CN116991955A - Data processing method, device, electronic equipment and computer storage medium - Google Patents
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Abstract
The application provides a data processing method, a device, an electronic device and a computer storage medium, which can be applied to the financial field or other fields, and the method comprises the following steps: classifying the original business data in the data warehouse into initial business data according to the business data type; integrating the initial business data into target business data based on business rules; determining dimension indexes and measurement indexes of target service data according to service analysis requirements; and acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on the permutation and combination algorithm, the dimension index and the measurement index. Therefore, the application classifies and integrates the business data firstly, and then realizes the automatic processing of the business data under the permutation and combination algorithm based on the measurement index and the dimension index corresponding to the business analysis requirement, thereby improving the efficiency of data processing.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method, a data processing device, an electronic device, and a computer storage medium.
Background
The data warehouse of the traditional data platform is generally divided into a source data layer, a data warehouse layer, a data mart layer and a data application layer, wherein the data warehouse layer is used for cleaning data of the source data layer and integrating fields with the same granularity so as to provide the data mart layer and the data application layer for better use.
With the development of internet technology, the data generated during the process of using the product by the user is exponentially increased, and the data volume and volume are huge. In the prior art, when the data demand is in the face of trillion data volume, the problems of long time consumption of data processing, low data processing efficiency, inaccurate data processing result and the like exist. How to efficiently and automatically process business analysis results for business analysis becomes a problem to be solved in the present application.
Disclosure of Invention
In view of the above, the present application provides a data processing method, apparatus, electronic device and computer storage medium, which can efficiently and automatically process a desired business analysis result.
In a first aspect, the present application provides a data processing method, the method comprising:
classifying the original business data in the data warehouse into initial business data according to the business data type;
integrating the initial business data into target business data based on business rules;
determining dimension indexes and measurement indexes of the target business data according to business analysis requirements;
and acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on an permutation and combination algorithm, the dimension index and the measurement index.
Optionally, the business rule includes a business topic rule and a business party usage rule;
the integrating the initial service data into target service data based on the service rule comprises the following steps:
constructing a service theme model according to the service theme rules based on the initial service data;
and summarizing the business topic models according to the common dimension of the business topic models to obtain target business data.
Optionally, the constructing a service theme model according to the service theme rule based on the initial service data includes:
determining target table name information and target field information according to the business theme rules;
extracting intermediate service data from the initial service data according to the target table information and the target field information;
and constructing the business theme model based on the intermediate business data.
Optionally, the acquiring, based on the permutation and combination algorithm, the dimension index and the metric index, the service analysis result corresponding to the service analysis requirement from the target service data includes:
determining service data to be processed from the target service data according to the dimension index and the measurement index;
and processing the service data to be processed based on an permutation and combination algorithm to obtain a service analysis result corresponding to the service analysis requirement.
Optionally, the method further comprises:
and displaying the service analysis results corresponding to the service analysis requirements according to the arrangement and combination results of the dimension indexes.
In a second aspect, the present application provides a data processing apparatus, the apparatus comprising:
the data acquisition module is used for classifying the original business data in the data warehouse into initial business data according to the business data type;
the data integration module is used for integrating the initial service data into target service data based on service rules;
the index refining module is used for determining dimension indexes and measurement indexes of the target business data according to business analysis requirements;
and the data processing module is used for acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on an permutation and combination algorithm, the dimension index and the measurement index.
Optionally, the business rule includes a business topic rule and a business party usage rule;
the data integration module comprises:
the model construction sub-module is used for constructing a service theme model according to the service theme rules based on the initial service data;
and the target data acquisition sub-module is used for summarizing the business topic models according to the common dimension of the business topic models so as to acquire target business data.
Optionally, the data processing module includes:
the data determining submodule is used for determining service data to be processed from the target service data according to the dimension index and the measurement index;
and the arrangement and combination sub-module is used for processing the service data to be processed based on an arrangement and combination algorithm to obtain a service analysis result corresponding to the service analysis requirement.
In a third aspect, the present application provides an electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the data processing method as described in the first aspect.
In a fourth aspect, the present application provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements a data processing method as described in the first aspect.
As can be seen from the above scheme, the present application provides a data processing method, which includes: classifying the original business data in the data warehouse into initial business data according to the business data type; integrating the initial business data into target business data based on business rules; determining dimension indexes and measurement indexes of the target business data according to business analysis requirements; and acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on an permutation and combination algorithm, the dimension index and the measurement index. Therefore, the application classifies and integrates the business data firstly, and then realizes the automatic processing of the business data under the permutation and combination algorithm based on the measurement index and the dimension index corresponding to the business analysis requirement, thereby improving the efficiency of data processing.
The embodiment of the application also provides a device, electronic equipment and a computer storage medium corresponding to the method, and the device and the method have the same beneficial effects as the method.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, the information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The embodiment of the application provides a data processing method, as shown in fig. 1, which specifically comprises the following steps:
step S100: the original business data in the data warehouse is classified into initial business data according to the business data type.
Data warehouses (Data warehouses) are typically created for the purpose of analytical reporting and decision support, providing Data support for enterprise level decision analysis, business reporting, and the like. The data warehouse typically contains business history data and a large amount of dimensional information, and illustratively, for banking businesses, the data warehouse includes all data generated by, for example, ATM transactions, and all data generated by the counter.
Specifically, the step can classify the service data obtained from each channel according to a preset category to obtain classified service data, namely initial service data.
For example, the business data in the data warehouse of the banking enterprise is classified according to the data categories of customer data, deposit data, loan data and the like, so as to obtain initial business data.
Step S200: the initial business data is integrated into target business data based on business rules.
Specifically, since the initial service data is only classified data, the initial service data relates to service data of each link of an enterprise, the data volume and the huge volume, and the processing and utilization efficiency of the initial service data is low. Therefore, in the step, the initial business data is screened and integrated based on the business rules to acquire target business data required by subsequent business index processing, so that the data processing is prevented from being directly carried out from hundreds of millions of data volumes in a data warehouse, and the efficiency of the data processing process can be greatly improved.
Step S300: and determining the dimension index and the measurement index of the target business data according to the business analysis requirement.
Specifically, the business party submits specific business analysis requirements, and dimension indexes and measurement indexes of the target business data are determined based on the business analysis requirements. The dimension index is a parameter used for analyzing the data, for example, when you want to analyze the loan amount, the dimension index can be selected to analyze according to the client type, the client credit level, the loan industry direction and the like, thus forming each dimension index. The metric is a specific value in the business data, such as sales, costs, loan amounts, etc.
Illustratively, the business party submits a specific business analysis requirement for analyzing the loan amount, and the determined dimension index may include: customer type dimension, customer credit rating dimension, loan industry posting dimension; the measurement indexes comprise: loan amount value.
Step S400: and acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on the permutation and combination algorithm, the dimension index and the measurement index.
Specifically, after determining the dimension index and the measurement index corresponding to the service analysis requirement, the corresponding service analysis result can be obtained from the target service data according to the permutation and combination algorithm.
The customer type dimension includes two dimensions of "individual" and "enterprise", the customer credit level dimension includes three dimensions of "good", "general" and "poor", the loan industry direction dimension includes four dimensions of "agriculture", "industry", "animal husbandry" and "service industry", and a total of 2 x 3 x 4=24 loan credit value results are finally obtained based on the permutation and combination algorithm, and for each permutation and combination, the corresponding loan credit value can be obtained.
As can be seen from the above scheme, the present application provides a data processing method, which includes: classifying the original business data in the data warehouse into initial business data according to the business data type; integrating the initial business data into target business data based on business rules; determining dimension indexes and measurement indexes of target service data according to service analysis requirements; and acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on the permutation and combination algorithm, the dimension index and the measurement index. Therefore, the application classifies and integrates the business data firstly, and then realizes the automatic processing of the business data under the permutation and combination algorithm based on the measurement index and the dimension index corresponding to the business analysis requirement, thereby improving the efficiency of data processing.
In an alternative implementation, the business rule may include a business topic rule and a business party usage rule, and the step S200 may include:
step S201: based on the initial business data, a business topic model is built according to business topic rules.
In particular, a business topic rule may include individual sub-businesses associated with a particular business, such as: for the business data of the data category of the loan data, the business topic rule corresponding to the loan business may include, for example, a loan issuing rule, a loan recycling rule, a loan risk rule, and the like, and correspondingly, constructing the business topic model by the initial business data according to the business topic rule may include: and constructing a loan issuing model, a loan recycling model, a loan risk model and the like based on the loan business data.
It should be noted that the business topic model constructed here is an abstraction of the features associated with the business data. As an alternative, the business topic model may be a relational model that organizes data in the form of record groups or data tables for storage and transformation using relationships between various entities and attributes.
In an alternative implementation manner, the step S201 specifically includes: determining target table name information and target field information according to the business topic rule; extracting intermediate service data from the initial service data according to the target table information and the target field information; and constructing a business theme model based on the intermediate business data.
In practical application, a target data table name corresponding to the service theme rule and a field name required in the data table can be determined according to the service theme rule and a preset mapping relation, corresponding service data is extracted from initial service data according to the target data table name and the field name, and a required service theme model is constructed according to the extracted service data.
Illustratively, the business theme rules include a loan issuing rule, and for loan business data, a customer account number field, a transaction amount field, a credit level field and a customer type field in a loan account information table are extracted, a customer account number field, a financial transaction detail field, a credit card transaction detail field and a contract transaction detail field in a loan transaction schedule are extracted, and finally a loan issuing model is constructed by the fields.
Step S202: and summarizing the business topic models according to the common dimension of the business topic models to obtain target business data.
Specifically, based on each service topic model constructed in step S201, summary may be performed according to the common dimension of the service topic models, to obtain the target service data. The loan issuing model, the loan recycling model and the loan risk model all comprise loan account dimensions, namely the loan account dimensions can be determined to be the common dimensions of the business topic model, and the business topic model is summarized by the loan account dimensions to obtain the target business data by the loan account dimensions.
It can be understood that, for a specific service, there may be each sub-service corresponding to the specific service, where each sub-service corresponds to each service data in the data repository, and in the embodiment of the present application, the common dimensions of the topic model corresponding to each sub-service are summarized in step S201 and step S202, so that service data in the common dimensions of the service topic model are re-carded, thereby facilitating subsequent processing of service data according to the required index dimensions.
In an alternative implementation, the step S400 may include: determining service data to be processed from the target service data according to the dimension index and the measurement index; processing the service data to be processed based on the permutation and combination algorithm to obtain a service analysis result corresponding to the service analysis requirement.
In practical application, the to-be-processed service data is determined from the target service data according to the dimension index and the measurement index corresponding to the service analysis requirement, and the to-be-processed service data is processed through the permutation and combination algorithm to obtain the service analysis result.
In an optional implementation manner, the data processing method provided in this embodiment further includes: and displaying the service analysis results corresponding to the service analysis requirements according to the arrangement and combination results of the dimension indexes.
Specifically, the service analysis result can be displayed according to the arrangement and combination result of the dimension indexes, and the service analysis result is intuitively and clearly displayed.
Illustratively, the displayed permutation and combination result of the dimension index includes 'enterprise, credit good, service industry throw' and a loan amount value corresponding to the permutation and combination result.
In correspondence to the above method, the embodiment of the present application further provides a data processing apparatus, referring to fig. 2, which shows a schematic structural diagram of the apparatus, where the apparatus may include:
a data acquisition module 201, configured to classify original service data in a data warehouse into initial service data according to a service data type;
a data integration module 202, configured to integrate the initial service data into the target service data based on the service rule;
an index refinement module 203, configured to determine a dimension index and a metric index of the target service data according to the service analysis requirement;
the data processing module 204 is configured to obtain a service analysis result corresponding to the service analysis requirement from the target service data based on the permutation and combination algorithm, the dimension index and the metric index.
In an alternative implementation, the business rules include business topic rules and business party usage rules; the data integration module 202 includes:
the model construction sub-module is used for constructing a service theme model according to the service theme rules based on the initial service data;
and the target data acquisition sub-module is used for summarizing the service topic model according to the common dimension of the service topic model so as to acquire target service data.
In an alternative implementation, the model building sub-module is specifically configured to: determining target table name information and target field information according to the business topic rule; extracting intermediate service data from the initial service data according to the target table information and the target field information; and constructing a business theme model based on the intermediate business data.
In an alternative implementation, the data processing module 204 includes:
the data determining submodule is used for determining service data to be processed from target service data according to the dimension index and the measurement index;
and the arrangement and combination sub-module is used for processing the service data to be processed based on an arrangement and combination algorithm to obtain a service analysis result corresponding to the service analysis requirement.
In an alternative implementation, the apparatus further includes: and the result display module is used for displaying the service analysis result corresponding to the service analysis requirement according to the arrangement and combination result of the dimension indexes.
It should be noted that, the steps and relevant technical features executed by each module in the data processing apparatus provided in the embodiments of the present application correspond to the data processing method provided in the application embodiment, and the description of the apparatus portion may refer to the embodiments of the foregoing method portion, which is not repeated herein.
As can be seen from the above, the present application provides a data processing apparatus, comprising: the data acquisition module is used for classifying the original business data in the data warehouse into initial business data according to the business data type; the data integration module is used for integrating the initial service data into target service data based on the service rule; the index refining module is used for determining dimension indexes and measurement indexes of the target business data according to business analysis requirements; and the data processing module is used for acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on the permutation and combination algorithm, the dimension index and the measurement index. Therefore, the application classifies and integrates the business data firstly, and then realizes the automatic processing of the business data under the permutation and combination algorithm based on the measurement index and the dimension index corresponding to the business analysis requirement, thereby improving the efficiency of data processing.
The data processing method, the data processing device, the electronic equipment and the computer storage medium provided by the application can be used in the financial field or other fields, for example, can be used in a scene of acquiring banking indexes. The foregoing is merely exemplary, and the application fields of the data processing method, the device, the electronic apparatus and the computer storage medium provided by the present application are not limited.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Another embodiment of the present application provides an electronic device, as shown in fig. 3, including:
one or more processors 301.
A storage device 302 having one or more programs stored thereon.
The one or more programs, when executed by the one or more processors 301, cause the one or more processors 301 to implement the method of transmitting data as described in any of the embodiments above.
Another embodiment of the present application provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for transmitting data according to any of the above embodiments.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application is not limited to the specific combinations of the features described above, but also covers other embodiments which may be formed by any combination of the features described above or their equivalents without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in the present application are replaced with each other.
Claims (10)
1. A method of data processing, comprising:
classifying the original business data in the data warehouse into initial business data according to the business data type;
integrating the initial business data into target business data based on business rules;
determining dimension indexes and measurement indexes of the target business data according to business analysis requirements;
and acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on an permutation and combination algorithm, the dimension index and the measurement index.
2. The method of claim 1, wherein the business rules include business topic rules and business party usage rules;
the integrating the initial service data into target service data based on the service rule comprises the following steps:
constructing a service theme model according to the service theme rules based on the initial service data;
and summarizing the business topic models according to the common dimension of the business topic models to obtain target business data.
3. The method of claim 2, wherein said constructing a business topic model according to said business topic rules based on said initial business data comprises:
determining target table name information and target field information according to the business theme rules;
extracting intermediate service data from the initial service data according to the target table information and the target field information;
and constructing the business theme model based on the intermediate business data.
4. The method of claim 1, wherein the obtaining, based on the permutation and combination algorithm, the dimension index, and the metric index, the service analysis result corresponding to the service analysis requirement from the target service data includes:
determining service data to be processed from the target service data according to the dimension index and the measurement index;
and processing the service data to be processed based on an permutation and combination algorithm to obtain a service analysis result corresponding to the service analysis requirement.
5. The method according to claim 1, wherein the method further comprises:
and displaying the service analysis results corresponding to the service analysis requirements according to the arrangement and combination results of the dimension indexes.
6. A data processing apparatus, the apparatus comprising:
the data acquisition module is used for classifying the original business data in the data warehouse into initial business data according to the business data type;
the data integration module is used for integrating the initial service data into target service data based on service rules;
the index refining module is used for determining dimension indexes and measurement indexes of the target business data according to business analysis requirements;
and the data processing module is used for acquiring a service analysis result corresponding to the service analysis requirement from the target service data based on an permutation and combination algorithm, the dimension index and the measurement index.
7. The apparatus of claim 6, wherein the business rules include business topic rules and business party usage rules;
the data integration module comprises:
the model construction sub-module is used for constructing a service theme model according to the service theme rules based on the initial service data;
and the target data acquisition sub-module is used for summarizing the business topic models according to the common dimension of the business topic models so as to acquire target business data.
8. The apparatus of claim 6, wherein the data processing module comprises:
the data determining submodule is used for determining service data to be processed from the target service data according to the dimension index and the measurement index;
and the arrangement and combination sub-module is used for processing the service data to be processed based on an arrangement and combination algorithm to obtain a service analysis result corresponding to the service analysis requirement.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-5.
10. A computer storage medium, characterized in that a computer program is stored thereon, wherein the computer program, when executed by a processor, implements the method according to any of claims 1 to 5.
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| CN202311105035.7A CN116991955A (en) | 2023-08-30 | 2023-08-30 | Data processing method, device, electronic equipment and computer storage medium |
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| CN202311105035.7A CN116991955A (en) | 2023-08-30 | 2023-08-30 | Data processing method, device, electronic equipment and computer storage medium |
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| CN112597308A (en) * | 2020-12-24 | 2021-04-02 | 北京金堤科技有限公司 | Text data processing method and device, electronic equipment and storage medium |
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