Enterprise Project Informatization Management Method, Device, System and Storage Medium

Abstract

The present application relates to an enterprise project informatization management method, device, system and storage medium. The method includes: obtaining enterprise project internal data, enterprise project external data, initial fuzzy inference parameters and project execution history data corresponding to a target enterprise; performing dynamic fuzzy inference on the enterprise project internal data and the enterprise project external data according to the initial fuzzy inference parameters to obtain dynamic fuzzy inference data; identifying the relationships between each execution object and each task detail in the target enterprise according to the dynamic fuzzy inference data and the project execution history data to obtain project behavior analysis data; structurally integrating the dynamic fuzzy inference data and the project behavior analysis data to obtain enterprise project integration data; scheduling the fixed resources and human resources of the target enterprise according to the enterprise project integration data to obtain enterprise project management information. Using this method can improve the efficiency of resource scheduling in enterprise management projects.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly to an enterprise project informatization management method, device, system, and storage medium. Background Art

[0002] In traditional technologies, enterprise project informatization management usually relies on various single and static software systems, such as project management tools, task scheduling software, and resource management systems. These systems help schedule various tasks and resources in the enterprise project life cycle by manually inputting and updating project information, such as time arrangements, progress tracking, budget control, etc. 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, resulting in low efficiency in resource scheduling for enterprise management projects. Summary of the Invention

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

[0004] In a first aspect, the present application provides an enterprise project informatization management method, including:

[0005] Obtaining enterprise project internal data, enterprise project external data, initial fuzzy inference parameters, and project execution history data corresponding to a target enterprise;

[0006] Performing dynamic fuzzy inference on the enterprise project internal data and the enterprise project external data according to the initial fuzzy inference parameters to obtain dynamic fuzzy inference data;

[0007] Identifying the relationships between each execution object and each task detail in the target enterprise according to the dynamic fuzzy inference data and the project execution history data to obtain project behavior analysis data;

[0008] Structurally integrating the dynamic fuzzy inference data and the project behavior analysis data to obtain enterprise project integration data;

[0009] Scheduling the fixed resources and human resources of the target enterprise according to the enterprise project integration data to obtain enterprise project management information.

[0010] In a second aspect, the present application further provides an enterprise project informatization management device, including:

[0011] A data acquisition module, configured to obtain enterprise project internal data, enterprise project external data, initial fuzzy inference parameters, and project execution history data corresponding to a target enterprise;

[0012] A fuzzy inference module, configured to perform dynamic fuzzy inference on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy inference parameters, so as to obtain dynamic fuzzy inference data;

[0013] A behavior analysis module, configured to identify the relationships between each execution object and each task detail in the target enterprise according to the dynamic fuzzy inference data and the project execution history data, so as to obtain project behavior analysis data;

[0014] A data fusion module, configured to perform structured fusion on the dynamic fuzzy inference data and the project behavior analysis data, so as to obtain enterprise project fusion data;

[0015] A resource scheduling module, configured to schedule the fixed resources and human resources of the target enterprise according to the enterprise project fusion data, so as to obtain enterprise project management information.

[0016] In a third aspect, the present application further provides an enterprise project information management system, where the system includes: a data processing end and a terminal, and when the data processing end executes a computer program, it implements the steps of an enterprise project information management method.

[0017] In a fourth aspect, the present application further provides an enterprise project information management storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by a processor in the data processing end, it implements the steps of an enterprise project information management method.

[0018] For the above enterprise project information management method, device, system, and storage medium, by obtaining the internal data of the enterprise project, the external data of the enterprise project, the initial fuzzy inference parameters, and the project execution history data corresponding to the target enterprise; performing dynamic fuzzy inference on the internal data of the enterprise project and the external data of the enterprise project according to the initial fuzzy inference parameters to obtain dynamic fuzzy inference data; identifying the relationships between each execution object and each task detail in the target enterprise according to the dynamic fuzzy inference data and the project execution history data to obtain project behavior analysis data; performing structured fusion on the dynamic fuzzy inference data and the project behavior analysis data to obtain enterprise project fusion data; scheduling the fixed resources and human resources of the target enterprise according to the enterprise project fusion data to obtain enterprise project management information.

[0019] By integrating the internal data of enterprise projects, the external data of enterprise projects, historical execution data, and initial fuzzy inference parameters, and applying dynamic fuzzy inference technology, it is possible to accurately analyze and deduce various types of data in the project, and capture and feedback the dynamic changes in project execution in real time. Further in-depth analysis of project behaviors can identify the potential associations between various execution objects and tasks, and then generate comprehensive project behavior analysis data. After structuring and integrating these data, the enterprise project integration data formed provides a reliable basis for resource scheduling. It not only improves the control of resource utilization efficiency to achieve the improvement of the efficiency of enterprise project management in resource scheduling, but also optimizes the allocation of fixed resources and human resources, ensures that the project can respond flexibly in a changing environment, improves the accuracy of decision-making; further helps to improve the intelligent level of enterprise project management, significantly reduces decision-making risks, and promotes the smooth execution and successful completion of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is an application environment diagram of the enterprise project information management method in an embodiment;

[0022] Figure 2 It is a schematic flowchart of the enterprise project information management method in an embodiment;

[0023] Figure 3 It is a schematic flowchart of the method for obtaining dynamic fuzzy inference data in an embodiment;

[0024] Figure 4 It is a schematic flowchart of the method for obtaining optimized fuzzy inference parameters in an embodiment;

[0025] Figure 5 It is a schematic flowchart of the method for obtaining project behavior analysis data in an embodiment;

[0026] Figure 6 It is a schematic flowchart of the method for obtaining project behavior analysis data in another embodiment;

[0027] Figure 7 It is a schematic flowchart of the method for obtaining enterprise project integration data in an embodiment;

[0028] Figure 8 It is a schematic flowchart of the method for obtaining initial project integration data in an embodiment;

[0029] Figure 9 It is a schematic flowchart of a method for obtaining enterprise project management information in an embodiment;

[0030] Figure 10 It is a structural block diagram of an enterprise project information management device in an embodiment;

[0031] Figure 11 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0032] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to 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.

[0033] An enterprise project information management method provided by an embodiment of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a 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 can be placed in 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 inference parameters, and the project execution history data corresponding to the target enterprise from the terminal 102; according to the initial fuzzy inference parameters, performs dynamic fuzzy inference on the internal data of the enterprise project and the external data of the enterprise project to obtain dynamic fuzzy inference data; according to the dynamic fuzzy inference data and the project execution history data, identifies the relationship between each execution object and each task detail in the target enterprise to obtain project behavior analysis data; structurally integrates the dynamic fuzzy inference data and the project behavior analysis data to obtain enterprise project integration data; according to the enterprise project integration data, schedules the fixed resources and human resources of the target enterprise to obtain enterprise project management information. Among them, the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0034] In an exemplary embodiment, as Figure 2 shown, an enterprise project information management method is provided. Taking the method applied to the Figure 1 server as an example, the following steps 202 to 210 are included. Among them:

[0035] Step 202, obtain the internal data of the enterprise project, the external data of the enterprise project, the initial fuzzy inference parameters, and the project execution history data corresponding to the target enterprise.

[0036] Among them, the internal data of enterprise projects can be information directly related to the projects and sourced from within the enterprise, including project budgets, schedules, resource allocations, task execution status, work status of team members, equipment utilization rates, etc.

[0037] Among them, the external data of enterprise projects can be external environmental factors affecting project execution, including market changes, competitive landscape, policies and regulations, economic conditions, customer needs, etc.

[0038] Among them, the initial fuzzy inference parameters can be set parameters used in the fuzzy inference system, including the definition of fuzzy sets, membership functions, fuzzy rules, etc.

[0039] Among them, the historical project execution data can be the execution records of projects implemented by the enterprise in the past, including task completion, schedule control, resource usage, cost control, team performance, etc.

[0040] Specifically, relevant data of the project are obtained from various information management systems of the target enterprise, including internal data of enterprise projects (such as budgets, schedules, resource usage, etc.), external data of enterprise projects (such as market changes, policy environment, etc.), initial fuzzy inference parameters (such as fuzzy sets, membership functions, etc.), and historical project execution data (such as execution records of previous projects, task completion, resource consumption, etc.).

[0041] Step 204: Perform dynamic fuzzy inference on the internal data of enterprise projects and the external data of enterprise projects according to the initial fuzzy inference parameters to obtain dynamic fuzzy inference data.

[0042] Among them, dynamic fuzzy inference can be to perform fuzzy inference processing on project data in the time dimension and update the inference results in real time as the project progresses.

[0043] Among them, the dynamic fuzzy inference data can be the output results obtained through the dynamic fuzzy inference process, including the inference values of the internal data and external data of the project after fuzzy processing.

[0044] Specifically, the internal data and external data of enterprise projects are processed using initial fuzzy inference parameters (such as the definition of fuzzy sets, the setting of membership functions, etc.). For the internal data of enterprise projects, such as project budgets, resource allocations, and progress controls, by setting appropriate fuzzification rules, quantitative data is converted into fuzzy values. For example, the project budget allocation status is classified into categories such as "over budget", "meeting expectations", or "below expectations". For the external data of enterprise projects, such as market trends, policy changes, and competitive landscapes, a similar method is used for fuzzification processing, and dynamic factors such as time series analysis are introduced to gradually update the fuzzy inference results. The resulting dynamic fuzzy inference data reflects the uncertainties and changes in the internal and external environments of the project, providing in-depth analysis data based on the real dynamic situation for further decision-making.

[0045] Step 206, based on the dynamic fuzzy inference data and the project execution historical data, identify the relationships between each execution object and each task detail in the target enterprise to obtain project behavior analysis data.

[0046] Among them, the execution object can be various participants who undertake tasks and responsibilities in enterprise projects, including project managers, team members, external suppliers, consultants, etc.

[0047] Among them, the task details can be information such as the content, objectives, progress, priorities, required resources, and key nodes of specific tasks during project execution.

[0048] Among them, the project behavior analysis data can be data obtained through in-depth analysis of the project execution process, mainly focusing on behavior patterns during project execution, the performance of execution objects, dependencies between tasks, potential risks and bottlenecks, etc.

[0049] Specifically, combined with in-depth behavior analysis, the system deeply mines the dynamic fuzzy inference data and the project execution historical data to identify the complex relationships 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, the in-depth behavior analysis technology uses machine learning and pattern recognition algorithms to analyze the behavior characteristics of each execution object in the project, task execution patterns, and their performances in different situations. By establishing an association model between execution objects and tasks, the system can gain insights into potential trends in project behavior. For example, the efficiency fluctuations that certain execution objects may exhibit in specific tasks, or the interdependencies between tasks in terms of time and resources. Based on in-depth behavior analysis, the generated project behavior analysis data helps project managers identify potential risks, optimize resource allocation, early warning of possible execution problems, and ensure the efficient progress of the project.

[0050] Step 208, structurally fuse the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data.

[0051] 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.

[0052] Among them, enterprise project fusion data can be a combination of various types of enterprise project data after structured fusion, including the project's internal data, external data, behavioral analysis results, etc.

[0053] 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.

[0054] 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.

[0055] Among them, fixed resources can be resources that cannot be adjusted at will during the project execution, including equipment, facilities, funds, etc.

[0056] 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.

[0057] Specifically, the system performs intelligent scheduling on fixed resources (such as equipment, funds, technical support, etc.) and human resources (such as project managers, team members, external partners, etc.) according to the enterprise project integration data, applying resource optimization scheduling algorithms (such as linear programming, integer programming, genetic algorithms, etc.); the goal of resource scheduling is to ensure that the project has sufficient and appropriate resource support at each stage, avoiding resource conflicts, waste or shortages. The system will automatically adjust the resource allocation strategy according to factors such as the priority of tasks, the capabilities of execution objects, the availability of resources, and time constraints, and optimize the use of various resources during the project execution process. Finally, the system will generate a complete set of enterprise project management information, including resource scheduling plans, project execution progress, task priorities, etc., providing real-time monitoring and decision-making support for project managers to ensure the efficient and smooth completion of the project.

[0058] In the above enterprise project information management method, by obtaining the enterprise project internal data, enterprise project external data, initial fuzzy inference parameters, and project execution history data corresponding to the target enterprise; according to the initial fuzzy inference parameters, performing dynamic fuzzy inference on the enterprise project internal data and enterprise project external data to obtain dynamic fuzzy inference data; according to the dynamic fuzzy inference data and project execution history data, identifying the relationships between each execution object and each task detail in the target enterprise to obtain project behavior analysis data; structurally integrating the dynamic fuzzy inference data and project behavior analysis data to obtain enterprise project integration data; according to the enterprise project integration data, scheduling the fixed resources and human resources of the target enterprise to obtain enterprise project management information.

[0059] By integrating enterprise project internal data, enterprise project external data, historical execution data, and initial fuzzy inference parameters, and applying dynamic fuzzy inference technology, it is possible to accurately analyze and deduce various types of data in the project, and capture and feedback the dynamic changes in project execution in real time. Further in-depth analysis of project behaviors can identify the potential relationships between each execution object and tasks, and then generate comprehensive project behavior analysis data. After structurally integrating these data, the formed enterprise project integration data provides a reliable basis for resource scheduling. It not only improves the control of resource utilization efficiency to achieve the improvement of the efficiency of the enterprise in managing projects in resource scheduling, but also optimizes the allocation of fixed resources and human resources to ensure that the project can flexibly respond in a changing environment, improving the accuracy of decision-making; further helps to improve the intelligent level of enterprise project management, significantly reduces decision-making risks, and promotes the smooth execution and successful completion of the project.

[0060] In an exemplary embodiment, such as Figure 3As shown, based on the initial fuzzy inference parameters, dynamic fuzzy inference is performed on the internal data and external data of the enterprise project to obtain dynamic fuzzy inference data, including steps 302 to 306. Among them:

[0061] Step 302: Based on the initial fuzzy inference parameters, perform preliminary fuzzy inference on the internal data and external data of the enterprise project to obtain preliminary fuzzy inference data.

[0062] Among them, the preliminary fuzzy inference data can be the result obtained by performing preliminary fuzzification processing on the internal data and external data of the enterprise project based on the initial fuzzy inference parameters.

[0063] Specifically, convert the internal data and external data of the enterprise project into fuzzy variables. For example, classify the project budget into categories such as "sufficient", "moderate", or "insufficient", and then perform inference on these fuzzy variables according to the preset initial fuzzy inference parameters (such as membership functions, fuzzy rules, etc.) to obtain the preliminary fuzzy inference data. The preliminary fuzzy inference data reflects the basic uncertainties and potential risks in project execution.

[0064] Step 304: Based on the preliminary fuzzy inference data, optimize the inference rules and membership functions in the initial fuzzy inference parameters to obtain optimized fuzzy inference parameters.

[0065] Among them, the optimized fuzzy inference parameters can be to adjust the key parameters in the fuzzy inference system according to the feedback results of the preliminary fuzzy inference data to improve the accuracy and adaptability of the inference model.

[0066] Specifically, evaluate the effectiveness of the current inference rules and membership functions according to the feedback results of the preliminary fuzzy inference data. Specifically, by comparing the preliminary fuzzy inference data with the actual project execution situation, identify the deficiencies in the initial fuzzy inference parameters of the current inference model (for example, some fuzzy rules are too broad or too narrow), and make adjustments and optimizations. This may include modifying the shape of the membership function or resetting the inference rules to more accurately reflect the fuzziness and uncertainty in the data. The obtained optimized fuzzy inference parameters can better adapt to the changes and dynamic requirements in project execution and improve the accuracy of the inference results.

[0067] Step 306: Based on the optimized fuzzy inference parameters, perform secondary fuzzy processing on the internal data and external data of the enterprise project to obtain dynamic fuzzy inference data.

[0068] Specifically, the optimized fuzzy inference parameters are used to perform fuzzy processing on the internal data and external data of enterprise projects once again. The inference optimization results of the previous stage are considered as the reference standard, and the adjusted inference rules and membership functions are applied to the further analysis of the data to reflect the changes in the project execution process in real time. This secondary fuzzy processing can more accurately identify the dynamic changes and complex associations in the data, generate more accurate dynamic fuzzy inference data, and provide higher-quality information support for the in-depth analysis and decision-making of the project.

[0069] In this embodiment, through the preliminary fuzzy inference of the internal data and external data of enterprise projects based on the initial fuzzy inference parameters, and optimizing the inference rules and membership functions according to the obtained preliminary data, finally, dynamic fuzzy inference data is obtained through secondary fuzzy processing, which can effectively improve the accuracy and adaptability of the inference process. This process not only optimizes the fuzzy inference parameters to make them more suitable for actual application requirements, but also can dynamically reflect the changes and complexities in enterprise projects, thereby improving the flexibility of data analysis and the quality of decision support, and providing a more accurate and feasible resource allocation and project management plan for the enterprise.

[0070] In an exemplary embodiment, as Figure 4 shown, according to the preliminary fuzzy inference data, the inference rules and membership functions in the initial fuzzy inference parameters are optimized to obtain the optimized fuzzy inference parameters, including steps 402 to 408. Among them:

[0071] Step 402, according to the preliminary fuzzy inference data, perform virtual scheduling on the fixed resources and human resources to obtain virtual scheduling information.

[0072] Specifically, the preliminary fuzzy inference data is used to perform virtual scheduling on 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 execute resource allocation, but adopts a computer simulation process of resource configuration. By considering factors such as the priority of project tasks, resource requirements, and time constraints, a digital virtual scheduling information is generated. 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, providing data support for optimizing the fuzzy inference parameters.

[0073] Step 404, according to the virtual scheduling information, adjust the inference rules and membership functions in the initial fuzzy inference parameters to obtain the adjusted fuzzy inference parameters.

[0074] Among them, adjusting the fuzzy inference parameters can be the result obtained by modifying and optimizing key parameters such as inference rules and membership functions according to the preliminary inference results and virtual scheduling information feedback during the fuzzy inference process.

[0075] Specifically, according to the virtual scheduling information generated by the computer for virtual scheduling, the inference rules and membership functions in the initial fuzzy inference parameters are adjusted. Specifically, analyze the results of virtual scheduling, identify deficiencies or unreasonable aspects in the resource scheduling process, such as problems like task execution time and uneven resource allocation, and then modify the fuzzy inference parameters based on these findings. For example, adjust the threshold of the membership function or modify the inference rules to better match the actual requirements of project execution. The adjusted inference rules and membership functions can more accurately reflect the real situation of project resource scheduling, thereby adjusting the fuzzy inference parameters.

[0076] Step 406, take the adjusted fuzzy inference parameters as the initial fuzzy inference parameters, and return to execute the step of performing preliminary fuzzy inference on the internal data and external data of the enterprise project according to the initial fuzzy inference parameters to obtain preliminary fuzzy inference data, until the difference information between at least two consecutive output adjusted fuzzy inference parameters is less than the preset difference information.

[0077] Among them, the preset difference information can be a tolerance standard used to judge whether to stop adjustment during the fuzzy inference optimization process. It defines the maximum acceptable difference value between adjustments of the fuzzy inference parameters.

[0078] Specifically, the adjusted fuzzy inference parameters will be re - used as the initial fuzzy inference parameters for the next round of fuzzy inference processing. This process will be repeated, that is, each time new adjusted parameters are used to re - perform preliminary fuzzy inference to obtain new preliminary fuzzy inference data. By comparing with the inference 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 (i.e., the difference has tended to be stable), it is considered that the optimization process has reached the convergence state, and the optimization is stopped and the final optimized parameters are determined.

[0079] Step 408, take the last output adjusted fuzzy inference parameters as the optimized fuzzy inference parameters.

[0080] Specifically, when after multiple rounds of adjustment, the last output adjusted fuzzy inference parameters no longer change significantly (i.e., the difference information is less than the preset threshold), at this time the system takes this final adjustment result as the optimized fuzzy inference parameters, which can more accurately reflect the real 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 inference and project management.

[0081] In this embodiment, by performing virtual scheduling on the preliminary fuzzy inference data and optimizing the initial fuzzy inference parameters according to the virtual scheduling information, fine-tuning of resource scheduling and data inference can be achieved. This process gradually optimizes the fuzzy inference parameters by repeatedly adjusting the inference rules and membership functions until the output of the model stabilizes and becomes accurate. This dynamic adjustment mechanism ensures that the resource scheduling and inference process can flexibly adapt to the actual situation and changes in enterprise projects, 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.

[0082] In an exemplary embodiment, as Figure 5 shown, based on the dynamic fuzzy inference data and the project execution historical data, identify the relationships between each execution object and each task detail in the target enterprise to obtain project behavior analysis data, including steps 502 to 506. Among them:

[0083] Step 502, defuzzify the dynamic fuzzy inference data to obtain dynamic defuzzified inference data.

[0084] Among them, the dynamic defuzzified inference data can be obtained by converting the fuzzy information output by the fuzzy inference system into specific numerical values or categories through the defuzzification process, so as to provide clear and quantitative dynamic data for further analysis.

[0085] Specifically, perform defuzzification processing on the dynamic fuzzy inference data. The purpose of the defuzzification processing is to convert the dynamic fuzzy inference data output by the fuzzy inference system into specific and clear numerical values or categories. Specifically, by adopting defuzzification algorithms (such as the centroid method, the maximum membership degree method, etc.), convert the membership degree values of the dynamic fuzzy inference data (such as "high", "medium", "low") into an accurate dynamic defuzzified inference 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.

[0086] Step 504, use the association rule fusion algorithm to fuse the dynamic defuzzified inference data and the project execution historical data to obtain behavior analysis fusion data.

[0087] Among them, the behavior analysis fusion data can be a set of comprehensive information formed by fusing data from different sources (such as dynamic defuzzified inference data and project execution historical data), reflecting the behavior patterns between each execution object and task details during project execution.

[0088] Specifically, since the dynamic de-blurring inference data has already transformed the fuzzy information into specific and actionable dynamic de-blurring inference data, these dynamic de-blurring inference data involve aspects such as resource allocation, task progress, and personnel load; while the project execution historical data contains information such as task completion, time consumption, and personnel performance during previous project executions. Using an association rule fusion algorithm (such as the Apriori algorithm or the FP-growth algorithm), the system analyzes these two sets of data to find the frequent patterns and association rules between them. For example, it can be identified that under certain resource configurations, the frequency of specific task execution delays is relatively high, or there is a strong association between the performance of specific execution objects and certain project characteristics (such as task scale and teamwork mode). Through the fusion of the association rule fusion algorithm, the system can not only reveal the correlation between tasks and resources, but also discover the rules behind the execution behavior, generating behavior analysis fusion data containing comprehensive information such as behavior patterns, resource utilization rates, and task execution efficiencies, providing rich data support for subsequent behavior analysis.

[0089] Step 506, based on the behavior analysis fusion data, analyze the behavioral association relationships between each execution object and each task detail to obtain project behavior analysis data.

[0090] Among them, the behavioral association relationship can be the mutual influence and dependence relationship between the execution object (such as team members, managers, etc.) and the task details (such as task progress, resource requirements, etc.) during the project execution process.

[0091] Specifically, by deeply mining the association patterns in the behavior analysis fusion data, identify the mutual influence and relationships between different execution objects (such as project managers, team members, external partners, etc.) and different task details (such as task priorities, completion times, required resources, etc.). Specifically, since the behavior analysis fusion data contains the behavior characteristics of various execution objects and the task execution situations, the analysis process reveals the roles of different execution objects in tasks by applying data analysis, pattern recognition, and statistical methods. For example, the system may discover the reaction pattern of a certain execution object when facing urgent tasks, or how a certain resource configuration affects the task completion time under a specific execution object. Through this analysis, it can be identified which execution objects perform excellently in specific tasks, which task details are most related to delays or failures, and how resource allocation affects the overall project progress. Ultimately, the project behavior analysis data can help decision-makers understand the potential rules in task execution, providing data-driven decision-making bases for future project optimization, resource scheduling, and task allocation.

[0092] In this embodiment, by defuzzifying the dynamic fuzzy inference data and integrating the project execution historical data, it is possible to effectively extract clearer and more intuitive behavior analysis data. Using the association rule fusion algorithm to combine the two 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. Through in-depth analysis of the behavior association relationships, this process provides a more accurate decision-making basis for project management, helps enterprises optimize resource allocation, improve task execution efficiency, and thus enhance the overall performance and success rate of the project.

[0093] In an exemplary embodiment, as Figure 6 shown, based on the behavior analysis fusion data, analyze the behavior association relationships between each execution object and each task detail to obtain project behavior analysis data, including steps 602 to 606. Among them:

[0094] Step 602, classify the project execution behaviors of each execution object according to the behavior analysis fusion data to obtain each project execution object set.

[0095] Among them, the project execution behavior can be various specific activities and characteristics shown by the execution object during the execution of the project task, including the time to complete the task, the efficiency of resource use, the collaboration method, the ability to handle unexpected problems, etc.

[0096] Among them, the project execution object set can be a group formed by classifying the project execution behaviors of the execution objects, and each set contains execution objects with similar behavior characteristics.

[0097] Specifically, the system will analyze the specific project execution behaviors of each execution object in the project, such as the type of task assignment, the timeliness of task completion, the efficiency of resource use, and the contribution to the project goal. Among them, the data of these project execution behaviors will lead the execution objects to be classified into different sets. According to the similarity of criteria such as task completion, resource consumption, and execution efficiency, the execution objects with similar performances are divided into the same group. For example, some team members may show behaviors of completing tasks efficiently and quickly, while other members may show a slower execution speed in complex tasks. Through this classification, the system can classify each execution object into a unique project execution object set for further causal relationship analysis.

[0098] Step 604, associate the behavior causal relationships between any project execution object set and each task detail to obtain each initial behavior analysis data.

[0099] Among them, associating the causal relationships can be to analyze the mutual influence between the execution object set and the task detail, and reveal the causal connection between them.

[0100] Among them, the initial behavior analysis data can be the first-round analysis results obtained after conducting a causal relationship analysis between the execution object set and task details, mainly including the specific association information between the behavior patterns of the execution objects and the task details.

[0101] Specifically, the system conducts an association analysis between each project execution object set and the details of specific tasks in the project to identify the behavioral causal relationship between the two. Among them, task details not only include the nature of the task (such as task type, difficulty, etc.), but also involve factors such as time, resource requirements, and priority during the task execution process. The system uses data mining techniques to analyze the performance of a specific project execution object set when performing a certain type of task and explores the causal relationship between it and the task details. For example, members in a certain set may require more time and resources when facing high-difficulty tasks, while other members are more efficient in simple tasks. Through this causal analysis, the system can reveal the deep connection between task details and the behavior of execution objects, obtain the initial behavior analysis data, provide data support for subsequent task optimization and resource allocation, and enable project managers to accurately identify which task details most affect the performance of execution objects.

[0102] Step 606: Concatenate the initial behavior analysis data for each item to obtain the project behavior analysis data.

[0103] Specifically, concatenate the initial behavior analysis data between each project execution object set and task details to form the complete project behavior analysis data. The concatenation process integrates the causal relationships between different execution objects and task details into a dataset to ensure that all behavior analysis results can be presented in a unified format. The final project behavior analysis data will include the overall analysis results regarding the performance of each execution object, the relevance to each task detail, and how these factors affect task completion.

[0104] In this embodiment, by classifying the behavior analysis fusion data, grouping the project execution behaviors of the execution objects and forming sets, the characteristics and patterns of different types of execution objects can be clearly shown. Further, conducting an association analysis of the behavioral causal relationship between these sets and task details can accurately reveal the relationship between the behavioral characteristics of execution objects and the task success factors. Finally, by concatenating the initial behavior analysis data to form the complete project behavior analysis data, it provides systematic behavioral insights for the enterprise. This process helps to optimize task allocation, improve resource utilization efficiency, and provides accurate and comprehensive data support for project management decisions, thereby promoting the success rate and overall efficiency of project execution.

[0105] In an exemplary embodiment, such as Figure 7As shown, the dynamic fuzzy inference data and the project behavior analysis data are structurally integrated to obtain enterprise project integration data, including steps 702 to 710. Among them:

[0106] Step 702, uniformly adjust the formats of the dynamic fuzzy inference data and the project behavior analysis data to obtain dynamic fuzzy adjusted data and project behavior adjusted data.

[0107] Among them, the dynamic fuzzy adjusted data and the project behavior adjusted data can be the data obtained after respectively adjusting the data formats of the dynamic fuzzy inference data and the project behavior analysis data.

[0108] Specifically, uniformly adjusting the formats of the dynamic fuzzy inference data and the project behavior analysis data means converting these two types of data into the same format for subsequent integration processing. Usually, data format unification involves standardizing data fields, data types, and units to ensure their compatibility and comparability. For example, the dynamic fuzzy inference data may include numerical task completion rates, resource consumption, etc., while the project behavior analysis data may contain categorical data such as task types, execution objects, etc. The system maps and transforms these two types of data so that they can be processed on the same platform to obtain standardized dynamic fuzzy adjusted data and project behavior adjusted data.

[0109] Step 704, according to the business data integration rules of the target enterprise, integrate the dynamic fuzzy adjusted data and the project behavior adjusted data to obtain initial project integration data.

[0110] Among them, the business data integration rules can be the rules used to guide how to effectively integrate different types of data according to the enterprise's specific business goals, data attributes, and analysis requirements during the multi-source data integration process.

[0111] Among them, the initial project integration data can be the preliminary data set obtained by integrating data from different sources after applying the business data integration rules. It synthesizes the dynamic fuzzy inference data and the project behavior analysis data, reflecting various aspects of the project, such as the efficiency of task execution, resource usage, the performance of execution objects, etc.

[0112] Specifically, it is necessary to effectively integrate the uniformly formatted dynamic blur adjustment data and project behavior adjustment data according to the specific business requirements of the enterprise. The business data integration rules are usually designed based on the business objectives, data characteristics, and decision-making requirements of the enterprise, and may include different methods such as weighted average, conditional selection, and priority ranking. For example, if the enterprise gives priority to resource utilization efficiency, then the part of the dynamic blur adjustment data related to resource utilization may be given a higher weight, while the execution efficiency part in the project behavior adjustment data may play a supplementary role. In addition, the business data integration rules also consider factors such as time series, project stages, and task categories, and comprehensively process the two types of data through integration algorithms to ensure that the integrated data can reflect a more accurate and comprehensive project execution status. Finally, the system will obtain the initial project integrated data.

[0113] Step 706: Generate the business data splitting rules for the target enterprise according to the business data integration rules.

[0114] Among them, the business data splitting rules can be the rules for splitting the integrated data according to the reverse operation requirements of the business data integration rules after the data integration is completed.

[0115] Specifically, the system reversely generates the business data splitting rules that match the specific business requirements of the target enterprise according to the business data integration rules of the enterprise. Since the business data integration rules are usually designed based on the enterprise business processes, project requirements, or resource management requirements. For example, some data may need to be split according to dimensions such as time, task type, region, and resource type for specific analysis at different management or decision-making levels; and the generation of the business data splitting rules is reversely designed according to the above-mentioned enterprise business processes, project requirements, or resource management requirements.

[0116] Step 708: Split the initial project integrated data according to the business data splitting rules to obtain the dynamic blur split data and the project behavior split data.

[0117] Among them, the dynamic blur split data and the project behavior split data are the data obtained by performing reverse operations on the initial project integrated data.

[0118] Specifically, based on the reverse-defined business data splitting rules, the system splits the initial project integration data according to business requirements and project characteristics. The purpose of the splitting process is to reverse-decompose the large-scale initial project integration data into two data sets before integration. The splitting rules may be carried out according to the reverse dimension of the business data integration rules. For example, if the business data integration rules are integrated according to task types (such as design, execution, review), time periods (such as weeks, months), project phases (such as planning, execution, evaluation), or resource types (such as human resources, material resources), etc., then the business data splitting rules are split reversely according to task types (such as design, execution, review), time periods (such as weeks, months), project phases (such as planning, execution, evaluation), or resource types (such as human resources, material resources), etc. In this way, the dynamically fuzzy split data and the project behavior split data can be respectively split into the dynamically fuzzy split data and the project behavior split data.

[0119] Step 710, when the dynamically fuzzy adjusted data is the same as the dynamically fuzzy split data, and the project behavior adjusted data is the same as the project behavior split data, the initial project integration data is used as the enterprise project integration data.

[0120] Specifically, if the dynamically fuzzy adjusted data is equal to the dynamically fuzzy split data (or the difference value is less than the first threshold), and the project behavior adjusted data is equal to the project behavior split data (or the difference value is less than the second threshold), it indicates that no error or data loss is introduced in the processes of splitting and integration. At this time, the validity of the initial project integration data can be confirmed. At this time, the system uses the initial project integration data as the final enterprise project integration data.

[0121] In this embodiment, by uniformly adjusting the formats of the dynamically fuzzy inference data and the project behavior analysis data and integrating them according to the business data integration rules of the target enterprise, it is possible to ensure the consistency and comparability of data from different sources, thereby improving the quality and efficiency of data integration. Generating and applying the business data splitting rules to further split the initial project integration data and refine the data processing process helps to analyze and process various business data more meticulously. By verifying the consistency between the split data and the adjusted data, the accuracy and integrity of data integration are ensured, and finally the enterprise project integration data is obtained. This process improves the accuracy and reliability of the data, provides more refined decision-making support for the enterprise in aspects such as resource scheduling and project management, and promotes the optimization of project execution and management.

[0122] In an exemplary embodiment, as Figure 8 shown, according to the business data integration rules of the target enterprise, integrating the dynamically fuzzy adjusted data and the project behavior adjusted data to obtain the initial project integration data, including steps 802 to 806. Among them:

[0123] Step 802: Classify the dynamically fuzzy adjusted data and the project behavior adjusted data according to the fusion classification constraints of the business data fusion rules, to obtain dynamically fuzzy classified data and project behavior classified data.

[0124] 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 requirements and data characteristics during the business data fusion process.

[0125] Among them, the dynamically fuzzy classified data and the project behavior classified data can be the results obtained by classifying the dynamically fuzzy adjusted data and the project behavior adjusted data respectively after applying the fusion classification constraints.

[0126] Specifically, classify the dynamically fuzzy adjusted data and the project behavior adjusted data according to the fusion classification constraints defined in the business data fusion rules of the target enterprise. The basis for classification is usually to divide the data into different groups according to different dimensions or data characteristics of the project, such as task category, resource consumption, execution stage, etc. In this way, each data point in the dynamically fuzzy adjusted data and the project behavior adjusted data will be assigned to the corresponding category according to its characteristics, obtaining the classification results of the two types of data, namely the dynamically fuzzy classified data and the project behavior classified data. Classification helps to more carefully adjust the data processing strategy according to different business goals in the subsequent fusion process.

[0127] Step 804: For any fusion classification constraint, fuse the dynamically fuzzy classified data and the project behavior classified data according to the data fusion weights of the business data fusion rules, to obtain the classified fusion data for each classification.

[0128] Among them, the data fusion weights can be the degrees of assigning different importance to different data according to business requirements during the data fusion process.

[0129] Among them, the classified fusion data can be the result obtained by weighted fusion of the classified data during the data fusion process.

[0130] Specifically, the system performs weighted fusion on the dynamically fuzzy classified data and the project behavior classified data of different categories obtained after classification according to the previous-step fusion classification constraints, in combination with the data fusion weights of different data in the same classification in the business data fusion rules. Since the fusion weight of each category is set according to the enterprise's priorities 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 the data category related to efficiency will be given a higher weight. The same operation is performed for each classification to obtain the weighted fusion result of each category, that is, the classified fusion data for each classification.

[0131] Step 806: Concatenate the classification fusion data of each category to obtain the initial project fusion data.

[0132] Specifically, the system concatenates the classification fusion data of each category. Here, concatenation means merging the fusion data of different categories into a unified data set for use in subsequent analysis and decision-making. This concatenation process does not lose any category data but integrates them into a complete data set. The obtained initial project fusion data is a comprehensive data set that can comprehensively reflect all aspects of project execution, providing strong data support for the enterprise's resource allocation, task optimization, and decision-making.

[0133] In this embodiment, by applying the fusion classification constraint in the business data fusion rule to classify the dynamic fuzzy adjustment data and the project behavior adjustment data, complex data can be effectively classified in an orderly manner, ensuring that the characteristics and behaviors of each category of data can be analyzed more accurately. Then, by fusing the classified data according to the data fusion weight, the importance of different data sources can be better balanced, improving the effect and accuracy of data fusion. Finally, by concatenating the classification fusion data, the initial project fusion data is obtained, providing more structured and targeted data support for subsequent analysis and decision-making. This process helps the enterprise to perform precise data integration in a complex environment, providing a more efficient and reliable basis for resource optimization, risk assessment, and strategic decision-making.

[0134] In an exemplary embodiment, as Figure 9 shown, according to the enterprise project fusion data, schedule the fixed resources and human resources of the target enterprise to obtain enterprise project management information, including steps 902 to 916. Among them:

[0135] Step 902: Construct an enterprise resource scheduling model corresponding to the target enterprise according to the enterprise project fusion data.

[0136] Among them, the enterprise resource scheduling model can be a mathematical or computational model used to optimize the allocation and scheduling of internal resources of the enterprise.

[0137] Specifically, enterprise project integration data is used to analyze and model the requirements and situations characterizing enterprise resource scheduling. In the construction of the enterprise resource scheduling model, factors such as the existing resource allocation, task assignment, and execution objectives of the enterprise need to be comprehensively considered to ensure the reasonable scheduling of the enterprise's fixed resources and human resources. The purpose of the enterprise resource scheduling model is to help the enterprise optimize resource allocation, ensure the maximization of resource utilization, and provide a basis for subsequent resource scheduling calculations. During the construction process, the model to be constructed classifies and associates 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), forming a multi-dimensional resource scheduling graph. This graph 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 consider various constraint conditions, such as the quantity, skill requirements, and priorities of resources. During the process of constructing the model, the system uses at least one optimization algorithm (such as linear programming, genetic algorithms, etc.) to select the most suitable resource scheduling plan and can dynamically adjust according to the actual situation to obtain the enterprise resource scheduling model. By introducing relevant scheduling constraint conditions (such as time, resource limitations, etc.), the enterprise can simulate the resource usage in different situations to achieve the optimal resource scheduling plan.

[0138] Step 904: Construct the resource scheduling capacity constraints of the target enterprise according to the available information and status information of the fixed resources and human resources.

[0139] Among them, the 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 of employees, skill levels, available working hours, etc.

[0140] Among them, the resource scheduling capacity constraints can be a series of restrictive conditions that must be complied with during the scheduling process, and these restrictive conditions come from the existing resource situation and task requirements of the enterprise.

[0141] Specifically, the system constructs the resource scheduling capacity constraints of the enterprise according to the actual situations of the fixed resources and human resources, combining their available information (such as remaining quantity, working ability, etc.) and status information (such as whether it is being used, available time period, etc.). Among them, the resource scheduling capacity constraints reflect the limitations of resources in actual operations. For example, some resources may not be available during a certain time period, or the working ability 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.

[0142] Step 906: Based on the resource scheduling capacity constraints, use the resource scheduling algorithm of the target enterprise to solve the enterprise resource scheduling model, and obtain the fixed resource scheduling information and the human resource scheduling information.

[0143] Among them, the resource scheduling algorithm can be the core method used to solve the enterprise resource scheduling model. This algorithm formulates the resource scheduling plan by adopting certain optimization strategies according to the requirements of the target enterprise, the requirements of the tasks, the available information of the resources, and the constraint conditions.

[0144] Among them, the fixed resource scheduling information can be the detailed information about the allocation and use of the enterprise's fixed resources (such as equipment, factories, materials, etc.). It includes the usage period, usage tasks, workload, etc. of each resource. The human resource scheduling information can be the information about personnel allocation, working hours, task allocation, etc., specifically including the working hour arrangements, work tasks, and skill matching of each employee.

[0145] Specifically, the system uses the matching resource scheduling algorithm to solve the enterprise resource scheduling model according to the resource scheduling capacity constraints constructed previously. The resource scheduling algorithm takes into account factors such as resource availability, task priorities, and scheduling objectives, and optimizes the resource allocation plan to generate specific fixed resource scheduling information and human resource scheduling information. These scheduling information provide the specific allocation and use plans of the resources and are the basis for actual resource scheduling operations.

[0146] 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 the enterprise resource utilization rate of the target enterprise.

[0147] Among them, the resource conflict information can be the situation where the same resource cannot be allocated to multiple tasks as required during the resource scheduling process. Usually, resource conflicts occur when two or more tasks need to use the same resource.

[0148] Specifically, the system conducts simulated 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 are resource conflicts. For example, two tasks simultaneously require the use of the same resource, or there are issues such as overlapping working hours of personnel. By identifying such conflict 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 generally involves comparing the actual usage of fixed resources and human resources with their total available amounts. The core of the calculation lies in measuring the ratio of the "actual usage time" to the "total available time" of each resource to obtain the actual utilization level of the resource. The resource utilization rate is a key indicator for measuring the resource usage efficiency, indicating the proportion of the resources actually used by the enterprise within a given time period to the available resources. By calculating the resource utilization rate, the enterprise can evaluate whether its resource allocation has reached the optimal state and whether there are problems of resource waste or resource shortage.

[0149] Step 910, when the resource conflict information is not a null value, adjust the available information and status information of the fixed resources and human resources, and return to execute the step of constructing the resource scheduling ability constraint of the target enterprise based on the available information and status information of the fixed resources and human resources until the resource conflict information is a null value.

[0150] Specifically, if the resource conflict information generated in the simulated scheduling is not a null value, that is, a resource conflict is discovered, the system will adjust the original available information and status information of the fixed resources and human resources through the latest available information and status information of the fixed resources and human resources to adjust the scheduling ability constraint. The purpose of this adjustment is to eliminate conflicts, such as by reallocating resources, adjusting task priorities, changing task execution times, etc. The system will conduct resource scheduling again based on the new constraints until all conflicts are eliminated and the resource conflict information is a null value, indicating that the scheduling plan has been optimized.

[0151] Step 912, when the enterprise resource utilization rate is less than the preset resource utilization rate, adjust the resource scheduling algorithm, and return to execute the step of using the resource scheduling algorithm of the target enterprise to solve the enterprise resource scheduling model based on the resource scheduling ability constraint to obtain the fixed resource scheduling information and human resource scheduling information until the enterprise resource utilization rate is greater than the preset resource utilization rate.

[0152] Among them, the preset resource utilization rate can be the resource utilization efficiency target that the enterprise sets and expects to achieve before the project execution. It is usually set based on factors such as historical data, industry standards, and project requirements.

[0153] Specifically, once the resource conflict is resolved, further check whether the enterprise resource utilization rate is lower than the preset resource utilization rate. If the calculated enterprise resource utilization rate is lower than the preset resource utilization rate, it indicates that the resources are not effectively utilized. At this time, the system needs to adjust the parameters of the resource scheduling algorithm. For example, by changing the scheduling policy parameters, optimizing the task allocation parameters, or re-evaluating the resource priorities, etc., to achieve the optimization of resource allocation and utilization efficiency. The adjusted algorithm will be used to solve the resource scheduling model again until the resource utilization rate reaches the preset resource utilization rate.

[0154] Step 914, regard the fixed resource scheduling information and the human resource scheduling information as the enterprise project management information.

[0155] Specifically, after all the adjustments and optimizations are completed, that is, when 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. These information provide a detailed resource allocation plan for the project manager, ensuring that the project can proceed smoothly within the scope allowed by the resources and achieving the enterprise's resource optimization goal.

[0156] In this embodiment, by constructing an enterprise resource scheduling model and setting the scheduling ability constraints in combination with the available information and status information of fixed resources and human resources, it can provide an accurate resource scheduling basis for enterprise project management. Using the resource scheduling algorithm to solve, and identifying potential resource conflicts and calculating the enterprise resource utilization rate through simulated scheduling, helps to timely discover resource bottlenecks and optimize resource allocation. In the case of resource conflicts and unqualified utilization rates, adjust the resource scheduling ability constraints and the scheduling algorithm to ensure that the scheduling plan maximizes the resource utilization efficiency while meeting the requirements. Through this process, it can effectively optimize resource allocation, reduce resource waste, improve the execution efficiency and cost-effectiveness of enterprise projects, and thus provide stable resource support and management decision-making basis for the long-term development of the enterprise.

[0157] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, 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. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0158] Based on the same inventive concept, an embodiment of the present application further provides an enterprise project informatization management device for implementing the enterprise project informatization management method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the enterprise project informatization management device provided below can refer to the limitations on an enterprise project informatization management method in the above text, and will not be repeated here.

[0159] In an exemplary embodiment, as Figure 10 shown, an enterprise project informatization 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, where:

[0160] The data acquisition module 1002 is configured to acquire enterprise project internal data, enterprise project external data, initial fuzzy reasoning parameters, and project execution historical data corresponding to the target enterprise;

[0161] The fuzzy reasoning module 1004 is configured to perform 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;

[0162] The behavior analysis module 1006 is configured to identify the relationships between each execution object and each task detail in the target enterprise according to the dynamic fuzzy reasoning data and the project execution historical data to obtain project behavior analysis data;

[0163] The data fusion module 1008 is configured to structurally fuse the dynamic fuzzy reasoning data and the project behavior analysis data to obtain enterprise project fusion data;

[0164] The resource scheduling module 1010 is configured 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.

[0165] Each module in the above enterprise project informatization management device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or independent of it, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0166] In an exemplary embodiment, a computer device is provided. This computer device can be a server, and its internal structure diagram can be as Figure 11As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, 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. Among them, 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 external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an enterprise project informatization management method.

[0167] Those skilled in the art can understand that Figure 11 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0168] In one embodiment, an enterprise project informatization management system is further provided. The system includes: a data processing end and a terminal. When the data processing end executes the computer program, it implements the steps of an enterprise project informatization management method.

[0169] In one embodiment, an enterprise project informatization management storage medium is further provided. The storage medium stores a computer program. When the computer program is executed by the processor in the data processing end, it implements the steps of an enterprise project informatization management method.

[0170] In one embodiment, a computer program product or a computer program is provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the above method embodiments.

[0171] 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 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 need to comply with relevant regulations.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories 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 memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0173] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in this specification.

[0174] The above-described embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended 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; The process of identifying 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 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; Analyze 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; Wherein, the behavior association relationship between each of the execution objects and each of the task details is analyzed according to the behavior analysis fusion data to obtain the project behavior analysis data, including: 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; splicing the initial behavior analysis data to obtain the 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 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.

5. The method according to claim 4, 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 concatenated to obtain the initial project fusion data.

6. The method according to any one of claims 1 to 5, 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.

7. 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; The process of identifying 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 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; Analyze 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; Wherein, the behavior association relationship between each of the execution objects and each of the task details is analyzed according to the behavior analysis fusion data to obtain the project behavior analysis data, including: 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; splicing the initial behavior analysis data to obtain the 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.

8. 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 claims 1 to 6 when executing a computer program.

9. 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 6 when executed by a processor in a data processing terminal.

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