Enterprise project intelligent visual management method based on big data

Abstract

The invention discloses an enterprise project intelligent visual management method based on big data, and belongs to the field of data visualization, and the method comprises the steps: collecting original data related to an enterprise project; preprocessing the original data to obtain a structured project data set; analyzing the project data set to obtain a project management mode and a project management rule, and constructing a project management knowledge graph based on the project management mode and the project management rule; a visual management platform is built, an interactive visual interface is generated in combination with the project management knowledge graph, and multi-dimensional visual display of the whole life cycle of the project is achieved. The technical problems that in the prior art, enterprise project management data is dispersed, effective integration and analysis are lacked, and project progress and risks are difficult to master comprehensively are solved, the project management knowledge graph is constructed based on big data analysis, full-life-cycle multi-dimensional visual management of projects is achieved, and the project management efficiency is improved. And the project management efficiency and the decision level are improved.

Description

Technical Field

[0001] The present invention relates to the field of data visualization, and in particular to an enterprise project intelligent visualization management method based on big data. Background Art

[0002] With the increasing complexity of enterprise project management, traditional project management methods can no longer meet the needs of modern enterprises. At present, enterprise project management generally has problems such as data dispersion and lack of effective integration and analysis, which makes it difficult for project managers to fully grasp project progress and risks, affecting project management efficiency and decision-making level. In the existing technology, some companies have tried to introduce information management tools, but there are still problems such as data islands and lack of global perspective. Some companies have tried to use big data analysis technology for project management, but lack of effective data integration and knowledge extraction methods, making it difficult to form a comprehensive and systematic project management knowledge system. At the same time, the existing project management systems generally lack intuitive and interactive visualization means, which is not conducive to project managers to quickly understand the project status and timely discover and respond to risks. Summary of the invention

[0003] This application provides an intelligent visualization management method for enterprise projects based on big data, aiming to solve the technical problems in the prior art that enterprise project management data is scattered, lacks effective integration and analysis, and is difficult to fully grasp project progress and risks.

[0004] The present application discloses an intelligent visualization management method for enterprise projects based on big data, including: determining enterprise projects, collecting original data related to the enterprise projects, the original data including project progress data, resource allocation data, budget execution data and risk management data; preprocessing the original data to obtain a structured project data set, the preprocessing including data cleaning, data integration and data conversion; analyzing the project data set to obtain project management models and rules, and constructing a project management knowledge graph based on the project management models and rules, the project management knowledge graph including key elements of project management, node relationships and semantic descriptions; building a visualization management platform, combining the project management knowledge graph to generate an interactive visualization interface, and realizing a multi-dimensional visualization display of the entire life cycle of the project.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] Due to the adoption of identifying enterprise projects, the original data related to the enterprise projects are collected. The original data includes project progress data, resource allocation data, budget execution data and risk management data. By comprehensively collecting project-related data, a data foundation is laid for subsequent data analysis and knowledge graph construction; the original data is preprocessed to obtain a structured project data set. The preprocessing includes data cleaning, data integration and data conversion. Through data preprocessing, the noise and redundancy in the original data are eliminated, and the data is standardized and structured, which is convenient for subsequent data analysis and mining; the project data set is analyzed to obtain project management models and rules, and a project management knowledge graph is constructed based on the project management models and rules. The project management knowledge graph includes key elements of project management, node relationships and semantic descriptions. Through data analysis, project The model and rules of project management are identified, and a knowledge graph is constructed based on this, forming a comprehensive and systematic project management knowledge system, providing strong knowledge support for project management; a visual management platform is built, and an interactive visual interface is generated in combination with the project management knowledge graph to achieve a multi-dimensional visual display of the entire project life cycle. Through the visual management platform, the knowledge in the project management knowledge graph is presented in an intuitive and interactive manner, realizing a technical solution for multi-dimensional visual management of the entire project life cycle, solving the technical problems in the existing technology of enterprise project management data being scattered, lacking effective integration and analysis, and making it difficult to fully grasp project progress and risks, achieving the technical effect of building a project management knowledge graph based on big data analysis, realizing multi-dimensional visual management of the entire project life cycle, and improving project management efficiency and decision-making level.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of an enterprise project intelligent visualization management method based on big data is provided for the embodiment of the present application;

[0009] Figure 2 A flowchart for generating raw data in an enterprise project intelligent visualization management method based on big data is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The overall idea of ​​the technical solution provided by this application is as follows:

[0011] The embodiment of the present application provides an intelligent visualization management method for enterprise projects based on big data. First, determine the enterprise project, collect multi-dimensional original data such as project progress, resource allocation, budget execution and risk management, and lay a data foundation for subsequent analysis. Secondly, perform pre-processing operations such as cleaning, integration and conversion on the original data to obtain a structured project data set for subsequent analysis and mining. Thirdly, use big data analysis technology to analyze and mine the project data set to discover the patterns and laws of project management, and on this basis, build a project management knowledge graph containing key elements, node relationships and semantic descriptions to form a comprehensive and systematic project management knowledge system. Finally, build a visualization management platform, combine the knowledge graph to generate an interactive visualization interface, and realize multi-dimensional visualization of the entire life cycle of the project.

[0012] To sum up, this application is based on big data analysis, constructs a project management knowledge graph, and presents and interacts with knowledge through a visual management platform, forming a comprehensive, systematic and intuitive project intelligent visual management solution, achieving the technical effect of improving project management efficiency and decision-making level, and providing strong support for the intelligence and refinement of enterprise project management.

[0013] After introducing the basic principles of the present application, the non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the drawings in the specification.

[0014] like Figure 1 As shown, the embodiment of the present application provides an enterprise project intelligent visualization management method based on big data, the method comprising:

[0015] Identify enterprise projects and collect original data related to the enterprise projects, including project progress data, resource allocation data, budget execution data and risk management data.

[0016] Specifically, first, determine the enterprise project. The enterprise project can be various projects within the enterprise, such as product research and development projects, marketing projects, information construction projects, etc. After determining the enterprise project, collect the original data related to the enterprise project. The collection of original data can be achieved in many ways, such as extracting from various information systems within the enterprise, sorting from project-related documents, and obtaining through manual entry. The original data includes project progress data, resource allocation data, budget execution data, and risk management data. Among them, the project progress data indicates the progress of the project, such as the completion percentage of the project; the resource allocation data indicates the allocation of human resources, equipment, materials, etc. required for the project; the budget execution data indicates the use of the project budget, such as the difference between actual expenditure and budget; the risk management data indicates the potential risks faced by the project and the implementation of risk response measures.

[0017] By collecting original data related to enterprise projects, we can fully understand the current status of project execution and provide a basis for subsequent data analysis and visual management.

[0018] The raw data is preprocessed to obtain a structured project data set, wherein the preprocessing includes data cleaning, data integration and data conversion.

[0019] Specifically, the collected raw data is preprocessed to convert the raw data into a structured project data set for subsequent data analysis and knowledge mining. The preprocessing process includes three steps: data cleaning, data integration, and data conversion. Among them, data cleaning refers to identifying and correcting errors, inconsistencies, duplications, or missing data in the raw data to improve data quality. Data integration refers to integrating raw data from different systems and formats into a consistent data storage to form a unified data view. Data conversion refers to converting the cleaned and integrated data into a data format and structure suitable for analysis and mining, such as structuring unstructured data and discretizing continuous data, so as to obtain a structured project data set for enterprise projects.

[0020] The project data set is analyzed to obtain project management patterns and rules, and a project management knowledge graph is constructed based on the project management patterns and rules. The project management knowledge graph includes key elements of project management, node relationships and semantic descriptions.

[0021] Specifically, after obtaining the project data set, the obtained project data set is analyzed and mined to discover the typical patterns and internal laws in the project management process, and based on this, a project management knowledge graph is constructed to provide a semantic basis for visual management.

[0022] Project management patterns and rules refer to the use of data mining algorithms to discover frequently occurring patterns, association rules, clusters, etc. from project data sets, reflecting the general rules of project management. For example, the association rules between project attributes are discovered, such as the association between "project schedule delay" and "low resource utilization"; for another example, the project data set is divided into several categories to identify the characteristic patterns of different types of projects. By analyzing the discovered frequent patterns, association rules, etc., the general rules and experiences in the project management process can be summarized. Then, based on the project management patterns and rules, the project management knowledge is further refined and semanticized to form a project management knowledge graph. The project management knowledge graph includes key elements of project management, node relationships, and semantic descriptions. Among them, key elements of project management refer to the core concepts in the project management process, such as projects, tasks, resources, risks, etc.; node relationships refer to the semantic associations between key elements, such as the allocation relationship between tasks and resources, the impact relationship between projects and risks, etc.; semantic descriptions refer to the textual definitions and explanations of key elements and node relationships.

[0023] Build a visual management platform, combine the project management knowledge graph to generate an interactive visual interface, and realize multi-dimensional visual display of the entire project life cycle.

[0024] Specifically, a visual management platform is built, combining the project management knowledge graph with visualization technology to generate an intuitive and easy-to-use visualization interface to help managers fully understand the project status and support project management decisions.

[0025] First, considering the business characteristics of project management and the analysis needs of users, an intuitive and easy-to-use interface layout and navigation mechanism are designed. For example, a modular and configurable interface layout is adopted to organize and present project information according to different dimensions (such as progress, cost, quality, etc.); multi-level information drilling and aggregation functions are provided to facilitate users to quickly locate and analyze project status; personalized views for different management roles are set to meet the needs of different user groups such as decision makers, managers, and executors. Then, in terms of developing visualization components, a general and flexible visualization control library is implemented to support the interactive display of multi-dimensional project data. Among them, the visualization components cover common data visualization charts, such as bar charts, line charts, scatter plots, radar charts, Gantt charts, etc., to support the visualization of key indicators such as project progress, resource usage, and budget execution; at the same time, the visualization components support rich interactive functions, such as zooming in and out, panning, filtering, and related highlighting, providing users with a flexible and convenient way to explore data. Then, the semantic information in the knowledge graph is integrated with the visualization interface to achieve knowledge-based visualization analysis and decision support. For example, an overview of the knowledge graph is presented in a visual interface, showing the connections between core concepts in the field of project management. When users browse specific project information, semantic association recommendations are made based on the knowledge graph, and intelligent suggestions are given on project optimization directions, risk response strategies, etc. When users conduct cross-project comparative analysis, the knowledge graph is used to achieve automatic matching and semantic conversion of the indicator system, thereby improving the consistency and coherence of the analysis.

[0026] Through the constructed visual management platform, data visualization of all aspects of the project life cycle can be realized, supporting multi-dimensional project status analysis and decision optimization, intuitively presenting key factors such as project progress trends, risk bottlenecks, resource usage, etc., helping project managers to grasp the overall picture of the project, respond to potential risks in a timely manner, and improve project management efficiency and quality.

[0027] Further, such as Figure 2 As shown, the embodiment of the present application also includes:

[0028] Determine the data collection indicator system for the enterprise project;

[0029] Collect project progress data, resource allocation data, budget execution data and risk management data from the project management system, enterprise resource planning system, financial management system and risk management system according to the data collection indicator system;

[0030] The project progress data, resource allocation data, budget execution data and risk management data are aggregated to generate original data.

[0031] In a feasible implementation, first, determine the data collection indicator system for enterprise projects. The data collection indicator system refers to a structured data collection catalog and standard designed for the information needs of project management, which clearly defines which project management data needs to be collected, and the metadata information such as the content, format, frequency, and source of the data. By comprehensively analyzing the project management business process, sorting out the key performance indicators and measurement dimensions of each link, and comprehensively considering factors such as the availability, timeliness, and accuracy of the data, a data collection indicator system is obtained. For example, collection indicators are designed from aspects such as project progress, resource utilization, budget execution, and risk control to form a collection framework in the form of an indicator tree or indicator matrix.

[0032] Then, according to the determined data collection indicator system, relevant raw data are collected from multiple business systems, including project progress data, resource allocation data, budget execution data and risk control data. Among them, the project progress data comes from the project management system, including the execution of the project task plan, completion percentage, etc.; the resource allocation data comes from the enterprise resource planning system, including project human resource allocation, equipment and material use, etc.; the budget execution data comes from the financial management system, including project cost expenditure, fund use progress, etc.; the risk control data comes from the risk management system, including project risk identification, assessment, and countermeasures implementation. Among them, the data collection process interfaces with the relevant business systems and exchanges data to ensure the timeliness, completeness and consistency of the collection. Subsequently, the collected project progress data, resource allocation data, budget execution data and risk control data are summarized and integrated to generate raw data, providing a data basis for subsequent data analysis and mining.

[0033] Furthermore, the embodiment of the present application also includes:

[0034] Clean the original data to remove noise and redundant data;

[0035] Integrate the cleaned data to eliminate the heterogeneity of data format and semantics;

[0036] The integrated data is transformed to generate a structured project data set.

[0037] In a feasible implementation, first, the collected raw data is cleaned to identify and remove noise data and redundant data in the data to improve data quality. Among them, noise data refers to data that is irrelevant to project management business or obviously wrong, such as null values, outliers, inconsistent timestamps, etc.; redundant data refers to data that is recorded repeatedly or derived, such as repeated records of different statistical calibers and frequencies of the same indicator. Then, the cleaned data is integrated to eliminate the heterogeneity between different data sources and form a consistent and standardized data view. Data heterogeneity includes format differences and semantic differences. Format differences refer to the inconsistency of data in different systems in terms of encoding, type, precision, etc., such as date format, numerical precision, character encoding, etc.; semantic differences refer to the inconsistency of naming, definition, calculation method, etc. of the same business concept in different systems. Data integration refers to the common data standards in the field of project management and the data dictionary within the enterprise to formulate a unified data model and metadata specification to convert heterogeneous data into a unified representation. For example, define a unified date format and measurement unit, and convert project date data from different sources; define standard project management indicator terminology and calculation caliber, and map and convert semantically heterogeneous indicator data, etc.

[0038] After that, the integrated data is transformed to further transform the data with unified format and semantics into a structured form suitable for visual display to generate a project data set. The main tasks of data conversion are dimensional modeling and measurement aggregation, that is, to store data in a structured manner according to the core analysis dimensions of project management (such as project, time, organization, resources, etc.) and measurement indicators (such as progress, cost, quality, etc.), forming multidimensional data models such as star and snowflake. Through dimensional modeling, flexible data slicing and roll-up summary analysis are supported; through measurement aggregation, statistics and display of indicators at different granularity levels are supported.

[0039] Furthermore, the embodiment of the present application also includes:

[0040] Using association rule mining algorithms to analyze the project data set to obtain frequent patterns and association rules in the project management process;

[0041] Using cluster analysis algorithms to group the project data set and identify typical patterns and categories in the project management process;

[0042] The frequent patterns, association rules, typical patterns and categories are integrated to form project management patterns and rules.

[0043] In a feasible implementation, first, an association rule mining algorithm is used to analyze the project data set to discover the frequently occurring patterns and implicit association rules in the project management process. The association rule mining algorithm reveals the association patterns between different project attributes by analyzing the association relationship between the variables in the data set, such as "if the project scale is large, the project cycle is long", "if key resources are lacking, the project schedule is delayed", etc. The association rule mining algorithm uses algorithms such as Apriori and FP-Growth, and controls the statistical significance and business relevance of the association rules by setting parameters such as support and confidence. For example, "support>5% and confidence>80%" are set as the filtering conditions of the association rules, and combined with the experience and judgment of project management experts, frequent patterns and association rules that frequently appear in the project management process are screened out.

[0044] At the same time, cluster analysis algorithms are used to group project data sets to identify typical patterns and categories in the project management process. By calculating the similarity between data objects, similar objects are automatically classified into the same category to form several data clusters with internal similarity and external differences. Through cluster analysis, the similarities and differences between different projects are discovered, and the typical characteristics and categories of projects are identified, such as being divided into large, medium and small projects according to dimensions such as project scale, complexity, and risk, or being divided into IT projects, manufacturing projects, marketing projects, etc. according to industry fields and technical characteristics. Then, the frequent patterns and association rules obtained are comprehensively analyzed, as well as the typical patterns and categories obtained, to summarize the common laws and experience patterns in the field of project management. Project management patterns refer to the universal laws and best practices followed by project activities, such as project life cycle models, project organizational structure models, project performance evaluation models, etc.; project management laws refer to the internal connections and constraints between project elements, such as the constraints of schedule-cost and the positive correlation between risk-return.

[0045] Furthermore, the embodiment of the present application also includes:

[0046] Define the project management domain ontology and standardize project management concepts and relationships;

[0047] Based on the domain ontology, the project management model and rules are semantically represented;

[0048] Build a project management knowledge graph to achieve structured organization and storage of project management knowledge.

[0049] In a preferred implementation, first, define the project management domain ontology, so as to standardize the core concepts, attributes and relationships in the project management field and form a consistent and shareable conceptual system. For example, adopt a top-down or bottom-up construction strategy, clarify the application scenarios and coverage of the ontology, refer to project management standards and industry best practices, define core concepts such as projects, tasks, teams, resources, risks, etc., characterize the semantic relationships such as classification, aggregation, and dependency between concepts, and form a standardized ontology glossary and relationship model. Then, based on the constructed project management domain ontology, the summarized project management patterns and laws are semantically represented, that is, the semantic elements such as concepts, attributes, and relationships defined in the ontology are used to formally express project management knowledge in a structured and computable manner to support knowledge sharing, reasoning, and application. To semanticize project management models and rules, it is necessary to express the frequent patterns and association rules obtained by mining in the form of ontology description. For example, the association rule "If the project scale is large, the project cycle is long" can be expressed as the association relationship between the concepts of "project scale" and "project cycle" in the ontology, and the strength of the association can be characterized by semantic constraints; the typical project categories obtained by cluster analysis can be expressed as the concept classification system in the ontology; management models such as project life cycle and organizational structure can be expressed as the temporal and structural relationships of concepts in the ontology.

[0050] Subsequently, based on the semantically expressed project management model and rules, a formalized project management knowledge graph is constructed to achieve the structured organization and storage of project management knowledge. The knowledge graph is a knowledge base that uses a graph data structure to represent the semantic association of conceptual entities. It uses nodes to represent concepts and edges to represent relationships, and intuitively presents the structure and context of domain knowledge in the form of a graph. To construct a project management knowledge graph is to organize the semantically expressed project management knowledge into a node-edge-attribute graph model supported by a graph database, and use a graph database for storage, retrieval, and calculation. For example, using a graph database such as Neo4j, nodes are used to represent core conceptual entities such as projects, resources, and risks, and edges are used to represent association rules, dependencies, etc., to build an end-to-end project management knowledge graph covering all aspects of the entire project life cycle.

[0051] Furthermore, the embodiment of the present application also includes:

[0052] Design visual interface layout and interaction methods;

[0053] Develop a visualization component library to support multi-dimensional data display and interactive operations;

[0054] Integrate the project management knowledge graph to realize the visual presentation of project management semantic information.

[0055] In a feasible implementation method, first, the interface layout and interaction mode of the visualization management platform are designed to provide project managers with an intuitive and easy-to-use human-computer interaction interface to achieve visual presentation and interactive operation of project management information. Among them, the design of the visualization interface follows the design principles of information visualization and human-computer interaction, comprehensively considers factors such as project management business characteristics, user role requirements, cognitive habits, and reasonably plans the interface layout, navigation mechanism, visual style, etc. For example, a responsive page layout is adopted to support adaptive display of different screen sizes; an intuitive information architecture and navigation mechanism is adopted to guide users to quickly locate and browse project information through menus, labels, breadcrumbs, etc.; color matching, icon design, interactive effects, etc. are used to create a professional and beautiful visual atmosphere and enhance user experience. At the same time, the visualization interface supports personalized customization and permission control, allowing project managers of different roles to customize interface elements and layouts according to needs, and control access and operation of sensitive information through authorization mechanisms.

[0056] Then, develop a visualization component library for the visualization management platform to provide general and flexible visualization controls for the display and interactive operation of multi-dimensional data. Among them, visualization components refer to visualization units with independent functions and interfaces, which realize the mapping and presentation of data to views through parameter configuration and data binding. Based on the theory and method of data visualization, the development of the visualization component library designs and implements a series of commonly used visualization charts and interactive controls, such as Gantt charts, work breakdown structure charts, resource histograms, risk matrices, etc., for the characteristics and analysis needs of project management data, covering the display and analysis of core project management data such as progress, cost, quality, resources, and risks. For example, for project progress management, develop a Gantt chart component to intuitively display the project schedule and execution status through visual elements such as task bars, milestone markers, and dependency lines. At the same time, the visualization component supports a variety of interactive operations, such as zooming, panning, filtering, drilling, linkage, etc., to facilitate users to explore and analyze data.

[0057] Subsequently, the constructed project management knowledge graph is integrated into the visual management platform to realize the visual presentation and interactive exploration of project management semantic information. Combining the knowledge graph with visual interaction helps project managers to more intuitively understand the semantic associations between project management concepts and promote the sharing and application of project management knowledge. The visual presentation of the integrated knowledge graph requires the design and development of a special knowledge graph visualization component. Through graphical grammars such as nodes, edges, and attributes, the semantic information such as concepts, instances, and relationships in the knowledge graph is mapped into a visual interactive view, supporting interactive functions such as global browsing, local exploration, and path recommendation of the knowledge graph. For example, the force-directed layout algorithm is used to present the project management knowledge graph as a dynamic network topology view, and interactive functions such as node expansion, edge filtering, and key path highlighting are provided to facilitate users to gain insight into the context of project management knowledge. At the same time, the knowledge graph is used to develop human-computer dialogue functions such as intelligent question and answer and semantic search, allowing users to ask questions and inquire in natural language. The system performs semantic understanding and reasoning based on the knowledge graph and returns relevant project management knowledge and insights.

[0058] Furthermore, the embodiment of the present application also includes:

[0059] Build a project progress prediction model,

[0060] The project progress is analyzed and predicted in real time through the project progress prediction model, and presented on the visualization interface.

[0061] In a preferred implementation, first, a project progress prediction model is established based on historical project data and real-time progress data to achieve intelligent prediction and early warning of future project progress. Collect and integrate historical project data from inside and outside the organization, including progress data of completed projects and ongoing projects, influencing factor data, etc., and clean, integrate and standardize the data; extract and construct feature sets highly related to project progress from the data through feature engineering, such as project scale, complexity, resource investment, team experience, etc., and use feature selection, dimensionality reduction and other methods to optimize feature subsets; on this basis, select a machine learning model for project progress prediction, such as logistic regression, decision tree, support vector machine, neural network, etc., and use historical project data to train and tune the model, and continuously iterate and optimize the model's prediction accuracy and generalization performance; evaluate the trained model, and use new project samples to test it to verify the model's prediction effect and obtain a project progress prediction model.

[0062] Subsequently, the project progress prediction model constructed is used to analyze and predict the progress of the ongoing project in real time, and the prediction results are presented to the project managers through a visual interface. For example, the project progress prediction model is integrated with the project management information system, performance management system, etc., and the progress data, milestone events, resource consumption and other data during the project execution are collected in real time, and input into the prediction model for real-time calculation and prediction. The prediction results include project completion time, progress deviation, critical path, risk warning and other contents to prompt project managers to take targeted control measures. For example, if the prediction shows that the project may be delayed, the high-risk tasks on the critical path are automatically identified, and the project manager is prompted to adjust resource input or optimize task dependencies; if the prediction shows that the project progress deviation exceeds the threshold, the warning information is triggered, and the optimization plan is recommended, such as adding personnel, adjusting the scope, changing the technical plan, etc. At the same time, the project progress prediction information and the actual progress data of the project are integrated into the visual interface for display, which helps project managers intuitively perceive the project progress status and make predictive decisions and risk control. For example, in the Gantt chart view, different colors or patterns are used to distinguish the actual progress from the predicted progress, and the degree of deviation is displayed; in the dashboard view, predictive indicators such as the probability distribution of project completion and risk level are displayed.

[0063] Furthermore, the embodiment of the present application also includes:

[0064] Setting up role-based and permission-based access control policies on the visualization management platform;

[0065] Provide personalized project management services to project managers and decision makers based on the access control policy.

[0066] In a preferred implementation, a role-based and permission-based access control strategy is set on the visual management platform to ensure the security, confidentiality and compliance of project management information, while meeting the differentiated information needs of different project management roles. First, according to the project management structure and process, different project management roles are defined, such as project managers, functional managers, senior managers, etc., and the responsibility boundaries and information needs of each role in project management activities are clarified; then, the project management information, functional modules, operation permissions, etc. in the visual management platform are sorted and classified to clarify the sensitivity, confidentiality level and access control requirements of various types of information and functions. On this basis, through the visual configuration tool, corresponding information access rights and functional operation permissions are assigned to different project management roles to form a comprehensive and fine-grained access control strategy. For example, the project manager can have a complete information view and management authority for the entire life cycle of the project, the functional manager can only access the subset of project information related to his function, and the senior manager can view the summary information and key performance indicators of the project status, but cannot modify the specific project plan and execution data.

[0067] Then, according to the set access control strategy, personalized project management services are provided for different project managers and decision makers. Personalized service refers to dynamically adjusting the information content, display mode and interactive behavior of the visual management platform according to the user's role, authority, preference and other factors to provide more intelligent, efficient and considerate project management support. For example, when the project manager logs in to the visual management platform, according to his role authority, the overall status view of the project, reminders of to-do tasks, risk warnings and other information are automatically presented, and relevant optimization decision-making plans are recommended; when the functional manager views the project information, according to his functional area, the relevant tasks, resources, performance and other information are highlighted, and professional analysis tools and optimization suggestions are provided; when senior managers browse the project cockpit, cross-project and cross-departmental status summary reports and key indicator trend analysis are generated, and potential opportunities and risks are intelligently identified to prompt decision makers to pay attention. At the same time, according to the user's preferences and behavioral habits, the layout style, color theme, interaction mode and other aspects of the visual interface are dynamically adjusted to provide a more friendly and comfortable user experience.

[0068] Through role- and permission-based access control and personalized services, the security, intelligence, and ease of use of the visualization management platform are further enhanced, providing highly customized, contextualized intelligent assistance for project managers in different roles, effectively improving project management efficiency and decision-making levels.

[0069] In summary, the enterprise project intelligent visualization management method based on big data provided by the embodiment of the present application has the following technical effects:

[0070] Identify enterprise projects and collect original data related to enterprise projects. Original data include project progress data, resource allocation data, budget execution data and risk management data. By clarifying the management object, i.e. enterprise projects, and comprehensively collecting original data of all dimensions of the project, a data foundation is provided for subsequent data analysis and knowledge mining. Preprocess the original data to obtain a structured project data set. Preprocessing includes data cleaning, data integration and data conversion. The original data is converted into a structured, high-quality project data set, laying the foundation for subsequent data analysis. Analyze the project data set to obtain the project management model and rules. Based on the project management model and rules, a project management knowledge graph is constructed. The project management knowledge graph includes key elements of project management, node relationships and semantic descriptions. Using big data analysis technology, the model and rules of project management are mined from the project data set, and the project management knowledge graph is constructed based on this. The key elements of project management and the relationships and semantics between them are presented in a structured form, forming a comprehensive and systematic project management knowledge system, providing knowledge support and guidance for project management. Build a visual management platform, combine the project management knowledge map to generate an interactive visual interface, realize the multi-dimensional visual display of the entire life cycle of the project, present the knowledge in the project management knowledge map to the project manager in an intuitive and interactive way, and realize the visual management of the entire life cycle of the project. Through multi-dimensional visual display, you can fully and intuitively understand key information such as project progress, resource use, budget execution, risk status, etc., which is convenient for timely discovery of problems, optimization of decision-making, and improvement of project management efficiency and quality.

[0071] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0072] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these modifications and variations.

Claims

1. An intelligent visualization management method for enterprise projects based on big data, characterized in that: The following steps are involved: Determine enterprise projects and collect original data related to enterprise projects, including project progress data, resource allocation data, budget execution data, and risk management and control data; Preprocessing the raw data to obtain a structured project data set, wherein the preprocessing includes data cleaning, data integration and data conversion; Analyze the project data set to obtain project management patterns and rules, and construct a project management knowledge graph based on the project management patterns and rules, wherein the project management knowledge graph includes key elements of project management, node relationships, and semantic descriptions; Build a visual management platform, combine the project management knowledge graph to generate an interactive visual interface, and realize multi-dimensional visual display of the entire project life cycle.

2. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: Collect original data related to the enterprise project, including: Determine the data collection indicator system for the enterprise project; Collect project progress data, resource allocation data, budget execution data and risk management data from the project management system, enterprise resource planning system, financial management system and risk management system according to the data collection indicator system; The project progress data, resource allocation data, budget execution data and risk management data are aggregated to generate original data.

3. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: Preprocessing the raw data includes: Clean the original data to remove noise and redundant data; Integrate the cleaned data to eliminate the heterogeneity of data format and semantics; The integrated data is transformed to generate a structured project data set.

4. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: The project data set is analyzed to obtain project management models and rules, including: Using association rule mining algorithms to analyze the project data set to obtain frequent patterns and association rules in the project management process; Using cluster analysis algorithms to group the project data set and identify typical patterns and categories in the project management process; The frequent patterns, association rules, typical patterns and categories are integrated to form project management patterns and rules.

5. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: Based on the project management model and rules, a project management knowledge graph is constructed, including: Define the project management domain ontology and standardize project management concepts and relationships; Based on the domain ontology, the project management model and rules are semantically represented; Build a project management knowledge graph to achieve structured organization and storage of project management knowledge.

6. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: Build a visual management platform, including: Design visual interface layout and interaction methods; Develop a visualization component library to support multi-dimensional data display and interactive operations; Integrate the project management knowledge graph to realize the visual presentation of project management semantic information.

7. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: The method further comprises: Build a project progress prediction model, The project progress is analyzed and predicted in real time through the project progress prediction model, and presented on the visualization interface.

8. The enterprise project intelligent visualization management method based on big data according to claim 1 is characterized in that: The method further comprises: Setting up role-based and permission-based access control policies on the visualization management platform; Provide personalized project management services to project managers and decision makers based on the access control policy.

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