CN110097386B - Method and device for data processing - Google Patents

Method and device for data processing Download PDF

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Publication number
CN110097386B
CN110097386B CN201810092410.1A CN201810092410A CN110097386B CN 110097386 B CN110097386 B CN 110097386B CN 201810092410 A CN201810092410 A CN 201810092410A CN 110097386 B CN110097386 B CN 110097386B
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commodity
stack
commodities
predetermined
boot
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CN110097386A (en
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赵苗苗
于盛昌
李闯
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Priority to PCT/CN2019/073684 priority patent/WO2019149189A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders

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  • General Business, Economics & Management (AREA)
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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application discloses a method and a device for data processing. The method relates to the field of computer information processing, and comprises the following steps: receiving and responding to a purchase instruction of a user on a predetermined commodity, wherein the predetermined commodity comprises a price floating commodity; extracting attribute information of the predetermined commodity through the related information of the predetermined commodity; determining one of a plurality of first commodities from a stack as boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity; and generating order data through the predetermined commodity and the boot commodity. The method and the device for data processing disclosed by the application can be used for carrying out the positioning of the price-forming supplementary commodity and the calibration of the commodity payable amount in the transaction of obtaining the real-time selling price of the supplier at the time of ordering for frequent price change, so that the experience problem and the contract dispute of the order in the commodity purchasing process are avoided.

Description

Method and device for data processing
Technical Field
The present invention relates to the field of computer information processing, and in particular, to a method and apparatus for data processing.
Background
On-line selling of travel commodities such as vacation is popular, but on-line quotation can only provide estimated price, commodity price displayed in the foreground is not necessarily commodity payable amount sold when a user's bill is not required to be lifted due to frequent commodity price fluctuation, a user often forms a temporary order in the purchasing process, and after off-line customer service is required to communicate with the user, whether the order can be paid after the order is changed according to the latest payable amount can be confirmed. And requires the user (or customer service instead) to make a second order to generate an order to be paid with accurate payable amount.
In the prior art, the method for solving the problems mainly comprises the following steps: text relevance, eigenvalue localization, etc. In the prior art, the above problem can be solved, for example, by text correlation, that is, a batch of features representing the commodity is extracted from the commodity name or description, and whether the target traversed by using the keyword search algorithm is related to the source text or not includes two positioning modes of fuzzy correlation and precise correlation. In the prior art, the above-mentioned problems can also be solved, for example, by feature value localization methods, typically by identifying the consistency of the characteristics of the image, for example whether it is the same or similar picture as the retrieved picture.
In the prior art, the floating part calibration of the amount due to the order can be completed only by finding out a proper spread supplementary commodity, and the spread supplementary commodity must belong to the same class and the same supplier as the commodity to be placed, so that the order can be submitted. First, the text correlation algorithm is more suitable for users to search commodities, but is not suitable for finding the differential price supplementary commodity of floating selling price for only the commodity with price, and the differential price supplementary commodity is not related through text correlation, but is positioned through the category of the commodity and the vendor of the commodity. And secondly, the characteristic value positioning algorithm is more suitable for searching pictures with source data, and positioning of categories and suppliers cannot be performed. Finally, the searching and ordering of the spread supplementary commodity is not perceived by the purchasing user, tp99 and above are needed for calculation timeliness, and the timeliness of the transaction cannot be achieved by the two methods in the prior art.
Thus, there is a need for a new method and apparatus for data processing.
The above information disclosed in the background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present invention provides a method and apparatus for data processing, which can perform positioning of a differential price supplementary commodity and calibration of commodity payable amount in a transaction of obtaining a real-time selling price of a supplier at a time of ordering for frequent price change, so as to avoid experience problems and contract disputes of orders in the commodity purchasing process.
Other features and advantages of the invention will be apparent from the following detailed description, or may be learned by the practice of the invention.
According to an aspect of the present invention, there is provided a method for data processing, the method comprising: receiving and responding to a purchase instruction of a user on a predetermined commodity, wherein the predetermined commodity comprises a price floating commodity; extracting attribute information of the predetermined commodity through the related information of the predetermined commodity; determining one of a plurality of first commodities from a stack as boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity; and generating order data through the predetermined commodity and the boot commodity.
In an exemplary embodiment of the present disclosure, further comprising: when the stack does not contain the first commodity corresponding to the attribute information of the preset commodity, the first commodity corresponding to the attribute information of the preset commodity is established.
In an exemplary embodiment of the present disclosure, further comprising: the stack is established by a plurality of first commodities.
In an exemplary embodiment of the present disclosure, the determining, from a stack, one of a plurality of first commodities as the boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity includes: extracting attribute information of the predetermined commodity; acquiring the weight of each first commodity in the plurality of first commodities; determining the first commodity corresponding to the preset commodity in the stack according to the attribute information and the weight; and using the first commodity as the boot commodity.
In an exemplary embodiment of the disclosure, the generating the order data by the predetermined commodity and the boot commodity includes: determining the number of boot commodities to generate boot commodity information; inputting the boot commodity information and the related information of the preset commodity into an order placing pipeline to generate the order placing data.
In an exemplary embodiment of the present disclosure, when the stack does not include the first commodity corresponding to the attribute information of the predetermined commodity, establishing the first commodity corresponding to the attribute information of the predetermined commodity includes: establishing a first commodity with preset unit price, wherein the attribute information of the first commodity is the same as that of the preset commodity; inserting the newly created first commodity into the stack; and maintaining an index of the stack through a MAX-HEAP-INSERT process.
In an exemplary embodiment of the present disclosure, the building the stack by a plurality of first commodities includes: acquiring an incidence matrix through all the preset commodities and the first commodity sequences; setting initial weight sequences of the first commodities; and determining a weight sequence through iterative calculation by the association matrix and the initial weight sequence.
In one exemplary embodiment of the present disclosure, the iterative computation includes:
Di＝L·Di-1；
Wherein, 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: a maximum stack is built as the stack using the process MAX-HEAPIFY in a bottom-up approach.
In an exemplary embodiment of the present disclosure, further comprising: and updating the incidence matrix at regular time according to the class heat, the historical sales and the merchant date.
In one exemplary embodiment of the present disclosure, there is provided: the receiving module is used for receiving and responding to a purchase instruction of a user on a preset commodity, wherein the preset commodity comprises a price floating commodity; the information module is used for extracting attribute information of the preset commodity through the related information of the preset commodity; boot module, configured to determine one of the plurality of first commodities from the stack as a boot commodity of the predetermined commodity according to attribute information of the predetermined commodity; and the order placing module is used for generating order placing data through the preset commodity and the boot commodity.
In an exemplary embodiment of the present disclosure, the boot module includes: and the judging sub-module is used for establishing the first commodity corresponding to the attribute information of the preset commodity when the stack does not contain the first commodity corresponding to the attribute information of the preset commodity.
In an exemplary embodiment of the present disclosure, further comprising: and the stack module is used for building the stack through a plurality of first commodities.
According to an aspect of the present invention, there is provided an electronic device including: one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the methods as described above.
According to an aspect of the invention, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, implements a method as described above.
According to the method and the device for data processing, in the transaction that the price is frequently changed and the real-time selling price of the supplier is required to be obtained at the time of ordering, the positioning of the price-forming supplementary commodity and the calibration of the commodity payable amount can be carried out, so that experience problems and order contract disputes in the commodity purchasing process are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the present invention and other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a system block diagram illustrating a method for data processing according to an exemplary embodiment.
FIG. 2 is a flowchart illustrating a method for data processing according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating a method for data processing according to another exemplary embodiment.
Fig. 4 is a block diagram illustrating an apparatus for data processing according to an example embodiment.
Fig. 5 is a block diagram illustrating an apparatus for data processing according to an example embodiment.
Fig. 6 is a block diagram of an electronic device, according to an example embodiment.
Fig. 7 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive 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 provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any one of the associated listed items and all combinations of one or more.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments and that the modules or flows in the drawings are not necessarily required to practice the invention and therefore should not be taken to limit the scope of the invention.
FIG. 1 is a system block diagram illustrating a method for data processing according to an exemplary embodiment.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background data management server (by way of example only) providing support for shopping-type websites browsed by users using the terminal devices 101, 102, 103. The background data management server can analyze and other data such as the received product information inquiry request and feed back processing results (such as target push information and product information) to the terminal equipment.
It should be noted that, the method for data processing provided by the embodiment of the present application is generally performed by the server 105, and accordingly, the web page generating device is generally disposed in the client 101.
FIG. 2 is a flowchart illustrating a method for data processing according to an exemplary embodiment.
As shown in fig. 2, in S202, a purchase instruction of a predetermined commodity including a price floating commodity is received and responded to by a user. The predetermined commodity can be, for example, travel commodity such as travel vacation, etc., can also be, for example, commodity that online quotation can only provide estimated price, and the predetermined commodity can also be, for example, price floating commodity, the application is not limited thereto. In the present application, the price is the lowest price of a commodity in a period of time and date (such as check-in time, travel time, take-off time, etc.). Floating selling price refers to: the real-time selling price of the commodity which can be made up under the trip and check-in time selected by the consumer is a value which floats along with the change of the date and time. In the present application, the predetermined commodity may also be, for example, a commodity to be calibrated: only the commodity is charged, and the commodity has floating selling price characteristics.
In S204, attribute information of the predetermined commodity is extracted by the related information of the predetermined commodity. As described above, the floating portion calibration of the amount due for the order may be accomplished by finding the appropriate spread supplement commodity, which must be of the same category and vendor as the commodity to be placed in order to complete the order. The attribute information of the commodity may include, for example, a class of the predetermined commodity, a merchant of the predetermined commodity sales, a commodity sales heat weight, and the like. In the present application, the payable amount means: when the consumer purchases the merchandise, the amount paid is then required.
In S206, one of the plurality of first commodities is determined from the stack as boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity. In one embodiment of the application, the stack is established by a plurality of first merchandise. Attribute information of the predetermined commodity may be extracted, for example; acquiring the weight of each first commodity in the plurality of first commodities; determining the first commodity corresponding to the preset commodity in the stack according to the attribute information and the weight; and using the first commodity as the boot commodity. Boot the commodity, which can also be called as a spread supplementary commodity, is required to be automatically supplemented with one commodity of an order in order to ensure that the commodity can be sold according to real-time selling price, and the difference (spread) between the price and the payable amount can be supplemented completely through the commodity. In the present application, to avoid confusion, boot items to be selected in the stack are defined as first items.
In S208, order data is generated by the predetermined commodity and the boot commodity. The number of boot items may be determined, for example, to generate boot item information; inputting the boot commodity information and the related information of the preset commodity into an order placing pipeline to generate the order placing data.
According to the method for data processing, through storing boot commodities in the pre-established stack, when the preset commodities are placed, the corresponding boot commodities are called in real time according to the attribute information of the preset commodities so as to generate the placing data, so that the price change is frequent, the commodity positioning and commodity payable amount calibration of the gap-making supplementary commodity can be carried out in the transaction of acquiring the real-time selling price of the supplier at the placing moment, and the experience problem and order contract dispute in the commodity purchasing process are avoided. And to ensure timeliness of transactions. The transaction timeliness refers to one of essential elements of a transaction system, and generally requires that an e-commerce system is smooth and has no delay and no blocking in the process of submitting an order, and performance indexes which are generally measured are tp90 and tp99.
It should be clearly understood that the present invention describes how to make and use specific examples, but the principles of the present invention are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
In an exemplary embodiment of the present disclosure, further comprising: when the stack does not contain the first commodity corresponding to the attribute information of the preset commodity, the first commodity corresponding to the attribute information of the preset commodity is established.
Fig. 3 is a flow chart illustrating a method for data processing according to another exemplary embodiment. Fig. 3 is an exemplary depiction of a method for data processing.
In S302, the user selects a commodity to prepare for submitting an order, and the user selects a commodity from among a plurality of predetermined commodities.
In S304, the first commodity stack is traversed, whether boot commodity exists or not is determined, when boot commodity exists in the stack, step S306 is entered, otherwise step S308 is entered.
In S306, boot items are determined.
In S308, a first commodity is newly created.
In S310, a new boot commodity is inserted into the stack.
In S312, the boot commodity performs a price float calibration operation with the predetermined commodity.
In S314, the price update is completed and ordered in accordance with the amount due.
In an exemplary embodiment of the present disclosure, further comprising: the stack is established by a plurality of first commodities. In the present application, the following settings are made:
the submitted order item set is s= { SKU 1,SKU2,...SKUn },
The predetermined commodity is k= { SKU x,SKUy,...SKUm },
The payable amount is
Commodity price-taking
The first merchandise index stack H.
When h= Null, the first commodity index stack H is initialized according to the following steps.
The first step: the first commodity weight sequence is set to d= [ SKU 1,SKU2,...,SKUm]T.
And a second step of: according to commodities issued by merchants at present, traversing and searching commodities sold by using the SKU in the first commodity weight sequence D at present to obtain a SKU association matrix:
Where l ab denotes the number of items offered for sale between SKUs a,SKUb.
And a third step of: initializing and setting the value in the first commodity weight sequence D as
Fourth step: at this time, the iteration formula is deduced to be D i＝L·Di-1, and it can be proved that after multiple iterations, D will finally converge, that is, D i≈Di-1, and finally the first commodity weight sequence is obtained.
Fifth step: the optimization in the step 4 can be performed by firstly averaging the values in the SKU correlation matrix L, if the values are the averageWhere ≡is a small constant. At this time, the SKU incidence matrix is considered to be sparse, and the iterative formula is optimized to be smooth:
Wherein I is an identity matrix.
Sixth step: based on the obtained first commodity weight sequence D, a maximum heap is constructed by using a process MAX-HEAPIFY in a bottom-up method.
Setting: the predetermined commodity is SKU α (alpha is more than or equal to x and alpha is less than or equal to m), and the payable amountCommodity price/>Obtaining the product types cid and the suppliers venderid, entering a first commodity stack H for traversing, and obtaining a first commodity SKU β of the SKU α;
When SKU β = Null, i.e., SKU β is not in the first product index stack H, a new first product SKU β is created and added to the first product index stack H;
setting the unit price of SKU β The meta, class cid, vendor venderid are all the same as SKU α;
Setting the comprehensive weight of the first commodity SKU β, and completing the first commodity insertion stack H according to a weight formula;
First, SKU β is added to the first commodity weight sequence D, SKU β is identified for sale with other SKUs, the first through fifth steps in building the stack index are repeated, and then the first commodity index stack H is maintained using the MAX-HEAP-INSERT procedure.
Dynamically updating the SKU association matrix L:
Periodically dynamically updating the SKU association matrix according to the category heat stack weight q, the history sales stack weight t and the merchant heat weight f The update algorithm can be adjusted according to the real-time situation of the market.
Floating selling price calibration value gamma
Combining is completed for the SKU α calibrator:
When alpha is smaller than m, only an initial step is needed to be carried out, and the calibration under all K= { SKU x,SKUy,...SKUm } is completed;
According to the method for data processing, in the transaction that the price is frequently changed and the real-time selling price of the supplier is required to be obtained at the time of ordering, the real-time selling price provided by the supplier interface is accurately ordered through the calibrator, the positioning of the spread supplementary commodity and the calibration of the commodity amount due are completed through the stacking and stacking traversing mechanism of comprehensive weight, the user experience is improved, the customer service pressure is reduced, and the transaction is rapidly and smoothly completed. The comprehensive weight is an average weight value integrating the aggregate class weight, the merchant weight and the sales weight.
In the present application, the creation of the first commodity may also be performed by stacking according to a first-in first-out or a last-in first-out method, and may be indexed according to a time sequence, and the stacking is completed for traversing.
In the application, the predetermined commodity can be calibrated in parallel without cyclic traversal, and the higher efficiency can be improved.
Those skilled in the art will appreciate that all or part of the steps implementing the above described embodiments are implemented as a computer program executed by a CPU. When executed by a CPU, performs the functions defined by the above-described method provided by the present invention. 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, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following are examples of the apparatus of the present invention that may be used to perform the method embodiments of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.
Fig. 4 is a block diagram illustrating an apparatus for data processing according to an example embodiment. The means 40 for data processing comprises: the receiving module 402, the information module 404, boot module 406, the ordering module 408.
The receiving module 402 is configured to receive and respond to a purchase instruction from a user for a predetermined commodity, the predetermined commodity including a floating price commodity. The predetermined commodity includes a price float commodity. The predetermined commodity can be, for example, travel commodity such as travel vacation, etc., can also be, for example, commodity that online quotation can only provide estimated price, and the predetermined commodity can also be, for example, price floating commodity, the application is not limited thereto. In the present application, the price is the lowest price of a commodity in a period of time and date (such as check-in time, travel time, take-off time, etc.). Floating selling price refers to: the real-time selling price of the commodity which can be made up under the trip and check-in time selected by the consumer is a value which floats along with the change of the date and time. In the present application, the predetermined commodity may also be, for example, a commodity to be calibrated: only the commodity is charged, and the commodity has floating selling price characteristics.
The information module 404 is configured to extract attribute information of the predetermined commodity according to the related information of the predetermined commodity. The floating part calibration of the amount due for the order can be completed only by finding out proper gap supplementary commodities, and the gap supplementary commodities must belong to the same class and the same supplier as the commodity to be placed in order to complete the order submission. The attribute information of the commodity may include, for example, a class of the predetermined commodity, a merchant of the predetermined commodity sales, a commodity sales heat weight, and the like. In the present application, the payable amount means: when the consumer purchases the merchandise, the amount paid is then required.
Boot module 406 is configured to determine, from the stack, one of the plurality of first commodities as a boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity. Further comprises: and the judging sub-module is used for establishing the first commodity corresponding to the attribute information of the preset commodity when the stack does not contain the first commodity corresponding to the attribute information of the preset commodity.
The order module 408 is configured to generate order data from the predetermined commodity and the boot commodity. Determining the number of boot commodities to generate boot commodity information; inputting the boot commodity information and the related information of the preset commodity into an order placing pipeline to generate the order placing data.
Further comprises: a stack module (not shown) is used to build the stack from the first plurality of items.
According to the data processing device, by storing boot commodities in the pre-established stack and calling the corresponding boot commodities in real time according to the attribute information of the preset commodities when the preset commodities are placed, and then generating the placing data, the invention can be used for carrying out the positioning of the price-forming supplementary commodities and the commodity payable amount calibration in the transaction of obtaining the real-time selling price of the suppliers at the placing moment when the price is changed frequently, and avoiding the experience problems and order contract disputes in the commodity purchasing process. And to ensure timeliness of transactions. The transaction timeliness refers to one of essential elements of a transaction system, and generally requires that an e-commerce system is smooth and has no delay and no blocking in the process of submitting an order, and performance indexes which are generally measured are tp90 and tp99.
Fig. 5 is a block diagram illustrating an apparatus for data processing according to an example embodiment. The apparatus 50 for data processing comprises: a receiving module 502, a calibrator module 504, a spread supplement commodity stack module 506, and an ordering module 508.
The receiving module 502 is configured to receive and respond to a purchase instruction of a predetermined commodity from a user. Providing floating sales price calibrator parameters (including the commodity SKU to be calibrated and the commodity payable amount under the currently selected time) only when the price-up commodity is ordered;
The calibrator module 504 searches for legitimate spread replenishment product based on the product type, merchant, sales heat weight of the product to be calibrated in the spread replenishment product stack with integrated weight stack.
And the stack module 506 is used for newly creating a gap supplementary commodity, and the newly created commodity needs to be piled and arranged according to the class, the merchant and the sales heat weight, and the gap supplementary commodity stack with the comprehensive weight piled and arranged is updated.
And the ordering module 508 calculates the replenishment quantity of the gap replenishment commodity, completes the calculation of the floating selling price calibrator, and enables the commodity SKU to be calibrated and the gap replenishment commodity ⑤ to enter an ordering pipeline at the same time, thereby completing the accurate payable amount ordering.
According to the device for data processing, the floating sales price calibrator can rapidly traverse and position the floating sales price calibrator according to the characteristics of the commodity to be calibrated and the comprehensive weight of the commodity heat weight, the merchant heat and the sales volume of the commodity to be calibrated in a stack. The newly established difference supplements commodities, and is piled and arranged according to the comprehensive weight, so that the subsequent quick retrieval is facilitated. And the floating selling price calibration of the commodity to be calibrated and the spread supplement commodity is completed, so that the joint ordering is realized.
Fig. 6 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 200 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 200 shown in fig. 6 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in fig. 6, the electronic device 200 is 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 memory unit 220, a bus 230 connecting the different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 210 such that the processing unit 210 performs the steps according to various exemplary embodiments of the present invention described in the electronic prescription stream processing method section above in this specification. For example, the processing unit 210 may perform the steps as shown in fig. 2, 3.
The memory unit 220 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 2201 and/or cache memory 2202, and may further include Read Only Memory (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 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.
Bus 230 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 200, and/or any device (e.g., router, modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 250. Also, the electronic device 200 may 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, through a network adapter 260. Network adapter 260 may communicate with other modules of electronic device 200 via bus 230. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 200, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the electronic prescription flow processing method according to the embodiments of the present disclosure.
Fig. 7 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the present disclosure.
Referring to fig. 7, a program product 400 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, 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), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs, which when executed by one of the devices, cause the computer-readable medium to perform the functions of: receiving and responding to a purchase instruction of a user on a predetermined commodity, wherein the predetermined commodity comprises a price floating commodity; extracting attribute information of the predetermined commodity through the related information of the predetermined commodity; determining one of a plurality of first commodities from a stack as boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity; and generating order data through the predetermined commodity and the boot commodity.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The exemplary embodiments of the present invention have been particularly shown and described above. It is to be understood that this invention is not limited to the precise arrangements, instrumentalities and instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
In addition, the structures, proportions, sizes, etc. shown in the drawings in the specification are used for the understanding and reading of the disclosure, and are not intended to limit the applicable limitations of the disclosure, so that any structural modification, change in proportion, or adjustment of size is not technically significant, and yet falls within the scope of the disclosure without affecting the technical effects and the objects that can be achieved by the disclosure. Also, the terms "upper", "first", "second", and "a" and the like recited in the present specification are also for descriptive purposes only and are not intended to limit the scope of the disclosure in which the invention may be practiced, but rather the relative relationship of the terms may be altered or modified without materially altering the technical context to the extent that the invention may be practiced.

Claims (6)

1. A method for data processing, comprising:
Receiving and responding to a purchase instruction of a user on a predetermined commodity, wherein the predetermined commodity comprises a price floating commodity;
Extracting attribute information of the predetermined commodity through the related information of the predetermined commodity;
Determining one of a plurality of first commodities from a stack as boot commodity of the predetermined commodity according to the attribute information of the predetermined commodity; and
Generating order data through the preset commodity and the boot commodity;
the method for determining one of the plurality of first commodities as boot commodity of the predetermined commodity from the stack according to the attribute information of the predetermined commodity comprises the following steps: extracting attribute information of the predetermined commodity; acquiring the weight of each first commodity in the plurality of first commodities; determining the first commodity corresponding to the preset commodity in the stack according to the attribute information and the weight; taking the first commodity as the boot commodity;
Wherein the building of the stack by the plurality of first commodities comprises:
Setting the first commodity weight sequence D;
According to the commodities issued at present, traversing and searching for commodities which are sold by using the SKU in the first commodity weight sequence D at present, and acquiring a SKU association matrix, wherein an element l ab in the association matrix represents the number of the commodities sold by each other between the SKUs a,SKUb;
initializing and setting a value in a first commodity weight sequence D to obtain an initial weight sequence; and
And determining a weight sequence through iterative calculation by the association matrix and the initial weight sequence, wherein the iterative calculation comprises the following steps: d i＝L·Di-1; wherein,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;
based on the obtained first commodity weight sequence D, constructing a maximum stack by using a process MAX-HEAPIFY in a bottom-up method to serve as the stack;
the method further comprises the steps of: when the stack does not contain the first commodity corresponding to the attribute information of the preset commodity, establishing a first commodity with preset unit price, wherein the first commodity is identical to the attribute information of the preset commodity, and inserting the newly established first commodity into the stack; and maintains an index of the stack through a MAX-HEAP-INSERT process.
2. The method of claim 1, wherein said generating order data from said predetermined commodity and said boot commodity comprises:
determining the number of boot commodities to generate boot commodity information;
inputting the boot commodity information and the related information of the preset commodity into an order placing pipeline to generate the order placing data.
3. The method as recited in claim 1, further comprising:
And updating the incidence matrix at regular time according to the class heat, the historical sales and the merchant heat.
4. An apparatus for data processing, comprising:
the receiving module is used for receiving and responding to a purchase instruction of a user on a preset commodity, wherein the preset commodity comprises a price floating commodity;
the information module is used for extracting attribute information of the preset commodity through the related information of the preset commodity;
Boot module, configured to determine, from a stack, one of a plurality of first commodities as a boot commodity of the predetermined commodity according to attribute information of the predetermined commodity, including: extracting attribute information of the predetermined commodity; acquiring the weight of each first commodity in the plurality of first commodities; determining the first commodity corresponding to the preset commodity in the stack according to the attribute information and the weight; taking the first commodity as the boot commodity; and
The ordering module is used for generating ordering data through the preset commodity and the boot commodity;
A stack module for building the stack from a plurality of first commodities, comprising: setting the first commodity weight sequence D; according to the commodities issued at present, traversing and searching for commodities which are sold by using the SKU in the first commodity weight sequence D at present, and acquiring a SKU association matrix, wherein an element l ab in the association matrix represents the number of the commodities sold by each other between the SKUs a,SKUb; initializing and setting a value in a first commodity weight sequence D to obtain an initial weight sequence; and determining a weight sequence through iterative computation by the association matrix and the initial weight sequence, wherein the iterative computation comprises the following steps: d i＝L·Di-1; wherein, 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; based on the obtained first commodity weight sequence D, constructing a maximum stack by using a process MAX-HEAPIFY in a bottom-up method to serve as the stack;
The stack module is further configured to: when the stack does not contain the first commodity corresponding to the attribute information of the preset commodity, establishing a first commodity with preset unit price, wherein the first commodity is identical to the attribute information of the preset commodity, and inserting the newly established first commodity into the stack; and maintains an index of the stack through a MAX-HEAP-INSERT process.
5. An electronic device, comprising:
One or more processors;
a storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-3.
6. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-3.
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