Multi-dimensional operation data analysis and method based on BI system

Abstract

The invention discloses a multi-dimensional operation data analysis and method based on a BI system, and relates to the technical field of multi-dimensional operation data analysis, and the system comprises a data integration module which is in butt joint with real-time data of a business system through a BI platform; the data logic processing module adopts a streaming computing framework to compute and update data in real time, and the timed task layer configures periodic batch tasks and executes historical data aggregation, trend analysis and cross-module association computation; the data warehouse module is used for storing the processed data and carrying out classified storage according to business dimensions to form a unified business data warehouse; the distributed computing engine module is used for scheduling and distributing tasks; and the visual report module is used for generating a multi-dimensional operation analysis report. Through real-time data integration, streaming computation and timed task configuration, efficient data processing and personalized requirements are supported, large-scale parallel computation and load balancing are supported, and disaster recovery capacity is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-dimensional business data analysis, and in particular to multi-dimensional business data analysis and methods based on a BI system. Background Art

[0002] As the level of informatization of modern enterprises continues to increase, various business systems have generated a large amount of real-time data. In order to effectively use this data for decision-making and optimize enterprise operations, traditional report analysis methods can no longer meet the increasingly complex analysis needs. Therefore, multi-dimensional business data analysis based on BI systems has emerged and has become an important tool for improving the quality and efficiency of enterprise decision-making. BI systems can help enterprises integrate data from various business systems, and through multi-dimensional analysis, real-time data processing and intelligent report display, help managers understand the business status of all levels of the enterprise in a timely manner, so as to make quick decisions. Specifically, BI systems ensure the real-time, accuracy and efficient processing of data through the collaborative work of modules such as data integration, logical processing, storage and analysis, and ultimately generate various business analysis reports to provide comprehensive data support for enterprises.

[0003] In multi-dimensional business data analysis, technical modules such as data integration, real-time data processing, distributed computing, data warehouse and visual reporting are crucial. Through the integrated application of these technologies, the BI system can process massive data from different business systems and support multi-dimensional and dynamic analysis needs. Summary of the Invention

[0004] In order to solve the above technical problems, a multi-dimensional business data analysis and method based on the BI system is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] Multi-dimensional business data analysis based on the BI system, including:

[0007] Data Integration Module: This module is used to connect to the real-time data of the business system through the BI platform, extract the original data using ETL tools, perform data cleaning, deduplication, format standardization, and perform real-time data updates;

[0008] Data logic processing module: The data logic processing module includes a real-time computing layer and a scheduled task layer. The real-time computing layer uses a streaming computing framework to calculate and update data in real time. The scheduled task layer configures periodic batch tasks to perform historical data aggregation, trend analysis, and cross-module correlation calculations.

[0009] Data warehouse module: The data warehouse module is used to store processed data and classify and store it according to business dimensions to form a unified business data warehouse and support multi-dimensional analysis;

[0010] Distributed computing engine module: The distributed computing engine module is used to schedule and allocate tasks, make data consistent and perform task parallel processing;

[0011] Visualization report module: The visualization report module is used to generate multi-dimensional business analysis reports, supports user-defined analysis dimensions, and updates report data in real time.

[0012] Preferably, the data integration module specifically includes:

[0013] Data stream processing component: processes real-time data through a streaming computing framework to ensure that data can be updated in a timely manner when it changes;

[0014] Data extraction component: Use ETL tools to extract real-time data from various business systems and perform cleansing and format standardization to ensure data quality.

[0015] Preferably, the data logic processing module specifically includes:

[0016] Real-time computing layer: used to process data update events in real time through the streaming computing framework and trigger corresponding analysis tasks;

[0017] Scheduled task layer: Configure scheduled tasks, aggregate and analyze historical data, use a sliding aggregation algorithm based on a time window to aggregate data, and use time series analysis formulas to generate trend analysis results.

[0018] Among them, the sliding aggregation algorithm formula is:

[0019]

[0020] Where A(t) is the average value of the sliding window at time t, w is the size of the window, t is the current time, i is the time index, and D i Represents the data value at time point i;

[0021] Among them, the time series analysis formula is:

[0022] Y(t)=β0+β1t

[0023] Where Y(t) represents the value of the dependent variable at time t, β0 and β are the parameters of linear regression, β1 represents the growth trend of the data. If β1>0, the data shows an increasing trend, and if β1<0, it shows a decreasing trend. t represents the time variable.

[0024] Preferably, the data warehouse module includes:

[0025] Data storage component: stores business data based on a distributed database, and shards the data by dimension based on the business module, supporting high concurrent access;

[0026] Data backup component: Ensures high data availability and disaster recovery capabilities through master-slave replication and redundant backup strategies.

[0027] Preferably, the distributed computing engine module includes:

[0028] Task scheduler: used to dynamically assign tasks to appropriate computing nodes based on task priority and computational complexity to achieve load balancing;

[0029] Resource management component: Dynamically adjusts the allocation strategy of computing resources to optimize the utilization efficiency of system resources.

[0030] Preferably, the visual report module includes:

[0031] Data display component: supports the generation of analytical reports covering multiple dimensions including sales, customers, projects, delivery, inventory, and procurement;

[0032] Visual interface: Users can view real-time reports through charts and trend graphs, and customize dimensions for analysis.

[0033] An intelligent multi-dimensional business data analysis method based on a BI system includes the following steps:

[0034] S1: Build a data integration layer to connect real-time data from ERP, OA, MES, PLM, and CRM business systems through the BI platform. Use ETL tools to extract, clean, and standardize data formats, and store the data in a distributed database.

[0035] S2: Build a data logic processing layer, which includes a real-time computing layer and a scheduled task layer. The real-time computing layer processes data in real time using a streaming computing framework, while the scheduled task layer performs historical data aggregation and trend analysis based on a preset period.

[0036] S3: Dynamically allocates computing tasks to appropriate computing nodes based on task type, priority, and resource requirements to optimize resource utilization;

[0037] S4: Generate multi-dimensional analysis reports. Use the BI platform to generate business analysis reports in sales, customers, projects, and delivery areas, and support user-defined dimensions.

[0038] S5: Update report data in real time. The system generates and updates reports by monitoring data changes in real time and automatically triggering corresponding analysis tasks.

[0039] Preferably, the task allocation process in S3 includes:

[0040] Based on the computing requirements of the tasks and the performance of the nodes, the task allocation algorithm is used to reasonably allocate tasks to ensure system load balancing and improve parallel computing capabilities;

[0041] Among them, the task allocation algorithm formula is:

[0042]

[0043] Where, L(N j ) is allocated to computing node N j Load capacity, N j represents the jth computing node, T i represents the i-th task in the task set T, T j represents the set of tasks assigned to the jth node, C(T i ) represents task T i Computational requirements;

[0044] Based on the load of the node, the load difference of each node is minimized to ensure load balancing.

[0045] Preferably, the real-time report updating process in S5 includes:

[0046] The system identifies the updated object type through the data change log, triggers data processing tasks in a timely manner, updates the business analysis data of relevant dimensions, and synchronizes reports through the data warehouse.

[0047] Preferably, the system further comprises:

[0048] Intelligent task allocation algorithm module: Dynamically optimizes task allocation strategies based on the relevance between tasks and computing resources to improve the system's computing efficiency and response speed.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The multi-dimensional business data analysis based on the BI system proposed in this invention improves decision-making efficiency and accuracy. Through real-time data integration, streaming computing and scheduled task configuration, it supports efficient data processing and personalized needs. The system ensures data quality and consistency, supports large-scale parallel computing and load balancing, enhances disaster recovery capabilities, and the visual reporting module updates data in real time to provide management with accurate decision-making basis. At the same time, through all-round business analysis, the system helps enterprises optimize operations from multiple dimensions, discover problems and adjust strategies in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a system framework diagram of the present invention;

[0052] Figure 2 It is a step flow framework diagram of the present invention. DETAILED DESCRIPTION

[0053] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0054] Reference Figure 1 As shown, the multi-dimensional business data analysis based on the BI system is characterized by including:

[0055] Data Integration Module: The data integration module is used to connect to the real-time data of the business system through the BI platform, extract the original data using ETL tools, perform data cleaning, deduplication, format standardization, and perform real-time data updates;

[0056] Data logic processing module: The data logic processing module includes a real-time computing layer and a scheduled task layer. The real-time computing layer uses a streaming computing framework to perform real-time calculations and update data. The scheduled task layer configures periodic batch tasks to perform historical data aggregation, trend analysis, and cross-module correlation calculations.

[0057] Data warehouse module: The data warehouse module is used to store processed data and classify and store it according to business dimensions to form a unified business data warehouse and support multi-dimensional analysis;

[0058] Distributed computing engine module: The distributed computing engine module is used to schedule and distribute tasks, make data consistent, and perform task parallel processing;

[0059] Visual reporting module: The visual reporting module is used to generate multi-dimensional business analysis reports, supports user-defined analysis dimensions, and updates report data in real time.

[0060] The data integration module specifically includes:

[0061] Data stream processing component: processes real-time data through a streaming computing framework to ensure that data can be updated in a timely manner when it changes;

[0062] Data extraction component: Use ETL tools to extract real-time data from various business systems and perform cleansing and format standardization to ensure data quality;

[0063] Real-time data updates ensure that the system can quickly respond to business changes, greatly improving the timeliness and accuracy of data. Especially for dynamically changing business environments, the introduction of streaming computing enables the system to process and respond to various types of dynamic data in real time, greatly improving the efficiency of data processing and the timeliness of business decisions.

[0064] The data logic processing module specifically includes:

[0065] Real-time computing layer: used to process data update events in real time through the streaming computing framework and trigger corresponding analysis tasks;

[0066] Scheduled task layer: Configure scheduled tasks, aggregate and analyze historical data, use a sliding aggregation algorithm based on a time window to aggregate data, and use time series analysis formulas to generate trend analysis results.

[0067] Among them, the sliding aggregation algorithm formula is:

[0068]

[0069] Where A(t) is the average value of the sliding window at time t, w is the size of the window, t is the current time, i is the time index, and D i Represents the data value at time point i;

[0070] Among them, the time series analysis formula is:

[0071] Y(t)=β0+β1t

[0072] Where Y(t) represents the value of the dependent variable at time t, β0 and β are the parameters of linear regression, β1 represents the growth trend of the data. If β1>0, the data shows an increasing trend, and if β1<0, it shows a decreasing trend. t represents the time variable.

[0073] The introduction of sliding aggregation and time series analysis algorithms enables the system to not only process real-time data, but also predict future trends through historical data analysis, thereby enhancing the foresight of decision-making.

[0074] The data warehouse module includes:

[0075] Data storage component: stores business data based on a distributed database, and shards the data by dimension based on the business module, supporting high concurrent access;

[0076] Data backup component: Ensures high data availability and disaster recovery capabilities through master-slave replication and redundant backup strategies;

[0077] Through distributed storage and backup strategies, not only is high data availability ensured, but stronger data recovery capabilities are also provided, ensuring the continuous operation of the system in the event of a failure.

[0078] The distributed computing engine module includes:

[0079] Task scheduler: used to dynamically assign tasks to appropriate computing nodes based on task priority and computational complexity to achieve load balancing;

[0080] Resource management component: dynamically adjusts the allocation strategy of computing resources to optimize the utilization efficiency of system resources;

[0081] Through intelligent scheduling and dynamic resource management, the system's parallel computing capability and resource utilization are improved, so that computing tasks can be efficiently and balancedly distributed to each node.

[0082] The visual reporting module includes:

[0083] Data display component: supports the generation of analytical reports covering multiple dimensions including sales, customers, projects, delivery, inventory, and procurement;

[0084] Visual interface: Users can view real-time reports through charts and trend graphs, and customize dimensions for analysis;

[0085] The introduction of visualization functions makes complex data easy to understand. Users can flexibly select analysis dimensions and present them through intuitive charts, which improves the ease of use of data analysis and user experience.

[0086] Reference Figure 2 As shown, an intelligent multi-dimensional business data analysis method based on a BI system is characterized by comprising the following steps:

[0087] S1: Build a data integration layer to connect real-time data from ERP, OA, MES, PLM, and CRM business systems through the BI platform. Use ETL tools to extract, clean, and standardize data formats, and store the data in a distributed database.

[0088] S2: Build a data logic processing layer, which includes a real-time computing layer and a scheduled task layer. The real-time computing layer processes data in real time using a streaming computing framework, while the scheduled task layer performs historical data aggregation and trend analysis based on a preset period.

[0089] S3: Dynamically allocates computing tasks to appropriate computing nodes based on task type, priority, and resource requirements to optimize resource utilization;

[0090] S4: Generate multi-dimensional analysis reports. Use the BI platform to generate business analysis reports in sales, customers, projects, and delivery areas, and support user-defined dimensions.

[0091] S5: Update report data in real time. The system monitors data changes in real time and automatically triggers corresponding analysis tasks to generate and update reports.

[0092] Through real-time data integration and intelligent task allocation, resource utilization and decision-making support processes are optimized, especially in the real-time analysis and report generation of multi-dimensional business data.

[0093] The task allocation process in S3 includes:

[0094] Based on the computing requirements of the tasks and the performance of the nodes, the task allocation algorithm is used to reasonably allocate tasks to ensure system load balancing and improve parallel computing capabilities;

[0095] Among them, the task allocation algorithm formula is:

[0096]

[0097] Where, L(N j ) is allocated to computing node N j Load capacity, N j represents the jth computing node, T i represents the i-th task in the task set T, T j represents the set of tasks assigned to the jth node, C(T i ) represents task T i Computational requirements;

[0098] Based on the node load, the load difference of each node is minimized to ensure load balancing;

[0099] According to the computing requirements of the tasks and the performance of the nodes, the task allocation algorithm is used to reasonably allocate tasks to ensure system load balancing and improve parallel computing capabilities. The introduction of the task allocation algorithm optimizes the resource utilization of computing nodes through a reasonable load balancing mechanism and ensures the smooth processing of high-concurrency tasks.

[0100] The real-time report update process in S5 includes:

[0101] The system identifies the updated object type through data change logs, triggers data processing tasks in a timely manner, updates business analysis data of relevant dimensions, and synchronizes reports through the data warehouse;

[0102] The system identifies the updated object type through data change logs, triggers data processing tasks in a timely manner, updates the business analysis data of relevant dimensions, and synchronizes reports through the data warehouse. This process ensures that the report data can quickly reflect business changes. The system's real-time update mechanism allows managers to always make decisions based on the latest data.

[0103] The system also includes:

[0104] Intelligent task allocation algorithm module: Dynamically optimizes task allocation strategies based on the relevance between tasks and computing resources to improve the system's computing efficiency and response speed;

[0105] Based on the correlation between tasks and computing resources, the task allocation strategy is dynamically optimized to improve the system's computing efficiency and response speed. The intelligent task allocation algorithm is optimized based on the dependencies between tasks and the actual situation of resources, which improves the system's flexibility and response speed when handling complex business and ensures efficient task processing.

[0106] Example 1:

[0107] Data integration module: Connect the ERP system, CRM system, and OA system through the BI platform, and use ETL tools to extract, clean, standardize the format of data from these business systems, and update them in real time;

[0108] Data stream processing component: performs streaming calculations on sales data to ensure that each transaction is reflected in the report in real time;

[0109] Data logic processing layer: Processes sales data in real time through a streaming computing framework, detects changes in sales trends, and triggers analysis tasks;

[0110] Scheduled task layer: Configure scheduled tasks to aggregate and analyze historical sales data, use a sliding window algorithm to analyze sales trends over the past week, and generate sales forecast reports.

[0111] Data warehouse module: stores cleaned sales data, sharding it by product, time, store, and other dimensions. It supports multi-dimensional analysis through efficient query structures, such as queries by region, category, and time.

[0112] Distributed computing engine module: Uses a task scheduler to distribute sales data analysis tasks to multiple computing nodes, ensuring smooth operation of the system under high concurrency conditions;

[0113] Visual reporting module: Generates multi-dimensional business analysis reports on sales, customers, inventory, procurement, etc. through the BI platform. Managers can view real-time sales status through charts and trend charts, and perform customized analysis based on different dimensions;

[0114] Example 2:

[0115] Data integration module: connects to MES system, PLM system and ERP system, and uses ETL tools to extract and clean data to ensure that all production data is updated in real time;

[0116] Data logic processing layer: monitors the working status of the production line in real time and processes the equipment operation data in real time. When equipment fails, the system immediately triggers an early warning and analyzes the cause of the failure.

[0117] Scheduled task layer: Configure scheduled tasks to analyze production data trends, predict future production bottlenecks, aggregate production data based on time windows, and generate monthly production efficiency reports.

[0118] Data warehouse module: stores and backs up processed production data by equipment, production line, time and other dimensions, supports high concurrent access, and ensures that historical data can be quickly queried during the production process;

[0119] Distributed computing engine module: dynamically allocates computing tasks, optimizes resource allocation based on the real-time status of the production line and the performance of computing nodes, and improves computing efficiency;

[0120] Visual reporting module: Generates multi-dimensional business analysis reports on production efficiency, equipment operation, production costs, etc. through the BI platform, and supports users to customize dimensions for detailed analysis.

[0121] The use process of the present invention is:

[0122] Step 1: Connect the BI platform to business systems, including ERP, OA, MES, PLM, and CRM, to extract real-time data;

[0123] Step 2: Use ETL tools to extract real-time data from various business systems, perform data cleaning, deduplication, and format standardization;

[0124] Step 3: Update the processed data to the distributed database in real time to ensure that the system can quickly respond to business changes;

[0125] Step 4: Use the streaming computing framework to process and update data in real time to ensure that data is updated in a timely manner when it changes;

[0126] Step 5: The data logic processing layer includes a real-time computing layer and a scheduled task layer. The real-time computing layer processes data in real time through a streaming computing framework, while the scheduled task layer periodically performs data aggregation and trend analysis.

[0127] Step 6: Configure scheduled tasks to aggregate historical data and perform cross-module correlation analysis;

[0128] Step 7: At the scheduled task layer, a sliding aggregation algorithm is used to aggregate the data to generate more accurate analysis results;

[0129] Step 8: Use time series analysis formulas to generate trend analysis results and enhance the foresight of decision-making;

[0130] Step 9: The data warehouse module is used to store processed data and classify and store it by business dimension, supporting multi-dimensional analysis;

[0131] Step 10: Store data based on a distributed database and shard data according to different business modules to support high concurrent access;

[0132] Step 11: Configure a data backup strategy to ensure high data availability and disaster recovery capabilities through master-slave replication and redundant backup.

[0133] Step 12: Schedule and allocate tasks through the distributed computing engine module to ensure parallel processing and data consistency;

[0134] Step 13: Use the task scheduler to dynamically allocate tasks based on task priority and computational complexity to ensure load balancing;

[0135] Step 14: Dynamically adjust the allocation strategy of computing resources to optimize the utilization efficiency of system resources;

[0136] Step 15: Generate multi-dimensional business analysis reports through the visual report module, support user-defined analysis dimensions, and update report data in real time;

[0137] Step 16: The system automatically triggers analysis tasks and generates and updates reports by monitoring data changes in real time;

[0138] Step 17: Dynamically optimize the task allocation strategy based on the relevance between tasks and computing resources to improve the computing efficiency and response speed of the system;

[0139] Step 18: The system identifies the updated objects through the data change log, triggers the data processing task in time, and updates the report synchronously.

[0140] In summary, the advantages of the present invention are:

[0141] Through real-time data integration and updates, enterprises can obtain real-time operational data, helping management make timely and accurate decisions. Based on multi-dimensional analysis reports and trend analysis, managers can more intuitively understand the dynamic changes of the business and adjust strategies in a timely manner.

[0142] Through streaming computing and scheduled task configuration, the system can process large amounts of data updates in real time. At the same time, it uses the distributed computing engine module for task scheduling and load balancing, ensuring efficient and scalable data processing.

[0143] The system allows users to customize analysis dimensions and display different levels of business data through visual reports, meeting the personalized analysis needs of different managers and decision makers;

[0144] The data integration module uses ETL tools to extract, clean, and standardize data formats, ensuring data consistency and quality. Meanwhile, the distributed computing engine module ensures data consistency and high availability in a distributed environment.

[0145] The distributed computing engine module and intelligent task allocation algorithm module optimize the use of computing resources, can efficiently schedule computing tasks, improve the system's parallel computing capabilities, and ensure task load balancing and optimal resource utilization;

[0146] The master-slave replication and redundant backup strategies of the data warehouse module ensure high data availability and disaster recovery capabilities. In the event of a system failure, data can be restored through backup to ensure business continuity.

[0147] The visual reporting module supports real-time data updates and automatically triggers analysis tasks through data change logs, ensuring the real-time and dynamic updates of report data, which provides management with more accurate decision-making basis;

[0148] Through multi-dimensional analysis, the system can generate comprehensive business reports on sales, customers, projects, inventory, delivery and other areas, helping companies to deeply analyze their business from multiple angles, identify potential problems and make optimizations.

[0149] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. Multi-dimensional business data analysis based on BI system, characterized by: include: Data Integration Module: This module is used to connect to the real-time data of the business system through the BI platform, extract the original data using ETL tools, perform data cleaning, deduplication, format standardization, and perform real-time data updates; Data logic processing module: The data logic processing module includes a real-time computing layer and a scheduled task layer. The real-time computing layer uses a streaming computing framework to calculate and update data in real time. The scheduled task layer configures periodic batch tasks to perform historical data aggregation, trend analysis, and cross-module correlation calculations. Data warehouse module: The data warehouse module is used to store processed data and classify and store it according to business dimensions to form a unified business data warehouse and support multi-dimensional analysis; Distributed computing engine module: The distributed computing engine module is used to schedule and allocate tasks, make data consistent and perform task parallel processing; Visualization report module: The visualization report module is used to generate multi-dimensional business analysis reports, supports user-defined analysis dimensions, and updates report data in real time.

2. The multi-dimensional business data analysis based on the BI system according to claim 1 is characterized in that: The data integration module specifically includes: Data stream processing component: processes real-time data through a streaming computing framework to ensure that data can be updated in a timely manner when it changes; Data extraction component: Use ETL tools to extract real-time data from various business systems and perform cleansing and format standardization to ensure data quality.

3. The multi-dimensional business data analysis based on the BI system according to claim 2 is characterized in that: The data logic processing module specifically includes: Real-time computing layer: used to process data update events in real time through the streaming computing framework and trigger corresponding analysis tasks; Scheduled task layer: Configure scheduled tasks, aggregate and analyze historical data, use a sliding aggregation algorithm based on a time window to aggregate data, and use time series analysis formulas to generate trend analysis results. Among them, the sliding aggregation algorithm formula is: Where A(t) is the average value of the sliding window at time t, w is the size of the window, t is the current time, i is the time index, and D i Represents the data value at time point i; Among them, the time series analysis formula is: Y(t)=β0+β1t Where Y(t) represents the value of the dependent variable at time t, β0 and β are the parameters of linear regression, β1 represents the growth trend of the data. If β1>0, the data shows an increasing trend, and if β1<0, it shows a decreasing trend. t represents the time variable.

4. The multi-dimensional business data analysis based on the BI system according to claim 3 is characterized in that: The data warehouse module includes: Data storage component: stores business data based on a distributed database, and shards the data by dimension based on the business module, supporting high concurrent access; Data backup component: Ensures high data availability and disaster recovery capabilities through master-slave replication and redundant backup strategies.

5. The multi-dimensional business data analysis based on the BI system according to claim 4 is characterized in that: The distributed computing engine module includes: Task scheduler: used to dynamically assign tasks to appropriate computing nodes based on task priority and computational complexity to achieve load balancing; Resource management component: Dynamically adjusts the allocation strategy of computing resources to optimize the utilization efficiency of system resources.

6. The multi-dimensional business data analysis based on the BI system according to claim 5 is characterized in that: The visual report module includes: Data display component: supports the generation of analytical reports covering multiple dimensions including sales, customers, projects, delivery, inventory, and procurement; Visual interface: Users can view real-time reports through charts and trend graphs, and customize dimensions for analysis.

7. An intelligent multi-dimensional business data analysis method based on a BI system, characterized by: The following steps are involved: S1: Build a data integration layer to connect real-time data from ERP, OA, MES, PLM, and CRM business systems through the BI platform. Use ETL tools to extract, clean, and standardize data formats, and store the data in a distributed database. S2: Build a data logic processing layer, which includes a real-time computing layer and a scheduled task layer. The real-time computing layer processes data in real time using a streaming computing framework, while the scheduled task layer performs historical data aggregation and trend analysis based on a preset period. S3: Dynamically allocates computing tasks to appropriate computing nodes based on task type, priority, and resource requirements to optimize resource utilization; S4: Generate multi-dimensional analysis reports. Use the BI platform to generate business analysis reports in sales, customers, projects, and delivery areas, and support user-defined dimensions. S5: Update report data in real time. The system generates and updates reports by monitoring data changes in real time and automatically triggering corresponding analysis tasks.

8. The intelligent multi-dimensional business data analysis method based on the BI system according to claim 7 is characterized in that: The task allocation process in S3 includes: Based on the computing requirements of the tasks and the performance of the nodes, the task allocation algorithm is used to reasonably allocate tasks to ensure system load balancing and improve parallel computing capabilities; Among them, the task allocation algorithm formula is: Where, L(N j ) is allocated to computing node N j Load capacity, N j represents the jth computing node, T i represents the i-th task in the task set T, T j represents the set of tasks assigned to the jth node, C(T i ) represents task T i Computational requirements; Based on the load of the node, the load difference of each node is minimized to ensure load balancing.

9. The intelligent multi-dimensional business data analysis method based on the BI system according to claim 8, characterized in that: The real-time report update process in S5 includes: The system identifies the updated object type through the data change log, triggers data processing tasks in a timely manner, updates the business analysis data of relevant dimensions, and synchronizes reports through the data warehouse.

10. The intelligent multi-dimensional business data analysis method based on the BI system according to claim 9, characterized in that: The system further comprises: Intelligent task allocation algorithm module: Dynamically optimizes task allocation strategies based on the relevance between tasks and computing resources to improve the system's computing efficiency and response speed.

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