PaaS platform-based enterprise management decision system

Abstract

The invention discloses an enterprise management decision-making system based on a PaaS platform. The enterprise management decision-making system comprises a data acquisition and preprocessing module, a data storage and management module, a decision-making model construction and management module, a decision-making analysis and visualization module and a user interaction and authority management module. The data acquisition and preprocessing module acquires data from multiple sources inside and outside an enterprise and cleans and standardizes the data in real time; the data storage and management module realizes efficient storage and management of data by using a distributed storage technology; the decision model construction and management module provides a visual tool to support construction and version management of various types of decision models; the decision analysis and visualization module is used for deeply analyzing data by using a data analysis technology and visually presenting a result; the user interaction and authority management module supports multi-terminal access and distributes authority according to enterprise architecture and business roles. According to the system, the enterprise deployment cost is reduced, the flexibility and expansibility are improved, efficient data processing capacity is achieved, and flexible decision model construction and cross-department collaboration are achieved.

Description

Technical Field

[0001] This invention relates to the field of enterprise management information technology, specifically to an enterprise management decision-making system based on a PaaS platform. Background Technology

[0002] In today's digital age, enterprises face massive amounts of data and a complex and ever-changing market environment. Efficient management decision-making is crucial for their survival and development. Traditional enterprise management decision-making systems are usually based on on-premises deployment, which suffers from high deployment costs, poor flexibility, and insufficient scalability.

[0003] With the development of cloud computing technology, some enterprises have begun to adopt SaaS (Software as a Service)-based management decision-making systems. However, SaaS systems often have limitations in terms of personalized customization and deep system integration, making it difficult to meet the increasingly diverse management decision-making needs of enterprises.

[0004] Existing management decision-making systems, whether deployed locally or based on a SaaS model, struggle to provide efficient, real-time, and personalized management decision support in areas such as data processing, decision model building, and cross-departmental collaboration. For example, in data processing, they cannot quickly integrate heterogeneous data from different business systems within an enterprise; in decision model building, they lack flexible customization features to adapt to the unique business logic of the enterprise; and in cross-departmental collaboration, poor data flow between systems used by different departments leads to delays in the transmission of decision-making information. Summary of the Invention

[0005] The purpose of this invention is to provide an enterprise management decision-making system based on a PaaS platform to solve the problems of high deployment costs, poor flexibility, inflexible data processing and decision model construction, and difficulties in cross-departmental collaboration in existing enterprise management decision-making systems.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: 1. System Architecture The enterprise management decision-making system based on the PaaS platform of this invention mainly includes a data acquisition and preprocessing module, a data storage and management module, a decision model construction and management module, a decision analysis and visualization module, and a user interaction and permission management module. These modules are built on the PaaS platform architecture, which provides underlying cloud computing resources, middleware services, and development tools, enabling the system to run in a high-performance platform environment.

[0007] 2. Data Acquisition and Preprocessing Module This module is responsible for collecting data from various internal business systems within the enterprise, such as ERP (Enterprise Resource Planning) systems, CRM (Customer Relationship Management) systems, and OA (Office Automation) systems, as well as external data sources, such as market research reports and industry dynamic data. It employs multiple data acquisition technologies, including ETL (Extract, Transform, Load) tools and API (Application Programming Interface) calls, to adapt to the data formats and interface specifications of different data sources. During the data acquisition process, the data undergoes real-time cleaning and preprocessing to remove noisy data, correct erroneous data formats, and perform data standardization to improve data quality and provide a reliable data foundation for subsequent data analysis and decision-making. For example, through preset data cleaning rules, it automatically identifies and corrects format errors in order data from the ERP system, ensuring data accuracy.

[0008] 3. Data Storage and Management Module Leveraging the distributed storage technology provided by the PaaS platform, the collected data is stored in a scalable distributed database. This database can automatically expand its storage capacity as the data volume grows and features high availability and data consistency guarantees. A tiered data storage strategy is employed, storing frequently accessed hot data on high-speed storage media to improve data retrieval speed, while storing cold data such as historical data on low-cost, high-capacity storage media to reduce storage costs. Simultaneously, data catalog management and metadata management enable effective organization and management of massive amounts of data, facilitating users' quick retrieval and use of the data they need. For example, users can quickly locate sales data for a specific business department within a specific time period through the data catalog.

[0009] 4. Decision Model Construction and Management Module This module provides users with a visual decision-making model building tool. Users do not need extensive programming knowledge to customize decision-making models through drag-and-drop and configuration. It supports various decision-making model types, including rule-based decision models, machine learning models (such as decision trees and neural networks), and operations research-based optimization models. Users can flexibly select and combine different model types according to their business needs and management decision-making scenarios. For example, when formulating production plans, users can build a decision model combining an operations research optimization model and a machine learning prediction model. The machine learning model predicts market demand, and the optimization model determines the optimal production quantity and resource allocation scheme. Simultaneously, the system has a decision model version management function, which can record the historical versions and change records of the model, allowing users to easily review and compare the performance of different versions.

[0010] 5. Decision Analysis and Visualization Module This module utilizes advanced data analysis algorithms and data mining techniques to perform in-depth analysis of data stored in the database. It supports both real-time and offline data analysis, and can quickly respond to user queries and analysis requests. Through various visualization technologies, such as bar charts, line charts, dashboards, and geographic information maps, the analysis results are presented to users in an intuitive and easy-to-understand manner. Users can customize visual reports and dashboards according to their needs, and monitor key business indicators and decision-making results in real time. For example, enterprise managers can use visual dashboards to understand real-time information such as sales performance, market share, and inventory levels in different regions, enabling timely decision-making. Simultaneously, the system supports data drill-down functionality, allowing users to click on data points in visual charts to delve deeper into the underlying detailed data and further analyze the reasons behind the data.

[0011] 6. User Interaction and Permission Management Module It provides a user-friendly interface and supports multi-terminal access, including desktop, mobile, and tablet devices, allowing enterprise employees to use the system for management and decision-making anytime, anywhere. Through user authentication and access control mechanisms, it ensures that only authorized users can access and operate the corresponding data and functional modules. Different permission levels are assigned to users based on the enterprise's organizational structure and business roles, such as administrator permissions, department manager permissions, and ordinary employee permissions. Users with different permission levels have different operating interfaces and functional visibility within the system. For example, administrators can perform system configuration, user management, and data permission allocation operations; department managers can only view and manage data within their department and use decision models and analytical functions related to their department's business; and ordinary employees can only view their own work data and perform related tasks.

[0012] In summary, due to the adoption of the above-mentioned technologies, the beneficial effects of this invention are: 1. Reduced deployment costs: Based on the PaaS platform, enterprises do not need to invest a lot of money in purchasing and maintaining hardware equipment and building complex software environments. They can use the system simply by accessing the platform via the Internet, which greatly reduces the cost of enterprise information technology construction.

[0013] 2. Enhanced Flexibility and Scalability: The characteristics of the PaaS platform enable the system to quickly expand its functionality and be customized according to the development and changes in enterprise business. Users can flexibly build decision-making models according to their own needs, and the system can easily integrate new data sources and business systems to adapt to the ever-changing management decision-making needs of enterprises.

[0014] 3. Highly efficient data processing capabilities: Through powerful data acquisition and preprocessing modules, it can quickly integrate heterogeneous data from different data sources and perform real-time cleaning and standardization. Combined with distributed storage and management technologies, it achieves efficient storage and management of massive amounts of data, providing high-quality data support for decision analysis.

[0015] 4. Flexible decision-making model building: Visual decision-making model building tools and a wide selection of model types enable enterprise users to easily build personalized decision-making models based on their own business logic and decision-making scenarios, thereby improving the scientific nature and accuracy of decision-making.

[0016] 5. Optimized Cross-Departmental Collaboration: The system breaks down data silos between departments within the enterprise, enabling cross-departmental data sharing and collaborative decision-making through unified data storage and management, as well as a visualized decision analysis and interactive interface. Each department can access the necessary data in real time and participate in the decision-making process, improving the overall operational efficiency and decision response speed of the enterprise. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention, making other features, objects, and advantages of the invention more apparent. The illustrative embodiments of the invention illustrated in the drawings and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, it should be understood that the terms indicating orientation or positional relationship are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing the invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0020] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific context of the specification.

[0021] This invention provides an enterprise management decision-making system based on a PaaS platform. Example 1: Production Decision Support for a Manufacturing Enterprise A manufacturing company uses the PaaS-based enterprise management decision-making system of this invention to optimize its production decision-making process. During the data acquisition phase, ETL tools and API interfaces are used to collect production order data and raw material inventory data from the company's ERP system, customer demand data from the CRM system, and equipment operating status data from the equipment management system. The data acquisition and preprocessing module cleans and standardizes this data before storing it in a distributed database. The company's production department personnel use the decision model building and management module to construct a rule-based and machine learning-based production decision-making model based on the company's production processes, cost structure, and market demand forecasts. This model uses machine learning algorithms to predict market demand trends for different products and then determines the optimal production plan based on preset production rules and cost constraints, including the types and quantities of products to be produced and the scheduling scheme for production equipment. The decision analysis and visualization module performs real-time analysis of production data and decision results and displays them to company managers and production department personnel through a visual dashboard. Managers can use the dashboard to understand key indicators such as production progress, equipment utilization, and production costs in real time, and can adjust production decisions promptly when abnormalities are detected during the production process. For example, when a sudden increase in market demand for a product is detected, managers can quickly adjust the production plan through the system, increase the production quantity of that product, and rationally allocate production resources to ensure that production tasks are completed on time. Simultaneously, through the user interaction and access control module, personnel from different departments can only access and operate data and functions relevant to their own work, ensuring data security and operational standardization. By using this system, the manufacturing company's production decision-making efficiency has been significantly improved, production costs have decreased by [X]%, and the accuracy of production planning has increased by [X]%, effectively enhancing the company's market competitiveness.

[0022] Example 2: Marketing Decision Optimization of a Retail Enterprise A retail company utilizes the system of this invention to optimize its marketing decisions. The data acquisition and preprocessing module collects data from multiple data sources, including the company's sales system, membership system, e-commerce platform, and external market research agencies, encompassing sales data, member consumption behavior data, and market trend data. After cleaning and preprocessing, the data is stored in a distributed database. Marketing personnel use the decision model building and management module to construct a decision model based on customer segmentation and marketing effectiveness prediction. This model analyzes member consumption behavior data to segment customers into different groups, develops personalized marketing strategies for each group, and uses machine learning algorithms to predict the effectiveness of different marketing strategies. The decision analysis and visualization module displays the effectiveness evaluation results of different marketing activities through visual reports and charts, such as sales growth and improved customer conversion rates. Marketing personnel can adjust marketing strategies in real time based on these analysis results. For example, if a promotional activity targeting a specific customer group is found to be ineffective, the promotional plan can be modified promptly, and the new promotional information can be quickly pushed to the target customer group through the system. The user interaction and access control module ensures that personnel in different positions can only access and operate corresponding data and functions, ensuring the orderly conduct of the marketing decision-making process. By using this system, the retail company's customer satisfaction increased by [X]%, the return on investment of its marketing activities increased by [X]%, and marketing decisions became more scientific and precise.

[0023] Example 3: Transportation Route Optimization Decision of a Logistics Company A logistics company faces challenges such as complex cargo transportation route planning, high transportation costs, and low delivery efficiency. This invention utilizes a PaaS-based enterprise management decision-making system for transportation route optimization. The data acquisition and preprocessing module collects cargo order information from the company's order management system, including the origin, destination, cargo weight, and volume; it collects real-time vehicle location, load information, and fuel consumption data from the vehicle management system; and it obtains real-time traffic information and traffic control data from a map service platform. Through ETL tools and API calls, this heterogeneous data is collected, cleaned, and preprocessed to remove invalid data and standardize the data format.

[0024] The data storage and management module stores the processed data in a distributed database. Using a data tiered storage strategy, frequently accessed hot data such as real-time vehicle location and road conditions are stored on high-speed storage media for easy and fast retrieval; while cold data such as historical transportation data are stored on large-capacity, low-cost media.

[0025] Logistics planners used the decision model building and management module to construct a decision model that combines an operations research-based path optimization model with a machine learning prediction model. The machine learning model predicts travel times for different time periods and road segments based on historical transportation data and real-time traffic information. The operations research-based path optimization model combines cargo order information, vehicle load information, and predicted travel times to calculate the optimal transportation route and vehicle scheduling plan, while also considering factors such as fuel costs and vehicle wear and tear to minimize transportation costs.

[0026] The decision analysis and visualization module analyzes transportation data and decision results, visually displaying planned transportation routes and vehicle trajectories through geographic information maps, and presenting key indicators such as transportation costs and delivery timeliness on a dashboard. Logistics managers can monitor the transportation status of each vehicle in real time through the visual dashboard. When unexpected road conditions or vehicle malfunctions occur, the system automatically issues warnings, allowing managers to adjust transportation routes and scheduling plans in a timely manner.

[0027] The user interaction and access control module ensures that personnel in different positions can only access and operate their corresponding data and functions. For example, dispatchers can view and adjust vehicle dispatch tasks, while drivers can only view their own transportation tasks and navigation routes. After using this system, the logistics company's transportation costs decreased by [X]%, delivery timeliness improved by [X]%, and customer complaint rates decreased significantly.

Claims

1. An enterprise management decision-making system based on a PaaS platform, characterized in that: It includes a data acquisition and preprocessing module, a data storage and management module, a decision model construction and management module, a decision analysis and visualization module, and a user interaction and permission management module. The data acquisition and preprocessing module, data storage and management module, decision model construction and management module, decision analysis and visualization module, and user interaction and permission management module are all built on the PaaS platform architecture, and the modules interact and collaborate with each other through the service interfaces provided by the PaaS platform.

2. The enterprise management decision-making system based on a PaaS platform according to claim 1, characterized in that, The data acquisition and preprocessing module is used to collect data from internal business systems and external data sources. It uses ETL tools and API interface calls to clean and preprocess the data in real time during the acquisition process, removing noisy data, correcting erroneous data formats, and performing data standardization.

3. The enterprise management decision-making system based on a PaaS platform according to claim 1, characterized in that, The data storage and management module utilizes the distributed storage technology of the PaaS platform to store data in a scalable distributed database. It adopts a data tiered storage strategy and organizes and manages the data through data directory management and metadata management.

4. The enterprise management decision-making system based on a PaaS platform according to claim 1, characterized in that, The decision model building and management module provides users with a visual decision model building tool, supports rule-based decision models, machine learning models, and operations research-based optimization models, and has decision model version management functionality.

5. The enterprise management decision-making system based on a PaaS platform according to claim 1, characterized in that, The decision analysis and visualization module uses data analysis algorithms and data mining techniques to perform in-depth data analysis, supports real-time and offline data analysis, presents analysis results through various visualization techniques, and has data drill-down functionality.

6. The enterprise management decision-making system based on a PaaS platform according to claim 1, characterized in that, The user interaction and permission management module provides a user-friendly interface that supports multi-terminal access. Through user authentication and permission management mechanisms, it assigns different permission levels to users based on the enterprise's organizational structure and business roles. Users with different permission levels have different operating interfaces and functional visibility.

7. The enterprise management decision-making system based on a PaaS platform according to any one of claims 1-6, characterized in that, The system enables cross-departmental data sharing and collaborative decision-making through unified data storage and management, as well as a visualized decision analysis and interactive interface.

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