Data index generation method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and mainly relates to a data index generation method and device, electronic equipment and a storage medium. The data are input into a description information output model, description information of the data indexes is obtained, and the description information at least comprises dimension information, measurement information and an analysis method of the data indexes; according to the description information and the data asset catalog, description fields of the data indexes are obtained, and the description fields at least comprise data source connection information, computational logic, dimension fields and measurement fields; compiling a calculation program of the data indexes according to the description fields; and the calculation program is operated, the calculation result of the data index is generated, the data index demand of the user can be automatically identified based on the description information output model and the data asset catalog, the calculation result of the data index is obtained, and the construction and delivery efficiency of the data index is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and mainly to a data indicator generation method, device, electronic device and storage medium. Background Art

[0002] Traditional data indicator development is generally carried out in a full-code or low-code manner. Users need to fully understand the relevant information of the data indicator before building the data indicator. This method is time-consuming for business personnel, and they still need to communicate with technical personnel before building. Therefore, there is still room for increasing efficiency and reducing costs in the construction of data indicators in related technologies. Therefore, how to automatically generate data indicators and improve the efficiency of building and delivering data indicators is an urgent problem to be solved. Summary of the invention

[0003] One of the purposes of this application is to provide a data indicator generation method to solve the problem of low efficiency in the construction and delivery of data indicators generated by full code or low code in related technologies; the second purpose is to provide a data indicator generation device; the third purpose is to provide an electronic device; and the fourth purpose is to provide a storage medium.

[0004] In order to achieve the above purpose, the technical solution adopted in this application is as follows:

[0005] A data indicator generation method, comprising:

[0006] Get the data entered by the user;

[0007] Input the data into a description information output model to obtain description information of the data indicator; the description information at least includes dimension information, measurement information and analysis method of the data indicator;

[0008] According to the description information and the data asset catalog, a description field of the data indicator is obtained, wherein the description field at least includes data source connection information, calculation logic, dimension field, and measurement field;

[0009] Compile a calculation program for the data indicator according to the description field;

[0010] Run the calculation program to generate calculation results of the data indicators.

[0011] Optionally, the step of inputting the data into a description information output model to obtain description information of the data indicator includes:

[0012] The data is input into the description information output model, and the entity information of the data indicator is identified by using the semantic recognition service of the description information output model; based on the entity information, the description information of the data indicator is written according to the preset description rules through the information extraction service of the description information output model.

[0013] Optionally, obtaining the description field of the data indicator according to the description information and the data asset catalog includes:

[0014] Through the data processing service, the first data is determined from the data asset catalog according to the dimension information and measurement information of the data indicator; the attribute description information of the first data includes the dimension information and measurement information of the data indicator; according to the first data, the data source connection information of the data indicator is determined; according to the dimension information and measurement information of the first data, the dimension field and measurement field of the data indicator are determined; according to the analysis method of the data indicator, the calculation logic of the data indicator is determined.

[0015] Optionally, the above method further includes:

[0016] When the description information output model fails to identify the data indicator entity, or cannot identify the data indicator entity expected by the user, a prompt word service is called to identify the data indicator entity; the prompt word service is used to prompt the user to narrow the description granularity, or prompt the user to select the desired data indicator entity.

[0017] Optionally, the above method further includes:

[0018] Acquire the recognition record of the prompt word service; and add the recognition record as a training sample to the sample library of the description information output model so as to retrain the description information output model.

[0019] Optionally, compiling a calculation program for the data indicator according to the description field includes:

[0020] The description field is translated into the logic code of the data indicator; the target code is generated according to the logic code and the syntax of the target data source; and the calculation program is generated after the target code is encapsulated.

[0021] A data indicator generating device, comprising:

[0022] Input module, used to obtain data input by users;

[0023] A first acquisition module is used to input the data into a description information output model to obtain description information of the data indicator; the description information at least includes dimension information, measurement information and analysis method of the data indicator;

[0024] A second obtaining module is used to obtain the description field of the data indicator according to the description information and the data asset catalog, and the description field at least includes data source connection information, calculation logic, dimension field and measurement field;

[0025] A compiling module, used to compile a calculation program of the data indicator according to the description field;

[0026] The calculation module is used to run the calculation program to generate the calculation results of the data indicators.

[0027] Optionally, the first obtaining module includes:

[0028] A semantic recognition module, used to input the data into the description information output model, and identify entity information of the data indicator by using the semantic recognition service of the description information output model;

[0029] The information extraction module is used to write the description information of the data indicator according to the entity information and the information extraction service of the description information output model according to the preset description rules.

[0030] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data indicator generation method when executing the computer program.

[0031] A computer-readable storage medium has a computer program installed thereon, and when the computer program is executed by a processor, the above-mentioned data indicator generation method is implemented.

[0032] Beneficial effects of this application:

[0033] The present application obtains data input by the user; inputs the data into a description information output model to obtain description information of the data indicator, the description information at least including dimension information, measurement information and analysis method of the data indicator; obtains description fields of the data indicator based on the description information and the data asset catalog, the description fields at least including data source connection information, calculation logic, dimension fields and measurement fields; compiles a calculation program for the data indicator based on the description fields; runs the calculation program to generate calculation results of the data indicator, and can automatically identify the user's data indicator requirements based on the description information output model and the data asset catalog to obtain the calculation results of the data indicator, thereby providing users with a zero-code data indicator generation method, realizing automatic generation of data indicators, and helping to improve the efficiency of data indicator construction and delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 This is a flow chart of a method for generating data indicators in one embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of the architecture of the data indicator generation method described in one embodiment of the present application;

[0037] Figure 3 This is a partial flow chart of the data indicator generation method described in one embodiment of the present application;

[0038] Figure 4 It is a schematic diagram of the framework of the data indicator generating device described in one embodiment of the present application.

[0039] Among them, 400-data indicator generating device, 401-input module, 402-first obtaining module, 403-second obtaining module, 404-compilation module, 405-calculation module. DETAILED DESCRIPTION

[0040] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0041] In an embodiment of the present application, the description information output model is a GPT (Generative Pre-Trained Transformer) model, that is, a generative pre-trained Transformer model, which is an Internet-based deep learning model that uses available data to train text generation.

[0042] AIGC (AI generated content) is also known as generative AI, which means artificial intelligence generates content. For example, AI text continuation, AI graphics that convert text to images, AI hosts, etc. are all applications of AIGC.

[0043] The data indicator model is the metadata of the data indicator, which is used to describe the data indicator, including the data indicator name, description, definition, dimension, measurement, calculation formula, data source information, management information, etc.

[0044] A data asset catalog is a system or tool used to manage and maintain all data assets within an organization. The data asset catalog provides basic information about data assets, the relationships between data assets, usage, and related compliance, security, etc. This invention mainly uses metadata information of data assets.

[0045] The technical concept of this application is to provide an automated process technology based on the combination of GPT or other descriptive information output models and data asset catalog metadata. This technology can use GPT technology to process natural language, so that machines can understand human natural language input, so as to better respond to business needs, learn from AIGC prompt words to converge and accurately identify business needs, and quickly build and have the ability to self-evolve models.

[0046] Specifically, in one embodiment of the present application, refer to Figure 1 , Figure 1 FIG. 1 is a flow chart of a method for generating data indicators in an embodiment of the present application. Figure 1 As shown, a data indicator generation method specifically includes the following steps:

[0047] Step S101, obtaining data input by the user.

[0048] The data of user data is in natural language, which is convenient for reducing the technical requirements for building data indicators, and users can enter some data at will. In the related technology, when users build data indicators, they need to understand the detailed information of the data indicators, such as related data, processing methods, etc. Different users have different standards when describing data indicators, which requires full communication between business personnel and technical personnel. In this application, the business needs for building data indicators can be automatically identified. Users can express their needs at will, and then they can obtain the calculation results of the required data indicators. Ordinary users can build data indicators and obtain the calculation results of data indicators. It reduces the communication costs of business personnel and technical personnel, and also reduces the technical requirements for building data indicators.

[0049] Step S102, input the data into a description information output model to obtain description information of the data indicator; the description information at least includes dimension information, measurement information and analysis method of the data indicator.

[0050] Specifically, the description information output model includes a GPT model, which is a pre-trained network model that can provide speech recognition and information extraction services. In related technologies, the GPT model can identify entities from the voice or text information input by the user, and then extract entity-related attributes, actions and other information from the learning library based on the entity, organize this information and output the description information of the data indicator. The description information includes at least the dimension information, measurement information and analysis method of the data indicator. In actual applications, the GPT model can generate the description information of the data indicator according to the writing rules of the data indicator model object. Alternatively, other preset models can be used for training to generate a description information output model to output the description information of the data indicator based on the data input by the user.

[0051] In a feasible implementation manner, step S102 includes: step S1021 and step S1022.

[0052] Step S1021: input the data into the description information output model, and use the semantic recognition service of the description information output model to identify the entity information of the data indicator.

[0053] Step S1022: Based on the entity information, the description information of the data indicator is written through the information extraction service of the description information output model according to the preset description rules.

[0054] Specifically, the data input by the user may be voice data or text data. For example, if the user inputs voice data, the GPT voice recognition function is preferentially called to convert the voice data into a text description in the corresponding language.

[0055] Specifically, the voice data is converted into a byte stream and uploaded to the OSS (Operation Support Systems) cloud shared storage; the GPT speech recognition service is called to identify the uploaded byte stream, convert it into a text description in the corresponding language, and return the text description corresponding to the voice data, releasing the current voice data in the OSS cloud shared storage.

[0056] Next, extract the data indicator entity information from the returned text description, including the data indicator entity, attributes, and actions, and fill in the description information of the data indicator according to the preset description rules. The preset description rules are the description rules of the data indicator model, that is, the dimension, measurement, analysis method, indicator name and other information are extracted from the aforementioned text description with the data indicator model as the schema.

[0057] For example, when a user says "generate a single-store sales indicator, and the analysis method is sum", GPT recognizes it as "[{indicator: single-store sales},{analysis method: [{sum}]},{metric: [{sales}]}]".

[0058] In this way, the description information of the data indicator is automatically generated, and the data indicator description information output according to the preset rules can be encapsulated into a Json object so as to be fed back to the data processing service and further converted into the description field of the data indicator. Then, step S103 is executed.

[0059] Step S103, obtaining description fields of the data indicators according to the description information and the data asset catalog, wherein the description fields at least include data source connection information, calculation logic, dimension fields, and metric fields.

[0060] Specifically, the description information is logical text information at this time, and the data asset catalog can provide metadata query. After finding the metadata matching the data indicator through the description information of the data indicator and the data asset catalog, the data source of the data indicator can be determined, and then the description field of the data indicator can be obtained. The description field includes at least data source connection information, calculation logic, dimension field, and measurement field, so as to generate targeted calculation programs.

[0061] In a feasible implementation, step S103 includes: executing steps S1031 to S1034 through data processing services:

[0062] Step S1031, determining first data from the data asset catalog according to the dimension information and measurement information of the data indicator; the attribute description information of the first data includes the dimension information and measurement information of the data indicator.

[0063] Specifically, the description information of the data indicator is logical text information, which needs to be mapped to a physical field. Therefore, the data source of the data indicator can be determined based on the correlation matching with the description information of the data indicator. For example, the description information of the data indicator is matched with the metadata according to the data asset catalog to determine the metadata with a matching value greater than 85%. 85% is the preset matching threshold, which can be adjusted according to actual needs.

[0064] When matching, the first data can be determined based on the dimension information and measurement information of the data indicator. The attribute description information of the first data is the metadata corresponding to the first data. If the attribute description information of the first data includes the same dimension information and measurement information as the data indicator, the first data can be determined to be the data source data of the data indicator. According to the matched metadata, the physical table corresponding to the metadata is determined, and the physical table corresponding to the metadata is the data source data of the data indicator. When matching, if there are multiple matching results, the user can be prompted to select.

[0065] Step S1032: Determine the data source connection information of the data indicator according to the first data.

[0066] Specifically, if the first data is data source data of the data indicator, the data source connection information of the data indicator can be determined so that when calculating the data indicator, the first data is extracted from the data source of the data indicator for calculation.

[0067] Step S1033: Determine the dimension field and metric field of the data indicator according to the dimension information and metric information of the first data.

[0068] Specifically, after determining the data source of the data indicator, the dimension fields mapped by each dimension information and the metric fields mapped by each metric information can be obtained from the data source metadata and the physical table corresponding to the metadata, so as to determine the dimension fields and metric fields of the data indicator based on these mapping relationships. For example, {sales:'sales_num'}, where "sales" is the metric information identified in the above steps, and sales_num is the physical field corresponding to this metric information.

[0069] Step S1034, determining the calculation logic of the data indicator according to the analysis method of the data indicator.

[0070] Specifically, the analysis method of data indicators, also known as the processing logic of data indicators, includes the processing logic of how to process the acquired data source data. According to the analysis method of data indicators, the calculation logic of data indicators is determined so that the calculation program can be generated later.

[0071] Step S104, compiling a calculation program for the data indicator according to the description field.

[0072] In the description field of the data indicator, you can obtain the name, measurement field, dimension field and calculation logic of the data indicator, understand which data to extract, which calculations to perform on the extracted data, and finally get the calculation results. You can generate the calculation program of the data indicator based on the description field of the data indicator.

[0073] Step S105, running the calculation program to generate calculation results of the data indicators.

[0074] Finally, after running the calculation program, the calculation results of the data indicators can be obtained.

[0075] Optionally, a preview view of the result data can be generated. Users can use the preview view to determine whether the calculation results of production indicators meet expectations, so as to make timely adjustments and improve delivery efficiency.

[0076] In this embodiment, data input by the user is obtained; the data is input into the description information output model to obtain the description information of the data indicator, the description information includes at least the dimension information, measurement information and analysis method of the data indicator; according to the description information and the data asset catalog, the description field of the data indicator is obtained, the description field includes at least the data source connection information, calculation logic, dimension field and measurement field; according to the description field, a calculation program for the data indicator is compiled; the calculation program is run to generate the calculation result of the data indicator. Based on the description information output model and the data asset catalog, the user's data indicator requirements can be automatically identified and the calculation result of the data indicator can be obtained, thereby providing the user with a zero-code data indicator generation method, realizing automatic generation of data indicators, and helping to improve the efficiency of data indicator construction and delivery.

[0077] In actual applications, data storage and data calculation are performed in different environments, and the data sources selected by users may be different. In a feasible implementation, when generating a calculation program for a data indicator, in order to quickly obtain the calculation results of the data indicator, the calculation program for the data indicator is compiled according to the description field, which may specifically include: translating the description field into the logic code of the data indicator; generating a target code according to the target data source syntax based on the logic code; and generating the calculation program after encapsulating the target code.

[0078] Specifically, the logic code is a pseudo code. Assuming that the target data source is Hive, the logic code is: select dealer stores, sum (sales volume) from retail volume group by dealer stores, then translate the logic code into SQL (Structured Query Language, database language) to generate an executable calculation program.

[0079] In this embodiment, different data sources can be applied to flexibly process data, generate executable calculation programs in a targeted manner, and finally obtain calculation results, thereby improving the applicability of generated data indicators.

[0080] In a feasible implementation, when the description information output model fails to identify the data indicator entity, or cannot identify the data indicator entity expected by the user, a prompt word service is called to identify the data indicator entity; the prompt word service is used to prompt the user to narrow the description granularity, or prompt the user to select the desired data indicator entity.

[0081] In this embodiment, if the description information output model does not identify any data indicator entity information from the data input by the user, or identifies multiple data indicator entities and cannot accurately output the data indicator entity expected by the user, then the prompt word service can be called to help output the data indicator entity so as to analyze the text description information of the data indicator based on the correct data indicator entity. Alternatively, if the description information output model does not extract the analysis method of the data indicator description information, the prompt word service can return the prompt word based on the relevance of the data indicator description information. For example, the user inputs "generate single-store sales index", and the input always fails to identify the analysis method. The prompt word will prompt "whether to produce summary indicators". If the user selects yes, then {analysis method: "sum"} is added to the data indicator description information.

[0082] In this way, the prompt word service can assist in extracting relevant information of data indicators, improve the speed and accuracy of identifying user business needs, and facilitate the automatic generation of data indicators.

[0083] In one embodiment of the present application, referring to Figure 2 and Figure 3 , Figure 2 Schematic diagram of the architecture of the data indicator generation method described in one embodiment of the present application, Figure 3 This is a partial flow chart of the data indicator generation method described in an embodiment of the present application. In this embodiment, the description information output model is a GPT model, and the data indicator generation method includes the following steps:

[0084] Step S301, extract the description information of the data indicator through the GPT model. If the description information extraction is successful, execute step S302, if the description information extraction fails, execute steps S304-S305;

[0085] Specifically, step S301 can refer to the above step S102, which will not be repeated here. In this step, if the GPT model extracts the description information of the data indicator normally, the description information is fed back to the data processing service and step S302 is executed. If not, step S304 is executed.

[0086] Step S302, clean and convert the description information through data processing services; identify the data source of the data indicator based on the metadata information of the data asset catalog, and align the dimensions, metrics and analysis methods to generate the description fields of the data indicator.

[0087] Specifically, step S302 can refer to the above step S103, which will not be described here. At this time, if the description field cannot be generated normally: there are multiple data asset catalog metadata that match the description information of the data indicator, or the metadata cannot be matched, then step S304 is executed. If the description field of the data indicator is generated normally, step S303 is executed.

[0088] Step S303, generating a calculation program for data indicators according to the description fields.

[0089] Specifically, step S303 may refer to the above embodiment and will not be described in detail here.

[0090] Step S304: Prompt word feedback to improve the quality and accuracy of generated output data.

[0091] Specifically, in this embodiment, if an abnormality occurs during the stage of extracting data indicator description information or the data processing stage, the prompt word service can be called to further receive user feedback on the prompt word service, and then adjust the identified and matched related data.

[0092] Step S305: training model feedback, recording more sample data for training.

[0093] In a feasible implementation, after calling the prompt word service, the recognition record of the prompt word service can be obtained; the recognition record is used as a training sample and added to the sample library of the description information output model to retrain the description information output model.

[0094] In this embodiment, the recognition record of the prompt word service includes the generation process of the prompt word and the user's feedback information in the process of the prompt word service helping the description information output model to identify the data indicator entity / description information. The recognition record is added as a training sample of the model, and the model is retrained to improve the accuracy and speed of the model in generating output data.

[0095] Optionally, the prompt word service can record different users and obtain the user's personalized historical data, so as to quickly respond to the subsequent data indicator generation requirements of the user and obtain correct recognition results.

[0096] In actual applications, there are situations where multiple prompts still cannot produce the correct result. In this case, the preset number of prompt words can be output. If the preset number is exceeded, manual processing is performed. The prompts output by the prompt word service and the relevant information of the manual processing process can be collected as training samples to retrain the model.

[0097] In this embodiment, in the process of generating data indicators based on GPT and data asset catalog, data indicators cannot be identified from the data input by the user, or the identified data indicators are different from the data indicators required by the user. At this time, AIGC prompt words can be used to converge and accurately identify business needs, quickly build data indicators, and have the ability of model self-evolution to improve the quality and accuracy of output data generated by the description information output model.

[0098] In one embodiment of the present application, a data indicator generating device is provided, referring to Figure 4 , Figure 4 : is a schematic diagram of a data indicator generating device 400 in an embodiment of the present application, wherein the device comprises:

[0099] Input module 401, used to obtain data input by the user;

[0100] A first obtaining module 402 is used to input the data into a description information output model to obtain description information of the data indicator; the description information at least includes dimension information, measurement information and analysis method of the data indicator;

[0101] The second obtaining module 403 is used to obtain the description field of the data indicator according to the description information and the data asset catalog, and the description field at least includes data source connection information, calculation logic, dimension field and measurement field;

[0102] A compiling module 404, used to compile a calculation program of the data indicator according to the description field;

[0103] The calculation module 405 is used to run the calculation program to generate the calculation results of the data indicators.

[0104] In this implementation, through the service of the descriptive information output model, the user's business needs are identified, the entity of the data indicator is determined, and the descriptive information of the data indicator is obtained, and the descriptive information at least includes the dimension information, measurement information and analysis method of the data indicator; based on the descriptive information and the data asset catalog, the description field of the data indicator is obtained, and the description field at least includes the data source connection information, calculation logic, dimension field and measurement field; based on the description field, the calculation program of the data indicator is compiled; the calculation program is run to generate the calculation result of the data indicator. Based on the descriptive information output model and the data asset catalog, the user's data indicator needs can be automatically identified and the calculation result of the data indicator can be obtained, which reduces the difficulty of generating data indicators and is conducive to improving the construction and delivery efficiency of data indicators.

[0105] In a feasible implementation manner, the first obtaining module 402 includes:

[0106] A semantic recognition module, used to input the data into the description information output model, and identify entity information of the data indicator by using the semantic recognition service of the description information output model;

[0107] The information extraction module is used to write the description information of the data indicator according to the entity information and the information extraction service of the description information output model according to the preset description rules.

[0108] In a feasible implementation manner, the second obtaining module 403 includes:

[0109] A data processing module is used to determine the first data from the data asset catalog through a data processing service according to the dimension information and measurement information of the data indicator; the attribute description information of the first data includes the dimension information and measurement information of the data indicator; according to the first data, the data source connection information of the data indicator is determined; according to the dimension information and measurement information of the first data, the dimension field and measurement field of the data indicator are determined; according to the analysis method of the data indicator, the calculation logic of the data indicator is determined.

[0110] In a feasible implementation manner, the above device 400 further includes:

[0111] A prompt word module is used to call a prompt word service to identify a data indicator entity when the description information output model fails to identify the data indicator entity, or cannot identify the data indicator entity expected by the user, or cannot extract the description information; the prompt word service is used to prompt the user to narrow the description granularity, or prompt the user to select the desired data indicator entity.

[0112] In a feasible implementation manner, the above device 400 further includes:

[0113] The training module is used to obtain the recognition record of the prompt word service; and add the recognition record as a training sample to the sample library of the description information output model to retrain the description information output model.

[0114] In a feasible implementation manner, the compiling module 404 includes:

[0115] A translation module, used for translating the description field into a logic code of the data indicator;

[0116] A generating module, used to generate target code according to the logic code and the target data source syntax;

[0117] The encapsulation module is used to encapsulate the target code and generate the computing program.

[0118] The description of the device embodiment is relatively sparse in the present application, and reference may be made to the above-mentioned method embodiment.

[0119] Optionally, in one embodiment of the present application, a data indicator generation system is provided, which integrates a data asset catalog, utilizes a description information output model and an AIGC prompt word service, can identify user needs, and automatically generate data indicators.

[0120] In one embodiment of the present application, an electronic device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the data indicator generation method described in the above embodiment is implemented.

[0121] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is installed. When the computer program is executed by a processor, the data indicator generation method described in the above embodiment is implemented.

[0122] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0123] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0124] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0127] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0128] The above is a detailed introduction to a data indicator generation method, device, electronic device and storage medium provided. This article uses specific examples to illustrate the principles and embodiments of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific embodiments and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for generating data indicators, characterized in that: The method comprises: Get the data entered by the user; Input the data into a description information output model to obtain description information of the data indicator; the description information at least includes dimension information, measurement information and analysis method of the data indicator; According to the description information and the data asset catalog, a description field of the data indicator is obtained, wherein the description field at least includes data source connection information, calculation logic, dimension field, and measurement field; Compile a calculation program for the data indicator according to the description field; Run the calculation program to generate calculation results of the data indicators.

2. The data indicator generation method according to claim 1, characterized in that: The step of inputting the data into a description information output model to obtain description information of the data indicators includes: Inputting the data into the description information output model, and using the semantic recognition service of the description information output model to identify entity information of the data indicator; According to the entity information, the description information of the data indicator is written according to the preset description rules through the information extraction service of the description information output model.

3. The data indicator generation method according to claim 1, characterized in that: The obtaining, according to the description information and the data asset catalog, the description field of the data indicator comprises: Determining, through the data processing service, first data from the data asset catalog according to the dimension information and the measurement information of the data indicator; the attribute description information of the first data includes the dimension information and the measurement information of the data indicator; Determine the data source connection information of the data indicator according to the first data; Determine the dimension field and the metric field of the data indicator according to the dimension information and the metric information of the first data; According to the analysis method of the data indicator, the calculation logic of the data indicator is determined.

4. The data indicator generation method according to claim 2, characterized in that: The method further comprises: When the description information output model fails to identify the data indicator entity, or cannot identify the data indicator entity expected by the user, a prompt word service is called to identify the data indicator entity; the prompt word service is used to prompt the user to narrow the description granularity, or prompt the user to select the desired data indicator entity.

5. The data indicator generation method according to claim 4, characterized in that: The method further comprises: Obtaining the recognition record of the prompt word service; The recognition records are used as training samples and added to the sample library of the description information output model so as to retrain the description information output model.

6. The data indicator generation method according to claim 1, characterized in that: Compiling a calculation program for the data indicator according to the description field includes: Translate the description field into the logic code of the data indicator; Generate target code according to the logic code and target data source syntax; After the target code is packaged, the computing program is generated.

7. A data indicator generating device, characterized in that: The device comprises: Input module, used to obtain data input by users; A first acquisition module is used to input the data into a description information output model to obtain description information of the data indicator; the description information at least includes dimension information, measurement information and analysis method of the data indicator; A second obtaining module is used to obtain the description field of the data indicator according to the description information and the data asset catalog, and the description field at least includes data source connection information, calculation logic, dimension field and measurement field; A compiling module, used to compile a calculation program of the data indicator according to the description field; The calculation module is used to run the calculation program to generate the calculation results of the data indicators.

8. The data indicator generating device according to claim 7, characterized in that: The first obtaining module comprises: A semantic recognition module, used to input the data into the description information output model, and identify entity information of the data indicator by using the semantic recognition service of the description information output model; The information extraction module is used to write the description information of the data indicator according to the entity information and the information extraction service of the description information output model according to the preset description rules.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data indicator generation method as described in any one of claims 1-6 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, characterized in that when the program is executed by a processor, the data indicator generation method as described in any one of claims 1-6 is implemented.

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