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

Abstract

The invention provides a data index recommendation method and device, electronic equipment and a computer readable storage medium, and is applied to the field of medical treatment or finance. The method provided by the invention comprises the following steps: acquiring executed sql data, and acquiring a current template according to the sql data; matching the current template with a historical template in a database, if the matching succeeds, updating the popularity of the historical template, and if the matching fails, writing the current template into the database; acquiring user input data, matching the user input data with the template in the database according to the type of the user input data, if the matching succeeds, determining a data index according to the popularity of the matched template, recommending the data index, and if the matching fails, recommending the data index according to the popularity of the matched template. And if yes, determining a data index according to the popularity of the aggregation template in the database, and recommending the data index. According to the method, the accuracy of the data indexes can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method, device, electronic device and computer-readable storage medium for recommending data indicators. Background Art

[0002] With the digital transformation in various fields such as healthcare and finance, obtaining data and using data modeling to solve corresponding problems has become a very common technical means. For example, in the financial field, each e-commerce platform will collect data such as users' product browsing records, and build a product recommendation model based on the collected data to recommend products to users. The indicators of data reflect the characteristics of the data, and the characteristics of the data reflect the characteristics of the business. The characteristics of the business will affect the decisions of enterprises. A good indicator can better reflect the characteristics of the data, and thus better support the decisions of enterprises.

[0003] At present, some of the data indicators are obtained based on the experience accumulated by predecessors or the industry, and the others are proposed by business personnel and sorted out by data developers in combination with business scenarios. The indicators obtained according to industry experience are easily affected by the inherent thinking framework, which is easy to lead people into misunderstandings and is not easy to adjust. The indicators proposed by business personnel are easily affected by personal factors. If there is a deviation in the understanding of the business, the calculated indicators will also have problems. Therefore, the accuracy rate of the data indicators developed by the existing indicator development methods is relatively low, and they may not be able to reflect the true characteristics of the data, resulting in the fact that the value of the underlying data is not fully reflected, and the human and material resources spent on data maintenance do not get the due return. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device, electronic device and computer-readable storage medium for recommending data indicators, so as to solve the technical problem of relatively low accuracy rate of the data indicators developed in the prior art.

[0005] The technical solution of the present invention is as follows. There is provided a method for recommending data indicators, including:

[0006] Obtain the executed sql data, and obtain the current template according to the sql data;

[0007] Match the current template with the historical templates in the database. If the match is successful, update the popularity of the historical template. If the match fails, write the current template into the database;

[0008] Obtain user input data. According to the type of the user input data, match the user input data with the templates in the database. If the match is successful, determine data metrics based on the popularity of the matched template and recommend the data metrics. If the match fails, determine data metrics based on the popularity of the aggregated templates in the database and recommend the data metrics.

[0009] Further, if the current template is the current aggregated template, match the current template with the historical templates in the database. If the match is successful, update the popularity of the historical template. If the match fails, write the current template into the database, including:

[0010] Match the current aggregated template with the historical aggregated templates in the database. If the match is successful, update the popularity of the historical aggregated template. If the match fails, write the current aggregated template into the database.

[0011] Further, if the current template is the current data template, match the current template with the historical templates in the database. If the match is successful, update the popularity of the historical template. If the match fails, write the current template into the database, including:

[0012] Match the current data template with the historical data templates in the database. If the match is successful, update the popularity of the historical data template. If the match fails, write the current data template into the database.

[0013] Further, according to the type of the user input data, match the user input data with the templates in the database. If the match is successful, determine data metrics based on the popularity of the matched template, including:

[0014] If the user input data is table data, match the table data with all the data templates in the database and determine data metrics according to the popularity of the successfully matched data templates and all the aggregated templates in the database;

[0015] If the user input data is aggregate function data, match the aggregate function data with all the aggregated templates in the database and determine data metrics according to the successfully matched aggregated templates, the aggregated templates with a corresponding similarity greater than a preset value, and the data templates in the database.

[0016] Further, after the current aggregated template fails to match the historical aggregated templates in the database, it also includes calculating the similarity between the current aggregated template and the historical aggregated templates in the database.

[0017] Further, if the matching fails, data metrics are determined according to the popularity of the aggregation templates in the database, including: if the matching fails, the aggregation template with the highest popularity is used as the data metric.

[0018] Further, the data metric recommendation method further includes: if the user adopts the data metric, the popularity of the template corresponding to the data metric is updated.

[0019] Another technical solution of the present invention is as follows. There is also provided a data metric recommendation device, including a data acquisition module, a matching module, and a metric recommendation module;

[0020] The data acquisition module is used to acquire the executed sql data and obtain the current template according to the sql data;

[0021] The matching module is used to match the current template with the historical templates in the database. If the matching is successful, the popularity of the historical template is updated. If the matching fails, the current template is written into the database;

[0022] The metric recommendation module is used to acquire user input data. According to the type of the user input data, the user input data is matched with the templates in the database. If the matching is successful, data metrics are determined according to the popularity of the matched templates and the data metrics are recommended. If the matching fails, data metrics are determined according to the popularity of the aggregation templates in the database and the data metrics are recommended.

[0023] Another technical solution of the present invention is as follows. There is also provided an electronic device, including a memory and a processor. The memory stores a computer program executable by the processor, and when the processor executes the computer program, the data metric recommendation method described in any one of the above technical solutions is implemented.

[0024] Another technical solution of the present invention is as follows. There is also provided a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data metric recommendation method described in any one of the above technical solutions is implemented.

[0025] The beneficial effects of the present invention are as follows: obtaining the executed SQL data, obtaining the current template according to the SQL data; matching the current template with the historical templates in the database, if the match is successful, updating the popularity of the historical template, if the match fails, writing the current template into the database; obtaining user input data, matching the user input data with the templates in the database according to the type of the user input data, if the match is successful, determining data metrics according to the popularity of the matched template, and recommending the data metrics, if the match fails, determining data metrics according to the popularity of the aggregated templates in the database, and recommending the data metrics; through the above technical solution, data metrics can be obtained in real time according to user input data for recommendation, improving the accuracy of data metrics. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flowchart of the data metric recommendation method provided by an embodiment of the present invention;

[0027] Figure 2 It is a schematic structural diagram of the data metric recommendation device provided by an embodiment of the present invention;

[0028] Figure 3 It is a schematic structural diagram of the electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0030] In the description of the present application, terms such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. In this specification, the terms "including", "comprising", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] Referring to "embodiments" herein means that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0032] Figure 1 It is a schematic flowchart of the data index recommendation method according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the data index recommendation method of the present invention is not limited to Figure 1 the process sequence shown. For example, Figure 1 as shown, the data index recommendation method mainly includes the following steps:

[0033] S101. Obtain the executed sql data, and obtain the current template according to the sql data;

[0034] In a specific embodiment, data points are set at the front and back ends of the system, especially the big data system, and the successfully executed sql data (statements) of the user are sent to the message middleware (for example, Kafka) to collect the executed sql data in real time. By using a real-time computing engine (for example, Flink) to consume the collected sql data, filter out the sql data containing aggregation (aggregation function), and perform semantic analysis to extract the aggregation function (aggregation feature) and data feature. Specifically, the fact table, association table dimension table, etc. related to the aggregation function and data feature can be obtained. It should be noted that in the financial field, the above sql data can be data related to transactions or data related to commodity browsing records, and in the medical field, the above sql data can be data related to medical diagnosis order information.

[0035] S102. Match the current template with the historical templates in the database. If the match is successful, update the popularity of the historical template. If the match fails, write the current template into the database;

[0036] In an alternative embodiment, if the current template is the current aggregation template, matching the current template with the historical templates in the database. If the match is successful, update the popularity of the historical template. If the match fails, write the current template into the database, including:

[0037] Match the current aggregation template with the historical aggregation templates in the database. If the match is successful, update the popularity of the historical aggregation template. If the match fails, write the current aggregation template into the database.

[0038] In a specific embodiment, the current aggregation template is extracted in real time according to the SQL data. For example, the percentage of the data that meets the preset conditions in the overall data is calculated. Then, according to this SQL data, an aggregation template of "first sum and then divide" can be extracted; the current aggregation template is matched with the historical aggregation templates in the database. If the match is successful, the popularity of the historical aggregation template is updated. The popularity is the popularity value, which can be represented by a number. The higher the number, the higher the popularity. For example, the number of successful matches within a preset time range (such as 7 days) can be used as the popularity. It should be noted that if the current aggregation template is exactly the same as the historical aggregation templates in the database, or the similarity between the two is greater than the first preset value, then the current aggregation template and the historical aggregation templates in the database match successfully.

[0039] In an alternative embodiment, after the current aggregation template fails to match the historical aggregation templates in the database, it further includes calculating the similarity between the current aggregation template and the historical aggregation templates in the database.

[0040] In a specific embodiment, if the current aggregation template fails to match the historical aggregation templates in the database, the similarity between the current aggregation template and the historical aggregation templates in the database is calculated, and the current aggregation template is written into the database as a new aggregation template.

[0041] In an alternative embodiment, if the current template is the current data template, the current template is matched with the historical templates in the database. If the match is successful, the popularity of the historical template is updated. If the match fails, writing the current template into the database includes:

[0042] The current data template is matched with the historical data templates in the database. If the match is successful, the popularity of the historical data template is updated. If the match fails, the current data template is written into the database.

[0043] In a specific embodiment, the current data template (data feature) is obtained according to the SQL data. The current data template is matched with the historical data templates in the database. If the match is successful, the popularity of the historical data template is updated. If the match fails, the current data template is written into the database as a new data template. If the current data template is exactly the same as the historical data templates in the database, or the similarity between the two is greater than the second preset value, the current data template and the historical data templates in the database match successfully.

[0044] S103. Obtain the user input data. According to the type of the user input data, match the user input data with the templates in the database. If the match is successful, determine the data metrics based on the popularity of the matched template, and recommend the data metrics. If the match fails, determine the data metrics based on the popularity of the aggregated templates in the database, and recommend the data metrics.

[0045] In an alternative embodiment, according to the type of the user input data, match the user input data with the templates in the database. If the match is successful, determine the data metrics based on the popularity of the matched template, including:

[0046] If the user input data is tabular data, match the tabular data with all the data templates in the database, and determine the data metrics according to the popularity of the successfully matched data templates and all the aggregated templates in the database;

[0047] If the user input data is aggregate function data, match the aggregate function data with all the aggregated templates in the database, and determine the data metrics according to the successfully matched aggregated templates, the aggregated templates with a corresponding similarity greater than a preset value, and the data templates in the database.

[0048] In a specific embodiment, obtain the user input data (context), send it to the message middleware, and use the real-time computing engine to infer the user's query intention in real time. Specifically, if the user input data is tabular data, match the tabular data with all the data templates in the database, and then comprehensively consider the popularity of the successfully matched data templates and all the aggregated templates, and select the top several of their respective popularities as the data metrics. If the user input data is aggregate function data, match the aggregate function data with all the aggregated templates in the database, select the top several of the respective popularities, or the top several of the total popularity, for the successfully matched aggregated templates and the aggregated templates with a corresponding similarity greater than a preset value, and select several of the data templates with the highest popularity under the aggregated templates as the data metrics.

[0049] In an alternative embodiment, the step of, if the match fails, determining the data metrics according to the popularity of the aggregated templates in the database includes: if the match fails, use the aggregated template with the highest popularity as the data metric.

[0050] In a specific embodiment, after obtaining the recommended metric data, the corresponding template is pushed to the user for selection. If no template is matched, it may mean that a new metric is needed. The most popular aggregation template is recommended as the data metric for the user to select, and the user is guided to sort out the metric logic, or the user is recommended to view the metric template library for inspiration. It should be noted that the user can also obtain the template with the highest popularity in the database at regular intervals. By calculating the popularity value and displaying it on the page, if the user approves of the template, it can be used as the data metric.

[0051] In an alternative embodiment, the data metric recommendation method further includes: if the user adopts the data metric, updating the popularity of the template corresponding to the data metric.

[0052] In a specific embodiment, after the user adopts the recommended data metric, the number of successful recommendations of the template data corresponding to the metric is incremented by one to update the popularity of the template corresponding to the data metric.

[0053] It should be noted that the above SQL data can be SQL data corresponding to business data, transaction data, or payment data.

[0054] The data metric recommendation method provided by the embodiments of the present invention obtains the executed SQL data, and obtains the current template according to the SQL data; matches the current template with the historical templates in the database. If the match is successful, the popularity of the historical template is updated. If the match fails, the current template is written into the database; obtains the user input data, and matches the user input data with the templates in the database according to the type of the user input data. If the match is successful, the data metric is determined according to the popularity of the matched template, and the data metric is recommended. If the match fails, the data metric is determined according to the popularity of the aggregation templates in the database, and the data metric is recommended; the data metric can be obtained in real time according to the user input data for recommendation, improving the accuracy of the data metric.

[0055] The data metric recommendation method of the embodiments of the present invention can obtain the correct metric based on the queries of a large number of users and in combination with the judgments of the users. In the stage of metric extraction, the large amount of user input ensures the diversity of the templates in the recommendation library, and calculates the similarity between the templates to ensure that the metrics will not be too single due to business lines. When recommending metrics, it is not limited to the current business line, and metrics similar to different templates will also be recommended, enabling users to access metrics from more perspectives; and finally, the use of the metrics is determined by the users, ensuring the rationality of the metrics. This method improves the richness of the metrics, reduces the difficulty of users developing metrics, improves the efficiency of users developing data metrics, and also more fully explores the value of the data.

[0056] The data index recommendation method provided by the embodiments of the present invention can be constructed based on artificial intelligence. By using artificial intelligence technology to obtain and process relevant data, an unattended artificial intelligence data index recommendation can be realized. Among them, artificial intelligence (AI) is to use a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results of theory, method, technology, and application system.

[0057] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0058] Figure 2 It is a schematic structural diagram of the data index recommendation device according to the embodiments of the present invention. As Figure 2 shown, the data index recommendation device 20 includes a data acquisition module 21, a matching module 22, and an index recommendation module 23;

[0059] The data acquisition module 21 is used to acquire executed sql data and obtain the current template according to the sql data;

[0060] The matching module 22 is used to match the current template with the historical templates in the database. If the match is successful, the popularity of the historical template is updated. If the match fails, the current template is written into the database;

[0061] The index recommendation module 23 is used to acquire user input data. According to the type of the user input data, the user input data is matched with the templates in the database. If the match is successful, data indexes are determined according to the popularity of the matched templates, and the data indexes are recommended. If the match fails, data indexes are determined according to the popularity of the aggregated templates in the database, and the data indexes are recommended.

[0062] In an alternative embodiment, the matching module 22, when the current template is the current aggregated template, matches the current template with the historical templates in the database. If the match is successful, the popularity of the historical template is updated. If the match fails, the current template is written into the database, including:

[0063] Match the current aggregation template with the historical aggregation templates in the database. If the match is successful, update the popularity of the historical aggregation template. If the match fails, write the current aggregation template into the database.

[0064] In a specific embodiment, the current aggregation template is extracted in real time according to the SQL data. For example, calculate the percentage of data that meets the preset conditions in the total data, and then an aggregation template of "first sum and then divide" can be extracted according to this SQL data. Match the current aggregation template with the historical aggregation templates in the database. If the match is successful, update the popularity of the historical aggregation template. The popularity can be represented by a number, and the higher the number, the higher the popularity. For example, the number of times in a preset time range (such as 7 days) can be used as the popularity. It should be noted that if the current aggregation template is exactly the same as the historical aggregation template in the database, or the similarity between the two is greater than the first preset value, then the current aggregation template and the historical aggregation template in the database match successfully.

[0065] In an alternative embodiment, the matching module 22, when the current template is the current data template, matches the current template with the historical templates in the database. If the match is successful, update the popularity of the historical template. If the match fails, write the current template into the database, including:

[0066] Match the current data template with the historical data templates in the database. If the match is successful, update the popularity of the historical data template. If the match fails, write the current data template into the database.

[0067] In a specific embodiment, obtain the current data template (data characteristics) according to the SQL data, match the current data template with the historical data templates in the database. If the match is successful, update the popularity of the historical data template. If the match fails, write the current data template into the database as a new data template. If the current data template is exactly the same as the historical data template in the database, or the similarity between the two is greater than the second preset value, the current data template and the historical data template in the database match successfully.

[0068] In an alternative embodiment, the metric recommendation module 23 matches the user input data with the templates in the database according to the type of the user input data. If the match is successful, determine the data metrics according to the popularity of the matched template, including:

[0069] If the user input data is table data, match the table data with all the data templates in the database, and determine the data metrics according to the popularity of the successfully matched data templates and all the aggregation templates in the database;

[0070] If the user input data is aggregate function data, match the aggregate function data with all the aggregate templates in the database, and determine the data metrics based on the successfully matched aggregate templates, the aggregate templates with corresponding similarity greater than the preset value, and the data templates in the database.

[0071] In a specific embodiment, obtain the user input data (context) and send it to the message middleware. Use the real-time computing engine to infer the user's query intention in real time. Specifically, if the user input data is table data, match the table data with all the data templates in the database, and then comprehensively consider the popularity of the successfully matched data templates and all the aggregate templates, and select the top several of their respective popularities as the data metrics. If the user input data is aggregate function data, match the aggregate function data with all the aggregate templates in the database, select the top several of the respective popularities, or the top several of the total popularity, for the successfully matched aggregate templates and the aggregate templates with corresponding similarity greater than the preset value, and select several of the data templates with higher popularity under the aggregate templates as the data metrics.

[0072] In an alternative embodiment, the data metric recommendation device 20 further includes a similarity calculation module, which is used to calculate the similarity between the current aggregate template and the historical aggregate templates in the database after the current aggregate template fails to match the historical aggregate templates in the database.

[0073] In an alternative embodiment, the metric recommendation module 23 is further used to use the aggregate template with the highest popularity as the data metric when the matching fails.

[0074] In a specific embodiment, after obtaining the recommended metric data, push the corresponding template to the user for selection. If no template is matched, it means that a new metric may be needed. Recommend the aggregate template with the highest current popularity as the data metric for the user to choose, and guide the user to sort out the metric logic, or recommend the user to view the metric template library for inspiration. It should be noted that the user can also obtain the template with the highest popularity in the database at regular intervals. The popularity value can be calculated and displayed on the page. If the user approves of the template, it can be used as the data metric.

[0075] In an alternative embodiment, the data metric recommendation device 20 further includes a popularity update module, which is further used to update the popularity of the template corresponding to the data metric when the user adopts the data metric.

[0076] Figure 3 It is a schematic structural diagram of the electronic device according to an embodiment of the present invention. As Figure 3As shown, the electronic device 30 includes a processor 31 and a memory 32 communicatively connected to the processor 31.

[0077] The memory 32 stores program instructions for implementing the data metric recommendation method of any of the above embodiments.

[0078] The processor 31 is configured to execute the program instructions stored in the memory 32 to perform data metric recommendation.

[0079] Among them, the processor 31 may also be referred to as a CPU (Central Processing Unit). The processor 31 may be an integrated circuit chip with signal processing capabilities. The processor 31 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0080] An embodiment of the present invention provides a storage medium. The storage medium of the embodiment of the present invention stores program instructions capable of implementing all the above methods. The storage medium may be non-volatile or volatile. Among them, the program instructions may be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program code, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0081] In several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces. The indirect coupling or communication connection of the apparatus or module may be in an electrical, mechanical, or other form.

[0082] In addition, in each embodiment of the present invention, each functional module can be integrated into a processing unit, or each module can exist physically alone, or two or more modules can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

[0083] The above are only the embodiments of the present invention. It should be noted here that for those of ordinary skill in the art, improvements can be made without departing from the inventive concept of the present invention, but these all belong to the protection scope of the present invention.

Claims

1. A data indicator recommendation method, characterized in that: include: Get the executed sql data, and get the current template according to the sql data; Match the current template with the historical template in the database, if the match is successful, update the heat of the historical template, if the match fails, write the current template into the database; Obtain user input data, and match the user input data with a template in the database according to the type of the user input data; if the match is successful, determine the data indicator according to the popularity of the matched template, and recommend the data indicator; if the match fails, determine the data indicator according to the popularity of the aggregated template in the database, and recommend the data indicator.

2. The data indicator recommendation method according to claim 1, characterized in that: If the current template is the current aggregate template, the current template is matched with the historical template in the database, and if the match is successful, the heat of the historical template is updated; if the match fails, the current template is written into the database, including: The current aggregation template is matched with the historical aggregation template in the database. If the match is successful, the heat of the historical aggregation template is updated. If the match fails, the current aggregation template is written into the database.

3. The data indicator recommendation method according to claim 2, characterized in that: If the current template is the current data template, the current template is matched with the historical template in the database, if the match is successful, the heat of the historical template is updated, if the match fails, the current template is written into the database, including: The current data template is matched with the historical data template in the database. If the match is successful, the heat of the historical data template is updated. If the match fails, the current data template is written into the database.

4. The data indicator recommendation method according to claim 3, characterized in that: According to the type of the user input data, the user input data is matched with the template in the database. If the match is successful, the data index is determined according to the heat of the matched template, including: If the user input data is table data, the table data is matched with all data templates in the database, and the data index is determined according to the popularity of the successfully matched data template and all the aggregated templates in the database; If the user input data is aggregation function data, the aggregation function data is matched with all aggregation templates in the database, and the data index is determined based on the successfully matched aggregation template, the corresponding aggregation template with a similarity greater than a preset value, and the data template in the database.

5. The data indicator recommendation method according to claim 4, characterized in that: After the current aggregate template fails to match the historical aggregate templates in the database, the method further includes calculating the similarity between the current aggregate template and the historical aggregate templates in the database.

6. The data indicator recommendation method according to claim 1, characterized in that: If the matching fails, determining the data indicator according to the popularity of the aggregate templates in the database includes: if the matching fails, using the aggregate template with the highest popularity as the data indicator.

7. The data indicator recommendation method according to claim 1, characterized in that: Also includes: If the user adopts the data indicator, the popularity of the template corresponding to the data indicator is updated.

8. A data indicator recommendation device, characterized in that: Including data acquisition module, matching module and indicator recommendation module; The data acquisition module is used to acquire the executed SQL data and acquire the current template according to the SQL data; The matching module is used to match the current template with the historical template in the database, and if the match is successful, update the heat of the historical template; if the match fails, write the current template into the database; The indicator recommendation module is used to obtain user input data, match the user input data with the template in the database according to the type of the user input data, and if the match is successful, determine the data indicator according to the popularity of the matched template and recommend the data indicator; if the match fails, determine the data indicator according to the popularity of the aggregated template in the database and recommend the data indicator.

9. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the processor executes the computer program, the data indicator recommendation method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data indicator recommendation method as described in any one of claims 1 to 7 is implemented.

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