CN110706049A - Data processing method and device - Google Patents
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Abstract
The embodiment of the invention provides a data processing method, which comprises the following steps: establishing basic order data according to an order table; generating repurchase data based on the base order data and the repurchase model, and generating first-purchase data based on the base order data and the first-purchase model; and generating first summary data based on the repurchase data, the first purchase data and the summary model, the first summary data comprising a plurality of analysis indicators based on the user concept. Basic order data, first purchase data, second purchase data and first summary data are sequentially obtained through three levels of data processing, data processing pressure is relieved through hierarchical processing, and therefore data processing efficiency is improved. The embodiment of the invention also provides a data processing device and a corresponding computer readable storage medium.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data processing method and device.
Background
With the rapid development of business, the importance of each layer of the e-commerce company on customer marketing is increased, and the product can support the macroscopic data consideration becomes very important. The business department provides various data reports in a targeted manner by building a data model, so that various data models can be generated at the same time. Existing data models, such as order detail models, which can characterize sales orders for a commodity granularity; a sales commodity model that represents information on commodities already sold at the granularity of a commodity sku (Stock Keeping Unit), and the like.
However, the inventor finds that for the theme of the category brand, the correlation model cannot support the report data analysis requirement of the business department. For example, when the business department analyzes the combination of multi-level product category + brand + jingxin value segment, data processing needs to be performed from the bottom layer, and report data of product category brand is finally generated through multiple steps, thereby causing waste of manpower and material resources. Moreover, because the data volume of the orders generated by the shopping mall every day is huge, and the brand of the product is changed frequently, the process of generating the report data also needs to consider the processing efficiency.
Therefore, there is a need to provide a more efficient data processing method to support the data analysis requirements of the brand of the category.
Disclosure of Invention
In view of this, embodiments of the present invention provide an efficient data processing method and apparatus to support the data analysis requirement for the category brands.
According to a first aspect of the embodiments of the present invention, there is provided a data processing method, including:
establishing basic order data according to an order table;
generating a plurality of repurchase data based on the basic order data and the plurality of repurchase models, and generating a plurality of first purchase data based on the basic order data and the plurality of first purchase models; and
generating first summary data based on the repurchase data, the first purchase data, and a summary model, the first summary data including a plurality of analytical indicators based on user concepts.
Preferably, the establishing the basic order data according to the order form includes:
and establishing the basic order data according to the order detail width table, the sales commodity table and the brand table.
Preferably, the generating the repurchase data comprises:
generating the re-purchasing data based on the order detail list, the user point grade list and the third-level class first-time purchasing model;
the generating of the first purchase data comprises:
and generating the first purchase data based on the order detail list, the user point grade list and the third-class first purchase model.
Preferably, the method further comprises: based on the first summary data, employing self-correlation of the summary model to generate second summary data comprising historical and current plurality of analytical indicators based on user concepts.
Preferably, the user concept is a user classification including a whole user, a new user outside the station, a new user inside the station and an old user.
Preferably, the first summary data and the second summary data patterns are stored in combined partitions based on time and brand identity.
According to a second aspect of embodiments of the present invention, there is provided a data processing apparatus including:
the data preparation unit is used for establishing basic order data according to the order table;
a first creating unit configured to generate a buyback data based on the base order data and a buyback model, and generate a first purchase data based on the base order data and a first purchase model;
a second creating unit configured to generate first summarized data based on the repurchase data, the first purchase data, and a summary model, the first summarized data including a plurality of analysis indicators based on a user concept.
Preferably, the data preparation unit includes:
and establishing the basic order data according to the order detail width table, the sales commodity table and the brand table.
Preferably, the first creating unit includes:
generating the re-purchasing data based on the order detail list, the user point grade list and the third-level class first-time purchasing model; and
and generating the first purchase data based on the order detail list, the user point grade list and the third-class first purchase model.
Preferably, the method further comprises the following steps: and the self-correlation unit is used for generating second summarized data by adopting self-correlation of a summary model based on the first summarized data, and the second summarized data comprises a plurality of historical and current analysis indexes based on user concepts.
Preferably, the user concept is a user classification including a whole user, a new user outside the station, a new user inside the station and an old user. .
Preferably, the first summary data and the second summary data types are grouped together based on time and brand identity.
According to a third aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed, implement the method of any one of claims 1 to 6.
According to a fourth aspect of the embodiments of the present invention, there is provided a data processing apparatus including:
a memory for storing computer instructions;
a processor coupled to the memory, the processor configured to perform the method described above based on computer instructions stored by the memory.
The embodiment of the invention has the following advantages or beneficial effects: basic order data, first purchase data, second purchase data and first summary data are sequentially obtained through three levels of data processing, data processing pressure is relieved through hierarchical processing, and therefore data processing efficiency is improved.
The preferred embodiments of the present invention have the following advantages or benefits: and presenting the historical analysis indexes and the current analysis indexes in the second summarized data, so that the trend analysis on the brand subjects of the categories is facilitated.
The preferred embodiments of the present invention have the following advantages or benefits: the method provides a user concept comprising a whole user, an off-site new user, an on-site new user and an old user, and obtains first summarized data and second summarized data based on the user concept, so that index management based on the user concept is improved.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing embodiments of the present invention with reference to the following drawings, in which:
FIG. 1 is a flow chart of a data processing method of an embodiment of the present invention;
FIG. 2 is an example of a data model of an embodiment of the present invention;
FIGS. 3a and 3b are schematic diagrams of an aggregation model and a self-associated aggregation model of an embodiment of the invention;
FIG. 4 is a block diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram of a data processing apparatus according to another embodiment of the present invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, and procedures have not been described in detail so as not to obscure the present invention. The figures are not necessarily drawn to scale.
Fig. 1 is a flowchart of a data processing method of an embodiment of the present invention. The method specifically comprises the following steps.
In step S101, basic order data is created from the order table.
The data sheet related to the theme of the brand of the article is various, and the order sheet is important data. In addition, brand data and commodity information in the data dictionary are also included therein. The data may be stored in different data sources, e.g. different databases, which may be integrated into the same data source. In this step, base order data is built according to the order sheet and other item brand theme related tables. The output base order data contains only key and important data items relative to the originally input order table.
In step S102, the buyback data is generated based on the base order data and the buyback model, and the first-purchase data is generated based on the base order data and the first-purchase model.
In this step, according to the pre-designed first-purchase model, according to the interface provided by the database platform, the mapping relationship between the basic model corresponding to the basic order data and the first-purchase model is established, so as to generate the first-purchase data, and similarly, according to the pre-designed second-purchase model, according to the interface provided by the database platform, the mapping relationship between the basic model corresponding to the basic order data and the second-purchase model is established, so as to generate the second-purchase data. The repurchase model contains data items relating to repurchase of the item type brand. The first purchase model contains data items relating to the first purchase of the brand of the category. It should be understood that the interfaces provided on different database platforms are different, and therefore after the repurchase model and the first-purchase model are designed, different interfaces need to be called to write codes reflecting the mapping relation.
In step S103, a summary model is obtained from the repurchase model and the first purchase model.
The summary model includes a plurality of analytical indicators based on user concepts. The user concept is based on a classification set by a user for a purchase of an item of brand goods. For example, the user concept based analysis metrics include the following analysis metrics: the number of effective order users, the re-purchasing rate, the effective order amount, the effective order quantity, the grade permeability, the channel-dividing ratio of the effective order users and the trend of the effective order users.
According to the embodiment of the invention, the basic order data, the first purchase data, the second purchase data and the first summary data are sequentially obtained through three levels of data processing, and the data processing pressure is relieved through the hierarchical processing, so that the data processing efficiency is improved.
In a preferred embodiment, the above embodiment further includes step S104, in step S104, based on the first summarized data, a second summarized data is generated by using self-correlation of the summarized model, and the second summarized data includes a plurality of historical and current analysis indexes based on the user concept. Since the first summarized data represent the current analysis index, the periodic variation rule of the analysis index cannot be represented. In step S104, the generated second summarized data includes both the historical analysis index and the current analysis index through the self-associated summary model. And the historical analysis indexes and the current analysis indexes are presented in the second summarized data and can be directly output to a report.
Fig. 2 is an example of a data processing method to which an embodiment of the present invention is applied.
Corresponding to step S101, an order detail table is obtained from the order detail table (gdm _ m04_ ord _ det _ sum), the sold commodity model (gdm _ m03_ sold _ item _ sku _ da), and the brand table (fdm _ forest _ brands _ chain). Specifically, the order detail table (gdm _ m04_ ord _ det _ sum) is used as a main table, and the sales commodity table (gdm _ m03_ sold _ item _ sku _ da) and the brand table (fdm _ forest _ brands _ chain) are associated to obtain basic order data of dimensions and indexes such as parent order ID, child order ID, order placing user, primary class, secondary class, tertiary class, brand, order placing amount and the like.
Optionally, before step S101, invalid orders in the order detail table are cleaned, so as to improve reliability of the basic order data.
Corresponding to step S102, a mapping relationship is established between the order detail table, the user point grade table, the tertiary class primary purchase model and the repurchase model, thereby obtaining the repurchase data. Meanwhile, a mapping relation is established among the order detail list, the user point grade list, the third-level class first-time purchasing model and the first-time purchasing model, and accordingly first-time purchasing data are obtained. The repurchase model and the first purchase model adopt two-stage combined partition storage: time (dt) + combination (tp). According to the permutation and combination of category + brand + jingxin value segmentation, the list of tp is as follows: 00: all users; 01: a first stage; 02: primary and secondary; 03: primary + secondary + tertiary; 04: first grade + brand; 05: first-level + second-level + brand; 06, brand of first grade + second grade + third grade +; 07: segmenting the Beijing Share value; 08, segmenting the primary + Beijing shared value; 09: segmenting the primary + secondary + Beijing shared value; 10: segmenting the first level, the second level, the third level and the Jingxiang value; 11: segmenting the grade + brand + Beijing shared value; 12: segmenting the first level, the second level, the brand and the Beijing share value; 13: the first level + the second level + the third level + the brand + the Beijing share value are segmented, and 14 combination conditions are totally realized. In the construction of the mapping relationship, the window function of hive is technically used: GROUPING SETS, the analytical functions typically used in OLAP, cannot be accumulated, and need to be varied. And using index statistics of dimensional drill-up and drill-down, specifically, in one GROUP BY query, performing aggregation according to different dimensional combinations, which is equivalent to performing UNION ALL on GROUP BY result sets of different dimensions. The GROUPING __ ID therein indicates which packet set the result belongs to. For example, tp ═ 00' full users, calculated by case conv (group __ ID,10,2) where 011000011the '00' are calculated. The user purchases the third-class brand re-purchase table model is a light summary of information such as the number of basic orders and the amount under the granularity of the first class, the second class, the third class, the brand, the Jingxiang value segment and the user. The model for purchasing the item type and the brand of the user for the first time is basic information such as a first-class type, a second-class type, a third-class type, a brand, a Beijing share value section and the granularity of the user, wherein the basic information includes a first-time purchase sub-order (effective), a first-time purchase parent order (effective), a first-time purchase shopping time (effective), a global first-order mark and the like.
Corresponding to step S103, association conditions are set among the repurchase model, the first purchase model, and the summary model according to an interface function provided by the database platform, and an association relationship is established, thereby generating first summary data. Continuing to use a two-stage partition mode, and performing segmented permutation, combination and integration processing on the category + brand + jingxian value to obtain a summary model reflecting the summary information of the multi-dimensional category brands, wherein the summary model comprises multiple analysis indexes based on the user concept, as shown in fig. 3 a. Notably, the user concept is defined in this example as a user category that encompasses whole users, new users off-site, new users on-site, and old users. Wherein, the whole user refers to an effective order user of the category in the statistical period; the new off-site user refers to the valid order users of the category in the statistical period, wherein the user purchases the category for the first time, and the category is the first valid order of the user in Jingdong; the new user in the station refers to the user who purchases the class for the first time in the valid order of the class in the statistical period, but the user does not purchase the valid order of the first time in the Jingdong; the old user refers to the user who does not purchase the item for the first time in the valid order users of the item in the counting period.
In the above example, a model of aggregation of item class brand topics is implemented based on user concepts in four dimensions, whole user, new user off site, new user on site, old user, which facilitates macro analysis of item class brand topics.
Corresponding to step S104, the self-correlation of the summary model is used to generate second summarized data, which includes the multidimensional historical analysis index and the multidimensional analysis index of the current time, as shown in fig. 3 b. The model continues to use a two-stage partition mode, the class + brand + Beijing shared value segments are arranged, combined and integrated, the class + brand + Beijing shared value segments are taken as a unit, and indexes such as the number of effective order users, the repurchase rate, the effective order amount, the effective order quantity, the class permeability, the effective order user channel division ratio, the effective order user trend and the like are analyzed and summarized through four dimensions of an integral user, an off-site new user, an in-site new user and an old user. The self-correlation of the summarizing model mainly comprises the steps of correlating the time partition of the previous month and the current year, and respectively obtaining the analysis indexes of the same ratio and the ring ratio.
Fig. 3a and 3b are schematic diagrams of an aggregation model and a self-associated aggregation model of an embodiment of the invention. Not all data items are shown due to the limited space of the drawing.
Referring to fig. 3a, the dimension data items of the summary model include date, first-level category, second-level category, third-level category, brand code, and kyshare value level code, and the index data items of the summary model include the number of users purchased in the month, the number of users purchased relatively for the first time, the number of users purchased again twice, the number of users purchased again for three times, and so on. Referring to fig. 3b, the dimension data items of the comparison model are the same as those of the summary model, and the index data items of the comparison model include the index of the previous period, the index of the last year, and the like, in addition to the index of the current time. The self-associated summary model is a data model derived from the summary model and comprises historical analysis indexes and current analysis indexes based on the same dimension. Data generated by the self-associated summary model can be directly output to a report, and the analysis and display of the periodic variation trend of the brand of the product can be facilitated.
Fig. 4 is a block diagram of a data processing apparatus 400 according to an embodiment of the present invention. Specifically, the data preparation unit 401, the first creation unit 402, and the second creation unit 403 are included.
The data preparation unit 401 is used to build basic order data according to the order table. In an alternative embodiment, the data preparation unit 401 includes building base order data from an order detail table, a sales item table, and a brand table.
The first creating unit 402 generates the buyback data based on the base order data and the buyback model, and generates the first-purchase data based on the base order data and the first-purchase model. Optionally, the first creating unit includes: generating re-purchasing data based on the order detail list, the user point grade list and the third-class first-time purchasing model; and generating first-purchase data based on the order detail list, the user point grade list and the third-class first-purchase model.
The second creating unit 403 generates first summarized data including a plurality of analysis indicators based on the user concept based on the buyback data, the first purchase data, and the summary model.
In an optional embodiment, the data processing apparatus 400 further includes: and the self-correlation unit is used for generating second summarized data by adopting self-correlation of the summary model based on the first summarized data, wherein the second summarized data comprises a plurality of historical and current analysis indexes based on the user concept.
In an alternative embodiment, the apparatus 400 may further include a self-correlation unit configured to self-correlate the summary model to obtain a comparison model, where the comparison model includes a plurality of historical and current analysis indicators based on the user concept.
Fig. 5 is a block diagram of a data processing apparatus according to another embodiment of the present invention. The apparatus shown in fig. 5 is only an example and should not limit the functionality and scope of use of embodiments of the present invention in any way.
Referring to fig. 5, the data processing apparatus includes a processor 501, a memory 502, and an input-output device 503, which are connected by a bus. Memory 502 includes Read Only Memory (ROM) and Random Access Memory (RAM), with various computer instructions and data required to perform system functions being stored in memory 502, and with various computer instructions being read by processor 501 from memory 502 to perform various appropriate actions and processes. An input/output device including an input portion of a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN card, a modem, or the like. The memory 502 also stores the following computer instructions to perform the operations specified by the data processing method of the embodiments of the present invention: establishing basic order data according to an order table; constructing a re-purchasing model and a first purchasing model based on the order data; and obtaining a summary model according to the repurchase model and the first purchase model, wherein the summary model comprises a plurality of analysis indexes based on the user concept.
Accordingly, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions that, when executed, implement operations specified by a data processing method.
The flowcharts and block diagrams in the figures and block diagrams illustrate the possible architectures, functions, and operations of the systems, methods, and apparatuses according to the embodiments of the present invention, and may represent a module, a program segment, or merely a code segment, which is an executable instruction for implementing a specified logical function. It should also be noted that the executable instructions that implement the specified logical functions may be recombined to create new modules and program segments. The blocks of the drawings, and the order of the blocks, are thus provided to better illustrate the processes and steps of the embodiments and should not be taken as limiting the invention itself.
The various modules or units of the system may be implemented in hardware, firmware or software. The software includes, for example, a code program formed using various programming languages such as JAVA, C/C + +/C #, SQL, and the like. Although the steps and sequence of steps of the embodiments of the present invention are presented in method and method diagrams, the executable instructions of the steps implementing the specified logical functions may be re-combined to create new steps. The sequence of the steps should not be limited to the sequence of the steps in the method and the method illustrations, and can be modified at any time according to the functional requirements. Such as performing some of the steps in parallel or in reverse order.
Systems and methods according to the present invention may be deployed on a single server or on multiple servers. For example, different modules may be deployed on different servers, respectively, to form a dedicated server. Alternatively, the same functional unit, module or system may be deployed in a distributed fashion across multiple servers to relieve load stress. The server includes but is not limited to a plurality of PCs, PC servers, blades, supercomputers, etc. on the same local area network and connected via the Internet.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (14)
1. A data processing method, comprising:
establishing basic order data according to an order table;
generating a plurality of repurchase data based on the basic order data and the plurality of repurchase models, and generating a plurality of first purchase data based on the basic order data and the plurality of first purchase models; and
generating first summary data based on the repurchase data, the first purchase data, and a summary model, the first summary data including a plurality of analytical indicators based on user concepts.
2. The data processing method of claim 1, wherein said building base order data from an order form comprises:
and establishing the basic order data according to the order detail width table, the sales commodity table and the brand table.
3. The data processing method of claim 2, wherein the generating the repurchase data comprises:
generating the re-purchasing data based on the order detail list, the user point grade list and the third-level class first-time purchasing model;
the generating of the first purchase data comprises:
and generating the first purchase data based on the order detail list, the user point grade list and the third-class first purchase model.
4. The data processing method of claim 1, wherein the method further comprises: based on the first summary data, employing self-correlation of the summary model to generate second summary data comprising historical and current plurality of analytical indicators based on user concepts.
5. The data processing method of claim 1, wherein the user concept is a user category comprising whole users, new users outside the station, new users inside the station, and old users.
6. The data processing method of claim 1, wherein the first summary data and the second summary data type are stored in a combined partition based on time and brand.
7. A data processing apparatus, comprising:
the data preparation unit is used for establishing basic order data according to the order table;
a first creating unit configured to generate a buyback data based on the base order data and a buyback model, and generate a first purchase data based on the base order data and a first purchase model;
a second creating unit configured to generate first summarized data based on the repurchase data, the first purchase data, and a summary model, the first summarized data including a plurality of analysis indicators based on a user concept.
8. The data processing apparatus of claim 7, wherein the data preparation unit comprises:
and establishing the basic order data according to the order detail width table, the sales commodity table and the brand table.
9. The data processing apparatus according to claim 8, wherein the first creating unit includes:
generating the re-purchasing data based on the order detail list, the user point grade list and the third-level class first-time purchasing model; and
and generating the first purchase data based on the order detail list, the user point grade list and the third-class first purchase model.
10. The data processing apparatus of claim 7, further comprising: and the self-correlation unit is used for generating second summarized data by adopting self-correlation of a summary model based on the first summarized data, and the second summarized data comprises a plurality of historical and current analysis indexes based on user concepts.
11. The data processing apparatus of claim 7, wherein the user concept is a user category comprising whole users, new users outside the station, new users inside the station, and old users.
12. The data processing apparatus of claim 7, wherein the first summary data and the second summary data type are grouped based on time and brand identity.
13. A computer-readable storage medium having computer instructions stored thereon which, when executed, implement the method of any one of claims 1 to 6.
14. A data processing apparatus, comprising:
a memory for storing computer instructions;
a processor coupled to the memory, the processor configured to perform implementing the method of any of claims 1-6 based on computer instructions stored by the memory.
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| CN113139835A (en) * | 2021-04-29 | 2021-07-20 | 北京深演智能科技股份有限公司 | Data processing method, data processing device, nonvolatile storage medium and processor |
| CN113781106A (en) * | 2021-08-26 | 2021-12-10 | 唯品会(广州)软件有限公司 | Commodity operation data analysis method, device, equipment and computer readable medium |
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