Data fusion method and device based on indexes and labels, equipment and medium

Abstract

The invention discloses a data fusion method and device based on indexes and labels, equipment and a medium. The method comprises the following steps: preprocessing business data generated by a preset data source and then transmitting the business data to a data concentration library; analyzing and counting the service data in the data concentration library according to service types to obtain key service data, and determining key service indexes according to the key service data; creating a corresponding label for each key business index through a preset data warehouse tool; and summarizing all the key business indexes corresponding to each piece of key business data and the corresponding labels, and generating fusion information with the key business indexes and the labels. By implementing the method provided by the invention, the problem that a data system of indexes and labels cannot be efficiently analyzed and operated can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of information fusion, and in particular to a data fusion method, device, equipment and medium based on indicators and labels. Background Art

[0002] In recent years, with the continuous advancement of technology and the growing business needs, the construction and transformation of the indicator and label system has achieved remarkable results, and the application scenarios have become increasingly rich, such as in the financial and insurance fields. However, while the indicator and label system is widely used, the problem of data consistency has gradually surfaced. Due to differences in data sources, processing procedures, storage methods, and other aspects, there are often inconsistencies between indicator and label data. This data inconsistency will not only affect the daily analysis and operation of the business, leading to wrong decisions and inefficiency, but also have a greater negative impact on the authority of the data. Moreover, the inconsistency of indicator and label data will increase the cost of explanation for business personnel when processing business, increase the difficulty of communication and coordination, and thus make it impossible for the indicator and label data system in the existing technology to efficiently perform daily analysis and operation. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, equipment and medium for data fusion based on indicators and labels, aiming to solve the problem that the data system of indicators and labels cannot be analyzed and operated efficiently.

[0004] In a first aspect, an embodiment of the present invention provides a data fusion method based on indicators and labels, which includes: pre-processing the business data generated by a preset data source and transmitting it to a data concentration library; analyzing and statistically analyzing the business data in the data concentration library according to business types to obtain key business data, and determining key business indicators based on the key business data; creating a corresponding label for each of the key business indicators through a preset data warehouse tool; summarizing all the key business indicators and their corresponding labels corresponding to each of the key business data to generate fusion information with the key business indicators and the labels.

[0005] In the second aspect, an embodiment of the present invention also provides a data fusion device based on indicators and labels, which includes: a processing unit, used to pre-process the business data generated by a preset data source and transmit it to a data concentration library; an acquisition unit, used to analyze and statistically analyze the business data in the data concentration library according to the business type to obtain key business data, and determine key business indicators based on the key business data; a creation unit, used to create a corresponding label for each of the key business indicators through a preset data warehouse tool; a fusion unit, used to summarize all the key business indicators and their corresponding labels corresponding to each of the key business data, and generate fusion information with the key business indicators and the labels.

[0006] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and the program instructions can implement the above method when executed by a processor.

[0008] The embodiment of the present invention provides a data fusion method, device, equipment and medium based on indicators and labels. The method includes: pre-processing the business data generated by the preset data source and transmitting it to the data concentration library; analyzing and statistically analyzing the business data in the data concentration library according to the business type to obtain key business data, and determining key business indicators according to the key business data; creating corresponding labels for each of the key business indicators through a preset data warehouse tool; summarizing all the key business indicators and their corresponding labels corresponding to each of the key business data, and generating fusion information with the key business indicators and the labels. The embodiment of the present invention obtains key business data in a unified business data bottom layer, and determines the key business indicators according to the key business data, so that the data and the data applied by the label are unified, avoiding the fusion of the indicator and the label system due to data differences. The label is created for each of the key business indicators, and the fusion information with the key business indicators and the labels is generated, so that the label and the indicator corresponding to each key business data are associated with the data, so that when the fusion data is used for data query and other operations through the indicator platform or the label platform, the application efficiency can be effectively improved, and the efficiency of business data analysis can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0010] Figure 1 A flowchart of a data fusion method based on indicators and labels provided in an embodiment of the present invention;

[0011] Figure 2 A schematic diagram of a sub-process of a data fusion method based on indicators and labels provided in an embodiment of the present invention;

[0012] Figure 3 A schematic diagram of a sub-process of a data fusion method based on indicators and labels provided in an embodiment of the present invention;

[0013] Figure 4 A schematic diagram of a sub-process of a data fusion method based on indicators and labels provided in an embodiment of the present invention;

[0014] Figure 5 A schematic diagram of a sub-process of a data fusion method based on indicators and labels provided in an embodiment of the present invention;

[0015] Figure 6 A schematic diagram of a sub-process of a data fusion method based on indicators and labels provided in an embodiment of the present invention;

[0016] Figure 7 A schematic block diagram of a data fusion device based on indicators and labels provided in an embodiment of the present invention;

[0017] Figure 8 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0020] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0021] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0022] See also Figure 1 , Figure 1 The present invention provides a flowchart of a method for data fusion based on indicators and labels. The method for data fusion based on indicators and labels in this embodiment can be applied to a data query system based on indicators and labels. When the data query system uses the method of the present invention to perform operations such as data query, it can effectively improve application efficiency and enhance business data analysis efficiency.

[0023] Figure 1 1 is a flow chart of a data fusion method based on indicators and labels provided by an embodiment of the present invention. As shown in the figure, the method includes the following steps S110-S140.

[0024] S110: pre-process the business data generated by the preset data source and transmit it to the data concentration library.

[0025] In this embodiment, the preset data source is a source that can generate business data. For example, the data generated by the insurance system can be business data, and the insurance system is the preset data source. The business data generated by the preset data source is pre-processed and transmitted to the data concentration library. Specifically, the collected business data is pre-processed through ODS (Operational Data Store, which is a key component in the data warehouse architecture, also known as the source layer). Specifically, the business data of business systems such as sales, inventory and customer management can be received and stored through ODS. ODS will perform necessary pre-processing on these data, such as data cleaning, integration and transformation. And the processed data is transmitted to the data concentration library, wherein the data concentration library refers to a common data model, which is a shared model that defines data and is used to ensure data consistency, for example, CDM (Common Data Model, data concentration layer). By pre-processing the business data generated by the preset data source and transmitting it to the data concentration library, a unified and reliable data source is formed. Multi-dimensional data analysis can be performed based on this data source, such as sales trend analysis, inventory status monitoring and customer behavior research. These analysis results can provide strong support for enterprises to formulate marketing strategies, optimize inventory management and improve customer satisfaction.

[0026] In one embodiment, if Figure 2 As shown, the step S110 also includes steps S111-S112.

[0027] S111, performing a pre-processing operation of data cleaning and / or data conversion on the business data;

[0028] S112: Transmit the pre-processed business data to the data concentration library.

[0029] In this embodiment, the preprocessing operation is an operation performed before the data is transferred to the data collection library. Preprocessing such as data cleaning and conversion can be performed before the data is transferred from the ODS to the data collection library, or preprocessing operations can be performed during the ODS transmission process. The data cleaning refers to the process of identifying and correcting errors or inconsistencies in the data set. These errors may include missing values, duplicate data, outliers (such as extremely large or small values), spelling errors, inconsistent data formats, etc. The goal of data cleaning is to ensure the quality of the data, making it more accurate, reliable and consistent, thereby providing a good foundation for subsequent data analysis or modeling. For example, for missing data, a variety of strategies can be adopted, such as deleting records containing missing values, using mean / median / mode to fill missing values, using interpolation to estimate missing values, or identifying and deleting duplicate records to avoid bias in data analysis. These operations are all data cleaning operations. The data conversion refers to the process of converting data from one format or structure to another format or structure, so as to better adapt to subsequent data analysis or model training needs. For example, converting a string type date to a date format is convenient for date-related calculations. By performing pre-processing operations of data cleaning and / or data conversion on the business data and then storing it in the data concentration library, the quality and applicability of the data are ensured, and unified management and efficient use of the data are facilitated.

[0030] S120: Analyze and collect statistics on the business data in the data concentration library according to business types to obtain key business data, and determine key business indicators based on the key business data.

[0031] In this embodiment, the key business indicator is a metric used to measure a certain goal or object, and is used to evaluate the state of a certain object. Specifically, if the present invention is applied to a financial system, the key business indicators may include business KPI indicators and operational indicators, wherein the business KPI indicator refers to the core indicator of the business concern, which is used to track and analyze the achievement of business performance. And the operational indicator refers to the process indicator generated by the business in daily operations. The business data in the data collection library is analyzed and statistically obtained according to the business type, and the key business data can be classified according to the business type. The business type can be sales, marketing, customer service, product development, etc., and the data is analyzed and processed to obtain data points or data sets that have a direct impact on business decisions, thereby obtaining key business data. Determine the key business indicators based on the key business data. Specifically, if sales is the main business of the enterprise, then sales growth rate, customer satisfaction, market share, etc. may be key business indicators. Once the key business indicators are determined, these indicators can be used to monitor and evaluate its business performance. By determining the key business indicators based on the key business data, it is convenient to establish the relationship between indicators and tags in the future, thereby realizing data fusion.

[0032] In one embodiment, if Figure 3 As shown, the step S120 includes steps S121-S122.

[0033] S121, performing data statistics and analysis on the business data according to the preset business requirements and preset business fields corresponding to the business types to obtain key business data;

[0034] S122: Perform business sorting according to the key business data to determine the key business indicators.

[0035] In this embodiment, the business type refers to different business lines or product lines operated by the enterprise, such as order processing, inventory management, user behavior analysis, etc. in e-commerce. The preset business requirements are a series of requirements set in advance based on factors such as corporate strategic goals, market trends, historical data, etc., to guide the direction and goals of data analysis. For example, an e-commerce company may be concerned about user conversion rate, average order amount, inventory turnover rate, etc. The preset business field refers to the division of business data according to specific fields or dimensions, such as sales field, market field, product field, etc., and each field may focus on different data points. Data statistics and analysis are based on the above-mentioned business types and fields, using statistical methods and analysis tools to process business data to reveal the patterns and trends behind the data. Key business data are data points obtained through the above analysis that have a direct impact on business decisions or important reference value. According to the preset business requirements and preset business fields corresponding to the business types, the business data is statistically analyzed and key business data is obtained. Specifically, the business data can be analyzed according to the preset business requirements and preset business fields through a preset data analysis program or software, wherein the specific statistical and analysis methods are not limited, for example, data analysis can be achieved through a large model or analysis software. According to the key business data, the business is sorted out and the key business indicators are determined. Specifically, according to the key business data obtained, the business processes, links, strategies, etc. can be re-sorted and reviewed to determine specific indicators for measuring business performance, monitoring business progress, and guiding business optimization. These indicators are usually quantitative and easy to track and evaluate. For example, after sorting out the insurance business, it can be found that the user insurance rate is a key business indicator. By determining the key business indicators, it is convenient to establish the relationship between indicators and tags in the future, thereby realizing data fusion.

[0036] S130: Create a corresponding label for each of the key business indicators using a preset data warehouse tool.

[0037] In this embodiment, the preset data warehouse tool is a tool that can provide functions such as data storage, query, and analysis, and supports data operations through languages ​​such as SQL. For example, tools such as Hive, Spark SQL, and Redshift are all data warehouse tools. In this embodiment, the preset data warehouse tool is Hive (a data warehouse tool based on Hadoop that provides a SQL-like query language HiveQL). The label is a way of classifying or marking data, which is used to describe and identify the characteristics or attributes of data. Create a corresponding label for each of the key business indicators through the preset data warehouse tool. Specifically, determine the name and description of the label to ensure that they can clearly express the meaning of the key business indicator. Determine the storage location and data structure of the label so that it can be effectively managed and queried in the data warehouse, and write SQL statements or use the interface provided by the data warehouse tool to associate the data of the key business indicator with the label. For example, in Hive, we can create a label called "pass" for "score 68". By creating a corresponding label for each of the key business indicators through the preset data warehouse tool, the efficiency of data management and analysis can be effectively improved.

[0038] In one embodiment, if Figure 4 As shown, the step S130 includes steps S131-S132.

[0039] S131, creating a label field in a preset indicator storage table through the preset data warehouse tool;

[0040] S132. Generate a corresponding tag value according to each of the key business indicators and store it in a corresponding tag field.

[0041] In this embodiment, the preset indicator storage table is a table that already exists in the data warehouse, which is used to store key business indicators and related information. These indicators may include sales, number of users, conversion rate, etc. A label field is created in the preset indicator storage table through the preset data warehouse tool. Specifically, a new column (field) is added to the preset indicator storage table to store the label value. For example, suppose we have a table called "SalesKPI" for storing key business indicators related to sales. A label is added to each sales indicator to indicate whether it has reached a certain preset goal. A new column named "PerformanceLabel" can be added to the "SalesKPI" table to store the label value corresponding to each indicator. According to each of the key business indicators, a corresponding label value is generated and stored in the corresponding label field. Specifically, for the actual value of each key business indicator, a corresponding label value is generated and stored in the label field just created. For example, a rule can be set that if the sales exceed 1 million, the label is "high", if the sales are between 500,000 and 1 million, the label is "medium", and if the sales are less than 500,000, the label is "low". By generating a corresponding tag value according to each of the key business indicators and storing it in the corresponding tag field, convenience is provided for subsequent data analysis, query and data fusion.

[0042] S140: Aggregate all the key business indicators corresponding to each of the key business data and their corresponding labels to generate fusion information having the key business indicators and the labels.

[0043] In this embodiment, the fusion information refers to the data obtained by summarizing the key business data, key business indicators and their labels. All the key business indicators and their corresponding labels corresponding to each of the key business data are summarized to generate fusion information with the key business indicators and the labels. For example, assuming that there is sales data of an insurance platform, we need to process these data to generate fusion information. Key business data: Sales: Sales data for the first quarter of 2023. Key business indicators: Total sales: Total sales in the first quarter of 2023 were 100 million yuan. Year-on-year growth rate: Sales increased by 20% compared with the first quarter of 2022. Month-on-month growth rate: Sales increased by 5% compared with the fourth quarter of 2022. For sales, we can mark it as "high" (such as sales exceeding 100 million yuan) according to the size of the total sales, so the sales in the first quarter of 2023 are labeled "high", and its growth rate is medium for the year-on-year growth rate and low for the month-on-month growth rate. Summarizing the information can obtain fusion information of sales with all label values ​​and key business indicators. By generating fusion information including the key business indicators and the tags, it is convenient to query the performance and status of the business data according to the tags.

[0044] In one embodiment, if Figure 5 As shown, the step S140 includes steps S141-S142.

[0045] S141, searching for all the key business indicators associated with the key business data;

[0046] S142: Associating each of the key business indicators and its corresponding label with the key business data to generate the fusion information.

[0047] In this embodiment, the key business data may involve multiple business indicators. For example, sales may correspond to total sales, year-on-year growth rate, and month-on-month growth rate. Therefore, it is necessary to search for all the key business indicators associated with the key business data. Specifically, the key business data can be used as a keyword to search for indicators in the indicator storage table in the data warehouse through the data warehouse tool Hive. For example, for a financial company, key business data may include sales, and the corresponding key business indicators may include monthly sales growth rate, new customer acquisition rate, etc. It should be noted that each key business indicator and its description (or label) are associated at this time. Therefore, the association between the three can be achieved by associating the indicator with the key business data. After determining the key business indicators related to "sales", such as "monthly sales growth rate" and "quarterly sales target achievement rate", this information is integrated. For example, a table is created, which lists the sales data, and the corresponding indicators (such as "monthly sales growth rate: +15%", "quarterly sales target achievement rate: 90%) and their labels (such as "measure sales changes over time", "evaluate whether the quarterly sales target has been achieved") are next to it, so as to obtain the corresponding fusion information. By obtaining fused information, the corresponding system can use its business data more effectively.

[0048] In one embodiment, if Figure 6 As shown, step S140 includes steps S1401-S1402.

[0049] S1401, encapsulating the key business indicators and corresponding labels into interfaces to generate corresponding label interfaces;

[0050] S1402: Call the tag interface to generate a query identifier that can perform a joint query on indicators and tags.

[0051] In this embodiment, the interface encapsulation refers to encapsulating the key business indicators and the corresponding tags into the form of API (application programming interface), so that these data and tags can be accessed by external systems or applications through network requests. Among them, the specific interface encapsulation method is not limited, and the corresponding interface can be generated. The key business indicators and the corresponding tags are interface-encapsulated to generate corresponding tag interfaces. Specifically, once the key business indicators and tags are interface-encapsulated, data can be obtained by calling these interfaces. The corresponding interface can be called according to customer instructions to obtain indicator and tag data. Among them, the query identifier is information such as the query button displayed in the indicator platform or tag platform that can call the fused data. This query identifier allows the user to directly obtain relevant query results through this identifier in the system. For example, when a user sees that a certain indicator data is abnormal or fluctuates greatly on the indicator platform, the population composition and portrait distribution of the corresponding abnormal indicator can be queried by clicking the query identifier. By calling the tag interface to generate a query identifier that can query indicators and tags together, a large amount of business data can be efficiently managed and queried, and the efficiency of data analysis and decision-making can be improved.

[0052] In one embodiment, step S1403 is further included after step S1402.

[0053] S1403: If it is detected that the query identifier is triggered, query the corresponding indicator data according to the tag corresponding to the triggered query identifier, and display the indicator data.

[0054] In this embodiment, if the query identifier is clicked by the user, it means that the query identifier is triggered. If it is detected that the user clicks the query identifier in the system that calls the query identifier, the index data related to the query identifier is retrieved from the database or data warehouse according to the tag of the triggered query identifier, and the index data is presented to the user or system in a certain form (such as chart, number, text, etc.). For example, a query identifier "Q123" is defined to track the sales of a certain financial product in the past week. If the user wants to query the corresponding sales through the tag platform, click the corresponding query identifier. The tag associated with the query identifier "Q123" may include product ID, sales time period, sales area information, and the system queries the index data associated with the tag from the database according to these tags, including sales quantity, sales volume, average unit price and other indicators. These index data are displayed to the operator in the form of charts (such as bar charts and line charts) so that they can quickly understand the sales of this product and make corresponding decisions. The query and display of specific data are realized by querying the corresponding index data according to the tag corresponding to the triggered query identifier.

[0055] Figure 7 is a schematic block diagram of a data fusion device 200 based on indicators and labels provided by an embodiment of the present invention. Figure 7 As shown, corresponding to the above data fusion method based on indicators and labels, the present invention also provides a data fusion device based on indicators and labels. The data fusion device based on indicators and labels includes a unit for executing the above data fusion method based on indicators and labels, and the device can be configured in a desktop computer, a tablet computer, a laptop computer, and other terminals. Specifically, please refer to Figure 7 The data fusion device based on indicators and labels includes a processing unit 210, an acquisition unit 220, a creation unit 230 and a fusion unit 240.

[0056] The processing unit 210 is used to pre-process the business data generated by the preset data source and then transmit it to the data concentration library.

[0057] In one embodiment, the processing unit 210 includes a cleaning unit and a transmission unit.

[0058] A cleaning unit, used for performing a pre-processing operation of data cleaning and / or data conversion on the business data;

[0059] The transmission unit is used to transmit the preprocessed business data to the data concentration library.

[0060] The acquisition unit 220 is used to analyze and collect statistics of the business data in the data concentration library according to business types to obtain key business data, and determine key business indicators based on the key business data.

[0061] In one embodiment, the acquisition unit 220 includes a cleaning unit and a transmission unit.

[0062] An analysis unit, used to perform data statistics and analysis on the business data according to preset business requirements and preset business fields corresponding to the business types, and obtain key business data;

[0063] The combing unit is used to perform business combing according to the key business data to determine the key business indicators.

[0064] The creation unit 230 is used to create a corresponding label for each of the key business indicators through a preset data warehouse tool.

[0065] In one embodiment, the creation unit 230 includes a label unit and a storage unit.

[0066] A label unit, used to create a label field in a preset indicator storage table through the preset data warehouse tool;

[0067] The storage unit is used to generate a corresponding tag value according to each of the key business indicators and store it in a corresponding tag field.

[0068] The fusion unit 240 is used to aggregate all the key business indicators corresponding to each of the key business data and their corresponding labels to generate fusion information having the key business indicators and the labels.

[0069] In one embodiment, the fusion unit 240 includes a configuration unit.

[0070] A search unit, used to search for all the key business indicators associated with the key business data;

[0071] The associating unit is used to associate each of the key business indicators and its corresponding label with the key business data to generate the fusion information.

[0072] In one embodiment, the fusion unit 240 includes a configuration unit.

[0073] The encapsulation unit is used to encapsulate the key business indicators and the corresponding labels into interfaces and generate corresponding label interfaces;

[0074] The calling unit is used to call the tag interface to generate a query identifier that can perform a joint query of indicators and tags.

[0075] In one embodiment, the fusion unit 240 includes a configuration unit.

[0076] The display unit is used to query the corresponding indicator data according to the tag corresponding to the triggered query identifier if it is detected that the query identifier is triggered, and display the indicator data.

[0077] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned indicator- and label-based data fusion device 200 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0078] The above-mentioned data fusion device based on indicators and labels can be implemented in the form of a computer program. The computer program can be used in Figure 8 Runs on the computer device shown.

[0079] See also Figure 8 Figure 85 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0080] See also Figure 8 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0081] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a data fusion method based on indicators and tags.

[0082] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0083] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a data fusion method based on indicators and labels.

[0084] The network interface 505 is used to communicate with other devices over the network. Figure 8 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0085] The processor 502 is used to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0086] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0087] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0088] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the above method.

[0089] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0090] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0091] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0092] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention 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.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0094] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A data fusion method based on indicators and labels, characterized in that: include: Pre-process the business data generated by the preset data source and transfer it to the data concentration library; Analyze and compile statistics of the business data in the data collection library according to the business type to obtain key business data, and determine key business indicators based on the key business data; Creating corresponding labels for each of the key business indicators through a preset data warehouse tool; All the key business indicators corresponding to each of the key business data and their corresponding labels are aggregated to generate fusion information having the key business indicators and the labels.

2. The method according to claim 1, characterized in that The step of pre-processing the business data generated by the preset data source and transmitting it to the data concentration library includes: Performing pre-processing operations of data cleaning and / or data conversion on the business data; The pre-processed business data is transmitted to the data concentration library.

3. The method according to claim 1, characterized in that The step of analyzing and collecting statistics of the business data in the data concentration library according to the business type to obtain key business data, and determining key business indicators according to the key business data, includes: Performing data statistics and analysis on the business data according to the preset business requirements and preset business fields corresponding to the business types to obtain key business data; Business is sorted out according to the key business data to determine the key business indicators.

4. The method according to claim 1, characterized in that: The step of creating a corresponding label for each of the key business indicators by using a preset data warehouse tool includes: Creating a label field in a preset indicator storage table by using the preset data warehouse tool; A corresponding tag value is generated according to each of the key business indicators and stored in the corresponding tag field.

5. The method according to claim 1, characterized in that The step of aggregating all the key business indicators and their corresponding labels corresponding to each of the key business data to generate fusion information having the key business indicators and the labels includes: Searching for all the key business indicators associated with the key business data; Each of the key business indicators and its corresponding label are associated with the key business data to generate the fusion information.

6. The method according to claim 1, characterized in that After the step of generating fusion information having the key business indicator and the label, the method further includes: Encapsulate the key business indicators and corresponding labels into interfaces to generate corresponding label interfaces; The tag interface is called to generate a query identifier that can perform a joint query on indicators and tags.

7. The method according to claim 6, characterized in that After the step of calling the tag interface to generate a query identifier for querying both the indicator and the tag, the following steps are included: If it is detected that the query identifier is triggered, the corresponding indicator data is queried according to the tag corresponding to the triggered query identifier, and the indicator data is displayed.

8. A data fusion device based on indicators and labels, characterized in that: include: A processing unit, used to pre-process the business data generated by the preset data source and then transmit it to the data concentration library; An acquisition unit, configured to analyze and collect statistics of the business data in the data collection library according to business types to acquire key business data, and determine key business indicators according to the key business data; A creation unit, used to create a corresponding label for each of the key business indicators through a preset data warehouse tool; The fusion unit is used to aggregate all the key business indicators corresponding to each of the key business data and their corresponding labels to generate fusion information having the key business indicators and the labels.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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