WO2019149189A1

WO2019149189A1 - Method and device for data processing

Info

Publication number: WO2019149189A1
Application number: PCT/CN2019/073684
Authority: WO
Inventors: 赵苗苗; 于盛昌; 李闯
Original assignee: 北京京东尚科信息技术有限公司; 北京京东世纪贸易有限公司
Priority date: 2018-01-30
Filing date: 2019-01-29
Publication date: 2019-08-08
Also published as: CN110097386B; CN110097386A

Abstract

A method and device for data processing are provided, relating to the field of computer information processing. The method comprises: receiving and responding to a purchase instruction of a user for a predetermined item, the predetermined item comprising a floating price item (S202); extracting attribute information of the predetermined item by means of related information of the predetermined item (S204); determining one of a plurality of first items from an item stack as a price differential compensation item of the predetermined item according to the attribute information of the predetermined item (S206); and generating order data via the predetermined item and the price differential compensation item (S208). The method and device can realize positioning of price compensation items and adjustment of the amount payable for items with respect to frequent price changes when real-time sales prices of suppliers are required to be provided for order placement, so as to avoid poor user experience and purchase order contractual disputes in a purchase process.

Description

Method and device for data processing

Technical field

The present disclosure relates to the field of computer information processing, and in particular to a method and apparatus for data processing.

Background technique

Online sales of travel and other travel goods are becoming more and more popular, but online quotation can only provide an estimated starting price. Due to frequent fluctuations in commodity prices, the price of goods displayed at the front desk is not necessarily the amount payable for goods sold by the user. In the process of purchasing, a temporary order is often formed, and the offline customer service is required to communicate with the user to confirm whether the order can be paid after the order is changed according to the latest amount due. And the user (or customer service instead) is required to make a second order to generate an order to be paid with an accurate amount due.

In the prior art, the methods for solving the above problems mainly include commodity sorting, searching and traversing methods, including: text correlation, feature value positioning, and the like. In the prior art, the above problem can be solved, for example, by text relevance, that is, whether a target that is traversed by the keyword retrieval algorithm is related to the source text, including whether a feature representing the commodity is extracted from the product name or description, including Fuzzy correlation and precise correlation are two kinds of positioning methods. In the prior art, the above problem can also be solved, for example, by a feature value localization method, typically by identifying the trait consistency of the image, such as whether the picture is the same or similar to the retrieved picture.

In the prior art, the floating part calibration of the order payable amount needs to find a suitable spread supplementary product to be completed, and the difference supplementary goods must belong to the same category and the same supplier as the goods to be placed, in order to complete the order submission. First of all, the text correlation algorithm is more suitable for users to search for goods, and is not suitable for finding the difference of the floating sales price for the goods with only the starting price. The difference supplementary goods are not related by text relevance, but by the need of goods. Positioned by the category and supplier. Secondly, the eigenvalue localization algorithm is more suitable for the retrieval of source data as pictures, and it is impossible to locate the categories and suppliers. Finally, the search for the price difference and the order placement, the purchase user must have no perception, and the calculation time limit of tp99 and above is required. The two methods in the prior art cannot achieve the transaction timeliness.

Therefore, there is a need for a new method and apparatus for data processing.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure, and thus it may include information that does not constitute a prior art known to those of ordinary skill in the art.

Summary of the invention

In view of this, the present disclosure provides a method and apparatus for data processing, which is capable of performing price adjustment and product amount adjustment in a transaction in which a real-time sales price of a supplier is obtained at a time when an order is placed. Avoid experiencing problems and order contract disputes during the purchase of goods.

Other features and advantages of the present disclosure will be apparent from the following detailed description.

According to an aspect of the present disclosure, a method for data processing is provided, the method comprising: receiving and responding to a user's purchase instruction for a predetermined item, the predetermined item comprising a price floating item; extracting related information of the predetermined item Attribute information of the predetermined item; determining, by the stack, one of the plurality of first items as a premium item of the predetermined item according to the attribute information of the predetermined item; and passing the predetermined item and the premium item Generate order data.

In an exemplary embodiment of the present disclosure, the method further includes: when the first item corresponding to the attribute information of the predetermined item is not included in the stack, establishing corresponding to the attribute information of the predetermined item The first item.

In an exemplary embodiment of the present disclosure, the method further includes: establishing the stack by the plurality of first commodities.

In an exemplary embodiment of the present disclosure, the determining, according to the attribute information of the predetermined item, one of the plurality of first items from the stack as the premium item of the predetermined item, comprising: extracting the predetermined item Attribute information of the commodity; obtaining a weight of each of the plurality of first commodities; determining, according to the attribute information and the weight, the first corresponding to the predetermined commodity in the stack a commodity; and the first commodity as the premium commodity.

In an exemplary embodiment of the present disclosure, the generating the order data by using the predetermined item and the premium item comprises: determining the quantity of the premium item, generating premium item information; The premium product information and the related information of the predetermined product are input into an order pipeline, and the order data is generated.

In an exemplary embodiment of the present disclosure, when the first item corresponding to the attribute information of the predetermined item is not included in the stack, establishing a location corresponding to the attribute information of the predetermined item The first commodity includes: a first commodity that establishes a predetermined unit price, the first commodity is identical to attribute information of the predetermined commodity; inserts the newly created first commodity into the stack; and passes MAX-HEAP- The INSERT process maintains an index of the stack.

In an exemplary embodiment of the present disclosure, the establishing the stack by using a plurality of first commodities includes: acquiring an association matrix by using all current predetermined commodities and the plurality of first commodity sequences; setting the plurality of An initial weight sequence of the first commodity; and an iterative calculation by the correlation matrix and the initial weight sequence to determine the weight sequence.

In an exemplary embodiment of the present disclosure, the iterative calculation includes:

D _i =L·D _i-1 ;

among them,

For the initial weight sequence, D _i is the i-th iteration value, L is the correlation matrix, and n is the number of first commodities.

In an exemplary embodiment of the present disclosure, further comprising: constructing a maximum heap as the stack using a bottom-up approach using the process MAX-HEAPIFY.

In an exemplary embodiment of the present disclosure, the method further includes: updating the association matrix periodically according to the category heat, the historical sales volume, and the merchant daily time.

In an exemplary embodiment of the present disclosure, the method includes: a receiving module, configured to receive and respond to a user purchase instruction for a predetermined item, the predetermined item including a price floating item; and an information module for passing the predetermined item Correlating information extracting attribute information of the predetermined item; a premium module, configured to determine, by the stack, one of the plurality of first items as a premium item of the predetermined item according to the attribute information of the predetermined item; and placing an order And a module, configured to generate order data by using the predetermined item and the premium item.

In an exemplary embodiment of the present disclosure, the premium module includes: a determination sub-module, configured to: when the stack does not include the first commodity corresponding to the attribute information of the predetermined item, Establishing the first item corresponding to the attribute information of the predetermined item.

In an exemplary embodiment of the present disclosure, the method further includes: a stacking module, configured to establish the stack by the plurality of first commodities.

According to an aspect of the present disclosure, an electronic device is provided, the electronic device comprising: one or more processors; a storage device for storing one or more programs; and one or more programs being one or more processors Executing, such that one or more processors implement the method as described above.

According to an aspect of the present disclosure, a computer readable medium is provided having stored thereon a computer program that, when executed by a processor, implements a method as hereinbefore described.

According to the method and apparatus for data processing according to the present disclosure, it is possible to perform price adjustment and product payable amount calibration in a transaction in which a price change is frequent and a supplier real-time sales price is required to be obtained at the time of placing an order, thereby avoiding the purchase of the product. Experience issues and order contract disputes.

The above general description and the following detailed description are merely exemplary and are not intended to limit the disclosure.

DRAWINGS

The above and other objects, features, and advantages of the present invention will become more apparent from the aspects of the description.

FIG. 1 is a system block diagram of a method for data processing, according to an exemplary embodiment.

2 is a flow chart of a method for data processing, according to an exemplary embodiment.

FIG. 3 is a flow chart showing a method for data processing, according to another exemplary embodiment.

FIG. 4 is a block diagram of an apparatus for data processing, according to an exemplary embodiment.

FIG. 5 is a block diagram of an apparatus for data processing, according to an exemplary embodiment.

FIG. 6 is a block diagram of an electronic device, according to an exemplary embodiment.

FIG. 7 schematically shows a schematic diagram of a computer readable storage medium in an exemplary embodiment of the present disclosure.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in a variety of forms and should not be construed as being limited to the embodiments set forth herein. To those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and the repeated description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are set forth However, one skilled in the art will appreciate that the technical solution of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

The block diagrams shown in the figures are merely functional entities and do not necessarily have to correspond to physically separate entities. That is, these functional entities may be implemented in software, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor devices and/or microcontroller devices. entity.

The flowcharts shown in the figures are merely illustrative, and not all of the contents and operations/steps are necessarily included, and are not necessarily performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially merged, so the actual execution order may vary depending on the actual situation.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components are not limited by these terms. These terms are used to distinguish one component from another. Thus, a first component discussed below could be termed a second component without departing from the teachings of the present disclosure. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that the drawings are only a schematic diagram of the exemplary embodiments, and the modules or processes in the drawings are not necessarily required to implement the disclosure, and therefore are not intended to limit the scope of the disclosure.

As shown in FIG. 1, system architecture 100 can include

terminal devices

101, 102, 103, network 104, and server 105. The network 104 is used to provide a medium for communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various types of connections, such as wired, wireless communication links, fiber optic cables, and the like.

The user can interact with the server 105 over the network 104 using the

terminal devices

101, 102, 103 to receive or transmit messages and the like. Various communication client applications, such as a shopping application, a web browser application, a search application, an instant communication tool, a mailbox client, a social platform software, and the like, may be installed on the

terminal devices

101, 102, and 103.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop portable computers, desktop computers, and the like.

The server 105 may be a server that provides various services, such as a back-end data management server (for example only) that provides support for a shopping site browsed by the user using the

terminal devices

101, 102, 103. The background data management server may perform processing such as analyzing the received product information query request and the like, and feed back the processing result (for example, target push information and product information) to the terminal device.

It should be noted that the method for data processing provided by the embodiment of the present disclosure is generally performed by the server 105. Accordingly, the webpage generating apparatus is generally disposed in the client 101.

As shown in FIG. 2, in S202, a purchase instruction for a predetermined item including a price floating item is received and responded to by the user. The predetermined item may be, for example, a travel item such as a travel vacation, and may also provide, for example, an item that is estimated to be an offer price for online quotation, and the predetermined item may also be, for example, a price floating item, and the disclosure is not limited thereto. In the present disclosure, the starting price refers to the lowest price of a commodity over a period of time, time (such as check-in time, travel time, departure time, etc.). The floating sales price refers to the real-time sales price of the goods that can be sold under the travel time and the check-in time selected by the consumer, which is a value that varies with the date and time. In the present disclosure, the predetermined item may also be, for example, an item to be calibrated: an item having only a starting price and having a floating selling price characteristic.

In step S204, attribute information of the predetermined item is extracted by the related information of the predetermined item. As mentioned above, the floating part calibration of the order payable amount needs to find the appropriate spread supplementary goods to complete, and the difference supplementary goods must belong to the same category and the same supplier as the goods to be placed, in order to complete the order submission. The attribute information of the merchandise may include, for example, a category of the predetermined merchandise, a merchant that the predetermined merchandise is sold, a merchandise sales heat weight, and the like. In the present disclosure, the amount payable refers to the amount that the consumer needs to pay when purchasing the product.

In step S206, one of the plurality of first items is determined as a premium item of the predetermined item from the stack based on the attribute information of the predetermined item. In one embodiment of the present disclosure, the stack is established by a plurality of first commodities. For example, extracting attribute information of the predetermined item; acquiring a weight of each of the plurality of first items; determining, in the stack, the reservation according to the attribute information and the weight The first item corresponding to the item; and the first item as the premium item. The premium product can also be called a price difference product. In order to ensure that the product can be sold at the real-time sales price, an item that needs to be automatically replenished with the order can be used to supplement the difference between the starting price and the amount payable (the difference). . In the present disclosure, to avoid confusion, the premium item to be selected in the stack is defined as the first item.

In step S208, order data is generated by the predetermined item and the premium item. For example, determining the quantity of the premium item, generating premium item information; and inputting the premium item information and related information of the predetermined item into an order pipeline to generate the order data.

According to the method for data processing of the present disclosure, when the premium item is stored in a pre-established stack, when the predetermined item is placed, the corresponding premium item is called in real time according to the attribute information of the predetermined item, and the order data is generated. In the transaction where the price changes frequently, it is necessary to obtain the real-time sales price of the supplier at the time of placing the order, and to perform the product positioning of the difference price and the calibration of the amount of the goods payable, thereby avoiding the experience problem and the contract contract dispute in the purchase process of the product. And to ensure the timeliness of the transaction. Among them, the timeliness of the transaction refers to one of the necessary elements of the trading system. Generally, the e-commerce system requires smooth and no delay and stuck in the order submission process. The commonly measured performance indicators are tp90 and tp99.

It should be clearly understood that the present disclosure describes how to make and use particular examples, but the principles of the present disclosure are not limited to the details of the examples. Rather, these principles can be applied to many other embodiments based on the teachings of the disclosure.

FIG. 3 is a flow chart showing a method for data processing, according to another exemplary embodiment. FIG. 3 is an exemplary depiction of a method for data processing.

In step S302, the user selects an item to prepare to submit an order, and the user selects an item from among a plurality of predetermined items.

In step S304, the first commodity stack is traversed to determine whether there is a premium product. If there is a premium product in the stack, the process proceeds to step S306, otherwise, the process proceeds to step S308.

In step S306, a premium item is determined.

In step S308, a new item is created.

In step S310, a new premium item is inserted into the stack.

In step S312, the premium item and the predetermined item are subjected to a price floating calibration operation.

In step S314, the price update is completed according to the amount due and the order is placed.

In an exemplary embodiment of the present disclosure, the method further includes: establishing the stack by the plurality of first commodities. In the present disclosure, the following settings are made:

Submit the order item collection as S={SKU ₁ , SKU ₂ ,...SKU _n },

The scheduled item is K={SKU _x , SKU _y ,...SKU _m },

The amount payable is

Commodity starting price

The first item index stack H.

When H==Null, the first commodity index stack H is initialized according to the following steps.

Step 1: Set the first product weight sequence to D=[SKU ₁ , SKU ₂ ,...,SKU _m ] ^T .

Step 2: According to the products currently published by the merchant, traverse the products currently spliced using the SKU in the first product weight sequence D, and obtain the SKU association matrix:

Where l _ab represents the number of items spliced between SKU _a and SKU _b .

The third step: initial setting the value in the first product weight sequence D

The fourth step: At this point, the iterative formula can be derived as D _i =L·D _i-1 , which can prove that D will eventually converge after multiple iterations, ie D _i ≈D _i-1 , and finally get the first commodity weight. The sequence is D.

Step 5: In the above step 4, the optimization process is to first average the values in the SKU correlation matrix L, if the mean value

Where ∝ is a smaller constant. At this point, the SKU correlation matrix is considered sparse, and the iterative formula is smoothed to optimize:

In the formula, I is a unit matrix.

Step 6: Based on the obtained first product weight sequence D, a bottom-up method is used to construct a maximum heap using the process MAX-HEAPIFY.

Set: The scheduled item is SKU _α (α≥x, α≤m), the amount payable

Commodity starting price

Obtaining its category cid and its supplier venderid, entering the first commodity stack H traversal, obtaining the first commodity SKU _{β of} SKU _α ;

When SKU _β == Null, that is, SKU _{β is} not in the first commodity index stack H, a new first commodity SKU _β needs to be created and added to the first commodity index stack H;

Set SKU _β unit price

The category cid and the supplier of the venderid are the same as the SKU _α ;

Set the comprehensive weight of the first commodity SKU _β , and complete the first commodity insertion stack H according to the weight formula;

First, add SKU _β to the first product weight sequence D, identify SKU _β and other SKU tying, re-establish the first step to the fifth step in the stack index, and then use the MAX-HEAP-INSERT process to maintain the A commodity index stack H.

Dynamically update the SKU association matrix L:

Regularly based on the category heat stack weight q, historical sales stack weight t, merchant heat weight f, dynamically updated SKU association matrix

The update algorithm can be adjusted according to the real-time situation of the market.

Floating sales price calibration value γ

Complete the combination of the SKU _alpha calibrator:

When α<m, then only the initial step is required to complete the calibration under all K={SKU _x , SKU _y ,...SKU _m };

According to the method for data processing of the present disclosure, in a transaction in which the price changes frequently and needs to obtain the real-time sales price of the supplier at the time of placing the order, the calibrator accurately places an order according to the real-time sales price provided by the supplier interface, and comprehensively weights The stacking and stack traversal mechanism completes the difference price replenishment product positioning, the commodity payable amount calibration, enhances the user experience, reduces the customer service pressure, and completes the transaction quickly and smoothly. The comprehensive weight refers to the average weight value of the aggregate category weight, the merchant weight and the sales weight.

In the present disclosure, the creation of the first item may also be performed, for example, in a first-in, first-out, first-in, first-out manner, and may be indexed in chronological order, and traversed after completion of the stacking.

In the present disclosure, predetermined items can be, for example, subjected to parallel calibration without the need for loop traversal, which can improve higher efficiency.

Those skilled in the art will appreciate that all or a portion of the steps to implement the above-described embodiments are implemented as a computer program executed by a CPU. When the computer program is executed by the CPU, the above-described functions defined by the above-described methods provided by the present disclosure are performed. The program may be stored in a computer readable storage medium, which may be a read only memory, a magnetic disk or an optical disk, or the like.

Further, it should be noted that the above-described drawings are merely illustrative of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processing shown in the above figures does not indicate or limit the chronological order of these processes. In addition, it is also easy to understand that these processes may be performed synchronously or asynchronously, for example, in a plurality of modules.

The following is an apparatus embodiment of the present disclosure, which may be used to implement the method embodiments of the present disclosure. For details not disclosed in the disclosed device embodiments, please refer to the method embodiments of the present disclosure.

FIG. 4 is a block diagram of an apparatus for data processing, according to an exemplary embodiment. The device 40 for data processing comprises: a receiving module 402, an information module 404, a premium module 406, and an ordering module 408.

The receiving module 402 is configured to receive and respond to a user purchase instruction for a predetermined item, the price item comprising a price floating item. The predetermined item includes a price floating item. The predetermined item may be, for example, a travel item such as a travel vacation, and may also provide, for example, an item that is estimated to be an offer price for online quotation, and the predetermined item may also be, for example, a price floating item, and the disclosure is not limited thereto. In the present disclosure, the starting price refers to the lowest price of a commodity over a period of time, time (such as check-in time, travel time, departure time, etc.). The floating sales price refers to the real-time sales price of the goods that can be sold under the travel time and the check-in time selected by the consumer, which is a value that varies with the date and time. In the present disclosure, the predetermined item may also be, for example, an item to be calibrated: an item having only a starting price and having a floating selling price characteristic.

The information module 404 is configured to extract attribute information of the predetermined item by using related information of the predetermined item. The floating part calibration of the amount payable for the order needs to find the appropriate price difference to complete the goods, and the difference supplementary goods must belong to the same category and the same supplier as the goods to be placed, in order to complete the order submission. The attribute information of the merchandise may include, for example, a category of the predetermined merchandise, a merchant that the predetermined merchandise is sold, a merchandise sales heat weight, and the like. In the present disclosure, the amount payable refers to the amount that the consumer needs to pay when purchasing the product.

The premium module 406 is configured to determine one of the plurality of first commodities from the stack as the premium commodity of the predetermined commodity according to the attribute information of the predetermined commodity. The method further includes: a determining submodule, configured to establish, when the stack does not include the first item corresponding to the attribute information of the predetermined item, the first item corresponding to the attribute information of the predetermined item.

The order module 408 is configured to generate order data by using the predetermined item and the premium item. Determining the quantity of the premium product, generating premium product information; inputting the premium product information and related information of the predetermined product into an order pipeline to generate the order data.

Also included is a stack module (not shown) for establishing the stack by a plurality of first items.

According to the apparatus for data processing of the present disclosure, when the premium item is stored in a pre-established stack, when the predetermined item is placed, the corresponding premium item is called in real time according to the attribute information of the predetermined item to generate the order data. In the transaction where the price changes frequently, it is necessary to obtain the real-time sales price of the supplier at the time of placing the order, and to perform the product positioning of the difference price and the calibration of the amount of the goods payable, thereby avoiding the experience problem and the contract contract dispute in the purchase process of the product. And to ensure the timeliness of the transaction. Among them, the timeliness of the transaction refers to one of the necessary elements of the trading system. Generally, the e-commerce system requires smooth and no delay and stuck in the order submission process. The commonly measured performance indicators are tp90 and tp99.

FIG. 5 is a block diagram of an apparatus for data processing, according to an exemplary embodiment. The apparatus 50 for data processing includes a receiving module 502, a calibrator module 504, a difference replenishment commodity stack module 506, and an ordering module 508.

The receiving module 502 is configured to receive and respond to a user's purchase instruction for a predetermined item. Only when the goods are placed at the starting price, the floating sales price calibrator parameters (including the SKU to be calibrated and the amount payable for the goods at the current selected travel time) are provided;

The calibrator module 504, in the stack with the comprehensive weight stacking, replenishes the commodity stack, and searches for the legal spread supplement product according to the category, merchant, and sales heat weight of the commodity to be calibrated.

The stack module 506 is configured to newly create a difference-price supplementary product, and the newly-built product needs to be stacked and applied according to the above-mentioned category, merchant, and sales heat weight, and the price difference supplementary commodity stack with the comprehensive weight stacking is updated.

The order module 508 calculates the supplementary quantity of the difference replenishment commodity, and completes the calculation of the floating sales price calibrator. The goods to be calibrated SKU and the difference replenishment commodity 5 enter the order pipeline at the same time, and the exact amount of the payable amount is completed.

According to the apparatus for data processing of the present disclosure, the floating sales price calibrator can quickly traverse and locate in the stack according to the comprehensive weight of the item heat weight, the merchant heat, and the sales volume of the goods to be calibrated according to the characteristics of the goods to be calibrated. The newly established difference-price supplementary goods are also stacked according to the comprehensive weights, which facilitates subsequent quick retrieval. And complete the floating sales price calibration of the goods to be calibrated and the price difference supplementary goods, and realize the joint order.

An electronic device 200 according to such an embodiment of the present disclosure is described below with reference to FIG. The electronic device 200 shown in FIG. 6 is merely an example and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.

As shown in Figure 6, electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to, at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including the storage unit 220 and the processing unit 210), a display unit 240, and the like.

Wherein, the storage unit stores a program code, and the program code may be executed by the processing unit 210, such that the processing unit 210 performs various exemplary embodiments according to the present disclosure described in the electronic recipe flow processing method section of the present specification described above. The steps of the embodiment. For example, the processing unit 210 can perform the steps as shown in FIG. 2, FIG.

The storage unit 220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 2201 and/or a cache storage unit 2202, and may further include a read only storage unit (ROM) 2203.

The storage unit 220 may also include a program/utility 2204 having a set (at least one) of the program modules 2205, including but not limited to: an operating system, one or more applications, other program modules, and programs. Data, each of these examples or some combination may include an implementation of a network environment.

Bus 230 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures. bus.

The electronic device 200 can also communicate with one or more external devices 300 (eg, a keyboard, pointing device, Bluetooth device, etc.), and can also communicate with one or more devices that enable the user to interact with the electronic device 200, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. This communication can take place via an input/output (I/O) interface 250. Moreover, electronic device 200 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of electronic device 200 via bus 230. It should be understood that although not shown in the figures, other hardware and/or software modules may be utilized in conjunction with electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives. And data backup storage systems, etc.

Through the description of the above embodiments, those skilled in the art will readily understand that the example embodiments described herein may be implemented by software or by software in combination with necessary hardware. Therefore, the technical solution according to an embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network. The instructions include a number of instructions to cause a computing device (which may be a personal computer, server, or network device, etc.) to perform the electronic recipe flow processing method described above in accordance with an embodiment of the present disclosure.

Referring to FIG. 7, a program product 400 for implementing the above method, which may employ a portable compact disk read only memory (CD-ROM) and includes program code, and may be at a terminal device, according to an embodiment of the present disclosure, is described. For example running on a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

The program product can employ any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive lists) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium can include a data signal that is propagated in a baseband or as part of a carrier, in which readable program code is carried. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable storage medium can also be any readable medium other than a readable storage medium that can transmit, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code embodied on a readable storage medium may be transmitted by any suitable medium, including but not limited to wireless, wireline, optical cable, RF, etc., or any suitable combination of the foregoing.

Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language, such as Java, C++, etc., including conventional procedural Programming language—such as the "C" language or a similar programming language. The program code can execute entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the remote computing device on the user computing device, or entirely on the remote computing device or server. Execute on. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any kind of network, including a local area network (LAN) or wide area network (WAN), or can be connected to an external computing device (eg, provided using an Internet service) Businesses are connected via the Internet).

The computer readable medium carries one or more programs that, when executed by one of the devices, cause the computer readable medium to perform the function of receiving and responding to a user's purchase instruction for a predetermined item, The predetermined item includes a price floating item; the attribute information of the predetermined item is extracted by the related information of the predetermined item; and one of the plurality of first items is determined from the stack as the predetermined item according to the attribute information of the predetermined item An premium product; and generating order data by the predetermined product and the premium product.

It will be understood by those skilled in the art that the above various modules may be distributed in the device according to the description of the embodiments, or may be correspondingly changed in one or more devices different from the embodiment. The modules of the above embodiments may be combined into one module, or may be further split into multiple sub-modules.

Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein may be implemented by software, or may be implemented by software in combination with necessary hardware. Therefore, the technical solution according to an embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network. A number of instructions are included to cause a computing device (which may be a personal computer, server, mobile terminal, or network device, etc.) to perform a method in accordance with an embodiment of the present disclosure.

The exemplary embodiments of the present disclosure have been particularly shown and described above. It is to be understood that the invention is not limited to the details of the details of

In addition, the structures, the proportions, the sizes, and the like shown in the drawings of the present disclosure are only for the purpose of the disclosure of the specification, and are intended to be understood by those skilled in the art. Therefore, it is not technically meaningful. Any modification of the structure, change of the proportional relationship, or adjustment of the size should be disclosed in the present disclosure without affecting the technical effects and the achievable objectives of the present disclosure. The technical content can be covered within the scope. In the meantime, the terms "upper", "first", "second", and "the" are used in the description, and are not intended to limit the scope of the disclosure. Changes or adjustments in the relative relationship are considered to be within the scope of the present disclosure without departing from the scope of the invention.

Claims

A method for data processing, comprising:

Receiving and responding to a user purchase instruction for a predetermined item, the predetermined item comprising a price floating item;

Extracting attribute information of the predetermined item by related information of the predetermined item;

Determining, by the attribute information of the predetermined item, one of the plurality of first items as a premium item of the predetermined item from the stack;

The order data is generated by the predetermined item and the premium item.
The method of claim 1 further comprising:

When the first item corresponding to the attribute information of the predetermined item is not included in the stack, the first item corresponding to the attribute information of the predetermined item is established.
The method of claim 1 further comprising:

The stack is established by a plurality of first items.
The method of claim 1, wherein the determining, by the stack based on the attribute information of the predetermined item, one of the plurality of first items as the premium item of the predetermined item, comprises:

Extracting attribute information of the predetermined item;

Obtaining a weight of each of the plurality of first commodities;

Determining, in the stack, the first item corresponding to the predetermined item based on the attribute information and the weight; and

The first commodity is used as the premium commodity.
The method of claim 1, wherein the generating the order data by the predetermined item and the premium item comprises:

Determining the quantity of the premium items to generate premium product information;

And inputting the premium commodity information and the related information of the predetermined commodity into an order pipeline to generate the order data.
The method according to claim 2, wherein when said first item corresponding to attribute information of said predetermined item is not included in said stack, said attribute information corresponding to said predetermined item is created The first commodity includes:

Establishing a first commodity of a predetermined unit price, the first commodity having the same attribute information as the predetermined commodity;

Inserting the newly created first item into the stack;

The index of the stack is maintained by the MAX-HEAP-INSERT procedure.
The method of claim 3 wherein said establishing said stack by said plurality of first products comprises:

Acquiring an association matrix by all current predetermined items and the plurality of first item sequences;

Setting an initial weighting sequence of the plurality of first commodities;

Through the correlation matrix and the initial weight sequence, the weight sequence is determined through iterative calculation.
The method of claim 7 wherein said iterative calculation comprises:

D i =L·D i-1

among them,
For the initial weight sequence, D i is the i-th iteration value, L is the correlation matrix, and n is the number of first commodities.
The method of claim 7 further comprising:

A bottom-up approach is used to build a maximum heap using the process MAX-HEAPIFY as the stack.
The method of claim 7 further comprising:

According to the category heat, historical sales, business heat, regularly update the correlation matrix.
An apparatus for data processing, comprising:

a receiving module, configured to receive and respond to a user purchase instruction for a predetermined item, the predetermined item comprising a price floating item;

An information module, configured to extract attribute information of the predetermined item by using related information of the predetermined item;

a premium module, configured to determine, by the stack, one of the plurality of first commodities as a premium commodity of the predetermined commodity according to attribute information of the predetermined commodity;

And an order module, configured to generate order data by using the predetermined item and the premium item.
The apparatus of claim 11, wherein the premium module comprises:

a judging submodule configured to: when the first item corresponding to the attribute information of the predetermined item is not included in the stack, establish the first item corresponding to the attribute information of the predetermined item.
The device of claim 11 further comprising:

a stack module for establishing the stack by a plurality of first commodities.
An electronic device, comprising:

One or more processors;

a storage device for storing one or more programs;

The one or more programs are executed by the one or more processors such that the one or more processors implement the method of any of claims 1-10.
A computer readable medium having stored thereon a computer program, wherein the program is executed by a processor to implement the method of any of claims 1-10.