CN106709794A - E-commerce background data processing system - Google Patents
E-commerce background data processing system Download PDFInfo
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- CN106709794A CN106709794A CN201710204989.1A CN201710204989A CN106709794A CN 106709794 A CN106709794 A CN 106709794A CN 201710204989 A CN201710204989 A CN 201710204989A CN 106709794 A CN106709794 A CN 106709794A
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- 238000004458 analytical method Methods 0.000 claims abstract description 84
- 238000002372 labelling Methods 0.000 claims abstract description 12
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- 230000003542 behavioural effect Effects 0.000 claims description 3
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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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
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Abstract
The invention relates to the technical field of warehousing and logistics, and particularly relates to an E-commerce background data processing system. The system comprises a user module, a logistics module, a core analysis module, a transaction processing module and a historical data module, wherein the core analysis module is used for performing data analysis on enterprise products, E-commerce orders and logistics orders, labeling product data, and screening optimal shopping schemes and more choices for users. The system can accurately analyze the time, expense and service level of logistics services of different logistics enterprises and provide the analysis results to customers for reference, thereby greatly improving the diverse choices of the customers and avoiding unnecessary waste.
Description
Technical field
The present invention relates to store in a warehouse and logistlcs technology field, more particularly to a kind of e-business back-end data processing system.
Background technology
Internet logistics service in recent years is developed rapidly, for numerous manufacturing business provide quick and cheap logistics clothes
Business, significantly reduces manufacturing logistics cost, on the other hand for China Internet commercial affairs economic development provides fertile hair
Exhibition soil.
What nowadays various shopping at network platform was such as emerged rapidly in large numbersBamboo shoots after a spring rain grows up, and whole Internet market is constantly carved up,
In order to be able to reach profit purpose in a short time, shopping platform is often signed an agreement with loglstics enterprise and has obtained optimal logistics
Service.Logistics service preferential in a short time can be that enterprise saves many pressure economically, but permanent single to observe species
Courier Service will be unable to meet the demand of each user.The competition of loglstics enterprise and the continuous change of policy, net purchase platform is just
Need constantly to adjust the logistics service of itself to be reached for enterprise's more interests of acquisition.Final single logistics service will be inapplicable
In internet shopping platform.
The net purchase platform that numerous logistics services are rolled into one not only allows platform consumer suitable according to itself selection
Logistics service, can also allow net purchase platform there is no concern that policy change and the logistics service company pressure that brings of competition to net purchase
Platform brings influence, as a result will be undertaken by consumer.
The content of the invention
To solve the shortcoming that prior art is present, for the logistics service of different loglstics enterprises provides comprehensive analysis, to disappear
The person of expense provides diversified selection.The invention provides a kind of e-business back-end data processing system, including
Line module:Userspersonal information is managed and updates, the net purchase consumer behavior custom to user carries out data statistics,
For core analysis module provides analyze data, the data that reception processing transaction model is returned;
Product module:The product information that management product supplier provides, responds the order processing of user, counts and analyze use
The operation information of family shopping process, the shopping association sequence information after the completion of Auto-matching user;
Logistics module:The logistics service that all loglstics enterprises are provided is managed by api interface, product vendor and thing is processed
Cooperative relationship between stream enterprise, processes product stream order, for core analysis module provides logistics analysis data;
Core analysis module:Digitization analyzing and processing is carried out to enterprise product, electric business order, logistics order, labeling is produced
Product data, are that user filters out optimal shopping scheme and more multi-option;
Transaction model:Result is accurately distributed to the result for responding core analysis module other modules;
Historical data module:Preserve and arrange user's history order data, logistics data and core analysis resume module
Analysis result, for analysis next time of core analysis module provides historical analysis data.
Further, the analyze data in above-mentioned core analysis module to data is divided into:Product treatment, customer analysis and fortune
Battalion's analysis,
Wherein product treatment includes labeling product data, analysis potential user crowd, the sale of product and management;
Customer analysis are mainly used in tracking the behavioural habits that user browses trace analysis user;
OA operation analysis is counted with product data mainly in combination with user data to product sales situation interior for a period of time,
Complete the investigation of enterprise product sale.
Further, above-mentioned historical data module also includes providing data-interface using the platform of the system for other;Adopt
Data are safeguarded with DBA, encrypted backup protection is provided for the data in local, strange land, cloud and mobile device and is recovered.
Further, the analysis method of labeling product data includes in above-mentioned core analysis module:It is price positioning, on probation
Crowd, territorial scope, user type, logistics information.
The present invention has following beneficial effect:
1) time, cost, service level required for can accurately analyzing the logistics service of different loglstics enterprises, and
It is supplied to consumer to refer to analysis result, greatly improves the diversified selection of consumer, it is to avoid unnecessary waste;
2) system tracks the consumption habit of each user, from it is huge browse whereabouts and history consumer record in analyze
The custom hobby and personality of user, then be its accurately and effectively analysis product consumption plan, suggested design.
3) accurately business analysis can be provided for product enterprise, huge transaction data is analyzed, is processed, integrate with
The form of Visual Report Forms shows enterprise customer.From consumer branch scope, product market share, various regions Logistics Service
Service level, user selection logistics mode occupation rate, customer consumption crowd ratio, consumers' opinions feedback aspect etc. with roll over
Line chart, bar chart, pie chart, the form of radar map are supplied to product enterprise to be referred to for operation.
4) platform with all use the system can be applicable, when user includes the platform of the system using other, point
Analysis system can still call conventional other platform historical datas that accurately judgement is made to the demand of user.
Brief description of the drawings
Fig. 1 is system architecture diagram of the invention;
Fig. 2 is analysis of product date flow chart of the present invention;
Fig. 3 is order processing analysis process figure of the present invention;
Fig. 4 is Product labelling analytical structure block diagram of the present invention.
Specific embodiment
It is as shown in Figure 1 system architecture diagram of the invention, the present invention includes:Line module:Manage and update user
People's information, the net purchase consumer behavior custom to user carries out data statistics, for core analysis module provides analyze data, receiving area
The data that reason transaction model is returned;
Product module:The product information that management product supplier provides, responds the order processing of user, counts and analyze use
The operation information of family shopping process, the shopping association sequence information after the completion of Auto-matching user;
Logistics module:The logistics service that all loglstics enterprises are provided is managed by api interface, product vendor and thing is processed
Cooperative relationship between stream enterprise, processes product stream order, for core analysis module provides logistics analysis data;
Core analysis module:Digitization analyzing and processing is carried out to enterprise product, electric business order, logistics order, labeling is produced
Product data, are that user filters out optimal shopping scheme and more multi-option;
Transaction model:Result is accurately distributed to the result for responding core analysis module other modules;
Historical data module:Preserve and arrange user's history order data, logistics data and core analysis resume module
Analysis result, for analysis next time of core analysis module provides historical analysis data.Historical data module also includes being used for other
The platform of the system provides data-interface;Data are safeguarded using DBA, is carried for the data in local, strange land, cloud and mobile device
Protected for encrypted backup and recovered.
Analysis of product date flow chart of the invention is illustrated in figure 2, is comprised the following steps:
1) product module receives and processes new product information, and new product information is sent to core analysis module deposits
Storage and analysis, core analysis module call the enterprise's related data in logistics module, such as:Major preferential activities of logistics company, enterprise
The Cooperation data of industry and logistics company, to area logistics service horizontal analysis report data (comprising logistics response speed,
Employee's service quality, means of distribution and service coverage etc. data) carry out network analysis;
2) core analysis module analysis process:Enterprise product, electric business order, logistics order are carried out at digitization analysis
Reason, and pass data to transaction model.
Data analysis is broadly divided into three major types:Product treatment, customer analysis and OA operation analysis,
Product treatment includes labeling product data, analysis potential user crowd, the sale of product and management;
Customer analysis are mainly used in tracking the behavioural habits that user browses trace analysis user;
OA operation analysis is counted with product data mainly in combination with user data to product sales situation interior for a period of time,
Complete the investigation of enterprise product sale.
The purpose of digitization analyzing and processing is according to the excellent of the conventional purchase data of user, current production business and platform offer
Favour activity, service analysis of each logistics etc. are that user's shopping filters out optimal shopping scheme, such as in terms of logistics:It is fragile article, expensive
Heavy articles order is then for user recommends the best logistics company of service;Rush order then points out user to select logistics speed most
Fast company;Then recommended user uses most cheap logistics mode to general order;
3) data that transaction model parsing core analysis module is analyzed, judge using not different data types
Same data processing method:Order data will be passed to line module and be shown, and Product labelling data will be passed to product
Module completes the distribution of initial product label;
4) historical data module backup arranges the data content after each core analysis module parsing, is core analysis module
History processing data is provided, enables core analysis module to be that next product analysis is prepared;
5) the product information data of labeling are passed into product module again, product module obtains a set of product and is applicable
The displaying label of this business platform, selects suitable popularization means to push product to vast according to corresponding label product module
User group.
Order processing analysis process figure of the invention is illustrated in figure 3, is comprised the following steps:
1) after commercial foreground user has carried out the consumption of order, product module can by the product information of customer consumption and
User related information passes to core analysis module;
2) core analysis module calls the history consumer record of user, user location logistics information, enterprise product, draws
The result of many levels, such as optimal distribution project, most fast distribution project and most cheap distribution project, in combination with electronics business
Business platform product activity filters out most favorable price.
3) the product sequence information data that transaction model is received and parsed through obtained by core analysis module carry out parsing point
The complete data result of analysis, line module is fed back to by result data;
4) historical data module backup arranges the sequence information of user;
5) line module obtains the instruction that transaction model sends and provides the user diversified logistics selection and optimal
Price selection, complete delivery operation simultaneously payment result is passed into product data module;
6) product data module notifies that product supplier carries out the delivery that delivery prepares and notifies logistics module upgrading products
State;
7) logistics data module obtains the physical state of product until completing whole logistics progress, history by API in real time
Data module records whole physical state, is that data analysis module prepares logistics layer data.
It is illustrated in figure 4 Product labelling analytical structure block diagram of the invention, including three partial contents, respectively data choosing
Select, analysis method and analysis result, the data source of Product labellingization analysis mainly includes user data, product data, logistics
Data and historical data;Analysis method includes:Price positioning, crowd, territorial scope, user type, logistics information on probation etc.;Root
The units such as speed, service, price are dispensed according to the applicable crowd of product, existing subscriber's hobby custom, regional culture, product stream with dispensing
The network analysis that element carries out diversification draws the label of product such as:Food, person in middle and old age, northeast, special product etc. refine label.
Claims (4)
1. a kind of e-business back-end data processing system, it is characterised in that:Including
Line module:Userspersonal information is managed and updates, the net purchase consumer behavior custom to user carries out data statistics, is core
Heart analysis module provides analyze data, the data that reception processing transaction model is returned;
Product module:The product information that management product supplier provides, responds the order processing of user, counts and analyze user's purchase
The operation information of thing process, the shopping association sequence information after the completion of Auto-matching user;
Logistics module:The logistics service that all loglstics enterprises are provided is managed by api interface, treatment product vendor is looked forward to logistics
Cooperative relationship between industry, processes product stream order, for core analysis module provides logistics analysis data;
Core analysis module:Digitization analyzing and processing, labeling product number are carried out to enterprise product, electric business order, logistics order
According to being that user filters out optimal shopping scheme and more multi-option;
Transaction model:Result is accurately distributed to the result for responding core analysis module other modules;
Historical data module:Preserve and arrange the analysis of user's history order data, logistics data and core analysis resume module
As a result, for analysis next time of core analysis module provides historical analysis data.
2. e-business back-end data processing system as claimed in claim 1, it is characterised in that:In the core analysis module
Analyze data to data is broadly divided into:Product treatment, customer analysis and OA operation analysis,
The product treatment includes labeling product data, analysis potential user crowd, the sale of product and management;
The customer analysis are mainly used in tracking the behavioural habits that user browses trace analysis user;
The OA operation analysis is counted with product data mainly in combination with user data to product sales situation interior for a period of time,
Complete the investigation of enterprise product sale.
3. e-business back-end data processing system as claimed in claim 1, it is characterised in that:The historical data module is also
Including providing data-interface using the platform of the system for other;Data are safeguarded using DBA, for local, strange land, cloud and movement
Data in equipment provide encrypted backup protection and recover.
4. e-business back-end data processing system as claimed in claim 1, it is characterised in that:Label in core analysis module
The analysis method for changing product data includes:Price positioning, crowd, territorial scope, user type, logistics information on probation.
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Cited By (5)
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CN107886279A (en) * | 2017-11-29 | 2018-04-06 | 商派软件有限公司 | A kind of full channel inventory allocation method based on businessman stock |
CN108876243A (en) * | 2018-06-20 | 2018-11-23 | 广州市公安局白云区分局 | Logistics information method for early warning, device and electronic equipment |
CN109829662A (en) * | 2019-03-28 | 2019-05-31 | 上海中通吉网络技术有限公司 | Enterprise portrait construction method, device and system based on logistics data |
CN111367927A (en) * | 2020-03-02 | 2020-07-03 | 壹车宜家信息科技有限公司 | Automobile consumption data management method based on big data analysis |
DE202022100697U1 (en) | 2022-02-07 | 2022-02-17 | Ganesh Agnihotri | Intelligent hybrid management system to predict e-commerce user churn in e-commerce using data mining and deep learning |
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CN104182879A (en) * | 2013-12-13 | 2014-12-03 | 赵鑫 | An e-commerce and logistics integrated system |
CN105069602A (en) * | 2015-08-28 | 2015-11-18 | 董方 | Logistics company recommending method and system |
CN106504006A (en) * | 2016-10-22 | 2017-03-15 | 肇庆市联高电子商务有限公司 | E-commerce system based on Internet of Things |
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CN102339447A (en) * | 2011-09-22 | 2012-02-01 | 用友软件股份有限公司 | Active service device and method |
CN103049865A (en) * | 2012-12-17 | 2013-04-17 | 中国农业大学 | Method and system for initiatively recommending product information service |
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
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CN107886279A (en) * | 2017-11-29 | 2018-04-06 | 商派软件有限公司 | A kind of full channel inventory allocation method based on businessman stock |
CN108876243A (en) * | 2018-06-20 | 2018-11-23 | 广州市公安局白云区分局 | Logistics information method for early warning, device and electronic equipment |
CN109829662A (en) * | 2019-03-28 | 2019-05-31 | 上海中通吉网络技术有限公司 | Enterprise portrait construction method, device and system based on logistics data |
CN111367927A (en) * | 2020-03-02 | 2020-07-03 | 壹车宜家信息科技有限公司 | Automobile consumption data management method based on big data analysis |
DE202022100697U1 (en) | 2022-02-07 | 2022-02-17 | Ganesh Agnihotri | Intelligent hybrid management system to predict e-commerce user churn in e-commerce using data mining and deep learning |
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