Data analysis method and device based on index system, computer equipment and medium

Abstract

The invention discloses a data analysis method and device based on an index system, computer equipment and a medium, and belongs to the field of data analysis and financial science and technology. The method comprises the following steps: acquiring business data to be analyzed; determining a target index corresponding to the business data through a preset index meta-model, and analyzing a data analysis result corresponding to the target index and the business data through the preset index meta-model; according to the scheme, the business data is processed through the constructed preset index meta-model, the index relation between different fields can be unified, the indexes can be reused, and the index specifications can be unified, so that the working efficiency of data analysis can be improved, the related cost of a company on index related operation can be reduced, and the enterprise experience is improved. The method can be applied to data analysis in the insurance industry.

Description

Technical Field

[0001] The present invention relates to the fields of data analysis and financial technology, and in particular to a data analysis method, device, computer equipment and medium based on an indicator system. Background Art

[0002] The construction of data warehouses in the insurance industry presents a series of unique and complex challenges. These issues are not only common in data warehouse construction, such as inconsistent indicator calibers, ambiguous indicators, siloed development, and difficulty for users to use data, but are also particularly prominent due to the unique characteristics of the insurance industry, especially within the life insurance sector. The life insurance business processes within insurance companies can be meticulously divided into 13 distinct areas, encompassing sales, claims, team management, customer service, and other aspects. However, the overlap and overlap between these areas has led to numerous disputes in actual operations. Furthermore, the fact that different development teams operate independently, resulting in blurred boundaries between responsibilities and even duplication of work, undoubtedly exacerbates the complexity of the issue. Furthermore, non-standardization in data processing is a major pain point. Each development team adheres to its own standards, lacking unified norms, making data management and maintenance extremely difficult. In summary, this data chaos not only reduces the efficiency of subsequent data analysis but also significantly increases the insurance company's operating costs.

[0003] Therefore, those skilled in the art are in urgent need of finding a new technical solution to solve the above technical problems. Summary of the Invention

[0004] Based on this, a data analysis method, device, computer equipment and medium based on an indicator system are provided to solve the technical problems in the existing technology that data confusion reduces the efficiency of subsequent data analysis and significantly increases the operating costs of insurance companies.

[0005] A data analysis method based on an indicator system, comprising: Obtain business data to be analyzed; A target indicator corresponding to the business data is determined through a preset indicator metamodel, and a data analysis result corresponding to the target indicator and the business data is analyzed through the preset indicator metamodel.

[0006] A data analysis device based on an indicator system, comprising: A first acquisition module is used to acquire business data to be analyzed; The analysis module is used to determine the target indicator corresponding to the business data through a preset indicator metamodel, and to analyze the data analysis results corresponding to the target indicator and the business data through the preset indicator metamodel.

[0007] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the data analysis method based on the indicator system is implemented.

[0008] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned data analysis method based on the indicator system.

[0009] The aforementioned indicator-based data analysis method, apparatus, computer equipment, and medium obtain business data to be analyzed; determine target indicators corresponding to the business data using a preset indicator metamodel; and analyze data analysis results corresponding to the target indicators and the business data using the preset indicator metamodel. This solution processes business data using a pre-established preset indicator metamodel, unifying indicator relationships across different domains, enabling the reuse of indicators, and standardizing indicator specifications. This not only improves data analysis efficiency but also reduces a company's costs associated with indicator-related operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] Figure 1 This is a schematic diagram of an application environment of a data analysis method based on an indicator system in one embodiment of the present invention; Figure 2 This is a process diagram of a data analysis method based on an indicator system in one embodiment of the present invention; Figure 3 is a schematic structural diagram of a data analysis device based on an indicator system in one embodiment of the present invention; Figure 4 FIG. 1 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0013] The data analysis method based on the index system provided by the present invention can be applied in Figure 1 In an application environment, a client communicates with a server via a network. The client may include, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. The server may be implemented as a standalone server or a server cluster consisting of multiple servers. For example, the present invention may be applied to the server corresponding to the data middleware.

[0014] In one embodiment, if Figure 2 As shown in the figure, a data analysis method based on the indicator system is provided. Figure 1 The server in the example is used as an example, and the steps are as follows: S10: Obtain the business data to be analyzed.

[0015] Understandably, the business data to be analyzed may be relevant data related to the insurance field, such as insurance company recruitment data, insurance company pre-sales data of insurance products, insurance company target sales data, insurance company claims data, customer satisfaction of insurance products, etc. The common feature of various initial data is that they have not been analyzed and processed by a dedicated data analysis platform.

[0016] S20, determining a target indicator corresponding to the business data through a preset indicator metamodel, and analyzing a data analysis result corresponding to the target indicator and the business data through the preset indicator metamodel.

[0017] It can be understood that the preset indicator metamodel is a special model, which is mainly used to describe and define the abstract syntax and semantics of models related to indicators. It can help organizations establish unified indicator definitions and management specifications, improve the consistency and accuracy of indicators, and it can also help designers understand the performance and status of the system, thereby designing a more efficient and reliable system architecture; the preset indicator metamodel is a metamodel pre-built according to specific business needs and business processes. In this model, a special indicator system and data analysis process are set up. In this way, the model after implementation can be used to determine the target indicators that match the business data and the data analysis results corresponding to the target indicators and business data from the corresponding indicator system. Among them, the data analysis results are used in the real-time data analysis process and may include at least one of trend analysis, comparative analysis and correlation analysis.

[0018] For example, life insurance in the insurance industry can complete the implementation of life insurance business data through the above-mentioned preset indicator meta-model. At the same time, it can analyze the data analysis results required by life insurance business data in business needs and business processes, which is beneficial to data decision-making of life insurance companies and related personnel.

[0019] In the embodiment of steps S10 to S20, by constructing a preset indicator metamodel to process business data, the indicator relationships between different fields can be unified, the indicators can be reused, and the indicator specifications can be unified. This not only improves the work efficiency of data analysis, but also reduces the company's related costs in indicator-related operations.

[0020] Furthermore, before determining the target indicator corresponding to the business data through the preset indicator meta-model, the method further includes: Obtain corresponding key indicators based on the analysis results of the preset data analysis tools; Determining evaluation indicators based on the key indicators; An indicator system corresponding to the preset indicator meta-model is constructed according to the evaluation indicators and the key indicators.

[0021] Understandably, the preset data analysis tool can be at least one of the tools with the same functions, such as Excel, Xmind, and Power BI. Specifically, the MECE principle is followed and the key links and key indicators of the business process are sorted out through the preset data analysis tool. For example, the key link related to the recruitment data of an insurance company is how to determine the corresponding personnel based on the target sales volume, and the key indicators are the target sales volume and whether the questionnaire test is passed. The evaluation indicators are based on the core indicators and more detailed evaluation indicators are formulated, such as the product purchase conversion rate of the corresponding personnel. The indicator system corresponding to the preset indicator meta-model is to determine the correlation between indicators through the association rule algorithm. In addition, in the process of constructing an indicator system corresponding to the preset indicator metamodel, it is equivalent to the process of constructing the corresponding preset indicator metamodel. The construction process also includes: determining the five elements corresponding to the indicator, and determining the five elements of the indicator according to business needs and business processes, namely measurement, dimension, time, space and condition; determining the smallest unit of the indicator - atomic indicator according to business needs and business processes; determining the dimensional attributes of the indicator according to business needs and business processes, and abstracting these attributes into shared dimensions; determining the derived indicators of the indicator according to business needs and business processes; For example, when the pre-set indicator metamodel is implemented, the business and data departments are closely linked, allowing the business to drive the definition of indicator data through goals, while the data in turn supports the business through decision-making. For example, in the business process of the insurance practitioner exam in the training domain, the subject is the insurance practitioner, the object is the exam paper, the relationship is the answer sheet, and the constraint is the training domain. The data department can adjust the training plan or improve teaching methods by analyzing the data on answer scores and the number of questions answered, thereby improving the learning effect and exam results of insurance practitioners. After completing the construction of the preset indicator meta-model pair, this embodiment forms a complete indicator system to provide strong data support for business decision-making. For example, through the indicator, the owner is determined, and the owner must be responsible for the indicator and create a new maintenance offline during the indicator life cycle.

[0022] It should be noted that the pre-built indicator metamodel allows for the division and management of indicators based on business domains and company structures. Business domains are composed of business processes, each of which forms an independent business module and contains at least one indicator dataset. The pre-built indicator metamodel decomposes goals into sub-goals layer by layer. Leaf goals map atomic indicators from the finest-grained goals, with business goals as the outcome-oriented approach. The pre-built indicator metamodel uses dimensions and qualifiers to form different metrics, known as derived indicators, for effective indicator tracking. Indicator dimensions are composed of subjects, objects, and relationships. Subjects are the subjective initiators of the indicator, objects are the passive recipients of the indicator, and relationships are the relationships between subjects and objects. These three elements do not necessarily need to be present in the indicator name; they can also be a relationship or an object. Furthermore, constraints are conditions on indicators, known as qualifiers. Different parameters or types can be combined to form different indicators. Within the business process, each indicator is defined only once, consolidating information such as the indicator's scope and metadata to enable full-process monitoring and early warning.

[0023] Furthermore, before constructing the indicator system corresponding to the preset indicator meta-model based on the evaluation indicators and the key indicators, the method further includes: Determine the elements and dimension attributes corresponding to the key indicators and evaluation indicators based on business needs and business processes.

[0024] In this embodiment, the elements and dimension attributes are the data required by the indicator system. Each indicator can correspond to the elements and dimension attributes, which is conducive to the comprehensiveness of subsequent decision-making.

[0025] Furthermore, after obtaining the corresponding key indicators based on the analysis results of the preset data analysis tool, the method further includes: After classifying the key indicators using a preset clustering algorithm, key indicators with similar indicator characteristics are divided into the same category to obtain the key indicators.

[0026] Understandably, the preset clustering algorithm may be K-means, DBSCAN, etc.; the key indicators of the indicator characteristics may be numerical value, change trend, etc., and one category may correspond to one indicator; Specifically, before cluster analysis, data is preprocessed. This includes data cleaning (such as removing duplicate values ​​and handling missing values), data transformation (such as normalization and standardization), and feature selection. The preprocessed key indicator data is input into a preset clustering algorithm for cluster analysis, where similar indicators are grouped into the same category based on their characteristics. After the cluster analysis is completed, the clustering results are interpreted and verified, including determining whether the clustering results meet expectations, whether the cluster centers are representative, and whether the number of clusters is reasonable. If the clustering results are unsatisfactory, the clustering algorithm is adjusted or data preprocessing is performed again. The preset clustering algorithm of this embodiment helps to better understand different indicators, discover and eliminate duplicate indicators, and form a complete indicator system, which is conducive to the indicator system to determine more accurate and effective analysis results.

[0027] Furthermore, the indicator system corresponding to the evaluation indicators and the key indicators and the preset indicator meta-model includes: Extract all items from multiple data sets corresponding to the evaluation indicators and the key indicators to form a single item set; Calculate the support of each of the single itemsets in the data set; Selecting single itemsets whose support is greater than and equal to the minimum support from all the single itemsets, and forming the single itemsets whose support is greater than and equal to the minimum support into a candidate itemset; generating a plurality of multinomial sets on the candidate item set by a preset connection operation; Calculating the support of each of the polynomials, and removing polynomials whose support is less than the minimum support, to obtain polynomials after removal; After performing an iterative process of determining candidate item sets on the multi-item set, a frequent item set is obtained; After determining the association rules based on the confidences corresponding to the frequent itemsets, an indicator system corresponding to the preset indicator meta-model and constructed based on the association rules is obtained.

[0028] In other words, an association rule is a rule that states that there is an association relationship between two or more items in a dataset. The support of an association rule refers to the probability of multiple items appearing at the same time, reflecting the frequency of the pattern in the rule. The confidence of an association rule refers to the strength of the implication, that is, the probability that a transaction containing item A also contains item B, which is a conditional probability. A frequent itemset is an item set that appears frequently in a dataset. If the support of an item set exceeds the minimum support threshold given by the user, the item set is called a frequent itemset. Specifically, 1. Extract all items from the independent indicator attributes of the data sets corresponding to the evaluation indicators and the key indicators, and group the extracted items into a single item set, such as the indicators of the same classification indicator attribute as an item; calculate the support of each single item set in the data set; 2. Select items with support greater than or equal to the minimum support from all single item sets, and group the items into a candidate item set; 3. Generate multi-item sets (such as two-item sets, three-item sets, etc.) through connection operations; 4. Calculate the support of each multi-item set and remove the item sets with support lower than the minimum support; 5. Iterate and repeat the above steps 1 to 4 until no new frequent item sets are found; 6. Extract all high-confidence association rules from the frequent item sets, such as screening association rules by calculating confidence (support (XY) / support (X)), where XY represents the antecedent and consequent of the association rule; This embodiment can be used to discover the correlation between indicators. Specifically, through reasonable data processing, useful indicator association rules can be mined and support can be provided for the subsequent formation of an indicator system. For example, through association rules, it may be found that the increase of a certain indicator is often accompanied by the decrease of another indicator. Correlation information is valuable for business decision-making and data governance.

[0029] Furthermore, the indicator system corresponding to the evaluation indicators and the key indicators and the preset indicator meta-model includes: Extracting frequent single itemsets from multiple data sets corresponding to the evaluation indicators and the key indicators; one frequent single itemset corresponds to one support degree; Arranging the frequent single itemsets corresponding to the support based on a preset support sorting rule, and inserting the sorted frequent single itemsets into a preset frequent pattern tree; Determining frequent item sets from the preset frequent pattern tree; After determining the association rules based on the confidences corresponding to the frequent itemsets, an indicator system corresponding to the preset indicator meta-model and constructed based on the association rules is obtained.

[0030] Understandably, the default frequent pattern tree is an FP-tree, a special type of tree data structure used to store a compressed version of a transaction database. In an FP-tree, each node represents an item and stores the number of times the item appears in the database. The main use of an FP-tree is to quickly discover frequent item sets and association rules in data mining. Specifically, the counts of all frequent single item sets are obtained from multiple data sets corresponding to the evaluation index and the key index, and items with support lower than a threshold are deleted; the frequent single item sets are put into the item header table and arranged in descending order of support; a preset frequent pattern tree is constructed, wherein, for each relevant indicator transaction, the items therein are inserted into the preset frequent pattern tree according to the order of the item header table; the conditional pattern base corresponding to the item in the item header table is determined from the bottom item of the item header table upward; the frequent item sets of the item in the item header table are recursively mined from the conditional pattern base; based on the mined frequent item sets, high-confidence association rules are generated; and the association rules corresponding to the indicators are screened by calculating the confidence, wherein the process is consistent with the above content and will not be repeated here; This embodiment can be used to discover the correlation between indicators. Specifically, through reasonable data processing, useful indicator association rules can be mined and support can be provided for the subsequent formation of an indicator system. For example, through association rules, it may be found that the increase of a certain indicator is often accompanied by the decrease of another indicator. Correlation information is valuable for business decision-making and data governance.

[0031] Furthermore, determining the target indicator corresponding to the business data through a preset indicator meta-model includes: After matching the business data with the preset indicator meta-model, determining the indicator level and dimension corresponding to the business data; The most relevant key indicators are used as the target indicators according to the indicator levels and dimensions.

[0032] Understandably, the indicator hierarchy refers to the hierarchical relationship between indicators. In the preset indicator metamodel, there are usually some high-level indicators (such as total sales, total number of users, etc.), as well as some lower-level, more specific indicators (such as sales of a certain product category, number of users in a certain region, etc.). Through the matching process, the hierarchical position of the indicators corresponding to the business data in the indicator metamodel can be determined; indicator dimensions refer to the different aspects or attributes used to describe and subdivide indicators. For example, time dimensions (such as year, month, day), geographic dimensions (such as country, region, city), product dimensions (such as product type, product ID), etc., can be determined based on the specific content of the business data. Specifically, after determining the indicator hierarchy and indicator dimensions, identify the key indicators most relevant to the business objectives. From these key indicators, select the target indicators that best meet the business objectives and needs. Target indicators should clearly reflect the core performance and key success factors of the business and provide strong support for subsequent decision-making and actions. This embodiment matches business data with a preset indicator meta-model to determine the indicator hierarchy and indicator dimension, which can help companies gain a deeper understanding of business performance, discover potential problems, and optimize decision-making strategies.

[0033] Furthermore, analyzing the data analysis results corresponding to the target indicator and the business data through the preset indicator meta-model includes: Determining at least one trend analysis, comparative analysis, and correlation analysis corresponding to the target indicator classification and correlation through the preset indicator meta-model; At least one trend analysis, comparison analysis and correlation analysis is performed on the target indicator through the preset indicator meta-model to obtain data analysis results corresponding to various analysis processes.

[0034] Understandably, trend analysis is mainly used to show how target indicators change over time; comparative analysis is used to compare target indicators between different time periods, departments or products; correlation analysis is used to explore the correlation between target indicators and other indicators; after the preset indicator meta-model is implemented, the corresponding data analysis results can be determined based on business data and indicator system. Specifically, 1. Identify trends in data (such as rise, fall, fluctuation, etc.) through trend charts corresponding to target indicators; calculate statistics such as trend slope and growth rate to quantify the strength of the trend; analyze possible causes of the trend, including internal factors (such as corporate strategy, operating strategy, etc.) and external factors (such as market environment, policy changes, etc.); based on the results of trend analysis, predict the future value of the target indicator; 2. Calculate the difference in target indicators between the comparison objects, using indicators such as absolute value difference and relative value difference (such as growth rate, ratio, etc.); analyze the reasons for the difference, including internal and external factors; use charts (such as bar charts, pie charts, etc.) to intuitively display the comparison results; Based on the results of the comparative analysis, draw conclusions or make recommendations; 3. Use the selected association analysis method to discover association rules between the target indicator and other indicators; evaluate the strength of the association rules, usually using indicators such as support and confidence; interpret the discovered association rules and analyze the causal relationship or correlation between them; formulate corresponding decisions or strategies based on the results of the association analysis; The trend analysis, comparative analysis, and correlation analysis in this embodiment are used for data analysis, which can help companies gain a deeper understanding of business conditions, identify potential problems, and optimize decision-making strategies. At the same time, the preset indicator meta-model can improve the efficiency of data analysis. In addition, data analysis results can be used to better manage data assets, understand which data is important and which data is relevant, and thus make better use of data. At the same time, by monitoring changes in indicators, we can understand the data quality in real time and promptly discover and deal with data problems. Through data classification and correlation analysis, we can also quantify the cost of data, such as calculating how much resources are needed to obtain data for a certain indicator, or how much it costs to maintain the quality of a certain data set.

[0035] In summary, the above provides a data analysis method based on an indicator system to obtain business data to be analyzed; determine the target indicator corresponding to the business data through a preset indicator metamodel, and analyze the data analysis results corresponding to the target indicator and the business data through the preset indicator metamodel. This solution processes business data by constructing a preset indicator metamodel, which can unify the indicator relationship between different fields, reuse indicators, and unify indicator specifications. In this way, it can not only improve the work efficiency of data analysis, but also reduce the company's related costs in indicator-related operations; in addition, indicators can be applied to multiple business areas of insurance. In terms of efficiency, the delivery speed of report requirements is improved, the efficiency of indicator search is improved, and the reuse of indicators is improved. In terms of quality, the indicator caliber is unified, and online problems caused by code changes are reduced. In terms of cost, the number of indicators is reduced, and the number of tables is reduced simultaneously. Due to the improvement of indicator reuse, manpower and operating costs are saved. In terms of security, authority management is implemented by role and field, in accordance with data security specifications.

[0036] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0037] In one embodiment, a data analysis device based on an indicator system is provided, and the data analysis device based on an indicator system corresponds one-to-one to the data analysis method based on an indicator system in the above embodiment. Figure 3 As shown, the data analysis device based on the indicator system includes a first acquisition module 11 and an analysis module 12. The functional modules are described in detail as follows: A first acquisition module 11 is used to acquire business data to be analyzed; The analysis module 12 is configured to determine a target indicator corresponding to the business data through a preset indicator meta-model, and to obtain a data analysis result corresponding to the target indicator and the business data through the preset indicator meta-model.

[0038] Furthermore, the data analysis device based on the indicator system also includes: The second acquisition module is used to obtain corresponding key indicators based on the analysis results of the preset data analysis tool; A determination module, configured to determine an evaluation indicator based on the key indicator; A construction module is used to construct an indicator system corresponding to the preset indicator meta-model based on the evaluation indicators and the key indicators.

[0039] Furthermore, the data analysis device based on the indicator system also includes: The classification module is used to classify the key indicators by using a preset clustering algorithm, and then classify the key indicators with similar indicator characteristics into the same category to obtain the key indicators.

[0040] Furthermore, the building blocks include: A first extraction unit is configured to extract all items from a plurality of data sets corresponding to the evaluation indicators and the key indicators to form a single item set; A calculation unit, configured to calculate the support of each of the single itemsets in the data set; a composition unit, configured to select single itemsets whose support is greater than and equal to a minimum support from all the single itemsets, and to combine the single itemsets whose support is greater than and equal to the minimum support into a candidate itemset; a generating unit, configured to generate a plurality of multinomial sets on the candidate item set through a preset connection operation; a removal unit, configured to calculate the support of each of the polynomials, and remove polynomials having a support less than a minimum support, to obtain polynomials after removal; A first determining unit is configured to obtain a frequent itemset by performing an iterative process of determining a candidate itemset on the multinomial set; The second determining unit is configured to determine an association rule based on the confidence corresponding to the frequent item set, and then obtain an indicator system corresponding to the preset indicator meta-model and constructed based on the association rule.

[0041] Furthermore, the building blocks include: A second extraction unit is configured to extract frequent single itemsets from a plurality of data sets corresponding to the evaluation index and the key index; one frequent single itemset corresponds to one support degree; an inserting unit, configured to arrange the frequent single itemsets corresponding to the support based on a preset support sorting rule, and insert the sorted frequent single itemsets into a preset frequent pattern tree; a third determining unit, configured to determine a frequent item set from the preset frequent pattern tree; The fourth determining unit is configured to determine an association rule based on the confidence corresponding to the frequent itemsets, and then obtain an indicator system corresponding to the preset indicator meta-model and constructed based on the association rule.

[0042] Furthermore, the analysis module includes: a fifth determining unit, configured to determine the indicator hierarchy and dimension corresponding to the business data after matching the business data with the preset indicator meta-model; As a unit, it is used to take the most relevant key indicator as the target indicator according to the indicator level and dimension.

[0043] Furthermore, the analysis module further includes: a sixth determining unit, configured to determine at least one trend analysis, comparative analysis, and correlation analysis corresponding to the target indicator classification and correlation through the preset indicator meta-model; The analysis unit is used to perform at least one trend analysis, comparative analysis and correlation analysis on the target indicator through the preset indicator meta-model to obtain data analysis results corresponding to various analysis processes.

[0044] For the specific definition of the data analysis device based on the indicator system, please refer to the definition of the data analysis method based on the indicator system above, which will not be repeated here. The various modules in the above-mentioned data analysis device based on the indicator system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0045] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data involved in the data analysis method based on the indicator system. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data analysis method based on the indicator system is implemented.

[0046] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the data analysis method based on the indicator system in the above embodiment are implemented, for example: Figure 2 Steps S10 to S20 shown include the following steps: Obtain business data to be analyzed; A target indicator corresponding to the business data is determined through a preset indicator metamodel, and a data analysis result corresponding to the target indicator and the business data is analyzed through the preset indicator metamodel.

[0047] Alternatively, when the processor executes the computer program, the functions of the modules / units of the data analysis device based on the indicator system in the above embodiment are realized, for example Figure 3 The functions of modules 11 and 12 are shown in FIG.

[0048] In one embodiment, a computer-readable storage medium is provided on which a computer program is stored. When the computer program is executed by a processor, the steps of the data analysis method based on the indicator system in the above embodiment are implemented, such as Figure 2 Steps S10 to S20 shown include the following steps: Obtain business data to be analyzed; A target indicator corresponding to the business data is determined through a preset indicator metamodel, and a data analysis result corresponding to the target indicator and the business data is analyzed through the preset indicator metamodel.

[0049] Alternatively, when the computer program is executed by the processor, the functions of the modules / units of the data analysis device based on the indicator system in the above embodiment are realized, for example Figure 3 The functions of modules 11 and 12 are shown in FIG.

[0050] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0051] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0052] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A data analysis method based on an indicator system, characterized in that: include: Obtain business data to be analyzed; A target indicator corresponding to the business data is determined through a preset indicator metamodel, and a data analysis result corresponding to the target indicator and the business data is analyzed through the preset indicator metamodel.

2. The data analysis method based on the indicator system according to claim 1, characterized in that: Before determining the target indicator corresponding to the business data through the preset indicator meta-model, the method further includes: Obtain corresponding key indicators based on the analysis results of the preset data analysis tools; Determining evaluation indicators based on the key indicators; An indicator system corresponding to the preset indicator meta-model is constructed according to the evaluation indicators and the key indicators.

3. The data analysis method based on the indicator system according to claim 2, characterized in that: After obtaining the corresponding key indicators based on the analysis results of the preset data analysis tool, the method further includes: After classifying the key indicators using a preset clustering algorithm, key indicators with similar indicator characteristics are divided into the same category to obtain the key indicators.

4. The data analysis method based on the indicator system according to claim 2, characterized in that: The indicator system corresponding to the evaluation indicator and the key indicator and the preset indicator meta-model includes: Extract all items from multiple data sets corresponding to the evaluation indicators and the key indicators to form a single item set; Calculate the support of each of the single itemsets in the data set; Selecting single itemsets whose support is greater than and equal to the minimum support from all the single itemsets, and forming the single itemsets whose support is greater than and equal to the minimum support into a candidate itemset; generating a plurality of multinomial sets on the candidate item set by a preset connection operation; Calculating the support of each of the polynomials, and removing polynomials whose support is less than the minimum support, to obtain polynomials after removal; After performing an iterative process of determining candidate item sets on the multi-item set, a frequent item set is obtained; After determining the association rules based on the confidences corresponding to the frequent itemsets, an indicator system corresponding to the preset indicator meta-model and constructed based on the association rules is obtained.

5. The data analysis method based on the indicator system according to claim 2, characterized in that: The indicator system corresponding to the evaluation indicator and the key indicator and the preset indicator meta-model includes: Extracting frequent single itemsets from multiple data sets corresponding to the evaluation indicators and the key indicators; one frequent single itemset corresponds to one support degree; Arranging the frequent single itemsets corresponding to the support based on a preset support sorting rule, and inserting the sorted frequent single itemsets into a preset frequent pattern tree; Determining frequent item sets from the preset frequent pattern tree; After determining the association rules based on the confidences corresponding to the frequent itemsets, an indicator system corresponding to the preset indicator meta-model and constructed based on the association rules is obtained.

6. The data analysis method based on the indicator system according to claim 1, characterized in that: The determining of the target indicator corresponding to the business data through a preset indicator meta-model includes: After matching the business data with the preset indicator meta-model, determining the indicator level and dimension corresponding to the business data; The most relevant key indicators are used as the target indicators according to the indicator levels and dimensions.

7. The data analysis method based on an indicator system according to any one of claims 1 to 6, characterized in that: The data analysis results corresponding to the target indicator and the business data are analyzed by the preset indicator meta-model, including: Determining at least one trend analysis, comparative analysis, and correlation analysis corresponding to the target indicator classification and correlation through the preset indicator meta-model; At least one trend analysis, comparison analysis and correlation analysis is performed on the target indicator through the preset indicator meta-model to obtain data analysis results corresponding to various analysis processes.

8. A data analysis device based on an indicator system, characterized in that: include: A first acquisition module is used to acquire business data to be analyzed; The analysis module is used to determine the target indicator corresponding to the business data through a preset indicator metamodel, and to analyze the data analysis results corresponding to the target indicator and the business data through the preset indicator metamodel.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the data analysis method based on the indicator system as described in any one of claims 1 to 7 is implemented.

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

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