CN111242685A - Product network competitive auction system based on big data support technology - Google Patents

Abstract

The invention discloses a product network competitive auction system based on big data support technology, which is characterized in that: a) establishing a bidder nonlinear item demand function based on big data analysis:

for an optimal bidding strategy, wherein q represents demand and p represents price; b) constructing a product network competition auction mode based on big data: through the research on the nonlinear item demand function of bidders and the analysis of dynamic market of buyers and sellers entering randomly according to the randomness of object-oriented market, Markov's balance is usedAnd measuring the estimation of the items in the market, and further constructing a big data-based product network competitive auction model.

Description

Product network competitive auction system based on big data support technology

Technical Field

The invention relates to the public welfare field, in particular to an online auction method realized through the Internet.

Background

With the rapid development of internet and electronic commerce, network transactions become an important market transaction form. The major auction platforms of the online auction market include dozens of auction sites such as ebay on a global scale, and the hot spot areas are mainly in the united states and european countries. The network auction can be rapidly started because the auction platform in the network auction can provide powerful commodity and price search service, so that a large amount of transaction cost is saved, the access thresholds of two parties of transaction are reduced, and the transaction success rate and the market matching efficiency of the network market are improved.

The network auction has characteristics unique to the network auction in addition to the characteristics of the traditional auction, and the characteristics enable the network auction to have important research value. The network auction has the following unique advantages: the buyer and the seller conduct online bidding and bargaining based on various services provided by the platform to reach the transaction; the virtual trading mode of the network auction has the characteristic of all-weather unreliability, and the time and space limitations are overcome; the access thresholds of auction suppliers, suppliers and consumers are low, and the number of participants is huge; more and more, an open, repeated and multi-round dynamic mode is adopted, and the information about the auction products and the bids is more transparent; the commodity range of the network auction is wider than that of the traditional auction and almost the commodity range is not covered; the network auction provides an optimal platform environment for the realization and good running of the combined auction.

Since the identity of the seller and the quality of the product are difficult to be effectively verified, a so-called 'seller fraud' phenomenon is easy to generate; since 1 buyer or seller can participate in the auction with multiple ID identities, there is a possibility of a false bid; since the open type price-increasing auction is mostly adopted in the network auction, the auction method is more likely to cause collusion behavior of the buyer than the sealed auction method. The network auction utilizes the convenience of the network, and enables the buyer and the seller to gather and trade on the network auction platform at the same time, the buyer has more commodities to select in the network auction, and the seller attracts the customers by the network auction. The network auction platform is generally a service-type website, which provides virtual transaction space (transaction platform) and online transaction service for buyers and sellers to engage in network auction, and is an independent third party in the network auction. The network auction platform mainly provides services for transaction parties (buyers and sellers) as follows: providing a platform for both buyers and sellers to reach a transaction; providing technical support and electronic services for transactions; and establishing a reputation rating system.

In the network auction, the bidding strategy of bidders is a very important research problem. Whereas the end bid effect in bidding strategies is very common in the practice of network auctions. The end bid-robbing effect refers to a bidding phenomenon in which a large number of bidders submit bids a short time (often only a few minutes or even a few seconds) before a bidding deadline in a network auction in which bids have a certain length of time (generally several days). One of the characteristics of the network auction is that neither buyer nor seller knows the identity of the other party, and simultaneously, the buyer cannot effectively discriminate the quality of the bidding product and other product attributes, i.e. the information of the auction is asymmetric. This leads to cursing problems similar to the common value model in auctions. Also, another important problem associated with cursing winners is fraud and trust in network auctions. Due to the asymmetry of the information about the auctioned goods by the buyer and the seller, the seller has an incentive to cheat bidders or buyers in the network auction in various ways to improve the estimation. This raises the reputation issues of the seller or bidder and the risk of fraud by the buyer or bidder. In practice, most network auctions, including eBay, Amazon and Yahoo, are traded using an english auction in which all auction participants know the real-time bidding information and the bids are gradually increasing. While this auction model has enjoyed great success in reality, it has resulted in easier collusion among buyers, which not only reduces the revenue for sellers, but the outcome of the auction can also be less efficient in configuration.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the product network competitive auction system based on the big data support technology is provided, and the problems that sellers cheat and buyers conspire, the income of sellers is reduced, and the result of auction is possibly low configuration efficiency in the prior art are solved.

The technical scheme of the invention is as follows: a big data support technology-based product network competitive auction system, a) establishing a big data analysis-based bidder nonlinear item demand function: q ═ f (p); q represents demand, and p represents price;

b) constructing big data based product network competitive auction model

Through the research on the nonlinear item demand function of bidders, and according to the randomness in the object-oriented market, dynamic markets in which buyers and sellers randomly enter are analyzed, the estimation of items in the markets is measured by Markov balance, and then a product network competitive auction mode based on big data is constructed.

The establishment of the bidder nonlinear item demand function based on big data analysis comprises the following specific steps:

step 1) starting from an auction theory, researching the quotation condition and the quantity of required articles allowed to be submitted by a bidder, and establishing a limit model based on a basic hypothesis model between the auctioneer and the bidder, namely infinite-dimension discrete time;

step 2) researching auction bidding basic rules based on the obtained basic hypothesis model between the auctioneer and the bidder, and researching the preference relationship between the bidder and the commodity, namely:

E(R(v_i ^tn)) means that the value of the commodity is v_i ^tExpected profit of, Y₁(n) represents the bidding strategies of n bidders, and δ w (n-1) represents the income generated by the commodity, so that the arbitrariness of quotation is reduced, and the auction efficiency is improved;

step 3) researching auction bidding rules to maximize self-benefit, researching auction allocation basic rules, and establishing a rule constraint model between commodity allocation quantity and transaction price, namely

E(R(v_i ^tN)) means that the value of the commodity is v_i ^tThe expected income W (n) represents the bargaining price, and then the optimal supply quantity and bargaining price are given according to the constraint relation;

step 4) according to the supply function relationship obtained in the step 3), researching balanced price and balanced bidding strategy analysis, and obtaining a bidder nonlinear item demand function based on big data analysis, wherein a specific demand function formula is as follows: q ═ f (p).

The invention has the beneficial effects that: through the comprehensive information platform, the advantage of big data is exerted, and the article of clapping and buyer do accurate butt joint, this reduction operating time that can be great improves work efficiency, also can promote the success rate of article of clapping. And (3) comparing the shot with other similar products in multiple aspects by using a big data technology, establishing a price prediction model, predicting the price of the shot and proposing the price for taking a beat.

Drawings

Fig. 1 shows an accurate docking model of a photographed article and a buyer.

Detailed Description

a) Establishing a bidder nonlinear item demand function based on big data analysis

The project planning starts from an auction theory, an auction theory bidding rule and an auction allocation rule are researched, a big data analysis-based bidder nonlinear item demand function is established by combining a balanced price analysis strategy and a balanced bidding strategy, and the method comprises the following specific steps of:

step 1) starting from an auction theory, researching the quotation condition and the quantity of required articles allowed to be submitted by a bidder, and establishing a limit model based on a basic hypothesis model between the auctioneer and the bidder, namely infinite-dimension discrete time;

step 2) researching auction bidding basic rules based on the obtained basic hypothesis model between the auctioneer and the bidder, and researching the preference relationship between the bidder and the commodity, namely:

E(R(v_i ^tn)) means that the value of the commodity is v_i ^tExpected profit of, Y₁(n) represents the bidding strategies of n bidders, and δ w (n-1) represents the income generated by the commodity, so that the arbitrariness of quotation is reduced, and the auction efficiency is improved;

step 3) researching auction bidding rules to maximize self benefits, researching auction allocation basic rules, and establishing a rule constraint model between commodity allocation quantity and transaction price, namely:

E(R(v_i ^tn)) means that the value of the commodity is v_i ^tThe expected income W (n) represents the bargaining price, and then the optimal supply quantity and bargaining price are given according to the constraint relation;

step 4) according to the supply function relationship obtained in the step 3), researching balanced price and balanced bidding strategy analysis, and obtaining a bidder nonlinear item demand function based on big data analysis, wherein a specific demand function formula is as follows: q ═ f (p);

b) constructing big data based product network competitive auction model

Through the research on the nonlinear item demand function of bidders, the dynamic market randomly entered by buyers and sellers is analyzed according to the randomness in the object-oriented market, the estimation of items in the market is measured by using Markov balance, the research is mainly carried out from three aspects, and a product network competitive auction mode based on big data is further constructed.

First, a sequential auction model in the form of a first-order price with reserve prices is studied, where buyers are to measure whether to buy now or to participate in future auctions in a dynamic market. When the number of bidders is large, competition is very fierce, and at the moment, the reserved price of the auction commodity is predicted in a Markov equilibrium mode;

secondly, researching the balance of expected benefits of buyers and the evaluation of the sellers on the benefits of the future market when a plurality of sellers and a plurality of buyers exist in the market;

finally, researching the dynamic market behavior of the buyer under the market condition, establishing a high-order difference equation of the buyer and the seller to obtain a solution vector for describing the future expected value of the commodity, and obtaining an accurate butt joint mode of the photographed product and the buyer as shown in figure 1;

c) auction user information data protection method based on generation type countermeasure network technology

In order to protect auction user information data, an auction user information data protection method based on a generational countermeasure network technology is studied. The main idea is as follows: and (3) judging the attack process from the network by using a generative countermeasure network (GAN), and then blocking the attack by adopting a protection strategy to protect the auction user information data. Firstly, setting G as an auction user information data generator network, and simultaneously setting a discriminant model D for discriminating whether input data come from auction user information data or from network attack, wherein a specific calculation formula is as follows:

wherein x is sampled in the real auction user information data distribution P_data(x) Z is sampled in the prior distribution p_z(z) (e.g., Gaussian noise distribution), E (or.) represents the calculated expectation, and the training dataset for discriminant model D is derived from the true dataset distribution P_data(x)。

Finally, a privacy data protection method based on an information theory and an encryption mechanism based on user information data are researched and obtained aiming at the attack process monitored by a generative countermeasure network.

Claims (2)

1. A product network competitive auction system based on big data support technology is characterized in that:

a) establishing a bidder nonlinear item demand function based on big data analysis:

for optimal bidding strategy

Wherein q represents demand and p represents price;

b) constructing big data based product network competitive auction model

Through the research on the nonlinear item demand function of bidders, and according to the randomness in the object-oriented market, dynamic markets in which buyers and sellers randomly enter are analyzed, the estimation of items in the markets is measured by Markov balance, and then a product network competitive auction mode based on big data is constructed.

2. The big data support technology-based product network competitive auction system according to claim 1, wherein: establishing a bidder nonlinear item demand function based on big data analysis, which comprises the following specific steps:

step 1) starting from an auction theory, researching the quotation condition and the quantity of required articles allowed to be submitted by a bidder, and establishing a limit model based on a basic hypothesis model between the auctioneer and the bidder, namely infinite-dimension discrete time;

step 2) researching auction bidding basic rules based on the obtained basic hypothesis model between the auctioneer and the bidder, and researching the preference relationship between the bidder and the commodity, namely:

E[R_l(v＇_i，n)]＝Y_l(n)+δW(n-1)

E(R(v_i ^tn)) means that the value of the commodity is v_i ^tExpected profit of, Y₁(n) indicates a bidding strategy of n bidders, and δ w (n-1) indicates a profit generated from the goods, thereby reducing the newspaperThe price is random, and the auction efficiency is improved;

step 3) researching auction bidding rules to maximize self-benefit, researching auction allocation basic rules, and establishing a rule constraint model between commodity allocation quantity and transaction price, namely

E(R(v_i ^tN)) means that the value of the commodity is v_i ^tThe expected income W (n) represents the bargaining price, and then the optimal supply quantity and bargaining price are given according to the constraint relation;

step 4) according to the supply function relationship obtained in the step 3), researching balanced price and balanced bidding strategy analysis, and obtaining a bidder nonlinear item demand function based on big data analysis, wherein a specific demand function formula is as follows: q ═ f (p).

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