CN112419016A - Big data-based grouping method and system - Google Patents
Big data-based grouping method and system Download PDFInfo
- Publication number
- CN112419016A CN112419016A CN202110094401.8A CN202110094401A CN112419016A CN 112419016 A CN112419016 A CN 112419016A CN 202110094401 A CN202110094401 A CN 202110094401A CN 112419016 A CN112419016 A CN 112419016A
- Authority
- CN
- China
- Prior art keywords
- data
- user
- probability
- grouping
- big
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
-
- 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
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0235—Discounts or incentives, e.g. coupons or rebates constrained by time limit or expiration date
-
- 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
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0239—Online discounts or incentives
-
- 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]
- G06Q30/0631—Item recommendations
Abstract
The invention discloses a big data-based grouping method and a big data-based grouping system, which belong to the technical field of big data, and immediately feed back grouped order information to an order placed by any user at any time for timely bargaining; the system monitors the piecing and uniting data in real time; automatically adjusting the weight of the grouping product according to the transaction data, and preferentially recommending commodities with high coefficient weight; the system sets commodity sharing probability, and the sharing probability is automatically adjusted according to the sharing group bargaining data. According to the invention, through automatic analysis, sequencing and fitting of transaction data and improvement of a preposed technical scheme of clustering logic, the trading efficiency of the whole clustering method is obviously optimized, the waste of user flow is avoided, the repurchase data and the conversion data are effectively improved, and a better solution of a transaction model is realized. Meanwhile, the waiting time of the user is reduced, the operation experience of the user is obviously improved, and a more convenient technical process path is achieved.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to a big data-based grouping method, a big data-based grouping system, a big data-based grouping medium and a big data-based grouping terminal device.
Background
The grouping mode is developed to date, and the grouping mode represented by the number of groups is changing the shopping consumption mode of people. The grouping is a technical scheme for feeding back the centralized will orders to the factory production through the platform, and the scheme realizes quick response of small orders in a certain sense, avoids the overstock risk of goods caused by redundant production and reduces the capital pressure. However, the technical scheme of grouping represented by a large number of groups also has obvious disadvantages, namely, the situations that the user waits for too long time for grouping and fails to group appear in the grouping process, the user waits for the operation interaction experience which can hurt the user, and the grouping failure is likely to cause the loss of the user, if the situations can be effectively avoided, the grouping efficiency is greatly improved, the user experience is improved, the cost of a platform and an operation merchant is saved, and the incremental value is created.
Disclosure of Invention
The technical problems solved by the invention are as follows: the invention provides a big data-based grouping method which improves grouping efficiency, avoids user traffic waste and reduces user waiting time.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a big data-based grouping method adopts a grouping preposition method to complete a deal, and a system immediately feeds back grouping order information and deals in time for orders placed by any user at any time; meanwhile, the system monitors the piecing and mating data in real time, automatically adjusts the weight of the piecing and mating product according to the mating data, and preferentially recommends commodities with high coefficient weight; setting commodity sharing probability in the system, wherein the sharing probability is automatically adjusted according to the sharing bargaining data; in addition, a compensation module is preset in the system, and if the user does not collect the commodity, the system automatically refunds the user and automatically issues compensation to the user account.
Further, grouping weight sorting is carried out on the same product, the background database monitors the grouping data in real time, a grouping coefficient is calculated by means of matching of the grouping data, and the higher the coefficient weight is, the recommendation can be preferentially obtained.
Further, the system performs clustering with more population of the reference clusters, and performs matching with more user traffic until the clustering is full of people, and then returns to the initial weight.
Further, if the piecing together data are rapidly promoted, the piecing probability is promoted in the next time of piecing together, otherwise, the piecing together data are reduced, and the piecing probability is reduced in the next time of piecing together.
Furthermore, threshold intervals are set for the splicing probability, the splicing group transaction data and the system monitoring period, the threshold is exceeded, the threshold is increased, the threshold is lowered when the threshold is lower, and finally platform user dynamic matching of the splicing probability is achieved.
Further, the transaction path of the big data-based grouping method mainly comprises the following steps:
s1: the data server creates transaction link information of the pieced products, including the product names, product details, product prices, the number of pieced people, the piecing probability, the compensation amount which is not pieced and the product inventory number of the pieced products;
s2: a user initiates a group-combining request through a client;
s3: and the data server receives the grouping request, judges whether the user is in grouping or not according to the grouping probability, and outputs result information to the client user.
Further, in step S3, if the client user matches the product, the order is reached, and a normal order fulfillment delivery process is entered; and if the client user does not piece together the product, entering a refund process and sending preset compensation to the user account.
Further, in step S3, the piece together is opened to other client users normally at the same time until the number of people in the whole piece together is full, and if the commodity piece together is full of people continuously and quickly, the system adjusts the middle piece-together probability; and if the commodity is poor in grouping and selling quantity, the probability of the middle grouping is lowered.
A system for realizing a big data-based grouping method comprises a data server and an equipment server, wherein the equipment server is provided with a client, and a user acquires transaction link information of the data server through the client and initiates a grouping request; the data server creates transaction link information of the pieced products, realizes monitoring, calculation, analysis and storage of pieced data information, and realizes pieced probability adjustment, weight adjustment and grouping information feedback; the data server adopts an entity server or a cloud server.
Has the advantages that: compared with the prior art, the invention has the following advantages:
the invention has the main technical characteristics that: firstly, feeding back the information of the grouped order immediately by the system for the order placed by any user at any time to be delivered in time; secondly, the system monitors the piecing and uniting data in real time; thirdly, automatically adjusting the weight of the conglomerate product according to the transaction data, and preferentially recommending the commodities with high coefficient weight; the system sets commodity sharing probability, and the sharing probability is automatically adjusted according to the sharing group bargaining data. Fourthly, the system presets the change compensation limit, if the user does not match the commodity, the system automatically refunds the user and automatically issues the change compensation to the user account.
According to the invention, through automatic analysis, sequencing and fitting of transaction data and improvement of a preposed technical scheme of clustering logic, the trading efficiency of the whole clustering method is obviously optimized, the waste of user flow is avoided, the repurchase data and the conversion data are effectively improved, and a better solution of a transaction model is realized. Meanwhile, the waiting time of the user is reduced, the operation experience of the user is obviously improved, and a more convenient technical process path is achieved.
Drawings
FIG. 1 is a schematic diagram of an operation flow of a big data-based clustering method;
FIG. 2 is a schematic diagram of a big data based clustering system.
Detailed Description
The present invention will be further illustrated by the following specific examples, which are carried out on the premise of the technical scheme of the present invention, and it should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention.
As shown in fig. 2, the big data-based grouping system of the present invention includes a data server and a device server; the equipment server is provided with client software, and a user acquires transaction link information of the data server through the client and initiates a group-combining request; the client can be downloaded through equipment servers such as a mobile phone, a PC (personal computer), a Pad and the like and is presented in the form of App software or a webpage; the user can log in/out of the client through the terminal device.
The data server can create transaction link information of the pieced products, realize the monitoring, calculation, analysis and storage of pieced data information, and realize the pieced probability adjustment, weight adjustment and grouping information feedback; the data server is an entity server or a cloud server, such as an Aliskian ECS server or other cloud servers.
As shown in fig. 1, the big data-based grouping method of the present invention adopts a grouping preposition method to complete a deal, and for any order placed by any user at any time, the system immediately feeds back grouping order information and deals in time; meanwhile, the system monitors the piecing and mating data in real time, automatically adjusts the weight of the piecing and mating product according to the mating data, and preferentially recommends commodities with high coefficient weight; setting commodity sharing probability in the system, wherein the sharing probability is automatically adjusted according to the sharing bargaining data; in addition, a compensation module is preset in the system, and if the user does not collect the commodity, the system automatically refunds the user and automatically issues compensation to the user account.
The invention designs a clustering preposition technology, no matter the number of people sharing a group is a few, the clustering order information is fed back immediately to the order placed by any user at any time, and the user places the order and deals immediately. The clustering preposition is a pre-order scheme, and under the condition that the platform has public domain flow, the matching and the dispatching of people and goods of subsequent orders can be realized through system recommendation. A transition from a person-seeking solution to a person-seeking solution of search logic is effected. Compared with the existing post-agglomeration, the pre-agglomeration is greatly improved in the exchange rate of the intention flow by more than a plurality of times, and is a great value in terms of economic efficiency.
Aiming at the self-adjustment of the weight, the system performs clustering weight sorting on the same product, the background database monitors the clustering data in real time, the clustering coefficient is calculated by matching the clustering number, and the higher the coefficient weight is, the recommendation can be preferentially obtained. For example: and two similar single products are obtained, wherein the commodity A already has 1 ginseng group, the commodity B has no ginseng group temporarily, the system preferentially recommends the commodity A to other platform users, and by analogy, the more people of the commodity A are grouped, the more user flow is matched until the group is full, and the initial weight is returned.
The advantages of using the clustering pre-and weight self-adjustment technique are: no wish orders are wasted, the user can place orders, and the user experience is smooth. For example, the clustering logic is that a plurality of users can cluster after being fully occupied, firstly the 1 st user initiates clustering and enters a clustering waiting period, if no other people participate in the clustering, the clustering automatically fails within a certain time, the willingness order of the 1 st user is wasted, and under the condition of a large number of users, the economic loss is huge. Even if the group is successfully opened, the 1 st and other users in the early stage need to wait for a certain time to complete order transaction, so that the user experience is poor, and the loss of the user is easily caused. The inventive self-adjusting scheme of clumping pre-and weight solves this problem.
The invention sets the middle-piecing probability and the middle-piecing probability self-adjusting technical scheme, the commodity middle-piecing probability is set in the piecing group, the piecing-together person can obtain discount low-price commodities and the un-pieced-together person refunds the money in full, the platform compensates from the commodity profit, and coupons, small gifts and the like are given to the un-pieced-together user, so that the expected piecing group is improved. The setting probability and the small amount of compensation can effectively leverage the ordering conversion rate of the user for the general-purpose commodity, which is obviously reflected in the experimental data provided below in the embodiment. The system monitors the piecing together and transaction data in real time, and the piecing probability is automatically adjusted according to the transaction data so as to test the demand intention of the market on the commodity. The essence of grouping is the requirement for grouping. And rapidly improving the piecing together hybridization data, improving the piecing together probability at the next time, and otherwise, reducing the piecing together hybridization data, and reducing the piecing together probability at the next time. The system is provided with a compensation module in advance, and if the user does not piece the commodity, the system automatically refunds the user and automatically issues compensation to the user account. The compensation can be small change, coupon, small gift and the like, the mode of piecing together the probability + the small change can improve the purchase demand of the user, and the test result shows that the transaction rate is improved by more than 50 percent compared with the common piecing together technology. For example: the initial piecing probability of the piecing commodity A is 4/8, the system monitors that the piecing success is far beyond that of the same type piecing commodity in the past period of time, the system automatically improves the piecing probability 5/8, and after the result is improved, the piecing success number still rapidly rises, and the system continues to improve the piecing probability; and (4) reducing the splicing probability until the successful number of the splicing groups begins to decrease in a period of time. The splicing probability, the successful grouping data and the monitoring period can be set to be within a certain threshold range, if the threshold is exceeded, the threshold is increased, if the threshold is lower, the threshold is decreased, and finally the platform user dynamic matching of the splicing probability is achieved.
The transaction path of the big data-based grouping method mainly comprises the following steps:
s1: the data server creates transaction link information of the pieced products, including the product names, product details, product prices, the number of pieced people, the piecing probability, the compensation amount which is not pieced and the product inventory number of the pieced products;
s2: a user initiates a group-combining request through a client;
s3: and the data server receives the group splicing request, judges whether the user splices in the group according to the splicing probability and outputs result information to the client user.
If the client user matches the product, the order is reached to be committed, and a normal order fulfillment delivery flow is entered; and if the client user does not piece together the product, entering a refund process and sending preset compensation to the user account.
The party is normally opened to other client users at the same time until the number of the whole party is full, and if the commodity party is continuously and quickly full, the system adjusts the probability in the party; and if the commodity is poor in grouping and selling quantity, the probability of the middle grouping is lowered.
According to the technical scheme of the grouping method for the big data, the actual test of the big sample is carried out, and the test data is as follows:
by using the method, the number of accumulated test user samples is 4334, wherein the number of ordering conversion is 1022, the number of repurchase users is 539, the number of clusters is 1436, the clustering is mainly 4 clusters, 8 clusters and 10 clusters, and the test period is 60 days.
According to the data, the lower single conversion rate is calculated to be 23.58 percent, and the repeated purchase rate is calculated to be 52.74 percent. The order placing conversion rate of the same industry of the e-commerce college is generally between 5 and 10 percent, the monthly repurchase rate is generally between 20 and 30 percent, and the data of the order placing conversion rate and the repurchase rate of the scheme are far higher than the same industry level.
The grouping is essentially a grouping requirement, the existing post scheme is not logic of 'buying and getting', the delivery efficiency is low on a matching transaction chain, the control risk is over emphasized, and the scheme is not a better technical scheme. According to the invention, through automatic analysis, sequencing and fitting of transaction data and improvement of a preposed technical scheme of clustering logic, the trading efficiency of the whole clustering method is obviously optimized, the waste of user flow is avoided, the repurchase data and the conversion data are effectively improved, and a better solution of a transaction model is realized. Meanwhile, the waiting time of the user is reduced, the operation experience of the user is obviously improved, a more convenient technical flow path is achieved, and the grouping flow matching transaction chain is reconstructed once.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (9)
1. A big data-based grouping method is characterized in that: completing the transaction by adopting a clustering preposition method, feeding back clustering order information immediately by the system for the order placed by any user at any time, and performing the transaction in time; meanwhile, the system monitors the piecing and mating data in real time, automatically adjusts the weight of the piecing and mating product according to the mating data, and preferentially recommends commodities with high coefficient weight; setting commodity sharing probability in the system, wherein the sharing probability is automatically adjusted according to the sharing bargaining data; in addition, a compensation module is preset in the system, and if the user does not collect the commodity, the system automatically refunds the user and automatically issues compensation to the user account.
2. The big-data-based grouping method according to claim 1, wherein: and (4) carrying out clustering weight sorting on the same product, monitoring the clustering data in real time by a background database, calculating a near clustering coefficient by means of matching of the clustering data, and preferentially obtaining recommendation if the coefficient weight is higher.
3. The big-data-based grouping method according to claim 2, wherein: the system is used for grouping the population of the reference group more, the user flow is more matched until the grouping is full of members, and the initial weight is returned.
4. The big-data-based grouping method according to claim 1, wherein: if the piecing together and handing over data is promoted rapidly, the piecing together probability is promoted at the next time, otherwise, the piecing together and handing over data is reduced, and the piecing together probability is reduced at the next time.
5. The big-data-based grouping method according to claim 1, wherein: threshold intervals are set for the splicing probability, the splicing group synthetic data and the system monitoring period, the threshold is exceeded, the threshold is increased, the threshold is lowered when the threshold is lower, and finally platform user dynamic matching of the splicing probability is achieved.
6. The big-data-based grouping method according to any one of claims 1 to 5, wherein the transaction path mainly comprises:
s1: the data server creates transaction link information of the pieced products, including the product names of the pieced products, the product details, the product prices, the number of the pieced people, the piecing probability, the compensation amount which is not pieced and the product inventory number;
s2: a user initiates a group-combining request through a client;
s3: and the data server receives the grouping request, judges whether the user is in grouping or not according to the grouping probability, and outputs result information to the client user.
7. The big-data-based grouping method according to claim 6, wherein: in step S3, if the client user matches the product, the order is reached, and a normal order fulfillment shipping process is entered; and if the client user does not piece together the product, entering a refund process and sending preset compensation to the user account.
8. The big-data-based grouping method according to claim 6, wherein: in step S3, the party is opened to other client users normally at the same time until the number of people in the whole party is full, if the commodity party is full of people continuously and quickly, the system increases the probability in the party; and if the commodity is poor in grouping and selling quantity, the probability of the middle grouping is lowered.
9. A system for implementing the big data based clustering method of any one of claims 1 to 8, wherein: comprises a data server and an equipment server side,
the equipment server is provided with a client, and a user acquires transaction link information of the data server through the client and initiates a group-combining request;
the data server creates transaction link information of the pieced products, realizes monitoring, calculation, analysis and storage of pieced data information, and realizes pieced probability adjustment, weight adjustment and grouping information feedback;
the data server adopts an entity server or a cloud server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110094401.8A CN112419016A (en) | 2021-01-25 | 2021-01-25 | Big data-based grouping method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110094401.8A CN112419016A (en) | 2021-01-25 | 2021-01-25 | Big data-based grouping method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112419016A true CN112419016A (en) | 2021-02-26 |
Family
ID=74782968
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110094401.8A Pending CN112419016A (en) | 2021-01-25 | 2021-01-25 | Big data-based grouping method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112419016A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113129096A (en) * | 2021-03-26 | 2021-07-16 | 南京石匠网络科技有限公司 | High-efficiency clustering method and system based on piecing probability |
| CN114926201A (en) * | 2022-05-07 | 2022-08-19 | 上海禹璨信息技术有限公司 | Grouping request pushing method, device, equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20040076297A (en) * | 2003-02-25 | 2004-09-01 | (주)비즈너커뮤니케이션 | Service method for buying Lottery Ticket cooperatively using Internet and Mobile Communication |
| CN103914780A (en) * | 2013-01-08 | 2014-07-09 | 纽海信息技术（上海）有限公司 | Group buying ordering system and method |
| CN110610401A (en) * | 2019-08-20 | 2019-12-24 | 苏州炜晔互联网科技有限公司 | Internet group-piecing shopping method, system, medium and terminal |
| CN111199459A (en) * | 2019-12-30 | 2020-05-26 | 深圳市盟天科技有限公司 | Commodity recommendation method and device, electronic equipment and storage medium |
-
2021
- 2021-01-25 CN CN202110094401.8A patent/CN112419016A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20040076297A (en) * | 2003-02-25 | 2004-09-01 | (주)비즈너커뮤니케이션 | Service method for buying Lottery Ticket cooperatively using Internet and Mobile Communication |
| CN103914780A (en) * | 2013-01-08 | 2014-07-09 | 纽海信息技术（上海）有限公司 | Group buying ordering system and method |
| CN110610401A (en) * | 2019-08-20 | 2019-12-24 | 苏州炜晔互联网科技有限公司 | Internet group-piecing shopping method, system, medium and terminal |
| CN111199459A (en) * | 2019-12-30 | 2020-05-26 | 深圳市盟天科技有限公司 | Commodity recommendation method and device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 上海交通大学海外教育学院连锁品牌战略研究所: "《企业数字化转型的风口之争 2019年度连锁品牌趋势观察报告》", 31 December 2018 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113129096A (en) * | 2021-03-26 | 2021-07-16 | 南京石匠网络科技有限公司 | High-efficiency clustering method and system based on piecing probability |
| CN114926201A (en) * | 2022-05-07 | 2022-08-19 | 上海禹璨信息技术有限公司 | Grouping request pushing method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112419016A (en) | Big data-based grouping method and system | |
| CN108182575A (en) | A kind of realization system and method for the multi-party income real time settlement of internet electric business | |
| CN112150243A (en) | Digital mall system based on business middling platform | |
| CN111553771A (en) | Intelligent e-commerce system | |
| CN111292149A (en) | Method and device for generating return processing information | |
| CN111311100A (en) | Enterprise electronic commerce digital purchasing platform | |
| CN111277625A (en) | E-commerce platform order pushing method and system | |
| CN115600956A (en) | Method for improving purchasing efficiency in purchasing cooperation | |
| CN111222956A (en) | Food production enterprise ordering system | |
| CN101964088A (en) | Automatic purchasing scheme generation system and method | |
| CN110503553A (en) | A kind of management service platform based on freight supply chain financial models | |
| TW507148B (en) | Verification method for replying work sheets in a collaboration transaction system | |
| CN109508930A (en) | A kind of the commodity display method and its system of online trading | |
| CN110889730A (en) | Commodity transaction method based on block chain | |
| CN115907815A (en) | Big data selection method based on intelligent farm trade | |
| KR102214456B1 (en) | Platform service system and method for goods dealing between companies adjusting the estimated value high | |
| CN114140177A (en) | Advertisement putting system and method based on e-commerce integrated management | |
| Chen et al. | Design and implement of a web-based e-procurement system platform for shipping line | |
| CN112581234A (en) | Background management method and system for regional business applets | |
| CN112541802A (en) | Electronic commerce transaction system | |
| CN112581231A (en) | Cloud customization integrated printing system | |
| CN214475136U (en) | Block chain Internet of things industrial production system | |
| KR102214457B1 (en) | Platform service system and method for goods dealing between companies including component with increasing value | |
| CN117455579B (en) | Commodity recommendation intervention method, commodity recommendation intervention device, medium and equipment | |
| KR102214458B1 (en) | Platform service system and method for goods dealing between companies including component with decreasing value |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |