Data generation method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of data processing, and relates to a data generation method which comprises the following steps: judging whether an index generation request triggered by a user in a preset index processing interface is received or not; if yes, displaying a pre-created dimension model list, and receiving a target dimension model selected by the user in the dimension model list; receiving a target access plug-in which is configured by a user and corresponds to the to-be-constructed index, and receiving a target access condition which is input by the user and corresponds to the target access plug-in; constructing a corresponding target access formula based on the target dimension model, the target access plug-in and the target access condition; performing index construction processing on the target dimension model, the target access plug-in, the target access condition and the target access formula to obtain a corresponding target index; and performing storage processing on the target index. The invention further provides a data generation device, computer equipment and a storage medium. According to the invention, the processing efficiency and processing accuracy of index generation are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a data generation method and device, a computer device and a storage medium. BACKGROUND

[0002] In today's data-driven era, enterprises are increasingly dependent on data analysis and decision support, and index management software, as an important tool for realizing data value mining, plays a key role. Currently, the industry generally follows a relatively fixed process when generating indexes by index management software.

[0003] Firstly, various data sources, such as database tables and data center datasets, need to be connected. This step aims to break down the barriers between different data sources and provide a unified data entry for subsequent index generation. However, due to the diversity and complexity of data sources, the process of establishing a connection often requires a lot of time and effort, and is prone to connection errors or data inconsistencies.

[0004] Secondly, manual identification of index dimensions and index data items is required for each data source. This process relies on the experience and knowledge of professionals, and requires manual analysis of fields and structures in the data source to determine which can be used as index dimensions and which can be used as index data items. Due to the large amount of data and complex data structure, manual identification is not only inefficient, but also easily affected by subjective factors, resulting in inaccurate identification results.

[0005] Finally, index creation is completed based on the manually identified dimensions and data items. This step needs to combine and configure the identified dimensions and data items based on the results of the previous two steps to generate the final index. However, due to the problems in the previous two steps, this step is also easily affected, resulting in a cumbersome, error-prone and inefficient index creation process.

[0006] For example, in a large e-commerce enterprise, the "sales of different regions of various categories of goods" index needs to be generated. According to the existing index generation process, the staff needs to first establish a connection between the enterprise's database tables and data center datasets, which involves the integration and debugging of multiple systems and takes several days. Then, the staff needs to manually identify the index dimensions and index data items of the data sources related to the sales of goods. Due to the large amount of data and the presence of a large number of redundant and irrelevant fields, the identification process lasted nearly a week and still had some inaccurate identification of dimensions and data items. Finally, in the index creation stage, due to the problems in the previous identification results, the index creation process repeatedly made errors and had to be repeatedly modified and adjusted, eventually taking nearly two weeks to complete the creation of the index. This cumbersome, error-prone and inefficient index generation method has seriously affected the efficiency of data analysis and decision-making in the enterprise.

[0007] Therefore, how to simplify the index generation process, improve processing efficiency and accuracy has become a technical problem to be solved in the field of index management software. SUMMARY

[0008] Embodiments of the present application aim to provide a data generation method and device, a computer device and a storage medium, to solve the technical problem of low processing efficiency and accuracy in the existing index generation process.

[0009] In a first aspect, a data generation method is provided, comprising:

[0010] determining whether an index generation request triggered by a user in a preset index processing interface is received;

[0011] if so, displaying a pre-created dimension model list and receiving a target dimension model selected by the user in the dimension model list;

[0012] receiving a target data extraction plug-in corresponding to a to-be-constructed index configured by the user, and receiving a target data extraction condition corresponding to the target data extraction plug-in input by the user;

[0013] constructing a corresponding target data extraction formula based on the target dimension model, the target data extraction plug-in and the target data extraction condition;

[0014] performing index construction processing on the target dimension model, the target data extraction plug-in, the target data extraction condition and the target data extraction formula to obtain a corresponding target index;

[0015] performing storage processing on the target index.

[0016] In a second aspect, a data generation device is provided, comprising:

[0017] a first determination module configured to determine whether an index generation request triggered by a user in a preset index processing interface is received;

[0018] a first processing module configured to, if so, display a pre-created dimension model list and receive a target dimension model selected by the user in the dimension model list;

[0019] a receiving module configured to receive a target data extraction plug-in corresponding to a to-be-constructed index configured by the user, and receive a target data extraction condition corresponding to the target data extraction plug-in input by the user;

[0020] a first construction module configured to construct a corresponding target data extraction formula based on the target dimension model, the target data extraction plug-in and the target data extraction condition;

[0021] a second construction module, configured to perform index construction processing on the target dimension model, the target data extraction plug-in, the target data extraction condition, and the target data extraction formula to obtain a corresponding target index;

[0022] a storage module, configured to perform storage processing on the target index.

[0023] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the data generation method when executing the computer program.

[0024] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the data generation method when executed by a processor.

[0025] In the above data generation method, device, computer device, and storage medium, first, it is determined whether a user triggers an index generation request in a preset index processing interface; if yes, a pre-created dimension model list is displayed, and a target dimension model selected by the user in the dimension model list is received; then, a target data extraction plug-in corresponding to a to-be-constructed index configured by the user is received, and a target data extraction condition corresponding to the target data extraction plug-in input by the user is received; subsequently, a target data extraction formula is constructed based on the target dimension model, the target data extraction plug-in, and the target data extraction condition; subsequently, the target dimension model, the target data extraction plug-in, the target data extraction condition, and the target data extraction formula are subjected to index construction processing to obtain a corresponding target index; finally, the target index is subjected to storage processing. Based on the above automatic processing procedure, the target dimension model, the target data extraction plug-in, and the target data extraction condition input by the user are processed based on the combination of the index processing interface and the dimension model list, so that the construction processing of the target index meeting the user demand can be efficiently and accurately completed, the processing efficiency and the processing accuracy of index generation are effectively improved, and the accuracy and the security of the generated target index are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the schemes in the present application, the drawings needed in the embodiments of the present application will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0027] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0028] Figure 2 is a flow chart of one embodiment of the data generation method according to the present application;

[0029] Figure 3 is a structural schematic diagram of one embodiment of the data generation apparatus according to the present application;

[0030] Figure 4 is a structural schematic diagram of one embodiment of the computer device according to the present application. DETAILED DESCRIPTION

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application; the present application will be described with reference to the drawings and terminology used by a person skilled in the art. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application. The words "comprising," "including," "containing," and "having," and variations thereof, are inclusive and do not exclude other components, integers, steps, or groups thereof. The words "first," "second," and "third," and variations thereof, are used only to distinguish one element from another, and are not intended to be limiting.

[0032] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all referring to a common set of embodiments. It is expressly understood that the described embodiments are merely examples from a

[0033] In order to make the technical personnel in the art better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings.

[0034] As shown in Figure 1 , the system architecture 100 can include a terminal device 101, a network 102, and a server 103. The terminal device 101 can be a notebook computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 can include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0035] A user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0036] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing, in addition to the notebook computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an electronic book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer and a desktop computer, etc.

[0037] The server 103 can be a server providing various services, for example, a background server providing support for a page displayed on the terminal device 101.

[0038] It should be noted that the data generation method provided by the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the data generation apparatus is generally arranged in a server / terminal device.

[0039] It should be understood that Figure 1 The number of terminal devices, networks and servers in

[0040] With reference to Figure 2 , a flow chart of one embodiment of the data generation method according to the present application is shown. The order of the steps in the flow chart can be changed according to different requirements, and some steps can be omitted. The data generation method provided by the embodiments of the present application can be applied to any scenario requiring generation of index data, and then the data generation method can be applied to products in these scenarios. The data generation method comprises the following steps:

[0041] Step S201: It is judged whether a request for generating an index triggered by a user in a preset index processing interface is received.

[0042] In the present embodiment, the electronic device (for example, the terminal device 101 or the server 103) on which the data generation method runs can be a terminal device, a server or any other electronic device. Figure 1The server / terminal device shown) can be obtained by wired connection or wireless connection. It should be noted that the above-mentioned wireless connection mode can include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, Wi MAX connection, Zigbee connection, UWB (ultra wide band) connection, and other now known or future developed wireless connection mode. The execution body of the present application is a data generation system, or an index generation system, which can be referred to as a system. The above-mentioned index processing page can be an index definition interface in the system. After the user logs in the system, the user navigates to the "index management" module, clicks the "new index" button, enters the index definition interface, and selects "new index" to trigger the index generation request corresponding to the target index to be constructed.

[0043] Before processing the user index generation request, the generation process of the dimension model list is further included, which specifically includes: 1. The user enters the dimension model creation interface through the dimension table interface service provided by the system. That is, after the user logs in the system, the user navigates to the "dimension model management" module, clicks the "new dimension model" button, and enters the dimension model creation interface. 2. The user defines the name and description of the dimension model, for example, "sales analysis dimension model". In the dimension model creation interface, the user inputs the name (such as "sales analysis dimension model") and description (such as "multi-dimensional model for sales data analysis") of the dimension model. 3. The user adds specific dimensions to the dimension model, such as "time dimension" (including year, quarter, month), "region dimension" (including province, city), "product dimension" (including product category, product name), etc. Specifically, the user clicks the "add dimension" button to select or create a dimension from the dimension library. For example: select "time dimension" and set the levels it contains (year, quarter, month). Select "region dimension" and set the levels it contains (province, city). Select "product dimension" and set the levels it contains (product category, product name). 4. The user saves the dimension model, and the system stores the dimension model information into the database for subsequent index definition. After the user completes the dimension addition, the user clicks the "save" button, and the system stores the dimension model information (including name, description and contained dimensions) into the database for subsequent use in index definition.

[0044] Step S202, if yes, the pre-created dimension model list is displayed, and the target dimension model selected by the user in the dimension model list is received.

[0045] In this embodiment, the user can select a suitable dimension model from the list of created dimension models according to actual personal needs, for example, the "sales analysis dimension model" created above. For example, in the indicator definition interface, the user selects the created dimension model (such as "sales analysis dimension model") from the drop-down list to provide a dimension basis for the indicator definition.

[0046] In step S203, the target data extraction plug-in corresponding to the to-be-constructed indicator configured by the user is received, and the target data extraction condition corresponding to the target data extraction plug-in input by the user is received.

[0047] In this embodiment, the user can configure the data item data source of the indicator according to actual needs and select a data extraction plug-in. For example, for the "sales amount" indicator, a data extraction plug-in related to "sales documents" is selected. Specifically, the user selects "data extraction plug-in" as the data source type in the data source configuration area and selects a data extraction plug-in related to "sales documents" (such as "sales document data extraction plug-in") from the plug-in list. In addition, the user can configure specific data extraction conditions in the data extraction plug-in according to needs, such as "time range: January 2023-December 2023", "region: East China", and the like. For example, the user sets specific data extraction conditions in the data extraction plug-in configuration interface. For example, the time range is set to "January 2023-December 2023". The region is set to "East China". The product category is set to "electronic products".

[0048] In step S204, a corresponding target data extraction formula is constructed based on the target dimension model, the target data extraction plug-in, and the target data extraction condition.

[0049] In this embodiment, the system generates a corresponding target data extraction formula according to the dimension model, data extraction plug-in, and data extraction condition selected by the user according to predefined rules. For example, for the "sales amount" indicator, the system generates a specific data extraction logic according to the rules of the "sales document" data extraction plug-in in combination with the time range and region condition. If the time range is January 2023-December 2023 and the region is East China, the data extraction formula may specify to filter out records that meet these two conditions from the "sales document" table. The generated document data extraction formula is in a structured form for subsequent processing and analysis by the system. The formula may include multiple parts, such as data source identification (indicating which data table or data set to obtain data from), filtering conditions (such as conditions for time, region, and other dimensions), field mapping (mapping fields in the data source to fields required by the indicator), and the like. These parts are combined according to certain syntax rules to form a complete data extraction formula.

[0050] In addition, the system designs special database tables to store the metric collection formulas. These tables usually contain fields such as indicator ID, collection formula content, creation time, update time, etc. The indicator ID is used to uniquely identify each indicator, the collection formula content field is used to store the generated collection formula, the creation time and update time fields are used to record the creation and modification time of the formula, facilitating subsequent maintenance and management. For example, taking the single document collection formula as an example, the formula is generated according to the specified rules for parsing, and the formula templates used by different single document types are different. For example, the formula template used by the management object is: {take management object attribute value ['short-term loan contract', 'content abstract', 'content abstract = D', 'actual termination date = empty']}, and the content of the generated single document collection formula can be as follows: GLDXATTRCOLLECT(['5113', 'DX ID', 0, ['ABSTRACT', '0', "], ['ACT_END_DATE', '0', "]]).

[0051] In addition, the generated target collection formula (such as the single document collection formula) can be further stored in the database as a metric collection formula. During storage, the system will perform necessary checks and conversions on the formula to ensure that it meets the storage requirements and format specifications of the database. For example, special characters in the formula are escaped, long text is appropriately truncated or segmented for storage, etc.

[0052] Step S205, performing indicator construction processing on the target dimension model, the target collection plug-in, the target collection condition, and the target collection formula to obtain the corresponding target indicator.

[0053] In this embodiment, the specific implementation process of the above-mentioned indicator construction processing on the target dimension model, the target collection plug-in, the target collection condition, and the target collection formula to obtain the corresponding target indicator will be further described in detail in the subsequent specific embodiments, and will not be described here.

[0054] Step S206, performing storage processing on the target indicator.

[0055] In this embodiment, the specific implementation process of the above-mentioned storage processing on the target indicator will be further described in detail in the subsequent specific embodiments, and will not be described here.

[0056] The application first judges whether a target generation request triggered by a user in a preset index processing interface is received; if yes, a pre-created dimension model list is displayed, and a target dimension model selected by the user in the dimension model list is received; then a target data acquisition plug-in corresponding to a to-be-constructed index configured by the user is received, and a target data acquisition condition corresponding to the target data acquisition plug-in input by the user is received; subsequently, a target data acquisition formula corresponding to the target dimension model, the target data acquisition plug-in and the target data acquisition condition is constructed; subsequently, the target dimension model, the target data acquisition plug-in, the target data acquisition condition and the target data acquisition formula are subjected to index construction processing to obtain a target index corresponding thereto; finally, the target index is subjected to storage processing. Based on the above automatic processing flow, the target dimension model, the target data acquisition plug-in and the target data acquisition condition input by the user are processed by using the combination of the index processing interface and the dimension model list, so that the construction processing of the target index meeting the user demand can be efficiently and accurately completed, the processing efficiency and the processing accuracy of the index generation are effectively improved, and the accuracy and the safety of the generated target index are ensured.

[0057] In some optional implementations, step S205 includes the following steps:

[0058] The target dimension model, the target data acquisition plug-in, the target data acquisition condition and the target data acquisition formula are subjected to information integration to obtain corresponding index information.

[0059] In this embodiment, the above information integration includes dimension model association, data extraction plug-in binding, data extraction condition storage, and data extraction formula association. Specifically, 1) dimension model association includes: when the user completes the dimension model selection, the system records the association relationship between the indicator and the selected dimension model. For example, if the user selects the "sales analysis dimension model", the system will bind the unique identifier (such as model ID) of this dimension model with the indicator information. In this way, in subsequent calculation and analysis, the system can clearly indicate which dimensions (such as time, region, product, etc.) the indicator needs to be organized and displayed according to. 2) Data extraction plug-in binding includes: after the user configures the data extraction plug-in, the system will associate the relevant information of the data extraction plug-in with the indicator. The data extraction plug-in information may include plug-in name, version, function description, etc., but the most important is its connection method with the data source and data processing logic. The system records this binding relationship so that when executing indicator calculation, it can accurately call the corresponding data extraction plug-in to obtain data. 3) Data extraction condition storage includes: the data extraction conditions set by the user, such as "time range is January 2023-December 2023" and "region is East China", will be stored in a structured manner by the system. These conditions will be saved together with other information of the indicator to form a complete indicator definition. The storage method can be in the form of key-value pairs, with each condition item (such as time range, region) as the key and the corresponding value (such as January 2023-December 2023, East China) as the value. 4) Data extraction formula association includes: the constructed indicator relies on the data extraction formula to realize its data acquisition function. In the process of indicator construction, the data extraction formula is generated according to the user's selection of dimension model, data extraction plug-in, and data extraction condition, etc. Only when the data extraction formula can correctly reflect these configuration information and match the dimension model, data extraction plug-in, and other elements, can the constructed indicator run normally to provide accurate and useful data analysis and decision support for users. For example, if the dimension model contains the region dimension, and the data extraction formula does not consider the region filtering condition, then the calculated indicator data cannot be accurately analyzed and displayed according to the region.

[0060] Obtaining a preset indicator construction strategy.

[0061] In the embodiment, the strategy content of the index construction strategy includes: for the construction of the index, the constructed index is a complete concept, containing information in multiple aspects. In addition to the data acquisition formula, it is also associated with the dimension model, the data acquisition plug-in, the name, description, data type, display method, and many other elements of the index. For example, the "sales" index not only has a data acquisition formula for obtaining sales data, but also clearly defines the dimension model (such as time, region, product category, etc.) on which the index is based when analyzing, and the data acquisition plug-in used to perform the data acquisition operation, as well as a clear index name and description to facilitate user understanding and use, and the format of the data when displayed (such as the number of decimal places to be retained, whether to display in currency, etc.).

[0062] Based on the index construction strategy, the index information is subjected to index construction processing to obtain a corresponding specified index.

[0063] In the embodiment, the index information can be subjected to index construction processing based on the strategy content of the index construction strategy, and a corresponding specified index is obtained.

[0064] The specified index is verified based on a preset index verification strategy.

[0065] In the embodiment, the specific implementation process of verifying the specified index based on the preset index verification strategy will be further described in detail in subsequent embodiments, and will not be described in detail here.

[0066] If the specified index passes the verification, the specified index is taken as the target index.

[0067] In the embodiment, by performing index verification, it can be ensured that the index can operate normally in subsequent calculation and analysis processes.

[0068] The application obtains corresponding index information by information integration of the target dimension model, the target data extraction plug-in, the target data extraction condition and the target data extraction formula. Then, a preset index construction strategy is acquired. Subsequently, the index information is subjected to index construction processing based on the index construction strategy, so as to obtain corresponding specified indexes. Subsequently, the specified indexes are subjected to verification based on a preset index verification strategy. If the specified indexes pass the verification, the specified indexes are taken as the target indexes. Based on the above processing procedure, the application obtains index information by information integration of a target dimension model, a target data extraction plug-in, a target data extraction condition and a target data extraction formula, and then obtains specified indexes by index construction processing of the index information based on the use of an index construction strategy. Furthermore, the specified indexes are subjected to verification based on the use of an index verification strategy, and the specified indexes passing the verification are taken as final target indexes. Therefore, the construction processing of the target indexes can be efficiently and accurately completed, the processing efficiency of index construction is improved, the accuracy and standardization of the target indexes are ensured, and the target indexes can normally operate in subsequent calculation and analysis processes.

[0069] In some optional implementations of the embodiment, the verification of the specified indexes based on the preset index verification strategy includes the following steps:

[0070] The specified indexes are subjected to integrity verification.

[0071] In the embodiment, the above integrity verification includes integrity verification of the constructed specified indexes, and checks whether the constructed specified indexes satisfy predefined rules and constraints. Specifically, it is checked whether a dimension model is selected, whether a data extraction plug-in and a data extraction condition are configured, whether a data extraction formula conforms to a syntax rule, and the like, and a corresponding integrity verification result is generated. The content of the integrity verification result includes that the specified indexes pass the integrity verification, or that the specified indexes do not pass the integrity verification. In addition, if it is found that the rules are not satisfied, the system prompts the user to correct. Furthermore, if it is detected that the specified indexes do not pass the integrity verification, it is directly determined that the specified indexes do not pass the verification.

[0072] If the specified indexes pass the integrity verification, the specified indexes are subjected to logic verification.

[0073] In the embodiment, the logical verification includes, in addition to the integrity verification, a logical consistency check. Specifically, the compatibility of the data acquisition condition with the dimension model is checked to ensure that the selected dimension can be reasonably applied in the data acquisition condition; whether the data acquisition plug-in can process the specified data source and data acquisition condition is checked, and a corresponding logical verification result is generated. The content of the logical verification result includes that the specified indicator passes the logical verification or that the specified indicator fails the logical verification. Through the logical consistency check, it is ensured that the indicator can normally operate in the subsequent calculation and analysis process.

[0074] If the specified indicator passes the logical verification, it is determined that the specified indicator passes the verification.

[0075] In the embodiment, only when it is detected that the specified indicator passes both the integrity verification and the logical verification, it is determined that the specified indicator passes the verification, otherwise it is determined that the specified indicator fails the verification.

[0076] If the specified indicator fails the logical verification, it is determined that the specified indicator fails the verification.

[0077] The application performs integrity verification on the specified indicator; if the specified indicator passes the integrity verification, it performs logical verification on the specified indicator; if the specified indicator passes the logical verification, it is determined that the specified indicator passes the verification; if the specified indicator fails the logical verification, it is determined that the specified indicator fails the verification. Based on the above processing flow, the application can efficiently and accurately complete the verification processing of the specified indicator, thereby effectively ensuring the accuracy of the generated indicator verification result.

[0078] In some optional implementations, step S206 includes the following steps:

[0079] The preset specified storage medium is called.

[0080] In the embodiment, the selection of the specified storage medium is not specifically limited and can be determined according to actual indicator storage requirements, for example, any one of a local database, a cloud server, a local disk, a blockchain, etc. can be used.

[0081] The preset target storage strategy is obtained.

[0082] In the embodiment, the target storage strategy can specifically use a cyclic coverage or a timing backup strategy to ensure that the target indicator to be stored will not be lost due to insufficient storage space.

[0083] Based on the target storage strategy, the target indicator is stored in the specified storage medium.

[0084] In the embodiment, the target index can be stored in the selected specified storage medium in the storage mode of the target storage strategy.

[0085] The application calls the preset specified storage medium, then acquires the preset target storage strategy, and subsequently stores the target index into the specified storage medium based on the target storage strategy. Based on the above processing procedure, after the construction of the target index is completed, the application intelligently calls the preset specified storage medium, acquires the target storage strategy corresponding to the target index, and then stores the target index based on the combined use of the obtained specified storage medium and target storage strategy, so as to ensure the data security of the target index and ensure that the target index will not be lost due to insufficient storage space, effectively improving the storage intelligence of the target index.

[0086] In some optional implementation manners, after step S206, the electronic device can further perform the following steps:

[0087] It is judged whether a specified user triggered index calculation request corresponding to the target index is received.

[0088] In the embodiment, the user can trigger manual calculation by triggering the manual calculation on the front-end interface of the system or the system automatically triggers the index calculation processing according to the preset scheduling task. For example, the user can manually select the index (such as the target index) to be calculated through the front-end interface, and click the “Calculate Now” button to trigger the manual calculation. Furthermore, the system automatically triggers the calculation task corresponding to the index according to the preset scheduling task (such as automatic calculation at 1 a.m. every day).

[0089] If yes, a corresponding calculation task is created based on the target index, and the calculation task is put into a preset thread pool.

[0090] In the embodiment, after receiving the calculation request, the back end creates a calculation task according to the index definition information (including the dimension model, the data acquisition plug-in, and the data acquisition condition) of the target index. Specifically, after receiving the calculation request, the back end reads the index definition information from the database, including the selected dimension model, the data acquisition plug-in, and the data acquisition condition. Furthermore, the system creates a specific calculation task according to these information, including the task ID, the index definition information, and the task state. Subsequently, the system adds the calculation task to the index calculation task thread pool and waits for the thread in the thread pool to acquire and execute. Specifically, the system puts the created calculation task into a pre-configured thread pool, and the thread in the thread pool acquires and executes the task according to the task priority and resource availability.

[0091] The calculation task is acquired from the thread pool, and the calculation task is executed and analyzed to obtain a corresponding analysis result.

[0092] In this embodiment, the execution and parsing of the above computing task includes the following steps: Step 1: An idle thread in the thread pool acquires the computing task. An idle thread in the thread pool acquires a computing task from the task queue and marks it as "in execution" state. Step 2: The idle thread parses the metric definition and determines the metric type (such as the document type metric). The idle thread reads the metric definition information from the computing task and parses the metric type. For example, determine whether the metric is a document type metric, an aggregation type metric, etc. Step 3: According to the metric type, the idle thread passes the task into the corresponding computing kernel for execution. For example, pass the document type metric into the document computing kernel. According to the metric type, the idle thread passes the task to the corresponding computing kernel. For example, pass the document type metric to the document computing kernel and the aggregation type metric to the aggregation computing kernel.

[0093] Wherein, the task execution and parsing is the process of acquiring and executing computing tasks in a multi-threaded environment. By parsing the metric definition, the system can determine how to acquire and process data, ensuring the correct execution of computing tasks. For example, the system can select appropriate computing kernels according to the metric type, thereby efficiently completing the computing task.

[0094] Based on the parsing result, the corresponding specified data is acquired from the preset data source using the target data acquisition formula.

[0095] In this embodiment, the process of acquiring the specified data includes the following steps: 1. The computing kernel acquires the target data acquisition formula (such as the document data acquisition formula) from the database or cache. The computing kernel reads the target data acquisition formula associated with the metric from the database or cache, which contains the data source and data acquisition conditions. 2. Parse the content of the target data acquisition formula to obtain the data type and specific data acquisition conditions. The computing kernel parses the target data acquisition formula (document data acquisition formula) and extracts the document type (such as "sales document") and specific data acquisition conditions (such as time range, region, product category, etc.). 3. According to the document type, call the corresponding data acquisition service to acquire the original data from the data source, i.e. the above specified data. For example, acquire the sales records that meet the conditions from the "sales document". For example, the computing kernel calls the corresponding data acquisition service according to the document type. For example, call the "sales document data acquisition service" to acquire the sales records that meet the conditions from the database.

[0096] Wherein, the data acquisition and conversion is the process of acquiring the original data from the data source and converting it into the metric data storage structure. By calling different data acquisition services, the system can flexibly handle different types of data sources, ensuring the accuracy and consistency of the data. For example, the system can acquire sales records from "sales documents" and convert them into the data structure of the "sales" metric.

[0097] generate a corresponding target SQL statement based on the specified data.

[0098] In the embodiment, the specific implementation process of generating a corresponding target SQL statement based on the specified data will be further described in detail in subsequent embodiments, and will not be described here.

[0099] Performing processing on the target SQL statement obtains corresponding target indicator data, and storing processing is performed on the target indicator data.

[0100] In the embodiment, the system stores the converted target indicator data into the indicator data table in the database by executing the generated target SQL statement, thereby completing the data persistence processing.

[0101] The application determines whether a specified user triggers an indicator calculation request corresponding to the target indicator; if so, creates a corresponding calculation task based on the target indicator, and puts the calculation task into a preset thread pool; then obtains the calculation task from the thread pool, and performs execution and analysis processing on the calculation task to obtain a corresponding analysis result; then based on the analysis result, uses the target data retrieval formula to obtain corresponding specified data from a preset data source; and generates a corresponding target SQL statement based on the specified data; subsequently, performing processing on the target SQL statement obtains corresponding target indicator data, and storing processing is performed on the target indicator data. Based on the above processing flow, the application can efficiently and accurately complete the processing of the indicator calculation request corresponding to the target indicator by creating and scheduling tasks, executing and analyzing tasks, data acquisition, and SQL statement generation and execution, thereby improving the processing efficiency of the indicator calculation request, and further improving the processing intelligence and processing accuracy of the indicator calculation.

[0102] In some optional implementations of the embodiment, the generating of a corresponding target SQL statement based on the specified data comprises the following steps:

[0103] Convert the specified data into an indicator data storage structure.

[0104] In the embodiment, the original data (i.e. the specified data) is analyzed and converted into an indicator data storage structure. For example, the sales amount, sales quantity and other data in the sales record are arranged according to the indicator data storage structure. Specifically, the calculation kernel analyzes the obtained original data, extracts the required fields (such as sales amount, sales quantity), and arranges and converts them according to the predefined indicator data storage structure to obtain the corresponding indicator data storage structure.

[0105] obtaining a preset analysis method.

[0106] In the embodiment, the analysis method is a method that is constructed in advance and has a function of generating a corresponding SQL statement according to an index data storage structure, and the analysis method has a function similar to a SQL statement generation module.

[0107] The index data storage structure is input into the analysis method to obtain a corresponding specified SQL statement.

[0108] In the embodiment, the converted index data storage structure (such as a field name and a field value) is input into the analysis method, and the analysis method generates a corresponding SQL statement according to the index data storage structure. For example, an INSERT statement is generated to insert “sales” data into an “index data table”.

[0109] The specified SQL statement is used as the target SQL statement.

[0110] The application converts the specified data into an index data storage structure, obtains a preset analysis method, inputs the index data storage structure into the analysis method to obtain a corresponding specified SQL statement, and uses the specified SQL statement as the target SQL statement. Based on the above processing flow, the application converts the specified data into an index data storage structure, and then inputs the index data storage structure into the analysis method, so that the required target SQL statement can be efficiently and accurately constructed, the construction efficiency and intelligence of the target SQL statement are effectively improved, and the accuracy and standardization of the obtained target SQL statement are ensured.

[0111] In some optional implementation manners of the embodiment, after step S206, the electronic device can further perform the following steps:

[0112] It is determined whether a target user triggers an index data query request corresponding to the target index.

[0113] In the embodiment, the system also provides a target user with a query function. The target user can enter the index query function through a front-end interface (such as an index data query interface) of the system. For example, after logging in to the system, the user navigates to an “index query” module and enters an index data query interface.

[0114] If yes, a query condition input by the target user is obtained.

[0115] In this embodiment, in the index data query interface, the user can input query conditions through input boxes, drop-down menus, date selectors, etc. For example: select the time range as "2023". Select the region as "East China". Select the index as "sales".

[0116] The target query data corresponding to the query conditions is retrieved from the preset index data table.

[0117] In this embodiment, the system converts the user input query conditions into a database query statement and retrieves data that meets the conditions from the index data table. Specifically, the system will perform exact matching or range query according to the dimensions (such as time, region) and indicators (such as sales) in the query conditions, and retrieve the data as the corresponding target query data. The above-mentioned index data table is a pre-constructed data table for storing index data, and relevant users can view and analyze these index data through the query function.

[0118] The target query data is displayed to the target user.

[0119] In this embodiment, the system displays the retrieved target query data to the user in the form of a table, chart or other visualization. The user can further analyze the data through tools on the interface, such as sorting, filtering, exporting, etc. For example, the user can query "sales in East China in 2023", and the system will retrieve and display the relevant data from the index data table to help the user understand the sales situation and make decisions.

[0120] The present application determines whether a target user triggers an index data query request corresponding to the target index; if so, the query conditions input by the target user are obtained; then the target query data corresponding to the query conditions is retrieved from the preset index data table; subsequently, the target query data is displayed to the target user. Based on the above processing flow, the present application can quickly and accurately obtain the data required by the user based on the index data table, support the user to make business decisions and analysis, improve the processing efficiency of index data query, and improve the user's experience.

[0121] In some optional implementations, the obtained user information is obtained with the consent of the user and in accordance with relevant laws and relevant policies.

[0122] In addition, the non-company software tools or components appearing in the embodiments of the present application are only examples and do not represent actual use.

[0123] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0124] It should be emphasized that, in order to further ensure the privacy and security of the above target indicators, the above target indicators can also be stored in a node of a block chain.

[0125] The blockchain referred to in the present application is a new application mode of distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm and other computer technologies. Blockchain, in essence, is a decentralized database, which is a series of data blocks associated using cryptographic methods, each data block containing information about a batch of network transactions, used to verify the validity of the information (anti-fake) and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer, and an application service layer.

[0126] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (Artificial Intelligence, AI) is the use of digital computers or digital computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. Theory, method, technology and application system.

[0127] The basic technology of artificial intelligence generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc. Several major directions.

[0128] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by computer readable instructions instructing related hardware, which can be stored in a computer readable storage medium. The program can include the processes of the above-mentioned embodiments when executed, wherein the storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) and other non-volatile storage media, or a random access memory (RAM) and the like.

[0129] It should be understood that although each step in the flowchart of the accompanying drawings is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless explicitly stated herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0130] Further referring to Figure 3 , as an implementation of the method shown in the above Figure 2 , the present application provides an embodiment of a data generation device, which corresponds to the method embodiment shown in Figure 2 , and the device can be specifically applied to various electronic devices.

[0131] As shown in Figure 3 , the data generation device 300 described in the embodiment includes a first judgment module 301, a first processing module 302, a receiving module 303, a first construction module 304, a second construction module 305, and a storage module 306. Among them:

[0132] The first judgment module 301 is configured to judge whether a user triggers an index generation request in a preset index processing interface;

[0133] The first processing module 302 is configured to, if yes, display a pre-created dimension model list, and receive a target dimension model selected by the user in the dimension model list;

[0134] The receiving module 303 is configured to receive a target data extraction plug-in corresponding to a to-be-constructed index configured by the user, and receive a target data extraction condition corresponding to the target data extraction plug-in input by the user;

[0135] The first construction module 304 is configured to construct a corresponding target data extraction formula based on the target dimension model, the target data extraction plug-in, and the target data extraction condition;

[0136] The second construction module 305 is configured to perform index construction processing on the target dimension model, the target data extraction plug-in, the target data extraction condition, and the target data extraction formula to obtain a corresponding target index;

[0137] The storage module 306 is configured to perform storage processing on the target index.

[0138] In the embodiment, the operations performed by the modules or units described above correspond to the steps of the data generation method of the foregoing embodiments one by one, and will not be described here again.

[0139] In some optional implementations of the embodiment, the second construction module 305 includes:

[0140] The integration sub-module is configured to integrate information of the target dimension model, the target data obtaining plug-in, the target data obtaining condition, and the target data obtaining formula to obtain corresponding index information.

[0141] The first acquisition sub-module is configured to acquire a preset index construction strategy.

[0142] The construction sub-module is configured to perform index construction processing on the index information based on the index construction strategy to obtain a corresponding specified index.

[0143] The verification sub-module is configured to verify the specified index based on a preset index verification strategy.

[0144] The first determination sub-module is configured to determine the specified index as the target index if the specified index passes the verification.

[0145] In the embodiment, the operations performed by the modules or units described above correspond to the steps of the data generation method of the foregoing embodiments one by one, and will not be described here again.

[0146] In some optional implementations of the embodiment, the verification sub-module includes:

[0147] The first verification unit is configured to perform integrity verification on the specified index.

[0148] The second verification unit is configured to perform logic verification on the specified index if the specified index passes the integrity verification.

[0149] The first determination unit is configured to determine that the specified index passes the verification if the specified index passes the logic verification.

[0150] The second determination unit is configured to determine that the specified index does not pass the verification if the specified index does not pass the logic verification.

[0151] In the embodiment, the operations performed by the modules or units described above correspond to the steps of the data generation method of the foregoing embodiments one by one, and will not be described here again.

[0152] In some optional implementations of the embodiment, the storage module 306 includes:

[0153] The calling sub-module is configured to call a preset specified storage medium.

[0154] a second obtaining sub-module, configured to obtain a preset target storage strategy;

[0155] a storage sub-module, configured to store the target indicator into the designated storage medium based on the target storage strategy.

[0156] In the embodiment, the above modules or units are respectively used for performing operations corresponding to steps of the data generation method of the foregoing embodiments, and thus no further description is given herein.

[0157] In some optional implementations of the embodiment, the data generation apparatus further includes:

[0158] a second judging module, configured to judge whether a target indicator calculation request triggered by a designated user is received;

[0159] a second processing module, configured to, if yes, create a corresponding calculation task based on the target indicator, and put the calculation task into a preset thread pool;

[0160] a third processing module, configured to obtain the calculation task from the thread pool, and perform execution and analysis processing on the calculation task to obtain a corresponding analysis result;

[0161] a first obtaining module, configured to obtain corresponding designated data from a preset data source by using the target data obtaining formula based on the analysis result;

[0162] a generation module, configured to generate a corresponding target SQL statement based on the designated data;

[0163] a fourth processing module, configured to perform execution processing on the target SQL statement to obtain corresponding target indicator data, and perform storage processing on the target indicator data.

[0164] In the embodiment, the above modules or units are respectively used for performing operations corresponding to steps of the data generation method of the foregoing embodiments, and thus no further description is given herein.

[0165] In some optional implementations of the embodiment, the generation module includes:

[0166] a conversion sub-module, configured to convert the designated data into an indicator data storage structure;

[0167] a third obtaining sub-module, configured to obtain a preset analysis method;

[0168] a transmission sub-module, configured to transmit the indicator data storage structure into the analysis method to obtain a corresponding designated SQL statement;

[0169] A second determining sub-module is configured to determine the specified SQL statement as the target SQL statement.

[0170] In the embodiment, the modules or units are respectively used for performing operations corresponding to steps of the data generation method of the foregoing embodiments, and thus no further description is provided herein.

[0171] In some optional implementation forms of the embodiment, the data generation apparatus further includes:

[0172] A third determining module is configured to determine whether a target user triggers a target index data query request corresponding to the target index;

[0173] A second obtaining module is configured to, if yes, obtain a query condition input by the target user;

[0174] A searching module is configured to search target query data corresponding to the query condition from a preset index data table;

[0175] A displaying module is configured to display the target query data to the target user.

[0176] In the embodiment, the modules or units are respectively used for performing operations corresponding to steps of the data generation method of the foregoing embodiments, and thus no further description is provided herein.

[0177] To solve the above technical problems, the embodiment of the present application further provides a computer device. For details, please refer to Figure 4 , Figure 4 The basic structure block diagram of the computer device of the embodiment is shown in the figure.

[0178] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 which are connected to each other through a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that all the shown components are not required to be implemented, and more or fewer components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device herein is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0179] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device can interact with a user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, or the like.

[0180] The memory 41 includes at least one type of readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, or the like), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, or the like. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, or the like. Of course, the memory 41 can include both an internal storage unit and an external storage device of the computer device 4. In this embodiment, the memory 41 is generally used to store an operating system and various application software installed in the computer device 4, such as computer readable instructions of the data generation method, or the like. In addition, the memory 41 can also be used to temporarily store various data that has been output or will be output.

[0181] The processor 42 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer readable instructions or process data stored in the memory 41, such as computer readable instructions of the data generation method.

[0182] The network interface 43 can include a wireless network interface or a wired network interface, and is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0183] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0184] In the embodiments of the present application, firstly, it is judged whether a target generation request triggered by a user in a preset index processing interface is received; if yes, a list of pre-created dimension models is displayed, and a target dimension model selected by the user in the list of dimension models is received; then, a target data acquisition plug-in corresponding to a target to-be-constructed index configured by the user is received, and a target data acquisition condition corresponding to the target data acquisition plug-in input by the user is received; after that, a corresponding target data acquisition formula is constructed based on the target dimension model, the target data acquisition plug-in and the target data acquisition condition; subsequently, the target dimension model, the target data acquisition plug-in, the target data acquisition condition and the target data acquisition formula are subjected to index construction processing to obtain a corresponding target index; finally, the target index is subjected to storage processing. Based on the above automatic processing flow, the present application processes the target dimension model, the target data acquisition plug-in and the target data acquisition condition input by the user by using the combination of the index processing interface and the list of dimension models, so that the construction processing of the target index meeting the user's demand can be efficiently and accurately completed, the processing efficiency and the processing accuracy of the index generation are effectively improved, and the accuracy and the security of the generated target index are ensured.

[0185] The present application also provides another embodiment, that is, a computer readable storage medium storing computer readable instructions, which can be executed by at least one processor to make the at least one processor execute the steps of the data generation method as described above.

[0186] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0187] In the embodiments of the present application, firstly, it is judged whether a target generation request triggered by a user in a preset index processing interface is received; if yes, a list of pre-created dimension models is displayed, and a target dimension model selected by the user in the list of dimension models is received; then, a target data acquisition plug-in corresponding to a target index to be constructed configured by the user is received, and a target data acquisition condition corresponding to the target data acquisition plug-in input by the user is received; after that, a corresponding target data acquisition formula is constructed based on the target dimension model, the target data acquisition plug-in, and the target data acquisition condition; subsequently, the target dimension model, the target data acquisition plug-in, the target data acquisition condition, and the target data acquisition formula are subjected to index construction processing to obtain a corresponding target index; finally, the target index is subjected to storage processing. Based on the above automatic processing flow, the target dimension model, the target data acquisition plug-in, and the target data acquisition condition input by the user are processed by the present application through the combined use of the index processing interface and the list of dimension models, so that the construction processing of the target index meeting the user's demand can be efficiently and accurately completed, the processing efficiency and the processing accuracy of index generation are effectively improved, and the accuracy and the security of the generated target index are ensured.

[0188] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and a necessary general hardware platform, and of course, it can also be realized by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk) and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the methods described in the various embodiments of the present application.

[0189] Obviously, the above-described embodiments are only some of the embodiments of the present application, rather than all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and contrary to the above-mentioned embodiments, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions recorded in the foregoing specific embodiments, or equivalently replace some of the technical features. Any equivalent structure made by using the contents of the specification and the drawings, directly or indirectly applied to other related technical fields, is also within the scope of the patent protection of the present application.

Claims

1. A data generation method, characterized in that: The steps include: Determine whether an indicator generation request triggered by a user in a preset indicator processing interface is received; If so, display a pre-created dimensional model list and receive a target dimensional model selected by the user from the dimensional model list; receiving a target data acquisition plug-in configured by the user and corresponding to the indicator to be constructed, and receiving a target data acquisition condition input by the user and corresponding to the target data acquisition plug-in; Constructing a corresponding target data retrieval formula based on the target dimension model, the target data retrieval plug-in, and the target data retrieval condition; Performing indicator construction processing on the target dimension model, the target data acquisition plug-in, the target data acquisition condition, and the target data acquisition formula to obtain corresponding target indicators; The target indicator is stored and processed.

2. The data generation method according to claim 1, wherein: The step of performing indicator construction processing on the target dimension model, the target data acquisition plug-in, the target data acquisition condition, and the target data acquisition formula to obtain corresponding target indicators specifically includes: Integrate the target dimension model, the target data acquisition plug-in, the target data acquisition condition, and the target data acquisition formula to obtain corresponding indicator information; Get the preset indicator construction strategy; Performing indicator construction processing on the indicator information based on the indicator construction strategy to obtain corresponding designated indicators; Verifying the specified indicator based on a preset indicator verification strategy; If the specified indicator passes the verification, the specified indicator is used as the target indicator.

3. The data generation method according to claim 2, characterized in that The step of verifying the specified indicator based on the preset indicator verification strategy specifically includes: Performing integrity check on the specified indicators; If the specified indicator passes the integrity check, a logic check is performed on the specified indicator; If the specified indicator passes the logic check, then it is determined that the specified indicator passes the check; If the specified indicator fails the logic check, it is determined that the specified indicator fails the check.

4. The data generation method according to claim 1, wherein: The step of storing and processing the target indicator specifically includes: Call the preset specified storage medium; Get the preset target storage policy; Based on the target storage policy, the target indicator is stored in the designated storage medium.

5. The data generation method according to claim 1, wherein: After the step of storing and processing the target indicator, the method further includes: Determining whether an indicator calculation request corresponding to the target indicator triggered by a specified user is received; If so, create a corresponding computing task based on the target indicator and put the computing task into a preset thread pool; Obtaining the computing task from the thread pool, and executing and parsing the computing task to obtain a corresponding parsing result; Based on the analysis result, the target data acquisition formula is used to obtain the corresponding specified data from the preset data source; Generate a corresponding target SQL statement based on the specified data; The target SQL statement is executed to obtain corresponding target indicator data, and the target indicator data is stored.

6. The data generation method according to claim 5, characterized in that: The step of generating a corresponding target SQL statement based on the specified data specifically includes: Converting the specified data into an indicator data storage structure; Get the preset parsing method; Pass the indicator data storage structure into the parsing method to obtain the corresponding specified SQL statement; The designated SQL statement is used as the target SQL statement.

7. The data generation method according to claim 1, characterized in that After the step of storing and processing the target indicator, the method further includes: Determining whether an indicator data query request corresponding to the target indicator triggered by a target user is received; If yes, obtain the query condition input by the target user; Retrieving target query data corresponding to the query condition from a preset indicator data table; The target query data is displayed to the target user.

8. A data generating device, characterized in that: include: A first judgment module is used to judge whether an indicator generation request triggered by a user in a preset indicator processing interface is received; A first processing module is configured to, if yes, display a pre-created dimensional model list and receive a target dimensional model selected by the user from the dimensional model list; A receiving module, configured to receive a target data acquisition plug-in configured by the user and corresponding to the indicator to be constructed, and receive a target data acquisition condition input by the user and corresponding to the target data acquisition plug-in; A first construction module is configured to construct a corresponding target data acquisition formula based on the target dimension model, the target data acquisition plug-in, and the target data acquisition condition; A second construction module is configured to perform indicator construction processing on the target dimension model, the target data acquisition plug-in, the target data acquisition condition, and the target data acquisition formula to obtain a corresponding target indicator; A storage module is used to store and process the target indicators.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the data generation method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data generation method according to any one of claims 1 to 7.

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