CN111881224A - Multidimensional data analysis method and system - Google Patents
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a multidimensional data analysis method and a multidimensional data analysis system, wherein the method comprises the following steps: performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component; carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model, and generating a corresponding multi-dimensional report according to a report rule defined by a user; and performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multi-dimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multi-dimensional analysis result taking the user requirements as rules. According to the invention, the multi-dimensional report can be generated through the data analysis model and then correlation analysis is carried out, multi-dimensional self-service analysis service of data is provided for users, the comprehensiveness and accuracy of multi-dimensional data analysis are improved, the analysis result can be displayed in various different information display forms, and the diversity and intuition of data analysis are improved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a multidimensional data analysis method and system.
Background
At present, with the increasingly wide application of databases and the increasingly abundant service types in different fields, the demand for large data intelligent analysis systems is also increased. However, in the process of research and practice of the prior art, the inventor of the present invention finds that the existing multidimensional data analysis system has the problems of too few dimension levels and low correlation between report data, and meanwhile, because the existing multidimensional data analysis system needs to load all data when loading content, the query is time-consuming, certain requirements are also required for system performance, and indexes concerned by a user cannot be freely set, so that the efficiency is low and the performance is poor when performing multidimensional data analysis. Accordingly, there is a need for a multidimensional data analysis system and method that overcomes the above-mentioned deficiencies.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a multidimensional data analysis method and system, which can generate a multidimensional report through a data analysis model and perform correlation analysis, so as to provide a self-service analysis service for a user, and trace back a root cause after a problem is found.
To solve the above problem, an embodiment of the present invention provides a multidimensional data analysis method, which at least includes the following steps:
performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component;
after the preprocessing is finished, carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model, and generating a corresponding multi-dimensional report according to a report rule defined by a user;
and performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multi-dimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multi-dimensional analysis result taking the user requirements as rules.
As a preferred scheme, the data preprocessing specifically includes:
firstly, analyzing collected data, and analyzing an MDX statement into a standard SQL statement;
and sequentially extracting, screening and summarizing the analyzed data to construct a plurality of data cubes and storing the data cubes into a bottom database.
As a preferred scheme, the data analysis model is constructed in a manner that:
after the data table is established and the corresponding data source is configured, the corresponding data table is loaded, the fact table and the dimension table are selected, the dimension and the index are selected, and finally the data analysis model is constructed.
As a preferable scheme, the multidimensional data analysis method further includes:
and slicing and rotating the data analysis model according to the user requirements.
As a preferable scheme, the multidimensional data analysis method further includes:
after the multi-dimensional analysis result is obtained, generating a corresponding dynamic analysis report, and displaying a report through a visual image; and displaying the report form through the visual image, wherein the report form comprises cockpit monitoring, a large data screen, graphic visualization and map analysis.
As a preferable scheme, the multidimensional data analysis method further includes:
and performing data mining including prediction, clustering and association rule mining according to the multi-dimensional analysis result, and extracting to obtain corresponding association data.
As a preferable scheme, the multidimensional data analysis method further includes:
and carrying out influence analysis and pedigree analysis on the collected data through a metadata management technology, and analyzing to obtain a reference relation and a dependency relation among the data.
One embodiment of the present invention provides a multidimensional data analysis system, comprising:
the data acquisition module is used for carrying out data acquisition and data preprocessing on the big data platform and the relational database through the distributed OLAP engine component;
the data analysis model module is used for carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model after the preprocessing is finished, and generating a corresponding multi-dimensional report according to a report rule defined by a user;
and the multidimensional analysis module is used for performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multidimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multidimensional analysis result taking the user requirements as rules.
An embodiment of the present invention provides a terminal device for multidimensional data analysis, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the multidimensional data analysis method as described above when executing the computer program.
An embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus on which the computer-readable storage medium is located to perform the multidimensional data analysis method as described above.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a multidimensional data analysis method and a multidimensional data analysis system, wherein the method comprises the following steps: performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component; after the preprocessing is finished, carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model, and generating a corresponding multi-dimensional report according to a report rule defined by a user; and performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multi-dimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multi-dimensional analysis result taking the user requirements as rules.
Compared with the prior art, the embodiment of the invention generates the multi-dimensional report form through the data analysis model and then performs the correlation analysis, provides the multi-dimensional self-service analysis service of the data for the user, improves the comprehensiveness and the accuracy of the multi-dimensional data analysis, can display the analysis result in various different information display forms, and improves the diversity and the intuition of the data analysis.
Drawings
Fig. 1 is a schematic flowchart of a multidimensional data analysis method according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for multidimensional data analysis according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart diagram illustrating an implementation of another multidimensional data analysis method according to the first embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a multidimensional data analysis method system according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of another multidimensional data analysis method system according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second", and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
First, an application scenario that can be provided by the present invention is introduced, for example, a method for performing multidimensional analysis on data is provided.
The first embodiment of the present invention:
please refer to fig. 1-3.
As shown in fig. 1, the present embodiment provides a multidimensional data analysis method, which at least includes the following steps:
s101, performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component;
specifically, for step S101, data acquisition is performed on each big data platform and each relational database docked by the analysis system through the distributed OLAP engine configuration, after a data interface is provided and data is acquired, data preprocessing is performed on the acquired data, so that basic data is provided for a subsequent multidimensional data analysis model, and after the preprocessing is completed, unified and standard conversion processing is performed on data in different formats, so that the efficiency of subsequent data analysis can be improved.
S102, after the preprocessing is finished, carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model, and generating a corresponding multi-dimensional report according to a report rule defined by a user;
specifically, for step S102, after the data preprocessing is completed, the multidimensional data analysis is performed on the data collected by the distributed OLAP engine component through the constructed data analysis model, and in the analysis process, a multidimensional data report based on the report rule defined by the user in advance is correspondingly generated according to the report rule defined by the user on the system.
S103, performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multi-dimensional reports, generating corresponding detail reports, performing statistical analysis, finally analyzing to obtain a multi-dimensional analysis result with the user requirements as rules, and drilling the detail reports from the multi-dimensional reports through performing correlation analysis among the multi-dimensional reports so as to find a root cause behind a problem.
Specifically, in step S103, according to different requirements of the user, drilling operation is performed on the data analysis model, and correlation analysis is performed on the multiple multidimensional data reports generated in the previous step, where the criterion of the correlation analysis is based on the requirement of the user, and a corresponding detailed report is generated after the correlation analysis is completed, so that multiple previous reports of a single dimension are correlated into a multiple-dimensional detailed report, and the detailed report is subjected to statistical analysis, thereby obtaining a multiple-dimensional analysis result corresponding to the requirement of the user.
For example, related institutions, government departments and mainstream media can broadcast epidemic situation dynamics in real time in the forms of epidemic situation maps, epidemic situation trends, domestic and foreign epidemic situations and the like through channels such as websites, public numbers, apps and the like according to a big data technology of a big data intelligent analysis system, and newly added and accumulated data details of confirmed diagnosis, suspected diagnosis and death of each province can be displayed by clicking each province in a system interface map, and even the details can be accurate to each cell.
In a preferred embodiment, the data preprocessing specifically includes:
firstly, analyzing collected data, and analyzing an MDX statement into a standard SQL statement;
and sequentially extracting, screening and summarizing the analyzed data to construct a plurality of data cubes and storing the data cubes into a bottom database.
Specifically, SQL query is performed on each big data platform and the relational database through the distributed OLAP engine set, and PB-level data is processed. When large-scale data is processed, a data cube is constructed on the large-scale data, efficient real-time query statistics is carried out on the large-scale data based on the data cube, massive data stored in each large data platform is preprocessed, target data is extracted, screened and summarized, the data cube is constructed and stored, so that a subsequent data analysis model can query the data, the situation that operation needs to be carried out on original data during subsequent analysis or query is avoided, the efficiency of data processing and data analysis is improved, and the response time and the occupied resources of a system are reduced.
In addition, the big Data intelligent analysis system Data adopted by the embodiment can also provide interface Data management capability and rich Data source support, and a user can construct a business model of the user on the basis of the source Data relationship to realize Data analysis. Characteristics of the big Data intelligent analysis system Data: the application difficulty of the user is greatly reduced and the user can use the system conveniently due to the abundant data source support; the system has the advantages of strong data set management and flexible semantic layer, provides strong data set management function for users, and enables the set to support SQL query, visual query, Java query, stored process query and multidimensional query.
In a preferred embodiment, the data analysis model is constructed in a manner that:
after the data table is established and the corresponding data source is configured, the corresponding data table is loaded, the fact table and the dimension table are selected, the dimension and the index are selected, and finally the data analysis model is constructed.
In a preferred embodiment, the multidimensional data analysis method further includes:
and slicing and rotating the data analysis model according to the user requirements.
Specifically, based on a multidimensional analysis technology, the OLAP server component provides a self-service interactive multidimensional analysis service for a user, the user can realize any operations such as slicing, rotating, drilling and the like based on a multidimensional model, and can further realize operations such as self-defining indexes and statistical analysis.
In a preferred embodiment, as shown in fig. 2, the multidimensional data analysis method further includes:
after the multi-dimensional analysis result is obtained, generating a corresponding dynamic analysis report, and displaying a report through a visual image; and displaying the report form through the visual image, wherein the report form comprises cockpit monitoring, a large data screen, graphic visualization and map analysis.
Specifically, after a multi-dimensional analysis result is obtained, a corresponding dynamic analysis report is generated according to the result, and a report is displayed through a visual image, such as a cockpit, a data large screen, a graphic visualization and a map analysis. The cockpit is an important component of a big data intelligent analysis system, and key indexes and business targets are monitored and analyzed by using graphics, instrument panels, early warning and the like, so that the cockpit keeps pace with a strategy. The leading cockpit aims to enable a user to know the business activities of the whole unit, so that the user can monitor KPIs, measure the KPIs and manage early warning and abnormity in a personalized mode. The large-screen display is performed through the large data screen, the large data screen is relative to the instrument panel, the information quantity is further concentrated, the interactivity is weakened, the display effect is strengthened, very flexible layout, style and graphic effect are supported, the design and online speed is very high, and the attractive effect and the development efficiency are improved.
The image visualization is to provide a series of graphic effects such as a bar graph, a horizontal bar graph, a scatter diagram, an area diagram, a line graph, a combination diagram, a waterfall diagram, a pie graph, a ring graph, a Nandingger rose diagram, an oil quantity diagram, a scatter diagram, a bubble diagram, a radar diagram, a relation diagram, a thermodynamic diagram, a word cloud diagram and the like by integrating Echart as a basic graphic control; and can complete more complex graphic design through spreadsheet drawing, such as: gantt chart, mountain chart, accordion chart, bullet chart, small and multiple chart, mini chart, funnel chart, progress chart, composite pie chart, multilayer pie chart, etc.; and setting conditional formats (data bars, color levels, icon sets) for the tables.
In addition, by means of geographic information technology, the map analysis function enables users to monitor business development and performance conditions of different regions very intuitively. The map area is displayed in different colors, or flags in different colors are placed on the map area, the decision layer can clearly know the execution condition of the service, and the root of the problem is explored and the insight is enhanced by realizing drill-down operation and linkage graphics on the map. Novel dynamic charts are supported, such as airline maps, thermal maps, bubble maps, and scatter maps.
In a preferred embodiment, as shown in fig. 3, the multidimensional data analysis method further includes:
and performing data mining including prediction, clustering and association rule mining according to the multi-dimensional analysis result, and extracting to obtain corresponding association data.
Specifically, after the multi-dimensional analysis result is obtained, the user can perform data mining on the analysis result according to the requirement, and finally extract and obtain the relevance data corresponding to the user requirement through methods such as prediction algorithm, clustering and relevance rule mining, so that the user can perform further multi-dimensional analysis on the relevance data obtained by mining subsequently, and the comprehensiveness and practicability of data analysis are improved.
In a preferred embodiment, the multidimensional data analysis method further includes:
and carrying out influence analysis and pedigree analysis on the collected data through a metadata management technology, and analyzing to obtain a reference relation and a dependency relation among the data.
Specifically, the present embodiment further provides an influence analysis and a pedigree analysis (dependency analysis) on the metadata of the system through the metadata management system, wherein through the influence analysis, when the source data item changes, which KPIs and reports are affected is identified; through dependency analysis, the data items from which the KPI data come are known, and the processing calculation is carried out. Managing metadata information in a BI application system, including managing information such as tables, fields, parameters, business topics, multidimensional models, queries, reports, instrument panels and the like; thereby better supporting the maintenance and operation of the application system. The pedigree analysis is an effective tool for tracing, and a user can check and analyze the dependency relationship between resources when browsing the report.
The multidimensional data analysis method provided by the embodiment comprises the following steps: performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component; after the preprocessing is finished, carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model, and generating a corresponding multi-dimensional report according to a report rule defined by a user; and performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multi-dimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multi-dimensional analysis result taking the user requirements as rules.
In the embodiment, the relevance analysis is performed after the multidimensional report is generated through the data analysis model, the multidimensional self-service analysis service of data is provided for the user, the comprehensiveness and the accuracy of the multidimensional data analysis are improved, the analysis result can be displayed in various different information display forms, and the diversity and the intuitiveness of the data analysis are improved.
Second embodiment of the invention:
please refer to fig. 4-5.
As shown in fig. 4, the present embodiment provides a multidimensional data analysis system, including:
the data acquisition module 100 is used for performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component;
specifically, for the data acquisition module 100, data acquisition is performed on each big data platform and each relational database that are docked by the analysis system through the distributed OLAP engine configuration, after a data interface and acquired data are provided, data preprocessing is performed on the acquired data, so that basic data are provided for a subsequent multidimensional data analysis model, and after the preprocessing is completed, unified and standard conversion processing is performed on data in different formats, so that the efficiency of subsequent data analysis can be improved.
The data analysis model module 200 is used for performing multidimensional data analysis on the acquired data through a pre-constructed data analysis model after the preprocessing is finished, and generating a corresponding multidimensional report according to a report rule defined by a user;
specifically, for the data analysis model module 200, after data preprocessing is completed, multidimensional data analysis is performed on data acquired by the distributed OLAP engine component through the constructed data analysis model, and in the analysis process, a multidimensional data report with the user-defined report rule as the reference is correspondingly generated according to the report rule pre-defined by the user on the system.
And the multidimensional analysis module 300 is configured to perform corresponding drilling operation on the data analysis model according to user requirements, perform correlation analysis on a plurality of multidimensional reports, generate corresponding detailed reports, perform statistical analysis, and finally analyze to obtain a multidimensional analysis result using the user requirements as rules.
Specifically, for the multidimensional analysis module 300, drilling operation is performed on the data analysis model according to different requirements of the user, and correlation analysis is performed on a plurality of multidimensional data reports generated in the previous step, wherein the criterion of the correlation analysis is based on the requirements of the user, and a corresponding detail report is generated after the correlation analysis is completed, so that a plurality of previous reports of a single dimension are correlated to form a multidimensional detail report, and statistical analysis is performed on the detail report, so that a multidimensional analysis result corresponding to the requirements of the user is obtained.
In a preferred embodiment, as shown in fig. 5, the multidimensional data analysis system further comprises:
the visualization module 400 is configured to generate a corresponding dynamic analysis report after the multidimensional analysis result is obtained, and display a report through a visualization image; and displaying the report form through the visual image, wherein the report form comprises cockpit monitoring, a large data screen, graphic visualization and map analysis.
Specifically, after a multi-dimensional analysis result is obtained, a corresponding dynamic analysis report is generated according to the result, and a report is displayed through a visual image, such as a cockpit, a data large screen, a graphic visualization and a map analysis.
In a preferred embodiment, as shown in fig. 5, the multidimensional data analysis system further comprises:
and the data mining module 500 is used for performing data mining including prediction, clustering and association rule mining according to the multi-dimensional analysis result, and extracting corresponding association data.
Specifically, after the multi-dimensional analysis result is obtained, the user can perform data mining on the analysis result according to the requirement, and finally extract and obtain the relevance data corresponding to the user requirement through methods such as prediction algorithm, clustering and relevance rule mining, so that the user can perform further multi-dimensional analysis on the relevance data obtained by mining subsequently, and the comprehensiveness and practicability of data analysis are improved.
The multidimensional data analysis system provided by the embodiment comprises: the data acquisition module 100 is used for performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component; the data analysis model module 200 is used for performing multidimensional data analysis on the acquired data through a pre-constructed data analysis model after the preprocessing is finished, and generating a corresponding multidimensional report according to a report rule defined by a user; and the multidimensional analysis module 300 is configured to perform corresponding drilling operation on the data analysis model according to user requirements, perform correlation analysis on a plurality of multidimensional reports, generate corresponding detailed reports, perform statistical analysis, and finally analyze to obtain a multidimensional analysis result using the user requirements as rules.
Compared with the prior art, the embodiment can generate the multidimensional report through the data analysis model and then perform the correlation analysis, provide the multidimensional self-service analysis service of the data for the user, improve the comprehensiveness and accuracy of the multidimensional data analysis, display the analysis result in various different information display forms, and improve the diversity and intuition of the data analysis.
An embodiment of the present invention provides a terminal device for multidimensional data analysis, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the multidimensional data analysis method as described above when executing the computer program.
An embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls an apparatus on which the computer-readable storage medium is located to perform the multidimensional data analysis method as described above.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the modules may be a logical division, and in actual implementation, there may be another division, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A multidimensional data analysis method is characterized by at least comprising the following steps:
performing data acquisition and data preprocessing on a big data platform and a relational database through a distributed OLAP engine component;
after the preprocessing is finished, carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model, and generating a corresponding multi-dimensional report according to a report rule defined by a user;
and performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multi-dimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multi-dimensional analysis result taking the user requirements as rules.
2. The multidimensional data analysis method according to claim 1, wherein the data preprocessing specifically comprises:
firstly, analyzing collected data, and analyzing an MDX statement into a standard SQL statement;
and sequentially extracting, screening and summarizing the analyzed data to construct a plurality of data cubes and storing the data cubes into a bottom database.
3. The multidimensional data analysis method according to claim 1, wherein the data analysis model is constructed in a manner that:
after the data table is established and the corresponding data source is configured, the corresponding data table is loaded, the fact table and the dimension table are selected, the dimension and the index are selected, and finally the data analysis model is constructed.
4. The method of multidimensional data analysis according to claim 1, further comprising:
and slicing and rotating the data analysis model according to the user requirements.
5. The method of multidimensional data analysis according to claim 1, further comprising:
after the multi-dimensional analysis result is obtained, generating a corresponding dynamic analysis report, and displaying a report through a visual image; and displaying the report form through the visual image, wherein the report form comprises cockpit monitoring, a large data screen, graphic visualization and map analysis.
6. The method of multidimensional data analysis according to claim 1, further comprising:
and performing data mining including prediction, clustering and association rule mining according to the multi-dimensional analysis result, and extracting to obtain corresponding association data.
7. The method of multidimensional data analysis according to claim 1, further comprising:
and carrying out influence analysis and pedigree analysis on the collected data through a metadata management technology, and analyzing to obtain a reference relation and a dependency relation among the data.
8. A multidimensional data analysis system, comprising:
the data acquisition module is used for carrying out data acquisition and data preprocessing on the big data platform and the relational database through the distributed OLAP engine component;
the data analysis model module is used for carrying out multi-dimensional data analysis on the acquired data through a pre-constructed data analysis model after the preprocessing is finished, and generating a corresponding multi-dimensional report according to a report rule defined by a user;
and the multidimensional analysis module is used for performing corresponding drilling operation on the data analysis model according to user requirements, performing correlation analysis on a plurality of multidimensional reports, generating corresponding detailed reports, performing statistical analysis, and finally analyzing to obtain a multidimensional analysis result taking the user requirements as rules.
9. A terminal device for multidimensional data analysis, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the multidimensional data analysis method as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls an apparatus to execute the multidimensional data analysis method according to any one of claims 1 to 7.
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