Data index definition and analysis method across multiple data sources

Abstract

The invention relates to a data index definition and analysis method across multiple data sources. The method comprises the following steps: performing atomic index definition based on a single data source and forming a composite index; initiating index query based on the composite index; performing atomic index result calculation on the data source under each atomic index through index cross-source disassembly; and according to the composite index definition logic, combining the index calculation results under the atomic indexes crossing different data sources obtained by the disassembly calculation to obtain the index result data of the composite index. According to the method, data widening of each data source is not needed, corresponding atomic indexes are directly defined based on a cross-source data set, then a composite index is defined, a calculation result similar to the achievement rate is directly obtained based on the composite index, result index calculation only surrounds the composite index definition with the index as the center, the data sources do not need to be paid attention to, and the calculation efficiency is improved. The index data can be re-refreshed at any time according to the composite index, the usability is high, and the method has more flexible index definition and management capability.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data analysis, and in particular to a method for defining and analyzing data indicators across multiple data sources, a device for defining and analyzing data indicators across multiple data sources, and an electronic device. Background Art

[0002] During the business operation analysis process, some operating indicators may come from two different data sets (such as the attached Figure 1 The order data set A and attached Figure 2 The situation of the sales target data set B) shown in the figure is, for example, "sales target achievement rate = actual completed sales performance / sales target". The actual completed sales performance comes from the statistics of the order table, and the sales target comes from a separately maintained data set.

[0003] However, in the BI-based analysis process, it is impossible to define a "sales target achievement rate" indicator from two data sets. It is necessary to build a materialized wide table or logical wide table model through the two data sets of orders and sales targets, and then implement the indicator definition at the wide table level.

[0004] As attached Figure 3 The wide table model shown, where the indicators are defined as follows:

[0005] 1.

Atomic Indicator

[0006] Calculation method: sum(C.order amount),

[0007] Analysis dimensions: C. Month, C. Sales team;

[0008] 2. [Atomic Indicator] Sales Target

[0009] Calculation caliber: No processing (C. sales target),

[0010] Analysis dimensions: C. Month, C. Sales team;

[0011] 3. [Compound indicator] Sales target achievement rate

[0012] Calculation basis: actual sales performance / sales target,

[0013] Analysis dimensions: C. Month, C. Sales team.

[0014] Although this wide table can view data sets under different business indicators to a certain extent, there are the following application problems: because sales targets are target data maintained offline, adjustments and changes often occur; and after the changes, the data in wide table C needs to be refreshed, which makes the wide table not very easy to use. Moreover, when the order data is too large, refreshing the historical data will cause additional performance consumption. Summary of the invention

[0015] In order to solve the above problems, the present application proposes a data indicator definition and analysis method across multiple data sources, a data indicator definition and analysis device and an electronic device for multiple data sources.

[0016] On the one hand, this application proposes a data indicator definition and analysis method across multiple data sources, including the following steps:

[0017] S1. Define atomic indicators based on a single data source, and form composite indicators based on atomic indicators defined by multiple data sources;

[0018] S2. Initiate an index query based on the composite index;

[0019] S3. Through cross-source decomposition of indicators, atomic indicator results are calculated for the data sources under each atomic indicator respectively;

[0020] S4. According to the composite index definition logic, the index calculation results under the atomic indexes across different data sources obtained by the decomposed calculations are merged to obtain the index result data of the composite index.

[0021] As an optional implementation scheme of the present application, optionally, S1, defining an atomic indicator based on a single data source, and forming a composite indicator based on the atomic indicators defined by multiple data sources, including:

[0022] Configure data sets from multiple data sources;

[0023] Define the atomic indicators of the data sets of each data source respectively, where the atomic indicators include the calculation caliber and analysis dimension of the corresponding atomic indicators;

[0024] Based on each atomic indicator, composite indicators are defined and constructed, wherein the atomic indicators include the corresponding calculation caliber and analysis dimension for calculating composite indicators on the data sets of each data source.

[0025] As an optional implementation scheme of the present application, optionally, S2, based on the composite index, initiates an index query, including:

[0026] Generate corresponding query content according to the calculation scope of the composite index;

[0027] Construct the required indicator query request based on the query content and the analysis dimension required for the query;

[0028] Issue an indicator query request.

[0029] As an optional implementation scheme of the present application, optionally, S3, by cross-source decomposition of indicators, atomic indicator result calculation is performed on the data sources under each atomic indicator respectively, including:

[0030] Receive and parse the indicator query request and obtain the indicator query plan;

[0031] The indicator query plan is decomposed according to the cross-source of indicators to obtain the indicator query sub-plan corresponding to each atomic indicator;

[0032] Each indicator query sub-plan is sent to the data source corresponding to each atomic indicator, and based on the data set of the corresponding data source, the corresponding atomic indicator query calculation is performed to obtain the query calculation results under each atomic indicator.

[0033] As an optional implementation scheme of the present application, optionally, S4, according to the composite index definition logic, merge the index calculation results under the atomic indexes across different data sources obtained by the decomposition calculation to obtain the index result data of the composite index, including:

[0034] Read the query calculation results under each atomic indicator;

[0035] According to the calculation caliber of the composite indicator, the query calculation results of each atomic indicator under different analysis dimensions are merged and processed to generate the indicator calculation results under the composite indicator;

[0036] Output and save the indicator calculation results under the composite indicator.

[0037] On the other hand, the present application proposes a data indicator definition and analysis device across multiple data sources, which is used to implement the data indicator definition and analysis method across multiple data sources, including:

[0038] The cross-source indicator definition module is used to define atomic indicators based on a single data source and to form composite indicators based on atomic indicators defined by multiple data sources;

[0039] The indicator query module is used to initiate indicator queries based on composite indicators;

[0040] The indicator cross-source decomposition query module is used to perform atomic indicator result calculations on the data sources under each atomic indicator through cross-source decomposition of indicators.

[0041] The cross-source query merging calculation module is used to merge the indicator calculation results under the atomic indicators across different data sources obtained by the decomposition calculation according to the composite indicator definition logic to obtain the indicator result data of the composite indicator.

[0042] In another aspect, the present application further provides an electronic device, comprising:

[0043] processor;

[0044] a memory for storing processor-executable instructions;

[0045] Among them, the processor is configured to implement data indicator definition and analysis methods across multiple data sources when executing executable instructions.

[0046] Technical effects of the present invention:

[0047] The present invention defines an atomic indicator based on a single data source, and forms a composite indicator based on the atomic indicators defined by multiple data sources; based on the composite indicator, an indicator query is initiated; through cross-source decomposition of the indicator, the atomic indicator result calculation is performed on the data source under each atomic indicator respectively; according to the composite indicator definition logic, the indicator calculation results under the atomic indicators across different data sources obtained by the decomposition calculation are merged to obtain the indicator result data of the composite indicator. It is possible to define the corresponding atomic indicators based on the cross-source data set without widening the data of each data source, and then define the composite indicator, and directly obtain the calculation result of the achievement rate based on the composite indicator. Therefore, the result indicator calculation only revolves around the composite indicator definition centered on the indicator, without paying attention to the data source, and the indicator data can be refreshed at any time according to the composite indicator, and the ease of use is high. And when refreshing before and after, even if the data is too large, the cross-source decomposition of the indicator is performed around the composite indicator without paying attention to the data source, so it will not cause additional performance consumption.

[0048] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0050] Figure 1 It is a schematic diagram of the data structure of the order data set A of the present invention;

[0051] Figure 2 It is a schematic diagram showing the data structure of the sales target data set B of the present invention;

[0052] Figure 3 It is a schematic diagram showing the data structure of the wide table C according to the present invention;

[0053] Figure 4 It is a schematic diagram of the cross-source indicator definition process of the present invention;

[0054] Figure 5 It is a schematic diagram of the process of performing cross-source index decomposition query and combined calculation based on composite index of the present invention;

[0055] Figure 6 It is shown as a schematic diagram of the implementation process of the present invention;

[0056] Figure 7 The application diagram of the electronic device of the present invention is shown as follows DETAILED DESCRIPTION

[0057] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0058] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0059] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure.

[0060] Cross-source: that is, across multiple data sources, so as to realize the indicator definition of different data sources and the indicator query and calculation under each indicator analysis dimension.

[0061] In this embodiment, the data sets from different data sources can be determined by the administrator / user, such as the order data set from the business end, the sales target data set from the management end, etc.

[0062] Example 1

[0063] As attached Figure 6 As shown, on the one hand, the present application proposes a data indicator definition and analysis method across multiple data sources, including the following steps:

[0064] S1. Define atomic indicators based on a single data source, and form composite indicators based on atomic indicators defined by multiple data sources;

[0065] S2. Initiate an index query based on the composite index;

[0066] S3. Through cross-source decomposition of indicators, atomic indicator results are calculated for the data sources under each atomic indicator respectively;

[0067] S4. According to the composite index definition logic, the index calculation results under the atomic indexes across different data sources obtained by the decomposed calculations are merged to obtain the index result data of the composite index.

[0068] The present invention directly defines the corresponding atomic indicators based on the cross-source data set, and then defines the composite indicators. The calculation result of the achievement rate is directly obtained based on the composite indicators. Therefore, the result indicator calculation only revolves around the composite indicator definition centered on the indicator, without paying attention to the data source. The indicator data can be refreshed at any time according to the composite indicator, and the usability is high. And when refreshing before and after, even if the data is too large, the cross-source disassembly of the indicator is performed around the composite indicator without paying attention to the data source, so it will not cause additional performance consumption.

[0069] The following will specifically describe the definition of cross-source indicators and the implementation principles of cross-source indicator decomposition, calculation and merging based on composite indicators.

[0070] 1. As Figure 4 As shown, the cross-source indicator definition

[0071] As an optional implementation scheme of the present application, optionally, S1, defining an atomic indicator based on a single data source, and forming a composite indicator based on the atomic indicators defined by multiple data sources, including:

[0072] Configure data sets from multiple data sources;

[0073] Define the atomic indicators of the data sets of each data source respectively, where the atomic indicators include the calculation caliber and analysis dimension of the corresponding atomic indicators;

[0074] Based on each atomic indicator, composite indicators are defined and constructed, wherein the atomic indicators include the corresponding calculation caliber and analysis dimension for calculating composite indicators on the data sets of each data source.

[0075] Here, we do not need to widen the data of A and B. We can directly define two atomic indicators based on data sets A and B respectively, and then define the composite indicator to directly obtain the calculation result of the achievement rate.

[0076] For the order dataset and sales target dataset, corresponding atomic indicators are constructed respectively.

[0077] 1. Define atomic indicators - actual sales performance:

[0078]

Atomic Indicator

[0079] Calculation method: sum(A.order amount),

[0080] Analysis dimensions: A. Month, A. Sales team;

[0081] 2. Define atomic indicators - sales targets:

[0082]

Atomic Indicator

[0083] Calculation caliber: No processing (B. Sales target),

[0084] Analysis dimensions: B. Month, B. Sales team;

[0085] 3. Define the composite indicator - sales target achievement rate (sales target achievement rate = actual sales performance / sales target):

[0086]

Compound indicator

[0087] Calculation basis: actual sales performance / sales target,

[0088] Analysis dimensions: month, sales team.

[0089] The constructed atomic indicators and composite indicators can be configured in the indicator center. When the business end and the management end perform indicator queries later, they can send corresponding indicator queries to the indicator center, perform indicator decomposition query calculations on data sets from different data sources based on the configured composite indicators, and finally calculate the indicator calculation results under the corresponding analysis dimension based on the calculation caliber of the composite indicators.

[0090] 2. If Figure 5 As shown, cross-source indicators are decomposed, calculated and merged based on composite indicators.

[0091] After the composite indicator is constructed, you only need to perform indicator query and calculation based on the calculation scope defined by the composite indicator without paying attention to the data source. Therefore, there is no need to calculate the indicator data of each atomic indicator one by one and widen the data of A and B. Instead, you can directly define two atomic indicators based on data sets A and B respectively, and then define the composite indicator to directly obtain the calculation result of the achievement rate.

[0092] By adopting the above method, there is no need to widen the data, which means that the step of re-refreshing the data of the wide table C is removed. Therefore, the present invention is easy to use and there will be no additional performance consumption when re-refreshing the historical data when the order data is too large.

[0093] As an optional implementation scheme of the present application, optionally, S2, based on the composite index, initiates an index query, including:

[0094] Generate corresponding query content according to the calculation scope of the composite index;

[0095] Construct the required indicator query request based on the query content and the analysis dimension required for the query;

[0096] Issue an indicator query request.

[0097] To query and calculate indicator data, you first need to initiate an indicator query.

[0098] The basis for initiating the indicator query here is to initiate the indicator query request under the corresponding analysis dimension based on the calculation caliber of the composite indicator.

[0099] The specific analysis dimension to be queried can be set to the corresponding month or sales team according to the administrator's needs. For example, calculate the sales target achievement rate of sales team A in September.

[0100] The query content of the corresponding analysis dimension is generated according to the calculation caliber, which can be achieved by generating and executing SQL query indicators. The generation and instructions of SQL instructions are not described in detail here.

[0101] Next, you need to execute the query request for the composite index.

[0102] As an optional implementation scheme of the present application, optionally, S3, by cross-source decomposition of indicators, atomic indicator result calculation is performed on the data sources under each atomic indicator respectively, including:

[0103] Receive and parse the indicator query request and obtain the indicator query plan;

[0104] The indicator query plan is decomposed according to the cross-source of indicators to obtain the indicator query sub-plan corresponding to each atomic indicator;

[0105] Each indicator query sub-plan is sent to the data source corresponding to each atomic indicator, and based on the data set of the corresponding data source, the corresponding atomic indicator query calculation is performed to obtain the query calculation results under each atomic indicator.

[0106] First, we need to perform the indicator query calculation decomposition around the indicator query plan of the composite indicator, perform the indicator cross-source decomposition, and obtain the indicator query sub-plans corresponding to different atomic indicators. Here, the indicator query sub-plan corresponds to the atomic indicators defined for the data sets of different data sources. Therefore, by decomposing the composite indicator query plan, we can obtain the indicator query sub-plans corresponding to different atomic indicators.

[0107] Subsequently, each indicator query sub-plan is executed respectively, and the indicator query sub-plan is sent to the data set of the corresponding atomic indicator to query and calculate the indicator data of the corresponding atomic indicator.

[0108] When the indicator query sub-plan is executed at each data set, the indicator query and calculation of the indicator query sub-plan can be implemented based on the data set to generate the query calculation results under the atomic indicator.

[0109] Therefore, by decomposing the indicators across sources, the results of multiple atomic indicators are calculated at one time, and the query calculation results under multiple atomic indicators are obtained.

[0110] Next, the query results of multiple atomic indicators are saved in memory and automatically merged and calculated.

[0111] In this embodiment, the LLM large language model (customized by the user or called through an API to call a third-party large language model such as Baidu's Wenxin Yiyan) can also be used to implement intelligent and automated cross-source decomposition and calculation of indicators.

[0112] Use LLM (Large Language Model) to decompose indicators across sources, and perform atomic indicator result calculation steps for the data sources under each atomic indicator. You can refer to the following steps:

[0113] 1. Clarify goals and targets

[0114] Identify your goals: Identify the specific business or analytical objectives you want to achieve through your LLM.

[0115] Define indicators: Based on the objectives, define the overall indicators and atomic indicators that need to be calculated. Atomic indicators are usually the smallest unit of measurement that cannot be divided.

[0116] 2. Data source identification and integration

[0117] Identify data sources: For each atomic indicator, identify all relevant data sources. These data sources may come from different systems, databases or files.

[0118] Data integration: Integrate the identified data sources to ensure that the data format, structure and quality meet the analysis requirements. This may involve operations such as data cleaning, transformation and merging.

[0119] 3. Indicator decomposition and mapping

[0120] Indicator decomposition: Decompose the overall indicator into individual atomic indicators, and clarify the calculation method and dependent data source of each atomic indicator.

[0121] Establishing mapping relationships: Establishing mapping relationships between atomic indicators and data sources to ensure that each atomic indicator can obtain the required data from the corresponding data source.

[0122] 4. Calculation using LLM

[0123] Model training (if necessary): Depending on the specific task, customized training of the LLM may be required to improve its performance in a specific domain or task.

[0124] Write calculation scripts: Use LLM's programming interface (such as API) or programming language (such as Python) to write calculation scripts or programs to implement the calculation logic of atomic indicators.

[0125] Execute calculations: Run the written calculation script or program, calculate each atomic indicator one by one, and store the calculation results.

[0126] 5. Result verification and optimization

[0127] Result verification: Verify the calculated atomic indicator results to ensure the accuracy and reliability of the results. This can be achieved by comparing with known data, logical checking or expert review.

[0128] Optimization and adjustment: Based on the verification results, the calculation script or program is optimized and adjusted to improve the calculation efficiency and accuracy.

[0129] 6. Summary and Reporting

[0130] Result summary: Summarize the calculation results of each atomic indicator to form a complete indicator system.

[0131] Generate reports: Generate analytical reports or visual presentations based on the summary results so that business personnel or decision makers can better understand and use these indicators.

[0132] 7. Continuous monitoring and updating

[0133] Continuous monitoring: Regularly monitor data sources and calculation results to ensure the timeliness and accuracy of data.

[0134] Update and Optimize: Update calculation scripts and models in a timely manner according to changes in business needs and data sources to maintain the applicability and effectiveness of the indicator system.

[0135] The use of the LLM large language model can improve indicator efficiency and intelligence.

[0136] As an optional implementation scheme of the present application, optionally, S4, according to the composite index definition logic, merge the index calculation results under the atomic indexes across different data sources obtained by the decomposition calculation to obtain the index result data of the composite index, including:

[0137] Read the query calculation results under each atomic indicator;

[0138] According to the calculation caliber of the composite indicator, the query calculation results of each atomic indicator under different analysis dimensions are merged and processed to generate the indicator calculation results under the composite indicator;

[0139] Output and save the indicator calculation results under the composite indicator.

[0140] When performing combined calculations, you need to read the query calculation results of each atomic indicator under the corresponding analysis dimension according to the calculation caliber of the composite indicator defined above, such as reading the actual completed sales performance under the order data set and the sales target under the sales target data set. According to the calculation caliber of the composite indicator:

[0141] Sales target achievement rate = actual sales performance / sales target;

[0142] Combine and calculate the results under the composite indicators to obtain the achievement rate result indicators under the corresponding required analysis dimensions.

[0143] The analysis dimensions in the composite indicator here can be selected and configured by the user.

[0144] Therefore, this application performs atomic indicator result calculations on the data sources under each atomic indicator through cross-source decomposition of indicators; and merges the atomic indicator results obtained by cross-source calculation of indicators to obtain result indicator data. It is possible to define the corresponding atomic indicators based on the cross-source data set without widening the data of each data source, and then define the composite indicator, and directly obtain the calculation result of the achievement rate based on the composite indicator. Therefore, the result indicator calculation only revolves around the composite indicator definition centered on the indicator without paying attention to the data source, and the indicator data can be refreshed at any time according to the composite indicator, which is easy to use. And when refreshing before and after, even if the data is too large, the cross-source decomposition of the indicator is performed around the composite indicator without paying attention to the data source, so it will not cause additional performance consumption.

[0145] Obviously, those skilled in the art should understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0146] Example 2

[0147] Based on the implementation principle of Example 1, on the other hand, the present application proposes a data indicator definition and analysis device across multiple data sources, which is used to implement the data indicator definition and analysis method across multiple data sources, including:

[0148] The cross-source indicator definition module is used to define atomic indicators based on a single data source and to form composite indicators based on atomic indicators defined by multiple data sources;

[0149] The indicator query module is used to initiate indicator queries based on composite indicators;

[0150] The indicator cross-source decomposition query module is used to perform atomic indicator result calculations on the data sources under each atomic indicator through cross-source decomposition of indicators.

[0151] The cross-source query merging calculation module is used to merge the indicator calculation results under the atomic indicators across different data sources obtained by the decomposition calculation according to the composite indicator definition logic to obtain the indicator result data of the composite indicator.

[0152] The composition of the above-mentioned device and the interaction of each module can be understood in conjunction with the corresponding description in Example 1, and will not be described in detail in this example.

[0153] The modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0154] Example 3

[0155] As attached Figure 7 As shown, further, in another aspect, the present application also proposes an electronic device, including:

[0156] processor;

[0157] a memory for storing processor-executable instructions;

[0158] Among them, the processor is configured to implement the data indicator definition and analysis method across multiple data sources of Example 1 when executing executable instructions.

[0159] The electronic device according to the embodiment of the present disclosure includes a processor and a memory for storing processor executable instructions, wherein the processor is configured to implement any of the above data indicator definition and analysis methods across multiple data sources when executing the executable instructions.

[0160] Here, it should be noted that the number of processors can be one or more. At the same time, the electronic device in the embodiment of the present disclosure may also include an input device and an output device. Among them, the processor, memory, input device and output device may be connected through a bus or in other ways, which are not specifically limited here.

[0161] The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs and various modules, such as the programs or modules corresponding to the data indicator definitions and analysis methods across multiple data sources in the embodiments of the present disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0162] The input device can be used to receive input numbers or signals. The signal can be a key signal related to user settings and function control of the device / terminal / server. The output device can include a display device such as a display screen.

[0163] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for defining and analyzing data indicators across multiple data sources, characterized in that: The steps include: S1. Define atomic indicators based on a single data source, and form composite indicators based on atomic indicators defined by multiple data sources; S2. Initiate an index query based on the composite index; S3. Through cross-source decomposition of indicators, atomic indicator results are calculated for the data sources under each atomic indicator respectively; S4. According to the composite index definition logic, the index calculation results under the atomic indexes across different data sources obtained by the decomposed calculations are merged to obtain the index result data of the composite index.

2. The method for defining and analyzing data indicators across multiple data sources according to claim 1 is characterized in that: S1. Define atomic indicators based on a single data source, and form composite indicators based on atomic indicators defined by multiple data sources, including: Configure data sets from multiple data sources; Define the atomic indicators of the data sets of each data source respectively, where the atomic indicators include the calculation caliber and analysis dimension of the corresponding atomic indicators; Based on each atomic indicator, composite indicators are defined and constructed, wherein the atomic indicators include the corresponding calculation caliber and analysis dimension for calculating composite indicators on the data sets of each data source.

3. The method for defining and analyzing data indicators across multiple data sources according to claim 2 is characterized in that: S2. Initiate an indicator query based on the composite indicator, including: Generate corresponding query content according to the calculation scope of the composite index; Construct the required indicator query request based on the query content and the analysis dimension required for the query; Issue an indicator query request.

4. The method for defining and analyzing data indicators across multiple data sources according to claim 3 is characterized in that: S3. Through cross-source decomposition of indicators, atomic indicator results are calculated for the data sources under each atomic indicator, including: Receive and parse the indicator query request and obtain the indicator query plan; The indicator query plan is decomposed according to the cross-source of indicators to obtain the indicator query sub-plan corresponding to each atomic indicator; Each indicator query sub-plan is sent to the data source corresponding to each atomic indicator, and based on the data set of the corresponding data source, the corresponding atomic indicator query calculation is performed to obtain the query calculation results under each atomic indicator.

5. The method for defining and analyzing data indicators across multiple data sources according to claim 4 is characterized in that: S4. According to the composite index definition logic, the index calculation results of the atomic indexes across different data sources obtained by the decomposition calculation are merged to obtain the index result data of the composite index, including: Read the query calculation results under each atomic indicator; According to the calculation caliber of the composite indicator, the query calculation results of each atomic indicator under different analysis dimensions are merged and processed to generate the indicator calculation results under the composite indicator; Output and save the indicator calculation results under the composite indicator.

6. A data indicator definition and analysis device across multiple data sources, used to implement any of the data indicator definition and analysis methods across multiple data sources in claims 1-5, characterized in that: include: The cross-source indicator definition module is used to define atomic indicators based on a single data source and to form composite indicators based on atomic indicators defined by multiple data sources; The indicator query module is used to initiate indicator queries based on composite indicators; The indicator cross-source decomposition query module is used to perform atomic indicator result calculations on the data sources under each atomic indicator through cross-source decomposition of indicators. The cross-source query merging calculation module is used to merge the indicator calculation results under the atomic indicators across different data sources obtained by the decomposition calculation according to the composite indicator definition logic to obtain the indicator result data of the composite indicator.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to implement any one of the data indicator definition and analysis methods across multiple data sources in claims 1-5 when executing executable instructions.

8. A computer-readable storage medium, characterized in that: A readable computer program is stored in the readable computer storage medium. When the readable computer program is read by a computer, the data indicator definition and analysis method across multiple data sources in any one of claims 1 to 5 is implemented.

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