned different permissions to access different functions and data of the system according to their roles and responsibilities, support access control for sensitive data, ensure that only users with specific permissions can view and modify sensitive information, track and record user operation logs, and record user behaviors in the system.

[0044] Furthermore, the notification and communication module plays an important role in the engineering progress management system in a design collaboration environment, promoting real-time communication among team members to promptly share information, solve problems, and maintain the collaborative work efficiency of the team. The notification and communication module supports a built-in notification system that can automatically send reminders, notifications, and updates to relevant team members to ensure that team members are always aware of the latest status of the project. In addition, the notification and communication module supports targeted notifications, that is, sending information to specific users, specific teams, and project managers. The targeted notification mechanism ensures that information can be accurately conveyed to relevant personnel, avoiding information overload and misdirection.

[0045] Advantages of the present invention: By co - designing the integrated module, the present invention achieves a high degree of integration of design collaboration tools, eliminates the problems of information isolation and insufficient collaboration caused by dispersed teams and different design tools in the traditional design process. Team members can share design data in real - time, perform collaborative editing, and implement version control on the same platform, significantly improving the team's collaboration efficiency, facilitating closer cooperation, and accelerating the design and decision - making processes. Traditional engineering projects often rely on static plans and are difficult to cope with changes and uncertainties during project execution. The intelligent progress optimization module dynamically adjusts the engineering progress plan, optimizes resource utilization and risk management, enabling project managers to more flexibly adapt to changes, avoid resource conflicts and task priority issues, thereby improving the overall project execution efficiency. The real - time monitoring and dashboard module provides an intuitive visualization tool for the project, displaying the project's key performance indicators, milestones, and real - time status, enabling project managers and team members to understand the current state of the project at any time, promptly discover potential problems, and make rapid decisions. The design of the dashboard presents project data in an intuitive and easy - to - understand form, effectively improving the manager's perception of the project and decision - making effectiveness.

[0046] The alarm and early - warning module, through an intelligent early - warning mechanism, monitors key project indicators in real - time, promptly discovers potential problems, and sends alarm and early - warning notifications to relevant personnel, which helps the project team take corresponding measures before the problem develops to an uncontrollable level. Through the preventive alarm mechanism, the team can quickly respond to problems, take corrective measures, minimize potential risks, and ensure the project progresses within a controllable range. The data analysis and reporting module divides data analysis into three stages: the data pre - processing stage, the feature engineering stage, and the model training stage. The innovation lies in that, in order to improve the performance and robustness of the model, the model input is optimized through feature scaling and feature selection, and the decision model integrated learning model logistic regression deviation The training model Y of this system is built. Through μ, the parameters for balancing the base learners in ensemble learning can be adjusted to enhance the adaptability of the system. The innovation of the permission and access control module lies in ensuring that only authorized personnel can access and modify specific information through a fine-grained permission management mechanism, which helps prevent information leakage and unauthorized access, improving the security and integrity of project data. At the same time, this module also supports access control for sensitive data, ensuring that only users with specific permissions can view or modify sensitive information; the notification and communication module promotes timely communication among team members through the built-in notification system and real-time communication tools, which can solve the problem of untimely information transmission, ensuring that team members can always keep abreast of the latest project developments. Through the real-time notification and communication mechanism, the team can respond to changes more quickly, jointly solve problems, and improve the overall collaboration efficiency of the team, providing a comprehensive, intelligent, and efficient management solution for engineering projects. Brief Description of the Drawings

[0047] The invention is further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the invention. For those of ordinary skill in the art, other drawings can also be obtained based on the following drawings without creative efforts.

[0048] Figure 1 It is a schematic structural diagram of the present invention. Detailed Embodiments

[0049] The present invention is further described in conjunction with the following embodiments.

[0050] See Figure 1, An engineering progress management optimization and monitoring system in a design collaboration environment, including a collaborative design integration module, a progress intelligent optimization module, a real-time monitoring and dashboard module, an alarm and early warning module, a data analysis and reporting module, a permissions and access control module, and a notification and communication module. The collaborative design integration module realizes the high integration of collaborative design tools, enabling team members to achieve real-time data sharing, collaborative editing, and version control on the same platform. Its intuitive user interface and multi-format file compatibility provide strong support for design collaboration; the progress intelligent optimization module realizes the dynamic adjustment of the engineering progress plan, resource optimization, and risk management through intelligent algorithms. Through data analysis, adaptive algorithms, and real-time adjustment, the project can flexibly respond to changes and maximize execution efficiency; the real-time monitoring and dashboard module provides an intuitive visualization tool for the project, displaying the real-time project status, key performance indicators, and milestones, helping project managers and team members understand the project progress at any time and make decisions quickly; the alarm and early warning module detects potential problems in real time through an intelligent early warning mechanism and sends timely alarm notifications to relevant personnel to prevent the development of problems; the data analysis and reporting module generates detailed reports through in-depth mining of project data, providing in-depth project analysis and decision support for the team; the permissions and access control module ensures the security of project data and prevents unauthorized access through a fine-grained permission management mechanism; the notification and communication module promotes timely communication among team members through a built-in notification system and real-time communication tools, ensuring the accuracy and efficiency of information transmission.

[0051] Preferably, the collaborative design integration module realizes the close combination of collaborative design tools and the engineering progress management system, promoting real-time data sharing, collaborative editing, and version control functions among team members. Through real-time data sharing, team members can view the latest design changes on the same platform, reducing information lag and errors. The collaborative editing function supports multi-user simultaneous editing of design documents, avoiding version conflicts and improving work efficiency. Version control ensures the integrity and traceability of design documents, enabling the team to trace back to previous versions to track design decisions. Real-time communication and collaborative work tools promote instant communication among team members to solve design problems. The permission management function ensures the security of design documents, restricting access permissions. File format compatibility, approval processes, automatic synchronization and updates, user interface friendliness, as well as logging and auditing functions further enhance the comprehensiveness and practicality of the collaborative design integration module.

[0052] Preferably, the intelligent progress optimization module realizes the dynamic adjustment of the project progress plan to maximize the execution efficiency of the project. The intelligent progress optimization module first realizes the dynamic adjustment of the progress plan by analyzing the specific conditions of the project, which include resource availability, task dependencies, and task priorities, to ensure that the entire project can be completed in the shortest time. In addition, the module considers various resources required for the project, including personnel, equipment, and materials, and realizes the optimal allocation and utilization of resources through intelligent scheduling algorithms to prevent resource bottlenecks and delays. The intelligent progress optimization module can assign appropriate priorities according to the urgency and importance indicators of tasks to ensure that critical tasks are processed in a timely manner and minimize the impact on the overall progress. The intelligent progress optimization module also has an intelligent risk management function, which reduces the risk of project delays by identifying potential problems in real time and providing corresponding countermeasures. In addition, the module supports real-time adjustment and feedback, can flexibly adjust the progress plan during the project execution process, and provides instant notifications about progress changes to team members and project managers through a real-time feedback mechanism to ensure that the team has a clear understanding of the project dynamics.

[0053] Preferably, the intelligent progress optimization module includes an engineering project with N tasks, and each task is described by a construction period t i and a resource requirement r i . The goal is to minimize the completion time of the entire project through appropriate resource allocation and task scheduling. Assume that each task is composed of task elements, and each task element contains n elements, denoted as G = {G1, G2, …, G n}, where G1, G2, …, G n represent the first, second, and nth elements respectively. The weighted sum of the task time and resource requirement is denoted as:

[0054]

[0055] where f1 represents the first fitness function, and α is a parameter that weighs the task time and resource requirement. Considering the dependencies between tasks, the intelligent progress optimization module has:

[0056]

[0057] f2 represents the second fitness function, β is a weighing factor for the dependencies between tasks, G i , G j represent the ith and jth elements respectively, and D ij is the dependency matrix. The present invention introduces adaptive temperature adjustment to make the temperature T change dynamically with the search process, having:

[0058]

[0059] Among them, T0 is the initial temperature, y is the number of iterations. Adjusting to make the temperature higher initially helps with global search. As the number of iterations increases, the temperature gradually decreases, which helps with local optimization. To further apply the adaptive temperature to the field applicable to the system provided by the present invention, further, a temperature-sensitive factor γ(t) for the relationship between tasks is established: After adding the temperature-sensitive factor, the first fitness function and the second fitness function are integrated as: f({G1, G2, …, G n}, t)

[0060] =(1 - γ(t))f1({G1, G2, …, G n}) + γ(t)f2({G1, G2, …, G n})

[0061] Among them, f is the fitness function. To prevent the system from falling into a local optimal solution during iteration, elements are exchanged according to probability. Assuming that in a task element, the probability of exchanging the i-th element and the j-th element is P a , there is:

[0062]

[0063] Among them, fit i is the fitness of the i-th element, fit j is the fitness of the j-th element. Denote the probability of the i-th element mutating as P b , there is:

[0064]

[0065] Among them, fit best is the optimal fitness. For the probability P i that element i is selected, there is: The difference in energy ΔE in simulated annealing is expressed as:

[0066] ΔE = f({G1, G2, …, G n}) - f({G`1, G`2, …, G` n})

[0067] f({G`1, G`2, …, G` n}) is the fitness function value of the new task arrangement obtained after exchange and mutation. Considering the gradient change of temperature drop The change in temperature should satisfy:

[0068]

[0069] Preferably, the real-time monitoring and dashboard module provides real-time project progress monitoring and visual display, so that project managers and team members can always understand the project status, key performance indicators, and milestones. Through real-time monitoring, the team can quickly identify potential problems, track project progress, and take timely actions. By presenting the project status, key performance indicators, and milestones on the dashboard, it provides an intuitive overall view for project managers, including task completion, resource utilization, and the status of the critical path. The dashboard is designed to be user-friendly, enabling team members to quickly understand and analyze the data, facilitating rapid decision-making. In addition, the real-time monitoring and dashboard module provides a customizable alert mechanism that can detect potential problems or schedule delays in a timely manner and notify relevant personnel through alerts, ensuring that the team can quickly respond to problems and take corrective measures to keep the project progress within control.

[0070] Preferably, the alarm and early warning module, through an intelligent alarm mechanism, monitors the key indicators of the project in real time and sends alarm and early warning notifications to relevant personnel in a timely manner in the event of potential problems or schedule delays, providing a mechanism for rapid response and preventing problem escalation to ensure the overall smooth progress of the project. The alarm and early warning module first monitors the key performance indicators of the project by setting predetermined thresholds and conditions. Once the system detects signs of potential problems or schedule delays, it will immediately trigger an alarm in the form of email, text message, or system notification. By analyzing the data, it identifies possible risk factors and sends early warning notifications in advance, enabling project managers and the team to take corresponding measures before the problems develop to an uncontrollable level. This preventive alarm mechanism helps reduce project risks and improve the controllability of overall project execution.

[0071] Preferably, the data analysis and reporting module collects, analyzes, and presents key performance indicators and statistical data related to project progress. Through customizable report generation tools, the system can automatically generate detailed project reports, support the comparison of historical data and trend analysis, and help the team understand the changes and improvements during project execution. By communicating the information to project stakeholders, the team can better evaluate the success factors of the project and adjust the plan in a timely manner to respond to changes.

[0072] Preferably, in the data analysis and reporting module, the data analysis function includes three stages: the data preprocessing stage, the feature engineering stage, and the model training stage. In the data preprocessing stage, missing values and outliers are processed to ensure the reliability of data quality. For missing values, the method of filling with the mean value is used, that is, for the missing elements, the mean value of the column is used for filling. For outliers, the Hampel filter is introduced to detect the median absolute deviation of the data, thereby effectively identifying and processing outliers. For the X ij th data, there are the following situations:

[0073]

[0074] Among them, X ij is the element in the i-th row and j-th column of the data matrix, and X j is the data in the j-th column, n is the total number of data, Hampel(X j , k) represents the Hampel filter with a window size of k. To improve the performance and robustness of the model, the model input is optimized through feature scaling and feature selection. In the feature scaling stage, the data features are scaled to the standard normal distribution:

[0075]

[0076] Among them, is the element in the i-th row and j-th column after feature scaling. In the feature selection stage, information gain is introduced as the criterion for feature selection. By evaluating the information gain of each feature, the feature that contributes the most to the model is selected, and there is:

[0077]

[0078]

[0079] Among them, RANK(X j ) represents the ranking of the data in the j-th column, Gain(X j ) is the information gain of the data in the j-th column, E(X j ) is the entropy of the data in the j-th column, P(X ij ) is the probability of the data in the j-th column X j taking the value in the i-th row. The training model Y is constructed as follows:

[0080]

[0081] Among them, Y is the predicted output of the system, sign is the sign function, a and b are index numbers, satisfying a + b = n, Tree i (X) is the output of the i-th tree, w1, w2 are the decision tree weights and the ensemble learning weights, is the weight corresponding to the output of the i-th tree, g i (X) is the output of the i-th base learner, is the weight corresponding to the output of the i-th base learner, β0, β1, β2, β n are the coefficients of logistic regression, X1, X2, X n are the data in the 1st column, 2nd column, and nth column. The data mining optimization objective function Obj is constructed as follows:

[0082]

[0083] Among them, Yi is the predicted output corresponding to the i-th data in the system, y i is the actual output corresponding to the i-th data in the system, λ is the weight of the regularization term, and there is: β i is the regression coefficient, Regularization is the regularization term of logistic regression, is the weight corresponding to the output of the j-th tree, is the weight corresponding to the output of the j-th base learner. For and there is:

[0084]

[0085]

[0086] Among them, μ is a parameter used to adjust the trade-off of base learners in ensemble learning.

[0087] Preferably, the permission and access control module is used to reasonably control the access to information and functions in the system, protect the security and integrity of project data. The permission and access control module allows system administrators and project managers to define user roles and corresponding permission levels. Through fine-grained permission management, team members are assigned different permissions to access different functions and data of the system according to their roles and responsibilities, support access control of sensitive data, ensure that only users with specific permissions can view and modify sensitive information, track and record user operation logs, and record user behaviors in the system.

[0088] Preferably, the notification and communication module plays an important role in designing an engineering progress management system in a collaborative environment, promoting real-time communication among team members to share information, solve problems in a timely manner, and maintain the collaborative work efficiency of the team. The notification and communication module supports a built-in notification system that can automatically send reminders, notifications, and updates to relevant team members to ensure that team members always understand the latest status of the project. In addition, the notification and communication module supports targeted notifications, that is, sending information to specific users, specific teams, and project managers. The targeted notification mechanism ensures that information can be accurately conveyed to relevant personnel, avoiding information overload and misinformation.

[0089] Advantages of the present invention: By synergistically designing the integrated module, the present invention achieves a high degree of integration of design collaboration tools, eliminates the problems of information isolation and insufficient collaboration caused by dispersed teams and different design tools in the traditional design process. Team members can share design data in real time, perform collaborative editing, and implement version control on the same platform, significantly improving the collaborative efficiency of the team, facilitating closer cooperation, and accelerating the design and decision-making processes. Traditional engineering projects often rely on static plans and are difficult to cope with changes and uncertainties during project execution. The intelligent progress optimization module dynamically adjusts the engineering progress plan, optimizes resource utilization, and manages risks, enabling project managers to more flexibly adapt to changes, avoid resource conflicts and task priority issues, thereby improving the overall project execution efficiency. The real-time monitoring and dashboard module provides an intuitive visualization tool for the project, displaying key performance indicators, milestones, and real-time status of the project, enabling project managers and team members to understand the current status of the project at any time, promptly discover potential problems, and make rapid decisions. The design of the dashboard presents project data in an intuitive and easy-to-understand form, effectively improving the manager's perception of the project and decision-making effectiveness.

[0090] The alarm and early warning module, through an intelligent early warning mechanism, monitors key project indicators in real time, promptly discovers potential problems, and sends alarm and early warning notifications to relevant personnel, which helps the project team take corresponding measures before the problems develop to an uncontrollable level. Through the preventive alarm mechanism, the team can quickly respond to problems, take corrective measures, minimize potential risks, and ensure the project progresses within a controllable range. The data analysis and reporting module divides data analysis into three stages: the data preprocessing stage, the feature engineering stage, and the model training stage. The innovation lies in that, in order to improve the performance and robustness of the model, the model input is optimized through feature scaling and feature selection, and the equilibrium decision model Integrated learning model Logistic regression deviation The training model Y of this system is built. The parameter for balancing the base learners in the ensemble learning can be adjusted by μ, enhancing the adaptability of the system. The innovation of the permission and access control module lies in the fine-grained permission management mechanism, which ensures that only authorized personnel can access and modify specific information, helping to prevent information leakage and unauthorized access, improving the security and integrity of project data. At the same time, this module also supports access control for sensitive data, ensuring that only users with specific permissions can view or modify sensitive information; the notification and communication module promotes timely communication among team members through the built-in notification system and real-time communication tools, solving the problem of untimely information transmission, ensuring that team members can always keep abreast of the latest project developments. Through the real-time notification and communication mechanism, the team can respond to changes more quickly, jointly solve problems, and improve the overall collaboration efficiency of the team, providing a comprehensive, intelligent, and efficient management solution for engineering projects.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. An engineering progress management optimization and monitoring system in a design collaboration environment, including a collaborative design integration module, a progress intelligent optimization module, a real-time monitoring and dashboard module, an alarm and early warning module, a data analysis and reporting module, a permissions and access control module, and a notification and communication module. The collaborative design integration module achieves a high degree of integration of collaborative design tools; the progress intelligent optimization module realizes dynamic adjustment of the engineering progress plan, resource optimization, and risk management through intelligent algorithms; the real-time monitoring and dashboard module provides an intuitive visualization tool for the project, displaying the real-time project status, key performance indicators, and milestones, helping project managers and team members understand the project progress at any time and make decisions quickly; The alarm and early warning module, through an intelligent early warning mechanism, detects potential problems in real time and sends timely alarm notifications to relevant personnel to prevent the development of problems; the data analysis and reporting module, through in-depth mining of project data, generates detailed reports to provide in-depth project analysis and decision-making support for the team; the permission and access control module ensures the security of project data and prevents unauthorized access through a fine-grained permission management mechanism; The notification and communication module promotes timely communication among team members through a built-in notification system and real-time communication tools; The intelligent progress optimization module realizes the dynamic adjustment of the project progress plan. First, by analyzing specific project conditions, including resource availability, task dependencies, and task priorities, the intelligent progress optimization module realizes the dynamic adjustment of the progress plan. In addition, the module considers various resources required for the project, including personnel, equipment, and materials, and realizes the optimal allocation and utilization of resources through intelligent scheduling algorithms to prevent resource bottlenecks and delays. The intelligent progress optimization module can assign appropriate priorities according to the urgency and importance indicators of tasks. The intelligent progress optimization module also has an intelligent risk management function, which can identify potential problems in real time and provide corresponding countermeasures. In addition, the module supports real-time adjustment and feedback; The project schedule intelligent optimization module contains a project with N tasks, and each task has a duration t i and a resource requirement r i Description. The goal is to minimize the completion time of the entire project through appropriate resource allocation and task scheduling. Assume that each task is composed of task elements, and each task element contains n elements. Denote the elements as G = {G1, G2, …, G n}, G1, G2, …, G n represent the first, second, and nth elements respectively. The weighted sum of task time and resource requirement is denoted as: Among them, f1 represents the first fitness function, and α is a parameter that weights task time and resource requirements. Considering the dependencies between tasks, the intelligent progress optimization module has: $f_2$ represents the second fitness function, $\beta$ is the trade-off factor for the dependencies between tasks, and $D$ ij is the dependency matrix, and $G$ i , $G$ j represents the $i$-th and $j$-th elements. An adaptive temperature adjustment is introduced to make the temperature $T$ change dynamically with the search process, and we have: Wherein, T0 is the initial temperature, t is the number of iterations. The adjustment is made such that the temperature is relatively high initially, which helps with global search. As the number of iterations increases, the temperature gradually decreases, which helps with local optimization. To further apply the adaptive temperature to the field applicable to the system provided by the present invention, further, a temperature-sensitive factor γ(t) for the relationship between tasks is established: After adding the temperature-sensitive factor, the first fitness function and the second fitness function are integrated as: f({G1,G2,…,G n},t) =(1 - γ(t))f1({G1,G2,…,G n}) + γ(t)f2({G1,G2,…,G n}) Among them, f is the fitness function. To prevent the system from falling into a local optimal solution during iteration, elements are exchanged according to probability. Assume that in a task element, the probability of exchanging the i-th element and the j-th element is P a , there is: where, fit i is the fitness of the i-th element, and fit j is the fitness of the j-th element. Denote the probability of mutation of the i-th element as P b , and there is: Among them, fit best is the optimal fitness. For the probability P i that element i is selected, there is: The difference in energy ΔE in simulated annealing is expressed as: ΔE = f({G1, G2, …, G n}) - f({G`1, G`2, …, G` n}) f({G`1,G`2,…,G` n}) is the fitness function value of the new task arrangement obtained after swapping and mutation. Considering the gradient change of temperature drop The change of temperature should satisfy: The data analysis and reporting module collects, analyzes, and displays key performance indicators and statistical data related to project progress. Through a customizable report generation tool, the system can automatically generate detailed project reports, support the comparison of historical data and trend analysis, help the team understand the changes and improvements during project execution, and enable the team to better evaluate the success factors of the project and adjust the plan in a timely manner to respond to changes by passing information to project stakeholders; In the data analysis and reporting module, the data analysis function consists of three stages: the data preprocessing stage, the feature engineering stage, and the model training stage. In the data preprocessing stage, missing values and outliers are processed to ensure the reliability of data quality. For missing values, the method of filling with the mean value is used, that is, for the missing elements, the mean value of the column is used for filling. For outliers, the Hampel filter is introduced to detect the median absolute deviation of the data, so as to effectively identify and process outliers. For the X ij th data, there are the following situations: Among them, X ij is the element in the i-th row and j-th column of the data matrix, X j is the data of the j-th column, n is the total number of data, Hampel(X j , k) represents the Hampel filter with a window size of k. To improve the performance and robustness of the model, the model input is optimized through feature scaling and feature selection. In the feature scaling stage, the data features are scaled to the standard normal distribution: Among them, is the element in the i-th row and j-th column after feature scaling. In the feature selection stage, information gain is introduced as the criterion for feature selection. By evaluating the information gain of each feature, the feature that contributes the most to the model is selected, and there is: Among them, RANK(X j ) represents the ranking of the data in the j-th column, Gain(X j ) is the information gain of the data in the j-th column, E(X j ) is the entropy of the data in the j-th column, P(X ij ) is the value probability of the data X j in the i-th row, and a training model Y is constructed: Among them, Y is the predicted output of the system, sign is the sign function, a and b are index numbers, satisfying a + b = n, and Tree i (X) is the output of the i-th tree, w1 and w2 are the decision tree weight and the ensemble learning weight, is the weight corresponding to the output of the i-th tree, and g i (X) is the output of the i-th base learner, is the weight corresponding to the output of the i-th base learner, and β0, β1, β2, β n are the coefficients of the logistic regression, and X1, X2, X n are the data in the first column, the second column, and the n-th column. Construct the data mining optimization objective function Obj: Among them, Y i is the predicted output corresponding to the i-th data in the system, y i is the actual output corresponding to the i-th data in the system, λ is the weight of the regularization term, and there is: β i is the regression coefficient, Regularization is the regularization term of logistic regression, is the weight corresponding to the output of the j-th tree, is the weight corresponding to the output of the j-th base learner. For and there is: Among them, μ is a parameter used to adjust the trade-off of base learners in ensemble learning.

2. The engineering progress management optimization and monitoring system in a design collaboration environment according to claim 1, characterized in that The collaborative design integration module realizes the close integration of collaborative design tools and the engineering progress management system, promoting real-time data sharing, collaborative editing, and version control functions among team members. Through real-time data sharing, team members can view the latest design changes on the same platform, reducing information lag and errors. The collaborative editing function supports multiple users to edit design documents simultaneously, avoiding version conflicts and improving work efficiency. Version control ensures the integrity and traceability of design documents, enabling the team to trace back to previous versions to track design decisions. Real-time communication and collaborative work tools promote instant communication among team members to solve design problems. The permission management function ensures the security of design documents and restricts access permissions. File format compatibility, approval processes, automatic synchronization and updates, user interface friendliness, as well as logging and auditing functions further enhance the comprehensiveness and practicality of the collaborative design integration module.

3. An engineering progress management optimization and monitoring system in a design collaboration environment according to claim 1, characterized in that The Real-time Monitoring and Dashboard Module provides real-time monitoring of project progress and visual display, enabling project managers and team members to keep track of the project status, key performance indicators, and milestones at any time. Through real-time monitoring, the team can quickly identify potential problems, track project progress, and take timely actions. By presenting the project status, key performance indicators, and milestones on the dashboard, it provides an intuitive overall view for project managers, including task completion, resource utilization, and the status of the critical path. The dashboard is designed to be user-friendly, enabling team members to quickly understand and analyze data, facilitating rapid decision-making. In addition, the Real-time Monitoring and Dashboard Module provides a customized alert mechanism that can detect potential problems or schedule delays in a timely manner and notify relevant personnel through alerts, ensuring that the team can respond quickly to problems and take corrective measures to keep the project progress under control.

4. An engineering progress management optimization and monitoring system in a design collaboration environment according to claim 1, characterized in that The Alarm and Early Warning Module, through an intelligent alarm mechanism, monitors the key indicators of the project in real time and sends alarm and early warning notifications to relevant personnel in the event of potential problems or schedule delays, providing a mechanism for rapid response and preventing problem escalation, ensuring the overall smooth progress of the project. The Alarm and Early Warning Module first monitors the project's key performance indicators by setting predefined thresholds and conditions. Once the system detects signs of potential problems or schedule delays, it will immediately trigger an alarm in the form of email, SMS, or system notification. By analyzing data, it identifies possible risk factors and sends early warning notifications in advance, enabling project managers and the team to take corresponding measures before the problem develops to an uncontrollable level. This preventive alarm mechanism helps reduce project risks and improve the controllability of overall project execution.

5. An engineering progress management optimization and monitoring system in a design collaboration environment according to claim 1, characterized in that, The Permission and Access Control Module is used to reasonably control access to information and functions in the system, protecting the security and integrity of project data. The Permission and Access Control Module allows system administrators and project managers to define user roles and corresponding permission levels. Through fine-grained permission management, team members are assigned different permissions to access different functions and data in the system according to their roles and responsibilities. It supports access control for sensitive data, ensuring that only users with specific permissions can view and modify sensitive information. It also tracks and records user operation logs and records user behavior in the system.

6. An engineering progress management optimization and monitoring system in a design collaboration environment according to claim 1, characterized in that, The Notification and Communication Module plays an important role in designing an engineering progress management system in a collaborative environment, promoting real-time communication among team members to share information, solve problems in a timely manner, and maintain the collaborative work efficiency of the team. The Notification and Communication Module supports a built-in notification system that can automatically send reminders, notifications, and updates to relevant team members, ensuring that team members are always aware of the latest project status. In addition, the Notification and Communication Module supports targeted notifications, that is, sending information to specific users, specific teams, and project managers. The targeted notification mechanism ensures that information can be accurately conveyed to relevant personnel, avoiding information overload and misdirection.

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