Enterprise project informatization management method, device and system and storage medium

Abstract

The invention relates to an enterprise project informatization management method, device and system and a storage medium. The method comprises the steps of obtaining enterprise project internal data, enterprise project external data, initial fuzzy reasoning parameters and project execution historical data corresponding to a target enterprise; performing dynamic fuzzy reasoning on the enterprise project internal data and the enterprise project external data according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data; according to the dynamic fuzzy reasoning data and project execution historical data, identifying a relationship between each execution object and each task detail in the target enterprise to obtain project behavior analysis data; performing structured fusion on the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; and scheduling fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information. By adopting the method, the resource scheduling efficiency of the enterprise management project can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an enterprise project information management method, device, system and storage medium. Background Art

[0002] In traditional technology, enterprise project information management usually relies on various single, static software systems, such as project management tools, task scheduling software, and resource management systems. These systems help enterprises schedule various tasks and resources in the project life cycle by manually inputting and updating project information, such as time scheduling, progress tracking, and budget control. However, in traditional methods, data is usually scattered, information updates require manual operations, and there is a lack of effective integration and automation between systems, which makes enterprise management projects less efficient in resource scheduling. Summary of the invention

[0003] Based on this, it is necessary to provide an enterprise project information management method, device, system and storage medium that can improve the efficiency of enterprise management projects in resource scheduling in response to the above technical problems.

[0004] In a first aspect, the present application provides an enterprise project information management method, comprising: Obtaining internal enterprise project data, external enterprise project data, initial fuzzy reasoning parameters and project execution history data corresponding to the target enterprise; According to the initial fuzzy reasoning parameters, dynamic fuzzy reasoning is performed on the internal data of the enterprise project and the external data of the enterprise project to obtain dynamic fuzzy reasoning data; According to the dynamic fuzzy reasoning data and the project execution history data, the relationship between each execution object and each task detail in the target enterprise is identified to obtain project behavior analysis data; Structurally fusion the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; According to the enterprise project fusion data, the fixed resources and human resources of the target enterprise are scheduled to obtain enterprise project management information.

[0005] In a second aspect, the present application also provides an enterprise project information management device, including: A data acquisition module is used to acquire internal enterprise project data, external enterprise project data, initial fuzzy reasoning parameters, and project execution history data corresponding to the target enterprise; A fuzzy reasoning module, used for performing dynamic fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data; A behavior analysis module, used to identify the relationship between each execution object and each task detail in the target enterprise according to the dynamic fuzzy reasoning data and the project execution history data, and obtain project behavior analysis data; A data fusion module, used for structurally fusing the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; The resource scheduling module is used to schedule the fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information.

[0006] In a third aspect, the present application also provides an enterprise project information management system, the system comprising: a data processing end and a terminal, the data processing end implementing the steps of an enterprise project information management method when executing a computer program.

[0007] In a fourth aspect, the present application also provides an enterprise project information management storage medium, in which a computer program is stored. When the computer program is executed by a processor in a data processing terminal, the steps of an enterprise project information management method are implemented.

[0008] The above-mentioned enterprise project information management method, device, system and storage medium obtain the enterprise project internal data, enterprise project external data, initial fuzzy reasoning parameters and project execution history data corresponding to the target enterprise; according to the initial fuzzy reasoning parameters, the enterprise project internal data and the enterprise project external data are dynamically fuzzy reasoned to obtain dynamic fuzzy reasoning data; according to the dynamic fuzzy reasoning data and the project execution history data, the relationship between each execution object and each task detail in the target enterprise is identified to obtain project behavior analysis data; the dynamic fuzzy reasoning data and the project behavior analysis data are structured and integrated to obtain enterprise project integrated data; according to the enterprise project integrated data, the fixed resources and human resources of the target enterprise are scheduled to obtain enterprise project management information.

[0009] By integrating the internal data of enterprise projects, external data of enterprise projects, historical execution data and initial fuzzy reasoning parameters, and using dynamic fuzzy reasoning technology, it is possible to accurately analyze and deduce various types of data in the project, and capture and feedback the dynamic changes of project execution in real time. Further in-depth analysis of project behavior can identify the potential associations between various execution objects and tasks, and then generate comprehensive project behavior analysis data. After the structured fusion of these data, the enterprise project fusion data formed provides a reliable basis for resource scheduling. It not only improves the control of resource utilization efficiency to improve the efficiency of enterprise management projects in resource scheduling, but also optimizes the allocation of fixed resources and human resources, ensuring that the project can respond flexibly in a changing environment and improve the accuracy of decision-making; it further helps to improve the level of intelligence of enterprise project management, significantly reduce decision-making risks, and promote the smooth execution and successful completion of projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 An application environment diagram of an enterprise project information management method in one embodiment; Figure 2 A flowchart of an enterprise project information management method in one embodiment; Figure 3 A schematic diagram of a flow chart of a method for obtaining dynamic fuzzy reasoning data in one embodiment; Figure 4 A schematic diagram of a flow chart of a method for optimizing fuzzy reasoning parameters in one embodiment; Figure 5 A schematic diagram of a process for obtaining project behavior analysis data in one embodiment; Figure 6 A schematic diagram of a flow chart of a method for obtaining project behavior analysis data in another embodiment; Figure 7 A schematic diagram of a process for obtaining enterprise project fusion data in one embodiment; Figure 8 A schematic diagram of a process for obtaining initial project fusion data in one embodiment; Fig. 9 A flowchart of a method for obtaining enterprise project management information in one embodiment; Fig.10A structural block diagram of an enterprise project information management device in one embodiment; Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] The enterprise project information management method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the internal data of the enterprise project, the external data of the enterprise project, the initial fuzzy reasoning parameters and the project execution history data corresponding to the target enterprise from the terminal 102; according to the initial fuzzy reasoning parameters, the internal data of the enterprise project and the external data of the enterprise project are dynamically fuzzy reasoned to obtain dynamic fuzzy reasoning data; according to the dynamic fuzzy reasoning data and the project execution history data, the relationship between each execution object and each task detail in the target enterprise is identified to obtain project behavior analysis data; the dynamic fuzzy reasoning data and the project behavior analysis data are structurally integrated to obtain enterprise project integration data; according to the enterprise project integration data, the fixed resources and human resources of the target enterprise are scheduled to obtain enterprise project management information. Among them, the server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0014] In an exemplary embodiment, Figure 2 As shown, a method for enterprise project information management is provided, which is applied to Figure 1 The server in the example is used to illustrate, including the following steps 202 to 210. Among them: Step 202, obtaining the internal data of the enterprise project, the external data of the enterprise project, the initial fuzzy reasoning parameters and the project execution history data corresponding to the target enterprise.

[0015] Among them, enterprise project internal data can be information directly related to the project and originating from within the enterprise, including the project's budget, progress, resource allocation, task execution, team members' work status, equipment utilization, etc.

[0016] Among them, enterprise project external data can be external environmental factors that affect project execution, including market changes, competitive situation, policies and regulations, economic conditions, customer needs, etc.

[0017] The initial fuzzy reasoning parameters may be setting parameters used in the fuzzy reasoning system, including the definition of fuzzy sets, membership functions, fuzzy rules, etc.

[0018] Among them, project execution history data can be the execution records of projects that the company has implemented in the past, including task completion, progress control, resource utilization, cost control, team performance, etc.

[0019] Specifically, relevant project data is obtained from various information management systems of the target enterprise, including internal data of the enterprise project (such as budget, progress, resource usage, etc.), external data of the enterprise project (such as market changes, policy environment, etc.), initial fuzzy reasoning parameters (such as fuzzy sets, membership functions, etc.) and project execution history data (such as execution records of previous projects, task completion status, resource consumption, etc.).

[0020] Step 204, performing dynamic fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data.

[0021] Among them, dynamic fuzzy reasoning can be to perform fuzzy reasoning on project data in the time dimension, and update the reasoning results in real time as the project progresses.

[0022] The dynamic fuzzy reasoning data may be the output result obtained through the dynamic fuzzy reasoning process, including the reasoning value based on the internal data and external data of the project after fuzzification processing.

[0023] Specifically, the initial fuzzy reasoning parameters (e.g., the definition of fuzzy sets, the setting of membership functions, etc.) are used to process the internal data of enterprise projects and the external data of enterprise projects. For internal data of enterprise projects, such as project budget, resource allocation, progress control, etc., quantitative data are converted into fuzzy values ​​by setting appropriate fuzzification rules, for example, the project budget allocation status is divided into categories such as "overspending", "in line with expectations" or "below expectations". For external data of enterprise projects, such as market trends, policy changes, competitive situations, etc., similar methods are used for fuzzification processing, and dynamic factors such as time series analysis are introduced to gradually update the fuzzy reasoning results. The dynamic fuzzy reasoning data obtained reflects the uncertainty and changes of the internal and external environment of the project, providing in-depth analysis data based on real dynamic situations for further decision-making.

[0024] Step 206, based on the dynamic fuzzy reasoning data and the project execution history data, identify the relationship between each execution object and each task detail in the target enterprise to obtain project behavior analysis data.

[0025] Among them, the execution objects can be various participants who undertake tasks and responsibilities in the enterprise project, including project managers, team members, external suppliers, consultants, etc.

[0026] Among them, task details can be information such as the content, objectives, progress, priority, required resources, key nodes, etc. of specific tasks during the project execution process.

[0027] Among them, project behavior analysis data can be data obtained through in-depth analysis of the project execution process, mainly focusing on behavioral patterns in project execution, the performance of execution objects, dependencies between tasks, potential risks and bottlenecks, etc.

[0028] Specifically, combined with deep behavioral analysis, the system conducts in-depth mining of dynamic fuzzy reasoning data and project execution history data to identify the complex relationship between each execution object (such as project managers, team members, external partners, etc.) and task details (such as task execution progress, resource allocation, dependencies, etc.). Among them, deep behavioral analysis technology uses machine learning and pattern recognition algorithms to analyze the behavioral characteristics, task execution modes, and performance of each execution object in the project in different situations. By establishing an association model between execution objects and tasks, the system can gain insight into potential trends in project behavior, such as efficiency fluctuations that certain execution objects may exhibit in specific tasks, or the interdependence between tasks in terms of time and resources. Based on deep behavioral analysis, the generated project behavior analysis data helps project managers identify potential risks, optimize resource allocation, and provide early warning of possible execution problems to ensure that the project can be efficiently advanced.

[0029] Step 208, structurally fuse the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data.

[0030] Among them, structured fusion can be the process of integrating data from different sources and formats according to a unified structure after preprocessing, standardization and cleaning.

[0031] Among them, enterprise project fusion data can be a combination of various enterprise project data after structured fusion, including the project's internal data, external data, behavioral analysis results, etc.

[0032] Specifically, data cleaning and preprocessing technology is used to ensure the consistency and reliability of dynamic fuzzy reasoning data and project behavior analysis data from different data sources. Further use data fusion algorithms, such as weighted average method, principal component analysis (PCA), Bayesian network, etc., to integrate the two types of data into a unified structured data set. Through structured data fusion, information from different sources such as project budget, progress, historical execution data, etc. can be organically combined, redundant information can be removed, key information can be highlighted, and a unified data framework can be generated. This enterprise project fusion data provides a more comprehensive and accurate basis for subsequent resource scheduling and decision analysis, which is convenient for efficient use.

[0033] Step 210, scheduling the fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information.

[0034] Among them, fixed resources can be resources that cannot be adjusted at will during the project execution, including equipment, facilities, funds, etc.

[0035] Among them, enterprise project management information can be various types of information used to support project management generated by collecting, analyzing and integrating various types of enterprise project data, including project progress, resource allocation, task completion status, risk warning, etc.

[0036] Specifically, the system uses resource optimization and scheduling algorithms (such as linear programming, integer programming, genetic algorithms, etc.) to intelligently schedule fixed resources (such as equipment, funds, technical support, etc.) and human resources (such as project managers, team members, external partners, etc.) based on the integrated data of enterprise projects; the goal of resource scheduling is to ensure that the project has sufficient and appropriate resource support at each stage to avoid resource conflicts, waste or shortages. The system will automatically adjust the resource allocation strategy based on factors such as task priority, the ability of the execution object, the availability of resources and time constraints, and optimize the use of various resources during project execution. Ultimately, the system will generate a complete set of enterprise project management information, including resource scheduling plans, project execution progress, task priorities, etc., to provide real-time monitoring and decision support for project managers to ensure efficient and smooth completion of projects.

[0037] In the above-mentioned enterprise project information management method, the enterprise project internal data, enterprise project external data, initial fuzzy reasoning parameters and project execution history data corresponding to the target enterprise are obtained; according to the initial fuzzy reasoning parameters, dynamic fuzzy reasoning is performed on the enterprise project internal data and the enterprise project external data to obtain dynamic fuzzy reasoning data; according to the dynamic fuzzy reasoning data and the project execution history data, the relationship between each execution object and each task detail in the target enterprise is identified to obtain project behavior analysis data; the dynamic fuzzy reasoning data and the project behavior analysis data are structured and integrated to obtain enterprise project integration data; according to the enterprise project integration data, the fixed resources and human resources of the target enterprise are scheduled to obtain enterprise project management information.

[0038] By integrating the internal data of enterprise projects, external data of enterprise projects, historical execution data and initial fuzzy reasoning parameters, and using dynamic fuzzy reasoning technology, it is possible to accurately analyze and deduce various types of data in the project, and capture and feedback the dynamic changes of project execution in real time. Further in-depth analysis of project behavior can identify the potential associations between various execution objects and tasks, and then generate comprehensive project behavior analysis data. After the structured fusion of these data, the enterprise project fusion data formed provides a reliable basis for resource scheduling. It not only improves the control of resource utilization efficiency to improve the efficiency of enterprise management projects in resource scheduling, but also optimizes the allocation of fixed resources and human resources, ensuring that the project can respond flexibly in a changing environment and improve the accuracy of decision-making; it further helps to improve the level of intelligence of enterprise project management, significantly reduce decision-making risks, and promote the smooth execution and successful completion of projects.

[0039] In an exemplary embodiment, Figure 3 As shown, according to the initial fuzzy reasoning parameters, dynamic fuzzy reasoning is performed on the internal data of the enterprise project and the external data of the enterprise project to obtain dynamic fuzzy reasoning data, including steps 302 to 306. Among them: Step 302, performing preliminary fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain preliminary fuzzy reasoning data.

[0040] The preliminary fuzzy reasoning data may be the result obtained by performing preliminary fuzzy processing on the internal data and external data of the enterprise project based on the initial fuzzy reasoning parameters.

[0041] Specifically, the internal data and external data of enterprise projects are converted into fuzzy variables, such as classifying project budgets into categories such as "sufficient", "moderate" or "insufficient", and then these fuzzy variables are inferred based on the preset initial fuzzy reasoning parameters (such as membership function, fuzzy rules, etc.) to obtain preliminary fuzzy reasoning data. The preliminary fuzzy reasoning data reflects the basic uncertainty and potential risks in project execution.

[0042] Step 304, based on the preliminary fuzzy reasoning data, the reasoning rules and the membership functions in the initial fuzzy reasoning parameters are optimized to obtain optimized fuzzy reasoning parameters.

[0043] Among them, optimizing the fuzzy reasoning parameters can be to adjust the key parameters in the fuzzy reasoning system according to the feedback results of the preliminary fuzzy reasoning data to improve the accuracy and adaptability of the reasoning model.

[0044] Specifically, based on the feedback results of the preliminary fuzzy reasoning data, the effectiveness of the current reasoning rules and membership functions is evaluated. Specifically, by comparing the preliminary fuzzy reasoning data with the actual project execution, the deficiencies in the initial fuzzy reasoning parameters of the current reasoning model (for example, some fuzzy rules are too broad or narrow) are identified and adjusted and optimized. This may include modifying the shape of the membership function or resetting the reasoning rules to more accurately reflect the fuzziness and uncertainty in the data. The optimized fuzzy reasoning parameters obtained can better adapt to the changes and dynamic needs in project execution and improve the accuracy of the reasoning results.

[0045] Step 306, performing secondary fuzzy processing on the internal data of the enterprise project and the external data of the enterprise project according to the optimized fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data.

[0046] Specifically, the internal data and external data of enterprise projects are fuzzy processed again using optimized fuzzy inference parameters, taking into account the previous inference optimization results as reference standards, and applying the adjusted inference rules and membership functions to further analyze the data to reflect the changes in the project execution process in real time. This secondary fuzzy processing can more accurately identify dynamic changes and complex associations in the data, generate more accurate dynamic fuzzy inference data, and provide higher quality information support for in-depth analysis and decision-making of the project.

[0047] In this embodiment, preliminary fuzzy reasoning is performed on the internal and external data of the enterprise project based on the initial fuzzy reasoning parameters, and the reasoning rules and membership functions are optimized according to the obtained preliminary data, and finally dynamic fuzzy reasoning data is obtained through secondary fuzzy processing, which can effectively improve the accuracy and adaptability of the reasoning process. This process not only optimizes the fuzzy reasoning parameters to make them more in line with actual application needs, but also can dynamically reflect the changes and complexity in the enterprise project, thereby improving the flexibility of data analysis and the quality of decision support, and providing enterprises with more accurate and feasible resource allocation and project management solutions.

[0048] In an exemplary embodiment, Figure 4 As shown, according to the preliminary fuzzy reasoning data, the reasoning rules and membership functions in the initial fuzzy reasoning parameters are optimized to obtain the optimized fuzzy reasoning parameters, including steps 402 to 408. Among them: Step 402, based on the preliminary fuzzy inference data, perform virtual scheduling on fixed resources and human resources to obtain virtual scheduling information.

[0049] Specifically, the preliminary fuzzy reasoning data is used to virtually schedule the fixed resources (such as equipment, funds, etc.) and human resources (such as project members, external partners, etc.) in the project. Among them, virtual scheduling does not directly perform resource allocation, but uses a computer to simulate the resource configuration process, and generates a digital virtual scheduling information by considering factors such as the priority, resource requirements, and time constraints of project tasks. Among them, the virtual scheduling information is used to evaluate whether the existing resource configuration is reasonable, and whether there are problems such as resource conflicts, waste or shortages. Through virtual scheduling, a set of virtual scheduling information can be obtained to provide data support for optimizing fuzzy reasoning parameters.

[0050] Step 404: adjust the inference rules and membership functions in the initial fuzzy inference parameters according to the virtual scheduling information to obtain adjusted fuzzy inference parameters.

[0051] Among them, adjusting the fuzzy reasoning parameters can be the result obtained by modifying and optimizing key parameters such as reasoning rules and membership functions according to the feedback of preliminary reasoning results and virtual scheduling information during the fuzzy reasoning process.

[0052] Specifically, the inference rules and membership functions in the initial fuzzy reasoning parameters are adjusted according to the virtual scheduling information generated by the computer for virtual scheduling. Specifically, the results of virtual scheduling are analyzed to identify deficiencies or unreasonableness in the resource scheduling process, such as task execution time and uneven resource allocation, and then the fuzzy reasoning parameters are modified based on these findings. For example, the threshold of the membership function is adjusted, or the inference rules are modified to better match the actual needs of project execution. The adjusted inference rules and membership functions can more accurately reflect the actual situation of project resource scheduling, thereby adjusting the fuzzy reasoning parameters.

[0053] Step 406, using the adjusted fuzzy reasoning parameters as the initial fuzzy reasoning parameters, returning to the step of performing preliminary fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain preliminary fuzzy reasoning data, until the difference information of the adjusted fuzzy reasoning parameters outputted for at least two consecutive times is less than the preset difference information.

[0054] The preset difference information may be a tolerance standard used to determine whether to stop adjusting during the fuzzy inference optimization process, and defines the maximum acceptable difference value between fuzzy inference parameter adjustments.

[0055] Specifically, the adjusted fuzzy inference parameters will be used as the initial fuzzy inference parameters for the next round of fuzzy inference processing. This process will be repeated, that is, each time the new adjustment parameters are used to re-perform preliminary fuzzy inference to obtain new preliminary fuzzy inference data. By comparing with the reasoning results of the previous round, if the difference information between the fuzzy inference parameters after two consecutive rounds of adjustment is less than the preset difference information (that is, the difference has stabilized), it is considered that the optimization process has reached a convergence state, the optimization is stopped and the final optimization parameters are determined.

[0056] Step 408: Use the last output adjusted fuzzy inference parameter as the optimized fuzzy inference parameter.

[0057] Specifically, after multiple rounds of adjustments, the adjusted fuzzy reasoning parameters outputted for the last time no longer change significantly (i.e., the difference information is less than the preset threshold). At this time, the system uses this final adjustment result as the optimized fuzzy reasoning parameter, which can more accurately reflect the actual situation of resource scheduling, task allocation and uncertainty factors during the execution of enterprise projects, and provide more effective decision-making support for subsequent dynamic fuzzy reasoning and project management.

[0058] In this embodiment, by virtually scheduling the preliminary fuzzy reasoning data and optimizing the initial fuzzy reasoning parameters based on the virtual scheduling information, it is possible to achieve fine-tuning of resource scheduling and data reasoning. This process gradually optimizes the fuzzy reasoning parameters by repeatedly iteratively adjusting the reasoning rules and membership functions until the output of the model is stable and tends to be accurate. This dynamic adjustment mechanism ensures that the resource scheduling and reasoning process can flexibly adapt to the actual situation and changes in the enterprise project, thereby improving the accuracy and efficiency of the decision-making process, and ultimately ensuring the optimization of enterprise resource scheduling and the efficient execution of project management.

[0059] In an exemplary embodiment, Figure 5 As shown, according to the dynamic fuzzy reasoning data and the project execution history data, the relationship between each execution object and each task detail in the target enterprise is identified to obtain the project behavior analysis data, including steps 502 to 506. Among them: Step 502, defuzzify the dynamic fuzzy reasoning data to obtain dynamic defuzzified reasoning data.

[0060] Among them, the dynamic defuzzified reasoning data can be the fuzzy information output by the fuzzy reasoning system converted into specific values ​​or categories through the defuzzification process, thereby providing clear and quantitative dynamic data for further analysis.

[0061] Specifically, the dynamic fuzzy reasoning data is defuzzified, wherein the purpose of the defuzzification process is to convert the dynamic fuzzy reasoning data output by the fuzzy reasoning system into specific, clear values ​​or categories. Specifically, by adopting a defuzzification algorithm (such as the centroid method, the maximum membership method, etc.), the membership value of the dynamic fuzzy reasoning data (such as "high", "medium", and "low") is converted into an accurate dynamic defuzzified reasoning data. The key to the conversion is to correspond the fuzzified data to the actual project status, so that the defuzzified data is easier to perform subsequent analysis and decision-making.

[0062] Step 504: Use an association rule fusion algorithm to fuse the dynamic defuzzification reasoning data and the project execution history data to obtain behavior analysis fusion data.

[0063] Among them, behavioral analysis fusion data can be a set of comprehensive information formed by fusing data from different sources (such as dynamic defuzzification reasoning data and project execution history data), which reflects the behavioral patterns between various execution objects and task details in project execution.

[0064] Specifically, since the dynamic defuzzification reasoning data has transformed the fuzzy information into specific and operational dynamic defuzzification reasoning data, these dynamic defuzzification reasoning data involve resource allocation, task progress, personnel load and other aspects; while the project execution history data contains information such as task completion, time consumption, and personnel performance in the previous project execution process. Using association rule fusion algorithms (such as Apriori algorithm or FP-growth algorithm), the system analyzes these two sets of data to find frequent patterns and association rules between them. For example, it can be identified that under certain resource configurations, the frequency of execution delays of specific tasks is high, or that there is a strong correlation between the performance of specific execution objects and certain project characteristics (such as task scale and teamwork mode). Through the fusion of association rule fusion algorithms, the system can not only reveal the correlation between tasks and resources, but also discover the laws behind execution behaviors, generate behavior analysis fusion data containing comprehensive information such as behavior patterns, resource utilization, and task execution efficiency, and provide rich data support for subsequent behavior analysis.

[0065] Step 506: Analyze the behavior association relationship between each execution object and each task detail based on the behavior analysis fusion data to obtain project behavior analysis data.

[0066] Among them, the behavioral association relationship can be the mutual influence and dependency between the execution objects (such as team members, managers, etc.) and task details (such as task progress, resource requirements, etc.) during the project execution process.

[0067] Specifically, by deeply mining the association patterns in the behavior analysis fusion data, the mutual influence and relationship between different execution objects (such as project managers, team members, external partners, etc.) and different task details (such as task priority, completion time, required resources, etc.) are identified. Specifically, since the behavior analysis fusion data contains the behavioral characteristics and task execution status of various execution objects, the analysis process reveals the role of different execution objects in the task by applying data analysis, pattern recognition and statistical methods. For example, the system may find the reaction pattern of a certain execution object when facing an urgent task, or how a certain resource configuration affects the completion time of a task under a specific execution object. Through this analysis, it is possible to identify which execution objects perform well on specific tasks, which task details are most related to delays or failures, and how resource configuration affects the overall project progress. Ultimately, the project behavior analysis data can help decision makers understand the underlying laws in task execution and provide data-driven decision-making basis for future project optimization, resource scheduling and task allocation.

[0068] In this embodiment, by defuzzifying the dynamic fuzzy reasoning data and integrating the project execution history data, clearer and more intuitive behavior analysis data can be effectively extracted. Combining the two using the association rule fusion algorithm can not only reveal the potential associations between the execution objects and task details in the project, but also identify important behavior patterns and relationships. This process provides a more accurate decision-making basis for project management by deeply analyzing the behavioral association relationship, helping enterprises optimize resource allocation and improve task execution efficiency, thereby improving the overall performance and success rate of the project.

[0069] In an exemplary embodiment, Figure 6 As shown, according to the behavior analysis fusion data, the behavior association relationship between each execution object and each task detail is analyzed to obtain the project behavior analysis data, including steps 602 to 606. Among them: Step 602: Classify the project execution behaviors of each execution object according to the behavior analysis fusion data to obtain a set of project execution objects.

[0070] Among them, project execution behavior can be various specific activities and characteristics exhibited by the execution object during the execution of the project task, including the time for task completion, efficiency of resource utilization, collaboration methods, ability to deal with unexpected problems, etc.

[0071] The project execution object set may be a group formed by classifying the project execution behaviors of the execution objects, and each set includes execution objects with similar behavior characteristics.

[0072] Specifically, the system will analyze the specific project execution behavior of each execution object in the project, such as the type of task assignment, timeliness of task completion, efficiency of resource use, and contribution to project goals, etc. The data of these project execution behaviors will lead the execution objects to be classified into different sets. Based on similarities such as task completion, resource consumption, and execution efficiency, execution objects with similar performance are divided into the same group. For example, some team members may exhibit behaviors of completing tasks efficiently and quickly, while other members may exhibit slower execution speeds in complex tasks. Through this classification, the system is able to classify each execution object into a unique set of project execution objects, which facilitates further causal analysis.

[0073] Step 604 , associating the behavioral causal relationship between any project execution object set and each task detail to obtain each initial behavior analysis data.

[0074] Among them, the causal relationship association can be to analyze the mutual influence between the execution object set and the task details, and reveal the causal relationship between them.

[0075] The initial behavior analysis data may be the first round of analysis results obtained after performing a causal relationship analysis between the execution object set and the task details, and mainly includes specific correlation information between the behavior pattern of the execution object and the task details.

[0076] Specifically, the system associates each set of project execution objects with the details of specific tasks in the project to identify the behavioral causal relationship between the two. The task details include not only the nature of the task (such as task type, difficulty, etc.), but also factors such as time, resource requirements, and priority during the task execution process. The system uses data mining technology to analyze the performance of a specific set of project execution objects when performing a certain type of task, and explores the causal relationship between it and the task details. For example, members of a set may need more time and resources when facing difficult tasks, while other members are more efficient under simple tasks. Through this causal analysis, the system can reveal the deep connection between task details and the behavior of execution objects, obtain initial behavioral analysis data, and provide data support for subsequent task optimization and resource allocation, so that project managers can accurately identify which task details most affect the performance of execution objects.

[0077] Step 606, concatenate the initial behavior analysis data to obtain project behavior analysis data.

[0078] Specifically, the initial behavior analysis data between each set of project execution objects and task details are spliced ​​to form the complete project behavior analysis data. The splicing process integrates the causal relationship between different execution objects and task details into one data set, ensuring that all behavior analysis results can be presented in a unified format. The final project behavior analysis data will contain the overall analysis results about the performance of each execution object, its correlation with each task detail, and how these factors affect task completion.

[0079] In this embodiment, by classifying the behavior analysis fusion data, the project execution behaviors of the execution objects are grouped and formed into sets, which can clearly show the characteristics and patterns of different types of execution objects. Furthermore, these sets are analyzed with the task details for behavioral causal relationships, which can accurately reveal the relationship between the behavioral characteristics of the execution objects and the factors that lead to the success of the tasks. Finally, by splicing the initial behavior analysis data to form complete project behavior analysis data, systematic behavioral insights are provided to the enterprise. This process helps to optimize task allocation, improve resource utilization efficiency, and provide accurate and comprehensive data support for project management decisions, thereby promoting the success rate of project execution and improving overall efficiency.

[0080] In an exemplary embodiment, Figure 7As shown, the dynamic fuzzy reasoning data and the project behavior analysis data are structurally fused to obtain enterprise project fusion data, including steps 702 to 710. Among them: Step 702, the dynamic fuzzy reasoning data and the project behavior analysis data are adjusted in a unified format to obtain dynamic fuzzy adjustment data and project behavior adjustment data.

[0081] The dynamic fuzzy adjustment data and the project behavior adjustment data may be data obtained after adjusting the data formats of the dynamic fuzzy reasoning data and the project behavior analysis data respectively.

[0082] Specifically, the dynamic fuzzy reasoning data and project behavior analysis data are formatted in a unified manner, which means converting the two types of data into the same format for subsequent fusion processing. Usually, data format unification involves standardizing data fields, data types, and units to ensure that they are compatible and comparable with each other. For example, dynamic fuzzy reasoning data may include numerical task completion, resource consumption, etc., while project behavior analysis data may contain categorical data such as task type, execution object, etc. The system uses mapping and conversion to enable these two types of data to be processed on the same platform, obtaining standardized dynamic fuzzy adjustment data and project behavior adjustment data.

[0083] Step 704 , according to the target enterprise's business data fusion rule, the dynamic fuzzy adjustment data and the project behavior adjustment data are fused to obtain initial project fusion data.

[0084] Among them, business data fusion rules can be rules used to guide how to effectively integrate different types of data according to the company's specific business goals, data attributes and analysis requirements during the multi-source data fusion process.

[0085] The initial project fusion data can be a preliminary data set obtained by fusing data from different sources after applying the business data fusion rules. It combines dynamic fuzzy reasoning data and project behavior analysis data, and reflects various aspects of the project, such as the efficiency of task execution, resource usage, and the performance of the execution object.

[0086] Specifically, the uniformly formatted dynamic fuzzy adjustment data and project behavior adjustment data need to be effectively integrated according to the specific business needs of the enterprise. The business data fusion rules are usually designed based on the business goals, data characteristics and decision-making needs of the enterprise, and may include different methods such as weighted average, conditional selection, and priority sorting. For example, if the enterprise prioritizes resource utilization efficiency, the part of the dynamic fuzzy adjustment data related to resource utilization may receive a higher weight, while the execution efficiency part of the project behavior adjustment data may play a complementary role. In addition, the business data fusion rules also consider factors such as time series, project stages, and task categories. The two types of data are comprehensively processed through the fusion algorithm to ensure that the fused data can reflect a more accurate and comprehensive project execution status. Finally, the system will obtain the initial project fusion data.

[0087] Step 706: Generate business data splitting rules for the target enterprise according to the business data fusion rules.

[0088] Among them, the business data splitting rule can be a rule for splitting the fused data according to the reverse operation requirements of the business data fusion rule after the data fusion is completed.

[0089] Specifically, the system reversely generates business data splitting rules that match the specific business needs of the target enterprise based on the enterprise's business data fusion rules. Since business data fusion rules are usually designed based on the enterprise's business processes, project requirements or resource management requirements. For example, some data may need to be split by time, task type, region, resource type and other dimensions in order to conduct specific analysis at different management or decision-making levels; and the generation of business data splitting rules is reversely designed based on the above-mentioned enterprise business processes, project requirements or resource management requirements.

[0090] Step 708, split the initial project fusion data according to the business data splitting rule to obtain dynamic fuzzy split data and project behavior split data.

[0091] Among them, the dynamic fuzzy split data and the project behavior split data are obtained by performing reverse operations on the initial project fusion data.

[0092] Specifically, the system splits the initial project fusion data according to business needs and project characteristics based on the reverse defined business data splitting rules. The purpose of the splitting process is to reversely decompose the large-scale initial project fusion data into two data sets before fusion. The splitting rules may be based on the reverse dimension of the business data fusion rules. For example, the business data fusion rules are fused according to task types (such as design, execution, review), time periods (such as weeks, months), project stages (such as planning, execution, evaluation) or resource types (such as human resources, material resources), and the business data splitting rules are reversely split according to task types (such as design, execution, review), time periods (such as weeks, months), project stages (such as planning, execution, evaluation) or resource types (such as human resources, material resources). In this way, dynamic fuzzy split data and project behavior split data can be split into dynamic fuzzy split data and project behavior split data respectively.

[0093] Step 710, when the dynamic fuzzy adjustment data and the dynamic fuzzy splitting data are the same, and the project behavior adjustment data and the project behavior splitting data are the same, the initial project fusion data is used as the enterprise project fusion data.

[0094] Specifically, if the dynamic fuzzy adjustment data is equal to the dynamic fuzzy split data (or the difference value is less than the first threshold), and the project behavior adjustment data is equal to the project behavior split data (or the difference value is less than the second threshold), it means that the process of splitting and fusion has not introduced errors or data loss, and the validity of the initial project fusion data can be confirmed. At this time, the system uses the initial project fusion data as the final enterprise project fusion data.

[0095] In this embodiment, by adjusting the format of dynamic fuzzy reasoning data and project behavior analysis data in a unified manner and fusing them according to the business data fusion rules of the target enterprise, it is possible to ensure that data from different sources are consistent and comparable, thereby improving the quality and efficiency of data integration. Generate and apply business data splitting rules, further split the initial project fusion data, and refine the data processing process, which helps to analyze and process various types of business data more carefully. By verifying the consistency of the split data and the adjusted data, the accuracy and completeness of the data fusion are ensured, and the enterprise project fusion data is finally obtained. This process improves the accuracy and reliability of the data, provides enterprises with more refined decision-making support in resource scheduling, project management, etc., and promotes the optimization of project execution and management.

[0096] In an exemplary embodiment, Figure 8 As shown, according to the target enterprise's business data fusion rule, the dynamic fuzzy adjustment data and the project behavior adjustment data are fused to obtain the initial project fusion data, including steps 802 to 806. Among them: Step 802, classify the dynamic fuzzy adjustment data and the project behavior adjustment data according to the fusion classification constraints of the business data fusion rules to obtain dynamic fuzzy classification data and project behavior classification data.

[0097] Among them, the fusion classification constraints can be rules or restrictions used to guide how to reasonably classify different types of data according to specific business needs and data characteristics during the business data fusion process.

[0098] Among them, the dynamic fuzzy classification data and the project behavior classification data can be the results obtained by classifying the dynamic fuzzy adjustment data and the project behavior adjustment data respectively after applying the fusion classification constraints.

[0099] Specifically, according to the fusion classification constraints defined in the target enterprise's business data fusion rules, the dynamic fuzzy adjustment data and the project behavior adjustment data are classified, wherein the classification is usually based on different dimensions or data characteristics of the project, such as task category, resource consumption, execution stage, etc., to divide the data into different groups. In this way, each data point in the dynamic fuzzy adjustment data and the project behavior adjustment data will be assigned to the corresponding category according to its characteristics, and the classification results of the two types of data, namely the dynamic fuzzy classification data and the project behavior classification data, are obtained. The classification helps to adjust the data processing strategy more carefully according to different business goals in the subsequent fusion process.

[0100] Step 804, for any fusion classification constraint, according to the data fusion weight of the business data fusion rule, the dynamic fuzzy classification data and the project behavior classification data are fused to obtain the fusion data of each classification.

[0101] The data fusion weight may be the degree to which different data are given different importance according to business needs during the data fusion process.

[0102] The classified fusion data may be a result obtained by weighted fusion of the classified data during the data fusion process.

[0103] Specifically, the system performs weighted fusion on the dynamic fuzzy classification data and project behavior classification data of the same category according to the dynamic fuzzy classification data and project behavior classification data of different categories obtained after the previous step of fusion classification constraint classification, combined with the data fusion weights of different data in the same category in the business data fusion rules. The fusion weight of each category is set according to the enterprise's priority for different tasks or project dimensions. For example, for some tasks, the enterprise may pay more attention to execution efficiency rather than resource consumption, so data categories related to efficiency will be given higher weights. The same operation is performed for each category to obtain the weighted fusion result of each category, that is, the fusion data of each category.

[0104] Step 806, concatenate the fusion data of each category to obtain the initial project fusion data.

[0105] Specifically, the system splices the classified fusion data of each category, where splicing is to merge the fusion data of different categories into a unified data set for use in subsequent analysis and decision-making. This splicing process will not lose any category data, but will integrate them into a complete data set. The initial project fusion data obtained is a comprehensive data set that can fully reflect all aspects of project execution and provide strong data support for the company's resource allocation, task optimization and decision-making.

[0106] In this embodiment, by applying the fusion classification constraints in the business data fusion rules, the dynamic fuzzy adjustment data and the project behavior adjustment data are classified and processed, which can effectively classify the complex data in an orderly manner, ensuring that the characteristics and behaviors of each type of data can be analyzed more accurately. Then, the classified data is fused according to the data fusion weight, which can better balance the importance of different data sources and improve the effect and accuracy of data fusion. Finally, by splicing the classified fusion data, the initial project fusion data is obtained, which provides more structured and targeted data support for subsequent analysis and decision-making. This process helps enterprises to accurately integrate data in complex environments and provides a more efficient and reliable basis for resource optimization, risk assessment and strategic decision-making.

[0107] In an exemplary embodiment, Fig. 9 As shown, according to the enterprise project integration data, the fixed resources and human resources of the target enterprise are scheduled to obtain the enterprise project management information, including steps 902 to 916. Among them: Step 902: construct an enterprise resource scheduling model corresponding to the target enterprise based on the enterprise project fusion data.

[0108] The enterprise resource scheduling model may be a mathematical or computational model used to optimize the allocation and scheduling of internal enterprise resources.

[0109] Specifically, the enterprise project fusion data is used to analyze and model the needs and situations of enterprise resource scheduling. The construction of the enterprise resource scheduling model needs to comprehensively consider the existing resource allocation, task allocation, execution goals and other factors of the enterprise to ensure that the fixed resources and human resources of the enterprise can be reasonably scheduled. The purpose of the enterprise resource scheduling model is to help enterprises optimize resource allocation, ensure the maximum utilization of resources, and provide a basis for subsequent resource scheduling calculations. During the construction process, the model to be constructed will classify and associate all tasks and required resources (including fixed resources such as equipment and space, and human resources such as the number of employees and working hours) to form a multi-dimensional resource scheduling diagram. This diagram associates information such as the relationship between resources and tasks, time arrangements, resource availability, and task priorities to ensure that the scheduling process is both efficient and practical. The model to be constructed also needs to take into account various constraints, such as the number of resources, skill requirements, priorities and other factors. During the construction of the model, the system will use at least one optimization algorithm (such as linear programming, genetic algorithm, etc.) to select the most appropriate resource scheduling solution, and can dynamically adjust it according to the actual situation to obtain the enterprise resource scheduling model. By introducing relevant scheduling constraints (such as time, resource limitations, etc.), enterprises can simulate resource usage under different situations in order to achieve the optimal resource scheduling solution.

[0110] Step 904: construct resource scheduling capability constraints of the target enterprise based on the available information and status information of the fixed resources and human resources.

[0111] Among them, available information and status information can be data related to resources that describe their current status. For example, the available information of fixed resources includes the idle status of equipment, the quantity of inventory, the available time of production lines, etc.; the available information of human resources includes the working hours, skill levels, available working hours, etc. of employees.

[0112] Among them, resource scheduling capacity constraints can be a series of restrictions that must be followed during the scheduling process. These restrictions come from the company's existing resource status and task requirements.

[0113] Specifically, the system constructs the resource scheduling capacity constraints of the enterprise based on the actual situation of fixed resources and human resources, combined with their available information (such as remaining quantity, working capacity, etc.) and status information (such as whether they are being used, available time period, etc.). The resource scheduling capacity constraints reflect the limitations of resources in actual operations. For example, some resources may not be available within a certain period of time, or the working capacity of some personnel is limited. Therefore, constructing these capacity constraints is a key step to ensure that the resource scheduling model is realistic and feasible.

[0114] Step 906, based on the resource scheduling capability constraint, use the resource scheduling algorithm of the target enterprise to solve the enterprise resource scheduling model, and obtain fixed resource scheduling information and human resource scheduling information.

[0115] Among them, the resource scheduling algorithm can be the core method for solving the enterprise resource scheduling model. The algorithm adopts certain optimization strategies to formulate resource scheduling plans based on the needs of the target enterprise, the requirements of the task, the available information of resources and the constraints.

[0116] Fixed resource scheduling information can be detailed information about the allocation and use of fixed resources (such as equipment, plant, materials, etc.) of an enterprise, including the usage period, usage tasks, workload, etc. of each resource. Human resource scheduling information can be information about personnel allocation, working period, task allocation, etc., including each employee's working hours, work tasks, and skill matching.

[0117] Specifically, the system uses the matching resource scheduling algorithm to solve the enterprise resource scheduling model based on the resource scheduling capacity constraints constructed earlier. The resource scheduling algorithm needs to consider factors such as resource availability, task priority, and scheduling goals, and optimize the resource allocation plan to generate specific fixed resource scheduling information and human resource scheduling information. These scheduling information provide specific resource allocation and usage plans, which are the basis for actual resource scheduling operations.

[0118] Step 908, use the fixed resource scheduling information and the human resource scheduling information to perform simulated scheduling, and identify the resource conflict information and enterprise resource utilization rate of the target enterprise.

[0119] The resource conflict information may be a situation in which the same resource cannot be allocated multiple times as required during the resource scheduling process. Usually, resource conflict occurs when two or more tasks need to use the same resource.

[0120] Specifically, the system simulates scheduling based on the generated fixed resource scheduling information and human resource scheduling information to verify the feasibility of the scheduling plan. During the simulation process, the system will detect whether there is a resource conflict, for example, two tasks need to use the same resource at the same time, or the working hours of personnel overlap. By identifying these conflicting information, the system can discover potential scheduling problems in advance and provide a basis for subsequent adjustments. At the same time, the system also calculates the enterprise resource utilization rate based on the generated fixed resource scheduling information and human resource scheduling information; the calculation of the enterprise resource utilization rate usually involves comparing the actual use of fixed resources and human resources with their total available amount. The core of the calculation is to measure the ratio of the "actual use time" to the "total available time" of each resource to obtain the actual utilization level of the resource. Resource utilization is a key indicator for measuring the efficiency of resource use, which indicates the proportion of resources actually used by the enterprise in a given time period to available resources. By calculating resource utilization, enterprises can evaluate whether their resource allocation has reached the optimal state and whether there is a problem of resource waste or insufficient resources.

[0121] Step 910, when the resource conflict information is not null, adjust the available information and status information of fixed resources and human resources, and return to execute the step of constructing the resource scheduling capability constraint of the target enterprise based on the available information and status information of fixed resources and human resources until the resource conflict information is null.

[0122] Specifically, if the resource conflict information generated by the simulated scheduling is not null, that is, a resource conflict is found, the system will adjust the original fixed resource and human resource availability information and status information through the latest fixed resource and human resource availability information and status information to adjust the scheduling capacity constraints. The purpose of this adjustment is to eliminate conflicts, such as by reallocating resources, adjusting task priorities, changing task execution time, etc. The system will re-schedule resources according to the new constraints until all conflicts are eliminated and the resource conflict information is null, indicating that the scheduling plan has been optimized.

[0123] Step 912, when the enterprise resource utilization is less than the preset resource utilization, adjust the resource scheduling algorithm, and return to the step of solving the enterprise resource scheduling model based on the resource scheduling capacity constraint using the target enterprise's resource scheduling algorithm to obtain fixed resource scheduling information and human resource scheduling information, until the enterprise resource utilization is greater than the preset resource utilization.

[0124] The preset resource utilization rate can be the resource utilization efficiency target that the enterprise sets before the project is executed and expects to achieve. It is usually set based on historical data, industry standards, project requirements and other factors.

[0125] Specifically, once the resource conflict is resolved, it is further checked whether the enterprise resource utilization is lower than the preset resource utilization. If the calculated enterprise resource utilization is lower than the preset resource utilization, it means that the resources are not used effectively. At this time, the system needs to adjust the parameters of the resource scheduling algorithm. For example, the allocation and utilization efficiency of resources can be optimized by changing the scheduling strategy parameters, optimizing the task allocation parameters, or re-evaluating the resource priority. The resource scheduling model will be solved again using the adjusted algorithm until the resource utilization reaches the preset resource utilization.

[0126] Step 914: Use the fixed resource scheduling information and the human resource scheduling information as enterprise project management information.

[0127] Specifically, after all adjustments and optimizations are completed, that is, the resource conflict information is null and the resource utilization rate reaches the preset resource utilization rate, the corresponding final fixed resource scheduling information and human resource scheduling information will be regarded as the final enterprise project management information and used for actual project management and execution. This information provides project managers with a detailed resource allocation plan to ensure that the project can proceed smoothly within the scope of resources allowed and achieve the enterprise's resource optimization goals.

[0128] In this embodiment, by constructing an enterprise resource scheduling model and combining the available information and status information of fixed resources and human resources to set scheduling capacity constraints, an accurate resource scheduling basis can be provided for enterprise project management. Using the resource scheduling algorithm to solve, and identifying potential resource conflicts and calculating enterprise resource utilization through simulation scheduling, it is helpful to timely discover resource bottlenecks and optimize resource allocation. In the case of resource conflicts and utilization rates not meeting the standards, the resource scheduling capacity constraints and scheduling algorithms are adjusted to ensure that the scheduling plan maximizes resource utilization efficiency while meeting demand. Through this process, resource allocation can be effectively optimized, resource waste can be reduced, and the execution efficiency and cost-effectiveness of enterprise projects can be improved, thereby providing stable resource support and management decision-making basis for the long-term development of the enterprise.

[0129] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0130] Based on the same inventive concept, the embodiment of the present application also provides an enterprise project information management device for implementing the enterprise project information management method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more enterprise project information management device embodiments provided below can refer to the above limitations on an enterprise project information management method, and will not be repeated here.

[0131] In an exemplary embodiment, Fig.10 As shown, an enterprise project information management device is provided, including: a data acquisition module 1002, a fuzzy reasoning module 1004, a behavior analysis module 1006, a data fusion module 1008 and a resource scheduling module 1010, wherein: The data acquisition module 1002 is used to acquire the internal data of the enterprise project, the external data of the enterprise project, the initial fuzzy reasoning parameters and the project execution history data corresponding to the target enterprise; The fuzzy reasoning module 1004 is used to perform dynamic fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data; The behavior analysis module 1006 is used to identify the relationship between each execution object and each task detail in the target enterprise based on the dynamic fuzzy reasoning data and the project execution history data, and obtain the project behavior analysis data; The data fusion module 1008 is used to structurally fuse the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; The resource scheduling module 1010 is used to schedule the fixed resources and human resources of the target enterprise according to the enterprise project integration data to obtain the enterprise project management information.

[0132] Each module in the above-mentioned enterprise project information management device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for information management of enterprise projects is implemented.

[0134] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] In one embodiment, an enterprise project information management system is also provided. The system includes: a data processing end and a terminal. When the data processing end executes a computer program, the steps of an enterprise project information management method are implemented.

[0136] In one embodiment, an enterprise project information management storage medium is also provided, in which a computer program is stored. When the computer program is executed by a processor in a data processing terminal, the steps of an enterprise project information management method are implemented.

[0137] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0139] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0140] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An enterprise project information management method, characterized in that: The method comprises: Obtaining internal enterprise project data, external enterprise project data, initial fuzzy reasoning parameters and project execution history data corresponding to the target enterprise; According to the initial fuzzy reasoning parameters, dynamic fuzzy reasoning is performed on the internal data of the enterprise project and the external data of the enterprise project to obtain dynamic fuzzy reasoning data; According to the dynamic fuzzy reasoning data and the project execution history data, the relationship between each execution object and each task detail in the target enterprise is identified to obtain project behavior analysis data; Structurally fusion the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; According to the enterprise project fusion data, the fixed resources and human resources of the target enterprise are scheduled to obtain enterprise project management information.

2. The method according to claim 1, characterized in that The step of performing dynamic fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data includes: Performing preliminary fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain preliminary fuzzy reasoning data; According to the preliminary fuzzy reasoning data, the inference rules and the membership functions in the initial fuzzy reasoning parameters are optimized to obtain optimized fuzzy reasoning parameters; According to the optimized fuzzy inference parameters, secondary fuzzy processing is performed on the internal data of the enterprise project and the external data of the enterprise project to obtain the dynamic fuzzy inference data.

3. The method according to claim 2, characterized in that The step of optimizing the inference rules and the membership functions in the initial fuzzy inference parameters according to the preliminary fuzzy inference data to obtain optimized fuzzy inference parameters includes: Performing virtual scheduling on the fixed resources and the human resources according to the preliminary fuzzy inference data to obtain virtual scheduling information; According to the virtual scheduling information, the inference rules and the membership functions in the initial fuzzy inference parameters are adjusted to obtain adjusted fuzzy inference parameters; The adjusted fuzzy reasoning parameter is used as the initial fuzzy reasoning parameter, and the step of performing preliminary fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameter to obtain preliminary fuzzy reasoning data is returned, until the difference information of the adjusted fuzzy reasoning parameter outputted for at least two consecutive times is less than the preset difference information; The last outputted adjusted fuzzy inference parameter is used as the optimized fuzzy inference parameter.

4. The method according to claim 1, characterized in that The identifying the relationship between each execution object and each task detail in the target enterprise according to the dynamic fuzzy reasoning data and the project execution history data to obtain project behavior analysis data includes: Defuzzifying the dynamic fuzzy reasoning data to obtain dynamic defuzzified reasoning data; Using an association rule fusion algorithm, the dynamic defuzzification reasoning data and the project execution history data are fused to obtain behavior analysis fusion data; According to the behavior analysis fusion data, the behavior association relationship between each of the execution objects and each of the task details is analyzed to obtain the project behavior analysis data.

5. The method according to claim 4, characterized in that The analyzing the behavior association relationship between each of the execution objects and each of the task details according to the behavior analysis fusion data to obtain the project behavior analysis data includes: Classifying the project execution behaviors of the execution objects according to the behavior analysis fusion data to obtain a set of project execution objects; Associating the behavioral causal relationship between any of the project execution object sets and each of the task details to obtain each initial behavioral analysis data; The initial behavior analysis data are spliced ​​to obtain the project behavior analysis data.

6. The method according to claim 1, characterized in that The structural fusion of the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data includes: The dynamic fuzzy reasoning data and the project behavior analysis data are adjusted in a unified format to obtain dynamic fuzzy adjustment data and project behavior adjustment data; According to the business data fusion rule of the target enterprise, the dynamic fuzzy adjustment data and the project behavior adjustment data are fused to obtain initial project fusion data; Generating a business data splitting rule for the target enterprise according to the business data fusion rule; According to the business data splitting rule, the initial project fusion data is split to obtain dynamic fuzzy split data and project behavior split data; When the dynamic fuzzy adjustment data is identical to the dynamic fuzzy splitting data, and the project behavior adjustment data is identical to the project behavior splitting data, the initial project fusion data is used as the enterprise project fusion data.

7. The method according to claim 6, characterized in that The step of fusing the dynamic fuzzy adjustment data and the project behavior adjustment data according to the business data fusion rule of the target enterprise to obtain initial project fusion data includes: According to the fusion classification constraint of the business data fusion rule, the dynamic fuzzy adjustment data and the project behavior adjustment data are classified to obtain dynamic fuzzy classification data and project behavior classification data; For any of the fusion classification constraints, the dynamic fuzzy classification data and the project behavior classification data are fused according to the data fusion weight of the business data fusion rule to obtain fusion data of each classification; The classified fusion data are spliced ​​to obtain the initial project fusion data.

8. The method according to any one of claims 1 to 7, characterized in that: The fixed resources and human resources of the target enterprise are scheduled according to the enterprise project fusion data to obtain enterprise project management information, including: Constructing an enterprise resource scheduling model corresponding to the target enterprise according to the enterprise project fusion data; Constructing resource scheduling capacity constraints of the target enterprise according to the available information and status information of the fixed resources and the human resources; Based on the resource scheduling capability constraint, using the resource scheduling algorithm of the target enterprise to solve the enterprise resource scheduling model to obtain fixed resource scheduling information and human resource scheduling information; Performing simulation scheduling using the fixed resource scheduling information and the human resource scheduling information to identify resource conflict information and enterprise resource utilization of the target enterprise; In the case where the resource conflict information is not a null value, the available information and the status information of the fixed resources and the human resources are adjusted, and the step of constructing the resource scheduling capability constraint of the target enterprise according to the available information and the status information of the fixed resources and the human resources is returned to be executed until the resource conflict information is a null value; In the case where the enterprise resource utilization rate is less than the preset resource utilization rate, the resource scheduling algorithm is adjusted, and the step of returning to the step of solving the enterprise resource scheduling model based on the resource scheduling capability constraint using the resource scheduling algorithm of the target enterprise to obtain fixed resource scheduling information and human resource scheduling information is executed until the enterprise resource utilization rate is greater than the preset resource utilization rate; The fixed resource scheduling information and the human resource scheduling information are used as the enterprise project management information.

9. An enterprise project information management device, characterized in that: The device comprises: A data acquisition module is used to acquire internal enterprise project data, external enterprise project data, initial fuzzy reasoning parameters, and project execution history data corresponding to the target enterprise; A fuzzy reasoning module, used for performing dynamic fuzzy reasoning on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy reasoning parameters to obtain dynamic fuzzy reasoning data; A behavior analysis module, used to identify the relationship between each execution object and each task detail in the target enterprise according to the dynamic fuzzy reasoning data and the project execution history data, and obtain project behavior analysis data; A data fusion module, used for structurally fusing the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data; The resource scheduling module is used to schedule the fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information.

10. An enterprise project information management system, characterized in that: The system comprises: a data processing end and a terminal, and the data processing end implements the steps of any one of the methods of claims 1 to 8 when executing a computer program.

11. An enterprise project information management storage medium, characterized in that: The storage medium stores a computer program, which implements the steps of any one of claims 1 to 8 when executed by a processor in a data processing terminal.

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