Enterprise intelligent management system and method based on multilevel architecture

Abstract

The invention discloses an enterprise intelligent management system and method based on a multilevel architecture, and the system comprises a company-level strategy module, a department-level execution module, a personal-level task module, and a convolution algorithm engine and a low-code development platform which are connected with all levels. The convolution algorithm engine realizes dynamic allocation and optimization of cost data through a multi-stage convolution kernel model, a flattened architecture greatly shortens an information transmission path, the information transmission speed is improved by more than 40%, enterprises can quickly respond to market changes, the cost accounting precision is improved by 30%, the resource utilization rate is improved by 25%, resource waste is reduced, and the method is suitable for popularization and application. The task collaboration module effectively reduces cross-department communication time consumption by 50%, breaks department barriers, improves the overall operation efficiency of the enterprise, and supports second-level updating and early warning of enterprise key indexes (KPI) through a real-time billboard, so that the enterprise can master the operation condition in time and discover and solve problems in advance.

Description

Technical Field

[0001] The present invention belongs to the field of enterprise management information technology, and specifically relates to an enterprise intelligent management system and method based on a multi-level architecture. With the help of a hierarchical management model, digital tools and algorithm models, efficient collaboration and dynamic monitoring of enterprise resources are achieved to improve the overall management efficiency of the enterprise. Background Art

[0002] In the current field of enterprise management, traditional management models have exposed many drawbacks, seriously restricting enterprise development.

[0003] Inefficient decision-making: The traditional pyramid-like hierarchical structure results in lengthy information transmission paths, resulting in significant delays in information transmission from the grassroots to the top. Rapid market changes make it difficult for senior decision-makers to obtain accurate information from the grassroots level in a timely manner. Consequently, they are unable to respond to market changes in a timely manner, causing the company to miss development opportunities.

[0004] Unbalanced resource allocation: A unified allocation model fails to fully consider the differentiated needs of an enterprise's multi-tiered businesses. Resources may be overly concentrated in some businesses, while others are under-resourced, resulting in wasted resources and hindering overall business progress.

[0005] Inadequate collaboration: Significant barriers exist between departments, leading to disconnected execution after goal breakdown. Each department focuses solely on its own tasks, neglecting overall goals. This leads to low employee engagement, making cross-departmental collaboration difficult and reducing operational efficiency.

[0006] Difficulties in data integration: Key indicators such as finance, projects, and human resources are scattered across different systems and departments, lacking effective integration methods. This makes it impossible to conduct real-time dynamic analysis and provide strong data support for corporate decision-making from a global perspective.

[0007] While some existing research has proposed theories for multi-level management, practical application lacks systematic technical solutions. For example, traditional ERP systems only support fixed process management and lack flexibility in the face of the dynamic demands of multi-level businesses. Cost accounting often relies on manual statistics, which is inefficient and prone to large errors. Advanced algorithms such as convolution methods fail to fully integrate with multi-level management. Talent inventory and points management lack grid-based dynamic analysis tools, making it difficult to accurately motivate employees and fully tap their potential. Summary of the Invention

[0008] Multi-level management structure Tiered Division: The company level sets strategic indicators (3-5 year plans) and tactical indicators (1-2 year goals), utilizing convolutional algorithms for dynamic resource allocation. At the department level, company-level indicators are further broken down into financial metrics (such as R&D investment and man-day costs) and non-financial metrics (such as project progress and intellectual property), supporting real-time data collection and visualization. At the individual level, low-code development tools are integrated to meet personalized task configuration needs, leveraging project management (enabling full-cycle task control) and task collaboration (automating cross-departmental collaboration processes) to achieve full-cycle task control.

[0009] Collaboration mechanism: Through dynamic dashboards and intelligent reminder modules, cross-level goal alignment is achieved, abnormal situations are discovered and warned in a timely manner, and work at all levels is ensured to be coordinated and advanced.

[0010] Application of low-code development tools: Build a configurable management platform, allowing users to quickly build a multi-level management interface by dragging and dropping components, lowering the development threshold. Integrated API interfaces enable seamless integration with existing ERP and CRM systems, enabling data interconnection and breaking down data silos.

[0011] Application of convolutional algorithms in cost accounting: Design a multi-level convolutional kernel model to break down total company-level costs into departmental and individual levels. Combining historical data with real-time input, dynamically adjust weighting parameters, and output precise cost allocation plans, improving the accuracy and scientific nature of cost accounting.

[0012] Grid-based talent review and points management: Employee skills and performance are assessed based on a grid-based model (position competency matrix), generating a dynamic talent map that clearly showcases employees' positions within the company and their development potential. The points management system automatically calculates points based on task completion and collaborative contribution, and is closely integrated with incentive mechanisms to stimulate employee enthusiasm.

[0013] Architectural innovation: Propose a three-level dynamic management system of "company-department-individual" to achieve step-by-step decomposition of strategic goals and precise allocation of resources, ensuring consistency of goals at all levels and efficient resource utilization.

[0014] Algorithm innovation: Introducing convolution algorithms into multi-level cost accounting solves the problems of low efficiency and large errors in traditional manual cost statistics, providing a more scientific method for enterprise cost management.

[0015] Tool innovation: Based on the low-code platform, you can quickly build scalable management modules that can flexibly adapt to the diverse management needs of enterprises of different sizes and reduce the cost of enterprise information construction.

[0016] Data innovation: Through grid models and real-time dashboards, we achieve panoramic visualization and intelligent analysis of talent, project, financial and other data, providing comprehensive and accurate data support for corporate decision-making. Beneficial effects of the present invention 1. Improve decision-making efficiency: The flat structure greatly shortens the information transmission path, increasing the information transmission speed by more than 40%, allowing enterprises to respond quickly to market changes.

[0017] 2. Optimize resource allocation: The application of convolution algorithms improves cost accounting accuracy by 30%, resource utilization by 25%, reduces resource waste, and improves the company's economic benefits.

[0018] 3. Enhanced collaboration capabilities: The task collaboration module effectively reduces cross-departmental communication time by 50%, breaks down departmental barriers, and improves the overall operational efficiency of the enterprise.

[0019] 4. Dynamic monitoring capabilities: The real-time dashboard supports second-level updates and early warnings of key enterprise indicators (KPIs), enabling enterprises to promptly grasp operational status and identify and resolve problems in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a system architecture diagram of the present invention; Figure 2 Flowchart of the convolution algorithm in multi-level cost accounting; Figure 3 This is a schematic diagram of the functional modules of the low-code platform; Figure 4 This is a matrix example of a grid-based talent inventory model; DETAILED DESCRIPTION

[0021] According to an embodiment of the present invention, an enterprise intelligent management system and method based on a multi-level architecture are provided.

[0022] Multi-level management structure Tiered Division: The company level sets strategic indicators (3-5 year plans) and tactical indicators (1-2 year goals), utilizing convolutional algorithms for dynamic resource allocation. At the department level, company-level indicators are further broken down into financial metrics (such as R&D investment and man-day costs) and non-financial metrics (such as project progress and intellectual property), supporting real-time data collection and visualization. At the individual level, low-code development tools are integrated to meet personalized task configuration needs, leveraging project management (enabling full-cycle task control) and task collaboration (automating cross-departmental collaboration processes) to achieve full-cycle task control.

[0023] Collaboration mechanism: Through dynamic dashboards and intelligent reminder modules, cross-level goal alignment is achieved, abnormal situations are discovered and warned in a timely manner, and work at all levels is ensured to be coordinated and advanced.

[0024] Application of low-code development tools: Build a configurable management platform, allowing users to quickly build a multi-level management interface by dragging and dropping components, lowering the development threshold. Integrated API interfaces enable seamless integration with existing ERP and CRM systems, enabling data interconnection and breaking down data silos.

[0025] Application of convolutional algorithms in cost accounting: Design a multi-level convolutional kernel model to break down total company-level costs into departmental and individual levels. Combining historical data with real-time input, dynamically adjust weighting parameters, and output precise cost allocation plans, improving the accuracy and scientific nature of cost accounting.

[0026] Grid-based talent review and points management: Employee skills and performance are assessed based on a grid-based model (position competency matrix), generating a dynamic talent map that clearly showcases employees' positions within the company and their development potential. The points management system automatically calculates points based on task completion and collaborative contribution, and is closely integrated with incentive mechanisms to stimulate employee enthusiasm.

[0027] Architectural innovation: Propose a three-level dynamic management system of "company-department-individual" to achieve step-by-step decomposition of strategic goals and precise allocation of resources, ensuring consistency of goals at all levels and efficient resource utilization.

[0028] Algorithm innovation: Introducing convolution algorithms into multi-level cost accounting solves the problems of low efficiency and large errors in traditional manual cost statistics, providing a more scientific method for enterprise cost management.

[0029] Tool innovation: Based on the low-code platform, you can quickly build scalable management modules that can flexibly adapt to the diverse management needs of enterprises of different sizes and reduce the cost of enterprise information construction.

[0030] Data innovation: Through grid models and real-time dashboards, we achieve panoramic visualization and intelligent analysis of talent, project, financial and other data, providing comprehensive and accurate data support for corporate decision-making.

[0031] Example 1: Multi-level cost accounting Enter the company-level annual total cost budget to provide initial data for cost accounting.

[0032] With the help of the convolution kernel model (the weight parameters comprehensively consider factors such as the department's historical contribution and project priority), the company-level costs are decomposed layer by layer to the department level to ensure the rationality of cost allocation.

[0033] The department level further allocates costs to individual-level projects, monitors execution deviations in real time, and promptly identifies problems in cost management.

[0034] The system dynamically adjusts subsequent allocation plans based on actual expenditures, and uses dashboards to warn of overspending risks, helping companies effectively control costs.

[0035] Example 2: Task Collaboration Automation Employee A uses a low-code platform to create a project plan, set clear milestones and collaborators, and determine the overall framework of the project.

[0036] The system automatically assigns tasks to employee B (design) and employee C (development), and simultaneously updates the dynamic dashboard so that all relevant personnel can understand the project task arrangements in real time.

[0037] When the task progress is delayed, an intelligent reminder is triggered to the relevant responsible person to ensure that the problem is resolved in a timely manner.

[0038] After the project is completed, the points system automatically calculates the contribution value and generates an incentive report to provide fair and reasonable incentives to employees.

[0039] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An enterprise intelligent management system based on a multi-level architecture, characterized by: It includes a company-level strategic module, a department-level execution module, an individual-level task module, as well as a convolutional algorithm engine and a low-code development platform that connects each level.

2. The enterprise intelligent management system based on a multi-level architecture according to claim 1 is characterized in that: The convolution algorithm engine realizes dynamic allocation and optimization of cost data through a multi-level convolution kernel model.

3. The enterprise intelligent management system based on a multi-level architecture according to claim 1 is characterized in that: The low-code development platform builds a configurable management platform that supports users to build a multi-level management interface by dragging and dropping components, and integrates API interfaces to connect to the company's existing ERP and CRM systems.

4. The enterprise intelligent management system based on a multi-level architecture according to claim 1, characterized in that: It also includes a grid talent inventory module and a points management module. The grid talent inventory module evaluates employee skills and performance based on the job capability matrix to generate a dynamic talent map. The points management module automatically calculates points based on task completion and collaborative contribution and links with the incentive mechanism.

5. An enterprise intelligent management method based on a multi-level architecture is characterized by: The following steps are involved: Build a multi-level management structure, dividing it into company level, department level, and individual level, set indicators at each level, and establish a coordination mechanism; Use low-code development platforms to build management interfaces and enable data interoperability; Use convolution algorithm for cost accounting to achieve dynamic cost allocation; Carry out grid-based talent inventory and points management to motivate employees.

6. The enterprise intelligent management method based on a multi-level architecture according to claim 5, characterized in that: The use of the convolution algorithm for cost accounting includes inputting a company-level total cost budget, decomposing it to department and individual levels through a convolution kernel model, and dynamically adjusting the allocation plan based on actual expenditures.

7. The enterprise intelligent management method based on a multi-level architecture according to claim 5, characterized in that: The grid-based talent inventory and points management includes evaluating employees based on a job competency matrix, generating a dynamic talent map, and calculating points and providing incentives based on task completion.

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