CN112085516A

CN112085516A - Agricultural product pricing method and device and computer readable storage medium

Info

Publication number: CN112085516A
Application number: CN202010778680.5A
Authority: CN
Inventors: 刘盼; 张凤杰
Original assignee: Henan Agricultural University
Current assignee: Henan Agricultural University
Priority date: 2020-08-05
Filing date: 2020-08-05
Publication date: 2020-12-15

Abstract

The invention provides an agricultural product pricing method, an agricultural product pricing device and a computer readable storage medium, wherein the agricultural product pricing method comprises the following steps: step one, constructing an agricultural product supply chain model; the supply chain comprises a central manufacturer, a seller and an information service provider; step two, determining a response model of the market demand based on the supply chain model; step three, determining the overall profit of the supply chain according to the response model of the market demand; and step four, processing the overall income of the supply chain to obtain the optimal income of the central producer, and formulating the optimal wholesale price of the central producer according to the optimal income of the central producer. The agricultural product pricing method can be well suitable for the agricultural product market, and meets the market requirements of users in an agricultural product supply chain on the basis of guaranteeing the income.

Description

Agricultural product pricing method and device and computer readable storage medium

Technical Field

The invention belongs to the technical field of agricultural big data, and particularly relates to an agricultural product pricing method, an agricultural product pricing device and a computer readable storage medium.

Background

Along with the improvement of living standard of people, green and safe agricultural products are more and more popular. But panic caused by a series of food safety incidents breaks through the bottom line of people and contusions the confidence of consumers. The food safety tracing is expected to improve the confidence of consumers, so that a tracing mechanism based on emerging information technologies such as Internet of things (IoT) and big data is provided, but the problems of information asymmetry and easiness in tampering cannot be thoroughly solved.

Due to the advantages of the block chain in terms of data tamper resistance and transparency, the above problems can be solved by the characteristics of tamper resistance, decentralization and the like of the block chain. However, the block chain technology is generally applied in the agricultural field for a short time and has limited data accumulation, and the market demand of consumers cannot be accurately predicted in a short time. Therefore, it is necessary to fuse big data and blockchain techniques to each other for better applications.

At present, the application of the fusion of a block chain and big data (whether the tracing is based on the block chain or the demand prediction is based on the big data, the essence of the fusion is to provide required information for users, so that the demand prediction precision is called as emerging information service for short) deeply influences the tracing flow and the demand prediction precision of agricultural products, and further causes the change of key pricing factors such as green degree and freshness of the agricultural products, for example, the demand prediction precision can be seriously restricted due to the shortage of the demand prediction based on the big data of consumers; affecting the pricing mechanism and revenue of the agricultural product supply chain. Users in the supply chain are generally priced in a manner that considers the respective benefit maximization, and such pricing method influences the balance of the overall benefits in the whole supply chain.

Therefore, there is a need to provide an improved solution to the above-mentioned deficiencies in the prior art.

Disclosure of Invention

The invention aims to provide an agricultural product pricing method, an agricultural product pricing device and a computer readable storage medium for use under the condition of blockchain and big data fusion.

To achieve the above object, the present invention provides a method for pricing agricultural products, comprising

Step one, an agricultural product supply chain model is constructed according to data in a supply chain;

the supply chain comprises a central manufacturer, a seller and an information service provider;

the central producer uploads the production data and production operation data of the agricultural products to the information service provider, and formulates wholesale prices of the agricultural products according to the information data in the information service provider; the production data comprises the production date, the life cycle and the green degree of the agricultural product; the production operation data comprises unit production cost and unit investment cost for emerging information services;

the information service provider provides information data in an agricultural product supply chain based on emerging information services; the information data comprises the production data and the production operation data uploaded by the central producer, market demands between the central producer and the seller obtained by the information service provider, and sales operation data uploaded by the seller; the emerging information service is a service for providing the information data for the central producer and the seller based on a block chain and big data;

the seller uploads sales operation data and formulates the selling price of the agricultural product according to the information data provided by the information service provider; the sales activity data comprises a unit sales cost and a unit investment cost for the emerging information services;

step two, determining a response model of the market demand based on the supply chain model;

step three, determining the overall profit of the supply chain according to the response model of the market demand;

and step four, processing the overall income of the supply chain to obtain the optimal income and profit of the central producer, and formulating the optimal wholesale price of the central producer according to the optimal income and profit of the central producer.

Further, in the second step, based on the supply chain model, determining a response model of the market demand under the action of an integrated decision; the integrated decision means that the central producer and the seller make a coordinated and unified decision to make the selling price of the agricultural product.

Further, under the action of the integrated decision, determining a response model of the market demand according to the selling price and freshness of the agricultural product, the green degree of the agricultural product and the credibility factor of the green degree of the agricultural product; the B mode refers to a pricing mode that members in the agricultural product supply chain adopt Xinxing information services to carry out integration decision.

Further, in step three, the overall profit of the supply chain is determined by: first, the market demand in B mode is obtained; secondly, determining the income of the central producer according to the market demand, the customized selling price of the agricultural product and information data provided by the information service provider in the B mode, wherein the third relevant information data comprises the unit production cost of the central producer, the unit investment cost of the central producer for the emerging information service, the unit production cost of the seller and the unit investment cost of the seller for the emerging information service.

Further, the overall benefits of the supply chain are:

π^C＝(p^C-φc_r-φc_w-c_ow-c_or)Q^C

in the formula, pi^CIs the overall revenue of the supply chain in the B mode; p is a radical of^CThe selling price of the agricultural products in the mode B; q^CIs the market demand in the B mode; c. C_wIs the unit production cost of the central manufacturer; c. C_owThe unit investment cost of the central producer to the emerging information service is saved; c. C_rA unit production cost for the vendor; c. C_orA unit investment cost for the vendor for emerging information services; phi is a fixed coefficient.

Further, in step four, the process of processing the overall profit of the supply chain to obtain the optimal profit of the central producer:

firstly, processing the whole income of the supply chain by adopting a reverse induction method to obtain the whole optimal income of the supply chain;

then, setting a cost sharing coefficient and a profit sharing coefficient between the central producer and the seller; the cost sharing coefficient represents the distribution proportion of the cost between a central manufacturer and a seller in the selling process of the agricultural products; the income sharing coefficient represents the distribution proportion of the income between a central producer and a seller in the selling process of the agricultural products;

and finally, obtaining the optimal profit of the central producer through the cost sharing coefficient, the profit sharing coefficient and the overall optimal profit of the supply chain.

Further, the fourth step further includes: and processing the overall income of the supply chain to obtain the optimal income of the seller.

Further, the determination process of processing the overall profit of the supply chain to obtain the optimal profit of the vendor is as follows:

then, setting a cost sharing coefficient and a profit sharing coefficient between the central producer and the seller;

and finally, obtaining the optimal profit of the sale through the cost sharing coefficient, the profit sharing coefficient and the overall optimal profit of the supply chain.

To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above agricultural product pricing method.

In order to achieve the above object, the present invention further provides an agricultural product pricing device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the agricultural product pricing method when executing the program.

Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:

according to the invention, each user participates in a new agricultural product supply chain, and can effectively avoid the situation that a central producer can misrepresent the agricultural product information in order to sell more agricultural products, namely the central producer survives misrepresentation behavior, when a block chain and big data fusion emerging information service technology is adopted. When a central producer and a seller adopt emerging information services provided by an information service provider, the block chain technology has the advantages of data tamper resistance and transparency, so that the misrepresentation behavior of the central producer can be reduced, the trust among participants can be enhanced, and the trust of a consumer on agricultural products can be improved.

The agricultural product pricing method is based on a block chain and a emerging information service technology of big data fusion, under the consideration of a coordination and unification decision between a central producer and a seller, namely the action of an integrated decision, market demand and overall income of a supply chain are respectively determined, then a reverse induction method is adopted so as to obtain the maximum income of the central producer and the maximum income of the seller, and the optimal wholesale price of the central producer and the optimal sale price of the seller are respectively made according to the maximum income of the central producer and the maximum income of the seller. The central producer and the seller can be considered uniformly by the pricing method, so that the profit of the whole supply chain is optimal, the central producer and the seller can obtain the optimal profit of the central producer and the seller respectively on the basis, the optimal wholesale price and the optimal sale price can be effectively determined according to the respective optimal profit, the pricing of agricultural products can meet the requirement of the market on the authenticity of the information of the agricultural products, and the central producer and the seller can also optimize the profit, namely although the investment cost of emerging information services is increased, the profit of users can not be reduced by a mode of pricing respectively on the basis of the overall profit of the supply chain. Therefore, the agricultural product pricing method can be well suitable for the agricultural product market, and on the basis of guaranteeing the income, the market requirements of users in the agricultural product supply chain are met.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate exemplary embodiments of the invention and, together with the description, serve to explain the invention and are not intended to limit the invention. Wherein:

FIG. 1 is a block chain and big data based agricultural product supply chain system diagram of the present invention;

FIG. 2 is a block chain and big data based model diagram of an agricultural product supply chain according to the present invention;

FIG. 3 is a flow chart of the present invention for constructing a mathematical model of the central producer revenue and the seller revenue in the N-mode;

FIG. 4 is a flow chart of the present invention for determining the central producer maximum revenue and the seller maximum revenue in the N mode;

FIG. 5 is a flow chart of the present invention for building a mathematical model of the central producer revenue and the vendor revenue in mode I;

FIG. 6 is a flow chart of the present invention for determining the central producer maximum revenue and the seller maximum revenue in mode I;

FIG. 7 is a flow chart of the present invention for determining the optimal revenue for the central producer and the seller in mode B;

FIG. 8 is a graph showing the relationship between the credibility factor of the green degree and the pricing in different modes of the present invention;

FIG. 9 is a diagram illustrating the relationship between wholesale prices, sales prices, and investment costs of the vendor's emerging information services in model N, I, B according to the present invention;

FIG. 10 is a graph illustrating the relationship between wholesale prices, sales prices, and investment costs for emerging information services of a central producer according to the present invention in model N, I, B;

FIG. 11 is a graph illustrating the relationship between wholesale prices, sales prices, and investment cost optimization coefficients for emerging information services according to the present invention in model N, I, B;

fig. 12 shows the trend of the difference between the profit sharing coefficient p and the overall profit of the agricultural product supply chain in the I, B mode according to the present invention.

Detailed Description

The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. The various examples are provided by way of explanation of the invention, and not limitation of the invention. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present invention encompass such modifications and variations as fall within the scope of the appended claims and equivalents thereof.

The agriculture production is the main strength of the agriculture operation entity in China, and in order to improve the agriculture production efficiency and promote the income increase of farmers, a novel agriculture operation mode is advocated at present, and comprises organization forms such as professional households, agricultural and civil cooperation agencies, and agricultural industrialization leading enterprises.

The novel agricultural operation mode has the inherent advantages of being large in scale and easy to manage in the aspects of block chain implementation and big data technology. Therefore, many trial projects based on blockchains and big data are selected to cooperate with the novel agricultural operation mode. This necessarily causes changes in the network structure of the supply chain, which in turn affects the relationships between users in the supply chain and ultimately the supply chain pricing strategy. Therefore, it is necessary to analyze and construct a network structure of the agricultural product supply chain under the fusion effect of big data and block chains.

By analyzing the big data and the effects of the blockchain on the agricultural product demand prediction and safety tracing processes, an agricultural product supply chain system based on the blockchain and the big data is constructed, as shown in fig. 1, an agricultural product supply chain model comprises a central producer, an information service provider, a wholesaler, a seller and a consumer.

The central producer, as the core of the production function, has the right to distribute production data and obligation to upload production job data. The central producer is mainly a novel agricultural producer (professional major, farmer cooperative, etc.) formed by scattered small farmers through cooperation, and provides information such as greenness, freshness, etc. of agricultural products to the information service provider. Meanwhile, in order to improve the production efficiency and meet the requirements of consumers (mainly referring to the quality safety tracing requirements of green agricultural products), a central producer cooperates with an information service provider (providing emerging information services based on big data and block chains) or directly purchases an information platform of the central producer to obtain required market demand information and agricultural product quality tracing services.

The information service provider is an emerging information service provider based on a big data and block chain fusion technology, and the essential of the information service provider is to provide required information for users no matter based on tracing of a block chain or demand prediction of the big data, so the emerging information service is defined as a service for providing information data for users in a supply chain including a central manufacturer and a seller based on the block chain and the big data in the application; the related information provided in the agricultural product supply chain not only includes production data and production operation data provided by the central producer, but also includes market demand, distribution data, transaction data, competitor data (wholesale price of competitive products) and the like obtained by the information service provider according to investigation or analysis. The consumer as the final user of the agricultural product needs to know the green degree, freshness, safety and other relevant information of the agricultural product.

Therefore, the agricultural products produced by the central producer finally reach the hands of consumers through wholesalers and retailers, the information service providers based on big data and block chain information service provide query bases for accurately releasing the agricultural products, reasonably ordering the agricultural products and completing quality tracing, and meanwhile, more benefits are obtained by providing the information service. The retailer and the wholesaler also need the platform of the information service provider to provide related information service based on big data and a block chain, and the related information comprises sales operation data, so that the retailer and the wholesaler can reasonably price the agricultural products according to the platform of the information service provider to obtain more benefits.

The central manufacturer, the retailer and the wholesaler need to verify the data provided by the platform of the information service provider, and the verification is generally realized by means of an intelligent contract.

The application provides an agricultural product supply chain pricing method based on a block chain and big data as shown in fig. 1, and is shown in fig. 3.

The method comprises the following steps:

step one, constructing an agricultural product supply chain model:

and S101, analyzing the members in the agricultural product supply chain system, and dividing according to the properties.

Specifically, based on the block chain and big data-based agricultural product supply chain system shown in fig. 1, wholesalers and retailers participating in the agricultural product supply chain system both belong to the category of sales, and small farmers and central producers both belong to the category of producers, and according to the attributes of each member in the agricultural product supply chain system, the agricultural product supply chain is regarded as a model consisting of a central producer, a seller, an information service provider and a consumer, and the agricultural product supply chain model is shown in fig. 2.

The central producer uploads production data and production operation data of the agricultural products to a platform provided by an information service provider, and formulates wholesale prices of the agricultural products according to the information data in the information service provider; wherein, the production data of the agricultural product refers to the freshness (i.e. the production date T and the life cycle T) and the green degree g of the agricultural product; the production operation data refers to the unit production cost c of the central manufacturer_wCost per investment c for emerging information services_ow。

A platform provided by an information service provider is used for providing needed information in an agricultural product supply chain based on a block chain and a big data fusion technology, namely information data in the agricultural product supply chain based on emerging information services; the seller uploads the sales operation data to the information facilitator, and the selling price of the agricultural products is formulated according to the information data in the information facilitator; wherein the sales operation data is a unit sales cost c of the seller_rAnd unit investment cost c for Xinxing information service_or；

The consumer as the end user of the agricultural product needs to know the green degree, freshness and other relevant information of the agricultural product.

S102, in order to analyze the agricultural product supply chain more conveniently, certain assumptions are made on each member in the agricultural product supply chain model:

in order to facilitate better and stable analysis of the agricultural product supply chain, assumptions are made about users in the agricultural product supply chain and the correlation conditions of the users to green agricultural products, and the assumed conditions are as follows:

(a) green agricultural products are monopolized for sale, and meanwhile, a central manufacturer has enough production capacity; the central producer does not increase the expenditure on the green innovative technology of the agricultural products, so that the actual green degree of the agricultural products is ensured to have no difference under different pricing modes;

(b) central producers and distributors are risk neutral and fully rational;

(c) consumers prefer green agricultural products and are willing to buy more green, fresher and lower-price agricultural products;

(d) members in the agricultural product supply chain system provide green agricultural products required by consumers, information credibility is guaranteed, trust among participants is enhanced, and a central producer and a central seller adopt emerging information services based on big data and a block chain.

In this embodiment, the technology based on blockchain and big data fusion is simply referred to as emerging information services. In the actual agricultural product supply chain system, because the central manufacturer controls the fresh-living degree information and the green degree information of the agricultural products, in order to encourage the sellers to increase the order quantity, the central manufacturer may misrepresent the production date and the green degree of the agricultural products, that is, the central manufacturer misrepresent the information of the agricultural products in order to sell more agricultural products, that is, the central manufacturer survives misrepresentation. When a central manufacturer and a seller adopt emerging information services provided by information service providers, the block chain technology has the advantages of data tamper resistance and transparency, so that the misrepresentation behavior of the central manufacturer can be reduced, the trust among participants can be enhanced, and the trust of consumers on agricultural products can be improved.

Step two, constructing a response model of market demands in the agricultural product supply chain;

in the agricultural product supply chain, a central manufacturer and a seller both have certain investment cost, and the unit production cost provided by the central manufacturer is c_wAnd green agricultural products with life cycle T, and publishes wholesale price w, production date T and green degree g of the green agricultural products. The seller decides the order lot Q and determines the sale price p.

In the agricultural product supply chain, based on the utility function theory, when a consumer purchases a product, the consumer will purchase the product only when the actual value of the product is greater than the consumer's perceived value.

In practice, the initial perception value of a consumer is influenced by the selling price, freshness and greenness of the agricultural product to change, in the application, the change of the safety perception of the consumer brought by tracing is reflected by the greenness g of the product, and after the actual agricultural product is seen, the consumer can buy the green agricultural product when the real perception value of the influenced consumer meets certain requirements, namely when U is greater than 0.

U＝v-p+θ(t)+λg (1)

Wherein U is the true perceptual value; v is the initial perceptual value; p is the selling price of the seller; theta (t) is the freshness of the green agricultural product, wherein theta (t) is 1-t²/T²(T is more than or equal to 0 and less than or equal to T), T is the production date, and T represents the life cycle of the product; lambda is a credible factor of green degree of the agricultural product; g is the green color of the agricultural product.

For a seller, the order lot is generally determined according to market demand, and herein, assuming that the order lot is considered to be the same as the market demand, the market demand is Q:

therefore, the market demand in the embodiment of the application is mainly constructed according to the selling price p of the seller, the freshness θ (t) of the agricultural product, the green degree g of the agricultural product and the credible factor λ of the green degree;

in this embodiment, the mathematical model of the market demand is shown in equation (2):

Qⁱ＝1-pⁱ+θⁱ(t)+λⁱg (2)

in the formula, I is a mode of whether members in the agricultural product supply chain adopt emerging information services, I is { N, I, B }, and N represents a mode that the members do not adopt the emerging information services, and is called an N mode; i represents a pricing mode that members in the agricultural product supply chain adopt emerging information services to make decentralized decisions, and is called an I mode; and B represents a pricing mode that all members in the agricultural product supply chain adopt emerging information services to carry out integrated decision, and is called as a B mode.

pⁱIs the selling price of the seller in the i mode; when in I mode and B mode, θⁱ(t) is expressed as theta (t), namely theta (t) is the freshness of the agricultural product when the emerging information service is adopted, wherein theta (t) is 1-t²/T²(T is more than or equal to 0 and less than or equal to T), T is the production date of the agricultural product, and T represents the life cycle of the agricultural product; when in the N mode, θⁱ(t) is expressed as theta (gamma t), namely theta (gamma t) is the freshness of the agricultural product when the emerging information service is not adopted, wherein gamma is a misrepresentation factor, and gamma belongs to [0,1 ]]，θ(γt)＝1-(γt)²/T²(0≤t≤T)。λⁱThe credibility factor of the green degree of the agricultural product; g is the green value of the agricultural product (the green value of the agricultural product reflects the safety).

Before members in the agricultural product supply chain do not adopt emerging information service based on big data and block chain, and when a central producer is supposed to lie about agricultural product related information, a market demand function in the N mode is Q^N＝1-p^N+θ(γt)+λ^Ng, i.e. order lot size Q between central producer and seller in the agricultural product supply chain^N＝1-p^N+θ(γt)+λ^Ng, the credibility factor of the green degree of the agricultural product is lambda at the moment^NWherein the lie production date is gamma t, and the lie factor gamma belongs to [0,1 ]]。

After members in the agricultural product supply chain adopt emerging information services based on big data and block chains, the market demand function in the I mode is Q^I＝1-p^I+θ(t)+λ^Ig, i.e. lots Q ordered between central producers and distributors in the agricultural product supply chain^I＝1-p^I+θ(t)+λ^Ig. At this time, agricultural productsThe green credible factor is lambda^I. The market demand function in B mode is Q^B＝1-p^B+θ(t)+λ^Bg, i.e. order lot size Q between central producer and seller in the agricultural product supply chain^B＝1-p^B+θ(t)+λ^Bg. At this time, the green credibility factor of the agricultural product is lambda^B. Technologies such as distributed storage of block chains and intelligent contracts promote information to be more transparent and safer, and the misrepresentation behavior of the central producer can be effectively weakened, so that the misrepresentation behavior of the central producer under the I mode after emerging information service is adopted is restrained.

Therefore, in the presence of lie, the credibility factor lambda of the green degree of the agricultural product^NLower confidence factor lambda than other modes^IAnd a confidence factor lambda^BI.e. λ^N<λ^I＝λ^B. Wherein, the credibility factor is obtained by statistical analysis. According to the method and the device, changes of the supply chain mode of the green agricultural products, the green degree (namely the safety degree) influencing the market demand and the freshness are considered, the demand function is revised based on the utility function theory, the shortage of demand function research under the background of block chain and big data fusion is made up, and therefore the accuracy of pricing can be improved.

Respectively determining the optimal pricing of a central manufacturer and a central seller in different modes according to a response model of market demands;

firstly, determining the optimal pricing of the N mode that a central manufacturer and a seller do not adopt emerging information services;

first, a mathematical model of the central producer's revenue and the seller's revenue in the N-mode, where the central producer and seller do not employ the emerging information services, is constructed, as shown in FIG. 3.

The central producer is used as a leader of the supply chain to decide wholesale price of the agricultural products, information such as production date, green degree and the like of the agricultural products is provided, and the seller is used as a follower of the supply chain to decide sale price of the agricultural products. Both the central producer and the seller will target pricing on respective interest maximization.

Since the emerging information service is not adopted in the N mode, the central manufacturer may misrepresent the freshness and the degree of greenness information of the agricultural products to encourage the seller to order more agricultural products, and therefore, in the N mode, the central manufacturer will misrepresent the behavior of misrepresenting the freshness and degree of greenness information of the products.

S301, acquiring order batch between central producer and seller, namely market demand Q in I mode^O；

Under model N, to encourage the seller to order more agricultural products, the manufacturer will have a misreported freshness behavior, while based on misreported information provided by the manufacturer, the seller will order Q^NThe product of (1). But this may result in a part of the product not selling and decaying, i.e. the actual sales volume of the seller should be equal to the sales volume Q of the central producer without the misrepresentation^o＝1-p^N+θ(t)+λ^Ng。

S302, determining a mathematical model of the income of the central producer through the wholesale price of the central producer to the agricultural products, the unit production cost of the central producer and the order batch between the central producer and the seller.

In this example, the mathematical model of the central producer revenue is:

wherein,

the revenue for the central producer in the N mode; w is a^NIs the wholesale price of the central producer in the N mode; c. C_wUnit sales cost for central manufacturers; q^OOrder lot between the central producer and the seller when the central producer does not lie in the N mode. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.

S303, the central manufacturer does not exist in the N mode through the selling price of the seller, the wholesale price of the central manufacturer and the unit selling cost of the sellerMarket demand (order batch) Q under lie reporting behavior^OAnd market demand (order batch) Q in N-mode^NA mathematical model of the vendor revenue is determined.

In this embodiment, the mathematical model of the vendor revenue is:

wherein,

as revenue to the vendor in I mode; w is a^NThe batch price of the central producer in the N mode is issued; c. C_rUnit sales cost for the lower vendor; q^NThe order lot between the central producer and the seller in the N mode, i.e., the market demand in the N mode. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.

It should be noted that, there is no chronological relationship between the steps S302 and S303, and the step S302 may be executed first, and then the step S303 may be executed; step S303 may be executed first, and then step S302 may be executed; or step S302 and step S303 are performed simultaneously. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.

Secondly, processing the income of the central producer and the income of the seller by a reverse induction method, and respectively determining the maximum income of the central producer and the maximum income of the seller, as shown in fig. 4;

under the N mode that the central producer and the seller do not adopt emerging information services, the income of the central producer and the income of the seller are processed based on a reverse induction method, and the selling price of the seller and the selling price of agricultural products are p^N(w^N)＝[c_r+θ(t)+w^N+λ^Ng+1]2; then p is added^N(w^N) Substituting into formula (3) to obtain

Obviously, there is an optimal wholesale price for the agricultural product

Can make central producer profit

And (4) maximizing.

In meeting the central manufacturer's income

In the case of maximization, the optimal wholesale price of the central producer is

The optimal selling price of the corresponding seller is

S311, according to the unit sale cost c of the central producer_wThe unit sales cost c of the seller_rThe freshness theta (gamma t) of the agricultural product when the misrepresentation action exists, the freshness theta (t) of the agricultural product when the misrepresentation action does not exist and a credibility factor lambda^NAnd green g to get the optimal wholesale price of the central producer.

In this example, the central producer's optimal wholesale price

Comprises the following steps:

it should be understood that the above description is only exemplary, and the embodiments of the present application are not limited thereto.

S312, according to the unit sale cost c of the central manufacturer_wThe unit sales cost c of the seller_rFreshness theta (gamma t) of agricultural product in case of misrepresentation, and no misrepresentationThe freshness theta (t) of the agricultural product and the confidence factor lambda^NAnd green g to obtain the optimum selling price of the seller.

In this embodiment, the optimum selling price of the seller

Comprises the following steps:

S313, determining the maximum benefit of the central manufacturer through the optimal wholesale price of the central manufacturer; determining a maximum profit for a seller by its optimal selling price

Specifically, the maximum benefit of the central producer is obtained through the optimal wholesale price of the central producer and a income mathematical model of the central producer;

in this embodiment, substituting equation (5) into equation (3) can find the maximum benefit of the central manufacturer, as shown in equation (7).

Specifically, the maximum profit of the seller is obtained through the obtained optimal selling price of the seller and the profit mathematical model of the seller.

In this embodiment, substituting equation (5) into equation (4) yields the maximum profit for the vendor, as shown in equation (8).

According to the formulas (5) and (6)

Therefore, as can be seen from the expression relationship between the optimal wholesale price and the maximum profit of the central producer, and the optimal sale price and the maximum profit of the seller, the optimal wholesale price, the optimal sale price and the maximum profit of the central producer increase with the increase of θ (γ t), but the maximum profit of the seller decreases. In addition, with the green degree credibility factor lambda of agricultural products^NThe optimal wholesale price, the optimal sale price and the maximum profit and profit of the user are all increased. That is, the central producer's misrepresentation will harm the retailer's interests. In addition, the credibility of the green information of agricultural products will influence the income and pricing.

Secondly, determining the optimal pricing of the central manufacturer and the central seller in the I mode of the emerging information service;

first, a pricing model is constructed in which the central producer and the seller make decentralized decisions using emerging information services, i.e., in I-mode, a mathematical model of the central producer's revenue and the seller's revenue, as shown in fig. 5.

S321, acquiring order batch between the central producer and the seller in the I mode, namely the market demand Q in the I mode^I；

S322, obtaining a mathematical model of the income of a central producer based on the market demand of the agricultural product supply chain and first related information provided by a platform of an information service provider; the first relevant information data provided by the platform of the information service provider is the wholesale price w of the competitive product^IUnit production cost c of the central producer_wAnd unit investment cost c of central producer for emerging information services_ow(ii) a Considering the influence of unit production cost on the income, therefore, an optimization coefficient phi is introduced to improve the income of a central producer;

in this example, the mathematical model of the central producer revenue is:

in the formula,

for revenue to the central producer in mode I; w is a^IThe batch sending price of competitive products in the I mode is set; phi is an optimization coefficient; c. C_wProduction costs for central manufacturers; c. C_owThe unit investment cost of the central producer for the emerging information services.

S323, market demand Q based on agricultural product supply chain^IObtaining a mathematical model of the seller income with second related information provided by the platform of the information service provider; the second related information provided by the information service provider is the selling price p of the competitive product^IUnit sales cost c of the seller_rAnd the unit investment cost c of the seller with respect to emerging information services_or(ii) a Considering the influence of unit sales cost on the profit, an optimization coefficient phi is introduced to improve the profit of the seller.

In this embodiment, the mathematical model of the vendor revenue is:

in the formula,

is the revenue of the seller in mode I; p is a radical of^IIs the selling price of competitive products in mode I; phi is an optimization coefficient; c. C_rA unit sales cost for the vendor; c. C_orThe unit investment cost for the vendor for emerging information services.

It should be noted that, there is no chronological relationship between step S322 and step S323, and step S322 may be executed first, and then step S323 may be executed; step S323 may be executed first, and then step S322 may be executed; or simultaneously performs step S322 and step S323. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.

Secondly, processing the mathematical models of the profits of the central producer and the sellers to obtain the maximum profits of the central producer and the maximum profits of the sellers;

in the I mode, the determination process of the maximum profit of the central producer and the maximum profit of the seller is shown in FIG. 6;

and processing the market demand, the income of the seller and the income of the central producer by a reverse induction method to obtain the optimal selling price, the maximum income of the central producer and the maximum income of the seller. A fixed coefficient is introduced in the process to improve the yield; preferably, the fixed coefficient is an optimization coefficient; s331, according to unit production cost c of central producer_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural products and credibility factor lambda^IGreen colour of_gCentral producer unit investment cost for emerging information services c_owUnit investment cost c of the seller for emerging information services_orAnd the optimization coefficient phi obtains the optimal selling price of the seller.

In this embodiment, in mode I, the optimum selling price of the seller

Is composed of

S332, according to the unit production cost c of the central manufacturer_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural product when no misrepresentation exists, and credibility factor lambda^IGreenness g, unit investment cost c of the central producer for emerging information services_owUnit investment cost c of the seller for emerging information services_orAnd optimizing the coefficient phi to obtain the optimal wholesale price of the central manufacturer.

In this embodiment, in mode I, the optimal wholesale price of the central producer

Is composed of

With the increase of the cost optimization coefficient phi of the emerging information service, the optimal wholesale price of the agricultural products is increased. In addition, with the increase of the credible factor of the green degree, the optimal wholesale price, the optimal sale price and the user income are increased.

S333, according to the unit sale cost c of the central manufacturer_rThe unit sales cost c of the seller_wFreshness theta (t) of agricultural products and credibility factor lambda^IGreen colour of_gUnit investment cost c for emerging information services by central producers_owUnit investment cost c of the seller for emerging information services_orAnd the optimization coefficient phi yields the market demand (optimal order lot) between the central producer and the seller.

In this embodiment, in mode I, market demand (best order lot)

Is composed of

And S334, respectively determining the maximum income of the central manufacturer and the central seller according to the market demand (optimal order batch).

Specifically, the maximum profit for the central producer is determined based on the market demand (optimal order lot) and the optimal wholesale price of the central producer.

In this embodiment, in mode I, the maximum profit for the central producer

Is composed of

As the emerging information service cost optimization factor phi increases, the maximum revenue for the central manufacturer will decrease. In addition, with the increase of the credible factor of the green degree, the optimal wholesale price, the optimal retail price and the participant income are increased.

Specifically, the maximum profit for the seller is determined based on the market demand (optimal order lot) and the seller's optimal selling price.

In this embodiment, in mode I, the maximum profit for the vendor

Comprises the following steps:

as the cost optimization coefficient φ for emerging information services increases, the maximum revenue for the vendor will decrease. In addition, with the increase of the credible factor of the green degree, the optimal wholesale price, the optimal retail price and the participant income are increased.

Based on the above description, with the increase of the cost optimization coefficient phi of the emerging information service, the optimal wholesale price and sale price of the agricultural products will increase, but the maximum profit of the user will decrease. As the cost of emerging information services increases, user revenue will decrease. In addition, with the increase of the credible factor of the green degree, the optimal batch sending price, the optimal retail price and the user income are increased.

According to the formula, the new information service based on big data and block chains influences the income and pricing strategy of the participants, and meanwhile, the credibility of the green degree of agricultural products is increased, so that the income and pricing strategy of the participants are influenced. But investment is profitable when the income of the user after investing in the emerging information service is larger than the income before investment. Without loss of generality

The following relationship is thereby obtained.

c_ow+c_or<θ(t)-φc_r-φc_w+λ^Ig+1 (16)

As can be seen from equation (16), the cost c of the emerging information services based on big data, blockchain_ow+c_orPhi is inversely related to the emerging information service cost optimization coefficient. That is, if a central manufacturer or retailer wants to reduce the cost risk brought by applying the emerging information service, it needs to try to draw the value in the new information and apply the value in the agricultural product supply chain cost optimization and efficiency improvement, and reduce the cost optimization coefficient phi of the emerging information service.

Through the formulas (11), (12), (14) and (15), the relationship between the green degree and the wholesale price of the central producer, the selling price of the seller, the maximum income of the central producer and the maximum income of the seller is obtained as follows:

wherein M is 1-c_or-c_ow+θ(t)-φc_r-φc_w+λ^Ig。

Therefore, the following steps are carried out: in the mode I, along with the improvement of the credible factor of the green degree of the product, the price of the agricultural product (wholesale price and sale price) is increased. Perhaps because the confidence of the consumer is enhanced by the increase in the credibility of the green color, the consumer's confidence is enhanced by the increase in the perceived value of the product, the retailer can set a higher retail price to obtain more revenue, and the wholesaler can set a higher wholesale price. Meanwhile, as can be seen from the formula (13), as the credibility of the green degree of the agricultural product is improved, the market demand is increased, which may be the reason for the increased income of the participator. And regarding the change of the credible factor lambda I of the green degree of the agricultural product, the optimal sale price of the agricultural product is more sensitive than the optimal wholesale price, and the income of a central manufacturer is more sensitive than the income of a retailer.

Through equations (11), (12), (14) and (15), the emerging information service cost optimization coefficients are related to the wholesale price of the central producer, the selling price of the seller, and the maximum profit of the central producer and the maximum profit of the seller:

wherein M is 1-c_or-c_ow+θ(t)-φc_r-φc_w+λ^Ig。

As can be seen from the above equation, in mode I, as the cost optimization coefficient of the emerging information services increases, the retail price of the green agricultural product increases, and the change of the optimal wholesale price is related to the sales cost between the retailer and the central producer (i.e. c)_w-c_r). When c is going to_w-c_r>0, as the emerging information service cost optimization coefficient increases, the optimal wholesale price will increase, otherwise it will decrease. Meanwhile, as the cost optimization coefficient of emerging information services increases, participant revenue will decrease. This also shows that if the participants want to gain more benefits after investing in emerging information services, they need to dig and draw the value of new information based on big data and block chains as much as possible.

Through equations (11), (12), (14) and (15), the relationship between the emerging information service cost and the wholesale price of the central producer and the sale price of the seller is obtained as follows:

wherein M is 1-c_or-c_ow+θ(t)-φc_r-φc_w+λ^Ig。

As can be seen, the cost of information services c is emerging with central manufacturers_owThe selling price of the agricultural products will increase and the wholesale price will decrease. This may be due to the central producer having to set higher wholesale prices to gain more revenue in order to fill the cost investment in emerging information services, resulting in an increase in the selling price of agricultural products. In contrast, as retailers emerge information service costs c_orThe retail price of the agricultural products will increase and the wholesale price will decrease. This may be because retailers have to set higher sales prices to gain more revenue in order to offset the cost investments in emerging information services. And the seller invests the 'overflow effect', the income of a central producer can be increased, and in order to maximize the income, the wholesaler incentivizes the retailers to order more agricultural products by setting a lower wholesale price.

Through equations (11), (12), (14) and (15), the relationship between the emerging information service cost and the maximum profit of the central producer and the maximum profit of the seller is obtained as follows:

wherein M is 1-c_or-c_ow+θ(t)-φc_r-φc_w+λ^Ig。

As can be seen, the cost of the emerging information services c of the central producer_owThe profit of the seller and the central producer is negatively related. In addition, retailer emerging information service cost c_orBut also negatively relate to the benefit of the seller and the central manufacturer. This indicates an emerging investmentWhen a central manufacturer and a seller after information service negotiate about emerging information service cost with a third-party information service company, the investment of the emerging information service cost is controlled as much as possible, and simultaneously, the value of new information based on big data and a block chain is mined and drawn as much as possible to expand the threshold value of the emerging information service cost.

Thirdly, determining the optimal pricing of the central producer and the central seller in the B mode adopting the emerging information service;

the method mainly considers that the members in the supply chain can carry out pricing according to own cost and investment condition in order to realize the maximization of respective income, namely pricing in a scattered decision mode; however, this does not fully utilize the information data provided by the information service provider, and ultimately results in the overall revenue of each member of the supply chain system being less than optimal. Therefore, in the present application, a method for pricing agricultural products under an integrated decision is proposed, that is, a central producer and a seller in a supply chain are considered uniformly when pricing is performed after an emerging information service of a big data and block chain is applied, and pricing is performed on the basis of a consistent and uniform decision coordinated by the central producer and the seller, as shown in fig. 7.

First, the overall yield of the agricultural product supply chain in the B mode of the integrated decision is determined based on the integrated decision-making action of the central producer and the seller.

S341, the central producer and the seller carry out coordinated and unified decision to obtain market demand in the B mode of the integrated decision;

in the integrated mode, when a central producer and a seller make a coordinated and unified decision, the selling price of the agricultural products in the integrated mode is determined, and the actual market demand Q is obtained in the B mode of the integrated decision^C(ii) a Where c characterizes the role of the integrated decision, here Q^C＝Q^B。

In this embodiment, the actual market demand in B-mode of integrated decision is Q^CComprises the following steps:

Q^C＝1-p^C+θ(t)+λ^Cg (17)

s342, obtaining the overall profit of the supply chain in the B mode of the integrated decision under the result of the coordinated unified decision;

in the process of obtaining the overall supply chain income of the integrated decision under the B mode under the condition of coordinating the result of the unified decision, firstly, the market demand Q under the B mode is obtained^C(ii) a Secondly, according to the market demand Q in the B mode^CThe established selling and selling prices of the agricultural products and third related information data provided by the information service providers determine a mathematical model of the overall income of the supply chain, wherein the third related information data comprises unit production cost of a central producer, unit investment cost of the central producer for emerging information services, unit production cost of a seller and unit investment cost of the seller for emerging information services.

Obtaining the whole income of the supply chain in the integration mode through the sales cost of a seller and the unit investment cost of the emerging information service, the production cost of a central producer and the unit investment cost of the emerging information service, and the selling price and the market demand of agricultural products in the determined integration mode; in addition, considering the influence of unit production cost and sale cost on the profit, a fixed coefficient is introduced to improve the profit; preferably, the fixed coefficient is an optimization coefficient;

in this embodiment, the overall supply chain benefit in B-mode of integrated decision is:

π^C＝(p^C-φc_r-φc_w-c_ow-c_or)Q^C (18)

in the formula, pi^CIs composed of^BSupply chain overall revenue under model; p is a radical of^CIs composed of^BSelling prices of agricultural products in the mode; q^CIs the actual market demand in mode B; c. C_wUnit production cost for central manufacturers; c. C_owUnit investment cost for central manufacturers for emerging information services; c. C_rUnit production cost for the vendor; c. C_orIs the unit investment cost of the marketer for the emerging information services.

Secondly, optimizing the whole income of the agricultural product supply chain in the B mode of the integrated decision to respectively obtain the optimal selling price, the optimal market demand and the optimal income;

according to market demands and the whole income analysis of the supply chain, the agricultural products have the optimal selling price in the decision-integrating mode B, so that the whole income of the supply chain tends to be the maximum.

And when the optimal selling price, the optimal market demand and the optimal profit are obtained for the whole profit of the supply chain in the B mode, the optimal selling price, the optimal market demand and the maximum profit are obtained for the whole profit of the supply chain in the B mode by adopting a reverse induction method and processing the whole profit of the supply chain in the B mode to obtain the optimal selling price, the optimal market demand and the whole optimal profit of the supply chain.

S351, according to the unit production cost c of a central manufacturer_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural products and credibility factor lambda^CGreen colour of_gUnit investment cost c for emerging information services by central producers_owUnit investment cost c of the seller for emerging information services_orAnd the optimization coefficient phi obtains the optimal selling price in the whole supply chain under the B mode.

In this embodiment, in mode I, the optimum selling price of the seller

Comprises the following steps:

S352, according to the unit production cost c of the central manufacturer_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural products and credibility factor lambda^CGreen colour of_gUnit investment cost c for emerging information services by central producers_owUnit investment cost c of the seller for emerging information services_orAnd optimizing the coefficient phi to obtain the optimal market demand of the supply chain under the B mode.

In this embodiment, in mode B, market demand (best order lot)

Comprises the following steps:

S353, according to the unit production cost c of the central manufacturer_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural products and credibility factor lambda^CGreen colour of_gUnit investment cost c for emerging information services by central producers_owUnit investment cost c of the seller for emerging information services_orAnd optimizing the coefficient phi to obtain the overall profit of the supply chain under the B mode to obtain the optimal profit.

In this embodiment, in mode B, the supply chain is optimized for revenue overall

Comprises the following steps:

Finally, processing the whole income of the agricultural product supply chain under the B mode of the integrated decision to obtain the optimal income of a central producer and the optimal income of a seller;

when the optimal income of the central producer and the optimal income of the seller are obtained by processing the whole income of the agricultural product supply chain under the B mode of the integrated decision, the optimal income of the central producer and the optimal income of the seller are respectively obtained by introducing the processing in the cost sharing income sharing contract mode

As can be seen from the formula (19) and the formula (11)

While

That is, the optimal selling price in the I-mode of the dispersion decision is higher than the optimal retail price in the B-mode of the integration decision, and the overall profit mode I of the supply chain is lower than the integration decision. Thus, there is a "bullwhip effect" of the supply chain in mode I. In order to reduce the influence of the effect and improve the income of the participants after the application of the emerging information service, under the B mode considering the integrated decision, a cost sharing income sharing contract is introduced, and the cost sharing income sharing contract is applied to a coordinated supply chain to reduce or even avoid the bullwhip effect.

S361, setting a cost sharing coefficient and a profit sharing coefficient between a central manufacturer and a seller; the cost sharing coefficient is the distribution proportion of the cost between a central producer and a seller in the selling process of the agricultural products; the income sharing coefficient is the distribution proportion of the income between the central producer and the seller in the agricultural product selling process;

s362, obtaining the optimal profit of the central producer through the cost sharing coefficient, the profit sharing coefficient and the overall optimal profit of the supply chain;

in the present embodiment, the cost sharing coefficient ζ, the profit sharing coefficient ρ, and the unit production cost c of the central manufacturer are determined based on_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural products and credibility factor lambda^CGreenness g, unit investment cost c of the central producer for emerging information services_owThe unit investment cost c of the seller on the Xinxing information service_orAnd optimizing the coefficient phi to obtain the optimal yield of the central producer in the B mode

In this example, the optimal revenue for the central producer in B mode

Comprises the following steps:

S363, obtaining the optimal profit of the seller according to the cost sharing coefficient, the profit sharing coefficient and the overall optimal profit of the supply chain;

in the present embodiment, the cost sharing coefficient ζ, the profit sharing coefficient ρ, and the unit production cost c of the central manufacturer are determined based on_wThe unit sales cost c of the seller_rFreshness theta (t) of agricultural products and credibility factor lambda^CGreenness g, unit investment cost c of the central producer for emerging information services_owThe unit investment cost c of the seller on the Xinxing information service_orAnd the optimization coefficient phi obtains the optimal profit of the seller in the B mode

In this embodiment, the optimal revenue for the vendor in B mode

Comprises the following steps:

Response model through market demand and optimal revenue for vendors in B-mode

As can be seen, the optimum selling price is obtained in the B mode

If the user wants to obtain the same income as the integrated decision, the optimal selling price is obtained

Should be equal to

Namely, it is

Thereby is easy to obtain

By

The optimum profit for the seller and the central producer is known.

Wherein F ═ φ c_r+φc_w+c_or+c_ow. When in use

Then, the manufacturer and the seller share the profit sharing contract with the cost.

Therefore, when rho is less than or equal to 0.5, the supply chain coordination after the emerging information service application can be realized by adopting the cost sharing income sharing contract, and the cost sharing coefficient does not influence the coordination relation.

By

And the optimal profit of the seller and the central producer is obtained:

it can be seen that the reliability lambda of green color information of agricultural products^BThe wholesale price of the agricultural products is not changed, and the retail price is increased. The contract coordination does not change the trend that the retail price of the agricultural products increases along with the change of the credibility of the green degree, but influences the change trend of the wholesale price. In addition, with λ^BThe trend of manufacturer-retailer revenue is similar for both mode I and mode B.

By

And the optimal profit of the seller and the central producer is obtained:

it can be known that as the cost optimization coefficient phi of the emerging information service increases, the selling price of the agricultural products in the mode B increases, but the change of the wholesale price is related to the selling cost of the seller and the wholesale merchant, namely when c is_w/(c_r+c_w)>ρ, the wholesale price will rise, otherwise it will fall. Meanwhile, as the optimization phi increases, the profits of both the manufacturer and the seller decrease. Therefore, if central manufacturers and distributors want to obtain more profits, efforts are made to draw and mine the potential value of new information to reduce the optimization coefficient phi.

By

And the optimal profit of the seller and the central producer is obtained:

as can be appreciated, cost of information services c as retailers emerge_orWill increase the retail price of the agricultural product, the wholesale price is related to the profit sharing coefficient and the cost sharing coefficient, namely when zeta>And rho, the wholesale price of the agricultural product is increased, and conversely, the wholesale price is decreased. Investment cost c for emerging information service units of central manufacturers_orThe change of retail price of agricultural products in the coordination mode is more sensitive than that in the I mode (decentralized decision model). Information service unit investment cost c as central manufacturers emerge_owThe sale price and the wholesale price of the agricultural products have the same change trend (both decrease) as the mode I. And the change of the wholesale price of the agricultural product in the mode B is related to the profit sharing coefficient.

By

And the optimal profit of the seller and the central producer is obtained:

as can be seen, the investment cost c of the information service unit emerging from the seller in the agricultural product supply chain_orInvestment cost c of emerging information service unit of central manufacturer_owThe revenue of both the central producer and the seller decreases. Contract coordination does not change the investment cost c of agricultural product supply chain participant income along with the emerging information service unit investment cost of the seller_orAnd investment cost c of emerging information service units of central manufacturers_owA reduced trend.

Pricing analysis of the different modes is verified through simulation:

in this embodiment, simulation analysis is performed in china by means of numerical simulation. The market demand is highWhen zero, so, let g be 5, c_r＝0.11、c_w＝0.1、c_ow＝0.1、c_or0.05, 0.6 ζ, 0.08 θ (t), 0.035 θ (γ t), and 0.65 Φ. The relationship shown in FIG. 8 can be obtained from the equations (5), (6), (11) and (12), and the credibility of the green degree is λ with the agricultural productⁱThe sale price and the wholesale price under the mode N, I are increased, but the wholesale price of the agricultural products under the mode B is not influenced, and the sale price is increased. This may be because of λⁱThe enhancement of the method improves the perception value of consumers and sellers, and the consumers and the sellers are willing to pay more for obtaining the agricultural products, so that a central producer can set a higher wholesale price, and the sellers can also set a higher sale price. In addition, with λⁱThe income of agricultural product supply chain users under the model I, N, B is increased, and when the investment cost of emerging information services based on big data and block chains is in a certain value range, more profit can be obtained by investment than non-investment.

When assuming λ^N＝0.4、λ^I＝λ^B＝λ^C0.9. FIG. 9 (a) shows the relationship between the batch price, the sale price, and the investment cost of the vendor emerging information services in mode N, I, B. Here the difference 0.3 between the cost sharing factor (taking the value 0.5) and the benefit sharing factor (taking the value 0.2) is greater than zero. Information service investment cost c as vendors emerge_orThe wholesale price of the agricultural products in the mode I is reduced, and the sale price is increased; both the wholesale price and the sale price in the mode B will increase. This may be due to manufacturer's shared c_orThe revenue is compensated for by the share of the seller, and the producer deliberately adjusts the wholesale price down in order to encourage the seller to order more agricultural products. FIG. 9 (b) shows user profits and c in the agricultural product supply chain in the mode N, I, B_orAnd (4) relationship. With c_orThe user's profit decreases in both mode I and B, but the user's profit is significantly higher in mode B than in mode I. This shows that the supply chain after the contract is adopted to coordinate investment can lead the users of the agricultural product supply chain to obtain more income.

FIG. 10 (a) shows the wholesale, sales and emerging from the central producer under model N, I, BInvestment cost of information service c_owThe relationship (2) of (c). With c_owThe wholesale price and the sale price of the agricultural products under the modes I and B are increased. This may be because the manufacturer has to increase the wholesale price of the agricultural products in order to fill the additional expenses incurred by applying the emerging information services, and the seller has to set a higher sales price after getting high wholesale price of the agricultural products. FIG. 10 (b) shows the yield and c of the participants in the agricultural product supply chain in the mode N, I, B_owThe relationship (2) of (c). With c_owThe gains for participants in both mode I and B decreased, but the gains for participants in mode B were significantly higher than in mode I. This shows that the supply chain after the investment is coordinated by the contract can make the agricultural product supply chain participant obtain more income. As can be additionally seen from fig. 11, the investment is advantageously available only when the investment cost of the manufacturer, the seller, for the emerging information services satisfies a certain value. In addition, with the supply chain after the coordination of the cost-sharing revenue sharing contracts, the investment cost threshold of the participants for emerging information services is larger.

FIG. 11 (a) shows the relationship between wholesale price, sales price and emerging information service cost optimization coefficient φ in mode N, I, B. As phi increases, the wholesale price and the sale price under the modes I and B increase. This may be because the manufacturer or the seller has to increase the wholesale price and the selling price of the agricultural products to cover the additional expenses caused by applying the emerging information services. FIG. 11 (b) shows the yield of the agricultural product supply chain participant versus φ in mode N, I, B. With increasing phi, the participant's profit decreases in both modes I and B. As can be seen from fig. 11 (a), the cost-sharing profit-sharing contract is not only beneficial to manufacturers and sellers, but also beneficial to consumers to purchase green and fresh high-quality agricultural products at low cost, thereby helping consumers to reduce living and consuming expenses. As can be seen from (b) in fig. 11, it is only profitable for a participant to employ an emerging information service when phi satisfies a certain value.

Fig. 12 shows the variation trend of the difference between the overall profits of the agricultural product supply chain with the increase of the benefit sharing coefficient ρ in the mode I and the mode B. As ρ increases, the difference in overall yield of the agricultural product supply chain will decrease. And when the rho is lower than 0.5, the supply chain coordination after the emerging information service is applied can be realized by adopting the cost sharing income sharing contract.

The embodiment of the device is as follows:

the invention also provides an agricultural product pricing device, which comprises a memory, a processor and a program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the agricultural product pricing method. Since the agricultural product pricing method is described in detail in the method embodiment, it is not described here in detail.

The Processor includes a Central Processing Unit (CPU), a Network Processor (NP), etc., and may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Computer-readable storage medium embodiments:

a computer-readable storage medium, on which a computer program is stored which, when executed by a processor, implements the above agricultural product pricing method.

In the embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. Further, the program code stored in the memory may be stored in a computer-readable storage medium if it is implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Compared with the prior art, each user in the supply chain participates in a new agricultural product, and an information technology of block chain and big data fusion is adopted, so that the situation that a central producer can misreport the information of the agricultural products in order to sell more agricultural products, namely, the central producer lives in misreport behavior can be effectively avoided. When the central manufacturer and the seller adopt the emerging information service provided by the information service provider, the block chain technology has the advantages of data tamper resistance and transparency, so that the misrepresentation behavior of the central manufacturer can be reduced, the trust among participants can be enhanced, and the trust of consumers on agricultural products can be improved.

Based on the agricultural product supply chain, a new pricing mechanism is needed to be well adapted to market operation, and therefore, the agricultural product pricing method is correspondingly provided, based on the information technology of block chain and big data fusion, market demands, central producer profits and seller profits are determined, then a reverse induction method is adopted to obtain the maximum profits of the central producer and the maximum profits of the seller, and the optimal wholesale price of the central producer and the optimal sale price of the seller are respectively formulated according to the maximum profits of the central producer and the maximum profits of the seller. Therefore, the agricultural product pricing method can be well suitable for the market, market requirements are met, and both central manufacturers and sellers can obtain the maximum benefits.

The technical scheme of the invention can lead the participants to obtain more benefits by adopting the supply chain after contract coordination investment. But investment is profitable only when the investment cost of the manufacturer and the seller for the emerging information services and the cost optimization coefficient of the emerging information services meet certain values. In addition, by utilizing the supply chain after the coordination of the cost-sharing benefit-sharing contract, the investment cost threshold value of the emerging information service is larger, and when and only when the benefit sharing coefficient is lower than 0.5, the coordination of the agricultural product supply chain applying the big data and the block chain can be realized by the investment and application cost-sharing benefit-sharing contract.

The invention provides a supply chain after the application cost sharing income sharing contract is coordinated and invested in the emerging information service, and enriches the application environment of the cost sharing income sharing contract. The proposed pricing result can provide a theoretical basis for agricultural product supply chain participants to formulate competitive pricing strategies under a new background; the method can provide theoretical support for the supervision, control and formulation of the macro policy of the price of the agricultural products for decision departments such as the national market supervision and management headquarter and the like from the perspective of supply chain operation management.

The above description is only exemplary of the invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the invention is intended to be covered by the appended claims.

Claims

1. A method for pricing agricultural products, comprising

the central producer uploads the production data and the production operation data of the agricultural products to the information service provider, and formulates the wholesale price of the agricultural products according to the information data in the information service provider; the production data comprises the production date, the life cycle and the green degree of the agricultural product; the production operation data comprises unit production cost and unit investment cost for emerging information services;

and step four, processing the overall income of the supply chain to obtain the optimal income of the central producer, and formulating the optimal wholesale price of the central producer according to the optimal income of the central producer.

2. An agricultural product pricing method according to claim 1, wherein in step two, a response model of the market demand is determined under the influence of an integrated decision based on the supply chain model; the integrated decision means that the central producer and the seller make a coordinated and unified decision to make the selling price of the agricultural product.

3. The commodity pricing method according to claim 2, wherein a response model for market demand in mode B is determined from the selling price of the commodity and the freshness of the commodity, the greenness of the commodity, and the credibility factors for the greenness of the commodity under the influence of the integrated decision; the B mode refers to a pricing mode that members in the agricultural product supply chain adopt emerging information services to carry out integration decision.

4. An agricultural product pricing method according to claim 3, wherein in step three, the determination of the overall revenue of the supply chain is by: firstly, acquiring the market demand in a B mode; secondly, determining the income of the central manufacturer according to the market demand, the established selling price of the agricultural product and information data provided by the information service provider in the B mode, wherein the third relevant information data comprises the unit production cost of the central manufacturer, the unit investment cost of the central manufacturer to the emerging information service, the unit production cost of the seller and the unit investment cost of the seller to the emerging information service.

5. Agricultural product pricing method according to claim 4, characterized in that the overall profit of the supply chain is:

π^C＝(p^C-φc_r-φc_w-c_ow-c_or)Q^C

in the formula, pi^CIs the overall revenue of the supply chain in the B mode; p is a radical of^CThe selling price of the agricultural products in the mode B is set; q^CIs the market demand in the B mode; c. C_wUnit production cost for the central producer; c. C_owA unit investment cost for the central producer for the emerging information services; c. C_rA unit production cost for the vendor; c. C_orA unit investment cost for the vendor for emerging information services; phi is a fixed coefficient.

6. An agricultural product pricing method according to claim 5, characterized in that in step four, the process of processing the overall revenue of the supply chain to obtain the optimal revenue of the central producer:

7. The agricultural product pricing method of claim 5, wherein step four further comprises: and processing the overall income of the supply chain to obtain the optimal income of the seller.

8. An agricultural product pricing method according to claim 7, wherein overall revenue of the supply chain is processed and the determination of the optimal revenue for the vendor is made by:

and finally, obtaining the optimal profit for sale through the cost sharing coefficient, the profit sharing coefficient and the overall optimal profit of the supply chain.

9. A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements a method of pricing agricultural products according to any of claims 1-8.

10. An agricultural product pricing device comprising a memory, a processor and a program stored in the memory and executable on the processor, the program when executed by the processor implementing the agricultural product pricing method of any of claims 1-8.