Enterprise project full-process intelligent management system

Abstract

The invention belongs to the technical field of enterprise project management, and particularly relates to an enterprise project full-process intelligent management system, which comprises a project starting module, a project planning module, a project execution module, a project monitoring module and a project ending module, and is characterized in that the project monitoring module comprises a progress monitoring unit, and the progress monitoring unit is connected with a performance monitoring unit; the performance monitoring unit is connected with a risk monitoring unit, the risk monitoring unit is connected with a problem monitoring unit, and the risk monitoring unit comprises a risk early warning unit and a risk management unit. Through data mining and natural language processing technologies, a project management team can be helped to discover and analyze potential risks and problems; through simulation and prediction, a team is helped to make strategies for coping with risks and problems, various aspects of enterprise operation can be monitored in real time, potential risk points can be identified, early warning can be given out in time, and the enterprise is helped to avoid risks in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise project management, and in particular, to an intelligent management system for the whole process of enterprise projects. Background Art

[0002] Enterprise project management involves multiple key links, including project establishment preparation, project approval budget, project startup organization establishment, etc. In the framework of project management, it usually includes five main processes: initiation, planning, execution, monitoring, and closure. These processes together constitute the entire life cycle of enterprise project management, ensuring that the project can be successfully completed according to the plan and achieve the expected goals within the established time and without exceeding the budget.

[0003] In the prior art, the intelligent management system for the whole process of enterprise projects cannot help the project management team discover and analyze potential risks and formulate coping strategies. Therefore, we propose an intelligent management system for the whole process of enterprise projects to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve the defect that the intelligent management system for the whole process of enterprise projects cannot help the project management team discover and analyze potential risks and formulate coping strategies, and to propose an intelligent management system for the whole process of enterprise projects.

[0005] In order to achieve the above purpose, the present invention adopts the following technical scheme:

[0006] An intelligent management system for the whole process of enterprise projects includes a project startup module, a project planning module, a project execution module, a project monitoring module, and a project closure module. The project monitoring module includes a progress monitoring unit, which is connected to a performance monitoring unit. The performance monitoring unit is connected to a risk monitoring unit. The risk monitoring unit is connected to a problem monitoring unit. The risk monitoring unit includes a risk warning unit and a risk management unit. The risk warning unit includes a risk identification subunit, which is connected to a risk quantification subunit. The risk quantification subunit is connected to a risk priority subunit. The risk priority subunit is connected to a risk response subunit. The risk management unit includes a data collection subunit, which is connected to a data integration subunit. The data integration subunit is connected to a risk warning subunit. The risk warning subunit is connected to a strategy formulation subunit. The strategy formulation subunit is connected to an execution feedback subunit.

[0007] Preferably, the risk warning unit uses data mining technology to identify potential risks that may affect project success from multi-dimensional data such as historical project data, market trends, and supply chain information. It uses natural language processing technology to analyze text information in project documents, meeting minutes, and communication records, extracts key risk points, quantitatively analyzes the identified risks, evaluates their likelihood and impact, and uses qualitative and quantitative methods such as risk matrices and sensitivity analyses to assist the project management team in understanding risks more accurately. Based on the severity and urgency of the risks, it prioritizes the risks, and through simulation and prediction technologies, provides the project management team with multiple risk response strategy options. The strategies include risk avoidance, risk mitigation, risk transfer, or risk acceptance. According to the specific situation of the project and the team's preferences, it recommends the most suitable strategy. During the project implementation process, it continuously monitors the risk status and its impact on the project, updates the risk list by regularly reviewing the risk management plan to ensure the effectiveness of the response measures, and provides adjustment suggestions when necessary.

[0008] Preferably, the risk management unit first collects data from various operational links of the enterprise. This data may include financial data, production process data, market dynamics data, and employee behavior data. The collected data is integrated into a unified platform or system for subsequent analysis and processing. It monitors the integrated data in real time to ensure that every link of the enterprise operation is under control. Through big data analysis and machine learning technologies, it analyzes the massive data to find the patterns and anomalies therein, thereby identifying potential risk points. Once potential risk points are identified, it immediately issues a warning signal to notify the enterprise management or relevant departments. The warning signal includes the specific type of risk, the possible impact degree, and the recommended response measures. After receiving the warning signal, the enterprise management formulates specific response strategies according to the suggestions provided by the system. The strategies include adjusting the production plan, optimizing inventory management, and triggering a stop-loss mechanism, aiming to minimize the impact of risks on the enterprise. The formulated response strategies are implemented, and the implementation effect of the strategies is continuously monitored. According to the implementation effect, feedback and suggestions are provided to help the enterprise continuously optimize the risk management process and improve the risk management efficiency.

[0009] Preferably, the project startup module includes a project determination unit, which is connected to a requirements analysis unit. The requirements analysis unit is connected to a project charter unit, and the project charter unit is connected to a component team unit.

[0010] Preferably, the project startup module is used to determine the ultimate goal of the project, ensure team alignment, understand the problems or opportunities to be solved by the project, the expected outcomes of the project, and the end-users or beneficiaries of the project, deeply understand the project requirements, lay a foundation for subsequent work, ensure the correct project direction, write the project charter, clarify the project scope, time, responsibilities, and resources, select or choose suitable team members, and assign roles and responsibilities.

[0011] Preferably, the project planning module includes a management plan unit, which is connected to a work breakdown unit, which is connected to a cost estimation unit, which is connected to a risk management unit.

[0012] Preferably, the project planning module is used to establish the management framework and processes of the project, lay a foundation for project execution, break down project tasks into executable work packages, clarify the responsible person, time node, and required resources for each task, arrange the sequence and time nodes of tasks, ensure the project progresses according to the plan, predict the resources and costs required for the project, prepare the budget, identify potential risks, formulate response strategies, and reduce project risks.

[0013] Preferably, the project execution module includes an assignment unit, which is connected to a task execution unit, which is connected to a quality management unit, which is connected to a communication management unit.

[0014] Preferably, the project execution module is used to allocate tasks and resources to team members according to the schedule plan and work packages, start executing tasks according to the plan, ensure that project activities are implemented according to the plan, ensure that the project quality meets the expected standards, conduct necessary inspections and tests, maintain effective communication within and outside the team, solve problems in a timely manner, and promote team collaboration.

[0015] Preferably, the project monitoring module is used to track the actual progress of the project, ensure progress according to the plan, promptly detect deviations and make adjustments, evaluate the performance of team members, provide feedback, adjust the allocation of team resources, manage change requests in the project, ensure that the project is not affected and proceeds smoothly, promptly identify and solve problems in the project, reduce risks, and ensure that the project goals are not affected.

[0016] Compared with the prior art, the advantages of the present invention are as follows:

[0017] Through data mining and natural language processing technologies, the present invention can help the project management team discover and analyze potential risks and problems, and through simulation and prediction, help the team formulate strategies to address risks and problems, be able to monitor all aspects of enterprise operations in real-time, identify potential risk points, and issue early warnings in a timely manner to help the enterprise avoid risks in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Block diagram of an intelligent management system for the entire process of enterprise projects proposed by the present invention;

[0019] Figure 2 Block diagram of the project startup module of an intelligent management system for the entire process of enterprise projects proposed by the present invention;

[0020] Figure 3 Block diagram of the project planning module of an intelligent management system for the entire process of enterprise projects proposed by the present invention;

[0021] Figure 4 Block diagram of the project execution module of an intelligent management system for the entire process of enterprise projects proposed by the present invention;

[0022] Figure 5 Block diagram of the project monitoring module of an intelligent management system for the entire process of enterprise projects proposed by the present invention;

[0023] Figure 6 Block diagram of the risk warning unit of an intelligent management system for the entire process of enterprise projects proposed by the present invention;

[0024] Figure 7 Block diagram of the risk management unit of an intelligent management system for the entire process of enterprise projects proposed by the present invention. Specific implementation manners

[0025] Next, the technical solutions in this embodiment will be clearly and completely described in conjunction with the accompanying drawings in this embodiment. Obviously, the described embodiments are only a part of the embodiments of this embodiment, rather than all the embodiments.

[0026] Embodiment 1

[0027] Referring to Figures 1-7 , an intelligent management system for the entire process of enterprise projects includes a project startup module, a project planning module, a project execution module, a project monitoring module, and a project closing module. The project monitoring module includes a progress monitoring unit, the progress monitoring unit is connected to a performance monitoring unit, the performance monitoring unit is connected to a risk monitoring unit, the risk monitoring unit is connected to a problem monitoring unit. The risk monitoring unit includes a risk warning unit and a risk management unit. The risk warning unit includes a risk identification subunit, the risk identification subunit is connected to a risk quantification subunit, the risk quantification subunit is connected to a risk priority subunit, the risk priority subunit is connected to a risk response subunit. The risk management unit includes a data collection subunit, the data collection subunit is connected to a data integration subunit, the data integration subunit is connected to a risk warning subunit, the risk warning subunit is connected to a strategy formulation subunit, and the strategy formulation subunit is connected to an execution feedback subunit.

[0028] In this embodiment, the risk early-warning unit uses data mining techniques to identify potential risks that may affect the success of the project from multi-dimensional data such as historical project data, market trends, and supply chain information. It uses natural language processing techniques to analyze text information in project documents, meeting minutes, and communication records, extracts key risk points, quantitatively analyzes the identified risks, evaluates their likelihood and impact degree, and uses qualitative and quantitative methods such as risk matrices and sensitivity analysis to assist the project management team in understanding risks more accurately. Based on the severity and urgency of the risks, the risks are prioritized. Through simulation and prediction techniques, multiple risk response strategy options are provided for the project management team. The strategies include risk avoidance, risk mitigation, risk transfer, or risk acceptance. According to the specific situation of the project and the preferences of the team, the most suitable strategy is recommended. During the project implementation process, the risk status and its impact on the project are continuously monitored. By regularly reviewing the risk management plan and updating the risk list, the effectiveness of the response measures is ensured, and adjustment suggestions are provided when necessary.

[0029] In this embodiment, the risk management unit first collects data from various operational links of the enterprise. This data may include financial data, production process data, market dynamics data, and employee behavior data. The collected data is integrated into a unified platform or system for subsequent analysis and processing. The integrated data is monitored in real time to ensure that every link of the enterprise operation is under control. Through big data analysis and machine learning techniques, a large amount of data is analyzed to find the patterns and anomalies therein, thereby identifying potential risk points. Once potential risk points are identified, it immediately issues a warning signal to notify the enterprise management or relevant departments. The warning signal includes the specific type of risk, the possible impact degree, and the recommended response measures. After receiving the warning signal, the enterprise management formulates specific response strategies according to the suggestions provided by the system. The strategies include adjusting the production plan, optimizing inventory management, and triggering a stop-loss mechanism, aiming to minimize the impact of risks on the enterprise. The formulated response strategies are implemented, and the implementation effect of the strategies is continuously monitored. According to the implementation effect, feedback and suggestions are provided to help the enterprise continuously optimize the risk management process and improve the risk management efficiency.

[0030] In this embodiment, the project startup module includes a project determination unit, which is connected to a requirements analysis unit. The requirements analysis unit is connected to a project charter unit, and the project charter unit is connected to a component team unit.

[0031] In this embodiment, the project startup module is used to determine the ultimate goal of the project, ensure team alignment, understand the problems or opportunities to be solved by the project, the expected outcomes of the project, and the ultimate users or beneficiaries of the project, deeply understand the project requirements, lay a foundation for subsequent work, ensure the correct project direction, prepare the project charter, clarify the project scope, time, responsibilities, and resources, select or choose suitable team members, and assign roles and responsibilities.

[0032] In this embodiment, the project planning module includes a management plan unit, which is connected to a work breakdown unit, which is connected to a cost estimation unit, which is connected to a risk management unit.

[0033] In this embodiment, the project planning module is used to establish the management framework and processes of the project, lay a foundation for project execution, break down project tasks into executable work packages, clarify the responsible person, time node, and required resources for each task, arrange the sequence and time nodes of tasks, ensure the project progresses according to the plan, predict the resources and costs required for the project, prepare a budget, identify potential risks, formulate coping strategies, and reduce project risks.

[0034] In this embodiment, the project execution module includes an assignment unit, which is connected to an execution task unit, which is connected to a quality management unit, which is connected to a communication management unit.

[0035] In this embodiment, the project execution module is used to assign tasks and resources to team members according to the schedule plan and work packages, start executing tasks according to the plan, ensure that project activities are implemented according to the plan, ensure that the project quality meets the expected standards, conduct necessary inspections and tests, maintain effective communication within and outside the team, solve problems in a timely manner, and promote team collaboration.

[0036] In this embodiment, the project monitoring module is used to track the actual progress of the project, ensure that it progresses according to the plan, promptly detect deviations and make adjustments, evaluate the performance of team members, provide feedback, adjust the team resource allocation, manage change requests in the project, ensure that the project is not affected and proceeds smoothly, promptly identify and solve problems in the project, reduce risks, and ensure that the project goals are not affected.

[0037] Embodiment 2

[0038] The differences from Embodiment 1 are as follows: It includes a project startup module, a project planning module, a project execution module, a project monitoring module, and a project closing module. The project monitoring module includes a progress monitoring unit, which is connected to a performance monitoring unit. The performance monitoring unit is connected to a risk monitoring unit, and the risk monitoring unit is connected to a problem monitoring unit. The risk monitoring unit includes a risk warning unit, a risk management unit, and a resource allocation optimization unit. The risk warning unit includes a risk identification subunit, which is connected to a risk quantification subunit. The risk quantification subunit is connected to a risk priority subunit, and the risk priority subunit is connected to a risk response subunit. The risk management unit includes a data collection subunit, which is connected to a data integration subunit. The data integration subunit is connected to a risk warning subunit, the risk warning subunit is connected to a strategy formulation subunit, and the strategy formulation subunit is connected to an execution feedback subunit; The resource allocation optimization unit collects the resource usage data of each department and link of the enterprise. The data includes information on human, material, and financial resources in multiple aspects. The collected data will be integrated into a unified platform or system for subsequent analysis and processing. The integrated resource usage data is deeply analyzed to identify the patterns and trends of resource usage. Through big data analysis and machine learning technologies, the value behind the data can be mined, potential problems and waste points in resource usage can be discovered. Based on the results of the resource usage analysis, the specific links and reasons for resource waste can be accurately identified. Waste may include various forms such as overuse, idleness, and inefficient use. For the identified resource waste links, AI proposes specific optimization suggestions and improvement measures, including various solutions such as adjusting resource allocation, optimizing work processes, and introducing new technologies or equipment. After receiving the optimization suggestions proposed by AI, the enterprise management will formulate an implementation plan and gradually promote it. During the implementation process, the usage of resources will be continuously monitored to ensure the effectiveness of the optimization measures and provide further adjustment suggestions if necessary.

[0039] The above is only the preferred specific implementation manner of this embodiment, but the protection scope of this embodiment is not limited thereto. Any person skilled in the art within the technical scope disclosed by this embodiment, according to the technical solution and inventive concept of this embodiment, makes equivalent substitutions or changes, and should be covered by the protection scope of this embodiment.

Claims

1. An enterprise project full-process intelligent management system, including a project initiation module, a project planning module, a project execution module, a project monitoring module and a project closing module, characterized in that: The project monitoring module includes a progress monitoring unit, the progress monitoring unit is connected to a performance monitoring unit, the performance monitoring unit is connected to a risk monitoring unit, the risk monitoring unit is connected to a problem monitoring unit, the risk monitoring unit includes a risk warning unit and a risk management unit, the risk warning unit includes a risk identification subunit, the risk identification subunit is connected to a risk quantification subunit, the risk quantification subunit is connected to a risk priority subunit, the risk priority subunit is connected to a risk response subunit, the risk management unit includes a data collection subunit, the data collection subunit is connected to a data integration subunit, the data integration subunit is connected to a risk warning subunit, the risk warning subunit is connected to a strategy formulation subunit, and the strategy formulation subunit is connected to an execution feedback subunit.

2. According to claim 1, the enterprise project full-process intelligent management system is characterized in that: The risk warning unit uses data mining technology to identify potential risks that may affect the success of the project from historical project data, market trends, and supply chain information data. It uses natural language processing technology to analyze project documents, meeting minutes, and communication record text information, extract key risk points, and conduct quantitative analysis on identified risks to assess their likelihood and impact. It uses qualitative and quantitative methods, such as risk matrix and sensitivity analysis, to assist the project management team in understanding risks more accurately, prioritizes risks based on their severity and urgency, and provides the project management team with a variety of strategic options for dealing with risks through simulation and prediction technology. Strategies include risk avoidance, risk mitigation, risk transfer, or risk acceptance. The most appropriate strategy is recommended based on the specific circumstances of the project and the team's preferences. During the project implementation process, the risk status and its impact on the project are continuously monitored, and the risk list is updated by regularly reviewing the risk management plan to ensure the effectiveness of the response measures and provide adjustment suggestions when necessary.

3. According to claim 2, the enterprise project full-process intelligent management system is characterized in that: The risk management unit will first collect data from various operational links of the enterprise. These data may include financial data, production process data, market dynamics data, and employee behavior data. The collected data will be integrated into a unified platform or system for subsequent analysis and processing, and the integrated data will be monitored in real time to ensure that every link of the enterprise's operation is under control. Through big data analysis and machine learning technology, massive data will be analyzed to find out the patterns and anomalies, so as to identify potential risk points. Once a potential risk point is identified, it will immediately issue an early warning signal to notify the enterprise management or relevant departments. The early warning signal includes the specific type of risk, the possible impact, and the recommended response measures. After receiving the early warning signal, the enterprise management will formulate specific response strategies based on the suggestions provided by the system. The strategies include adjusting production plans, optimizing inventory management, and triggering stop-loss mechanisms, aiming to minimize the impact of risks on the enterprise. The formulated response strategies will be executed, and the execution effect of the strategies will be continuously monitored. Based on the execution effect, feedback and suggestions will be provided to help enterprises continuously optimize risk management processes and improve risk management efficiency.

4. According to claim 3, the enterprise project full-process intelligent management system is characterized in that: The project initiation module comprises a project determination unit, the project determination unit is connected to a demand analysis unit, the demand analysis unit is connected to a project charter unit, and the project charter unit is connected to a component team unit.

5. According to claim 4, the enterprise project full-process intelligent management system is characterized in that: The project initiation module is used to determine the ultimate goal of the project, ensure team alignment, understand the problems or opportunities to be solved by the project, the expected results of the project and the end users or beneficiaries of the project, gain in-depth understanding of project requirements, lay the foundation for subsequent work, ensure the correct direction of the project, write a project charter, clarify the scope, time, responsibilities and resources of the project, select or choose appropriate team members, and assign roles and responsibilities.

6. According to claim 5, the enterprise project full-process intelligent management system is characterized in that: The project planning module includes a management plan unit, the management plan unit is connected to a decomposition work unit, the decomposition work unit is connected to a cost estimation unit, and the cost estimation unit is connected to a risk management unit.

7. The enterprise project full-process intelligent management system according to claim 6 is characterized in that: The project planning module is used to establish the management framework and process of the project, lay the foundation for project execution, break down project tasks into executable work packages, clarify the responsible person, time node and required resources for each task, arrange the sequence and time nodes of tasks, ensure that the project proceeds as planned, predict the resources and costs required for the project, prepare a budget, identify potential risks, formulate response strategies, and reduce project risks.

8. The enterprise project full-process intelligent management system according to claim 7 is characterized in that: The project execution module includes an allocation unit, the allocation unit is connected to a task execution unit, the task execution unit is connected to a quality management unit, and the quality management unit is connected to a communication management unit.

9. The enterprise project full-process intelligent management system according to claim 8 is characterized in that: The project execution module is used to assign tasks and resources to team members according to the schedule and work packages, start executing tasks as planned, ensure that project activities are implemented as planned, ensure that project quality meets expected standards, conduct necessary inspections and tests, maintain effective communication within and outside the team, solve problems in a timely manner, and promote team collaboration.

10. The enterprise project full-process intelligent management system according to claim 9 is characterized in that: The project monitoring module is used to track the actual progress of the project, ensure that it proceeds as planned, promptly identify deviations and make adjustments, evaluate the performance of team members, provide feedback, adjust team resource allocation, manage change requests in the project, ensure that the project is not affected and proceeds smoothly, identify and solve problems in the project in a timely manner, reduce risks, and ensure that project goals are not affected.

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