Visual platform configuration method and device, electronic equipment and storage medium

Abstract

The invention provides a big data visualization platform configuration method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The configuration method of the big data visualization platform comprises the steps of performing analysis operation on a data index of a data warehouse model based on a model type of a data warehouse to obtain a plurality of meta-indexes representing different business domain granularities, so as to generate a meta-index function pool representing a subject domain based on the plurality of meta-indexes; performing combined configuration on meta-indexes in a meta-index function pool based on the query and aggregation dimension of the visual platform, and generating a meta-index wildcard character SQL based on a combined configuration result; configuring a visual platform interface based on a meta-index wildcard SQL, and generating an executable SQL statement; and in response to a display interface demand of a front end, converting an execution result of the executable SQL statement into a visual query interface adapted to the front end based on a visual platform interface. Through the technical scheme of the invention, SaaS demand capability configuration of different users is realized.

Description

Background Art

[0002] At present, a serial link development method is usually adopted to develop visualization pages for big data reports, such as visualization drag operations based on data warehouse models or rapid interface generation operations, to achieve direct service access. However, the front-end pages generated by the above development method are usually developed based on a single requirement and lack flexible configuration capabilities and data reuse capabilities.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] The purpose of the present disclosure is to provide a configuration method for a big data visualization platform, a configuration device for a big data visualization platform, an electronic device and a computer-readable storage medium, which can at least to some extent improve the problem of lack of flexible configuration capability and data reuse capability in the development of visualization platforms in related technologies.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a configuration method for a big data visualization platform is provided, comprising: performing a parsing operation on data indicators of a data warehouse model based on a model type of the data warehouse to obtain a plurality of meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing a subject domain based on the plurality of meta-indicators; performing combination configuration on the meta-indicators in the meta-indicator function pool based on the query and aggregation dimensions of the visualization platform, and generating a meta-indicator wildcard SQL based on the combination configuration result; configuring a visualization platform interface based on the meta-indicator wildcard SQL, and generating an executable SQL statement; and in response to a display interface requirement of a front-end, converting an execution result of the executable SQL statement into a visualization query interface adapted to the front-end based on the visualization platform interface.

[0007] In one embodiment, the data warehouse model type is based on which grammatical and semantic parsing operations are performed on the data indicators of the data warehouse model to obtain multiple meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing the subject domain based on the multiple meta-indicators, including: determining the corresponding semantic and grammatical verification method based on the model type to perform grammatical and semantic verification on SQL engines of different types of the data warehouse model to obtain an abstract syntax tree that passes the verification; performing structural splitting based on the abstract syntax tree to obtain the multiple meta-indicators, as well as the query and aggregation dimensions supported by the meta-indicators; and obtaining the subject domain association information of the multiple meta-indicators to generate the meta-indicator function pool based on the subject domain association information.

[0008] In one embodiment, the method of determining the corresponding semantic and grammatical verification method based on the model type to perform grammatical and semantic verification on SQL engines of different types of the data warehouse model to obtain an abstract syntax tree that passes the verification includes: performing grammatical analysis on SQL engines of different types of the data warehouse model based on the corresponding verification method to obtain a corresponding initial abstract syntax tree; performing SQL dialect semantic verification on the initial abstract syntax tree to obtain a verified intermediate abstract syntax tree; based on a pre-selection operation, switching to a target data source corresponding to the presentation interface requirement based on an aspect-oriented programming operation among multiple data sources; and verifying the intermediate abstract syntax tree based on the target data source to obtain the verified abstract syntax tree.

[0009] In one embodiment, the structural splitting based on the abstract syntax tree to obtain the multiple meta-indicators, as well as the query and aggregation dimensions supported by the meta-indicators, includes: obtaining the SQL node structure in the abstract syntax tree; performing a selection parsing operation on the SQL node structure based on a SQL parsing tool to obtain corresponding selection nodes; structurally splitting the selection nodes to obtain multiple initial meta-indicators; filtering the initial meta-indicators to determine the retained SQL fragment information as the meta-indicators; and obtaining the query and aggregation dimensions supported by the meta-indicators based on a nested determination operation and an identification determination of the SQL node structure.

[0010] In one embodiment, the acquiring of the subject domain association information of the multiple meta-indicators to generate the meta-indicator function pool based on the subject domain association information includes: performing fingerprint encryption processing on the original data in the data warehouse model to obtain an encrypted fingerprint; generating the subject domain association information of the meta-indicator based on the encrypted fingerprint, the subject domain node information to which the meta-indicator belongs, the corresponding source information, the corresponding query and aggregation dimensions and the path global unique identifier; generating the meta-indicator function pool based on a panoramic representation of indicator data of the subject domain association information of the multiple meta-indicators.

[0011] In one embodiment, the query and aggregation dimensions based on the visualization platform combine and configure the meta-indicators in the meta-indicator function pool, and generate meta-indicator wildcard SQL based on the combined configuration result, including: determining the meta-indicators belonging to the same source based on the identification of the encrypted fingerprint; merging the meta-indicators belonging to the same source; structurally combining the corresponding selection nodes based on the parsing operation of the abstract syntax tree and the indicator semantic dictionary and the result of the merging process to generate a new SQL node structure; and generating the meta-indicator wildcard SQL based on the new SQL node structure.

[0012] In one embodiment, configuring the visualization platform interface based on the meta-indicator wildcard SQL and generating an executable SQL statement includes: replacing the standardized parameters in the standardized interface based on the meta-indicator wildcard SQL to obtain the visualization platform interface; generating the executable SQL statement based on the visualization platform interface to execute the executable SQL statement on the target data source.

[0013] In one embodiment, in response to the display interface requirement of the front end, the execution result of the executable SQL statement is converted into a visual query interface adapted to the front end based on the visualization platform interface, including: obtaining interface information in the display interface requirement; configuring a data structure corresponding to the interface information based on an interface specification; converting the execution result of the executable SQL statement into a data result adapted to the data structure based on the visualization platform interface; and generating the visual query interface for displaying the data result based on the feature information of the front end.

[0014] In one embodiment, the visual query interface for displaying the data results is generated based on the characteristic information of the front end, including: determining the display form of the data results based on the characteristic information of the front end; configuring the visual query interface based on the display form, wherein the characteristic information of the front end includes terminal characteristic information and / or user characteristic information.

[0015] In one embodiment, it also includes: identifying the caching capability, monitoring capability and expansion capability of the visualization platform interface based on aspect-oriented programming (AOP); and configuring the concurrency capability of the visualization platform based on the caching capability, monitoring capability and expansion capability.

[0016] According to another aspect of the present disclosure, a configuration device for a big data visualization platform is provided, including: a parsing module, which is used to perform grammatical and semantic parsing operations on data indicators of a data warehouse model based on a model type of the data warehouse, and obtain multiple meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing a subject domain based on the multiple meta-indicators; a combination module, which is used to combine and configure the meta-indicators in the meta-indicator function pool based on the query and aggregation dimensions of the visualization platform, and generate a meta-indicator wildcard SQL based on the combined configuration result; a configuration module, which is used to configure a visualization platform interface based on the meta-indicator wildcard SQL, and generate an executable SQL statement; a visualization module, which is used to respond to the display interface requirements of the front end, and convert the execution result of the executable SQL statement into a visualization query interface adapted to the front end based on the visualization platform interface.

[0017] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the configuration method of the big data visualization platform described in any one of the first aspect above, or the configuration method of the big data visualization platform described in any one of the second aspect above, by executing the executable instructions.

[0018] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any of the above-mentioned configuration methods of the big data visualization platform is implemented.

[0019] The configuration scheme of the big data visualization platform provided by the embodiment of the present disclosure performs syntax and semantic parsing operations on the data indicators of the data warehouse model to realize the splitting of the underlying data analysis meta-indicators, so as to generate meta-indicators of various business domains and generate a data panorama, thereby avoiding the problem of duplicate construction and ensuring the ability to reuse data assets. Furthermore, different meta-indicators are combined and configured based on the query and aggregation dimensions of the visualization platform, and corresponding meta-indicator wildcard SQL is generated. Combined with different front-end display interface requirements, the SaaS demand capability configuration of different users is realized, and the SaaS demand of different users and different terminals for the underlying data to be connected is flexibly met.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0022] Figure 1 A schematic diagram showing a configuration system structure of a big data visualization platform in an embodiment of the present disclosure;

[0023] Figure 2 A flowchart showing a configuration method of a big data visualization platform in an embodiment of the present disclosure is shown;

[0024] Figure 3 A flowchart showing another configuration method of a big data visualization platform in an embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0026] Figure 5 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0027] Figure 6 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0028] Figure 7 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0029] Figure 8 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0030] Fig. 9 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0031] Fig.10 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0032] Fig.11 A schematic diagram showing a configuration scheme of another big data visualization platform in an embodiment of the present disclosure;

[0033] Fig.12 A schematic diagram showing a configuration device of a big data visualization platform in an embodiment of the present disclosure;

[0034] Fig.13A schematic diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0036] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0037] To facilitate understanding, several terms involved in this application are first explained below.

[0038] SaaS (Software as a Service) allows customers to order the required application software services from manufacturers through the Internet based on actual work needs, pay manufacturers according to the amount of ordered services and the length of time, and obtain services provided by SaaS platform providers through the Internet. In a broad sense, the same service provides certain differences in data services when facing different users, such as different interface functions seen by different users, different users see different indicators, etc., which is the SaaS capability mentioned in this article.

[0039] Abstract Syntax Tree (AST) is an abstract representation of the syntax structure of source code. It represents the syntax structure of a programming language in a tree-like form, and each node in the tree represents a structure in the source code.

[0040] The current big data report development platform solution still has the following problems:

[0041] Although pages generated through interfaces or drag-and-drop capabilities can empower specific users, when faced with SaaS service needs, that is, different users see different indicator data, this flexible capability is not available in existing products.

[0042] Since the platform model itself focuses on fast and efficient development, while improving the efficiency of single-demand development, it is inevitable that the full-link development model is still inevitable for one demand and one development at a time. At the same time, this demand-based development model has certain data product isolation characteristics and often lacks the ability to precipitate panoramic data from an overall perspective. At present, in the big data indicator service development scenario, there are not many research results that have the reuse of full-link development capabilities and data precipitation assets.

[0043] In addition, in terms of small team development or commercialization, the commercial capability requirements for private domains and cloud deployments require a centralized model of platform capabilities that is too onerous. For example, when facing private domain operation scenarios, a common problem is that it is difficult for the platform to adapt to different permission management and support different data sources.

[0044] The main reason for encountering the above problems is that the above platform products focus on improving the efficiency of demand development, that is, at the key nodes of the entire service development, the platform capabilities are used to replace the manual development method to achieve efficient development of a single demand. However, when facing the demands of different users, that is, when facing the demand for SaaS capabilities, the drawbacks of the serial development model are not solved, and there is a lack of data panoramic capabilities and reusability.

[0045] The solution provided in the present application performs syntax and semantic parsing operations on the data indicators of the data warehouse model to achieve the splitting of the underlying data analysis meta-indicators, so as to generate meta-indicators for each business domain and generate a data panorama, thereby avoiding duplication of construction problems and ensuring the ability to reuse data assets. Furthermore, different meta-indicators are combined and configured based on the query and aggregation dimensions of the visualization platform, and corresponding meta-indicator wildcard SQL is generated. Combined with different front-end display interface requirements, the SaaS demand capability configuration of different users is achieved, and the SaaS demand of different users and different terminals for the underlying data can be flexibly met.

[0046] Figure 1 1 is a schematic diagram of a computer system provided by an exemplary embodiment of the present application. The system includes: a plurality of terminals 120 and a server cluster 140.

[0047] The terminal 120 may be a mobile terminal such as a mobile phone, a game console, a tablet computer, an e-book reader, smart glasses, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a smart home device, an AR (Augmented Reality) device, a VR (Virtual Reality) device, or the terminal 120 may also be a personal computer (PC), such as a laptop computer and a desktop computer.

[0048] The terminal 120 may be installed with an application for providing a configuration of a big data visualization platform.

[0049] The terminal 120 is connected to the server cluster 140 via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0050] The server cluster 140 is a server, or is composed of several servers, or is a virtualization platform, or is a cloud computing service center. The server cluster 140 is used to provide background services for the application program that provides the configuration of the big data visualization platform. Optionally, the server cluster 140 undertakes the main computing work, and the terminal 120 undertakes the secondary computing work; or, the server cluster 140 undertakes the secondary computing work, and the terminal 120 undertakes the main computing work; or, the terminal 120 and the server cluster 140 adopt a distributed computing architecture for collaborative computing.

[0051] In some optional embodiments, the server cluster 140 is used to store configuration program information of the big data visualization platform.

[0052] Optionally, the client of the application installed in different terminals 120 is the same, or the client of the application installed on the two terminals 120 is the client of the same type of application of different control system platforms. Based on the different terminal platforms, the specific form of the client of the application may also be different, for example, the application client may be a mobile client, a PC client or a World Wide Web (Web) client.

[0053] Those skilled in the art will appreciate that the number of the terminals 120 may be more or less. For example, there may be only one terminal, or there may be dozens or hundreds of terminals, or a greater number. The embodiment of the present application does not limit the number and device type of the terminals.

[0054] Optionally, the system may also include a management device ( Figure 1(not shown), the management device is connected to the server cluster 140 via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0055] Optionally, the above-mentioned wireless network or wired network uses standard communication technology and / or protocol. The network is usually the Internet, but it can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network). In some embodiments, technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged through the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec), etc. can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0056] Below, the configuration method of the big data visualization platform in this example implementation will be described in more detail with reference to the accompanying drawings and embodiments.

[0057] like Figure 2 As shown, a configuration method of a big data visualization platform according to an embodiment of the present disclosure includes:

[0058] Step S202, based on the model type of the data warehouse, grammatical and semantic parsing operations are performed on the data indicators of the data warehouse model to obtain multiple meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing the subject domain based on the multiple meta-indicators.

[0059] Among them, the data warehouse model includes but is not limited to CK (ClickHouse) database, Mysql, and private domain SQL library.

[0060] Data indicators, including data-related indicators and structured query language SQL.

[0061] Specifically, the meta-indicator parsing engine automatically parses the data and data analysis SQL from the data warehouse model, performs syntax and semantic analysis of different database SQL based on different databases, such as MySQL and ClickHouse, further splits the original SQL, performs fingerprint encryption, and finally generates indicators with the smallest business domain granularity - meta-indicators. When these indicator data are accumulated, a set of panoramic data for the entire data subject domain can be generated, namely the meta-indicator function pool.

[0062] Step S204, combining and configuring the meta-indicators in the meta-indicator function pool based on the query and aggregation dimensions of the visualization platform, and generating a meta-indicator wildcard SQL based on the combined configuration result.

[0063] Among them, the combination configuration of meta-indicators in the meta-indicator function pool can be realized based on the indicator combination engine. Specifically, the indicators in the meta-indicator function pool are arbitrarily combined and automatically merged by the processing engine. The meta-indicators here are freely combined and recognized by the engine. Finally, a combined indicator system for different terminals and different users will be generated. The combination engine is an automated assembly device for realizing the SaaS solution.

[0064] Step S206, configuring the visualization platform interface based on the meta-indicator wildcard SQL, and generating an executable SQL statement.

[0065] Step S208, in response to the display interface requirements of the front end, the execution results of the executable SQL statements are converted into a visual query interface adapted to the front end based on the visualization platform interface.

[0066] Among them, the visual query interface is implemented based on the dynamic query engine. By supporting the dynamic switching capability of different data sources in the underlying configuration, it can automatically switch between different database queries, as well as support aspect-oriented capabilities, unified caching, monitoring, etc., and generate real executable SQL based on different parameters. Based on different terminals (APP, PC, POS and other terminals), the results are converted into the data structure of the ECharts component specification, realizing the automated interface capability to adapt to different front-end display formats (such as bar charts, line charts, etc.).

[0067] In this embodiment, the data indicators of the data warehouse model are parsed in terms of syntax and semantics to achieve the splitting of the underlying data analysis meta-indicators, so as to generate meta-indicators for each business domain and a data panorama, thereby avoiding duplication of construction and ensuring the ability to reuse data assets. Furthermore, different meta-indicators are combined and configured based on the query and aggregation dimensions of the visualization platform, and corresponding meta-indicator wildcard SQL is generated. Combined with different front-end display interface requirements, the SaaS demand capability configuration for different users is achieved, and the SaaS demand for the underlying data to connect to different users and different terminals is flexibly met.

[0068] like Figure 3 As shown, in one embodiment, in step S202, a grammatical and semantic parsing operation is performed on the data indicators of the data warehouse model based on the model type of the data warehouse to obtain multiple meta indicators representing different business domain granularities, and a specific implementation method for generating a meta indicator function pool representing the subject domain based on the multiple meta indicators includes:

[0069] Step S302, determining the corresponding semantic and grammatical verification method based on the model type, so as to perform grammatical and semantic verification on the SQL engines of different types of data warehouse models, and obtain an abstract syntax tree that passes the verification.

[0070] Different data warehouse models have different verification methods, that is, different ways of parsing data and SQL. Therefore, a common abstract syntax tree can be obtained by executing different verification methods.

[0071] Step S304, performing structural splitting based on the abstract syntax tree to obtain multiple meta-indicators, as well as query and aggregation dimensions supported by the meta-indicators.

[0072] Step S306, obtaining subject domain association information of multiple meta-indicators to generate a meta-indicator function pool based on the subject domain association information.

[0073] In this embodiment, different parsing methods are used to automatically parse the data and data analysis SQL from the data warehouse model. Based on different databases, such as MySQL and Click House, syntax and semantic parsing of different database SQLs are performed to split the original SQL and perform fingerprint encryption. Finally, meta-indicators with the smallest business domain granularity are generated. By combining the subject domain association information of multiple meta-indicators and accumulating these indicator data, a set of panoramic data for the entire subject domain of the data can be generated, namely, a meta-indicator function pool. The meta-indicator function pool is reusable and prevents data duplication.

[0074] In one embodiment, the corresponding semantic and grammatical verification methods are determined based on the model type to perform grammatical and semantic verification on the SQL engines of different types of data warehouse models to obtain an abstract syntax tree that passes the verification, including:

[0075] Based on the corresponding verification method, the SQL engine of different types of data warehouse models is parsed to obtain the corresponding initial abstract syntax tree.

[0076] Among them, open source Calcite is used for SQL abstract syntax tree AST parsing.

[0077] The SQL dialect semantics is verified on the initial abstract syntax tree to obtain a verified intermediate abstract syntax tree.

[0078] Based on the pre-selection operation, a target data source corresponding to the display interface requirement is switched among multiple data sources based on aspect-oriented programming operations.

[0079] Among them, the underlying layer uses AbstractRoutingDataSource to configure multiple data sources, realize the dynamic switching capability of supporting different data sources, and dynamically switch the data source when querying different data sources. Different thread accesses are identified through the underlying data source and switched to the current database link pool DataSource, which is equivalent to supporting multiple SQL database query services, and well meets the support of different underlying data capabilities.

[0080] The intermediate abstract syntax tree is verified based on the target data source to obtain a verified abstract syntax tree.

[0081] In this embodiment, since the SQL syntax of different types of databases has certain differences, the SQL is subjected to syntax and semantic verification based on different SQL database engines, and SQLDialect verification of syntax and semantics, that is, SQL dialect semantic verification, is performed. Then, based on the pre-selection operation, the target data source is switched to connect to the real database for SQL query verification. Only valid SQL can pass the verification and perform subsequent processing to ensure the validity of the meta-indicators generated by the splitting.

[0082] In one embodiment, the structure is split based on the abstract syntax tree to obtain multiple meta-indicators, as well as query and aggregation dimensions supported by the meta-indicators, including:

[0083] Obtain the SQL node structure in the abstract syntax tree; perform selective parsing operations on the SQL node structure based on the SQL parsing tool to obtain the corresponding selection nodes; perform structural decomposition on the selection nodes to obtain multiple initial meta-indicators; perform filtering operations on the initial meta-indicators to determine the retained SQL fragment information as meta-indicators; based on the nested determination operation and the identification and determination of the SQL node structure, obtain the query and aggregation dimensions supported by the meta-indicators.

[0084] The SQL parsing tool may specifically be calcite. Apache Calcite is an SQL parsing and optimization engine independent of storage and execution, and is widely used in various offline, search, and real-time query engines.

[0085] In this embodiment, an open source SQL parsing tool is used to further parse the AST abstract syntax tree. After the SQL node structure SqlNode in the abstract syntax tree is obtained, the SqlNode structure of the underlying structure is parsed to obtain the selection node SqlSelect, and the SqlSelect node information is obtained. Each SelectNode is split and stored, and each meta-indicator filters other indicator information and only keeps the SQL fragment information of the current indicator, that is, the meta-indicator. By determining the nesting of complex SQL and the identification and determination of the underlying SqlKind, the query dimension supported by the meta-indicator, the aggregation dimension analysis and other information are obtained, including the where condition, the group by dimension, etc., to facilitate the dimensional analysis in the combination process.

[0086] In one embodiment, acquiring subject domain association information of a plurality of meta-indicators to generate a meta-indicator function pool based on the subject domain association information includes:

[0087] Fingerprint encryption is performed on the original data in the data warehouse model to obtain an encrypted fingerprint; the subject domain association information of the meta-indicator is generated based on the encrypted fingerprint, the subject domain node information to which the meta-indicator belongs, the corresponding source information, the corresponding query and aggregation dimension, and the path global unique identifier; a meta-indicator function pool is generated based on a panoramic representation of indicator data based on the subject domain association information of multiple meta-indicators.

[0088] In this embodiment, fingerprint encryption processing is performed on the original data in the data warehouse model, which is specifically implemented based on MD5 encryption processing of the original SQL, and is saved as meta-indicator original SQL information to facilitate identification of whether the meta-indicators come from the same source.

[0089] In addition, the associated information of other meta-indicators also includes: SQL fragment information, subject domain node information, source information, dimension information, etc. Each indicator has a globally unique identifier with a path.

[0090] For example, if the meta-indicator is the number of members indicator, the corresponding global path definition is as follows: subject domain: marketing, secondary subject: members, tertiary subject: member overview, indicator description: number of members indicator. Through these subject domain indicator information, a set of full subject domain indicator data panorama can be gradually accumulated.

[0091] like Figure 4 As shown, the meta-indicator parsing engine 400 includes a semantic determination layer 402, a meta-indicator splitting layer 404 and a parse tree storage layer 406, wherein the semantic determination layer 402 includes multiple data sources 4022, an SQL syntax verification module 4024, an SQL semantic verification module 4026, a data source switching module 4028 and an SQL executable verification module 4030.

[0092] The meta-indicator splitting layer 404 includes a SQL node structure traversal module 4042 , a SQL selection node information acquisition module 4044 , a SQL fragment splitting module 4046 , and a dimension parsing module 4048 .

[0093] The parsing tree storage layer 406 includes an original SQL fingerprint encryption module 4062 and a meta-indicator business tree generation module 4064 .

[0094] Based on the meta-index parsing engine 400, the data and SQL statements in the data warehouse model are split to obtain meta-indexes.

[0095] like Figure 5 As shown, the meta-indicator function pool generation system includes: a static business asset module 502, a parsing module 504 and a meta-indicator function pool 506.

[0096] The static business asset module 502 includes an offline data model 5022, a real-time data model 5024 and a tag library 5026. Based on the above models, multiple subject domains 5028 are obtained.

[0097] The parsing module 504 includes the data analysis SQL 5042 and the above-mentioned meta-indicator parsing engine 400 , and generates a meta-indicator function pool 506 based on the meta-indicator parsing engine 400 .

[0098] like Figure 6 As shown, in one embodiment, the meta-indicators in the meta-indicator function pool are combined and configured based on the query and aggregation dimensions of the visualization platform, and the meta-indicator wildcard SQL is generated based on the combined configuration result, including:

[0099] Step S602: determining meta-indicators belonging to the same source based on the identification of the encrypted fingerprint.

[0100] Step S604: merging meta-indicators of the same origin.

[0101] Step S606: Based on the results of the parsing operation and merging processing of the abstract syntax tree and the indicator semantic dictionary, the corresponding selection nodes are structurally combined to generate a new SQL node structure.

[0102] Step S608: Generate a meta-indicator wildcard SQL based on the new SQL node structure.

[0103] In this embodiment, the merging processing engine for combining meta-indicators, through encrypted homology fingerprint recognition, identifies whether the meta-indicators in the indicator pool are mergeable when performing combination configuration of any dimension. Indicators of the same source can be merged. By parsing the AST abstract syntax tree structure of each meta-indicator, the SqlSelect of the meta-indicator is parsed and combined to generate a new SqlNode structure based on the Calcite abstract syntax tree, that is, executable SQL is generated, and the dimensional analysis information is further followed up to perform SQL-like processing of all dimensional wildcard structures of SqlNode.

[0104] For example, the query condition:

[0105] WHERE tenant_id=1and reg_tm='2022-12-01'

[0106] Through dimension parameter recognition, automatic identification and replacement

[0107] WHERE tenant_id=${tenant_id}and reg_tm=${reg_tm}

[0108] This facilitates subsequent dynamic query capabilities. This combination configuration method is a reflection of SaaS service capabilities, and different indicator systems are selected for different users.

[0109] like Figure 7 As shown, the meta-indicator combination and merging engine includes a SAAS combination configuration module 702 and a meta-indicator combination engine 704.

[0110] The SAAS combination configuration module 702 includes a meta-indicator business tree generation module 7022 and an indicator language semantic dictionary 7024 .

[0111] Based on the meta-indicator combination engine 704, the following process is implemented:

[0112] SQL fingerprint recognition, obtaining SQL selection node information of homologous SQL fragments, reassembling SQL node structure, generating executable SQL, dimension parsing, and generating meta-indicator wildcard SQL.

[0113] like Figure 8 As shown, in one embodiment, the visualization platform interface is configured based on the meta-indicator wildcard SQL, and an executable SQL statement is generated, including:

[0114] Step S802, replacing the standardized parameters in the standardized interface based on the meta-indicator wildcard SQL to obtain a visualization platform interface.

[0115] Step S804: Generate an executable SQL statement based on the visualization platform interface to execute the executable SQL statement on the target data source.

[0116] In this embodiment, the parameter dynamic replacement capability based on wildcard SQL will replace the standardized parameters in the standardized interface and generate dynamic executable SQL. For example, the wildcard SQL above will parse and replace the parameters tenant_id and reg_tm passed in the standardized interface, implement dynamic SQL capabilities, and generate executable SQL query statements to query the database.

[0117] like Fig. 9 As shown, in one embodiment, in response to the display interface requirements of the front end, the execution results of the executable SQL statements are converted into a visual query interface adapted to the front end based on the visualization platform interface, including:

[0118] Step S902: Obtain interface information in the display interface requirement.

[0119] Step S904: configuring a data structure corresponding to the interface information based on the interface specification.

[0120] Among them, the data structure is specifically the ECharts graphic data structure, and ECharts is a data visualization chart library based on JavaScript.

[0121] Step S906: Convert the execution result of the executable SQL statement into a data result that is compatible with the data structure based on the visualization platform interface.

[0122] Step S908: Generate a visual query interface for displaying data results based on the feature information of the front end.

[0123] In this embodiment, the SQL execution results are displayed according to the requirements of different front-end display style interfaces, such as providing interface information such as indicators, line charts, and pie charts. The interface specifications are defined according to the ECharts data structure. Through the adaptation layer, it is adapted to the unified interface capabilities of various terminals to meet the interface requirements of multi-terminal and multi-page display forms. At the same time, since a flexible underlying data structure can be provided, the terminal adaptation layer can be connected with other visualization platforms.

[0124] In one embodiment, a visual query interface for displaying data results is generated based on the feature information of the front end, including:

[0125] Determine the display form of the data results based on the characteristic information of the front end; configure the visual query interface based on the display form, wherein the characteristic information of the front end includes terminal characteristic information and / or user characteristic information.

[0126] In this embodiment, by acquiring terminal characteristic information and / or user characteristic information,

[0127] In one embodiment, it further includes:

[0128] Based on aspect-oriented programming (AOP), the caching, monitoring and expansion capabilities of the visualization platform interface are identified; based on the caching, monitoring and expansion capabilities, the concurrency capabilities of the visualization platform are configured.

[0129] In this embodiment, aspect-oriented capability support is supported, and unified caching, monitoring, etc. are performed. Aspect caching is to uniformly cache method input parameters through redis. The same input parameters can use the same cache, especially for offline data queries. The caching capability can greatly increase the OLAP (Online Analytical Processing) data query performance. The overall aspect capability is very important and is also an important capability that the framework can continuously expand. The underlying dynamic query engine supports the dynamic switching capability of different data sources, can automatically switch queries from different databases, and aspect-oriented capability support, unified caching, monitoring, etc., based on different parameters to generate real executable SQL, and based on different terminals (APP, PC, POS and other terminals), the results are converted into ECharts component specification data structures to achieve automatic interface capabilities that adapt to different front-end display formats (such as bar charts, line charts, etc.).

[0130] Based on the dynamic SQL query engine, through the conversion of front-end API adaptation, it is possible to display different visualization interfaces based on different tenants and / or different product ends.

[0131] like Fig.10 As shown, a configuration method of a big data visualization platform according to another embodiment of the present disclosure includes:

[0132] Step S1002: dynamically switch the data source.

[0133] Step S1004, identifying the corresponding platform capabilities based on the cache capability AOP aspect, the monitoring capability AOP aspect and the extension capability AOP aspect.

[0134] Step S1006: Based on the dynamic replacement capability, the input interface parameters are replaced with wildcard SQL to generate executable dynamic SQL.

[0135] Step S1008, performing API conversion on the front end based on the executable dynamic SQL.

[0136] Step S1010, based on the dynamic Form capability of the front end, generate a visual query interface and visual graphics adapted to the front end.

[0137] Specifically, when faced with the demand for member indicator dashboards, User 1 and User 2 look at different indicators. For example, User 2 is an offline store with real card-opening member indicators, while User 1 is an online store and only focuses on registered member indicators.

[0138] Taking the SaaS scenario as an example, in response to the member dashboard needs, different tenants display indicators differently, as shown in Table 1.

[0139] Table 1

[0140]

[0141] SQL description: Query the aggregate indicators of different channels of the user (tenant_id) under the date (reg_tm) (dimension: channel open_channel_cd), including: channel information (open_channel_cd), overall channel member data indicators (totalVipNum), number of registered members (createVipNum), number of card-bound members (regBindVipNum), ordering member data (totalOnlFirstOrdVipNum)

[0142] like Fig.11 As shown, according to the configuration scheme of the big data visualization platform disclosed in the present invention, based on the SQL indicator automatic parsing module 1102, multiple data warehouse models, including data warehouse model 1 to data warehouse model N, are parsed to obtain a data panoramic indicator pool, that is, a meta-indicator function pool, and further, based on the SAAS interface service module 1104, multiple meta-indicators are automatically combined and merged to realize SaaS interface service, that is, visualization platform interface service, so as to generate different front-end visualization interfaces for different users, including front-end page 1, front-end page 2 and front-end page N, etc.

[0143] SaaS data product assembly capability: By splitting the underlying data analysis meta-indicators, we can generate meta-indicators for each business domain, and through any combination of indicators, we can achieve SaaS demand capability configuration for different users, and flexibly meet the SaaS needs of different users and different terminals for the underlying data to connect.

[0144] Realize the reuse capability of data indicators: transform the traditional code development mode into an assembly development mode, connect the upstream and downstream relationships of the processed data, generate a data panorama, avoid duplication of construction, and ensure the reuse capability of data assets.

[0145] Full-process automation capability for data development: Achieve full-process automated analysis to avoid the limitations of intrusive development, extract indicator dictionaries and configuration information, and automatically generate personalized service capabilities through any combination of configurations, without requiring intermediate development links, to form a unified overall system data, interface, and service. At the same time, providing services through lightweight jars can avoid the problem of excessive asset references during private domain operations.

[0146] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0147] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0148] Refer to the following Fig.12 To describe the configuration device 1200 of the big data visualization platform according to this embodiment of the present invention. Fig.12 The configuration device 1200 of the big data visualization platform shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0149] The configuration device 1200 of the big data visualization platform is presented in the form of a hardware module. The components of the configuration device 1200 of the big data visualization platform may include but are not limited to: a parsing module 1202, which is used to perform grammatical and semantic parsing operations on the data indicators of the data warehouse model based on the model type of the data warehouse, and obtain multiple meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing the subject domain based on multiple meta-indicators; a combination module 1204, which is used to perform combination configuration of the meta-indicators in the meta-indicator function pool based on the query and aggregation dimensions of the visualization platform, and generate a meta-indicator wildcard SQL based on the combination configuration result; a configuration module 1206, which is used to configure the visualization platform interface based on the meta-indicator wildcard SQL, and generate an executable SQL statement; a visualization module 1208, which is used to respond to the display interface requirements of the front end, and convert the execution result of the executable SQL statement into a visualization query interface adapted to the front end based on the visualization platform interface.

[0150] Refer to the following Fig.13 An electronic device 1300 according to this embodiment of the present invention is described. Fig.13The electronic device 1300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0151] like Fig.13 As shown, the electronic device 1300 is in the form of a general computing device. The components of the electronic device 1300 may include but are not limited to: at least one processing unit 1310, at least one storage unit 1320, and a bus 1330 connecting different system components (including the storage unit 1320 and the processing unit 1310).

[0152] The storage unit stores program codes, which can be executed by the processing unit 1310, so that the processing unit 1310 performs the steps of various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 1310 can perform the following steps: Figure 2 Steps S202 and S208 shown in , as well as other steps defined in the configuration method of the big data visualization platform of the present disclosure.

[0153] The storage unit 1320 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 13201 and / or a cache storage unit 13202 , and may further include a read-only storage unit (ROM) 13203 .

[0154] The storage unit 1320 may also include a program / utility 13204 having a set (at least one) of program modules 13205, such program modules 13205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0155] Bus 1330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0156] The electronic device 1300 may also communicate with one or more external devices 1360 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device 1300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1350. Furthermore, the electronic device 1300 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1350. As shown, the network adapter 1350 communicates with other modules of the electronic device 1300 via a bus 1330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0157] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0158] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0159] The program product for implementing the above method according to an embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0160] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0161] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0162] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0163] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0164] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0165] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0166] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A configuration method for a big data visualization platform, characterized in that: include: Performing grammatical and semantic parsing operations on data indicators of the data warehouse model based on the model type of the data warehouse to obtain multiple meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing the subject domain based on the multiple meta-indicators; Combining and configuring the meta-indicators in the meta-indicator function pool based on the query and aggregation dimensions of the visualization platform, and generating a meta-indicator wildcard SQL based on the combined configuration result; Based on the meta-indicator wildcard SQL, a visualization platform interface is configured and an executable SQL statement is generated; In response to the display interface requirements of the front end, the execution results of the executable SQL statements are converted into a visual query interface adapted to the front end based on the visualization platform interface.

2. The configuration method of the big data visualization platform according to claim 1, characterized in that: The data warehouse model type is based on the data warehouse, and the data indicators of the data warehouse model are parsed in syntax and semantics to obtain multiple meta-indicators representing different business domain granularities, so as to generate a meta-indicator function pool representing the subject domain based on the multiple meta-indicators, including: Determine the corresponding semantic and grammatical verification method based on the model type, so as to perform grammatical and semantic verification on the SQL engine of the different types of data warehouse models, and obtain an abstract syntax tree that passes the verification; Performing structural splitting based on the abstract syntax tree to obtain the multiple meta-indicators and the query and aggregation dimensions supported by the meta-indicators; The subject domain association information of the plurality of meta-indicators is obtained to generate the meta-indicator function pool based on the subject domain association information.

3. The configuration method of the big data visualization platform according to claim 2, characterized in that: The determining of the corresponding semantic and grammatical verification method based on the model type to perform grammatical and semantic verification on the SQL engines of different types of the data warehouse models to obtain an abstract syntax tree that passes the verification includes: Performing syntax analysis on the SQL engines of different types of the data warehouse models based on the corresponding verification methods to obtain corresponding initial abstract syntax trees; Performing SQL dialect semantic verification on the initial abstract syntax tree to obtain a verified intermediate abstract syntax tree; Based on the pre-selection operation, switching to a target data source corresponding to the display interface requirement among multiple data sources based on aspect-oriented programming operation; The intermediate abstract syntax tree is verified based on the target data source to obtain the abstract syntax tree that passes the verification.

4. The configuration method of the big data visualization platform according to claim 2, characterized in that: The performing structural splitting based on the abstract syntax tree to obtain the multiple meta-indicators and the query and aggregation dimensions supported by the meta-indicators includes: Obtaining the SQL node structure in the abstract syntax tree; Performing a selection parsing operation on the SQL node structure based on a SQL parsing tool to obtain a corresponding selection node; Performing structural decomposition on the selected node to obtain a plurality of initial meta-indicators; Performing a filtering operation on the initial meta-indicator, and determining the retained SQL fragment information as the meta-indicator; Based on the nested determination operation and the identification and determination of the SQL node structure, the query and aggregation dimensions supported by the meta-indicator are obtained.

5. The configuration method of the big data visualization platform according to claim 4, characterized in that: The acquiring of the subject domain association information of the plurality of meta-indicators to generate the meta-indicator function pool based on the subject domain association information includes: Performing fingerprint encryption processing on the original data in the data warehouse model to obtain an encrypted fingerprint; Generate subject domain association information of the meta-indicator based on the encrypted fingerprint, subject domain node information to which the meta-indicator belongs, corresponding source information, corresponding query and aggregation dimensions and path global unique identifier; The meta-indicator function pool is generated based on the indicator data panoramic representation of the subject domain association information of the multiple meta-indicators.

6. The configuration method of the big data visualization platform according to claim 5, characterized in that: The query and aggregation dimensions based on the visualization platform are combined to configure the meta-indicators in the meta-indicator function pool, and a meta-indicator wildcard SQL is generated based on the combined configuration result, including: Determining the meta-indicators belonging to the same source based on the identification of the encrypted fingerprints; Merging the meta-indicators belonging to the same source; Based on the parsing operation of the abstract syntax tree and the indicator semantic dictionary and the result of the merging process, structurally combining the corresponding selection nodes to generate a new SQL node structure; The meta-indicator wildcard SQL is generated based on the new SQL node structure.

7. The configuration method of the big data visualization platform according to claim 1, characterized in that: The step of configuring the visualization platform interface based on the meta-indicator wildcard SQL and generating executable SQL statements includes: Replacing the standardized parameters in the standardized interface based on the meta-indicator wildcard SQL to obtain the visualization platform interface; The executable SQL statement is generated based on the visualization platform interface to execute the executable SQL statement on the target data source.

8. The configuration method of the big data visualization platform according to claim 1, characterized in that: The step of converting the execution result of the executable SQL statement into a visual query interface adapted to the front end based on the visual platform interface in response to the display interface requirement of the front end includes: Obtaining interface information in the display interface requirement; Configuring a data structure corresponding to the interface information based on an interface specification; Converting the execution result of the executable SQL statement into a data result adapted to the data structure based on the visualization platform interface; The visual query interface for displaying the data results is generated based on the feature information of the front end.

9. The configuration method of the big data visualization platform according to claim 8, characterized in that: The generating the visual query interface for displaying the data results based on the feature information of the front end includes: Determining a display format of the data result based on the characteristic information of the front end; Configure the visual query interface based on the display format, The characteristic information of the front end includes terminal characteristic information and / or user characteristic information.

10. The configuration method of the big data visualization platform according to any one of claims 1 to 9, characterized in that: Also includes: Identify the caching capability, monitoring capability and expansion capability of the visualization platform interface based on aspect-oriented programming (AOP); The concurrency capability of the visualization platform is configured based on the cache capability, the monitoring capability, and the expansion capability.

11. A configuration device for a big data visualization platform, characterized in that: include: A parsing module, used to perform a parsing operation on the data indicators of the data warehouse model based on the model type of the data warehouse, to obtain a plurality of meta indicators representing different business domain granularities, so as to generate a meta indicator function pool representing the subject domain based on the plurality of meta indicators; A combination module, used for combining and configuring the meta-indicators in the meta-indicator function pool based on the query and aggregation dimensions of the visualization platform, and generating a meta-indicator wildcard SQL based on the combination configuration result; A configuration module, used to configure the visualization platform interface based on the meta-indicator wildcard SQL and generate executable SQL statements; The visualization module is used to respond to the display interface requirements of the front end and convert the execution results of the executable SQL statement into a visualization query interface adapted to the front end based on the visualization platform interface.

12. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the configuration method of the big data visualization platform described in any one of claims 1 to 10 by executing the executable instructions.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the configuration method of the big data visualization platform described in any one of claims 1 to 10 is implemented.

Images