Project management system based on artificial intelligence and big data

Abstract

The invention provides a project management system integrating artificial intelligence and big data, and aims to improve the project management efficiency and effect through an intelligent means. The system is composed of eight core modules. A comprehensive portal provides a unified entrance for project participants. And the data support center is responsible for maintaining the consistency and accuracy of the data. And the project management module covers whole-process management from project approval to acceptance. The data visualization module visually displays the project dynamic state through the functions of a situation large screen, a multi-level project view and the like. And the evaluation statistical module constructs an evaluation system, generates a report and supports statistical screening. The information achievement management module establishes a knowledge base and promotes knowledge sharing. The artificial intelligence module provides intelligent project management and decision support through machine learning and an intelligent algorithm. The big data module provides deeper insight and prediction for project management by mining and analyzing a large amount of data in the project. All the modules work cooperatively, and efficient management and control of the whole life cycle of the project are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and in particular to a project management system based on artificial intelligence and big data. Background Art

[0002] With the rapid development of information technology, project management plays an increasingly important role in all walks of life. However, traditional project management methods often have problems such as low efficiency, information islands, and difficulty in data integration, which cannot meet the needs of modern project management. Therefore, there is an urgent need for a system that can integrate artificial intelligence and big data technology to achieve efficient project management and intelligent decision-making. Summary of the invention

[0003] The present invention provides a project management system based on artificial intelligence and big data, which aims to improve the efficiency and effect of project management through intelligent means. The system includes eight modules: comprehensive portal, data support center, project management, data visualization, evaluation statistics, information achievement management, artificial intelligence and big data. Each module cooperates with each other to jointly achieve efficient management of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0005] Figure 1 It is a functional framework diagram of the comprehensive portal in the present invention;

[0006] Figure 2 It is a functional framework diagram of the data support center in the present invention;

[0007] Figure 3 This is a functional framework diagram of project management in the present invention;

[0008] Figure 4 This is a functional framework diagram of evaluation statistics in the present invention;

[0009] Figure 5 This is a technical architecture diagram of the artificial intelligence in the present invention. DETAILED DESCRIPTION

[0010] 1. Comprehensive portal, functional framework such as Figure 1 As shown, including:

[0011] Unified information: supports the release of different types of information such as notifications, announcements, official documents, system messages, and early warning messages; can publish information to selected users, and supports attachments and reply functions.

[0012] Unified To-Do: It can display the current user's to-do items, support viewing item details, and provide reminder and reminder functions for overdue to-do items.

[0013] Unified schedule: can display and edit the current user's schedule information, and support automatic establishment of schedules by associating project node data.

[0014] Personal Center: supports displaying and modifying basic personnel information, supports users to modify passwords, and supports displaying user system operation records.

[0015] Application integration: supports hierarchical and classified display of applications and provides application search and recommendation functions.

[0016] Application management: supports installation, update, and uninstallation of single and batch applications.

[0017] Application authorization: Provides application permission management function and authorizes application usage permissions based on user type.

[0018] 2. Data Support Center, functional framework Figure 2 As shown, including:

[0019] Department data management: supports the establishment of a multi-level organizational structure based on affiliation. It can visualize the organizational structure through a tree diagram, and provide information maintenance and permission configuration functions for departments of different levels and types.

[0020] User data management: Ability to add, modify, delete user types and configure permissions based on usage requirements, support user registration, login, and logout, and have user information management and maintenance functions.

[0021] Dictionary engine: It can store and manage basic data in the system, support adding, querying, modifying, deleting and batch operations, and provide multi-level classification management functions.

[0022] Process Engine: It can build and manage project management process models, support model addition, query, modification, copy, deletion and batch operations, and provide graphical design functions for processes.

[0023] Form engine: supports adding, editing, copying, querying, deleting and batch operations of system form data, and provides graphical design functions for forms.

[0024] Rule Engine: supports users to define, edit, and maintain evaluation logic rules. It can be integrated with other engines and system components to provide graphical design functions for rules.

[0025] Visualization engine: supports associating and visualizing project data information, can select display data on the large screen, and adjust the interface layout.

[0026] Report engine: supports adding, editing, copying, querying, deleting and batch operations of report templates, and provides graphical design functions for report templates.

[0027] 3. Project management, functional framework such as Figure 3 As shown, including:

[0028] Template tools: Integrate form templates, business process templates, project templates, and official document template functions to uniformly provide various template tools required for project management, and use template tools to flexibly combine and configure project management processes.

[0029] Project justification: It can realize project application, project approval and plan issuance, provide project set management and attachment management functions, and support the display of project lists and project relationship diagrams.

[0030] Organization and implementation: Provides project process management functions, supports users to promote project progress by filling in work logs, provides file upload, node approval, information change and other functions, and has the ability to verify the integrity and logic of the reported data. At the same time, provides project file management functions, supports batch upload, download, delete, category view, online rename, preview and other operations of files.

[0031] Process supervision: Supports setting project status according to lag time, can display and warn project progress and node progress, and supports batch issuance of reminders, message notifications and other operations for projects with different progress statuses. Provides project log and quick communication functions, and can quickly view personnel information based on project operation history.

[0032] Project acceptance: Ability to set up a separate project acceptance stage in the project process, support project achievement information registration, provide unified management of acceptance materials, and upload checklist comparison functions.

[0033] 4. Data Visualization

[0034] Situation large screen configuration: Supports setting the content of each section of the interface through editable methods such as dragging and dropping. Design multi-level large screen templates according to actual needs.

[0035] Comprehensive situation of multi-level projects: Data visualization display templates can be applied to display the comprehensive situation of project data at all levels and departments, and support filtering the scope of displayed data by limiting conditions such as project start time and progress status.

[0036] 5. Evaluation statistics, functional framework Figure 4 As shown, including:

[0037] Evaluation indicator system: It can obtain project evaluation indicators through automatic collection of project data or form filling, establish a project evaluation indicator library, provide indicator classification management functions, and support indicator addition, deletion, modification and batch operation.

[0038] Evaluation model: Supports the establishment of evaluation models by calling indicators and algorithm rules of the project evaluation indicator library through workflow and logic diagram. Centrally manage various evaluation models and provide model rapid maintenance functions.

[0039] Evaluation Report: Provides online display of various evaluation result reports, and supports template configuration and one-click generation of evaluation reports.

[0040] Statistical screening: You can select multiple groups of limiting conditions (project funds, project cycle, department, etc.) for project statistics, and support adjusting the statistical results by setting the header of the project statistics list.

[0041] Project report: Ability to set project report templates, customize project report elements and styles, support initiating report document multi-person collaborative online editing projects, support project report generation and export. At the same time, support associated project data, and set conditions such as time range and category range of associated project data.

[0042] Project file statistics: supports statistical display of relevant attachments in project reports, which can be displayed by category and exported in batches.

[0043] VI. Information Achievement Management

[0044] Knowledge Catalog: Provides a multi-dimensional knowledge classification system and supports the construction of a multi-dimensional knowledge system.

[0045] Knowledge base: Provides public and personal information achievement management functions, and supports the collection and entry of project information achievement related documents into the project knowledge base.

[0046] Knowledge search: It can recommend historically similar projects and assist users in project management. It builds a knowledge service platform, supports full-text search, provides one-stop knowledge search and discovery, and provides knowledge navigation based on project knowledge types.

[0047] User portrait knowledge base construction: supports the construction of user portraits for project managers, provides targeted indexing options, and supports intelligent recommendations based on roles, selection preferences, etc.

[0048] 7. Artificial Intelligence, Technical Architecture Figure 5 shown.

[0049] Knowledge graph: Promote team collaboration and decision-making by visually displaying dependencies between project tasks, resource allocation, etc. The system can automatically identify and update information in the knowledge graph to ensure the real-time and accuracy of the data. The TransE model is used for knowledge graph embedding, and its loss function is defined as follows:

[0050]

[0051] Among them, d(x,y)d(x,y) represents the distance between vectors xx and yy, γγ is the interval hyperparameter, SS is the correct set of triplets, and S′S′ is the set of incorrect triplets obtained by negative sampling.

[0052] Intelligent question-answering system: Using natural language processing technology, it helps project managers and team members quickly obtain project-related information and answers. Users can ask questions in natural language, and the system will automatically parse the questions and provide accurate answers, covering aspects such as project progress, resource allocation, and risk assessment. The core idea of ​​the question-answering system based on the Transformer model is to capture the global dependencies in the input sequence through the self-attention mechanism.

[0053] Automatic document generation: Based on machine learning algorithms, the system can automatically generate documents such as contracts and plans according to project requirements and templates. Users only need to enter relevant parameters and requirements, and the system can quickly generate documents that meet the requirements.

[0054] Similar project recommendation: Use deep learning algorithms to analyze the characteristics and requirements of the current project, and find similar cases from historical projects for recommendation. The cosine similarity in collaborative filtering measures the similarity of project features. The formula is:

[0055]

[0056] Among them, AA and BB represent the vector representation of two items respectively.

[0057] Chat File: provides a new mode of interaction with project documents. Users can ask questions to the system through dialogue, and the system will extract relevant information from the project documents and give answers. The information retrieval algorithm used is BM25, which is a scoring formula for measuring the relevance of queries and documents. The formula is:

[0058]

[0059] Where DD represents the document, QQ represents the query, titi represents the ii-th term in the query, nn is the total number of terms in the query, IDF(ti) is the inverse document frequency of the term titi, f(ti,D) is the frequency of the term titi in the document DD, k1k1 and bb are tuning parameters, DLDL is the length of the document, and DFDF is the average length of the document collection.

[0060]

[0061] Natural Language Processing (NLP): Use NLP technology to conduct in-depth analysis of project documents, automatically identify problems and risk points, and provide corresponding suggestions and warnings. The text classification algorithm formula is:

[0062] where yy represents the class label, xx represents the feature vector, nn is the number of features, P(y)P(y) is the prior probability of class yy, P(xi|y)P(xi|y) is the conditional probability of feature xixi given class yy, and P(x)P(x) is the marginal probability of feature vector xx (as a normalization factor).

[0063] Application of blockchain technology: Provide a secure, transparent and tamper-proof data storage and exchange platform for project management information systems. By recording key data on the blockchain, the integrity and credibility of the data can be ensured to prevent data from being tampered with or lost.

[0064] 8. Big Data

[0065] Data collection and processing: Collect and integrate data from multiple data sources, clean, transform and integrate. During data cleaning, a Bloom filter is used to determine whether an element exists in a set. The formula is as follows.

[0066] The result of the Hickey function, xx is the element to be judged.

[0067]

[0068] Data analysis and decision support: cost analysis, performance analysis, risk analysis, etc. The linear regression model is used to predict project cost and progress, and its formula is: where yy is the target variable (such as cost or progress), wiwi is the feature weight, xixi is the feature variable, and bb is the bias term.

[0069]

[0070] Resource optimization and allocation: Comprehensively analyze the utilization of project resources and allocate resources reasonably. The objective function and constraints of linear programming can be expressed as:

[0071] \begin{align*}

[0072] \text{maximize} & \quad c^T x \\

[0073] \text{subject to} & \quad Ax \leq b \\

[0074] & \quad x_i \geq 0, \quad i = 1, 2, ..., n

[0075] Among them, cc is the coefficient vector of the objective function, xx is the decision variable vector, AA is the coefficient matrix of the constraints, and bb is the right boundary vector of the constraints.

[0076] Real-time monitoring and early warning: Monitor project progress in real time and predict potential risks in advance. The Isolation Forest algorithm in anomaly detection isolates anomalies by building multiple random trees. For each tree, a random feature and a random threshold are recursively selected to divide the data set.

[0077] Data visualization and report generation: Automatically generate various reports and charts to intuitively display project status trends.

Claims

1. A project management system based on artificial intelligence and big data, comprising: Comprehensive portal module for unified information release, displaying user to-do items, editing schedule information, managing personal information, integrating and managing applications, and authorizing application use; Data support center module, used to manage organizational structure, user data, dictionary engine, process engine, form engine, rule engine, visualization engine and report engine; The project management module provides project template tools, project justification, organization and implementation, process supervision and project acceptance functions; Data visualization module, supporting large-screen configuration of situation and comprehensive situation display of multi-level projects; The evaluation statistics module provides the establishment of evaluation indicator system, creation of evaluation model, generation of evaluation report, project statistics screening, report setting and export, and project file statistics; Information achievement management module, used to build knowledge catalogs, manage public and personal information achievements, realize knowledge search and build user portrait knowledge base; Artificial intelligence module, including knowledge graph, intelligent question and answer, writing task automation, similar project recommendation, document assistant Chat file, natural language processing NLP and blockchain technology application; The big data module has the capabilities of data collection and processing, data analysis and decision support, resource optimization allocation, real-time monitoring and early warning, and data visualization report generation.

2. The project management system according to claim 1, wherein the integrated portal module further comprises: Information release mechanisms in different forms such as notices, announcements, official documents, etc. Unified message center, receiving system messages and warning messages; Targeted information release and online collaborative data collection.

3. The project management system according to claim 1, wherein the data support center module further comprises: Organization department data management function, visually maintain the organizational structure through the organizational chart; User data management functions, including user registration, login, logout and permission configuration; The dictionary engine, process engine, form engine, rule engine, visualization engine and report engine are used to manage basic data, custom approval processes, custom form data, business logic rule configuration, visual BI large screen and custom reports respectively.

4. The project management system according to claim 1, wherein the project management module further comprises: Template tools integrate form templates, business process templates, project templates, and official document templates, and uniformly provide various template tools required for project management. Template tools can be used to flexibly combine and configure project management processes. Project justification function to promote the early stages of the project, including project application, project approval, and plan issuance; The organization and implementation function promotes the project progress by filling in the work log, and has the functions of completeness verification and logic verification of the filled data. At the same time, it provides project file management function. The process supervision function realizes the status display and early warning of project progress and node progress, and batch issues reminders, message notifications and other operations for projects with different progress statuses. Project acceptance process management and results information registration.

5. The project management system according to claim 1, wherein the data visualization module further comprises: Editable status screen configuration; Multi-level project comprehensive situation display, data range can be filtered according to limited conditions; The travel status of personnel is displayed, and project data is associated and presented through map tools.

6. The project management system according to claim 1, wherein the evaluation statistics module further comprises: Evaluation indicator system, automatic collection or form filling to obtain project evaluation indicators; Evaluation model, call the evaluation index library and establish algorithm rules; Evaluation reports are displayed online and generated with one click; Project statistics screening, select multiple groups of limiting conditions for project statistics; Customized editing of project reports and multi-person collaborative online editing.

7. The project management system according to claim 1, wherein the information achievement management module further comprises: Construction of a multi-dimensional knowledge classification system; Management of public and personal information outcomes; Recommendation of historically similar projects to assist project management; Knowledge service platform, providing full-text search and one-stop search discovery.

8. The project management system according to claim 1, wherein the artificial intelligence module further comprises: Knowledge graph, showing project task dependencies and resource allocation; Intelligent question-and-answer system to quickly obtain project-related information; Automatically write contracts, plans and other documents; Intelligent recommendation of similar projects; Document project assistant Chat file, providing insights into project documents; Natural language processing (NLP) to identify issues and risks in project documents; Blockchain technology provides a secure and transparent data storage and exchange platform.

9. The project management system according to claim 1, wherein the big data module further comprises: Data collection and processing, integrating data from multiple data sources; Cost analysis, performance analysis and risk analysis, providing decision support through data analysis; Resource optimization and allocation, reasonable allocation of project resources; Real-time monitoring and early warning to predict potential risks in advance; Data visualization and report generation to intuitively display project status and trends.

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