Multi-dimensional data association analysis system and method based on unified modeling

Abstract

The invention discloses a multi-dimensional data association analysis system and method based on unified modeling, and relates to the technical field of big data intelligent analysis. Comprising the following steps: 1, constructing a semantic model; step 2, carrying out query compiling and optimization; 3, performing query execution and scheduling; 4, interactive analysis and visualization are carried out, wherein a multi-dimensional report designer MRD is used for providing end-to-end full-process visualization display, and interactive drilling and narrative analysis are supported; step 5, performing closed-loop optimization; according to the method, the complexity of an underlying data structure is shielded through the semantic model, and automatic correlation analysis of a cross-fact table is supported, so that business personnel can independently complete deep data exploration and decision support without mastering professional technologies such as SQL (Structured Query Language) and the like.

Description

TECHNICAL FIELD

[0001] The application discloses a system and method for multi-dimensional data correlation analysis based on unified modeling, and relates to the technical field of intelligent analysis of big data. BACKGROUND

[0002] In enterprise data analysis, data is usually stored in multiple fact tables such as a sales fact table, a stock fact table, and a user behavior fact table, and these fact tables are correlated through shared dimension tables such as a time dimension, a product dimension, and a user dimension table. Existing analysis methods mainly rely on a star / snowflake model of a data warehouse and SQL queries. However, this approach still has limitations, such as: 1. The model is rigid and slow to respond to business changes. When a new analysis dimension or indicator is proposed by the business, a data engineer needs to modify the data model and the ETL process, and the process is long and cannot respond quickly.

[0003] 2. Multi-fact table correlation analysis is complex. If a business person needs to analyze an indicator across fact tables, such as "analyze the sales conversion rate of high inventory goods", a complex SQL statement containing multiple JOINs needs to be written, which has a high technical threshold and is prone to errors.

[0004] 3. The report development cycle is long: even if there is a BI tool, it still takes technical personnel to develop a new multi-dimensional analysis report, especially a drill-down report, and business personnel cannot independently conduct exploratory analysis, which seriously hinders the efficiency of data-driven decision-making.

[0005] Although there are some self-service BI tools on the market, they only provide drag-and-drop functions, and their underlying structure usually relies on a single wide table or a pre-defined data model, which still lacks flexible support for dynamic and multi-fact table correlation scenarios, and cannot achieve true cross-model and cross-fact table free drilling and correlation analysis. SUMMARY

[0006] To solve the problems of the prior art, the application provides a system and method for multi-dimensional data correlation analysis based on unified modeling, which shields the complexity of the underlying data structure through a semantic model, supports automatic correlation analysis across fact tables, and enables business personnel to independently complete deep data exploration and decision support without mastering professional techniques such as SQL.

[0007] The specific scheme provided by the application is: The application provides a method for multi-dimensional data correlation analysis based on unified modeling, comprising: Step 1: semantic model construction: Connect multiple sources of data: connect multiple heterogeneous databases through an interface, automatically scan the data table structure, Intelligent recommendation association: according to the scanning result, the main foreign key recommendation, field name similarity and data sampling analysis are carried out, the association between dimensions and facts is obtained, and the association is visualized in the form of graph, Define dimension level and index: define dimension level, set index aggregation rule and calculation expression, According to the association between dimensions and facts, the dimension level index aggregation rule and the calculation expression form a semantic model; Step 2: query compilation and optimization: Parse query intent: parse user query intent, get query intent, Multi-objective cost optimization: according to the query intent, use the semantic model to find the association path between multiple fact tables and dimension tables, generate multiple candidate physical execution plans according to the association path, and select the optimized physical execution plan output, Step 3: query execution and scheduling: Multi-engine adaptation: build a multi-engine adaptation layer, select an execution engine according to the physical execution plan, Step 4: interactive analysis and visualization: use multi-dimensional report designer MRD to provide end-to-end full-process visual display, and support interactive drilling and narrative analysis, Step 5: closed-loop optimization: Monitoring and feedback: collect query performance, resource usage and user behavior data, Automatic optimization model and plan: according to the collected data, regularly optimize the semantic model and physical execution plan.

[0008] Further, the step 1 of the method for multi-dimensional data association analysis based on unified modeling manages the semantic model, including: Version unification and consistency check: store the semantic model in a versioned storage manner, record version number, change content and personnel information for all semantic model changes, support rollback and difference comparison, and perform semantic consistency check on the semantic model, automatically detect index ambiguity, circular dependency and logical conflict, and ensure that the semantic model is rigorous and reliable.

[0009] Further, the step 2 of the method for multi-dimensional data association analysis based on unified modeling performs distributed cost evaluation on the candidate physical execution plan, including: Calculate the total cost TotalCost for each candidate physical execution plan, the formula is: TotalCost=a*(SF / DV)+b*CL+c*(1-CH); Wherein DV is the abbreviation of Data Volume, representing the amount of data to be processed, reflecting the data reading or transmission overhead, SF is the abbreviation of Scale Factor, representing the preset data scaling factor, which is adjusted based on the cluster benchmark performance, and is used for normalizing the data volume influence, CL is the abbreviation of Cluster Load, representing the cluster load rate, ranging from 0 to 1, reflecting the current cluster resource competition degree, CH is the abbreviation of Cache Hit Rate, representing the cache hit rate, ranging from 0 to 1, a, b, c: weight coefficients, adjusted through actual scene, satisfying a+b+c=1; Some candidate physical execution plans with smaller total cost are selected, in a distributed environment, a coordination node is selected, local costs of each node are collected, and the local costs of each node are aggregated into a global cost, The physical execution plan with the minimum global cost is selected as the output physical execution plan after optimization.

[0010] Further, in step 2 of the method, a materialized view is also generated and cached: for high-frequency queries, an incremental materialized view is generated, and incremental updates are triggered based on logs or timestamps, ensuring the balance between query performance and data real-time performance, Query rewriting and physical execution plan caching are also performed, and physical execution plans are automatically reused for semantically equivalent queries, reducing repeated optimization overhead.

[0011] Further, in step 3 of the method, for the cross-fact table association query in the physical execution plan, a phased execution is performed to balance the row load: the filtering and aggregation operations are performed on each fact table in parallel, and then the join operation is performed on the intermediate results, At the same time, failover and monitoring are performed: the query execution state is monitored in real time, and dynamic resource allocation is used for failover, automatically responding to node failures or load tilting, ensuring query high availability and stability.

[0012] The application also provides a system for multi-dimensional data association analysis based on unified modeling, comprising a semantic model construction module, a compilation module, an execution module, a visualization management module and a feedback module, The semantic model construction module performs semantic model construction: Connect multiple source data: connect multiple heterogeneous databases through interfaces, and automatically scan data table structures, Intelligent recommendation of association relationship: according to the scanning results, the primary key recommendation, field name similarity and data sampling analysis are performed to obtain the association relationship between dimensions and facts, and the association relationship is visualized and displayed in the form of a graph, define dimension hierarchy and indicators: define dimension hierarchy, set indicator aggregation rules and calculation expressions, form a semantic model according to the association relationship between dimensions and facts, dimension hierarchy indicator aggregation rules and calculation expressions; The compiling module performs query compilation and optimization: Parse query intent: parse user query intent, get query intent, Multi-objective cost optimization: according to the query intent, use the semantic model to find the association path between multiple fact tables and dimension tables, generate multiple candidate physical execution plans according to the association path, perform distributed cost evaluation optimization on the candidate physical execution plans, select the optimized physical execution plan output, The execution module performs query execution and scheduling: Multi-engine adaptation: build a multi-engine adaptation layer, select an execution engine according to the physical execution plan, Interactive analysis and visualization: use multi-dimensional report designer MRD to provide end-to-end full-process visual display, and support interactive drilling and narrative analysis, Feedback module for closed-loop optimization: Monitoring and feedback: collect query performance, resource usage and user behavior data, Automatic optimization model and plan: according to the collected data, regularly optimize the semantic model and physical execution plan automatically.

[0013] Further, the semantic model construction module of the multi-dimensional data association analysis system based on unified modeling manages the semantic model, including: Version unification and consistency check: store the semantic model in a versioned storage manner, record version number, change content and personnel information for all semantic model changes, support rollback and difference comparison, and perform semantic consistency check on the semantic model, automatically detect indicator ambiguity, circular dependency and logical conflict, and ensure that the semantic model is rigorous and reliable.

[0014] Further, the compiling module of the multi-dimensional data association analysis system based on unified modeling performs distributed cost evaluation on the candidate physical execution plan, including: Calculate the total cost TotalCost for each candidate physical execution plan, the formula is: TotalCost=a*(SF / DV)+b*CL+c*(1-CH); Where DV is the abbreviation of DataVolume, indicating the amount of data to be processed, reflecting data reading or transmission overhead, SF is the abbreviation of ScaleFactor, which represents the preset data scaling factor, is adjusted based on the cluster benchmark performance, and is used for normalizing the data volume influence, CL is the abbreviation of ClusterLoad, which represents the cluster load rate, ranges from 0 to 1, and reflects the current cluster resource competition degree, CH is the abbreviation of CacheHitRate, which represents the cache hit rate, ranges from 0 to 1, a, b, c: weight coefficients, are adjusted through actual scenes, and satisfy a+b+c=1; Some candidate physical execution plans with smaller total cost are selected, in a distributed environment, a coordination node is selected, local costs of each node are collected, and the local costs of each node are aggregated into a global cost, The physical execution plan with the minimum global cost is selected as the output physical execution plan after optimization.

[0015] Further, the compiling module of the system for multi-dimensional data correlation analysis based on unified modeling further generates materialized views and caches: for high-frequency queries, an incremental materialized view is generated, and incremental updating is triggered based on logs or timestamps, so that the balance between query performance and data real-time performance is ensured, Query rewriting and physical execution plan caching are also performed, and the physical execution plan is automatically reused for semantically equivalent queries, thereby reducing repeated optimization overhead.

[0016] Further, the execution module of the system for multi-dimensional data correlation analysis based on unified modeling performs phased execution for the correlation query across the fact tables in the physical execution plan to balance the row load: the filtering and aggregation operations are performed on each fact table in parallel, and then the join operation is performed on the intermediate results, Fault transfer and monitoring are also performed: the query execution state is monitored in real time, fault transfer is performed by using dynamic resource allocation, node failure or load inclination is automatically coped with, and the high availability and stability of the query are ensured.

[0017] The present application has the following advantages: A business-friendly unified semantic model is constructed, complex physical data models are abstracted into business terms such as indicators and dimensions, and the association relationship is clearly defined, thereby reducing the understanding cost of business personnel; Automatic intelligent association of multiple fact tables is realized, the join, aggregation and drill-down analysis between multiple fact tables are automatically processed according to the association relationship defined in the semantic model, and complex SQL does not need to be manually written; Zero-code multi-dimensional report generation capability is provided, business users can independently and flexibly configure dimensions and indicators through completely visual drag-and-drop operations, and multi-dimensional analysis reports can be generated and interactively explored in real time; Guarantee the query performance of mass data, optimize through model compilation, pre-computation, distributed query and other technologies, and ensure that complex queries based on the semantic layer can be quickly responded to. In summary, the present application realizes agile delivery from data model to analysis application, shields the complexity of the underlying data structure through the semantic model, supports automatic association analysis across fact tables, and enables business personnel to independently complete deep data exploration and decision support without mastering professional techniques such as SQL. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a schematic diagram of the method flow of the present application. DETAILED DESCRIPTION

[0019] The present application will be further described below in combination with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0020] Embodiment 1: The present application provides a method for multi-dimensional data association analysis based on unified modeling, comprising: Step 1: semantic model construction and management: Connect multi-source data: connect multi-source heterogeneous databases through JDBC / ODBC or data lake interface, automatically scan data table structure, Intelligent recommendation of association relationship: according to the scanning result, recommend primary and foreign keys, analyze field name similarity and data sampling, obtain the association relationship between dimensions and facts, and visualize the association relationship in the form of a graph, Define dimension hierarchy and indicators: define dimension hierarchy such as time: year-> season-> month-> day, set indicator aggregation rules such as SUM / AVG, etc., and calculation expressions such as "gross profit rate = (revenue-cost) / revenue", etc., Form a semantic model according to the association relationship between dimensions and facts, dimension hierarchy, indicator aggregation rules and calculation expressions, Version unification and consistency check: store the semantic model in a versioned storage manner, record version number, change content and personnel information for all semantic model changes, support rollback and difference comparison, and perform semantic consistency check of the semantic model, automatically detect indicator ambiguity, circular dependency and logical conflict, and ensure that the semantic model is rigorous and reliable.

[0021] Step 2: query compilation and optimization: Parse query intent: parse user query intent, obtain query intent, Multi-objective cost optimization is performed: according to a query intention, a semantic model is used to find an associated path between multiple fact tables and dimension tables, multiple candidate physical execution plans are generated according to the associated path, distributed cost evaluation optimization is performed on the candidate physical execution plans, and an optimized physical execution plan is selected and output.

[0022] The distributed cost evaluation on the candidate physical execution plans includes: A total cost TotalCost is calculated for each candidate physical execution plan, and a formula is as follows: TotalCost = a * (SF / DV) + b * CL + c * (1-CH); DV is an abbreviation of DataVolume, representing a data volume to be processed, reflecting data reading or transmission overhead, SF is an abbreviation of ScaleFactor, representing a preset data scaling factor, which is adjusted based on a cluster benchmark performance and is used for normalizing data volume influence, CL is an abbreviation of ClusterLoad, representing a cluster load rate, ranging from 0 to 1, reflecting a current cluster resource competition degree, CH is an abbreviation of CacheHitRate, representing a cache hit rate, ranging from 0 to 1, a, b, and c are weight coefficients, which are adjusted through an actual scene and satisfy a+b+c = 1; Some candidate physical execution plans with smaller total costs are selected, nodes are coordinated in a distributed environment, local costs of the nodes are collected, and the local costs of the nodes are aggregated into a global cost, A physical execution plan with the smallest global cost is selected as an optimized physical execution plan.

[0023] A materialized view is also generated and cached: for a high-frequency query, an incremental materialized view is generated, and an incremental update is triggered based on a log or a timestamp, so that the balance between query performance and data real-time performance is ensured, Query rewriting and physical execution plan caching are also performed, and a physical execution plan is automatically reused for semantically equivalent queries, so that repeated optimization overhead is reduced.

[0024] Step 3: query execution and scheduling are performed: Multi-engine adaptation is performed: a multi-engine adaptation layer is constructed, such as Presto, Spark, and Druid query engines, an execution engine is selected according to a query SQL complexity, a data size, and a SLA timeliness requirement involved in the physical execution plan, and a plan is executed, such as Presto for interactive query, Spark for ETL processing, and Druid for pre-aggregation query, For cross-fact table join queries in the physical execution plan, perform them in stages to achieve load balancing: first, perform filtering and aggregation operations in parallel on each fact table, and then perform join operations on the intermediate results. This can significantly reduce the amount of shuffle data.

[0025] Simultaneously, failover and monitoring are performed: query execution status is monitored in real time, failover is performed using dynamic resource allocation, and node failures or load imbalances are automatically addressed to ensure high availability and stability of queries.

[0026] Step 4: Perform interactive analysis and visualization. Utilize the Multidimensional Report Designer (MRD) to provide end-to-end visualization of the entire process, supporting interactive drill-down and narrative analysis. Drag and drop to generate queries: You can drag and drop dimensions and metrics onto the canvas, and the multidimensional report designer (MRD) will generate query statements in real time and send them to the multi-engine adaptation layer. It will also automatically recommend visualization schemes, such as line charts for time series data and pie charts for percentage data. Natural Language Parsing: Users can directly generate reports through natural language input. Based on NLP technology, the query intent is parsed and mapped to the semantic model. Intelligent visualization and drill-down: When performing interactive drill-down and narrative analysis, clicking on any area of ​​the chart, such as a bar chart for a certain month, will automatically drill down to the daily granular data and record the drill-down path to generate an analysis storyline. It supports multi-user collaborative editing, commenting and sharing, and can be exported to PDF or published to a large screen with one click.

[0027] Step 5: Perform closed-loop optimization: Monitoring and feedback: Collect data on query performance, resource usage, and user behavior. Automatic Model and Plan Optimization: Based on collected data, the system automatically optimizes the semantic model and physical execution plan periodically, such as adjusting materialized view strategies, updating statistics, and optimizing association paths. It also provides model health scores and optimization suggestions to help administrators continuously improve model quality and the analytics experience, forming a closed-loop self-optimization mechanism.

[0028] Example 2: This invention also provides a system for multidimensional data association analysis based on unified modeling, including a semantic model construction module, a compilation module, an execution module, a visualization management module, and a feedback module. The semantic model building module constructs the semantic model: Connecting to multi-source data: Connecting to heterogeneous databases from multiple sources via an interface, automatically scanning the data table structure. Intelligent recommendation of associations: Based on the scanning results, primary and foreign key recommendations, field name similarity analysis, and data sampling analysis are performed to obtain the associations between dimensions and facts, and the associations are visualized in the form of a graph. Define dimensional hierarchy and metrics: Define the dimensional hierarchy, set the metric aggregation rules and calculation expressions. A semantic model is formed based on the relationship between dimensions and facts, the aggregation rules of dimension-level indicators, and the calculation expressions. The compilation module performs query compilation and optimization: Parsing query intent: Parsing the user's query intent to obtain the query intent. Multi-objective cost optimization is performed: based on the query intent, a semantic model is used to find association paths between multiple fact tables and dimension tables. Multiple candidate physical execution plans are generated based on the association paths. Distributed cost evaluation and optimization are performed on the candidate physical execution plans, and the optimized physical execution plan is output. The execution module performs query execution and scheduling: Multi-engine adaptation: Construct a multi-engine adaptation layer to select the execution engine execution plan based on the physical execution plan. The visualization management module enables interactive analysis and visualization: Utilizing the multidimensional report designer (MRD), it provides end-to-end, full-process visualization and supports interactive drill-down and narrative analysis. The feedback module undergoes closed-loop optimization: Monitoring and feedback: Collect data on query performance, resource usage, and user behavior. Automatic model and plan optimization: The semantic model and physical execution plan are automatically optimized periodically based on the collected data.

[0029] The information interaction and execution process between the modules in the above system are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description in the method embodiment of the present invention, and will not be repeated here.

[0030] Similarly, the advantages of the system of the present invention are: Build a business-friendly unified semantic model to abstract complex physical data models into business terms such as "indicators and dimensions" and clearly define their relationships to reduce the understanding cost for business personnel. It enables automatic and intelligent association of multiple fact tables, automatically handling joins, aggregations, and drill-down analysis between multiple fact tables based on the association relationships defined in the semantic model, without the need for manually writing complex SQL. It provides zero-code multidimensional report generation capabilities, allowing business users to independently and flexibly configure dimensions and metrics through fully visual drag-and-drop operations, and instantly generate and interactively explore multidimensional analysis reports; To ensure query performance for massive amounts of data, technologies such as model compilation optimization, pre-computation, and distributed querying are used to ensure that complex queries based on the semantic layer can be responded to quickly. In summary, the system of this invention enables agile delivery from data model to analytical application. By shielding the complexity of the underlying data structure through semantic model, it supports automatic correlation analysis across fact tables, enabling business personnel to independently complete in-depth data exploration and decision support without needing to master professional technologies such as SQL.

[0031] It should be noted that not all steps and modules in the above processes and system structures are mandatory; some steps or modules can be omitted as needed. The execution order of the steps is not fixed and can be adjusted as required. The system structures described in the above embodiments can be physical or logical structures. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be implemented by certain components in multiple independent devices.

[0032] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for multidimensional data correlation analysis based on unified modeling, characterized by: include: Step 1: Construct a semantic model: Connecting to multi-source data: Connecting to heterogeneous databases from multiple sources via an interface, automatically scanning the data table structure. Intelligent recommendation of associations: Based on the scanning results, primary and foreign key recommendations, field name similarity analysis, and data sampling analysis are performed to obtain the associations between dimensions and facts, and the associations are visualized in the form of a graph. Define dimensional hierarchy and metrics: Define the dimensional hierarchy, set the metric aggregation rules and calculation expressions. A semantic model is formed based on the relationship between dimensions and facts, the aggregation rules of dimension-level indicators, and the calculation expressions. Step 2: Perform query compilation and optimization: Parsing query intent: Parsing the user's query intent to obtain the query intent. Multi-objective cost optimization is performed: based on the query intent, a semantic model is used to find association paths between multiple fact tables and dimension tables. Multiple candidate physical execution plans are generated based on the association paths. Distributed cost evaluation and optimization are performed on the candidate physical execution plans, and the optimized physical execution plan is output. Step 3: Perform query execution and scheduling: Multi-engine adaptation: Construct a multi-engine adaptation layer to select the execution engine execution plan based on the physical execution plan. Step 4: Perform interactive analysis and visualization: Utilize the Multidimensional Report Designer (MRD) to provide end-to-end visualization of the entire process, supporting interactive drill-down and narrative analysis. Step 5: Perform closed-loop optimization: Monitoring and feedback: Collect data on query performance, resource usage, and user behavior. Automatic model and plan optimization: The semantic model and physical execution plan are automatically optimized periodically based on the collected data.

2. The method for multidimensional data correlation analysis based on unified modeling according to claim 1, characterized in that: Step 1 involves managing the semantic model, including: Perform version unification and consistency checks: The semantic model is stored in a versioned storage manner. All changes to the semantic model are recorded with version number, change content and personnel information. Rollback and difference comparison are supported. At the same time, semantic consistency checks are performed on the semantic model, automatically detecting ambiguity of indicators, circular dependencies and logical conflicts to ensure that the semantic model is rigorous and reliable.

3. The method for multidimensional data correlation analysis based on unified modeling according to claim 1, characterized in that: Step 2 involves a distributed cost evaluation of the candidate physical execution plans, including: Calculate the total cost (TotalCost) for each candidate physical execution plan using the following formula: TotalCost=a*(SF / DV)+b*CL+c*(1-CH); DV stands for DataVolume, representing the amount of data to be processed and reflecting the overhead of data reading or transmission. SF is an abbreviation for ScaleFactor, representing a preset data scaling factor. It is adjusted based on the cluster's baseline performance and is used to normalize the impact of data volume. CL is an abbreviation for ClusterLoad, representing the cluster load rate, ranging from 0 to 1, and reflecting the current level of resource contention within the cluster. CH is an abbreviation for CacheHitRate, representing the cache hit rate, ranging from 0 to 1. a, b, c: weighting coefficients, adjusted according to the actual scenario to satisfy a+b+c=1; Select a few candidate physical execution plans with smaller total costs. In a distributed environment, coordinate nodes, collect the local costs of each node, and aggregate the local costs of each node into a global cost. The physical execution plan with the lowest global cost is selected as the optimized physical execution plan.

4. The method for multidimensional data correlation analysis based on unified modeling according to claim 1, characterized in that: Step 2 also generates and caches materialized views: For high-frequency queries, incremental materialized views are generated, and incremental updates are triggered based on logs or timestamps to ensure a balance between query performance and data real-time performance. It also performs query rewriting and physical execution plan caching, automatically reusing physical execution plans for semantically equivalent queries to reduce redundant optimization overhead.

5. The method for multidimensional data correlation analysis based on unified modeling according to claim 1, characterized in that: In step 3, for cross-fact table join queries in the physical execution plan, the execution is carried out in stages to achieve load balancing: first, filtering and aggregation operations are performed in parallel on each fact table, and then join operations are performed on the intermediate results. Simultaneously, failover and monitoring are performed: query execution status is monitored in real time, failover is performed using dynamic resource allocation, and node failures or load imbalances are automatically addressed to ensure high availability and stability of queries.

6. A system for multidimensional data correlation analysis based on unified modeling, characterized by: It includes a semantic model building module, a compilation module, an execution module, a visualization management module, and a feedback module. The semantic model building module constructs the semantic model: Connecting to multi-source data: Connecting to heterogeneous databases from multiple sources via an interface, automatically scanning the data table structure. Intelligent recommendation of associations: Based on the scanning results, primary and foreign key recommendations, field name similarity analysis, and data sampling analysis are performed to obtain the associations between dimensions and facts, and the associations are visualized in the form of a graph. Define dimensional hierarchy and metrics: Define the dimensional hierarchy, set the metric aggregation rules and calculation expressions. A semantic model is formed based on the relationship between dimensions and facts, the aggregation rules of dimension-level indicators, and the calculation expressions. The compilation module performs query compilation and optimization: Parsing query intent: Parsing the user's query intent to obtain the query intent. Multi-objective cost optimization is performed: based on the query intent, a semantic model is used to find association paths between multiple fact tables and dimension tables. Multiple candidate physical execution plans are generated based on the association paths. Distributed cost evaluation and optimization are performed on the candidate physical execution plans, and the optimized physical execution plan is output. The execution module performs query execution and scheduling: Multi-engine adaptation: Construct a multi-engine adaptation layer to select the execution engine execution plan based on the physical execution plan. The visualization management module enables interactive analysis and visualization: Utilizing the multidimensional report designer (MRD), it provides end-to-end, full-process visualization and supports interactive drill-down and narrative analysis. The feedback module undergoes closed-loop optimization: Monitoring and feedback: Collect data on query performance, resource usage, and user behavior. Automatic model and plan optimization: The semantic model and physical execution plan are automatically optimized periodically based on the collected data.

7. A system for multidimensional data correlation analysis based on unified modeling as described in claim 6, characterized in that: The semantic model building module manages the semantic model, including: Perform version unification and consistency checks: The semantic model is stored in a versioned storage manner. All changes to the semantic model are recorded with version number, change content and personnel information. Rollback and difference comparison are supported. At the same time, semantic consistency checks are performed on the semantic model, automatically detecting ambiguity of indicators, circular dependencies and logical conflicts to ensure that the semantic model is rigorous and reliable.

8. A system for multidimensional data correlation analysis based on unified modeling according to claim 6, characterized in that: The compilation module performs a distributed cost evaluation of the candidate physical execution plans, including: Calculate the total cost (TotalCost) for each candidate physical execution plan using the following formula: TotalCost=a*(SF / DV)+b*CL+c*(1-CH); DV stands for DataVolume, representing the amount of data to be processed and reflecting the overhead of data reading or transmission. SF is an abbreviation for ScaleFactor, representing a preset data scaling factor. It is adjusted based on the cluster's baseline performance and is used to normalize the impact of data volume. CL is an abbreviation for ClusterLoad, representing the cluster load rate, ranging from 0 to 1, and reflecting the current level of resource contention within the cluster. CH is an abbreviation for CacheHitRate, representing the cache hit rate, ranging from 0 to 1. a, b, c: weighting coefficients, adjusted according to the actual scenario to satisfy a+b+c=1; Select a few candidate physical execution plans with smaller total costs. In a distributed environment, coordinate nodes, collect the local costs of each node, and aggregate the local costs of each node into a global cost. The physical execution plan with the lowest global cost is selected as the optimized physical execution plan.

9. A system for multidimensional data correlation analysis based on unified modeling according to claim 6, characterized in that: The compilation module also generates and caches materialized views: for high-frequency queries, it generates incremental materialized views and triggers incremental updates based on logs or timestamps, ensuring a balance between query performance and data real-time performance. It also performs query rewriting and physical execution plan caching, automatically reusing physical execution plans for semantically equivalent queries to reduce redundant optimization overhead.

10. A system for multidimensional data correlation analysis based on unified modeling according to claim 6, characterized in that: The execution module performs cross-fact table join queries in the physical execution plan in stages to achieve load balancing: first, filtering and aggregation operations are performed in parallel on each fact table, and then join operations are performed on the intermediate results. Simultaneously, failover and monitoring are performed: query execution status is monitored in real time, failover is performed using dynamic resource allocation, and node failures or load imbalances are automatically addressed to ensure high availability and stability of queries.

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