CN111127129A - Commodity transaction system and method - Google Patents
Commodity transaction system and method Download PDFInfo
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- CN111127129A CN111127129A CN201910236003.8A CN201910236003A CN111127129A CN 111127129 A CN111127129 A CN 111127129A CN 201910236003 A CN201910236003 A CN 201910236003A CN 111127129 A CN111127129 A CN 111127129A
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- 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]
Abstract
The invention is suitable for the field of computers, and provides a commodity transaction system and a method, wherein the transaction system comprises a server, a purchasing client and a selling client, wherein the purchasing client is used for sending a purchasing request to the server; the sales client is used for sending a sales request to the server; and the server is used for determining an acceptable purchase price according to the purchase request and the purchase price regression model, determining an acceptable sale price according to the sale request and the sale price regression model, matching the acceptable price according to the price difference matching model to determine a transaction client combination, and sending matching success information to the transaction client combination to promote transaction. The commodity transaction system provided by the embodiment of the invention ensures that the final price can meet the psychological expectation of both parties based on the model trained by big data, and the buyer can buy the commodity at a desirable price while not excessively pressing the profit of the seller.
Description
Technical Field
The invention belongs to the field of computers, and particularly relates to a commodity transaction system and a commodity transaction method.
Background
With the development of science and technology, the development of electronic commerce is faster and faster, and more people choose to shop on the internet, which also leads to the appearance of more commodity sales platforms.
However, the existing goods sales platforms are all built under the unequal sales relations, and often sellers designate sales prices, and the sales prices often exceed the psychological expectation prices of buyers, but in most cases, the buyers have to accept the prices. On the other hand, sellers often use preferential sales promotion, which means that there is some interest-offering space for sellers in normal sales, and the sales amount is effectively promoted in preferential sales promotion.
Therefore, the benefits of the buyers are infringed due to the existing commodity sales mode under the condition of the non-peer sales relationship, so that the volume of the transaction is reduced, and further the benefits of the sellers are lost.
Disclosure of Invention
The embodiment of the invention aims to provide a commodity transaction system and a commodity transaction method, and aims to solve the technical problem that benefits of buyers and sellers are damaged in the existing commodity sales mode.
The embodiment of the invention is realized in such a way that the commodity transaction system comprises a server, at least one purchasing client and at least one selling client, wherein the purchasing client and the selling client are communicated with the server;
the purchase client is used for sending a purchase request to the server, wherein the purchase request carries intention purchase commodity information;
the sales client is used for sending a sales request to the server, and the sales request carries intention sales commodity information;
the server is used for receiving a purchase request sent by a purchase client, determining an acceptable purchase price of the purchase client for the intention to purchase commodities according to the purchase request corresponding to the purchase client and a purchase price regression model, wherein the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client; receiving a selling request sent by a selling client, and determining the acceptable selling price of the selling client for the intention selling goods according to the selling request corresponding to the selling client and a selling price regression model, wherein the selling price regression model is generated by pre-training the traded information of the corresponding selling client; and matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination, and sending matching success information to a purchase client and a sale client in the optimal transaction client combination.
Another objective of an embodiment of the present invention is to provide a commodity transaction method, which is operated on a server, where the server communicates with at least one purchasing client and at least one selling client, and the commodity transaction method includes:
receiving a purchase request sent by a purchase client;
determining an acceptable purchase price of the purchase client for the intention purchase of the commodity according to a purchase request corresponding to the purchase client and a purchase price regression model, wherein the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client;
receiving a sales request sent by a sales client;
determining the acceptable sale price of the sales client to the intention sale commodity according to the sale request corresponding to the sales client and a sale price regression model, wherein the sale price regression model is generated by pre-training the transaction information of the corresponding sales client;
matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination;
and sending matching success information to the purchasing client and the selling client in the optimal trading client combination.
The commodity transaction system provided by the embodiment of the invention comprises a server, at least one purchasing client and at least one selling client which are communicated with the server, wherein the purchasing client and the selling client are respectively used for sending a purchasing request and a selling request to the server, the server is used for receiving the purchasing request sent by the purchasing client and the selling request sent by the selling client, determining the acceptable purchasing price of the purchasing client for the intention to purchase commodities according to the purchasing request corresponding to each purchasing client and a purchasing price regression model, determining the acceptable selling price of the selling client for the intention to sell commodities according to the selling request corresponding to each selling client and the selling price regression model, and matching the plurality of acceptable purchasing prices and the acceptable selling prices according to a price difference matching model generated by pre-training to determine the optimal transaction client combination, and then sending matching success information to the purchasing client and the selling client in the optimal trading client combination. According to the commodity transaction system provided by the embodiment of the invention, the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client, the sale price regression model is generated by pre-training the traded information of the corresponding sale client, so that the determined acceptable purchase price and the acceptable sale price meet the psychological expectations of both parties, and the price difference matching model generated by pre-training is used for matching the price, so that the psychological requirements of both parties in transaction can be met during final transaction.
Drawings
Fig. 1 is a diagram of an application environment of a commodity transaction system according to an embodiment of the present invention;
fig. 2 is a block diagram of a commodity transaction system according to an embodiment of the present invention;
FIG. 3 is a timing diagram of a merchandise transaction system according to an embodiment of the present invention;
fig. 4 is a detailed structural diagram of a commodity transaction system according to an embodiment of the present invention;
fig. 5 is a detailed block diagram of another commodity transaction system according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a purchase price regression model training unit according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a transaction matching unit according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a product matching module according to an embodiment of the present invention;
FIG. 9 is a flowchart illustrating steps of a method for trading commodities according to an embodiment of the present invention;
FIG. 10 is a flow chart illustrating steps of another method for trading commodities according to an embodiment of the present invention;
FIG. 11 is a flowchart of the steps for transaction matching according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
The embodiment of the invention receives the purchase request from the purchase client and the sale request from the sale client through the server, and carries out transaction matching based on a big data algorithm, so that the probability of transaction of both parties of the finally matched transaction is high, and the final transaction price simultaneously meets the psychological needs of both parties.
Fig. 1 is a diagram of an application environment of a commodity transaction system according to an embodiment of the present invention, as shown in fig. 1, in the application environment, a terminal 110 and a computer device 120 are included.
The computer device 120 may be an independent physical server or terminal, may also be a server cluster formed by a plurality of physical servers, and may be a cloud server providing basic cloud computing services such as a cloud server, a cloud database, a cloud storage, and a CDN.
The terminal 110 may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal 110 and the computer device 120 may be connected through a network, and the present invention is not limited thereto.
As shown in fig. 2, in an embodiment, a commodity transaction system is proposed, which is mainly exemplified by the application of the system to the terminal 110 (or the computer device 120) in fig. 1, and the commodity transaction system includes a server 210, and at least one purchasing client 220 and at least one selling client 230 communicating with the server.
The purchase client 220 is configured to send a purchase request to the server.
In the embodiment of the present invention, the purchase client runs on the terminal 110 shown in fig. 1
In the embodiment of the invention, the purchase request carries intention purchase commodity information.
As a preferred embodiment of the present invention, the purchase request may further carry an intended purchase price for the intended purchase of the commodity, and at this time, data analysis is performed in combination with the intended purchase price, so that the acceptable purchase price determined by the subsequent server is closer to the psychological needs of the purchaser.
The sales client 230 is configured to send a sales request to the server.
In the embodiment of the present invention, the sales client runs on the terminal 110 shown in fig. 1.
In the embodiment of the invention, the sales request carries information of the intention sales commodity.
As a preferred embodiment of the present invention, the sales request may also carry an intended sales price of an intended sales commodity, and at this time, data analysis is performed in combination with the intended sales price, so that an acceptable sales price determined by a subsequent server may be closer to a psychological demand of a seller.
In the embodiment of the present invention, it can be understood that the purchasing client and the selling client only refer to clients playing different roles in order to distinguish between them in one transaction process, and in fact, in the whole system, the purchasing client and the selling client are completely equivalent and can be mutually converted, that is, in different transaction processes, the clients may be the purchasing client and the selling client.
The server 210 is configured to receive a purchase request sent by a purchasing client, and determine, according to the purchase request corresponding to the purchasing client and a purchase price regression model, an acceptable purchase price of the purchasing client for an intention to purchase a commodity; receiving a selling request sent by a selling client, determining the acceptable selling price of the selling client to the intention selling commodity according to the selling request corresponding to the selling client and a selling price regression model, matching a plurality of acceptable purchasing prices and a plurality of acceptable selling prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination, and sending matching success information to a purchasing client and a selling client in the optimal transaction client combination.
In the embodiment of the present invention, the server 210 runs on the computer device 120 shown in fig. 1.
In the embodiment of the present invention, the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client, and further, the traded information of the purchase client includes the information of the traded commodity, the information of the trade time, and the actual trade price.
In the embodiment of the present invention, the sales price regression model is generated by pre-training the traded information of the corresponding sales client, and further, the traded information of the sales client also includes the information of the traded goods, the information of the trading time, and the actual trading price.
The commodity transaction system provided by the embodiment of the invention comprises a server, at least one purchasing client and at least one selling client which are communicated with the server, wherein the purchasing client and the selling client are respectively used for sending a purchasing request and a selling request to the server, the server is used for receiving the purchasing request sent by the purchasing client and the selling request sent by the selling client, determining the acceptable purchasing price of the purchasing client for the intention to purchase commodities according to the purchasing request corresponding to each purchasing client and a purchasing price regression model, determining the acceptable selling price of the selling client for the intention to sell commodities according to the selling request corresponding to each selling client and the selling price regression model, and matching the plurality of acceptable purchasing prices and the acceptable selling prices according to a price difference matching model generated by pre-training to determine the optimal transaction client combination, and then sending matching success information to the purchasing client and the selling client in the optimal trading client combination. According to the commodity transaction system provided by the embodiment of the invention, the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client, the sale price regression model is generated by pre-training the traded information of the corresponding sale client, so that the determined acceptable purchase price and the acceptable sale price meet the psychological expectations of both parties, and the price difference matching model generated by pre-training is used for matching the price, so that the psychological requirements of both parties in transaction can be met during final transaction.
As shown in fig. 3, a timing diagram of the merchandise transaction system shown in fig. 2 is provided.
In the embodiment of the present invention, it should be noted that there is no strict sequence between the purchase client sending the purchase request to the server and the sale client sending the sale request to the server, and in fact, the server continuously receives the purchase request sent by the purchase client and the sale request sent by the sale client.
As shown in fig. 4, there is also provided a schematic structural diagram of the commodity transaction system shown in fig. 2, in the embodiment of the present invention, the server 210 includes a purchase request receiving unit 401, an acceptable purchase price determining unit 402, a sales request receiving unit 403, an acceptable sale price determining unit 404, a transaction matching unit 405, and a matching success information sending unit 406, the purchase client 220 includes a sales request sending unit 407, and the sales client 230 includes a sales request sending unit 408.
The purchase request receiving unit 401 is configured to receive a purchase request sent by a purchase client.
In the embodiment of the invention, the purchase request carries intention purchase commodity information.
As a preferred embodiment of the present invention, the purchase request may further carry an intended purchase price for the intended purchase of the commodity, and at this time, data analysis is performed in combination with the intended purchase price, so that the acceptable purchase price determined by the subsequent server is closer to the psychological needs of the purchaser.
The acceptable purchase price determining unit 402 is configured to determine an acceptable purchase price of the purchase client for the intended purchase of the commodity according to the purchase request corresponding to the purchase client and a purchase price regression model.
In the embodiment of the present invention, the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client, and further, the traded information of the purchase client includes the information of the traded commodity, the information of the trade time, and the actual trade price.
The sales request receiving unit 403 is configured to receive a sales request sent by a sales client.
In the embodiment of the invention, the sales request carries information of the intention sales commodity.
As a preferred embodiment of the present invention, the sales request may also carry an intended sales price of an intended sales commodity, and at this time, data analysis is performed in combination with the intended sales price, so that an acceptable sales price determined by a subsequent server may be closer to a psychological demand of a seller.
The acceptable selling price determining unit 404 is configured to determine an acceptable selling price of the selling client for the intended selling goods according to the selling request corresponding to the selling client and the selling price regression model.
In the embodiment of the present invention, the sales price regression model is generated by pre-training the traded information of the corresponding sales client, and further, the traded information of the sales client also includes the information of the traded goods, the information of the trading time, and the actual trading price.
The transaction matching unit 405 is configured to match the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination.
In the embodiment of the present invention, the transaction matching unit continuously matches the prices, and therefore, in the matching process, a plurality of pairs of optimal transaction client combinations are determined.
In the embodiment of the invention, the optimal transaction client combination determined by the price difference matching model shows that the transaction probability of the two parties is the maximum.
In the embodiment of the present invention, please refer to fig. 7 and its explanation for the specific structure of the transaction matching unit.
The matching success information sending unit 406 is configured to send matching success information to the purchasing client and the selling client in the optimal transaction client combination.
In the embodiment of the invention, after the purchasing client and the selling client receive the matching success information, the transaction can be further confirmed through communication.
As shown in fig. 5, another schematic structural diagram of the article sales system shown in fig. 2 is provided, and is different from the schematic structural diagram of the article sales system shown in fig. 4 in that the server further includes a recommended price sending unit 501.
The recommended price sending unit 501 is configured to determine a recommended price according to the acceptable purchase price, the acceptable sale price, and the standard transaction price, and send the purchase client and the sale client in the optimal transaction client combination.
In the embodiment of the invention, when the acceptable purchase price and the acceptable sale price of the commodity in the optimal transaction client combination are determined to be different, the transaction of two parties needs to be promoted, therefore, the recommended price is determined according to the acceptable purchase price, the acceptable sale price and the standard transaction price, and is sent to the two parties, and the recommended price is determined based on the acceptable purchase price, the acceptable sale price and the standard transaction price, so that the two parties can easily accept the recommended price, and the transaction efficiency is effectively improved.
In the embodiment of the present invention, when both parties agree with the recommended price, the recommended price is used as the actual transaction price, and when both parties disagree with the recommended price, other steps are performed, for example, the recommended price is determined by each of both parties or the recommended price is determined by the buyer/seller, the other party confirms, and further, when the transaction cannot be facilitated, the matching relationship between both parties is cancelled.
As another embodiment of the present invention, the server further includes a purchase price regression model training unit and a sale price regression model training unit, which are respectively used for pre-training to generate a purchase price regression model and a sale price regression model, wherein the purchase price regression model training unit and the sale price regression model training unit are different only in training targets and have the same specific training process, so that a specific functional module schematic diagram is listed by taking the purchase price regression model training unit as an example, and the sale price regression model training unit has a similar structure, which is not described herein again.
As shown in fig. 6, a schematic diagram of a structure of a purchase price regression model training unit is provided, and the purchase price regression model training unit includes a traded information receiving module 601, a standard trading price obtaining module 602, and a purchase price regression model training module 603.
The traded information receiving module 601 is configured to receive traded information sent by a purchasing client.
In an embodiment of the present invention, the traded information includes information of traded goods, information of trading time, and actual trading price.
The standard transaction price obtaining module 602 is configured to obtain a standard transaction price corresponding to the transaction commodity and the transaction time according to the transaction commodity information and the transaction time information.
In an embodiment of the present invention, the standard transaction price is a transaction price of the commodity determined by the seller in the same transaction time period.
The purchase price regression model training module 603 is configured to generate a purchase price regression model according to the standard transaction price and the actual transaction price training.
In the embodiment of the invention, a mapping relation can be established according to standard transaction prices and actual transaction price training of different commodities, after the standard transaction prices of other commodities are known, the actual transaction prices of users can be directly predicted, for example, the most basic discount acceptable for a buyer can be calculated and determined, the discount is used as a unique parameter to establish a purchase price regression model, but further, other parameters can be set to improve the accuracy of the purchase price regression model, such as attribute information of the commodities, the highest transaction price, the lowest transaction price and the like, and the accuracy of determining the acceptable purchase price according to the commodity information is effectively ensured through the purchase price regression model trained by a large number of samples.
As shown in fig. 7, in an embodiment, the transaction matching unit 405 specifically includes a product matching module 701, a price difference determination module 702, and a transaction matching module 703.
The commodity matching module 701 is configured to determine a matched commodity according to the intention commodity purchasing information and the intention commodity selling information.
In the embodiment of the invention, the commodity information needs to be matched first, and the commodity matching is successful, so that the judgment is continued.
The price difference determination module 702 is configured to calculate a price difference between a plurality of acceptable purchase prices and a plurality of acceptable sale prices in the matched merchandise.
The transaction matching module 703 is configured to determine a combination of an optimal acceptable purchase price and an acceptable sale price according to the size of the price difference, and determine an optimal transaction client combination.
In the embodiment of the present invention, the price difference may be used as an index for determining the best combination of transaction clients, and further, other indexes may be included, such as the time for sending the transaction request, and the higher priority matching right for sending the transaction request first.
As shown in fig. 8, in one embodiment, the product matching module 701 specifically includes an identical product matching sub-module 801 and a related product matching sub-module 802.
The same commodity matching sub-module 801 is configured to determine the same commodity according to the commodity intention purchase commodity information and the intention sale commodity information, and determine the same commodity as a matching commodity.
The related commodity matching sub-module 802 is configured to determine related commodities according to the correlation between the commodity intention purchase commodity information, the intention sale commodity information, and preset commodity information, and determine the related commodities as matched commodities.
In the embodiment of the present invention, the related commodities are also determined as the matching commodities, where the related means that the product categories are similar or the product attributes are similar, for example, when the commodity is intended to be purchased as a certain cosmetic, the commodity related to the odor, the effect, the price, and the brand of the cosmetic is acquired as the matching product.
In the embodiment of the invention, the related commodities are determined as the matched commodities, so that the matching efficiency is improved, and the selection margin of a user is improved
In one embodiment, there is further provided a commodity transaction method, running on a server, as shown in fig. 9, the commodity transaction method including:
step S902, receiving a purchase request sent by the purchase client.
In the embodiment of the invention, the purchase request carries intention purchase commodity information.
As a preferred embodiment of the present invention, the purchase request may further carry an intended purchase price for the intended purchase of the commodity, and at this time, data analysis is performed in combination with the intended purchase price, so that the acceptable purchase price determined by the subsequent server is closer to the psychological needs of the purchaser.
Step S904, determining an acceptable purchase price of the purchasing client for the intended purchased goods according to the purchase request corresponding to the purchasing client and the purchase price regression model.
In the embodiment of the present invention, the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client, and further, the traded information of the purchase client includes the information of the traded commodity, the information of the trade time, and the actual trade price.
Step S906, receives the sales request sent by the sales client.
In the embodiment of the invention, the sales request carries information of the intention sales commodity.
As a preferred embodiment of the present invention, the sales request may also carry an intended sales price of an intended sales commodity, and at this time, data analysis is performed in combination with the intended sales price, so that an acceptable sales price determined by a subsequent server may be closer to a psychological demand of a seller.
Step S908, determining an acceptable selling price of the selling client for the intended goods to be sold according to the selling request corresponding to the selling client and the selling price regression model.
In the embodiment of the present invention, the sales price regression model is generated by pre-training the traded information of the corresponding sales client, and further, the traded information of the sales client also includes the information of the traded goods, the information of the trading time, and the actual trading price.
Step S910, matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to the price difference matching model generated by pre-training to determine the optimal transaction client combination.
In the embodiment of the present invention, the transaction matching unit continuously matches the prices, and therefore, in the matching process, a plurality of pairs of optimal transaction client combinations are determined.
In the embodiment of the invention, the optimal transaction client combination determined by the price difference matching model shows that the transaction probability of the two parties is the maximum.
In the embodiment of the present invention, please refer to fig. 10 for the detailed steps of step S910.
Step S912, sending matching success information to the purchasing client and the selling client in the optimal transaction client combination.
In one embodiment, as shown in fig. 10, another commodity transaction method is provided, which is different from the one shown in fig. 9 in that it further includes:
step S1002, determining a recommended price according to the acceptable purchase price, the acceptable sale price and the standard transaction price, and sending the purchase client and the sale client in the optimal transaction client combination.
In the embodiment of the invention, when the acceptable purchase price and the acceptable sale price of the commodity in the optimal transaction client combination are determined to be different, the transaction of two parties needs to be promoted, therefore, the recommended price is determined according to the acceptable purchase price, the acceptable sale price and the standard transaction price, and is sent to the two parties, and the recommended price is determined based on the acceptable purchase price, the acceptable sale price and the standard transaction price, so that the two parties can easily accept the recommended price, and the transaction efficiency is effectively improved.
In the embodiment of the present invention, when both parties agree with the recommended price, the recommended price is used as the actual transaction price, and when both parties disagree with the recommended price, other steps are performed, for example, the recommended price is determined by each of both parties or the recommended price is determined by the buyer/seller, the other party confirms, and further, when the transaction cannot be facilitated, the matching relationship between both parties is cancelled.
In an embodiment, as shown in fig. 11, the step S810 of matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to the price difference matching model generated by pre-training to determine the optimal transaction client combination specifically includes:
in step S1102, the matched commodity is determined according to the intention purchase commodity information and the intention sale commodity information.
In the embodiment of the invention, the commodity information needs to be matched first, and the commodity matching is successful, so that the judgment is continued.
In step S1104, price differences between a plurality of acceptable purchase prices and a plurality of acceptable sale prices in the matched goods are calculated.
Step S1106, determining a combination of the optimal acceptable purchase price and the acceptable sale price according to the size of the price difference, and determining an optimal transaction client combination.
In the embodiment of the present invention, the price difference may be used as an index for determining the best combination of transaction clients, and further, other indexes may be included, such as the time for sending the transaction request, and the higher priority matching right for sending the transaction request first.
In one embodiment, a computer device is also presented, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
receiving a purchase request sent by a purchase client;
determining an acceptable purchase price of the purchase client for the intention purchase of the commodity according to a purchase request corresponding to the purchase client and a purchase price regression model, wherein the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client;
receiving a sales request sent by a sales client;
determining the acceptable sale price of the sales client to the intention sale commodity according to the sale request corresponding to the sales client and a sale price regression model, wherein the sale price regression model is generated by pre-training the transaction information of the corresponding sales client;
matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination;
and sending matching success information to the purchasing client and the selling client in the optimal trading client combination.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of:
receiving a purchase request sent by a purchase client;
determining an acceptable purchase price of the purchase client for the intention purchase of the commodity according to a purchase request corresponding to the purchase client and a purchase price regression model, wherein the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client;
receiving a sales request sent by a sales client;
determining the acceptable sale price of the sales client to the intention sale commodity according to the sale request corresponding to the sales client and a sale price regression model, wherein the sale price regression model is generated by pre-training the transaction information of the corresponding sales client;
matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination;
and sending matching success information to the purchasing client and the selling client in the optimal trading client combination.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A commodity transaction system is characterized by comprising a server side, at least one purchasing client side and at least one selling client side, wherein the purchasing client side and the selling client side are communicated with the server side;
the purchase client is used for sending a purchase request to the server, wherein the purchase request carries intention purchase commodity information;
the sales client is used for sending a sales request to the server, and the sales request carries intention sales commodity information;
the server is used for receiving a purchase request sent by a purchase client, determining an acceptable purchase price of the purchase client for the intention to purchase commodities according to the purchase request corresponding to the purchase client and a purchase price regression model, wherein the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client; receiving a selling request sent by a selling client, and determining the acceptable selling price of the selling client for the intention selling goods according to the selling request corresponding to the selling client and a selling price regression model, wherein the selling price regression model is generated by pre-training the traded information of the corresponding selling client; and matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination, and sending matching success information to a purchase client and a sale client in the optimal transaction client combination.
2. The merchandise transaction system of claim 1, wherein the purchase client comprises:
a purchase request sending unit, configured to send a sales request to a server, where the sales request includes intention sales commodity information;
the sales client includes:
a sale request sending unit, configured to send a sale request to a server, where the sale request includes information of an intended sale commodity;
the server side comprises:
a purchase request receiving unit, configured to receive a purchase request sent by a purchase client;
the acceptable purchase price determining unit is used for determining the acceptable purchase price of the purchase client to the intention purchase commodity according to the purchase request corresponding to the purchase client and a purchase price regression model, and the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client;
the sales request receiving unit is used for receiving a sales request sent by a sales client;
the acceptable sale price determining unit is used for determining the acceptable sale price of the sale client to the intention sale commodity according to the sale request corresponding to the sale client and a sale price regression model, and the sale price regression model is generated by pre-training the deal information of the corresponding sale client;
the transaction matching unit is used for matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination;
and the matching success information sending unit is used for sending matching success information to the purchasing client and the selling client in the optimal transaction client combination.
3. The merchandise transaction system of claim 2, wherein the server further comprises:
and the recommended price sending unit is used for determining the recommended price according to the acceptable purchase price, the acceptable sale price and the standard transaction price and sending the purchase client and the sale client in the optimal transaction client combination.
4. The merchandise transaction system of claim 2, wherein the server further comprises:
the purchase price regression model training unit is used for training in advance to generate the purchase price regression model;
and the sales price regression model training unit is used for training in advance to generate the sales price regression model.
5. The commodity transaction system according to claim 4, wherein the purchase price regression model training unit includes:
the trading information receiving module is used for receiving the trading information sent by the purchasing client, and the trading information comprises trading commodity information, trading time information and actual trading price;
the standard trading price acquisition module is used for acquiring a standard trading price corresponding to the trading commodity and the trading time according to the trading commodity information and the trading time information;
and the purchase price regression model training module is used for training and generating a purchase price regression model according to the standard transaction price and the actual transaction price.
6. The commodity transaction system according to claim 2, wherein the transaction matching unit includes:
the commodity matching module is used for determining matched commodities according to the intention commodity purchasing information and the intention commodity selling information;
a price difference determination module for calculating a price difference between a plurality of acceptable purchase prices and a plurality of acceptable sale prices in the matched commodity;
and the transaction matching module is used for determining the combination of the optimal acceptable purchase price and the acceptable sale price according to the price difference and determining the combination of the optimal transaction client.
7. The merchandise transaction system of claim 6, wherein the merchandise matching module comprises:
the same commodity matching secondary module is used for determining the same commodities according to the commodity intention purchasing commodity information and the intention selling commodity information and determining the same commodities as matched commodities;
and the related commodity matching secondary module is used for determining related commodities according to the correlation degree between the commodity intention purchase commodity information, the intention sale commodity information and the preset commodity information, and determining the related commodities as matched commodities.
8. A commodity transaction method is characterized in that the commodity transaction method runs on a server side, the server side is communicated with at least one purchasing client side and at least one selling client side, and the commodity transaction method comprises the following steps:
receiving a purchase request sent by a purchase client;
determining an acceptable purchase price of the purchase client for the intention purchase of the commodity according to a purchase request corresponding to the purchase client and a purchase price regression model, wherein the purchase price regression model is generated by pre-training the traded information of the corresponding purchase client;
receiving a sales request sent by a sales client;
determining the acceptable sale price of the sales client to the intention sale commodity according to the sale request corresponding to the sales client and a sale price regression model, wherein the sale price regression model is generated by pre-training the transaction information of the corresponding sales client;
matching the plurality of acceptable purchase prices and the plurality of acceptable sale prices according to a price difference matching model generated by pre-training to determine an optimal transaction client combination;
and sending matching success information to the purchasing client and the selling client in the optimal trading client combination.
9. The method of claim 8, wherein the step of matching a plurality of acceptable purchase prices with a plurality of acceptable sale prices according to a pre-trained price difference matching model to determine an optimal transaction client combination specifically comprises:
determining matched commodities according to the intention commodity purchasing information and the intention commodity selling information;
calculating price differences between a plurality of acceptable purchase prices and a plurality of acceptable sale prices in the matched commodities;
and determining the combination of the best acceptable purchase price and the acceptable sale price according to the size of the price difference, and determining the combination of the best trading client.
10. The merchandise transaction method according to claim 8, wherein the merchandise transaction method further comprises:
and determining a recommended price according to the acceptable purchase price, the acceptable sale price and the standard transaction price, and sending the purchase client and the sale client in the optimal transaction client combination.
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