A method and system for intelligent data analysis and mining supporting multiple dimensions and multiple scenarios

Abstract

The present invention discloses a method and system for intelligent data analysis and mining that supports multiple dimensions and multiple scenarios, and the method includes: determining the business needs of users, determining the user screening dimension indicators according to the business needs, building a custom report according to the screening dimension indicators and retrieving business data to import into the custom report; obtaining the user's data drill-down statistical needs, determining the statistical data level according to the data drill-down statistical needs, and tracking and screening the imported business data based on the statistical data level; generating a statistical report based on business objects according to the statistical business data in the custom report and displaying it in a variety of ways; pre-saving the statistical report as a template for subsequent data analysis through a preset bookmark function. Reports can be intelligently generated according to the user's business needs for rapid analysis, which improves the efficiency of data analysis while synchronizing the data statistical caliber to achieve data collection with the same concept and ensure data quality.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to an intelligent data analysis and mining method and system supporting multiple dimensions and multiple scenarios. Background Art

[0002] Intelligent data analysis and mining methods that support multi-dimensional and multi-scenario analysis are technologies for analyzing complex and diverse data, aiming to discover the underlying laws and relationships behind the data and provide users with valuable decision support. In the current information society, data in various fields are highly complex and diverse, so traditional data analysis methods can no longer meet these needs. Since there are multiple business systems between various departments and branches of an enterprise, and the data between each business system is independent of each other, there is a lack of data resource sharing mechanism. Therefore, data sharing platforms came into being. Existing data sharing platforms are all uploaded by various enterprises. Due to different statistical calibers, the data quality is uneven, data analysis is difficult, and traditional data visualization processing is highly dependent on manual operations. Handwriting, word, excel and other methods are often used for chart drawing and data analysis, which reduces the efficiency and practicality of data analysis. Summary of the invention

[0003] In response to the above-mentioned problems, the present invention provides an intelligent data analysis and mining method and system that supports multiple dimensions and scenarios to solve the problems mentioned in the background technology, such as uneven data quality due to different statistical calibers, high difficulty in data analysis, and traditional data visualization processing that is highly dependent on manual operations, mostly using handwriting, Word, Excel and other methods for chart drawing and data analysis, which reduces the efficiency and practicality of data analysis.

[0004] A multi-dimensional and multi-scenario intelligent data analysis and mining method includes the following steps:

[0005] Determine the user's business needs, determine the user's screening dimension indicators based on the business needs, build a custom report based on the screening dimension indicators, and retrieve business data and import it into the custom report;

[0006] Obtain the user's data drill-down statistical requirements, determine the statistical data level according to the data drill-down statistical requirements, and track and filter the imported business data based on the statistical data level;

[0007] Generate business object-based statistical reports based on the statistical business data in the custom report and display them in various ways;

[0008] Use the preset bookmark function to save statistical reports as templates for subsequent data analysis.

[0009] Preferably, the determining of the user's business needs, determining the user's screening dimension indicators according to the business needs, building a custom report according to the screening dimension indicators and retrieving business data and importing it into the custom report includes:

[0010] Collect user operation behavior data, determine user business needs based on the operation behavior data, and determine user data usage scenarios based on business needs;

[0011] Determine the user's business analysis needs based on the data usage scenario, and determine the expected business dimensions and expected business indicators based on the business analysis needs;

[0012] Determine the filter dimension indicators based on the expected business dimensions and expected business indicators and build custom reports;

[0013] Determine business projects based on expected business dimensions and expected business indicators, and obtain data attributes corresponding to the business projects;

[0014] Build a data model based on business items and their corresponding data attributes, and write dynamic SQL statements based on the data model;

[0015] Use SQL query tools to query dynamic SQL statements in the database to retrieve dynamic business data and import the dynamic business data into custom reports.

[0016] Preferably, obtaining the user's data drill-down statistical requirements, determining the statistical data level according to the data drill-down statistical requirements, and tracking and screening the imported business data based on the statistical data level include:

[0017] Obtain the user's comparative analysis requirements, and determine the user's expected comparative analysis details based on the comparative analysis requirements;

[0018] Determine the user's data drill-down statistical needs based on the expected comparative analysis details, and determine the data statistical caliber and data focus level based on the data drill-down statistical needs;

[0019] Determine the data statistical level according to the data statistical caliber and data focus level, and determine the data mining indicators based on the data statistical level;

[0020] Track and filter the imported business data based on data mining indicators.

[0021] Preferably, generating a statistical report based on business objects according to the statistical business data in the custom report and displaying it in a variety of ways include:

[0022] Convert statistical business data into time series data and load it into the data computing framework, and preprocess the loaded data through the data computing framework;

[0023] Obtain the business objects corresponding to the statistical business data, determine the business attributes of the business objects, and determine the business analysis dimensions based on the business attributes;

[0024] Perform data analysis based on the business analysis dimension through the data model, and generate statistical reports based on business objects according to the analysis results;

[0025] Determine the display method based on the user's business scenario and needs, and display the statistical report according to the display method.

[0026] Preferably, pre-storing the statistical report as a template for subsequent data analysis by using a preset bookmark function includes:

[0027] Save the statistical report as a bookmark template through the preset bookmark function, and edit and modify the storage location of the bookmark in the template view;

[0028] Determine the adjustable dimensions in the statistical report and the indicator items corresponding to each adjustable dimension;

[0029] Perform data screening and subsequent analysis on the subsequently imported data according to the indicator items corresponding to each adjustable dimension;

[0030] Perform data screening and subsequent analysis on the subsequent imported data based on the indicator items corresponding to each adjustable dimension, including:

[0031] Obtain the data quantification standard of the indicator item corresponding to each adjustable dimension, and determine the generalized analysis data indicator based on the quantification standard;

[0032] Obtain multiple narrow analysis data indicators corresponding to the broad analysis data indicator, and obtain entity attributes of each narrow analysis data indicator;

[0033] Extract relevant analytical data from subsequent imported data based on entity attributes and user analytical data indicators;

[0034] Conduct subsequent analysis on relevant analytical data.

[0035] An intelligent data analysis and mining system supporting multiple dimensions and multiple scenarios, the system comprising:

[0036] The retrieval module is used to determine the user's business needs, determine the user's screening dimension indicators based on the business needs, build a custom report based on the screening dimension indicators, and retrieve business data and import it into the custom report;

[0037] The screening module is used to obtain the user's data drill-down statistical requirements, determine the statistical data level according to the data drill-down statistical requirements, and track and screen the imported business data based on the statistical data level;

[0038] The generation module is used to generate statistical reports based on business objects according to the statistical business data in the custom report and display them in various ways;

[0039] The pre-storage module is used to pre-save statistical reports as templates for subsequent data analysis through the preset bookmark function.

[0040] Preferably, the calling module includes:

[0041] The first determination submodule is used to collect the user's operation behavior data, determine the user's business needs based on the operation behavior data, and determine the user's data usage scenario based on the business needs;

[0042] The second determination submodule is used to determine the user's business analysis needs according to the data usage scenario, and determine the expected business dimensions and expected business indicators according to the business analysis needs;

[0043] Construct submodules to determine the screening dimension indicators and build custom reports based on the expected business dimensions and expected business indicators;

[0044] The third determination submodule is used to determine the business project according to the expected business dimension and the expected business indicator, and obtain the data attributes corresponding to the business project;

[0045] Write submodules to build data models based on business items and their corresponding data attributes, and write dynamic SQL statements based on the data models;

[0046] The retrieval submodule is used to use the SQL query tool to query dynamic SQL statements in the database to retrieve dynamic business data and import the dynamic business data into a custom report.

[0047] Preferably, the screening module comprises:

[0048] The fourth determination submodule is used to obtain the user's comparative analysis requirements and determine the user's expected comparative analysis details according to the comparative analysis requirements;

[0049] The fifth determination submodule is used to determine the user's data drill-down statistical needs according to the expected comparative analysis details, and determine the data statistical caliber and data focus level according to the data drill-down statistical needs;

[0050] A sixth determination submodule is used to determine the data statistical level according to the data statistical caliber and the data focus level, and determine the data mining index based on the data statistical level;

[0051] The filtering submodule is used to track and filter the imported business data according to data mining indicators.

[0052] Preferably, the generating module comprises:

[0053] The preprocessing submodule is used to convert the statistical business data into time series data and load it into the data computing framework, and preprocess the loaded data through the data computing framework;

[0054] A seventh determination submodule is used to obtain the business object corresponding to the statistical business data, determine the business attributes of the business object, and determine the business analysis dimension based on the business attributes;

[0055] The generation submodule is used to perform data analysis based on the business analysis dimension through the data model and generate statistical reports based on business objects according to the analysis results;

[0056] The display submodule is used to determine the display method according to the user's business scenarios and needs, and to display the statistical report according to the display method.

[0057] Preferably, the pre-stored module includes:

[0058] The Save As submodule is used to save the statistical report as a bookmark template through the preset bookmark function, and edit and modify the storage location of the bookmark in the template view;

[0059] An eighth determination submodule is used to determine the adjustable dimensions in the statistical report and the indicator items corresponding to each adjustable dimension;

[0060] The analysis submodule is used to filter and analyze the subsequent imported data according to the indicator items corresponding to each adjustable dimension;

[0061] Analysis submodules include:

[0062] A determination unit, used to obtain a data quantification standard of an indicator item corresponding to each adjustable dimension, and determine a generalized analysis data indicator based on the quantification standard;

[0063] An acquisition unit, used to acquire multiple narrow analysis data indicators corresponding to the broad analysis data indicator, and acquire entity attributes of each narrow analysis data indicator;

[0064] An extraction unit, used to extract relevant analytical data from the subsequently imported data according to entity attributes and the analytical data indicators of the user;

[0065] The analysis unit is used for subsequent analysis of the relevant analysis data.

[0066] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0067] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0069] Figure 1 A workflow diagram of an intelligent data analysis and mining method supporting multiple dimensions and multiple scenarios provided by the present invention;

[0070] Figure 2 Another workflow diagram of an intelligent data analysis and mining method supporting multi-dimensional and multi-scenario provided by the present invention;

[0071] Figure 3 A schematic diagram of the structure of an intelligent data analysis and mining system supporting multiple dimensions and multiple scenarios provided by the present invention;

[0072] Figure 4 This is a structural schematic diagram of a screening module in an intelligent data analysis and mining system supporting multiple dimensions and multiple scenarios provided by the present invention. DETAILED DESCRIPTION

[0073] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0074] The intelligent data analysis and mining method supporting multi-dimensional and multi-scenario is a technology for analyzing complex and diversified data, aiming to discover the potential laws and relationships behind the data and provide valuable decision support for users. In the current information society, data in various fields are highly complex and diverse, so traditional data analysis methods can no longer meet these needs. Since there are multiple business systems between various departments and branches of an enterprise, the data between each business system is independent of each other, so there is a lack of data resource sharing mechanism. Therefore, a data sharing platform came into being. The existing data sharing platforms are all uploaded by various enterprises. Due to different statistical calibers, the data quality is uneven, the data analysis is difficult, and the traditional data visualization processing is highly dependent on manual operations. Handwriting, word, excel and other methods are often used for chart drawing and data analysis, which reduces the efficiency and practicality of data analysis. In order to solve the above problems, this embodiment discloses an intelligent data analysis and mining method supporting multi-dimensional and multi-scenario based on unified data statistical caliber and intelligent analysis.

[0075] An intelligent data analysis and mining method that supports multiple dimensions and multiple scenarios, such as Figure 1 As shown, the following steps are included:

[0076] Step S101: determine the business needs of the user, determine the user screening dimension indicators according to the business needs, build a custom report according to the screening dimension indicators, and retrieve business data and import it into the custom report;

[0077] Step S102: obtaining the user's data drill-down statistical requirements, determining the statistical data level according to the data drill-down statistical requirements, and tracking and filtering the imported business data based on the statistical data level;

[0078] Step S103: Generate a statistical report based on the business object according to the statistical business data in the custom report and display it in various ways;

[0079] Step S104: Pre-save the statistical report as a template through the preset bookmark function for subsequent data analysis.

[0080] The working principle of the above technical solution is: determine the user's business needs, determine the user's filtering dimension indicators based on the business needs, build a custom report based on the filtering dimension indicators and retrieve the business data and import it into the custom report; obtain the user's data drill-down statistical needs, determine the statistical data level based on the data drill-down statistical needs, and track and filter the imported business data based on the statistical data level; generate a statistical report based on the business object according to the statistical business data in the custom report and display it in a variety of ways; use the preset bookmark function to pre-save the statistical report as a template for subsequent data analysis.

[0081] The beneficial effects of the above technical solution are: by building custom reports and importing dynamic business data for comparative analysis and statistical display, reports can be intelligently generated according to the user's business needs for rapid analysis, thereby improving data analysis efficiency. At the same time, by determining the statistical data level according to the statistical needs of data drilling down to filter or add data, the statistical caliber of data can be synchronized to achieve data collection with the same concept, thereby ensuring data quality. This solves the problems mentioned in the prior art that due to different statistical calibers, data quality is uneven, data analysis is difficult, and traditional data visualization processing is highly dependent on manual operations, and chart drawing and data analysis are mostly carried out by handwriting, word, excel, etc., which reduces the efficiency and practicality of data analysis.

[0082] In this embodiment, before data tracking and filtering are performed on the imported business data based on the statistical data level, the following steps are also included:

[0083] Obtain the user's analytical data indicators, and extract relevant analytical data from the business data based on the analytical data indicators;

[0084] Determine data representation, data core indicators and data format based on preset data standard parameters, and determine statistical caliber based on data representation and data core indicators;

[0085] Perform data conversion processing on the relevant analysis data according to the statistical caliber to obtain the converted relevant analysis data;

[0086] Determine the descriptive statistics of the extracted core data according to the statistical caliber, and screen the converted related analysis data according to the descriptive statistics;

[0087] According to the screening results, the sub-datasets that meet the requirements are obtained from the relevant analysis data, and the sub-datasets are confirmed as core data.

[0088] The beneficial effect of the above technical solution is that by screening the converted related analysis data according to the data descriptive statistics, the consistency between the core data can be ensured, which not only ensures the high-quality representation of the data but also lays the foundation for subsequent comparative analysis, further improving the practicality and reliability.

[0089] In this embodiment, before building a custom report based on the screening dimension index and retrieving business data and importing it into the custom report, it also includes:

[0090] Obtaining data source object attributes of various source data, and determining data query attributes and data entry attributes of various source data according to the data source object attributes;

[0091] Determine the query path of various source data based on data query attributes and data entry attributes, and obtain query condition parameters and verification indicator parameters for each query path;

[0092] Determine query qualification parameters for each type of source data based on query condition parameters and verification indicator parameters, and define the operator set for each type of source data according to the query qualification parameters;

[0093] Determine the response function based on the operator set, and configure the query import variables for various source data in the source database according to the response function;

[0094] Determine logical conditions based on query import variables, and determine relevant business attributes of various source data based on logical conditions;

[0095] Pre-retrieve matching target source data based on relevant business attributes and user business needs as business data samples to be retrieved;

[0096] The target business data selected by the user in the business data sample to be retrieved is obtained as the report import data.

[0097] In this embodiment, the data source object attributes include: state-owned enterprises, private enterprises, and shopping malls;

[0098] In this embodiment, the data query attributes include: first-level encrypted query, second-level encrypted query, and ordinary encrypted query;

[0099] In this embodiment, the data entry attributes are represented as data entry tag attributes of each source data, including: enterprise solutions, enterprise projects, shopping mall brands, etc.

[0100] The beneficial effects of the above technical solution are: by pre-retrieval of business samples, relevant business data samples can be quickly screened out in the first time according to the user's business needs, thereby improving data screening efficiency, and at the same time providing users with a reasonable data sample reference range for selection, thereby improving work efficiency and user experience.

[0101] In one embodiment, determining the business needs of the user, determining the user screening dimension indicators according to the business needs, building a custom report according to the screening dimension indicators, and retrieving business data and importing it into the custom report includes:

[0102] Collect user operation behavior data, determine user business needs based on the operation behavior data, and determine user data usage scenarios based on business needs;

[0103] Determine the user's business analysis needs based on the data usage scenario, and determine the expected business dimensions and expected business indicators based on the business analysis needs;

[0104] Determine the filter dimension indicators based on the expected business dimensions and expected business indicators and build custom reports;

[0105] Determine business projects based on expected business dimensions and expected business indicators, and obtain data attributes corresponding to the business projects;

[0106] Build a data model based on business items and their corresponding data attributes, and write dynamic SQL statements based on the data model;

[0107] Use SQL query tools to query dynamic SQL statements in the database to retrieve dynamic business data and import the dynamic business data into custom reports.

[0108] The beneficial effects of the above technical solution are: by building custom reports, report templates can be quickly generated according to the user's business scenario requirements, achieving thousands of reports for thousands of people, and improving the user experience. Furthermore, by dynamically querying business data, dynamic business data can be obtained in real time to ensure data reliability and quality.

[0109] In one embodiment, Figure 2 As shown, the method of obtaining the user's data drill-down statistical requirements, determining the statistical data level according to the data drill-down statistical requirements, and tracking and filtering the imported business data based on the statistical data level includes:

[0110] Step S201: Obtain the comparative analysis requirements of the user, and determine the expected comparative analysis details of the user according to the comparative analysis requirements;

[0111] Step S202: Determine the user's data drill-down statistical requirements according to the expected comparative analysis details, and determine the data statistical caliber and data focus level according to the data drill-down statistical requirements;

[0112] Step S203: determining the data statistical level according to the data statistical caliber and the data focus level, and determining the data mining index based on the data statistical level;

[0113] Step S204: Track and filter the imported business data according to the data mining indicators.

[0114] The beneficial effects of the above technical solution are: through drill-down analysis of multi-dimensional indicators, in-depth mining of transaction data, insight into data information, realization of data penetration function, tracking of the smallest granularity data, and precise analysis at the most microscopic level, thereby improving data quality and accuracy.

[0115] In one embodiment, the statistical report based on the business object is generated according to the statistical business data in the custom report and displayed in a variety of ways, including:

[0116] Convert statistical business data into time series data and load it into the data computing framework, and preprocess the loaded data through the data computing framework;

[0117] Obtain the business objects corresponding to the statistical business data, determine the business attributes of the business objects, and determine the business analysis dimensions based on the business attributes;

[0118] Perform data analysis based on the business analysis dimension through the data model, and generate statistical reports based on business objects according to the analysis results;

[0119] Determine the display method based on the user's business scenario and needs, and display the statistical report according to the display method.

[0120] The beneficial effects of the above technical solution are: by determining the business analysis dimension and then conducting rapid data analysis, the analysis focus and analysis elements can be quickly determined to conduct data analysis, thereby improving analysis efficiency. Furthermore, by determining the display method, the user's preferred display method can be provided according to user needs, thereby improving the user experience.

[0121] In one embodiment, pre-storing the statistical report as a template for subsequent data analysis by using a preset bookmark function includes:

[0122] Save the statistical report as a bookmark template through the preset bookmark function, and edit and modify the storage location of the bookmark in the template view;

[0123] Determine the adjustable dimensions in the statistical report and the indicator items corresponding to each adjustable dimension;

[0124] Perform data screening and subsequent analysis on the subsequently imported data according to the indicator items corresponding to each adjustable dimension;

[0125] Perform data screening and subsequent analysis on the subsequent imported data based on the indicator items corresponding to each adjustable dimension, including:

[0126] Obtain the data quantification standard of the indicator item corresponding to each adjustable dimension, and determine the generalized analysis data indicator based on the quantification standard;

[0127] Obtain multiple narrow analysis data indicators corresponding to the broad analysis data indicator, and obtain entity attributes of each narrow analysis data indicator;

[0128] Extract relevant analytical data from subsequent imported data based on entity attributes and user analytical data indicators;

[0129] Conduct subsequent analysis on relevant analytical data.

[0130] The beneficial effects of the above technical solution are: by constructing template tags, users can directly perform data analysis by adjusting parameters such as dates when performing subsequent data retrieval and analysis, thereby improving analysis efficiency and practicality. Furthermore, by extracting relevant analysis data from subsequent imported data based on entity attributes and user's analysis data indicators, core analysis data can be quickly screened out, thereby improving data screening accuracy and efficiency.

[0131] In one embodiment, this embodiment also discloses an intelligent data analysis and mining system that supports multiple dimensions and multiple scenarios, such as Figure 3 As shown, the system includes:

[0132] The retrieval module 301 is used to determine the business needs of the user, determine the user screening dimension indicators according to the business needs, build a custom report according to the screening dimension indicators, and retrieve the business data and import it into the custom report;

[0133] The screening module 302 is used to obtain the user's data drill-down statistical requirements, determine the statistical data level according to the data drill-down statistical requirements, and track and screen the imported business data based on the statistical data level;

[0134] A generating module 303, used to generate a statistical report based on business objects according to the statistical business data in the custom report and display it in various ways;

[0135] The pre-storage module 304 is used to pre-storage the statistical report as a template for subsequent data analysis through a preset bookmark function.

[0136] In one embodiment, the calling module includes:

[0137] The first determination submodule is used to collect the user's operation behavior data, determine the user's business needs based on the operation behavior data, and determine the user's data usage scenario based on the business needs;

[0138] The second determination submodule is used to determine the user's business analysis needs according to the data usage scenario, and determine the expected business dimensions and expected business indicators according to the business analysis needs;

[0139] Construct submodules to determine the screening dimension indicators and build custom reports based on the expected business dimensions and expected business indicators;

[0140] The third determination submodule is used to determine the business project according to the expected business dimension and the expected business indicator, and obtain the data attributes corresponding to the business project;

[0141] Write submodules to build data models based on business items and their corresponding data attributes, and write dynamic SQL statements based on the data models;

[0142] The retrieval submodule is used to use the SQL query tool to query dynamic SQL statements in the database to retrieve dynamic business data and import the dynamic business data into a custom report.

[0143] In one embodiment, Figure 4 As shown, the screening module 302 includes:

[0144] The fourth determination submodule 3021 is used to obtain the comparative analysis requirements of the user and determine the expected comparative analysis details of the user according to the comparative analysis requirements;

[0145] The fifth determination submodule 3022 is used to determine the user's data drill-down statistical requirements according to the expected comparative analysis details, and determine the data statistical caliber and data focus level according to the data drill-down statistical requirements;

[0146] The sixth determination submodule 3023 is used to determine the data statistical level according to the data statistical caliber and the data focus level, and determine the data mining index based on the data statistical level;

[0147] The screening submodule 3024 is used to track and screen the imported business data according to the data mining indicators.

[0148] In one embodiment, the generating module comprises:

[0149] The preprocessing submodule is used to convert the statistical business data into time series data and load it into the data computing framework, and preprocess the loaded data through the data computing framework;

[0150] A seventh determination submodule is used to obtain the business object corresponding to the statistical business data, determine the business attributes of the business object, and determine the business analysis dimension based on the business attributes;

[0151] The generation submodule is used to perform data analysis based on the business analysis dimension through the data model and generate statistical reports based on business objects according to the analysis results;

[0152] The display submodule is used to determine the display method according to the user's business scenarios and needs, and to display the statistical report according to the display method.

[0153] In one embodiment, the pre-stored module includes:

[0154] The Save As submodule is used to save the statistical report as a bookmark template through the preset bookmark function, and edit and modify the storage location of the bookmark in the template view;

[0155] An eighth determination submodule is used to determine the adjustable dimensions in the statistical report and the indicator items corresponding to each adjustable dimension;

[0156] The analysis submodule is used to filter and analyze the subsequent imported data according to the indicator items corresponding to each adjustable dimension;

[0157] Analysis submodules include:

[0158] A determination unit, used to obtain a data quantification standard of an indicator item corresponding to each adjustable dimension, and determine a generalized analysis data indicator based on the quantification standard;

[0159] An acquisition unit, used to acquire multiple narrow analysis data indicators corresponding to the broad analysis data indicator, and acquire entity attributes of each narrow analysis data indicator;

[0160] An extraction unit, used to extract relevant analytical data from the subsequently imported data according to entity attributes and the analytical data indicators of the user;

[0161] The analysis unit is used for subsequent analysis of the relevant analysis data.

[0162] In one embodiment, the data middle platform uses public cloud, private cloud, and government cloud as computing resources, based on a technical framework deployed with middleware, container support, computing engine, scheduling engine, etc. The data source includes the collection of internal business system data such as construction projects, property rights transactions, and sunshine leasing, as well as external data from public resource transaction centers in various provinces.

[0163] In terms of data warehouse, the data source is extracted, converted and loaded into the data warehouse. The data warehouse is built according to the concept of layered design, which includes the source layer, standard implementation layer, detail layer, summary layer and application layer from bottom to top. It also supports storage and calculation of unstructured files such as bidding documents and contract documents.

[0164] In terms of data application, the platform realizes the service-oriented model result of the data application layer, supports the provision of data services to various data applications in the form of API interfaces, and realizes dynamic monitoring and management of data services.

[0165] Among them, data is divided into six parts: data preparation, data access, data storage, data processing, data warehouse storage, and query. Data preparation and access include various types of data sources, such as FTP data sources corresponding to file data, business system data, API data, log data, and website data. Through ETL, data is stored in Hbase, HDFS, and Kafka databases. After data processing by KylinCube data analysis engine, MapReduce distributed computing program programming framework, Spark and Storm stream processing components, the data results are stored in the data master and slave databases, and finally queried through dynamic SQL. The query structure can be dynamically adjusted according to different conditions, which greatly improves the flexibility of the application in data processing. The front end of the system allows users to enter a combination of multiple dimensions and indicators, and the back end flexibly constructs query statements based on the input content. In this way, the system realizes the dynamic query function, which can meet the needs of different users and achieve the effect of "thousands of tables for thousands of people".

[0166] In this system, there are multiple functional components, specifically:

[0167] 1.1 Page filter items, including multiple filter conditions, support global filtering, can expand the filter items, enter the query conditions;

[0168] 1.2 Indicator card: Based on data standardization work, the indicator card displays the core indicators of concern in different business scenarios, with caliber descriptions attached;

[0169] 1.3 Reports, including customization, display of all data details, and data export functions, support user-defined report indicators and dimensions, and realize one thousand tables for one thousand people. Click "all data details" or report values ​​to drill down to data details, that is, detailed information of specific projects, and export reports and all detailed data tables;

[0170] 1.4 Customization supports the realization of one thousand reports for one thousand people by selecting dimensions and indicators. For example: Scenario 1: Business personnel analyze the number of bid opening projects in each region and other situations. Users can click the Customize button, check the dimension "Project Region", check the indicator "Number of bid opening projects", etc., to generate a report. The first column of the report is the project area segmentation, and the second column is the result value of indicators such as the number of bid opening projects. Scenario 2: Business personnel analyze the number of bid opening projects in each region in each month and other situations. You can add the dimension "Bid Opening Date" before "Project Region" to generate a report to obtain the project bid opening conditions in each region in each month;

[0171] 1.5 All data details, based on custom dimensions and indicators, get the report data of the report. You can further click the report value or directly click the All Data Details button to drill down the data, including project number, project name, section name, purchaser, etc.; enter the filter conditions, click the "Customize" button, select the header field, and customize the data display;

[0172] 1.6 Data export, supports exporting custom reports in excel and csv formats, select the file format, apply for export and make approval;

[0173] 1.7 Add bookmarks, support saving report query results as "bookmarks", click the bookmark to jump to the data dashboard, and the data display results are the data generated by the data filtering conditions at that time. For example: Scenario 1: Data analysis of bidding projects every quarter, you can filter the time range of a certain quarter, customize the data report, save it as a bookmark, and when analyzing other quarters, you can directly open the bookmark and adjust the time range to the second quarter without repeatedly filtering the report fields.

[0174] Those skilled in the art should understand that the first and second in the present invention merely refer to different application stages.

[0175] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0176] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for intelligent data analysis and mining that supports multiple dimensions and multiple scenarios, characterized in that: The following steps are involved: Determine the user's business needs, determine the user's screening dimension indicators based on the business needs, build a custom report based on the screening dimension indicators, and retrieve business data and import it into the custom report; Obtain the user's data drill-down statistical requirements, determine the statistical data level according to the data drill-down statistical requirements, and track and filter the imported business data based on the statistical data level; Generate business object-based statistical reports based on the statistical business data in the custom report and display them in various ways; Pre-save statistical reports as templates for subsequent data analysis through the preset bookmark function; Determining the business needs of the user, determining the user screening dimension indicators according to the business needs, building a custom report according to the screening dimension indicators, and retrieving business data and importing it into the custom report, includes: Collect user operation behavior data, determine user business needs based on the operation behavior data, and determine user data usage scenarios based on business needs; Determine the user's business analysis needs based on the data usage scenario, and determine the expected business dimensions and expected business indicators based on the business analysis needs; Determine the filter dimension indicators based on the expected business dimensions and expected business indicators and build custom reports; Determine business projects based on expected business dimensions and expected business indicators, and obtain data attributes corresponding to the business projects; Build a data model based on business items and their corresponding data attributes, and write dynamic SQL statements based on the data model; Use SQL query tools to query dynamic SQL statements in the database to retrieve dynamic business data and import the dynamic business data into custom reports; The obtaining of the user's data drill-down statistical requirements, determining the statistical data level according to the data drill-down statistical requirements, and tracking and filtering the imported business data based on the statistical data level include: Obtain the user's comparative analysis requirements, and determine the user's expected comparative analysis details based on the comparative analysis requirements; Determine the user's data drill-down statistical needs based on the expected comparative analysis details, and determine the data statistical caliber and data focus level based on the data drill-down statistical needs; Determine the data statistical level according to the data statistical caliber and data focus level, and determine the data mining indicators based on the data statistical level; Track and filter the imported business data according to data mining indicators; The statistical report is pre-saved as a template for subsequent data analysis through the preset bookmark function, including: Save the statistical report as a bookmark template through the preset bookmark function, and edit and modify the storage location of the bookmark in the template view; Determine the adjustable dimensions in the statistical report and the indicator items corresponding to each adjustable dimension; Perform data screening and subsequent analysis on the subsequently imported data according to the indicator items corresponding to each adjustable dimension; Perform data screening and subsequent analysis on the subsequent imported data based on the indicator items corresponding to each adjustable dimension, including: Obtain the data quantification standard of the indicator item corresponding to each adjustable dimension, and determine the generalized analysis data indicator based on the quantification standard; Obtain multiple narrow analysis data indicators corresponding to the broad analysis data indicator, and obtain entity attributes of each narrow analysis data indicator; Extract relevant analytical data from subsequent imported data based on entity attributes and user analytical data indicators; Conduct subsequent analysis on relevant analytical data.

2. The intelligent data analysis and mining method supporting multi-dimensional and multi-scenario according to claim 1 is characterized in that: The statistical report based on the business object is generated according to the statistical business data in the custom report and displayed in various ways, including: Convert statistical business data into time series data and load it into the data computing framework, and preprocess the loaded data through the data computing framework; Obtain the business objects corresponding to the statistical business data, determine the business attributes of the business objects, and determine the business analysis dimensions based on the business attributes; Perform data analysis based on the business analysis dimension through the data model, and generate statistical reports based on business objects according to the analysis results; Determine the display method based on the user's business scenario and needs, and display the statistical report according to the display method.

3. An intelligent data analysis and mining system supporting multiple dimensions and multiple scenarios, characterized in that: The system includes: The retrieval module is used to determine the user's business needs, determine the user's screening dimension indicators based on the business needs, build a custom report based on the screening dimension indicators, and retrieve business data and import it into the custom report; The screening module is used to obtain the user's data drill-down statistical requirements, determine the statistical data level according to the data drill-down statistical requirements, and track and screen the imported business data based on the statistical data level; The generation module is used to generate statistical reports based on business objects according to the statistical business data in the custom report and display them in various ways; Pre-storage module, used to save statistical reports as templates for subsequent data analysis through the preset bookmark function; The calling module includes: The first determination submodule is used to collect the user's operation behavior data, determine the user's business needs based on the operation behavior data, and determine the user's data usage scenario based on the business needs; The second determination submodule is used to determine the user's business analysis needs according to the data usage scenario, and determine the expected business dimensions and expected business indicators according to the business analysis needs; Construct submodules to determine the screening dimension indicators and build custom reports based on the expected business dimensions and expected business indicators; The third determination submodule is used to determine the business project according to the expected business dimension and the expected business indicator, and obtain the data attributes corresponding to the business project; Write submodules to build data models based on business items and their corresponding data attributes, and write dynamic SQL statements based on the data models; The retrieval submodule is used to use the SQL query tool to query dynamic SQL statements in the database to retrieve dynamic business data and import the dynamic business data into a custom report; The screening module comprises: The fourth determination submodule is used to obtain the user's comparative analysis requirements and determine the user's expected comparative analysis details according to the comparative analysis requirements; The fifth determination submodule is used to determine the user's data drill-down statistical needs according to the expected comparative analysis details, and determine the data statistical caliber and data focus level according to the data drill-down statistical needs; A sixth determination submodule is used to determine the data statistical level according to the data statistical caliber and the data focus level, and determine the data mining index based on the data statistical level; The filtering submodule is used to track and filter the imported business data according to the data mining indicators; The pre-stored module includes: The Save As submodule is used to save the statistical report as a bookmark template through the preset bookmark function, and edit and modify the storage location of the bookmark in the template view; An eighth determination submodule is used to determine the adjustable dimensions in the statistical report and the indicator items corresponding to each adjustable dimension; The analysis submodule is used to filter and analyze the subsequent imported data according to the indicator items corresponding to each adjustable dimension; Analysis submodules include: A determination unit, used to obtain a data quantification standard of an indicator item corresponding to each adjustable dimension, and determine a generalized analysis data indicator based on the quantification standard; An acquisition unit, used to acquire multiple narrow analysis data indicators corresponding to the broad analysis data indicator, and acquire entity attributes of each narrow analysis data indicator; An extraction unit, used to extract relevant analytical data from the subsequently imported data according to entity attributes and the analytical data indicators of the user; The analysis unit is used for subsequent analysis of the relevant analysis data.

4. The intelligent data analysis and mining system supporting multiple dimensions and multiple scenarios according to claim 3 is characterized in that: The generating module comprises: The preprocessing submodule is used to convert the statistical business data into time series data and load it into the data computing framework, and preprocess the loaded data through the data computing framework; A seventh determination submodule is used to obtain the business object corresponding to the statistical business data, determine the business attributes of the business object, and determine the business analysis dimension based on the business attributes; The generation submodule is used to perform data analysis based on the business analysis dimension through the data model and generate statistical reports based on business objects according to the analysis results; The display submodule is used to determine the display method according to the user's business scenarios and needs, and to display the statistical report according to the display method.

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