Purchase data analysis method and system based on metadata driving

Abstract

The invention discloses a purchase data analysis method and system based on metadata driving, and relates to the technical field of purchase data management, and the method comprises the steps: obtaining data access configuration and data mapping rules of a plurality of purchase data sources, so as to construct an analysis data set; processing target data in the analysis data set based on a preset business rule to obtain index data; in response to a configuration operation on the visual component, generating a visual billboard, the visual billboard being used for displaying a data state; responding to configuration operation on the business rule and the visual billboard; through a configurable data access mode, flexible business rule setting and a visual billboard construction function, business personnel can autonomously adjust data analysis content and a display mode according to purchase strategy changes and market demands, and program codes do not need to be modified by relying on technicians; the service requirement adapting period of the system is obviously shortened, and the maintenance cost is reduced.

Description

Technical Field

[0001] This application relates to the field of procurement data management technology, and in particular to a procurement data analysis method and system based on metadata-driven approaches. Background Technology

[0002] In the steel manufacturing industry, raw material and material procurement management has a critical impact on enterprise cost control. Due to the massive amount of multi-source data involved, and the rapidly changing market prices and supply chain situation, enterprises need to conduct real-time and flexible analysis of procurement data to quickly support business decisions and thereby enhance their core competitiveness.

[0003] Currently, the data analysis systems commonly used in the industry are all built on a pre-fixed rigid architecture. The data model, key analysis indicators, dimensions, and final visualization logic of such systems all need to be predefined and coded by technical personnel during the system development phase. Their overall structure is tightly coupled, and the business logic is encoded in the program, forming a closed and fixed analysis environment.

[0004] When procurement strategies change, business product structure needs adjustment, or new market analysis needs emerge, the existing system cannot quickly adjust its analysis logic. Business personnel need to propose modification requests and rely on IT personnel to redevelop or modify the underlying code of the system. This process not only has a long response cycle and huge investment of manpower and funds, but also has seriously lagging data analysis capabilities, which cannot effectively support the company's rapid decision-making under high-frequency market changes and is difficult to adapt to the dynamic changes in business needs. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a metadata-driven procurement data analysis method and system, which solves the problems of existing systems being unable to quickly adjust their analysis logic and adapt to dynamic changes in business needs.

[0006] In a first aspect, embodiments of this application provide a metadata-driven procurement data analysis method, comprising: acquiring data access configurations and data mapping rules from multiple procurement data sources to construct an analysis dataset; processing target data in the analysis dataset based on preset business rules to obtain indicator data; generating a visualization dashboard in response to configuration operations on a visualization component, the visualization dashboard being used to display data status; updating the driving metadata in response to configuration operations on the business rules and the visualization dashboard to obtain updated driving metadata; and performing data analysis and dashboard rendering based on the updated driving metadata.

[0007] In one feasible implementation, the data access configuration for acquiring multiple procurement data sources includes: acquiring internal enterprise data sources and external enterprise data sources.

[0008] In one feasible implementation, the response to the configuration operation of the visualization component includes: responding to the configuration operation of the visualization component through a graphical configuration interface; the response to the configuration operation of the business rule and the visualization dashboard includes: responding to the configuration operation of the business rule and the visualization dashboard through a graphical configuration interface.

[0009] In one feasible implementation, the processing of target data in the analysis dataset based on preset business rules includes: processing target data in the analysis dataset based on formulas for calculating indicator data and thresholds used to determine business status.

[0010] In one feasible implementation, the processing of target data in the analysis dataset based on preset business rules further includes: performing association queries and data fusion calculations on data tables from different data sources according to user-configured data association rules.

[0011] In one feasible implementation, the configuration operation of the visualization component includes: inputting the component into the visualization dashboard by dragging and dropping, binding the analysis indicators or indicator data to the component, and setting the linkage filtering rules between components.

[0012] Secondly, embodiments of this application provide a metadata-driven procurement data analysis system. The system includes: a configurable data access and integration layer for acquiring data access configurations and data mapping rules from multiple procurement data sources to construct an analysis dataset; a module analysis service layer for processing target data in the analysis dataset based on preset business rules to obtain indicator data; an interactive visualization construction layer for generating a visualization dashboard in response to configuration operations on visualization components, the dashboard displaying data status; and a metadata-driven engine for updating driver metadata in response to configuration operations on the business rules and the visualization dashboard, obtaining updated driver metadata, and performing data analysis and dashboard rendering based on the updated driver metadata.

[0013] In one feasible implementation, the metadata-driven engine includes: a metadata parsing unit for parsing the driving metadata to obtain a parsing result; a query generation unit for generating a data query statement based on the parsing result; a calculation execution unit for scheduling and executing indicator calculation logic based on the parsing result; and a rendering instruction unit for generating interface rendering instructions based on the parsing result.

[0014] In one feasible implementation, the preset analysis functions in the module analysis service layer are configured as independent and callable microservices.

[0015] In one feasible implementation, the procurement data source includes internal enterprise data sources and external enterprise data sources; the internal enterprise data source includes at least one of an enterprise resource planning system and a supplier relationship management system.

[0016] This invention provides a metadata-driven procurement data analysis method and system, which has the following beneficial effects:

[0017] This invention enables business personnel to independently adjust the content and presentation of data analysis based on changes in procurement strategies and market demands through configurable data access methods, flexible business rule settings, and visual dashboard construction functions, without relying on technical personnel to modify program code. It significantly shortens the cycle of the system adapting to business needs, reduces maintenance costs, and ensures that data analysis capabilities keep pace with the pace of enterprise decision-making, effectively supporting rapid response and accurate decision-making in a dynamic market environment. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the implementation of the invention and, together with the description, serve to explain the principles of the embodiments of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0019] Figure 1 A flowchart illustrating a metadata-driven procurement data analysis method provided in an embodiment of the present invention;

[0020] Figure 2 A flowchart for metadata-driven application generation provided in this embodiment of the invention. Detailed Implementation

[0021] 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 some embodiments of the present invention, and not all embodiments. 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.

[0022] To facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art, the technical terms involved in the embodiments of this application will be explained below.

[0023] A metadata-driven engine is a software architecture that designs and runs a system around metadata. Metadata is data that describes the characteristics and relationships of the data itself. This engine dynamically controls the application's behavior, data model, and user interface by reading and interpreting these predefined metadata configurations. It achieves separation of business logic from the underlying code, meaning that when business requirements change, typically only the metadata configuration needs to be updated without modifying and redeploying the program code. This greatly improves the system's flexibility and scalability, enabling rapid adaptation to business changes and significantly reducing long-term software maintenance costs.

[0024] An Application Programming Interface (API) is a set of well-defined rules, protocols, and tools for building and integrating software applications. It acts as a bridge between different software components, allowing them to communicate and exchange data without needing to understand each other's internal implementation details. By calling specific functions or services provided by the API, developers can efficiently reuse existing functionality, thus avoiding redundant development. It promotes software modularity, seamless integration between systems, and efficient collaboration between services, and is a key technology for achieving interoperability and improving development efficiency in modern software development.

[0025] Currently, the data analysis systems commonly used in the industry are all built on a pre-fixed rigid architecture. The data model, key analysis indicators, dimensions, and final visualization logic of such systems all need to be predefined and coded by technical personnel during the system development phase. Their overall structure is tightly coupled, and the business logic is encoded in the program, forming a closed and fixed analysis environment.

[0026] When procurement strategies change, business product structure needs adjustment, or new market analysis needs emerge, the existing system cannot quickly adjust its analysis logic. Business personnel need to propose modification requests and rely on IT personnel to redevelop or modify the underlying code of the system. This process not only has a long response cycle and huge investment of manpower and funds, but also has seriously lagging data analysis capabilities, which cannot effectively support the company's rapid decision-making under high-frequency market changes and is difficult to adapt to the dynamic changes in business needs.

[0027] This invention provides a metadata-driven procurement data analysis method and system to solve the problem that existing systems cannot quickly adjust their analysis logic and are difficult to adapt to dynamic changes in business needs.

[0028] The embodiments of this application will now be described with reference to the accompanying drawings.

[0029] This application provides a metadata-driven procurement data analysis method. Please refer to [link / reference]. Figure 1 , Figure 1This invention provides a flowchart illustrating a metadata-driven procurement data analysis method, which includes:

[0030] Step 101: Obtain the data access configuration and data mapping rules of multiple procurement data sources to build an analysis dataset; deploy the data analysis component on the enterprise server or cloud platform, establish a connection with multiple procurement data sources through the application programming interface (API), define the access configuration and data mapping rules of the procurement data sources through the graphical configuration interface, clean and transform the data, and then build the analysis dataset.

[0031] By connecting and graphically configuring multiple procurement data source application programming interfaces, complex data integration is transformed into intuitive configuration operations, significantly reducing the technical threshold and improving the efficiency of dataset construction. At the same time, heterogeneous data is cleaned and transformed through customizable data mapping rules to ensure the consistency, accuracy and reliability of the final analysis dataset, providing a data foundation for high-value procurement data analysis.

[0032] In some embodiments, the data access configuration for acquiring multiple procurement data sources includes: acquiring internal enterprise data sources and external enterprise data sources; for example, internal enterprise data sources can be Enterprise Resource Planning (ERP) systems, Supplier Relationship Management (SRM) systems, etc.; external enterprise data sources can be industry websites, commodity data platforms, etc.; by integrating internal and external heterogeneous data sources, a comprehensive and multi-dimensional procurement analysis view can be constructed, providing a solid data foundation for accurate cost analysis, supplier evaluation, and market trend forecasting. In addition, by correlating and comparing internal transaction data with external benchmark data, procurement price deviations can be effectively identified, and potential risks in the supply chain can be discerned, thereby improving the scientific and forward-looking nature of enterprise procurement decisions.

[0033] By defining data mapping rules, raw data fields from different sources with inconsistent naming and formats can be transformed into a set of data with unified semantic definitions and standard formats that meet business analysis needs. This can effectively resolve semantic ambiguities and structural conflicts between multi-source data, ensuring the consistency of integrated data in logic and format, thereby providing a high-quality and highly reliable data foundation for performing accurate cross-source correlation queries and comparative analyses.

[0034] Taking alloy procurement as an example, a specific example is as follows: Data analysis components are deployed on enterprise servers or cloud platforms, connecting to the enterprise's internal Enterprise Resource Planning (ERP) system and Supplier Relationship Management (SRM) system via Application Programming Interfaces (APIs), as well as external industry information websites and commodity data platforms, to build a comprehensive data collection network covering internal and external data sources. Data access, usage, and visualization dashboard configuration permissions are configured for target users, including but not limited to procurement managers. Internal and external data sources are configured through the data management interface. Internal data sources use pre-built connectors to select the purchase order table, supplier information table, material master data table, and inbound inspection record table from the ERP system as internal data sources. External data sources are accessed through third-party APIs to obtain daily market price data for ferrovanadium and ferroniobium from the Yangtze River Nonferrous Metals Network. Data mapping relationships are established in the graphical user interface, associating the supplier ID field in the purchase order table with the supplier information table, and associating the material code with the material master data table. The associated data undergoes automated cleaning and transformation to construct a dataset suitable for procurement analysis.

[0035] By deeply integrating internal and external data, and using pre-built connectors and standardized interfaces, the complexity of system integration is significantly reduced. The graphical user interface greatly lowers the technical threshold, automated data processing effectively ensures data quality, real-time updated market data provides dynamic reference for procurement decisions, the constructed complete dataset supports multi-dimensional analysis needs, flexible deployment solutions adapt to different enterprise sizes, and the access control mechanism promotes collaborative work while ensuring data security. Overall, the automation and intelligence level of procurement data management is improved, providing comprehensive and reliable data support for enterprise procurement strategy formulation and daily decision-making.

[0036] Step 102: Based on preset business rules, the target data in the analysis dataset is processed to obtain indicator data. By transforming business rules into executable data processing logic, the system can automatically generate core indicators such as procurement price rationality assessment, supplier performance scoring, and procurement cost ratio analysis, significantly improving the efficiency and accuracy of data analysis. Automatic processing based on preset rules ensures the consistency and repeatability of indicator calculations, effectively avoiding errors that may be caused by manual operation. The generated indicator data provides a quantitative basis for procurement decisions, supports in-depth business applications such as procurement price monitoring, supplier hierarchical management, and procurement strategy optimization, and significantly improves the refinement and intelligence of procurement management.

[0037] In some embodiments, processing the target data in the analysis dataset based on preset business rules includes: processing the target data in the analysis dataset based on formulas for calculating indicator data and thresholds for determining business status; this processing can automatically generate key indicators such as procurement price index, supplier on-time delivery rate, and quality pass rate, and identify business statuses such as price anomalies and supplier performance levels in real time based on preset thresholds, significantly improving the intelligence and real-time performance of data analysis; formula calculation can ensure the accuracy and consistency of indicator generation, effectively eliminating subjective biases caused by manual calculation; the status determination based on thresholds provides clear early warnings and classification criteria for procurement decisions, enabling data-driven refined management of business links such as procurement price monitoring, supplier evaluation, and procurement strategy optimization, significantly improving the scientific nature and decision-making efficiency of procurement management.

[0038] In some embodiments, processing the target data in the analysis dataset based on preset business rules further includes: performing association queries and data fusion calculations on data tables from different data sources according to user-configured data association rules; this process can automatically generate key indicators such as procurement price index and supplier comprehensive performance score, and achieve in-depth fusion analysis of procurement cost and quality data through cross-source data association, which greatly improves the dimensional completeness and business insight of data analysis; the data fusion mechanism based on configurable association rules ensures the flexibility and accuracy of multi-source heterogeneous data integration.

[0039] Taking the ferrovanadium purchase price difference index as an example, this index is used to quantify the deviation between the unit price of ferrovanadium alloy purchased by an enterprise and the fair market price. It is a key performance indicator for assessing the enterprise's ability to control procurement costs and negotiate prices. The formula for calculating the purchase price difference is: (Purchase order price including tax - Average price on Changjiang.com on the day) / Average price on Changjiang.com on the day. 100. The core business rule of this formula is to calculate the percentage deviation of each purchase order price for a specific material, ferrovanadium, from the market benchmark price. The business logic requires that only purchase orders with a status of "received" or "completed" be counted, and that the purchase order date be completely consistent with the market price date. While defining the purchase price difference indicator, threshold rules for judging the business status need to be configured. For example: when the calculated price difference is less than or equal to -2%, it is marked as advantageous purchase; when the price difference is between -2% and 3%, it is marked as reasonable range; when the price difference is greater than or equal to 3%, an abnormal price difference warning is triggered.

[0040] The specific implementation process is as follows: The unit price and order date of the specified material code are obtained from the purchase order table; the average market price for the corresponding date is obtained from the Yangtze River Nonferrous Metals Network price table; the purchase order date and market price date are associated; the associated data are calculated according to a preset purchase price difference calculation formula to generate the price difference result for each order; the price difference result is compared with preset threshold rules, and the corresponding business status is automatically marked for each purchase order. This process not only generates quantifiable performance indicators and realizes real-time, automatic identification and classification of procurement business status, but also provides efficient and accurate data support for procurement cost monitoring, supplier performance evaluation, and immediate intervention in abnormal procurement, significantly improving the intelligence level and risk control capabilities of procurement management.

[0041] Taking the on-time delivery rate as an example, this indicator is used to quantify the supplier's ability to deliver goods on time as agreed in the contract. It is a key indicator for evaluating the supplier's performance and the stability of the supply chain. The formula for calculating the on-time delivery rate is: (Number of on-time delivered orders / Total number of delivered orders). 100. The core business rule is to count the proportion of all completed orders within a specified period whose actual delivery date is no later than the required delivery date. When defining the on-time delivery rate indicator, threshold rules for judging the business status need to be configured. For example, when the on-time delivery rate is higher than 98%, it is marked as excellent; between 95% and 98%, it is marked as good; and when it is lower than 95%, an improvement warning status is triggered.

[0042] The specific implementation process is as follows: The purchase order table and receiving record table are linked from the enterprise's internal data source. The delivery date is required to be taken from the agreed delivery date field in the purchase order table. The actual arrival date is obtained from the receiving record table, but needs to be determined according to business rules: if the final completion date of the order is used, the latest receipt date among all receiving records for that order is taken; if the first arrival date is used, the earliest receipt date is taken. The purchase order table and receiving record table are linked through the order number, and based on the above date determination logic, conditional statements are used to mark each completed receipt order as on-time. When the actual final receipt date is earlier than or equal to the required delivery date, it is marked as on-time; otherwise, it is marked as untimely. The number of on-time marked orders is used as the numerator, and the total number of all completed receipt orders is used as the denominator. A percentage calculation is performed and the result is output. The result is compared with a preset threshold rule, and the corresponding business status is automatically marked for each delivery order. Through this process, accurate quantification and automated monitoring of supplier delivery performance can be achieved, providing continuous and reliable data support for supplier hierarchical management, procurement strategy optimization, and supply chain risk early warning.

[0043] Step 103: In response to the configuration operation of the visualization component, a visualization dashboard is generated. The visualization dashboard is used to display the data status; it realizes the deep integration of data visualization and analysis process. Users can quickly build monitoring views that meet specific management scenarios by configuring the dashboard layout and display content themselves; the visualization dashboard intuitively presents the status and change patterns of indicators through color block annotation, trend curves and other methods, which greatly improves the efficiency of data perception; the dashboard update mechanism based on real-time data-driven mechanism ensures the immediate visibility of business status and the rapid identification of abnormal situations, effectively supporting procurement performance monitoring, supplier evaluation and management decision-making, and significantly improving data utilization efficiency and business insight.

[0044] In some embodiments, responding to configuration operations on visualization components includes: responding to configuration operations on visualization components through a graphical configuration interface; enabling users to define dashboard layouts and data bindings independently without coding, significantly reducing the technical threshold; intuitively presenting data patterns through visual elements such as color block alerts and trend curves, improving information perception efficiency; and ensuring that business status is instantly visible and anomalies are quickly identified based on a real-time data-driven update mechanism, thereby improving the flexibility and accuracy of data analysis.

[0045] In some embodiments, the configuration operations of visualization components include: inputting components into the visualization dashboard via drag and drop, binding analysis indicators or indicator data to components, and setting linkage filtering rules between components; supporting users to build personalized data views independently in a zero-coding manner, greatly reducing the construction threshold of visualization dashboards; ensuring the accuracy and relevance of data display through flexible binding of indicator data to components; and leveraging the linkage filtering mechanism between components, user operations on any visualization component will automatically trigger synchronous filtering and updates of other related components, realizing the correlation analysis of multi-dimensional data, significantly improving the exploration efficiency and analysis depth of multi-dimensional data, and providing highly integrated and real-time responsive visualization support for procurement monitoring and decision-making.

[0046] Taking an alloy procurement monitoring visualization dashboard as an example, a specific process involves: adding a time-series line chart, binding the data to the configured ferrovanadium procurement price difference indicator, and displaying monthly trend changes with the order date as the horizontal axis; adding a bar chart, binding the data to the on-time delivery rate indicator, and displaying delivery status by supplier name on the horizontal axis; configuring the dashboard component and setting alarm rules, displaying a red alert when the overall procurement price difference exceeds 5%; adding a data table component to display detailed orders with price differences exceeding 3%, including order number, supplier, purchaser, and purchase quantity; setting a time filter component and enabling global filtering, allowing all charts in the dashboard to dynamically change according to the selected time range; through this visualization configuration process, centralized monitoring of procurement indicators, real-time early warning of abnormal situations, and multi-dimensional data correlation analysis can be achieved, providing intuitive and comprehensive visualization support for procurement decisions.

[0047] Step 104: In response to configuration operations on business rules and visual dashboards, update the driver metadata to obtain the updated driver metadata. Perform data analysis and dashboard rendering based on the updated driver metadata. This ensures real-time synchronization between business rule adjustments and data display effects. Parameter modifications or component configuration changes can be reflected in the analysis results and dashboard interface. This improves the timeliness of data monitoring and management flexibility. Users can obtain the latest analysis view without waiting for technical team intervention or system redeployment. At the same time, based on real-time updated data and rules, managers can quickly identify procurement anomalies, evaluate supplier performance, and optimize procurement strategies, effectively improving the accuracy and decision-making efficiency of procurement management.

[0048] In some embodiments, responding to configuration operations on business rules and visual dashboards includes: responding to configuration operations on business rules and visual dashboards through a graphical configuration interface; responding to user configuration operations on business rule thresholds, indicator calculation formulas, and visualization components in the graphical configuration interface, the system automatically updates the corresponding driver metadata, and triggers the re-execution of the data analysis process and the dynamic rendering of the visual dashboard in real time based on the updated metadata; this allows business personnel to independently adjust monitoring rules and display content without technical support, greatly improving the system's flexibility and response speed in adapting to business changes.

[0049] After the configuration of the visual dashboard is completed, the release process is executed in response to the target user's release command. The metadata-driven engine automatically parses all configuration information, generates corresponding data query statements, indicator calculation logic, and interface rendering instructions, and completes the real-time release of the dashboard. After successful release, the target user can immediately access the visual dashboard and monitor the status of various key indicators of alloy procurement in real time. When business needs change, such as adding a quality pass rate assessment requirement one month later, the calculation formula can be added to the indicator management interface as (qualified warehouse entry quantity / total arrival quantity). The metric 100 can be added to the existing visualization dashboard by dragging and dropping, thus expanding the analysis functionality. The entire optimization process requires no coding or technical support from the IT department, significantly improving the system's agility in adapting to business changes and enabling procurement managers to respond independently and quickly to various new data analysis needs.

[0050] Please see Figure 2 , Figure 2 The metadata-driven application generation flowchart provided in this embodiment of the invention includes a configuration phase and a metadata-driven engine execution phase. In the configuration phase, business personnel complete the following operations through a visual interface: First, define the access configuration for multi-source data such as ERP systems, SRM systems, and market data, including data connection parameters and access permission settings; then, configure calculation indicators and business rules such as purchase amount statistics and supplier evaluation, and define the calculation formulas and data processing logic for each indicator; next, select functional modules such as trend analysis and comparative analysis from the system's analysis module library and assemble them by dragging and dropping; finally, design the overall layout of the visual dashboard, configure the correspondence between various chart components and data indicators, and set style themes; after configuration, the system integrates all settings to generate structured driving metadata.

[0051] During the metadata-driven engine execution phase, the system engine first parses the received driver metadata and, based on the metadata content, schedules the corresponding data integration service to access multi-source data, performing data extraction, cleaning, and fusion calculations. Then, it schedules the analysis model service to perform indicator calculations and data processing according to business rules. Finally, it schedules the visualization rendering service to dynamically generate interactive charts based on layout configuration, assembling them into a complete analytical application. Business personnel monitor and analyze data through the generated analysis dashboards. When business needs change or new analytical requirements are discovered, they can return to the configuration phase to adjust data sources, modify business rules, or reorganize analysis modules in a low-code manner, forming a closed-loop management process for continuous optimization. Throughout this process, the data-driven engine automatically updates the analytical application content by parsing the updated driver metadata.

[0052] This application provides a metadata-driven procurement data analysis system, comprising: a configurable data access and integration layer for acquiring data access configurations and data mapping rules from multiple procurement data sources to construct an analysis dataset; a module analysis service layer for processing target data in the analysis dataset based on preset business rules to obtain indicator data; an interactive visualization construction layer for generating visualization dashboards in response to configuration operations on visualization components, the visualization dashboards being used to display data status; and a metadata-driven engine for updating driver metadata in response to configuration operations on business rules and visualization dashboards, obtaining updated driver metadata, and performing data analysis and dashboard rendering based on the updated driver metadata.

[0053] Through collaborative operations at all levels, the system achieves configurable and automated management of the entire procurement data analysis process. It can flexibly access multi-source data from both internal and external sources and build a unified analysis dataset. It supports defining business rules through a graphical interface to automatically generate key performance indicators (KPIs), and provides interactive dashboard construction functionality to intuitively display data status. Driven by a metadata-driven engine, any modification to business rules or dashboard configurations can trigger the re-execution of the data analysis process and dynamic updates to visualization content in real time, ensuring that system output remains synchronized with business needs. This solution significantly reduces the technical barriers and maintenance costs of procurement data analysis, enhances the system's agility in responding to business changes, and provides efficient, accurate, and timely data support for procurement cost control, supplier management, and strategic decision-making.

[0054] In some embodiments, the metadata-driven engine includes: a metadata parsing unit for parsing driver metadata to obtain parsing results; a query generation unit for generating data query statements based on the parsing results; a calculation execution unit for scheduling and executing indicator calculation logic based on the parsing results; and a rendering instruction unit for generating interface rendering instructions based on the parsing results. The metadata-driven engine automatically generates data query statements, schedules indicator calculations, and generates interface rendering instructions by parsing updated driver metadata. This achieves full automation from configuration changes to dashboard updates, significantly improving the system's efficiency in responding to changes in business needs. Any adjustments users make to business rules or dashboard settings can be reflected in the data analysis results and visualizations in real time, significantly reducing maintenance costs and operational delays, and providing efficient and accurate data decision support for procurement management.

[0055] In some embodiments, the preset analysis functions in the module analysis service layer are configured as independent and callable microservices; this enables each analysis function to run, expand, and maintain independently, significantly improving the system's flexibility and manageability; when a specific analysis function needs to be added or modified, only the corresponding microservice needs to be adjusted without affecting other parts of the system, effectively reducing the complexity of system updates and maintenance. At the same time, this design supports the rapid combination and reuse of different analysis modules, providing a highly scalable technical foundation for procurement data analysis and enabling rapid response to various customized analysis needs.

[0056] In some embodiments, the procurement data source includes internal and external data sources; the internal data source includes at least one of an enterprise resource planning (ERP) system and a supplier relationship management (SRM) system; the internal data source includes at least an ERP system and a SRM system, while the external data source covers various market price platforms and industry websites. By integrating internal and external data, the system can simultaneously acquire procurement execution data and market information, forming a complete procurement data view. This multi-source data fusion mechanism significantly improves the comprehensiveness and accuracy of data analysis.

[0057] This invention enables business personnel to independently adjust the content and presentation of data analysis based on changes in procurement strategies and market demands through configurable data access methods, flexible business rule settings, and visual dashboard construction functions, without relying on technical personnel to modify program code. It significantly shortens the cycle of the system adapting to business needs, reduces maintenance costs, and ensures that data analysis capabilities keep pace with the pace of enterprise decision-making, effectively supporting rapid response and accurate decision-making in a dynamic market environment.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A metadata driven based procurement data analysis method, characterized in that, The method comprises the following steps: acquiring data access configurations and data mapping rules of a plurality of procurement data sources to construct an analysis dataset; processing target data in the analysis dataset based on preset business rules to obtain index data; in response to a configuration operation on a visualization component, generating a visualization dashboard for displaying data states; in response to configuration operations on the business rules and the visualization dashboard, updating driving metadata to obtain updated driving metadata, and performing data analysis and dashboard rendering based on the updated driving metadata.

2. The metadata driven procurement data analysis method of claim 1, wherein, The acquisition of the data access configurations of the plurality of procurement data sources comprises: acquiring enterprise internal data sources and enterprise external data sources.

3. The metadata-driven procurement data analysis method according to claim 1, wherein: the response to the configuration operation on the visualization component comprises:

4. The metadata driven procurement data analysis method of claim 1, wherein, responding to the configuration operation on the visualization component through a graphical configuration interface; 5. The metadata driven procurement data analysis method of claim 4, wherein, the response to the configuration operations on the business rules and the visualization dashboard comprises:

6. The metadata driven procurement data analysis method of claim 1, wherein, responding to the configuration operations on the business rules and the visualization dashboard through the graphical configuration interface.

7. A metadata driven based procurement data analysis system characterized in that, The processing of the target data in the analysis dataset based on the preset business rules comprises: processing the target data in the analysis dataset based on formulas for calculating index data and threshold values for determining business states. The processing of the target data in the analysis dataset based on the preset business rules further comprises: performing associated queries and data fusion calculations on data tables from different data sources according to user-configured data association rules. The configuration operation on the visualization component comprises:

8. The metadata driven procurement data analysis system of claim 7, wherein, inputting components to the visualization dashboard through a drag-and-drop manner, binding analysis indexes or index data with the components, and setting linkage filtering rules between the components. The system comprises: a configurable data access and integration layer for acquiring data access configurations and data mapping rules of a plurality of procurement data sources to construct an analysis dataset; a module analysis service layer for processing target data in the analysis dataset based on preset business rules to obtain index data; an interactive visualization construction layer for generating a visualization dashboard in response to a configuration operation on a visualization component, the visualization dashboard being used for displaying data states; 9. The metadata driven procurement data analysis system of claim 7, wherein, a metadata driving engine for updating driving metadata in response to configuration operations on the business rules and the visualization dashboard to obtain updated driving metadata, and performing data analysis and dashboard rendering based on the updated driving metadata. The metadata driving engine comprises: a metadata analysis unit for analyzing the driving metadata to obtain an analysis result; a query generation unit for generating a data query statement according to the analysis result; a calculation execution unit for scheduling and executing index calculation logic according to the analysis result; a rendering instruction unit for generating an interface rendering instruction according to the analysis result. The preset analysis functions in the module analysis service layer are configured as independent and callable microservices.

10. The metadata driven procurement data analysis system of claim 7, wherein, The procurement data sources include enterprise internal data sources and enterprise external data sources; the enterprise internal data sources include at least one of an enterprise resource planning system and a supplier relationship management system. The procurement data sources include enterprise internal data sources and enterprise external data sources; the enterprise internal data sources include at least one of an enterprise resource planning system and a supplier relationship management system. The procurement data sources

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