Enterprise management optimization methods and systems empowered by big data ERP

Abstract

This invention discloses a method and system for enterprise management optimization using big data to empower ERP. This method relates to the field of enterprise management technology. The method includes: creating enterprise R&D tasks and acquiring R&D resource information to construct a dynamic knowledge graph of task-resource-technology relationships; adjusting task priorities based on multi-task game theory to reconstruct the task-resource-technology task line; adaptively decomposing the reconstructed task line to generate task key points and establish parallel subtask lines; further optimizing the task line and formulating a cross-departmental collaboration plan to promote enterprise management optimization. This method achieves real-time perception and dynamic priority response, efficient resource allocation, and enhanced cross-departmental collaboration.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management technology, and in particular to an enterprise management optimization method and system that enables ERP with big data. Background Art

[0002] Enterprise R&D management often requires concurrently managing multiple R&D projects and rationally allocating limited R&D resources (such as manpower, equipment, and funding). Existing technologies often rely on traditional project management tools (such as Gantt charts and resource allocation tables) to plan tasks and allocate resources. These tools statically define task priorities and resource allocation strategies to ensure timely project completion. However, as the complexity and dynamism of enterprise R&D projects continue to grow, traditional approaches are increasingly lacking in handling multi-task collaboration and resource optimization. For example, product development management for trading companies often faces risks and challenges such as mismatches between R&D and demand, disconnects between production and sales, and slow supply chain responses leading to delays.

[0003] Traditional methods often set task priorities based on fixed rules or historical data, failing to dynamically perceive the real-time relationship between tasks and resources. Furthermore, they lack efficient cross-departmental collaboration mechanisms in multi-tasking scenarios, leading to severe information silos. In summary, existing technologies for enterprise R&D project management suffer from poor management responsiveness and real-time performance, uneven and delayed resource allocation, and inefficient cross-departmental collaboration. Summary of the Invention

[0004] The present invention provides an enterprise management optimization method and system that empowers ERP with big data to solve the technical problems in the existing technology, such as poor management responsiveness and real-time performance, unbalanced and delayed resource allocation, and low efficiency of cross-departmental collaboration, and achieves the technical effects of real-time perception and dynamic priority response, efficient resource allocation, and improved cross-departmental collaboration.

[0005] In a first aspect, the present invention provides a method for optimizing enterprise management by enabling ERP with big data, wherein the method comprises:

[0006] Create multiple enterprise R&D project tasks and obtain the enterprise's R&D resource information.

[0007] A dynamic knowledge graph of task-resource-technology association is constructed based on the multiple enterprise R&D project tasks and the R&D resource information.

[0008] A multi-task game is performed on the multiple enterprise R&D project tasks, and after the task priorities of the enterprise R&D project tasks are adjusted according to the multi-task game results, the task-resource-technology task line is reconstructed.

[0009] The reconstructed task-resource-technology task line is used to perform adaptive task granularity decomposition, and task key points are created based on the adaptive task decomposition results to establish parallel subtask lines.

[0010] After optimizing the task-resource-technology task line according to the parallel subtask lines, a cross-departmental collaborative solution decision is created, and enterprise management optimization is performed according to the solution decision.

[0011] In a feasible implementation, the performing of multi-task game on the multiple enterprise R&D project tasks includes:

[0012] Model multiple enterprise R&D project tasks as task agents.

[0013] A state space is established, wherein the state space includes the current resource occupancy state, task state, task dependency graph, and executed records.

[0014] After the task agent selects an execution action in the dynamic knowledge graph of task-resource-technology association, the game analysis under the game goal is performed based on the state space to complete the multi-task game.

[0015] In a feasible implementation, the performing of game analysis under the game objective based on the state space includes:

[0016] A game objective is created, wherein the game objective includes an overall project benefit maximization objective, a conflict minimization objective, and a local optimal matching objective, wherein the local optimal matching objective is an alternative discarded objective.

[0017] After all task agents have visited the state space, they perform a self-evaluation of the current situation.

[0018] Based on the self-evaluation results of the current situation, each task agent searches for actions that maximize its own benefits, establishes agent actions, and the evaluation indicators of its own benefits include task value, delay penalty, and request resource cost.

[0019] Establish update actions, which include self-scheduling actions, delayed resource release actions, conflict priority enhancement actions, and shared cooperation actions. Establish resource conflict degrees based on the agent actions. After selecting update actions using the resource conflict degrees, execute task agent updates and conduct game evaluation using the game objectives.

[0020] Complete the game analysis based on the game evaluation results.

[0021] In a feasible implementation, the selecting an update action by utilizing the resource conflict degree includes:

[0022] Performing task correlation analysis on the multiple enterprise R&D project tasks to establish correlation analysis results, wherein the correlation analysis results include sequential correlation, collaborative correlation, and conflict correlation.

[0023] An action update constraint is established using the association analysis result, and an update action is selected using the action update constraint and the resource conflict degree.

[0024] In a feasible implementation, completing the game analysis according to the game evaluation result includes:

[0025] The game round cycle is set. In each game round, the task agent performs task body updates according to the selected update actions and optimizes the actions according to the updated state space and benefit function.

[0026] Simulate the action interactions of all task agents and detect resource conflicts and dependency contradictions.

[0027] Establish game feedback, optimize the update action of the next game round, and complete the game when the game round cycle is met or the game stable state appears.

[0028] In a feasible implementation, the adaptive task granularity decomposition using the reconstructed task-resource-technology task line and the creation of task key points based on the adaptive task decomposition results include:

[0029] According to the complexity, resource dependency and technical connectivity of the tasks, the tasks are adaptively decomposed to establish subtask sets.

[0030] Key point identification features are configured, and the key point identification features include task conversion bottleneck features, connectivity hub features, and technology breakthrough features.

[0031] The key point identification features are used to identify the key task points of the subtask set and create task key points.

[0032] In a feasible implementation, establishing the parallel sub-task lines includes:

[0033] An expert knowledge base is called and used to identify alternative technical paths for key points of the task and to establish branch technical paths.

[0034] The branching technical paths are used to establish parallel sub-task lines.

[0035] In a feasible implementation, the performing of multi-task game on the plurality of enterprise R&D project tasks further includes:

[0036] An expected revenue model is established, and the expected revenue model is used to predict the revenue of enterprise R&D project tasks.

[0037] A customer strategy weight factor is created, wherein the evaluation indicators of the customer strategy weight factor include stability indicator, profit contribution indicator, cooperation history indicator, and strategic fit indicator.

[0038] A profit function is established according to the expected profit model and the customer strategy weight factor, and the game evaluation of the multi-task game is performed using the profit function.

[0039] In a feasible implementation, the optimizing of enterprise management according to the solution decision includes:

[0040] A monitoring time window is created using the scenario decisions.

[0041] The execution of the solution decision is monitored during the monitoring time window, and monitoring feedback is established.

[0042] The monitoring feedback is used to predict the execution impact, generate a decision optimization plan, and optimize the management of the plan decision through the decision optimization plan.

[0043] In a second aspect, the present invention further provides an enterprise management optimization system that enables ERP using big data, wherein the enterprise management optimization system that enables ERP using big data includes:

[0044] The task and resource information acquisition module is used to create multiple enterprise R&D project tasks and obtain the enterprise's R&D resource information.

[0045] A dynamic graph module is used to construct a dynamic knowledge graph of task-resource-technology association based on the multiple enterprise R&D project tasks and the R&D resource information.

[0046] The priority reconstruction module is used to conduct a multi-task game on the multiple enterprise R&D project tasks, adjust the task priorities of the enterprise R&D project tasks according to the multi-task game results, and reconstruct the task-resource-technology task line.

[0047] The key point definition module is used to use the reconstructed task-resource-technology task line to perform adaptive task granularity decomposition, create task key points based on the adaptive task decomposition results, and establish parallel sub-task lines.

[0048] The collaborative decision-making module is used to optimize the task-resource-technology task line according to the parallel sub-task lines, create a cross-departmental collaborative solution decision, and optimize enterprise management according to the solution decision.

[0049] The present invention discloses an enterprise management optimization method and system for big data-enabled ERP, comprising: creating multiple enterprise R&D project tasks and obtaining relevant R&D resource information; constructing a dynamic knowledge graph of task-resource-technology association based on these R&D project tasks and R&D resource information. A multi-task game is performed for these R&D project tasks, and the priority of the tasks is adjusted according to the game results, thereby reconstructing the task-resource-technology task line. The reconstructed task-resource-technology task line is used to perform adaptive task granularity decomposition, and task key points are created based on the decomposition results, thereby establishing parallel subtask lines. The task-resource-technology task line is optimized based on the parallel subtask lines, a cross-departmental collaborative solution decision is created, and enterprise management is optimized based on the decision. The enterprise management optimization method and system for big data-enabled ERP disclosed in the present invention solve the technical problems of poor management responsiveness and real-time performance, unbalanced and delayed resource allocation, and low cross-departmental collaboration efficiency, and achieves the technical effects of real-time perception and dynamic priority response, efficient resource allocation, and improved cross-departmental collaboration level. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the enterprise management optimization method of big data-enabled ERP of the present invention.

[0051] Figure 2 This is a structural diagram of the enterprise management optimization system that enables ERP with big data in the present invention.

[0052] Explanation of the accompanying symbols: task and resource information acquisition module 11, dynamic graph module 12, priority reconstruction module 13, key point definition module 14, collaborative decision-making module 15. DETAILED DESCRIPTION

[0053] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0054] Example 1, as Figure 1 The flowchart of the enterprise management optimization method of big data-enabled ERP of the present invention is as follows:

[0055] S100: Create multiple enterprise R&D project tasks and obtain the enterprise's R&D resource information.

[0056] Specifically, first, the complex enterprise R&D goals are systematically and structurally decomposed into multiple executable independent tasks (multiple enterprise R&D project tasks), where the task information of each enterprise R&D project task includes clear goals, time limits, resource requirements, etc.

[0057] Specifically, based on the multiple enterprise R&D project tasks created, all R&D-related resource information within the enterprise is collected through data collection and integration, including human resources (such as R&D personnel skills and available time), equipment (such as laboratory instruments, testing equipment), funds (such as budget allocation) and other technical resources (such as patents, technical documents, etc.).

[0058] S200: Constructing a dynamic knowledge graph of task-resource-technology associations based on the multiple enterprise R&D project tasks and the R&D resource information.

[0059] Specifically, the dynamic knowledge graph of task-resource-technology relationships is a knowledge network constructed through dynamic relationships, with tasks, resources, and technologies as core nodes. This dynamic graph can reflect the dynamic relationships between tasks, resources, and technologies in real time and supports real-time updates and reasoning. The process of building a dynamic knowledge graph includes data collection, preprocessing, entity and relationship extraction, graph construction and storage, and real-time updates and reasoning.

[0060] Specifically, first, structured and unstructured data is obtained from multiple data sources within the enterprise (such as project management systems, resource management systems, technical document libraries, etc.) and cleaned, formatted, and extracted to ensure data consistency and availability; then, task, resource, and technical entities are extracted from the text through named entity recognition technology or large language models, and the relationships between them are identified. For example, LLM (large language model) can be used to extract relationships such as "Task A requires support from Technology B, and Technology B depends on Resource C" from technical documents; then, the extracted entities and relationships are mapped to a graph structure and stored using a time-series graph database (such as TigerGraph), where each entity and relationship is attached with a timestamp attribute to support time window queries and historical backtracking.

[0061] Optionally, the graph structure can be updated in real time through streaming computing and incremental learning technologies, and real-time reasoning can be performed using graph neural networks (GNNs) or rule engines. By building a dynamic knowledge graph, the dynamic relationship between tasks, resources, and technologies can be perceived in real time, providing data support and intelligent decision-making basis for subsequent multi-task game optimization and cross-departmental collaboration.

[0062] S300: Performing a multi-task game on the plurality of enterprise R&D project tasks, adjusting the task priorities of the enterprise R&D project tasks according to the multi-task game results, and reconstructing the task-resource-technology task line.

[0063] Specifically, when resources are limited and there are dependencies and competition between tasks, game theory is introduced to optimize task scheduling, determining task priority allocation strategies while maximizing overall benefits or achieving local balance. The Task-Resource-Technology Task Line is a chain of mapping relationships between tasks, allocable resources, and compatible technologies during the R&D process, serving as the foundation for task execution planning and resource allocation.

[0064] In some embodiments, performing a multi-task game on the plurality of enterprise R&D project tasks includes:

[0065] Multiple enterprise R&D project tasks are modeled as task agents; a state space is established, which includes the current resource occupancy status, task status, task dependency graph, and execution records; after the task agent selects an execution action in the dynamic knowledge graph of task-resource-technology association, game analysis under the game goal is performed based on the state space to complete the multi-task game.

[0066] Specifically, the task agent is an intelligent entity with independent decision-making capabilities, abstracted from each independent enterprise R&D project task. It is used to simulate its behavioral choices under different states and resource conditions. The state space represents the current multidimensional information set during task scheduling and execution, including resource utilization (such as developer, equipment, and budget utilization), task status (such as completed, in progress, and not started), task dependency graph (indicating the order and dependencies between tasks), and execution records (including historical task decisions and execution feedback).

[0067] Specifically, the system first abstractly models multiple enterprise R&D project tasks into independent task agents based on information such as task requirements, task return rates, and task costs. This allows them to perform state-based judgments and engage in strategic game-playing. In other words, each agent possesses clear goals (such as completion time and resource requirements) and decision-making capabilities (such as selecting execution actions). A multidimensional state space, encompassing resources, tasks, dependencies, and execution history, is then constructed to fully describe the current operational state of the R&D system. Next, a dynamic knowledge graph is used to model the connections between tasks, their required resources, and adaptive technologies. This allows task agents to select optimal execution actions (such as requesting resources, adjusting execution order, and requesting collaboration) within this graph structure. Game analysis is then conducted based on the state space and the determined execution actions, thereby evaluating the task completion performance (such as completion time and resource utilization) under different strategies and selecting the optimal strategy.

[0068] Specifically, after the intelligent agent selects an action, it conducts multi-task game analysis based on preset game objectives (such as shortest total execution time, maximum output benefit, minimum conflict rate, etc.), calculates the optimal decision-making strategy for each task in the current state, and dynamically adjusts task priorities to ensure that key tasks can be completed in a timely manner. Based on the adjustment results, the task-resource-technology task line is reconstructed to ensure the efficiency of task execution and the rationality of resource utilization, and to achieve dynamic and optimal R&D resource scheduling and execution path configuration.

[0069] The above process, through the introduction of a multi-task game mechanism, abstractly transforms previously independent, static enterprise R&D project tasks into task agents with dynamic game behaviors, thereby improving task scheduling flexibility and overall system efficiency. Furthermore, by leveraging the task-resource-technology knowledge graph and state-space modeling, the system can fully exploit inter-task dependencies and collaborative potential, achieving efficient reconstruction of task pipelines.

[0070] In some implementations, performing game analysis based on the game objective based on the state space includes:

[0071] Create game objectives, which include the goal of maximizing the overall project benefit, the goal of minimizing conflicts, and the goal of local optimal matching, wherein the local optimal matching goal is an alternative discarded goal; after all task agents access the state space, perform a self-assessment of the current situation; based on the results of the self-assessment of the current situation, search for actions that maximize the self-benefit of each task agent, establish agent actions, and the evaluation indicators of self-benefit include task value, delay penalty, and request resource cost; establish update actions, which include self-scheduling actions, delayed resource release actions, conflict priority enhancement actions, and shared cooperation actions; establish a resource conflict degree based on the agent actions, use the resource conflict degree to select the update action, execute the task agent update, and conduct game evaluation through the game objectives; complete the game analysis based on the game evaluation results.

[0072] Specifically, the game goal is the optimization goal in the set multi-task game, including the goal of maximizing the overall project benefits (i.e., the highest overall task value and resource utilization efficiency), the goal of minimizing conflicts (i.e., the least resource request or task dependency conflicts), and the goal of local optimal matching (referring to the solution of allocating tasks and resources nearby within a specific time window, but it is a suboptimal strategy and can be abandoned when the conflict is serious).

[0073] Specifically, the current situation self-assessment is a predictive analysis of the current task-resource matching pattern conducted by the task agent after reading the state space information. According to the results of the current situation self-assessment, it can search for actions that maximize its own benefits. That is, the task agent tries to select the strategic action with the best benefits under the current resource conditions based on the situation assessment results. Among them, its own benefits are composed of the task value (the contribution of the task to the overall goal), the delay penalty (the penalty coefficient caused by the task not being completed as planned) and the requested resource cost (the cost or scarcity corresponding to the requested resources).

[0074] Specifically, update actions refer to actions chosen by task agents based on the results of game analysis. These include self-scheduling actions (such as advancing or delaying task execution), delayed resource release actions (i.e., delaying the release of resources that should have been released to mitigate risk), conflict priority-raising actions (increasing the priority of urgent tasks in the game), and shared cooperation actions (sharing resources with other tasks for mutual benefit). The resource conflict degree refers to the degree of conflict between task agents due to resource competition and is used to select the optimal update action.

[0075] Specifically, first, establish game objectives: maximizing the overall project benefit (e.g., improving project completion rate and resource utilization), minimizing conflict (e.g., reducing resource conflicts between tasks), and achieving a local optimal match (e.g., optimizing resource allocation for individual tasks). The local optimal match can be discarded if necessary to prioritize the first two objectives.

[0076] Then, it conducts a self-assessment of the current situation: each task agent accesses the state space, evaluates its own situation based on the current resource occupancy status, task status, task dependency graph, and execution records, and searches for possible actions based on its own benefit evaluation indicators (task value, delay penalty, and request resource cost), and selects the action that maximizes its own benefit.

[0077] Next, the task agent selects update actions based on the search results, including self-scheduling actions (such as adjusting the order of task execution), delayed resource release actions (such as delaying the release of currently occupied resources), conflict priority improvement actions (such as increasing the priority of high-conflict tasks), and shared cooperation actions (such as sharing resources with other task agents).

[0078] Furthermore, based on the agent actions after the action update, the resource conflict degree between task agents is calculated to select the update action with the lowest conflict degree for execution. After the update action is executed, the entire game process is evaluated according to the game goal (maximizing the overall project benefits and minimizing conflicts) to ensure the optimization of task priority and resource allocation.

[0079] Through the above-mentioned multi-task game analysis, we can dynamically adjust task priorities and resource allocation, optimize the order of task execution, reduce resource conflicts, and improve the overall efficiency of the project.

[0080] In some implementations, selecting an update action using the resource conflict degree includes:

[0081] Perform task correlation analysis on the multiple enterprise R&D project tasks and establish correlation analysis results, which include sequential correlation, collaborative correlation, and conflict correlation; use the correlation analysis results to establish action update constraints, and use the action update constraints and the resource conflict degree to select update actions.

[0082] Specifically, first, we systematically model and classify the relationships between tasks in multiple enterprise R&D projects to obtain correlation analysis results, including sequential correlation (task A must be completed before task B), collaborative correlation (multiple tasks need to be executed in parallel or collaboratively within a certain time window), and conflict correlation (multiple tasks compete for the same type of resources or there are logical exclusions). The structured output of the above correlations serves as the basic data for subsequent task scheduling and behavioral strategy optimization.

[0083] Specifically, action update constraints are restrictions set based on the relationships between tasks. For example, if tasks have a sequential relationship, subsequent tasks cannot be advanced; collaborative tasks must be synchronized in time. Resource conflict quantifies the degree to which multiple tasks compete for resources and is an important factor in determining whether an update action is necessary in task scheduling optimization.

[0084] Furthermore, combined with the current resource conflict level—that is, the intensity of competition among multiple tasks for key resources—and taking into account the action update constraints and the degree of conflict, the most suitable update action type is selected, such as delayed release, collaborative sharing, or priority adjustment. The behavior plan of the task agent is updated accordingly, in order to avoid resource competition as much as possible while complying with the logical dependencies of the tasks and optimize the overall scheduling strategy.

[0085] In some implementations, completing the game analysis based on the game evaluation results includes:

[0086] Set a game round cycle. In each game round, the task agent performs task body updates according to the selected update action and optimizes the action according to the updated state space and benefit function. Simulate the action interactions of all task agents to detect resource conflicts and dependency contradictions. Establish game feedback and optimize the update action of the next game round. Complete the game when the game round cycle is met or the game stable state appears.

[0087] Specifically, a game round is a discrete unit of time during the game, used to synchronize the strategy updates and interactions of each agent. In other words, during a game round, the task agent adjusts its state and strategy based on the selected update actions (such as self-scheduling actions and delayed resource release actions). Game stability occurs when the task agent's strategy and resource allocation reach a relatively stable state after multiple rounds of game play. At this point, the game analysis is considered complete.

[0088] Specifically, by simulating the actions of all task agents (such as resource applications, task migration, and priority adjustments), we detect resource conflicts (such as two tasks simultaneously applying for the same GPU) and dependency inconsistencies (such as task B depending on task A but task A is not yet completed). The results of the current round provide reference information for adjusting the strategy for the next round (such as resource conflict degree, task completion rate, and profit function value), guiding them to adjust their strategies in the next round (such as avoiding highly competitive resources and adjusting execution order). This process continues until the preset upper limit of game rounds is reached or the system enters a stable state (such as a Nash equilibrium or a resource-free state).

[0089] This step enables dynamic collaboration and resource optimization scheduling among multi-tasking agents, avoiding local optimality problems caused by static allocation or single-agent optimization. The game round mechanism ensures the orderliness and traceability of strategy updates, while the feedback mechanism enhances the system's adaptability. At the same time, by detecting resource conflicts and dependency contradictions, it effectively reduces operational risks such as system deadlock and task blocking.

[0090] In some embodiments, the performing of multi-task game on the plurality of enterprise R&D project tasks further includes:

[0091] Establish an expected profit model, which is used to predict the profits of enterprise R&D project tasks; create a customer strategy weight factor, and the evaluation indicators of the customer strategy weight factor include stability index, profit contribution index, cooperation history index, and strategic fit index; establish a profit function based on the expected profit model and the customer strategy weight factor, and use the profit function to conduct game evaluation of multi-task games.

[0092] Specifically, the expected revenue model is a mathematical model used to predict the potential returns of an enterprise's R&D project tasks. This expected revenue model can be constructed based on historical data, market trends, and project characteristics. The customer strategy weight factor is a weighted indicator used to assess the customer's strategic importance to the enterprise. Its evaluation indicators include stability (the stability of the customer's business), profit contribution (the profit generated by the customer for the enterprise), cooperation history (the duration and frequency of cooperation with the customer), and strategic fit (the degree of alignment between the customer and the enterprise's strategic goals).

[0093] Specifically, the profit function refers to a function that combines the expected profit model and the customer strategy weight factor, which is used to quantify the comprehensive profit of the task and provide an evaluation basis for multi-task games. For example, the function can be expressed as: profit = expected profit × customer strategy weight factor, where the expected profit comes from the expected profit model, and the customer strategy weight factor is calculated based on the above four indicators.

[0094] By establishing an expected return model and customer strategy weight factors, and using the profit function to evaluate multi-task games, we can more comprehensively and accurately evaluate the comprehensive benefits of tasks and avoid decision-making bias caused by single-indicator evaluation; at the same time, the introduction of customer strategy weight factors ensures that customer tasks that are critical to corporate development can be given priority; the comprehensively constructed profit function provides a quantitative evaluation basis for multi-task games, making task priority adjustment and resource allocation more scientific and reasonable.

[0095] S400: Adaptively decompose the task into granularity using the reconstructed task-resource-technology task line, create task key points based on the adaptive task decomposition results, and establish parallel subtask lines.

[0096] Specifically, adaptive task granularity decomposition dynamically decomposes tasks into multiple subtasks based on their complexity, resource dependencies, and technical connectivity, improving the flexibility and efficiency of task execution. Task keypoints are task nodes in a task network that have a decisive impact on overall performance or structural stability. A subtask set is a collection of subtasks formed by decomposing a task, each with a clear objective and execution steps.

[0097] In some embodiments, the adaptive task granularity decomposition using the reconstructed task-resource-technology task line and creating task key points based on the adaptive task decomposition results include:

[0098] The tasks are adaptively decomposed according to their complexity, resource dependency, and technical connectivity to establish a subtask set; key point identification features are configured, and the key point identification features include task conversion bottleneck features, connectivity hub features, and technical breakthrough features; the key point identification features are used to identify the key task points of the subtask set and create task key points.

[0099] Specifically, key point identification features are features used to identify key nodes in a task, including task transition bottleneck features (such as long transition time between tasks), connectivity hub features (such as the connection point between multiple subtasks of a task), and technology breakthrough features (such as nodes in a task that require key technology breakthroughs).

[0100] Specifically, first, based on the reconstructed task-resource-technology task line, the original task is adaptively decomposed into granularity. For example, it includes analyzing the complexity of each task (such as the number of dependencies, execution logic depth), resource dependencies (such as whether it depends on specific hardware or shared resources), and technical connectivity (such as whether it connects multiple technical paths or modules), and dynamically decomposing the task into multiple subtasks. For example, a complex product development task can be decomposed into multiple subtasks such as design, testing, and optimization. Then, the decomposed subtasks are organized into subtask sets, each of which stores clear goals, resource requirements, and execution steps. Preferably, the subtask sets are managed through a tree structure to facilitate tracking and adjustment.

[0101] Furthermore, a set of characteristic indicators for identifying key task points is configured, including: task conversion bottleneck characteristics (such as resource switching delay and task waiting time), connectivity hub characteristics (such as the degree centrality of the task in the task network), and technological breakthrough characteristics (such as whether new algorithms or key processes are introduced). Based on these characteristics, the subtask set is analyzed to identify the subtasks that have the greatest impact on the task process and mark them as task key points.

[0102] The purpose of this process is to manage complex tasks more flexibly, adapt to task complexity and changes, and improve task execution efficiency through adaptive task granularity decomposition and identification of key task points. Identifying key task points helps prioritize resource allocation to critical tasks, improving resource utilization efficiency.

[0103] In some embodiments, establishing parallel sub-task lines includes:

[0104] An expert knowledge base is called, and the expert knowledge base is used to identify alternative technical paths for key points of the task, and a branch technical path is established; and the branch technical path is used to establish a parallel subtask line.

[0105] Specifically, the original linear task process is expanded into multiple sub-processes (sub-task lines) that can be executed in parallel. These sub-processes can run simultaneously under the premise of satisfying dependencies, so as to further improve execution efficiency and enhance the stability of task execution. Among them, the expert knowledge base is a data system that contains knowledge such as industry experience, process rules, and technical alternatives. It is used to provide the general knowledge and domain information required for intelligent reasoning and completion in complex decision-making.

[0106] Specifically, an alternative technical path is an alternative technical solution or process flow, provided by the expert knowledge base, at a critical point in a task, in addition to the default technical implementation path. In other words, the alternative technical path is functionally equivalent or compatible with the existing technical path at the critical point in the task. A branch technical path refers to an optional execution path formed outside the original task mainline based on the aforementioned alternative technical path, i.e., the branch structure that constitutes the task flow.

[0107] Specifically, in addition to the original task main line, one or more functionally equivalent branch paths are introduced for key task nodes and organized into structured parallel sub-task lines. The parallel sub-task line forms a concurrent execution relationship with the main task line, providing the possibility of dynamically selecting the optimal path based on resource status, performance feedback, fault conditions, etc. when the task is in progress, which plays a role in enhancing the flexibility of the task process, improving task parallelism and system fault tolerance.

[0108] S500: After optimizing the task-resource-technology task line according to the parallel subtask lines, a cross-departmental collaborative solution decision is created, and enterprise management optimization is performed according to the solution decision.

[0109] Specifically, after the task line is optimized, cross-departmental collaborative plan decisions are made based on the task execution model, resource distribution status and technical capability boundaries. The plan decisions include key elements such as task allocation, resource scheduling, division of responsibilities and time nodes.

[0110] Specifically, create cross-departmental collaborative decision-making plans to break down barriers between departments and promote information sharing and collaborative work. For example, this can be achieved through consistent goals, assignment of task responsibilities, information sharing platforms (such as building a digital dashboard so that the R&D department can instantly access market feedback data, shortening product iteration decision-making time), and hierarchical decision-making mechanisms (such as setting up a joint decision-making group composed of technology, marketing, and production managers to shorten the approval cycle through a pre-empowerment mechanism).

[0111] Specifically, by introducing a cross-departmental collaborative decision-making mechanism, enterprises can achieve a deep integration between task execution processes and organizational management. For example, in a smart manufacturing enterprise, the R&D task of a new denitrification catalyst is broken down into multiple parallel subtasks: ① Material ratio optimization (responsible for the R&D department), ② Sample trial production and performance testing (coordinated by the trial production workshop and quality inspection department), ③ Process parameter modeling (completed by the data analysis department), and ④ Production line deployment (responsible for the engineering and operations departments). The collaborative plan automatically generated based on the task-resource-technology model can clearly define the person in charge of each task node, cross-departmental contacts, required resources, time nodes, and data interaction interfaces.

[0112] Preferably, through the above-mentioned cross-departmental collaborative solution decision-making mechanism, creating a cross-departmental collaborative solution decision to optimize enterprise management also includes:

[0113] The Knowledge R&D Assistant is used to synchronize knowledge content and standardize terminology across multiple business departments. It supports the automatic extraction of key terms and knowledge points from internal department documents, meeting minutes, and project materials. Through semantic analysis and knowledge graph technology, it achieves term alignment, concept unification, and knowledge sharing. It helps improve cross-departmental communication efficiency and reduce misunderstandings caused by inconsistent terminology.

[0114] The intelligent recommendation module is used to provide multi-dimensional intelligent recommendation services based on the current project or task requirements, including but not limited to: Personnel recommendation: recommending the most suitable personnel based on capability profile, historical project experience, and current workload; Tool recommendation: recommending appropriate collaboration tools or platforms based on task type and departmental tools; Methodology recommendation: recommending standard processes, analysis methods, or industry best practices applicable to the current task; Scheduling recommendation: intelligently generating feasible project scheduling suggestions based on the resource usage of each department.

[0115] The dynamic monitoring module is used to monitor the execution process of cross-departmental collaborative tasks in real time and synchronize the progress. It supports dynamic display and early warning of task status, key nodes, resource usage and other information. An automatic coordination mechanism can be set up. When a department lags behind in progress, the system automatically notifies the relevant responsible person or adjusts the schedule. This ensures that multi-departmental collaborative tasks are completed on schedule and reduces the risk of delays.

[0116] The Technical Document Assistant is used to intelligently analyze unstructured data such as technical documents and project materials; automatically extract key entity content, such as functional modules, technical parameters, interface information, dependencies, etc.; support the generation of structured knowledge cards to facilitate rapid understanding and reuse across departments; at the same time, it can be linked with the Knowledge R&D Assistant module to achieve the sedimentation and accumulation of knowledge.

[0117] The above process, through the optimization of the task-resource-technology task line and the establishment of a cross-departmental collaboration mechanism, can improve task execution efficiency and resource utilization, while promoting collaborative work between departments and enhancing the overall operational efficiency and competitiveness of the enterprise.

[0118] In some embodiments, performing enterprise management optimization according to the solution decision includes:

[0119] A monitoring time window is created using the solution decision; execution of the solution decision is monitored in the monitoring time window, and monitoring feedback is established; the monitoring feedback is used to predict the execution impact, generate a decision optimization solution, and optimize the management of the solution decision through the decision optimization solution.

[0120] Specifically, the monitoring time window refers to a specific time period or time length set according to the task execution plan, resource scheduling cycle and key node time points for real-time monitoring and evaluation of the execution of the plan. The window has attributes such as start and end time, monitoring frequency and monitoring indicators.

[0121] Specifically, within the monitoring time window, real-time or periodic data collection and status analysis are conducted on the actual implementation of the plan decision, including task progress, resource usage, cross-departmental collaboration status, etc., so as to obtain data results and status assessment information, including the identification of problems such as task delays, resource conflicts, and collaboration bottlenecks, and the output is monitoring feedback.

[0122] Furthermore, based on monitoring feedback, combined with preset impact prediction models (such as rule-based, time series models trained based on historical data, causal reasoning models, etc.), trend analysis and result deduction are carried out to determine the subsequent task delays, resource bottlenecks, collaboration failures and other problems that may be caused by the current execution status to predict the execution impact; then, decision optimization plans are automatically generated based on the prediction results, for example, advancing a task node, changing the order of resource allocation, adjusting the collaboration interface, etc.; finally, the optimization plan is fed back to the enterprise decision-making process, and the original plan is dynamically adjusted to achieve closed-loop control of optimization management.

[0123] For example, taking a certain environmental protection equipment manufacturing company as an example, when carrying out a modular design task of an SCR denitrification system, the system has generated parallel sub-task lines and cross-departmental collaboration plans, and set a 48-hour monitoring time window, collecting task execution status every 30 minutes. During the monitoring process, it was found that the "module interface standardization" task responsible for the design department was delayed by more than 4 hours, resulting in the subsequent structural simulation task unable to start on time. At this time, monitoring feedback is immediately generated, and the impact prediction model is executed to determine: If not adjusted, the delay will cause the overall delivery time to be delayed by 1.5 days. At this time, the corresponding generation optimization plan recommends changing some simulation tasks to use historical data for pre-simulation, and temporarily calling outsourced resources to assist in completing the interface standardization task, so that the project can be delivered on time.

[0124] Through the above mechanism, enterprises can achieve real-time monitoring and dynamic optimization of solution decisions during task execution, significantly improving management agility and execution efficiency.

[0125] In summary, the enterprise management optimization method of big data-enabled ERP provided by the present invention has the following technical effects:

[0126] By creating multiple enterprise R&D project tasks and obtaining relevant R&D resource information, a dynamic knowledge graph of task-resource-technology relationships is constructed based on these R&D project tasks and resource information. A multi-task game is conducted for these R&D project tasks, and task priorities are adjusted based on the game results, thereby reconstructing the task-resource-technology task line. The reconstructed task-resource-technology task line is then used to perform adaptive task granularity decomposition, and task key points are created based on the decomposition results, thereby establishing parallel subtask lines. The task-resource-technology task line is optimized based on the parallel subtask lines, creating cross-departmental collaborative solution decisions, and optimizing enterprise management based on these decisions. This achieves the technical effects of real-time perception and dynamic priority response, efficient resource allocation, and improved cross-departmental collaboration.

[0127] Example 2, as Figure 2 This is a schematic diagram of the structure of the enterprise management optimization system of the present invention that enables ERP with big data. For example, Figure 1 The flow chart of the enterprise management optimization method of big data empowering ERP in the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0128] Based on the same concept as the enterprise management optimization method of big data-enabled ERP in the above embodiment, the present invention also provides an enterprise management optimization system of big data-enabled ERP, including:

[0129] The task and resource information acquisition module 11 is used to create multiple enterprise R&D project tasks and obtain the enterprise's R&D resource information.

[0130] The dynamic graph module 12 is used to construct a dynamic knowledge graph of task-resource-technology association based on the multiple enterprise R&D project tasks and the R&D resource information.

[0131] The priority reconstruction module 13 is used to perform a multi-task game on the multiple enterprise R&D project tasks, adjust the task priorities of the enterprise R&D project tasks according to the multi-task game results, and reconstruct the task-resource-technology task line.

[0132] The key point definition module 14 is used to use the reconstructed task-resource-technology task line to perform adaptive task granularity decomposition, create task key points based on the adaptive task decomposition results, and establish parallel sub-task lines.

[0133] The collaborative decision module 15 is used to optimize the task-resource-technology task line according to the parallel sub-task lines, create a cross-departmental collaborative solution decision, and optimize enterprise management according to the solution decision.

[0134] In some embodiments, the priority reconstruction module 13 includes:

[0135] The task agent modeling unit is used to model multiple enterprise R&D project tasks into task agents.

[0136] The state space establishing unit is used to establish a state space, wherein the state space includes the current resource occupancy state, task state, task dependency graph, and executed records.

[0137] The multi-task game execution unit is used to select the execution action of the task agent in the dynamic knowledge graph of task-resource-technology association, and then rely on the state space to perform game analysis under the game goal to complete the multi-task game.

[0138] In some implementations, the multi-task game execution unit in the priority reconstruction module 13 includes:

[0139] The game objective creation subunit is used to create game objectives, which include the overall project benefit maximization objective, conflict minimization objective, and local optimal matching objective, wherein the local optimal matching objective is an alternative abandonment objective.

[0140] The situation self-assessment subunit is used to perform self-assessment of the current situation after all task agents have accessed the state space.

[0141] The agent action search subunit is used to search for actions that maximize the self-benefit of each task agent based on the self-assessment results of the current situation, establish agent actions, and the evaluation indicators of self-benefit include task value, delay penalty, and request resource cost.

[0142] The update action establishment subunit is used to establish update actions, which include self-scheduling actions, delayed resource release actions, conflict priority improvement actions, and shared cooperation actions. The resource conflict degree is established according to the intelligent agent actions. After selecting the update action using the resource conflict degree, the task intelligent agent is updated and the game evaluation is performed through the game goal.

[0143] The game analysis completion subunit is used to complete the game analysis based on the game evaluation results.

[0144] Furthermore, the execution step of the update action establishment subunit further includes:

[0145] Performing task correlation analysis on the multiple enterprise R&D project tasks to establish correlation analysis results, wherein the correlation analysis results include sequential correlation, collaborative correlation, and conflict correlation.

[0146] An action update constraint is established using the association analysis result, and an update action is selected using the action update constraint and the resource conflict degree.

[0147] Furthermore, the execution steps of the game analysis completion sub-unit also include:

[0148] The game round cycle is set. In each game round, the task agent performs task body updates according to the selected update actions and optimizes the actions according to the updated state space and benefit function.

[0149] Simulate the action interactions of all task agents and detect resource conflicts and dependency contradictions.

[0150] Establish game feedback, optimize the update action of the next game round, and complete the game when the game round cycle is met or the game stable state appears.

[0151] In some embodiments, the key point definition module 14 includes:

[0152] The task adaptive decomposition unit is used to adaptively decompose tasks according to their complexity, resource dependency, and technical connectivity to establish subtask sets.

[0153] The key point identification feature configuration unit is used to configure key point identification features, where the key point identification features include task conversion bottleneck features, connectivity hub features, and technology breakthrough features.

[0154] The task key point creation unit is used to identify the key task points of the subtask set using the key point recognition features and create task key points.

[0155] In some embodiments, the key point definition module 14 further includes:

[0156] The backup technical path identification unit is used to call the expert knowledge base, use the expert knowledge base to identify the backup technical paths of the key points of the task, and establish a branch technical path.

[0157] A parallel subtask line establishing unit is used to establish a parallel subtask line using the branch technical path.

[0158] In some embodiments, the priority reconstruction module 13 further includes:

[0159] The expected profit model establishment unit is used to establish an expected profit model, and the expected profit model is used to predict the profit of the enterprise's R&D project tasks.

[0160] The customer strategy weight factor creation unit is used to create a customer strategy weight factor, wherein the evaluation indicators of the customer strategy weight factor include stability indicator, profit contribution indicator, cooperation history indicator, and strategic fit indicator.

[0161] The profit function establishment unit is used to establish a profit function according to the expected profit model and the customer strategy weight factor, and use the profit function to perform game evaluation of the multi-task game.

[0162] In some embodiments, the collaborative decision-making module 15 includes:

[0163] The monitoring time window creating unit is used to create a monitoring time window using the solution decision.

[0164] The execution monitoring and feedback unit is used to monitor the execution of the solution decision in the monitoring time window and establish monitoring feedback.

[0165] A decision optimization scheme generating unit is used to use the monitoring feedback to predict the execution impact, generate a decision optimization scheme, and optimize the management of the scheme decision through the decision optimization scheme.

[0166] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the enterprise management optimization system of big data-enabled ERP described in embodiment two. For the sake of brevity of the specification, no further elaboration will be given here.

[0167] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. The enterprise management optimization method of big data-enabled ERP is characterized by: include: Create multiple enterprise R&D project tasks and obtain enterprise R&D resource information; Constructing a dynamic knowledge graph of task-resource-technology associations based on the multiple enterprise R&D project tasks and the R&D resource information; Conducting a multi-task game for the multiple enterprise R&D project tasks, adjusting the task priorities of the enterprise R&D project tasks according to the multi-task game results, and reconstructing the task-resource-technology task line; Use the reconstructed task-resource-technology task line to perform adaptive task granularity decomposition, create task key points based on the adaptive task decomposition results, and establish parallel sub-task lines; After optimizing the task-resource-technology task line according to the parallel subtask lines, a cross-departmental collaborative solution decision is created, and enterprise management optimization is performed according to the solution decision; The multi-task game for the multiple enterprise R&D project tasks includes: Model multiple enterprise R&D project tasks as task agents; Establishing a state space, the state space including the current resource occupancy state, task state, task dependency graph, and executed records; After the task agent selects an action to be executed in the dynamic knowledge graph of task-resource-technology association, the game analysis under the game goal is performed based on the state space to complete the multi-task game; The game analysis based on the state space and the game objective includes: Creating a game objective, wherein the game objective includes a project overall benefit maximization objective, a conflict minimization objective, and a local optimal matching objective, wherein the local optimal matching objective is an alternative discarded objective; After all task agents have accessed the state space, they perform a self-assessment of the current situation; Based on the self-evaluation results of the current situation, each task agent searches for actions that maximize its own benefits and establishes agent actions. The evaluation indicators of its own benefits include task value, delay penalty, and request resource cost. Establish update actions, including self-scheduling actions, delayed resource release actions, conflict priority improvement actions, and shared cooperation actions; establish resource conflict degrees based on the agent actions; use the resource conflict degrees to select update actions, execute task agent updates, and conduct game evaluation based on the game objectives; Complete the game analysis based on the game evaluation results.

2. The enterprise management optimization method of big data-enabled ERP according to claim 1, characterized in that: The selecting an update action by utilizing the resource conflict degree includes: Performing task correlation analysis on the multiple enterprise R&D project tasks to establish correlation analysis results, wherein the correlation analysis results include sequential correlation, collaborative correlation, and conflict correlation; An action update constraint is established using the association analysis result, and an update action is selected using the action update constraint and the resource conflict degree.

3. The enterprise management optimization method of big data-enabled ERP according to claim 1, characterized in that: The game analysis is completed according to the game evaluation results, including: Set the game round cycle. In each game round, the task agent performs task body updates according to the selected update action and optimizes the action according to the updated state space and benefit function. Simulate the action interactions of all task agents and detect resource conflicts and dependency contradictions; Establish game feedback, optimize the update action of the next game round, and complete the game when the game round cycle is met or the game stable state appears.

4. The enterprise management optimization method of big data-enabled ERP according to claim 1, characterized in that: The adaptive task granularity decomposition using the reconstructed task-resource-technology task line and the creation of task key points based on the adaptive task decomposition results include: Adaptively decompose tasks based on their complexity, resource dependencies, and technical connectivity to create subtask sets; Configuring key point identification features, wherein the key point identification features include task conversion bottleneck features, connectivity hub features, and technology breakthrough features; The key point identification features are used to identify the key task points of the subtask set and create task key points.

5. The enterprise management optimization method of big data-enabled ERP according to claim 4, characterized in that: The establishment of parallel sub-task lines includes: Calling an expert knowledge base, using the expert knowledge base to identify alternative technical paths for key tasks, and establishing branch technical paths; The branching technical paths are used to establish parallel sub-task lines.

6. The enterprise management optimization method of big data-enabled ERP according to claim 1, characterized in that: The multi-task game for the multiple enterprise R&D project tasks further includes: Establishing an expected revenue model, which is used to predict the revenue of enterprise R&D project tasks; Creating a customer strategy weight factor, wherein the evaluation indicators of the customer strategy weight factor include stability indicator, profit contribution indicator, cooperation history indicator, and strategic fit indicator; A profit function is established according to the expected profit model and the customer strategy weight factor, and the game evaluation of the multi-task game is performed using the profit function.

7. The enterprise management optimization method of big data-enabled ERP according to claim 1, characterized in that: The enterprise management optimization according to the solution decision includes: Creating a monitoring time window using the solution decision; Performing execution monitoring of the solution decision within the monitoring time window and establishing monitoring feedback; The monitoring feedback is used to predict the execution impact, generate a decision optimization plan, and optimize the management of the plan decision through the decision optimization plan.

8. Big data empowers ERP's enterprise management optimization system, which is characterized by: The enterprise management optimization method for implementing the big data-enabled ERP according to any one of claims 1 to 7 comprises: The task and resource information acquisition module is used to create multiple enterprise R&D project tasks and obtain the enterprise's R&D resource information; A dynamic graph module, configured to construct a dynamic knowledge graph of task-resource-technology associations based on the multiple enterprise R&D project tasks and the R&D resource information; a priority reconstruction module, configured to conduct a multi-task game on the plurality of enterprise R&D project tasks, adjust the task priorities of the enterprise R&D project tasks according to the multi-task game results, and reconstruct the task-resource-technology task line; The key point definition module is used to use the reconstructed task-resource-technology task line to perform adaptive task granularity decomposition, create task key points based on the adaptive task decomposition results, and establish parallel sub-task lines; The collaborative decision-making module is used to optimize the task-resource-technology task line according to the parallel sub-task lines, create a cross-departmental collaborative solution decision, and optimize enterprise management according to the solution decision.

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