Data index identification method and device, equipment and storage medium

Abstract

The application relates to a data index identification method and device, equipment and a storage medium, and relates to the field of data processing. The method comprises the following steps: based on historical data, analyzing and processing each data field in a plurality of data fields to determine at least one first data field; based on a preset dimension matching rule, analyzing and processing each data field to determine at least one second data field; based on a preset metric matching rule, analyzing and processing each data field to determine at least one third data field; and based on the at least one first data field, the at least one second data field and the at least one third data field, determining the type of each data field. Thus, the dimensions and metrics of the data index can be automatically identified based on the historical data, the preset dimension matching rule and the preset metric matching rule, and the technical problem that a large amount of manpower is consumed and the efficiency is low when the dimensions and metrics of the data index are manually identified can be solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a data indicator identification method, apparatus, device, and storage medium. Background Technology

[0002] In the era of big data, data processing has become a crucial foundation for enterprise decision-making and management. Accurately defining data metrics to measure the performance of various business operations (such as marketing, manufacturing, and finance) is paramount. The dimensions of a data metric (e.g., time, geographic location, product type, organization) are attribute metrics used to describe the category to which the data belongs; the measures of a data metric (e.g., sales revenue, page views, conversion rate) are numerical metrics used to measure the specific attribute value of the data. Dimensions and measures, as key elements of data metrics, help users understand and analyze data to make informed decisions. Currently, the dimensions and measures of data metrics can be manually identified to define data metrics based on these dimensions and measures.

[0003] However, with the rapid growth of business data, the rapid adjustment of business models, and the increasingly complex relationships between business data, manually identifying the dimensions and measurement of data metrics requires a lot of manpower, is inefficient and costly, and thus has poor efficiency in identifying data metrics. Summary of the Invention

[0004] The purpose of this invention is to provide a data indicator identification method, apparatus, device, and storage medium to solve the technical problem that manually identifying the dimensions and measurements of data indicators is labor-intensive, inefficient, and costly. The technical solution of this application is as follows:

[0005] According to the first aspect of this application, a data indicator identification method is provided, comprising: analyzing and processing each data field among multiple data fields included in a target business detail data table based on historical data, determining at least one first data field from the multiple data fields, wherein the historical data includes multiple data fields with common dimensions, and the at least one first data field is a data field with common dimensions, the common dimensions including at least one of the following: time dimension, organization dimension, product dimension; analyzing and processing each data field among the multiple data fields based on preset dimension matching rules, determining at least one second data field from the multiple data fields, the at least one second data field being a data field with private dimensions, the private dimensions including at least one of the following: order status, order type, access channel; analyzing and processing each data field among the multiple data fields based on preset metric matching rules, determining at least one third data field from the multiple data fields, the at least one third data field being a data field of a preset type, the preset type including at least one of the following: numerical type, time type; and determining the type of each data field among the multiple data fields based on at least one first data field, at least one second data field, and at least one third data field.

[0006] Based on the aforementioned technical means, this application can determine a first data field as a common dimension from multiple data fields included in the target business detail data table based on historical data, a second data field as a private dimension from multiple data fields based on preset dimension matching rules, and a third data field as a preset type from multiple data fields based on preset metric matching rules. In other words, it can determine data fields with common dimensions, data fields with private dimensions, and data fields with metrics from multiple data fields included in the target business detail data table. This solves the problem of low efficiency and high cost in the prior art when manually identifying data indicators in data fields of business detail data tables, thereby improving the efficiency of identifying data indicators.

[0007] In one possible implementation, based on historical data, each data field in the target business detail data table is analyzed and processed to determine at least one first data field from the multiple data fields. This includes: for any data field in the target business detail data table, comparing each data field with each common dimension data field in the historical data; if any data field exists in the multiple common dimension data fields in the historical data, determining that data field as the first data field, and labeling the data field with the common dimension label.

[0008] Based on the aforementioned technical means, this application can compare each data field included in the target business detail data table with the data field of each common dimension included in the historical data to determine the data fields that are common dimensions from the data fields included in the target business detail data table. By determining the data fields of common dimensions through historical data, the ambiguity of common dimensions can be eliminated.

[0009] In one possible implementation, the preset dimension matching rules include multiple dimension matching rules, each of which corresponds to a different priority. Based on the preset dimension matching rules, each of the multiple data fields is analyzed and processed to determine at least one second data field from the multiple data fields. This includes: based on the priority of each dimension matching rule in the multiple dimension matching rules, analyzing and processing each of the multiple data fields included in the target business detail data table in descending order of priority to determine at least one second data field from the multiple data fields.

[0010] Based on the aforementioned technical means, this application can determine the data fields that are private dimensions from multiple data fields included in the target business detail data table based on the priority corresponding to the preset dimension matching rules. Users can define and manage the dimension matching rules and the priority of the dimension matching rules themselves, thereby improving the flexibility of identifying private dimensions of data indicators.

[0011] In one possible implementation, the preset metric matching rules include multiple metric matching rules, each of which corresponds to a different priority. Based on the preset metric matching rules, each of the multiple data fields is analyzed and processed to determine at least one third data field from the multiple data fields. This includes: based on the priority corresponding to each of the multiple metric matching rules, analyzing and processing each of the multiple data fields included in the target business detail data table in descending order of priority to determine at least one third data field from the multiple data fields.

[0012] Based on the aforementioned technical means, this application can determine the data fields of the preset type from multiple data fields included in the target business detail data table based on the priority corresponding to the preset metric matching rules. Users can define and manage the metric matching rules and the priority of the metric matching rules themselves, thereby improving the flexibility of identifying data indicators.

[0013] According to a second aspect of this application, a data indicator identification device is provided, comprising a determining module; the determining module is configured to analyze and process each of multiple data fields included in a target business detail data table based on historical data, and determine at least one first data field from the multiple data fields, wherein the historical data includes multiple data fields with common dimensions, and at least one first data field is a data field with common dimensions, the common dimensions including at least one of the following: time dimension, organization dimension, and product dimension; the determining module is further configured to analyze and process each of the multiple data fields based on preset dimension matching rules, and determine from the multiple data fields... The module is configured to: output at least one second data field, wherein the at least one second data field is a private dimension data field, and the private dimension includes at least one of the following: order status, order type, access channel; the determination module is further configured to: analyze and process each of the multiple data fields based on a preset metric matching rule, and determine at least one third data field from the multiple data fields, wherein the at least one third data field is a data field of a preset type, and the preset type includes at least one of the following: numeric type, time type; the determination module is further configured to: determine the type of each of the multiple data fields based on at least one first data field, at least one second data field, and at least one third data field.

[0014] In one possible implementation, the data indicator identification device further includes a processing module; the processing module is used to compare any data field among multiple data fields included in the target business detail data table with each data field of a common dimension included in the historical data; the determination module is also used to determine any data field as the first data field if any data field exists among the multiple common dimension data fields included in the historical data; the processing module is also used to mark any data field with a common dimension label.

[0015] In one possible implementation, the preset dimension matching rules include multiple dimension matching rules, each of which corresponds to a different priority; the determining module is further configured to analyze and process each data field in the target business detail data table in descending order of priority based on the priority of each dimension matching rule in the multiple dimension matching rules, and determine at least one second data field from the multiple data fields.

[0016] In one possible implementation, the preset metric matching rules include multiple metric matching rules, each of which corresponds to a different priority; the determining module is further configured to analyze and process each data field in the target business detail data table in descending order of priority based on the priority of each metric matching rule in the multiple metric matching rules, and determine at least one third data field from the multiple data fields.

[0017] According to a third aspect provided in this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.

[0018] According to a fourth aspect provided in this application, a computer-readable storage medium is provided that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0019] According to the fifth aspect provided in this application, a computer program product is provided, the computer program product including computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0020] Therefore, the above-mentioned technical features of this application have the following beneficial effects:

[0021] (1) Based on historical data, the first data field that is a common dimension can be determined from multiple data fields included in the target business detail data table. Based on the preset dimension matching rules, the second data field that is a private dimension can be determined from multiple data fields. Based on the preset metric matching rules, the third data field that is a preset type can be determined from multiple data fields. That is, the data field that is a common dimension, the data field that is a private dimension, and the data field that is a metric can be determined from multiple data fields included in the target business detail data table. This solves the problem of low efficiency and high cost when manually identifying data indicators of data fields in the business detail data table in the existing technology, thereby improving the efficiency of identifying data indicators.

[0022] (2) The data fields included in the target business detail data table can be compared one by one with the data fields of each common dimension included in the historical data to determine the data fields that are common dimensions from the data fields included in the target business detail data table. By determining the data fields of common dimensions through historical data, the ambiguity of common dimensions can be eliminated.

[0023] (3) Based on the priority of the preset dimension matching rules, the data fields that are private dimensions can be determined from the multiple data fields included in the target business detail data table. Users can define and manage the dimension matching rules and the priority of the dimension matching rules themselves, thereby improving the flexibility of identifying private dimensions of data indicators.

[0024] (4) Based on the priority of the preset metric matching rule, the data field of the preset type can be determined from the multiple data fields included in the target business detail data table. Users can define and manage the metric matching rule and the priority of the metric matching rule, thereby improving the flexibility of the measurement of the identified data indicators.

[0025] It should be noted that the technical effects of any of the implementation methods in aspects two through five can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0028] Figure 1 This is a schematic diagram illustrating the structure of a data indicator recognition system according to an exemplary embodiment;

[0029] Figure 2 This is a flowchart illustrating a data indicator identification method according to an exemplary embodiment;

[0030] Figure 3 This is a flowchart illustrating yet another data indicator identification method according to an exemplary embodiment;

[0031] Figure 4 This is a flowchart illustrating yet another data indicator identification method according to an exemplary embodiment;

[0032] Figure 5 This is a flowchart illustrating yet another data indicator identification method according to an exemplary embodiment;

[0033] Figure 6 This is a block diagram illustrating a data indicator recognition device according to an exemplary embodiment;

[0034] Figure 7 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0035] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0036] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0037] For ease of understanding, the data indicator identification method provided in this application will be described in detail below with reference to the accompanying drawings.

[0038] The data indicator identification method provided in this application embodiment can be applied to a data indicator identification system. Figure 1 This is a schematic diagram illustrating the structure of a data indicator recognition system according to an exemplary embodiment. For example... Figure 1 As shown, the data indicator recognition system 10 includes: a data indicator definition module 11, a dimension measurement recognition module 12, a master data management module 13, and a rule engine 14.

[0039] The data indicator definition module 11 is used to send data indicator dimension identification requests and data indicator measurement identification requests to the dimension measurement identification module 12 for the target business detail data table, and to determine the type of each data field among the multiple data fields included in the target business detail data table based on at least one first data field, at least one second data field, and at least one third data field.

[0040] The dimension measurement identification module 12 is used to send a master data consistency analysis request for the target business detail data table to the master data management module 13 based on the data indicator dimension identification request for the target business detail data table; determine at least one first data field from multiple data fields included in the target business detail data table based on the master data consistency analysis result; send a dimension rule matching request for the target business detail data table to the rule engine 14 based on the data indicator dimension identification request for the target business detail data table; determine at least one second data field from multiple data fields included in the target business detail data table based on the dimension matching result; send a measurement rule matching request for the target business detail data table to the rule engine 14 based on the data indicator measurement identification request for the target business detail data table; and determine at least one third data field from multiple data fields included in the target business detail data table based on the measurement matching result.

[0041] The master data management module 13 is used to receive the master data consistency analysis request for the target business data detail table sent by the dimension measurement and identification module 12, analyze and process each data field in the target business detail data table based on historical data to obtain the master data consistency analysis result, and send the master data consistency analysis result to the dimension measurement and identification module 12.

[0042] The rule engine 14 is used to receive the dimension rule matching request for the target business data detail table sent by the dimension measurement and recognition module 12, analyze and process each data field in multiple data fields based on the preset dimension matching rules to obtain the dimension matching result, send the dimension matching result to the dimension measurement and recognition module 12, receive the measurement rule matching request for the target business data detail table sent by the dimension measurement and recognition module 12, analyze and process each data field in multiple data fields based on the preset measurement matching rules to obtain the measurement matching result, and send the measurement matching result to the dimension measurement and recognition module 12, so as to identify the data indicators based on the data indicator definition module 11, the dimension measurement and recognition module 12, the master data management module 13, and the rule engine 14.

[0043] Figure 2 This is a flowchart illustrating a data indicator identification method according to an exemplary embodiment, such as... Figure 2 As shown, the data indicator identification method includes the following steps:

[0044] S201. Based on historical data, analyze and process each of the multiple data fields included in the target business detail data table, and determine at least one first data field from the multiple data fields.

[0045] The historical data includes multiple common dimension data fields, with at least one first data field being a common dimension data field. The common dimensions include at least one of the following: time dimension, organization dimension, and product dimension.

[0046] Optionally, historical data may include multiple master data (MDs), each master data corresponding to a common dimension data field, and each master data may include an encoding field and its information description field.

[0047] The data metric definition module can assist users in selecting the target business detail data table through a guided interactive interface, and initiate data metric dimension identification requests and data metric measurement identification requests for that target business detail data table to the dimension measurement identification module. Upon receiving these requests, the dimension measurement identification module can send a master data consistency analysis request for the target business detail data table to the master data management module.

[0048] When the master data management module receives a request for master data consistency analysis of the target business data detail table, it can perform master data consistency analysis on each of the multiple data fields included in the target business data detail table based on historical data, and return the master data consistency analysis results to the dimension measurement identification module. The dimension measurement identification module can determine at least one first data field (i.e., identify the common dimension data field from the multiple data fields included in the target business data detail table) based on the master data consistency analysis results.

[0049] For example, the target business detail data table can be a sales order table or a user access log table. If a master data corresponds to a data field of an organization dimension, then the master data is the organization master data. The organization master data includes an organization code field and detailed descriptions of the organization represented by the organization code, such as the organization name, establishment time, and responsible person.

[0050] It should be noted that the data metric definition module provides metric definition services to users through a guided interactive interface. The dimensional measurement identification module controls and manages the entire process of data metric dimension identification and data metric measurement identification. The master data management module provides master data information maintenance and master data consistency analysis services. Master data (also known as baseline data) refers to shared data between systems (such as data related to customers, suppliers, accounts, and organizational departments). Compared to transaction data, which records business activities and fluctuates significantly, master data changes relatively slowly.

[0051] S202. Based on the preset dimension matching rules, analyze and process each of the multiple data fields to determine at least one second data field from the multiple data fields.

[0052] Among them, at least one of the second data fields is a private dimension data field, and the private dimension includes at least one of the following: order status, order type, and access channel.

[0053] Optionally, after identifying the common dimension data fields from the multiple data fields included in the target business data detail table, the dimension measurement and identification module can send a dimension rule matching request for the target business data detail table to the rule engine. When the rule engine receives the dimension rule matching request for the target business data detail table, it can perform matching analysis processing on each data field included in the target business data detail table, excluding the common dimension data fields, according to preset dimension matching rules, to obtain the matching results, and return the matching results to the dimension measurement and identification module. Based on the matching results, the dimension measurement and identification module can determine at least one second data field from the data fields included in the target business data detail table, excluding the common dimension data fields (i.e., identify the private dimension data field from the data fields included in the target business data detail table, excluding the common dimension data fields).

[0054] For example, the data fields of the private dimension included in the sales order table can be order status and order type.

[0055] S203. Based on the preset metric matching rules, analyze and process each of the multiple data fields to determine at least one third data field from the multiple data fields.

[0056] Among them, at least one third data field is a data field of a preset type, and the preset type includes at least one of the following: numeric type and time type.

[0057] Optionally, after identifying the private dimension data fields from the multiple data fields included in the target business data detail table, the dimension measurement identification module can send a measurement rule matching request for the target business data detail table to the rule engine. When the rule engine receives the measurement rule matching request for the target business data detail table, it can perform matching analysis processing on each data field included in the target business data detail table, excluding the public dimension data fields and the private dimension data fields, according to the preset measurement matching rules, to obtain the matching results, and return the matching results to the dimension measurement identification module. Based on the matching results, the dimension measurement identification module can determine at least one third data field from the data fields included in the target business data detail table, excluding the public dimension data fields and the private dimension data fields (i.e., identify a preset type of data field from the data fields included in the target business data detail table, excluding the public dimension data fields and the private dimension data fields).

[0058] For example, the sales order table may include preset data fields such as order amount and order duration (order completion time - order creation time).

[0059] S204. Based on at least one first data field, at least one second data field, and at least one third data field, determine the type of each of the plurality of data fields.

[0060] Optionally, the dimension measurement identification module can send at least one first data field, at least one second data field, and at least one third data field to the data indicator definition module. When the data indicator definition module receives at least one first data field, at least one second data field, and at least one third data field, it assists the user in confirming the dimension and measurement of the data indicator (i.e., determining the type of each data field among the multiple data fields) through a guided interactive interface to complete the data indicator definition.

[0061] One possible implementation is to automatically identify the type of each data field in the multiple data fields included in the target business data details table. This can reduce the high cost of manually identifying the dimensions and measurement of data indicators, improve the efficiency of identifying and defining data indicators, and facilitate a quick understanding of the company's current business situation and timely promotion of company growth through management.

[0062] Figure 3 This is a flowchart illustrating yet another data indicator identification method according to an exemplary embodiment, such as... Figure 3 As shown, the method in step S201 above specifically includes the following steps:

[0063] S301. For any data field among the multiple data fields included in the target business detail data table, compare each data field with the data fields of each common dimension included in the historical data.

[0064] Optionally, the master data management module can determine the data type of any data field among the multiple data fields included in the target business detail data table, and determine the sampling strategy corresponding to the data field based on the data type of the data field. Based on the sampling strategy corresponding to the data field, data is sampled for the data field to obtain sampled data of the data field. The sampled data of the data field is then compared one by one with the data fields of the common dimension of each master data in the multiple master data (i.e., the data fields of each common dimension included in the historical data) to determine whether the data field exists in the multiple master data.

[0065] For example, for the product data field among the time data field, organization data field, and product data field included in the sales order table, the product data field is compared one by one with the time master data, organization master data, and product master data included in multiple master data to determine whether the product data field exists in the time master data, organization master data, and product master data.

[0066] It should be noted that each data field corresponds to a data type, and each data type corresponds to a sampling strategy.

[0067] S302. If any data field exists among the multiple common dimensions of the historical data, determine that data field as the first data field and mark that data field with the label of the common dimension.

[0068] Optionally, if the dimension measurement and identification module determines that any data field exists in multiple master data, the data field can be identified as the first data field, and the data field can be labeled with the common dimension label.

[0069] For example, if it is determined that a product data field exists in the time master data, organization master data, and product master data included in multiple master data sets, the product data field can be determined as the first data field, and the product data field can be labeled with a product dimension label.

[0070] One possible implementation involves matching multiple data fields from the business detail data table with publicly available master data to obtain common dimension data fields. This can eliminate the ambiguity of common dimension data metrics, reduce communication costs incurred during the use of data metrics, and reduce the risk of metric operators making incorrect decisions.

[0071] Figure 4This is a flowchart illustrating another data indicator identification method according to an exemplary embodiment. The preset dimension matching rules include multiple dimension matching rules, each of which corresponds to a different priority, such as... Figure 4 As shown, the method in step S202 above specifically includes the following steps:

[0072] S401. Based on the priority of each dimension matching rule in the multi-dimensional matching rules, analyze and process each data field in the target business detail data table in order of priority from high to low, and determine at least one second data field from the multiple data fields.

[0073] Optionally, a rule engine can be used to perform matching analysis on each data field in the target business detail data table (excluding common dimension data fields) in descending order of priority, based on the priority of each dimension matching rule among multiple dimension matching rules. The matching results are then returned to the dimension measurement and identification module. Based on the matching results, the dimension measurement and identification module can identify private dimension data fields (i.e., determine at least one second data field from multiple data fields) from the data fields in the target business detail data table.

[0074] For example, the preset dimension matching rules of the sales order table may include two dimension matching rules: dimension matching rule 1: "the result list after deduplication does not exceed 10" and dimension matching rule 2: "the field type is string". The priority of dimension matching rule 1 can be 1, and the priority of dimension matching rule 2 can be 2.

[0075] Based on dimension matching rule 1, each data field in the target business detail data table (excluding common dimension data fields) can be sequentially matched and analyzed. If any data field in the target business detail data table (excluding common dimension data fields) satisfies dimension matching rule 1, then that data field is determined to be a private dimension data field. If none of the data fields in the target business detail data table (excluding common dimension data fields) satisfies dimension matching rule 1, then based on dimension matching rule 2, each data field in the target business detail data table (excluding common dimension data fields) can be sequentially matched and analyzed.

[0076] If any data field in the target business detail data table, excluding the common dimension data fields, satisfies dimension matching rule 2, then that data field is determined to be a private dimension data field; if none of the data fields in the target business detail data table, excluding the common dimension data fields, satisfy dimension matching rule 2, then it is determined that there is no private dimension data field in the target business detail data table.

[0077] Figure 5 This is a flowchart illustrating another data indicator identification method according to an exemplary embodiment. The preset metric matching rules include multiple metric matching rules, each of which corresponds to a different priority, such as... Figure 5 As shown, the method in step S203 above specifically includes the following steps:

[0078] S501. Based on the priority of each metric matching rule in the multiple metric matching rules, analyze and process each data field in the multiple data fields included in the target business detail data table in order of priority from high to low, and determine at least one third data field from the multiple data fields.

[0079] Optionally, a rule engine can be used to perform matching analysis on each data field in the target business detail data table (excluding public and private dimension data fields) in descending order of priority, based on the priority of each metric matching rule. The matching results are then returned to the dimension metric identification module. Based on the matching results, the dimension metric identification module can identify a preset type of data field (i.e., determine at least one third data field from multiple data fields) from the data fields in the target business detail data table (excluding public and private dimension data fields).

[0080] For example, the preset metric matching rules of the sales order table may include two metric matching rules: metric matching rule 1: "field is numeric type" and metric matching rule 2: "field type is duration type". The priority of metric matching rule 1 can be 1, and the priority of metric matching rule 2 can be 2.

[0081] Based on metric matching rule 1, each data field in the target business detail data table (excluding common and private dimensions) can be sequentially matched and analyzed. If any data field in the target business detail data table (excluding common and private dimensions) satisfies metric matching rule 1, then that data field is determined to be a data field of a preset type. If none of the data fields in the target business detail data table (excluding common and private dimensions) satisfies metric matching rule 1, then based on metric matching rule 2, each data field in the target business detail data table (excluding common and private dimensions) can be sequentially matched and analyzed.

[0082] If any data field in the target business detail data table, excluding the data fields of the public and private dimensions, satisfies metric matching rule 2, then that data field is determined to be a data field of the preset type; if none of the data fields in the target business detail data table, excluding the data fields of the public and private dimensions, satisfy metric matching rule 2, then it is determined that there is no data field of the preset type in the target business detail data table.

[0083] This application provides a data indicator identification method. On the one hand, this method can reduce the high cost of manually identifying the dimensions and measuring data indicators, and improve the efficiency of data indicator definition, so that enterprises can quickly grasp the current business situation and promote enterprise growth through management in a timely manner. On the other hand, by applying master data information, the ambiguity of the common dimensions of data indicators can be eliminated, thereby reducing the communication costs generated during the use of data indicators and reducing the risk of indicator operators making incorrect decisions.

[0084] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the data indicator recognition device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] This application embodiment can, based on the above method, exemplarily divide a data indicator recognition device or electronic device into functional modules. For example, the data indicator recognition device or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0086] Figure 6 This is a block diagram illustrating a data indicator recognition device according to an exemplary embodiment. (Refer to...) Figure 6 The data indicator identification device 70 includes: a determination module 701;

[0087] The determination module 701 is used to analyze and process each of the multiple data fields included in the target business detail data table based on historical data, and determine at least one first data field from the multiple data fields. The historical data includes multiple data fields with common dimensions, and at least one first data field is a data field with common dimensions. The common dimensions include at least one of the following: time dimension, organization dimension, and product dimension.

[0088] The determination module 701 is also used to analyze and process each of the multiple data fields based on the preset dimension matching rules, and determine at least one second data field from the multiple data fields. The at least one second data field is a data field of a private dimension, and the private dimension includes at least one of the following: order status, order type, and access channel.

[0089] The determination module 701 is also used to analyze and process each of the multiple data fields based on the preset measurement matching rules, and determine at least one third data field from the multiple data fields. The at least one third data field is a data field of a preset type, and the preset type includes at least one of the following: numeric type and time type.

[0090] The determination module 701 is further configured to determine the type of each of the plurality of data fields based on at least one first data field, at least one second data field, and at least one third data field.

[0091] In one possible implementation, the data indicator identification device 70 further includes a processing module 702; the processing module 702 is used to compare any data field among the multiple data fields included in the target business detail data table with each data field of a common dimension included in the historical data; the determination module 701 is also used to determine any data field as the first data field if any data field exists among the multiple common dimension data fields included in the historical data; the processing module 702 is also used to mark any data field with a common dimension label.

[0092] In one possible implementation, the preset dimension matching rules include multiple dimension matching rules, each of which corresponds to a different priority; the determining module 701 is further used to analyze and process each data field in the target business detail data table in order of priority from high to low based on the priority corresponding to each dimension matching rule in the multiple dimension matching rules, and determine at least one second data field from the multiple data fields.

[0093] In one possible implementation, the preset metric matching rules include multiple metric matching rules, each of which corresponds to a different priority; the determining module 701 is further configured to analyze and process each data field in the target business detail data table in order of priority from high to low based on the priority corresponding to each metric matching rule in the multiple metric matching rules, and determine at least one third data field from the multiple data fields.

[0094] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0095] Figure 7 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Figure 7 As shown, the electronic device 80 includes, but is not limited to, a processor 801 and a memory 802.

[0096] The memory 802 described above is used to store the executable instructions of the processor 801. It is understood that the processor 801 is configured to execute instructions to implement the data indicator recognition method in the above embodiments.

[0097] It should be noted that those skilled in the art will understand that Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 7This may indicate more or fewer components, or a combination of certain components, or a different arrangement of components.

[0098] The processor 801 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802, and by calling data stored in the memory 802, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 801 may include one or more processing modules. Optionally, the processor 801 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 801.

[0099] The memory 802 can be used to store software programs and various data. The memory 802 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and application programs required by at least one functional module (such as an acquisition unit, a determination unit, a processing unit, etc.). Furthermore, the memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0100] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 802 including instructions, which can be executed by a processor 801 of an electronic device 800 to implement the data index identification method in the above embodiments.

[0101] In actual implementation, Figure 6 The functions of the determining module 701 and the processing module 702 can both be provided by Figure 7 The processor 801 calls the computer program stored in the memory 802 to implement the process. The specific execution process can be found in the description of the data indicator identification method section of the previous embodiment, and will not be repeated here.

[0102] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0103] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by a processor 801 of an electronic device to complete the data index identification method in the above embodiments.

[0104] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the electronic device, they implement the various processes of the above-described data indicator recognition method embodiments and achieve the same technical effect as the above-described data indicator recognition method. To avoid repetition, they will not be described again here.

[0105] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0110] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data indicator identification method, characterized in that, include: Based on historical data, each data field in the target business detail data table is analyzed and processed to determine at least one first data field from the multiple data fields. The historical data includes multiple data fields with common dimensions, and the at least one first data field is a data field with common dimensions. The common dimensions include at least one of the following: time dimension, organization dimension, and product dimension. Based on preset dimension matching rules, each of the multiple data fields is analyzed and processed to determine at least one second data field. The at least one second data field is a private dimension data field, and the private dimension includes at least one of the following: order status, order type, and access channel. Based on preset metric matching rules, each of the multiple data fields is analyzed and processed to determine at least one third data field. The at least one third data field is a data field of a preset type, and the preset type includes at least one of the following: numerical type and time type. The at least one first data field, the at least one second data field, and the at least one third data field are sent to the data indicator definition module, so that when the data indicator definition module receives the at least one first data field, the at least one second data field, and the at least one third data field, it assists the user in determining the type of each of the multiple data fields through a guided interactive interface to complete the data indicator definition.

2. The method according to claim 1, characterized in that, The process involves analyzing and processing each data field in the target business detail data table based on historical data, and determining at least one first data field from the multiple data fields, including: For any one of the multiple data fields included in the target business detail data table, compare each data field with the data fields of each common dimension included in the historical data; If any of the data fields in the multiple common dimensions included in the historical data are found to exist, then the data field is determined to be the first data field, and the data field is labeled with the label of the common dimension.

3. The method according to claim 1 or 2, characterized in that, The preset dimension matching rules include multiple dimension matching rules, and each dimension matching rule in the multiple dimension matching rules corresponds to a different priority; The step of analyzing and processing each of the plurality of data fields based on preset dimension matching rules to determine at least one second data field from the plurality of data fields includes: Based on the priority of each dimension matching rule in the multiple dimension matching rules, each data field in the multiple data fields included in the target business detail data table is analyzed and processed in order of priority from high to low, and at least one second data field is determined from the multiple data fields.

4. The method according to claim 1 or 2, characterized in that, The preset metric matching rules include multiple metric matching rules, and each of the multiple metric matching rules corresponds to a different priority; The step involves analyzing and processing each of the multiple data fields based on preset metric matching rules to determine at least one third data field, including: Based on the priority of each of the multiple metric matching rules, each data field in the target business detail data table is analyzed and processed in descending order of priority, and at least one third data field is determined from the multiple data fields.

5. A data indicator recognition device, characterized in that, The data indicator identification device includes a determination module; The determining module is used to analyze and process each of the multiple data fields included in the target business detail data table based on historical data, and determine at least one first data field from the multiple data fields. The historical data includes multiple data fields with common dimensions, and the at least one first data field is a data field with common dimensions. The common dimensions include at least one of the following: time dimension, organization dimension, and product dimension. The determining module is further configured to analyze and process each of the plurality of data fields based on preset dimension matching rules, and determine at least one second data field from the plurality of data fields. The at least one second data field is a private dimension data field, and the private dimension includes at least one of the following: order status, order type, and access channel. The determining module is further configured to analyze and process each of the plurality of data fields based on a preset metric matching rule, and determine at least one third data field from the plurality of data fields. The at least one third data field is a data field of a preset type, and the preset type includes at least one of the following: numerical type and time type. The determining module is further configured to send the at least one first data field, the at least one second data field, and the at least one third data field to the data indicator defining module, so that when the data indicator defining module receives the at least one first data field, the at least one second data field, and the at least one third data field, it assists the user in determining the type of each of the plurality of data fields through a guided interactive interface to complete the data indicator definition.

6. The data indicator recognition device according to claim 5, characterized in that, The data indicator identification device also includes a processing module; The processing module is used to compare any one of the multiple data fields included in the target business detail data table with each data field of the common dimension included in the historical data. The determining module is further configured to determine any data field as the first data field if any data field exists among the data fields of the multiple common dimensions included in the historical data; The processing module is also used to label any of the data fields with the label of the common dimension.

7. The data indicator recognition device according to claim 5 or 6, characterized in that, The preset dimension matching rules include multiple dimension matching rules, and each dimension matching rule in the multiple dimension matching rules corresponds to a different priority; The determining module is further configured to analyze and process each of the multiple data fields included in the target business detail data table in order of priority from high to low, based on the priority of each of the multiple dimension matching rules, and determine the at least one second data field from the multiple data fields.

8. The data indicator recognition device according to claim 5 or 6, characterized in that, The preset metric matching rules include multiple metric matching rules, and each of the multiple metric matching rules corresponds to a different priority; The determining module is further configured to analyze and process each of the multiple data fields included in the target business detail data table in order of priority from high to low, based on the priority corresponding to each of the multiple metric matching rules, and determine the at least one third data field from the multiple data fields.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is capable of performing the method as described in any one of claims 1 to 4.

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