Intelligent project management system based on supervision integration

Abstract

The invention provides an intelligent project management system based on supervision integration, and the system employs a cloud platform for deployment, and comprises a project management module which is used for task management, progress tracking and document sharing, so as to achieve the planning, starting, execution, monitoring and ending of a whole life cycle of a project; the data acquisition and integration module is used for collecting project data in real time and performing integration processing; the risk assessment and early warning module is used for performing risk assessment on the project data and giving out early warning when the risk exceeds a preset threshold value; the intelligent decision support module is used for providing decision suggestions for a project manager based on the data analysis result; and the visual display module is used for visually displaying information such as a project state, progress, cost and quality in forms such as charts and reports. According to the system, through an integrated management platform and an intelligent analysis tool, real-time monitoring, risk assessment, resource optimization and decision support of the full life cycle of a project are realized, so that the project management efficiency is improved, and the project risk is reduced.

Description

Technical Field

[0001] This application belongs to the technical fields of information technology and project management, and particularly relates to an intelligent project management system based on integrated supervision. Background Art

[0002] Traditional project management methods often rely on manual operations and paper documents, suffering from problems such as untimely information updates, low data accuracy, and insufficient decision-making support. With the rapid development of information technology, especially the widespread application of Internet, Internet of Things, big data, and artificial intelligence technologies, project management is gradually turning towards intelligence, digitization, and integration. However, most existing intelligent project management systems have single functions and are difficult to achieve all-round supervision and integrated management throughout the project life cycle. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide an intelligent project management system based on integrated supervision, which can achieve real-time monitoring, risk assessment, resource optimization, and decision-making support throughout the project life cycle through an integrated management platform and intelligent analysis tools, thereby improving project management efficiency and reducing project risks.

[0004] This application provides an intelligent project management system based on integrated supervision. The system is deployed on a cloud platform and includes:

[0005] A project management module for task management, progress tracking, and document sharing to achieve planning, initiation, execution, monitoring, and closure of the entire project life cycle;

[0006] A data collection and integration module for real-time collection and integrated processing of project data;

[0007] A risk assessment and early warning module for risk assessment of project data and issuing an early warning when the risk exceeds a preset threshold;

[0008] An intelligent decision-making support module for providing decision-making suggestions for project managers based on data analysis results;

[0009] A visualization display module for intuitively displaying information such as project status, progress, cost, and quality in the form of charts, reports, etc.

[0010] Further, the project management module includes:

[0011] A task management unit for creating tasks, assigning them to designated team members, setting task deadlines and priorities, and real-time updating task status so that project managers can keep track of task progress at any time;

[0012] A progress tracking unit, which is used to compare the actual progress of a task with the planned progress, generate a progress deviation report, so that project managers can timely discover and solve problems;

[0013] A document sharing unit, which is used to share project-related document materials to ensure the accuracy and consistency of information.

[0014] Furthermore, the data acquisition and integration module includes:

[0015] A data interface unit, which is used to provide data interfaces with various devices at the project site, and at the same time support multiple data formats and communication protocols to ensure the real-time and accuracy of collecting project data;

[0016] A data integration unit, which is used to integrate the collected project data into a unified database, and perform cleaning, denoising and standardization processing.

[0017] Furthermore, the risk assessment and early warning module includes:

[0018] A risk assessment unit, which is used to establish a risk assessment model based on historical data and real-time data, and perform real-time analysis on project data to calculate the probability and impact degree of risk occurrence;

[0019] An early warning unit, which is used to automatically send out early warning information when the risk reaches a preset early warning threshold. Among them, the early warning information includes: risk type, risk level and early warning suggestions.

[0020] Furthermore, the intelligent decision-making support module is specifically used for:

[0021] Using machine learning or deep learning algorithms, establish a project development trend prediction model, and predict the development trend of the project based on historical data and real-time data to generate decision-making suggestions.

[0022] Furthermore, the visualization display module is specifically used for:

[0023] Adopting advanced visualization tools and technologies to generate visualization elements including charts and dashboards from project data to achieve data display. Among them, the visualization tools include: ECharts, D3.js, and the project data includes: project progress, cost, and quality data.

[0024] Furthermore, the system also includes a background management module, and the background management module includes:

[0025] A data backup unit, which is used to regularly and automatically perform data backup by adopting advanced encryption technology to ensure rapid recovery in case of data loss or damage, and at the same time protect the security of the backup data;

[0026] The system upgrade unit is used to automatically detect and download the latest upgrade package, and ensure the integrity and continuity of data during the upgrade process to avoid the risk of data loss or system crash;

[0027] The user management unit is used for user creation, deletion, password modification, and permission assignment.

[0028] Furthermore, the system further includes a system integration and interface module. The system integration and interface module adopts a hierarchical structure, specifically including:

[0029] The data access layer is used to establish connections with databases of different enterprise information systems to achieve transparent access to data, and provide interfaces for data reading, writing, and updating operations to ensure the atomicity, consistency, isolation, and durability of data operations;

[0030] The business logic layer is used to convert data from different data sources into a unified format to implement data mapping rules, perform data conversion operations to meet the compatibility requirements of data formats between different systems, and conduct data verification to ensure the accuracy and integrity of data;

[0031] The interface layer is used to standardize API interfaces and make the standard API interfaces follow the RESTful and SOAP protocols to ensure the interoperability and cross-platform capabilities of the interfaces, and customize API interfaces according to specific business requirements to meet personalized integration scenarios;

[0032] The security management layer is used to encrypt the data transmission process, control system access operations, and monitor the system to ensure the security of data during transmission and storage, and prevent data leakage and unauthorized access.

[0033] The intelligent project management system based on regulatory integration proposed in this application constructs an all-round, multi-level and integrated project management platform by integrating advanced information technologies, including the Internet, Internet of Things, big data, artificial intelligence, etc. It has the following beneficial effects: (1) Full life cycle management: The system covers all stages of project planning, initiation, execution, monitoring and closure, ensuring effective supervision and control of the project from start to end. (2) Intelligent data analysis: Using big data analysis and artificial intelligence algorithms, the system can deeply mine project data, provide accurate predictions and optimization suggestions, greatly improving the scientificity and accuracy of decision-making. (3) Real-time monitoring and early warning: Through real-time data collection and dynamic analysis, the system can timely detect project risks and deviations and issue early warnings, helping the project team quickly take measures to ensure the project progresses as planned. (4) High degree of integration and collaboration: The system realizes seamless docking with other enterprise information systems, such as ERP, CRM, etc., breaking information silos and promoting collaborative work among departments. (5) User experience optimization: The system has a friendly interface and simple operation, reducing the learning cost of users and improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The framework diagram of the intelligent project management system based on regulatory integration provided by the embodiment of this application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions and advantages of this technical solution clearer and more understandable, the following further details this technical solution in conjunction with specific embodiments. It should be understood that these descriptions are exemplary and not intended to limit the scope of this technical solution.

[0036] Please refer to Figure 1 the framework diagram of the intelligent project management system based on regulatory integration as shown. As Figure 1 shown, the intelligent project management system 10 includes:

[0037] A project management module 11 for task management, progress tracking and document sharing to achieve the planning, initiation, execution, monitoring and closure of the entire project life cycle;

[0038] A data collection and integration module 12 for collecting project data in real time and performing integration processing;

[0039] A risk assessment and early warning module 13 for performing risk assessment on project data and issuing early warnings when the risk exceeds a preset threshold;

[0040] An intelligent decision support module 14 for providing decision-making suggestions for project managers based on the data analysis results;

[0041] The visualization display module 15 is used to visually display information such as project status, progress, cost, quality, etc. in the form of charts, reports, etc.

[0042] Specifically, the project management module 11 includes:

[0043] The task management unit 111 is used to create tasks, assign them to designated team members, set the deadline and priority of the tasks, and update the task status in real time so that project managers can keep track of the task progress at any time.

[0044] Implementation method of the task management unit 111: Front-end users can fill in task details and submit them for creation through the Web interface. The back-end system receives the data, stores it in the database, and allows task assignment and priority setting. The task list is dynamically sorted according to the priority, and users can update the task status in real time.

[0045] The progress tracking unit 112 is used to compare the actual progress of the task with the planned progress, generate a progress deviation report so that project managers can discover and solve problems in a timely manner.

[0046] Implementation method of the progress tracking unit 112: The front-end uses a chart library to display the Gantt chart to show the task progress. The back-end calculates the progress difference regularly and generates a brief report.

[0047] The document sharing unit 113 is used to share project-related document materials to ensure the accuracy and consistency of information.

[0048] Implementation method of the document sharing unit 113: The front-end provides file upload and download functions. The back-end processes file storage and implements version control.

[0049] Specifically, the data collection and integration module 12 includes:

[0050] The data interface unit 121 is used to provide data interfaces with various devices at the project site, and at the same time support multiple data formats and communication protocols to ensure the real-time and accuracy of collecting project data;

[0051] Implementation method of the data interface unit 121: (1) Hardware connection: Ensure the physical connection between hardware devices such as sensors and cameras and the data collection module, which can be carried out through wired networks, wireless networks or other suitable communication methods. (2) Interface development: A series of software interfaces supporting multiple data formats (such as JSON, XML, etc.) and communication protocols (such as HTTP, MQTT, Modbus, etc.) have been developed to meet the access requirements of different devices. (3) Real-time data collection: Through the developed interfaces, data collection scripts or programs have been written to realize the reception and preliminary verification of real-time data, ensuring the integrity and availability of the data.

[0052] A data integration unit 122, which is used to integrate the collected project data into a unified database and perform cleaning, denoising, and standardization processing.

[0053] Implementation methods of the data integration unit 122: (1) Data storage: A central database is designed and implemented, which has efficient data storage capabilities and can store the data collected from various data interfaces, providing a solid foundation for subsequent data processing and analysis. (2) Data cleaning: Data cleaning scripts are developed to identify and correct errors in the data, including removing invalid or duplicate data records, improving the quality of the data. (3) Data denoising: Advanced algorithms are applied to denoise the data, effectively eliminating sensor noise and other interference factors, ensuring the accuracy of the data. (4) Data standardization: The cleaned data is converted into a unified format and standard for subsequent data analysis and processing. (5) Data query and export. Query function: A database query interface is provided, allowing users to retrieve data according to specific conditions, supporting complex queries and conditional filtering to meet the needs of different management levels. Export function: The data export function is implemented, supporting the export of data into common file formats such as CSV, Excel, etc., facilitating project managers to perform data analysis and report preparation in different environments and tools.

[0054] Specifically, the risk assessment and early warning module 13 includes:

[0055] A risk assessment unit 131, which is used to establish a risk assessment model based on historical data and real-time data and perform real-time analysis on project data to calculate the probability and impact degree of risk occurrence;

[0056] Implementation of the risk assessment unit 131: (1) Data preparation: A large amount of historical project data was collected and sorted out, including successful and failed project cases, as well as records of related risk events, ensuring that the training data for the model was sufficient and representative. (2) Feature extraction: Key features affecting risks were extracted from the project data, which may include project progress, cost, resource allocation, market changes, team collaboration, etc., providing a multi-dimensional analysis perspective for risk assessment. (3) Model construction: Appropriate algorithms were selected, such as logistic regression, decision tree, random forest, neural network, etc. The model was trained using historical data, and the model parameters were optimized through methods such as cross-validation. A risk assessment model was constructed to enable it to predict the probability and impact degree of risk occurrence based on the input project data. (4) Model verification: A part of the data not involved in training was used to test the model to verify the accuracy and reliability of the model and ensure its effectiveness in actual applications. (5) Model deployment: The trained risk assessment model was deployed into the project management system, realizing the analysis and processing of real-time data and providing continuous risk monitoring for the project.

[0057] The early warning unit 132 is used to automatically send out early warning information by the system when the risk reaches a preset early warning threshold. Among them, the early warning information includes: risk type, risk level, and early warning suggestions.

[0058] Implementation of the early warning unit 132: (1) Early warning threshold setting: According to project characteristics and risk management strategies, the threshold for risk early warning is set, such as when the risk probability is greater than a certain percentage or the risk impact degree exceeds a certain level. (2) Early warning rule definition: Early warning rules are formulated, including when to trigger the early warning, the format and content of the early warning information, etc. (3) Early warning system development: The early warning module is developed and integrated into the project management system. An interface with the risk assessment model is implemented to obtain the risk assessment results in real time. The display interface and notification methods of the early warning information are designed, such as pop-up windows, emails, text messages, etc. (4) Early warning triggering and notification: The project data is monitored in real time, and the risk assessment model is used for risk analysis. When the analysis result reaches the early warning threshold, the system automatically triggers the early warning mechanism and generates early warning information according to the early warning rules, and notifies the project manager in a timely manner through the preset method. (5) Early warning response: After receiving the early warning information, the project manager takes corresponding risk response measures according to the early warning suggestions. The system records the situation of the early warning response, providing valuable reference data for subsequent risk management, thereby continuously improving the risk assessment and early warning mechanisms.

[0059] Specifically, the intelligent decision support module 14 is specifically used for:

[0060] Using machine learning or deep learning algorithms, establish a prediction model for the project development trend, and predict the project development trend based on historical data and real-time data to generate decision-making suggestions.

[0061] Implementation method of the intelligent decision-making support module 14:

[0062] 1. Specific implementation method of the algorithm model: (1) Data collection and preprocessing: Collect historical data of the project, including project progress, cost, quality, resource usage, etc. Clean the collected data, including removing invalid and incorrect data, handling missing values, and data standardization to ensure the quality and consistency of the data. (2) Feature engineering: Analyze the data and extract features that have a significant impact on the project development trend. Optimize the feature set through feature selection and feature extraction techniques, such as principal component analysis (PCA) or autoencoders, to improve the performance of the model. (3) Model selection and training: Select appropriate machine learning or deep learning algorithms according to the project characteristics, such as time series analysis, regression analysis, neural networks, etc. Train the model using historical data and adjust the model parameters through techniques such as grid search and Bayesian optimization to optimize the prediction accuracy. (4) Model validation and optimization: Validate the model using the reserved test data set and evaluate the prediction ability of the model. Tune the model according to the validation results, including adjusting the algorithm structure, adding regularization terms, etc., until the model meets the requirements of project management. (5) Model deployment: Deploy the trained model to the project management system to ensure that the model can receive real-time data and output prediction results.

[0063] 2. Specific implementation of the decision support interface: (1) Interface design: Design an intuitive and user-friendly decision support interface to ensure that project managers can quickly capture and understand key information, thereby improving decision-making efficiency. The interface design should integrate various visualization elements such as charts, dashboards, and trend graphs, as well as clear and concise text information, so that users can view data from different perspectives. The interface should include diverse visualization elements, such as charts, dashboards, and trend graphs, and provide detailed text information. These elements work together to help project managers comprehensively and deeply understand the meaning behind the data. (2) Function implementation: Implement the interface connection with the backend algorithm model to ensure that the prediction results and decision-making suggestions of the project can be obtained in real time, providing the latest data support for project managers. At the same time, develop the interface logic to display complex prediction results and decision-making suggestions in the form of intuitive charts and easy-to-understand text to the users. Develop the interface logic to convert the prediction results and decision-making suggestions output by the algorithm model into charts and text forms, and present them to users in an intuitive and easy-to-understand way, enabling them to quickly make data-based decisions. (3) Interaction design: Design a set of interaction logics with clear logic and simple operations, allowing project managers to effectively interact with the system through the interface, such as adjusting prediction parameters, exploring different prediction scenarios, etc. At the same time, provide detailed decision-making suggestion information, including the basis of the prediction, possible influencing factors, etc., to enhance the user's decision-making confidence. Through the carefully designed interaction logic, project managers can easily adjust prediction parameters, view different prediction scenarios, and the system will also provide detailed information on decision-making suggestions to help users make more accurate and reasonable decisions. (4) Interface integration: Seamlessly integrate the decision support interface into the overall architecture of the project management system to ensure that users can conveniently access and use the decision support module without leaving the main system, thereby maintaining the coherence of the work process. Closely combine the decision support interface with other parts of the project management system, enabling users to smoothly switch to the decision support module in the system without additional login or complex operations, enhancing the user experience. (5) User training and feedback: Provide comprehensive and in-depth interface usage training for project managers to ensure that they can fully understand and master the various functions of the decision support module, so as to maximize its effectiveness in actual work. Establish an effective user feedback mechanism, regularly collect the usage experiences and suggestions of project managers on the interface and functions, and based on this, continuously optimize and improve the interface and functions.

[0064] Specifically, the visualization display module 15 is specifically used for:

[0065] Using advanced visualization tools and technologies to generate visualization elements including charts and dashboards from project data for data display. Among them, the visualization tools include: ECharts, D3.js, and the project data includes: project progress, cost, and quality data.

[0066] Specific implementation of the visualization module 15:

[0067] 1. Selection and integration methods of visualization tools: (1) Tool selection: According to the specific requirements and technology stack of the project, carefully select visualization tools suitable for the project characteristics. For example, if the project needs to display complex interactive charts, ECharts can be selected because it provides a rich variety of chart types and interactive functions; while if the project requires highly customized data visualization, D3.js may be more appropriate because it offers great flexibility and control capabilities, allowing developers to deeply customize according to requirements. (2) Environment setup: Install and configure the selected visualization tools in the project development environment to ensure that these tools can be seamlessly integrated with the existing project management system without compatibility issues, and at the same time lay a solid foundation for subsequent data display and interactive functions. (3) Interface development: Develop efficient data interfaces that will be responsible for extracting the data required by the visualization tools from the project database, ensuring the real-time and accuracy of the data, and at the same time ensuring the security of the interfaces to prevent data leakage.

[0068] 2. Specific implementation of data display: (1) Data preparation: Clearly define the key data indicators to be displayed in the project, such as project progress, cost, quality, etc., and conduct a detailed sorting and classification of these data. Preprocess the data, including cleaning, deduplication, conversion, etc., to ensure that the data used for display is accurate and meets the requirements of visualization display. (2) Visualization design: Design the layout of charts and dashboards, including selecting appropriate chart types (such as line charts, bar charts, pie charts, radar charts, etc.). Determine visual elements such as the color, font, and size of the visualization elements to provide a clear and beautiful display effect. (3) Function implementation: Use the selected visualization tools to implement the specific functions of charts and dashboards according to the design drawings. Develop interactive functions such as click-through drilling, data filtering, time range selection, etc., which enable users to more conveniently explore and analyze data, enhancing the interactivity and user experience of the entire system. (4) Customized display: Provide an easy-to-operate custom configuration interface that allows project managers to select the data indicators and chart types to be displayed according to their own needs, realizing personalized data display. At the same time, implement the saving and loading functions of custom configurations to facilitate users to quickly switch between different display modes and improve work efficiency. (2) System integration: Seamlessly integrate the visualization module into the front-end part of the project management system to ensure that users can easily access these functions in the system and work in coordination with other system functions, providing users with a unified and efficient project management platform.

[0069] The system 10 further includes a background management module 16, and the background management module 16 includes:

[0070] The data backup unit 161 is used to automatically perform data backup regularly by adopting advanced encryption technology to ensure rapid recovery in case of data loss or damage, while protecting the security of the backup data;

[0071] The system upgrade unit 162 is used to automatically detect and download the latest upgrade package, and ensure the integrity and continuity of data during the upgrade process, avoiding the risks of data loss or system crash;

[0072] The user management unit 163 is used for user creation, deletion, password modification, and permission assignment.

[0073] Specific implementation of the background management module 16:

[0074] 1. User account management: Administrators can create new users, delete users, or modify user passwords through the background management interface. The system supports efficient batch user operations, so that administrators can process a large amount of user information in a short time without operating one by one, thus significantly improving management efficiency and reducing the workload of administrators.

[0075] 2. Permission control: The system adopts the role-based access control (RBAC) model. By assigning corresponding permissions to different user roles, it ensures the reasonable use and security of system resources. This permission control mechanism can precisely restrict users' behaviors, enabling them to only access and operate functions and data within their permission scope, effectively preventing the risks of unauthorized access and data leakage. By implementing refined permission control, the system ensures that each user strictly complies with permission regulations during operation, which not only improves the security of the system but also enables users to focus more on their own work areas when using the system.

[0076] 3. Login and operation logs: The system details the user's login time, login IP address, operation type, and operation result. These log information is crucial for the security audit and problem tracking of the system. By analyzing the login and operation logs, administrators can promptly discover abnormal behaviors and take corresponding security measures. The log recording function provides a reliable security audit tool for the system. It helps administrators monitor users' operation activities and ensure the stable operation of the system. In case of security incidents or system failures, the operation logs can provide valuable information for problem tracking, helping to quickly locate and solve problems.

[0077] 4. Encryption technology and log recording: To ensure the security of the system, the background management module adopts advanced encryption technology to encrypt and store sensitive data during storage and transmission. In addition, the system also records the historical records of system operations in detail through the log recording function, including operation time, operation type, operation result, etc., for review and traceability when necessary.

[0078] (1) Encryption technology: Encrypt sensitive data, including user passwords, communication data, etc. Use protocols such as HTTPS, SSL / TLS to ensure the security of data transmission.

[0079] (2) Logging: Design a logging system to ensure that all key operations are recorded and traceable. The logs should contain sufficient information for detailed analysis in case of security incidents or system failures.

[0080] The system 10 further includes a system integration and interface module 17. The system integration and interface module 17 adopts a hierarchical structure, specifically including:

[0081] A data access layer 171, used to establish connections with databases of different enterprise information systems to achieve transparent data access, and provide interfaces for data reading, writing, and updating operations to ensure the atomicity, consistency, isolation, and durability of data operations;

[0082] A business logic layer 172, used to convert data from different data sources into a unified format to implement data mapping rules, perform data conversion operations to meet the compatibility requirements of data formats between different systems, and conduct data verification to ensure the accuracy and integrity of data;

[0083] An interface layer 173, used to standardize API interfaces and make the standard API interfaces follow the RESTful and SOAP protocols to ensure the interoperability and cross-platform capabilities of the interfaces, and customize API interfaces according to specific business requirements to meet personalized integration scenarios;

[0084] A security management layer 174, used to encrypt the data transmission process, control system access operations, and monitor the system to ensure the security of data during transmission and storage, and prevent data leakage and unauthorized access.

[0085] Specific implementation of the system integration and interface module 17:

[0086] (1) Standard API interface implementation: When designing the API interface, the best practices of the RESTful and SOAP protocols are fully considered to ensure the generality and usability of the interface. Provide detailed API documentation, including function descriptions, request parameters, response formats, etc. of the interface, for easy use and understanding by developers. Implement a strict permission control mechanism to ensure the security of the API interface through authentication and authorization. Conduct performance testing and optimization on the API interface to reduce response time and improve data exchange efficiency.

[0087] (2) Implementation of customized integration solutions: Conduct in-depth communication with customer enterprises to comprehensively understand their business processes and information system architectures. Based on the specific needs of customers, tailor integration solutions, including data mapping rules, interface design, etc. Through system integration testing, verify the feasibility and stability of the customized solutions to ensure that they meet the business needs of customers. Continuously optimize and upgrade the integration solutions according to the business development of enterprises to maintain the flexibility and adaptability of the systems.

[0088] (3) Implementation of data exchange and synchronization functions: Develop an efficient data exchange engine to support real-time data processing, asynchronous message queues, and batch data processing modes. Implement an error handling mechanism and logging function to facilitate quick location and resolution of problems during data exchange. Design a refined data synchronization strategy to ensure the consistency and real-time nature of data across multiple systems. Continuously monitor the performance of data exchange and synchronization functions to ensure the stable operation of the system under high loads.

[0089] (4) Ensuring security and stability: Adopt encryption technologies such as SSL / TLS to ensure the security of data during transmission, prevent data from being intercepted or tampered with during transmission, safeguard the confidentiality and integrity of information, and thus maintain the security of user data. Implement data access permission control. By setting strict user permissions and role management, prevent unauthorized data access, ensure that only authorized users can access sensitive data, and reduce the risk of data leakage. Adopt technical means such as load balancing and fault tolerance mechanisms to improve the availability and stability of the system. By distributing requests to multiple servers, avoid single points of failure and ensure that the system can still operate normally in the face of high traffic or hardware failures. Regularly conduct security vulnerability scans and risk assessments on the system. Through professional security detection tools and expert evaluations, promptly discover and repair potential security hazards in the system to ensure the long-term secure operation of the system and reduce the probability of security incidents.

[0090] In addition, based on the above structure, the system further includes: an intelligent analysis algorithm module 18, and the intelligent analysis algorithm module 18 is specifically used for:

[0091] 1. Data preprocessing:

[0092] (1) Extract multi-dimensional features from the fused data, including time series features, text features, image features, etc., to provide basic data for subsequent complex analysis.

[0093] (2) Use principal component analysis (PCA) or autoencoder technology to perform dimensionality reduction on the data, reducing the computational complexity while retaining the main information of the data.

[0094] 2. Self-developed algorithms:

[0095] (1) Time series prediction: The long short-term memory network (LSTM) is used for time series prediction, which is suitable for dealing with and predicting the sequential dependence of project data.

[0096] h t = o t ·tanh(W h h t-1 + W x x t + b h )

[0097] In the formula, h t is the hidden state, o t is the output gate, W h and W x are weight matrices, b h is the bias term, and x t is the input at the current time step.

[0098] Specifically, the hidden state is the core of the LSTM network. It is passed along the sequence and carries information from past time steps. At each time step, the hidden state is updated and used for the calculation of the next time step. The output gate determines how much information in the hidden state at the current time step needs to be output to the next hidden state and the final output. The value of the output gate ranges between 0 and 1, where 1 represents full output and 0 represents no output. The weight matrix W includes the input weight matrix, forget gate weight matrix, output gate weight matrix, and input gate weight matrix. These weight matrices are used in the LSTM cell to calculate different gating signals and control the information flow. The bias term b is the bias vector corresponding to each gate, and they can be used to adjust the output of the activation function of the gate. The input at the current time step is one data point in the sequence, and it will be processed together with the hidden state of the previous time step to generate the output of the current time step.

[0099] (2) Classification and clustering algorithms:

[0100] Classification algorithm: The support vector machine (SVM) is used for classification, which is suitable for classifying projects into different categories.

[0101]

[0102] In the formula, sign is the sign function, α i is the Lagrange multiplier, y i is the class label, K is the kernel function, x i is the support vector, and b is the bias term.

[0103] Specifically, the output of the SVM is a sign function that maps the value of the decision function to the positive or negative class. If the value of the decision function is greater than 0, it is predicted as the positive class; if it is less than 0, it is predicted as the negative class. In the SVM, Lagrange multipliers are used to solve the constrained optimization problem, that is, while maximizing the classification margin, ensuring that the data points are correctly classified. The class labels indicate the class to which each training sample belongs, usually represented by +1 and -1. The kernel function is used to map the input data to a high-dimensional feature space so that the optimal separation hyperplane can be found. Common kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. Support vectors are those data points located near the decision boundary, and they are crucial for constructing the classification boundary. The bias term is a constant term in the decision function that determines the distance between the classification boundary and the origin.

[0104] Clustering algorithm: The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is adopted, which is suitable for discovering natural clusters in project data.

[0105] CorePoint:|N ε (x)|≥MinPts

[0106] where N ε (x) is the set of points within the neighborhood of x with a distance less than or equal to ε, and MinPts is the minimum number of points required to form a cluster.

[0107] Specifically, for each point in the dataset, DBSCAN calculates its ε-neighborhood, which is the set of all points with a distance less than or equal to ε from that point. MinPts is a threshold used to determine whether a region is of high density. If the ε-neighborhood of a point contains at least MinPts points, then that point is a core point.

[0108] The basic steps of the DBSCAN algorithm are as follows: Mark all core points. Connect all core points to form clusters. If the neighborhoods of two core points intersect, they belong to the same cluster. Assign border points (those that are not core points but are within the neighborhood of a core point) to the clusters. Noise points are not assigned to any cluster.

[0109] (3) Data integration: Use weighted average or model fusion techniques to integrate the results of different analysis algorithms.

[0110]

[0111] where R is the integrated analysis result, A i is the result of the i-th algorithm, and ω i is the corresponding weight.

[0112] Specifically, the integrated result is a summary of the results of multiple algorithms, which is usually more accurate or robust than the result of a single algorithm. The results generated by each algorithm may be classification labels, prediction values, clustering labels, etc. Weights are used to reflect the importance of different algorithm results during the integration process. The selection of weights can be based on the performance of the algorithms, such as accuracy or cross-validation scores.

[0113] 3. Module Function Implementation:

[0114] (1) Algorithm Selection and Optimization: During the implementation of the functions of the intelligent analysis algorithm module, the selection and optimization of algorithms are crucial steps, which directly affect the final analysis results and the performance of the system.

[0115] Algorithm Selection: First, according to the specific analysis objectives and data characteristics of the project, select appropriate algorithms. For example, for time series prediction problems, the Long Short-Term Memory network (LSTM) is selected because it can capture long-term dependencies in time series data. For classification tasks, the Support Vector Machine (SVM) is chosen due to its strong generalization ability and classification ability for different types of data. For clustering tasks, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is adopted because it can discover clusters of arbitrary shapes and effectively handle noise points.

[0116] Algorithm Optimization: After the algorithm is selected, use gradient descent or its efficient variants (such as the Adam optimizer) to optimize the parameters of the model. These optimizers can accelerate convergence by adjusting the learning rate, improving the efficiency and performance of model training. During the optimization process, it is also necessary to adjust the hyperparameters of the algorithm, such as the learning rate, batch size, number of hidden layer nodes, etc., to find the optimal model configuration.

[0117] 4. Testing and Optimization:

[0118] (1) Algorithm Performance Evaluation: Algorithm performance evaluation is a key link to ensure the effectiveness of the intelligent analysis algorithm module. It quantitatively evaluates the performance of the algorithm on various test cases to determine the advantages and disadvantages of the algorithm.

[0119] Use metrics such as cross-validation, Mean Squared Error (MSE), or Accuracy to evaluate the algorithm performance.

[0120]

[0121] Wherein, P is the average performance index, which is the final result in the entire evaluation process and represents the average performance of the algorithm on all test cases. This index can be the average value of accuracy, mean square error, or any other index used to measure the performance of the algorithm. The smaller this average performance index is, the better the performance of the algorithm, that is, the smaller the difference between the predicted value and the true value. N is the number of test cases, which represents the total number of independent test cases used to evaluate the performance of the algorithm. In cross-validation, this may refer to the number of test cases in each fold multiplied by the total number of folds. δ is the performance evaluation function, which is used to calculate the performance index of a single test case. For different tasks, this function can be different. For example, in a regression problem, it may be a function that calculates the difference between the predicted value and the true value, such as the calculation formula of mean square error (MSE); in a classification problem, it may be a function that calculates accuracy. A i is the algorithm output, which represents the prediction result of the algorithm for the i-th test case. In a regression problem, this is the numerical value predicted by the algorithm; in a classification problem, this is the class label predicted by the algorithm. R i is the true value, which represents the actual result of the i-th test case, that is, the value used as a comparison standard when evaluating the performance of the algorithm.

[0122] The intelligent project management system based on regulatory integration proposed in this application constructs an all-round, multi-level, and integrated project management platform by integrating advanced information technologies, including the Internet, Internet of Things, big data, artificial intelligence, etc. It has the following beneficial effects: (1) Full life cycle management: The system covers all stages of project planning, initiation, execution, monitoring, and closure, ensuring that the project can be effectively supervised and controlled from start to end. (2) Intelligent data analysis: Using big data analysis and artificial intelligence algorithms, the system can deeply mine project data, provide accurate predictions and optimization suggestions, and greatly improve the scientificity and accuracy of decision-making. (3) Real-time monitoring and early warning: Through real-time data collection and dynamic analysis, the system can timely detect project risks and deviations and issue early warnings, helping the project team quickly take measures to ensure that the project progresses according to the plan. (4) High degree of integration and collaboration: The system realizes seamless docking with other enterprise information systems, such as ERP, CRM, etc., breaks information silos, and promotes collaborative work among departments. (5) User experience optimization: The system has a friendly interface and simple operation, reduces the learning cost of users, and improves work efficiency.

[0123] The above content is only a preferred embodiment of the present invention. For those of ordinary skill in the art, many changes can be made in the specific implementation manners and application scopes according to the idea of the present technical content. As long as these changes do not depart from the concept of the present invention, they all belong to the protection scope of the present invention.

Claims

1. A smart project management system based on integrated supervision, characterized in that: The system is deployed on a cloud platform and includes: Project management module, which is used for task management, progress tracking and document sharing to achieve planning, initiation, execution, monitoring and closing of the entire project life cycle; Data collection and integration module, used to collect project data in real time and perform integration processing; Risk assessment and early warning module, which is used to conduct risk assessment on project data and issue early warning when the risk exceeds the preset threshold; Intelligent decision support module, used to provide decision suggestions to project managers based on data analysis results; The visual display module is used to intuitively display project status, progress, cost, quality and other information in the form of charts, reports, etc.

2. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The project management module includes: The task management unit is used to create tasks and assign them to designated team members, set task deadlines and priorities, and update task status in real time so that project managers can keep track of task progress at any time; The progress tracking unit is used to compare the actual progress of the task with the planned progress and generate progress deviation reports so that project managers can find and solve problems in a timely manner; Document sharing unit, used to share project-related documents and materials to ensure the accuracy and consistency of information.

3. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The data collection and integration module includes: Data interface unit, used to provide data interface with various equipment on the project site, and supports multiple data formats and communication protocols to ensure the real-time and accuracy of collected project data; The data integration unit is used to integrate the collected project data into a unified database and perform cleaning, denoising and standardization.

4. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The risk assessment and early warning module includes: The risk assessment unit is used to establish a risk assessment model based on historical data and real-time data, and to perform real-time analysis on project data to calculate the probability and impact of risk occurrence; The early warning unit is used to automatically issue an early warning message when the risk reaches a preset early warning threshold, wherein the early warning information includes: risk type, risk level and early warning suggestions.

5. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The intelligent decision support module is specifically used for: Use machine learning or deep learning algorithms to establish a project development trend prediction model, and predict the project development trend based on historical data and real-time data to generate decision-making recommendations.

6. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The visual display module is specifically used for: Advanced visualization tools and technologies are used to generate visualization elements including charts and dashboards from project data to achieve data display. The visualization tools include ECharts and D3.js, and the project data include project progress, cost, and quality data.

7. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The system also includes a background management module, which includes: Data backup unit, which uses advanced encryption technology to automatically back up data regularly to ensure rapid recovery in the event of data loss or damage, while protecting the security of backup data; System upgrade unit, used to automatically detect and download the latest upgrade package, while ensuring data integrity and continuity during the upgrade process to avoid the risk of data loss or system crash; The user management unit is used to create, delete, modify passwords, and assign permissions to users.

8. The smart project management system based on integrated supervision as claimed in claim 1, characterized in that: The system further includes a system integration and interface module, which adopts a hierarchical structure and specifically includes: The data access layer is used to build connections with different enterprise information system databases to achieve transparent access to data and provide interfaces for data reading, writing, and updating operations to ensure the atomicity, consistency, isolation, and persistence of data operations; The business logic layer is used to convert data from different data sources into a unified format to implement data mapping rules, perform data conversion operations to meet the compatibility requirements of data formats between different systems, and perform data verification to ensure the accuracy and integrity of the data; The interface layer is used to standardize API interfaces and make them comply with RESTful and SOAP protocols to ensure interoperability and cross-platform capabilities. API interfaces can be customized according to specific business requirements to meet personalized integration scenarios. The security management layer is used to encrypt the data transmission process, continuously control system access operations, and monitor the system to ensure the security of data during transmission and storage, and prevent data leakage and unauthorized access.

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