CN113220783B

CN113220783B - Data processing method, device, electronic equipment and storage medium

Info

Publication number: CN113220783B
Application number: CN202110493268.3A
Authority: CN
Inventors: 邹春莉; 谢苏; 周念; 胡廷伟
Original assignee: Shanghai Enmu Information Technology Co ltd; Shenzhen Yuemu Information Technology Co ltd
Current assignee: Shanghai Enmu Information Technology Co ltd; Shenzhen Yuemu Information Technology Co ltd
Priority date: 2021-05-07
Filing date: 2021-05-07
Publication date: 2024-03-26
Anticipated expiration: 2041-05-07
Also published as: CN113220783A

Abstract

The invention provides a data processing method, a data processing device, electronic equipment and a storage medium. The data processing scheme comprises the following steps: pulling original data from a database to obtain each original data table; the original data in the original data table are regrouped, and a target data table corresponding to the original data table is organized and generated; traversing the target data table respectively to perform de-duplication treatment; and matching a target result corresponding to the target product according to a preset matching strategy aiming at the target data table after the duplication removal. By the processing scheme, homologous/different source data and the same/different coding modes can be intelligently processed, so that cost reduction and efficiency improvement are realized for enterprises.

Description

Data processing method, device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of computer technologies, and in particular, to a data processing method, a data processing device, an electronic device, and a storage medium.

Background

Currently, commodity merchants, including electronic commerce enterprises and physical commodity sales enterprises, typically derive data tables from databases manually, then manually extract data of corresponding fields, calculate data results by manually checking the corresponding field-related table data, and then compare with other calculated data results to determine field data. The data processing scheme can calculate the result for the electronic commerce industry, and the accuracy of the data can be ensured through manual compound inspection.

However, with the rapid development of e-commerce, such as live and commercial e-commerce sales, sales of products, inventory and other data are significantly increased, so that the e-commerce industry faces data with huge data volume and more complex data forms, such as data stored in databases with more complex data structures, such as the same/different databases may also store the data in different data coding modes, and at this time, the data cannot be maintained in time by using the traditional manual mode, such as the data of complex fields, such as the data of systems with different codes, and the operations of relying on manual identification, matching field information and the like become more and more difficult, and the maintenance cost for the enterprise is significantly increased, such as more manpower and material resources are spent, and such as data confusion caused by untimely maintenance, enterprise operation confusion and the like.

For the increasing mass data of electronic commerce industry, a new and efficient data processing scheme is needed.

Disclosure of Invention

In view of the above, the invention provides a data processing method, a device, an electronic device and a storage medium, which can intelligently process mass data and reduce cost and increase efficiency for enterprises.

The invention provides the following technical scheme:

the invention provides a data processing method, which comprises the following steps: acquiring an original sales table, an original inventory table and an original product table from a plurality of databases; respectively carrying out data grouping on the original sales table, the original inventory table and the original product table, and respectively generating a corresponding target sales table, a corresponding target inventory table and a corresponding target product table, wherein in the data grouping, the target sales table, the target inventory table and the corresponding target product table all comprise data columns of target coding fields, the target coding fields are used for representing numbers of corresponding target products, and a row in which the target coding fields are positioned comprises sales information, inventory information or product information of the target products corresponding to the target coding fields; according to the target coding field, traversing the target sales table, the target inventory table and the target product table respectively, and carrying out duplicate removal processing on sales information, inventory information or product information corresponding to the target coding field; and matching target results corresponding to the target products according to a preset matching strategy aiming at the data in the target sales table, the target inventory table and the target product table after the duplication removal.

The invention also provides a data processing device, comprising: the acquisition module acquires an original sales table, an original inventory table and an original product table from a plurality of databases; the grouping module is used for respectively grouping the original sales table, the original inventory table and the original product table and respectively generating a corresponding target sales table, a target inventory table and a target product table, wherein in the data grouping, the target sales table, the target inventory table and the target product table all comprise data columns of target coding fields, the target coding fields are used for representing numbers of corresponding target products, and the rows of the target coding fields comprise sales information, inventory information or product information of the target products corresponding to the target coding fields; the duplication removing module respectively traverses the target sales table, the target inventory table and the target product table according to the target coding field and carries out duplication removing treatment on sales information, inventory information or product information corresponding to the target coding field; and the matching module is used for matching the target results corresponding to the target products according to a preset matching strategy aiming at the data in the target sales table, the target inventory table and the target product table after the duplication removal.

The invention also provides an electronic device for data processing, comprising:

at least one processor; and a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

acquiring an original sales table, an original inventory table and an original product table from a plurality of databases;

respectively carrying out data grouping on the original sales table, the original inventory table and the original product table, and respectively generating a corresponding target sales table, a corresponding target inventory table and a corresponding target product table, wherein in the data grouping, the target sales table, the target inventory table and the corresponding target product table all comprise data columns of target coding fields, the target coding fields are used for representing numbers of corresponding target products, and a row in which the target coding fields are positioned comprises sales information, inventory information or product information of the target products corresponding to the target coding fields;

according to the target coding field, traversing the target sales table, the target inventory table and the target product table respectively, and carrying out duplicate removal processing on sales information, inventory information or product information corresponding to the target coding field;

And matching target results corresponding to the target products according to a preset matching strategy aiming at the data in the target sales table, the target inventory table and the target product table after the duplication removal.

The present invention also provides a computer storage medium for data processing, the computer storage medium storing computer-executable instructions configured to:

Compared with the prior art, the at least one technical scheme adopted by the invention has the beneficial effects that at least the beneficial effects comprise:

obtaining data from a database to obtain a corresponding original data table, regrouping the data in the original data table into a new target data table by utilizing a target coding field, so as to be suitable for processing the data of the same source/different source and processing the data of the same coding mode/different coding modes, traversing the data in the target data table to perform de-duplication processing, cleaning repeated data and correcting error data, ensuring the uniqueness of the data, then matching a target result corresponding to a target product according to a preset matching strategy, and calculating by utilizing the data in a field matching association table to obtain an accurate result. The data processing method provided by the invention has the advantages that manual calculation is not needed, the efficiency is greatly improved, data processing can be performed on massive data and the like from different systems, such as data of complex fields, such as data of different coding systems, such as huge e-commerce sales data, field information can be accurately identified and matched, accurate calculation results are obtained, an intelligent data processing scheme is provided for enterprises, automatic management is performed on inventory in sales according to the data results in time, and cost reduction and synergy are realized for the enterprises.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic structural diagram of a data processing scheme according to the present invention.

Fig. 2 is a flowchart of a data processing method provided by the present invention.

Fig. 3 is a schematic diagram of an inventory table in a data processing method according to the present invention.

Fig. 4 is a schematic diagram of a sales table in a data processing method according to the present invention.

Fig. 5 is a schematic diagram of a product table in a data processing method according to the present invention.

Fig. 6 is a schematic diagram of a data processing engine according to the present invention.

Fig. 7 is a schematic structural diagram of a data processing apparatus according to the present invention.

Fig. 8 is a schematic structural diagram of an electronic device for data processing according to the present invention.

Detailed Description

Embodiments of the present application are described in detail below with reference to the accompanying drawings.

Other advantages and effects of the present application will become apparent to those skilled in the art from the present disclosure, when the following description of the embodiments is taken in conjunction with the accompanying drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present application, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, apparatus may be implemented and/or methods practiced using any number and aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should also be noted that the illustrations provided in the following embodiments merely illustrate the basic concepts of the application by way of illustration, and only the components related to the application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided in order to provide a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details. The terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining a description of "a first," "a second," etc. may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.

At present, with the rapid development of electronic commerce, the data of electronic commerce enterprises are increasingly huge, and the data are also becoming more and more complex, for example, in a live broadcast and on-the-spot scene, sales data of the electronic commerce enterprises can come from a live broadcast front end, stock information can come from a rear end warehouse, product information can come from a factory and the like, so that not only are data sources various, but also data coding formats can be different, and the existing scheme cannot timely process and obtain accurate results through simple identification, field information matching and the like.

Based on the analysis of the data in the new scene, the inventor provides a data processing scheme applicable to different data sources and different coding systems, effectively provides an intelligent processing scheme for processing mass data for enterprises, ensures that the enterprises obtain accurate data results in time in the E-commerce sales, carries out E-commerce sales and protection navigation for the enterprises, improves the data maintenance efficiency of the enterprises, and reduces the operation and management cost of the enterprises.

As shown in fig. 1, in the data processing scheme provided by the invention, raw data are respectively acquired from a plurality of databases to form various raw data tables, such as a sales table, an inventory table and a product table, which are pulled to be used as raw data tables, wherein the sales table can reflect sales information of products, such as daily sales, monthly sales and the like, the inventory table can reflect inventory information of products, such as inventory number, ex-warehouse number, warehouse-in number and the like, and the product table can reflect product information, such as production, processing, circulation and the like; the data sources may be the same, for example, the data sources may be all the same from a database of an enterprise, for example, may be different from a sales enterprise, a warehouse enterprise, a processing enterprise, etc., and the coding modes of the data may be the same, or may be different, at this time, the data tables may be regrouped and organized by using preset coding fields (i.e., target coding fields) before processing, that is, the data in the pulled sales table, inventory table and product table may be regrouped to generate a new target data table, for example, a target sales table, a target inventory table and a target product table, so that each target data table after grouping includes a data column of the coding field, where each coding field is located, the data row of each coding field correspondingly includes product data corresponding to the coding field, for example, sales information, inventory information or product information, etc., and after regrouping, the subsequent processing may be prevented from being affected by different data sources, data coding modes, etc.; and calculating target result data according to the data in the target data table according to a preset matching strategy, wherein the target result data can be data required by an enterprise in e-commerce sales, such as whether stock in sales is sufficient, whether stock is out of stock, when stock replenishment is required, and the like.

By regrouping the original data, the data from different data sources can be processed, and the data from different coding systems can be processed, so that the mass data of enterprises can be intelligently and rapidly processed, support is provided for the enterprises in the fast electronic commerce sales, and the cost reduction and efficiency improvement of the enterprises are realized.

The following describes the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.

The invention provides a data processing method, which can be used for carrying out intelligent processing on data in the operation management of an e-commerce enterprise, as shown in fig. 2, and can comprise the following steps:

step S202, an original sales table, an original stock table and an original product table are obtained from a plurality of databases.

In practice, the required original data may be obtained from several databases, for example, according to the storage schemes of sales data, inventory data and product data in practical applications, for example, three are from the same database, for example, three are from three different databases respectively, and the data are obtained from the databases accordingly, so that the pulled corresponding sales data forms an original sales table, the corresponding inventory data forms an original inventory table, and the corresponding product data forms an original product table.

Wherein the sales table may store data reflecting sales information of the product, such as daily sales, monthly sales, etc., the inventory table may store inventory information reflecting the product, such as inventory number, ex-warehouse number, in-warehouse number, etc., and the product table may store information reflecting the product, such as production information, processing information, logistics information, suppliers, prices, brands, classifications (e.g., primary classifications, secondary classifications, etc.), etc.

For example, as shown in fig. 3, the pulled inventory table may include field values such as code (number), item_name (product name), branch_name (sort name), qty (inventory number), and the like.

For example, as shown in fig. 4, the pulled SALES table may include field values of CODE (number), m_pn (product number), DESCRIPTION (DESCRIPTION), SALES TOTAL QTY (TOTAL SALES), SALES TOTAL AMOUNT (TOTAL SALES), ROUGH pro fit (gross PROFIT), etc. in the SALES table.

For example, as shown in fig. 5, the pulled item table (i.e., product table) may include field values such as code (number), provider (number), provider_code (number), provider_price (price of provider), brand (Brand), cat (primary category), s_cat (secondary category). In a specific implementation, in the original sales table, the original inventory table, and the original product table, at least one identical code field (such as the code number field in the foregoing schematic diagrams 3-5) is provided for the same product, so as to identify the product, for example, the code field may be a name corresponding to the product, for example, the code field may be a bar code, a two-dimensional code, etc. corresponding to the product.

The databases may be the same or different databases, and the encoding modes used for storing the data in the databases may be the same or different, which are not limited herein, such as the database, the manner in which the data is obtained from the database, and the like.

The raw data, i.e., the data collection, may be obtained from a database, and the required data (or data table) may be derived from the existing database system one by one and summarized, or the required data (or data table) may be collected from the data collection system and summarized, which is not limited herein.

Step S204, respectively carrying out data grouping on the original sales table, the original inventory table and the original product table, and respectively generating a corresponding target sales table, a target inventory table and a target product table.

In implementation, in the data packet, the target sales table, the target inventory table and the target product table each include a data column of a target coding field, the target coding field is used for representing a number of a corresponding target product, and a row where the target coding field is located includes sales information, inventory information or product information of the target product corresponding to the target coding field.

By regrouping and organizing the data of the original data table by using the target coding field, for example, extracting core data from the original data table, wherein the core data can be data concerned in processing, for example, sales data (such as sales volume), stock data (such as stock volume), for example, production data (such as production period), and further, the core data is used for generating a corresponding target data table, so that subsequent data processing according to the target data table is facilitated. For example, for the original inventory table in the foregoing example, three items of code (number), item_name (name) and qty (inventory number) may be taken as core data, and field values corresponding to the three items of data are extracted again to generate a new inventory table.

For example, for the original SALES table in the foregoing example, three items of CODE (number), m_pn (product number), SALES TOTAL QTY (TOTAL SALES) may be taken as core data, and a new SALES table may be regenerated.

For example, for the original product table (i.e., the item table) in the foregoing example, two items of code (number), supplier (vendor number) may be taken as core data, and a new product table may be regenerated.

It should be noted that the target code field may be used to identify the uniqueness of the product in each data table, and the core data is regrouped and organized by the target code field, so as to obtain the key field data required by the user processing. In practice, the target code field may be determined according to the actual application, for example, the product number may be used as the field identifier to uniquely identify the product in the process, which is not limited herein.

Step S206, according to the target coding field, traversing the target sales table, the target inventory table and the target product table respectively, and performing duplication removal processing on sales information, inventory information or product information corresponding to the target coding field.

After reorganizing each data table by adopting the target coding field, each data in each target data table can be de-duplicated by traversing each data in each target data table, so that the data in each target data table is ensured to be effective and unique, and the subsequent data processing is facilitated.

For example, multiple pieces of data of the same product may be combined. For example, when traversing the inventory table, it is found that a product a has a plurality of inventory records (e.g., two inventory records respectively stored at different times), where the inventory records may be combined into one record; for example, when traversing the sales table, it is found that a product B corresponds to a plurality of sales records (e.g., records sold to a and B, respectively), and these sales records may be combined into one record; for example, when traversing the product table, it is found that a product C corresponds to a plurality of pieces of logistics data that are distributed to the same target warehouse through a plurality of logistics paths, the plurality of pieces of logistics data may be combined.

For example, invalid data corresponding to the same product may be deleted, such as deleting product data that is no longer sold in a sales table (or inventory table, or product table).

Step S208, matching the target sales table, the target inventory table and the data in the target product table after the duplication removal according to a preset matching strategy to obtain a target result corresponding to the target product.

In practice, according to practical application requirements, such as the sales condition of a certain product (i.e. a target product) needs to be monitored, such as the inventory condition of a certain product (i.e. a target product) needs to be monitored, such as the production condition of a certain product (i.e. a target product) is monitored, the required target result can be matched from the data tables according to a preset matching strategy, so that a required data support is provided for the business operation.

It should be noted that, the matching policy may be a processing policy for performing business data processing on a target product, for example, a policy for acquiring a target result from a target data table, for example, a data processing policy for acquiring data from the data table and performing data processing (such as calculation, analysis, summarization, etc.) according to the acquired result, and specifically, the matching policy may be preset according to data required by enterprise business, so as to intelligently provide data support for an enterprise.

Through the steps S202-S208, the original data is pulled from the database to form the original data table, the original data table is further regrouped, the target data table which can be processed uniformly is organized and generated, the data from different data sources, the data from different coding systems and the like can be processed intelligently, the target data table is de-duplicated, the result data required by enterprises can be conveniently matched from the de-duplicated target data table, data support is provided for enterprise operation, and cost reduction and synergy of the enterprises are realized.

In some embodiments, the data in the original data table may be regrouped according to the data attribute, so as to divide the data in the data table into different groups, thereby forming data that can facilitate subsequent unified data processing. The data attribute may be a characteristic of characterizing the data in enterprise operation, such as a factory attribute, a logistics attribute, such as a warehouse entry attribute, a warehouse exit attribute, a stock attribute, such as a pre-sale attribute, a promotion attribute, a hot sale attribute, a diapause attribute, and the like, which are not listed one by one.

For example, for the original inventory table in the foregoing example, fields such as code (number), item_name (name), and qty (inventory number) may be used as data attributes; for the original SALES table in the foregoing example, fields such as CODE (number), m_pn (product number), SALES TOTAL QTY (TOTAL SALES) and the like may be used as data attributes; for the original product table in the foregoing example, fields such as code (number), supplier (number), and the like may be used as data attributes.

For example, data in sales tables may be grouped according to inventory, production, etc., facilitating monitoring of marketable conditions.

For example, data in the inventory table may be grouped by a degree of popularity (e.g., hot, diapause, etc.) to facilitate monitoring the inventory of the sold product.

For example, data in the product table can be grouped according to the diapause grade, so that the production, delivery, circulation and the like of the product can be monitored conveniently.

In some embodiments, after the original data tables (such as the original sales table, the original inventory table, the original product table, etc.) are regrouped by using the target coding field to obtain the target tables (such as the target sales table, the target inventory table, the target product table, etc.) which are convenient to process, the data results (i.e. the target results) required by the e-commerce user in the operation management can be quickly obtained from the target tables, so that the e-commerce user can perform the operation management in time.

In some embodiments, the sales links may be managed by daily average sales. In this case, the target result may include daily sales of the product to be sold (i.e., the target product).

In implementation, the average daily sales can be obtained according to the following steps, namely when the target result corresponding to the target product is matched according to the preset matching strategy, the method can comprise the following steps: inquiring sales volume corresponding to the target product from the target sales table according to the target coding field corresponding to the target product; and calculating the average daily sales volume according to the sales volume and the preset sales days, wherein the average daily sales volume = sales volume/(preset sales days).

It should be noted that the preset sales days may be preset, adjusted or re-input according to the operation requirement, which is not limited herein.

The target coding field can be used for quickly inquiring the sales volume corresponding to the target product from the target sales table, so that the daily sales volume can be timely obtained, and the electric business can conveniently manage the electric business sales according to the daily sales volume, such as screening out which hot sales products exist, which diapause products exist, which products with good sales promotion effect exist, and the like.

In some embodiments, the marketing segment may be managed by the number of days available for sale. In this case, the target result may include the number of days available for selling the product (i.e., the target product).

In a specific implementation, the number of days available for sale may be obtained as follows, that is, in calculating the number of days available for sale, the data processing method provided by the present invention may further include the steps of: inquiring a first stock quantity corresponding to the target product from the target stock table according to the target coding field corresponding to the target product; and calculating the number of days available for sale according to the average daily sales and the first inventory quantity, wherein the number of days available for sale = first inventory quantity/(average daily sales).

Through the target coding field, the inventory quantity corresponding to the target product can be quickly inquired from the target inventory table, the number of days on sale can be timely obtained, and the electric business can conveniently manage the sales of the electric business according to the number of days on sale, such as goods intake, backlog reminding and the like.

In some embodiments, sales links may be managed through an backout list. At this time, the target result may include a stock out list (i.e., a first stock out list) of the products for sale (i.e., target products).

In implementation, the first backorder list may be acquired according to the following steps, that is, in acquiring the first backorder list, the data processing method provided by the present invention may further include the steps of: inquiring the arrival period corresponding to the target product from the target product table according to the target coding field corresponding to the target product; and determining whether the target product is listed in the backorder list according to the number of days available and the arrival period.

It should be noted that, the arrival period may be a period of time when the product enters the inventory corresponding to the marketable product, the period may be days, weeks, months, etc., and the arrival period may be preset, adjustable, or re-input, which is not limited herein.

Through the number of days available for sale and the arrival period, whether the product to be sold (namely the target product) belongs to the backorder or not can be predicted, so that the electric business can adjust the management in time.

In some embodiments, whether to be out of stock may be determined based on sales.

In practice, logic may be employed to determine whether a target product is out of stock, based on the days of sale and the arrival period, the determining whether the target product is listed in the out-of-stock list may include: and when the number of days available for sale is smaller than the arrival period and the average daily sales quantity is larger than zero, listing the target product in the backorder list.

It should be noted that, in the example, for a product with a daily sales volume of zero, logic decision can be performed according to other monitoring conditions in practical application, for example, although the daily sales volume of the target product is zero, the number of days available for sale cannot be calculated, if the inventory is less than or equal to zero, the product can be counted into the stock-out list, so that the electric business user can adjust the operation and management in time according to the stock-out list.

In some embodiments, whether to be out of stock may be determined jointly based on sales and inventory conditions.

In practice, the logic may be used to determine whether the target product is out of stock, that is, in the data processing method provided by the present invention, the target result may further include a second out-of-stock list, and the data processing method may further include: inquiring a second inventory quantity corresponding to the target product from the target inventory table according to the target coding field corresponding to the target product; and determining whether the target product is listed in the second stock out list according to the average daily sales and the second stock quantity.

For example, according to the stock quantity and average daily sales, according to the current sales situation, the calculated number of days that the product can be sold may not fall within a preset threshold (for example, the threshold is several days), for example, the number of days that the product can continue to be sold may be less than zero, at this time, the target product may be listed in the stock-out list, and the electric user may be timely reminded to make a replenishment.

For example, according to the stock quantity and average daily sales quantity, the number of days for which the product can be sold is calculated to be near a preset threshold value, but according to the sales condition, the product is known to be a hot-sold product, at this time, the target product can be listed in the stock-out list, and the electric user can be timely reminded of carrying out replenishment.

In some embodiments, after obtaining the target result corresponding to the target product, various application data may be generated according to the target result, so that the electric business user can conveniently and timely make operation management according to the application number, where the application data may be data required by the electric business user for operation management, such as hot-sell products, diapause products, products to be promoted, products with long goods intake period, and the like.

In some implementations, in the data processing method provided in any one of the foregoing embodiments, the data processing method may further include: grouping the target results corresponding to the target products according to a preset grouping strategy and outputting the grouped target results; and/or combining the target results corresponding to the target products according to a preset combination strategy and outputting the combined target results.

For example, the grouping may be by sales, obtaining a hot list, a bad list, etc.

For example, the groupings may be by inventory condition, obtaining inventory adequacy, inventory backorder, inventory risky, etc.

For example, the supplementary manifest, the inventory list, etc. are obtained by combining the sales condition and the inventory condition.

For example, a close-up list is obtained, there is a risk of production sales, etc. in accordance with the combination of sales and production of the product.

For example, the product list with risk of restocking, the product list with long warehouse-in period and the like are obtained by combining according to the stock condition and the product production condition.

It should be noted that, the preset grouping policy and the combination policy may be preset according to the user operation and management requirement of the electric user, and the above examples are only illustrative, and do not limit the application.

In some embodiments, corresponding prompt information can be generated and output, and the electric business can be timely prompted to perform corresponding operation management.

It should be noted that, the prompt information may be determined according to an actual application scenario, for example, a backorder prompt, a replenishment prompt, a promotion prompt, a hot-order prompt, a diapause prompt, etc., or various data may be obtained for each of the foregoing embodiments, and/or a packet output result of the data, and/or a combination output result of the data, which is not limited herein.

Based on the same inventive concept, the present invention provides a data processing engine.

In the implementation, the data processing method provided by the invention can form a data processing engine, so that an electric user can conveniently use the data processing engine to process homologous/different source data and the same/different coding modes data, and various data required by management can be obtained in time.

As shown in fig. 6, in the processing block diagram of the data processing engine, raw data is collected from a database, such as a sales table of sales data, an inventory table of inventory data, a product table of product production data, etc. are pulled from the database; then, the target coding field is adopted to reorganize the original data table to reorganize the homologous/different homologous data and the data with the same/different coding modes into a new target data table (such as a sales table, an inventory table and a product table), so that various data processing can be performed based on the target data table; then cleaning the target data table, such as deduplication, deleting the data which is not concerned, and leaving core key data, such as sales, such as inventory, such as arrival cycle, and the like; the cleaned data is imported into an algorithm system, and logic decision algorithm is used for carrying out logic processing to generate various data required by a user of the electric business, such as average daily sales, number of days on sale, a stock-out list and the like; further, the generated various data can be classified and/or combined according to the application needs of the e-commerce user, so that final application data, such as reminding information, such as a summary table, can be generated, and the e-commerce user can conduct management, such as replenishment, such as sales promotion, such as putting down goods, and the like, according to the various data and/or the application data.

It should be noted that, the algorithm system may be an algorithm for matching a logic decision of a target result corresponding to a target product according to a matching policy, and an algorithm for performing the logic decision (such as a matching algorithm, such as a statistical algorithm, etc.) may be set according to an actual application requirement of an e-commerce user, which is not limited herein.

The data processing engine is used for processing various data, so that various data can be provided for the electric business user, inventory can be automatically managed, stock shortage reminding and the like, the electric business user can conveniently conduct operation management, management efficiency is improved, and cost reduction and efficiency improvement are realized for enterprises.

Based on the same inventive concept, the invention also provides a data processing device, an electronic device and a computer storage medium corresponding to the data processing method.

Fig. 7 is a schematic diagram of a data processing apparatus according to the present invention.

As shown in fig. 7, the data processing apparatus may include the following processing function modules: the acquisition module 401 acquires an original sales table, an original inventory table and an original product table from a plurality of databases; the grouping module 403 is configured to perform data grouping on the original sales table, the original inventory table, and the original product table, and generate a corresponding target sales table, a target inventory table, and a target product table, where in the data grouping, the target sales table, the target inventory table, and the target product table each include a data column of a target coding field, the target coding field is used to represent a number of a corresponding target product, and a row where the target coding field is located includes sales information, inventory information, or product information of the target product corresponding to the target coding field; the duplication removing module 405 respectively traverses the target sales table, the target inventory table and the target product table according to the target coding field, and performs duplication removing processing on sales information, inventory information or product information corresponding to the target coding field; the matching module 407 is configured to match, according to a preset matching policy, a target result corresponding to a target product with respect to the data in the target sales table, the target inventory table, and the target product table after the duplication removal.

Alternatively, the target result may include a daily sales volume.

The matching of the target result corresponding to the target product according to the preset matching strategy comprises the following steps: inquiring sales volume corresponding to the target product from the target sales table according to the target coding field corresponding to the target product; and calculating the average daily sales volume according to the sales volume and the preset sales days.

Optionally, the target result may further include a number of days available for sale.

The data processing apparatus may further include: a first query calculation module (not shown in the figure) queries a first inventory quantity corresponding to the target product from the target inventory table according to the target code field corresponding to the target product, and calculates the number of days on sale according to the average daily sales quantity and the first inventory quantity.

Optionally, the target result may further include a first backorder list.

The data processing apparatus may further include: a second query computation module (not shown in the figure) queries a arrival period corresponding to the target product from the target product table according to the target code field corresponding to the target product, and determines whether the target product is listed in the backorder list according to the number of days available and the arrival period.

Optionally, determining whether the target product is listed in the backorder list according to the days on sale and the arrival period includes: and when the number of days available for sale is smaller than the arrival period and the average daily sales quantity is larger than zero, listing the target product in the backorder list.

Optionally, the target result may further include a second backorder list.

The data processing apparatus may further include: a third query calculation module (not shown in the figure) queries a second inventory quantity corresponding to the target product from the target inventory table according to the target code field corresponding to the target product, and determines whether the target product is listed in the second stock list according to the average daily sales quantity and the second inventory quantity.

Optionally, the data grouping the original sales table, the original inventory table, and the original product table respectively includes: and respectively grouping the original sales table, the original inventory table and the original product table according to data attributes.

Optionally, the data processing apparatus may further include: a classification module (not shown in the figure) for grouping the target results corresponding to the target products according to a preset grouping strategy and outputting the grouped target results;

And/or, the data processing apparatus may further include: and the combination module (not shown in the figure) is used for combining the target results corresponding to the target products according to a preset combination strategy and outputting the combined target results.

Optionally, the data processing apparatus may further include: a prompt module (not shown in the figure) generates prompt information and outputs the prompt information.

Fig. 8 is a schematic structural diagram of an electronic device for data processing according to the present invention, where the electronic device 500 is shown in the structure of the electronic device for implementing the data processing scheme according to the present invention, and the electronic device 500 is merely an example, and should not be limited to the functions and the application scope of the embodiments of the present invention.

As shown in fig. 8, in the electronic device 500, it may include: at least one processor 510; the method comprises the steps of,

a memory 520 communicatively coupled to the at least one processor; wherein,

the memory 520 stores instructions executable by the at least one processor 510, the instructions being executable by the at least one processor 510 to enable the at least one processor 510 to:

It should be noted that the electronic device 500 may be represented in the form of a general purpose computing device, which may be a server device, for example.

In practice, components of electronic device 500 may include, but are not limited to: the at least one processor 510, the at least one memory 520, and a bus 530 connecting the various system components including the memory 520 and the processor 510, wherein the bus 530 may include a data bus, an address bus, and a control bus.

In implementation, memory 520 may include volatile memory, such as Random Access Memory (RAM) 5201 and/or cache memory 5202, and may further include Read Only Memory (ROM) 5203.

Memory 520 may also include a program tool 5205 having a set (at least one) of program modules 5204, such program modules 5204 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

The processor 510 executes various functional applications and data processing by running computer programs stored in the memory 520.

The electronic device 500 may also communicate with one or more external devices 540 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 550. Moreover, electronic device 500 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet via network adapter 560, and network adapter 560 communicates with other modules in electronic device 500 via bus 530. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 500, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.

It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.

Note that the computer storage medium may include, but is not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.

In a possible embodiment, the invention may also provide that the data processing is implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps of the method as described in any of the preceding embodiments, when said program product is run on said terminal device.

Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment focuses on differences from other embodiments. In particular, for the product examples described later, since they correspond to the methods, the description is relatively simple, and reference is made to the description of the method examples in the section.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of data processing, comprising:

respectively carrying out data grouping on the original sales table, the original inventory table and the original product table, and respectively generating a corresponding target sales table, a corresponding target inventory table and a corresponding target product table, wherein in the data grouping, the target sales table, the target inventory table and the corresponding target product table all comprise data columns of target coding fields, the target coding fields are used for representing numbers of corresponding target products, and a row in which the target coding fields are positioned comprises sales information, inventory information or product information of the target products corresponding to the target coding fields; wherein the data grouping of the original sales table, the original inventory table, and the original product table, respectively, comprises: respectively grouping the original sales table, the original inventory table and the original product table according to data attributes;

2. The data processing method according to claim 1, wherein the target result includes a daily sales volume;

the matching of the target result corresponding to the target product according to the preset matching strategy comprises the following steps:

inquiring sales volume corresponding to the target product from the target sales table according to the target coding field corresponding to the target product;

and calculating the average daily sales volume according to the sales volume and the preset sales days.

3. The data processing method of claim 2, wherein the target result further comprises days on sale;

the data processing method further comprises the following steps:

inquiring a first stock quantity corresponding to the target product from the target stock table according to the target coding field corresponding to the target product;

And calculating the number of days on sale according to the average daily sales and the first inventory quantity.

4. A data processing method according to claim 3, wherein the target result further comprises a first backorder list;

the data processing method further comprises the following steps:

inquiring the arrival period corresponding to the target product from the target product table according to the target coding field corresponding to the target product;

and determining whether the target product is listed in the backorder list according to the number of days available for sale and the arrival period.

5. The data processing method of claim 4, wherein determining whether the target product is listed in the backorder list based on the days of sale and the arrival period comprises:

and when the number of days available for sale is smaller than the arrival period and the average daily sales quantity is larger than zero, listing the target product in the backorder list.

6. The data processing method of claim 2, wherein the target result further comprises a second backorder list;

the data processing method further comprises the following steps:

inquiring a second inventory quantity corresponding to the target product from the target inventory table according to the target coding field corresponding to the target product;

And determining whether the target product is listed in the second stock out list according to the average daily sales and the second stock quantity.

7. The data processing method according to any one of claims 1 to 6, characterized in that the data processing method further comprises:

grouping the target results corresponding to the target products according to a preset grouping strategy and outputting the grouped target results;

and/or combining the target results corresponding to the target products according to a preset combination strategy and outputting the combined target results.

8. The data processing method according to claim 7, characterized in that the data processing method further comprises: generating prompt information and outputting the prompt information.

9. A data processing apparatus, comprising:

the acquisition module acquires an original sales table, an original inventory table and an original product table from a plurality of databases;

the grouping module is used for respectively grouping the original sales table, the original inventory table and the original product table and respectively generating a corresponding target sales table, a target inventory table and a target product table, wherein in the data grouping, the target sales table, the target inventory table and the target product table all comprise data columns of target coding fields, the target coding fields are used for representing numbers of corresponding target products, and the rows of the target coding fields comprise sales information, inventory information or product information of the target products corresponding to the target coding fields; wherein the data grouping of the original sales table, the original inventory table, and the original product table, respectively, comprises: respectively grouping the original sales table, the original inventory table and the original product table according to data attributes;

The duplication removing module respectively traverses the target sales table, the target inventory table and the target product table according to the target coding field and carries out duplication removing treatment on sales information, inventory information or product information corresponding to the target coding field;

and the matching module is used for matching the target results corresponding to the target products according to a preset matching strategy aiming at the data in the target sales table, the target inventory table and the target product table after the duplication removal.

10. An electronic device for data processing, comprising:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,

11. A computer storage medium for data processing, the computer storage medium storing computer executable instructions configured to: