CN112085518A - New agricultural product supply chain pricing method and device - Google Patents

New agricultural product supply chain pricing method and device Download PDF

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CN112085518A
CN112085518A CN202010779834.2A CN202010779834A CN112085518A CN 112085518 A CN112085518 A CN 112085518A CN 202010779834 A CN202010779834 A CN 202010779834A CN 112085518 A CN112085518 A CN 112085518A
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刘盼
张凤杰
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Henan Agricultural University
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Abstract

The invention provides a pricing method and a device for a new supply chain of agricultural products, wherein the pricing method comprises the following steps: step one, constructing a new supply chain model of agricultural products; step two, constructing a response model of the market demand based on the new supply chain model; step three, determining a mathematical model of the central producer income and the seller income according to the response model of the market demand; and step four, processing the mathematical models of the income of the central producer and the income of the seller by adopting a reverse induction method to obtain the maximum income of the central producer and the maximum income of the seller. The maximum earnings of the central producer and the central seller can be obtained respectively through the pricing method, the pricing can be effectively carried out according to the respective maximum earnings, the requirement of the market for the authenticity of the agricultural product information can be met, and the income maximization of the central producer and the central seller can be met, so that the agricultural product new supply chain pricing method can meet the market requirement of each user in the agricultural product new supply chain.

Description

New agricultural product supply chain pricing method and device
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a new supply chain pricing method and device for agricultural products.
Background
Along with the improvement of living standard of people, green and safe agricultural products are more and more popular. But the panic caused by the outbreak of a series of food safety events 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 blockchain in terms of data tamper resistance and transparency, the problems can be solved through the characteristics of tamper resistance, decentralization and the like of the blockchain. However, the block chain technology has short general application time and limited data accumulation in the agricultural field, and the market demand of consumers cannot be accurately predicted in a short time.
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 a pricing method and a pricing device for a new supply chain of agricultural products under the fusion of a block chain and big data.
In order to achieve the above purpose, the invention provides the following technical scheme:
a new supply chain pricing method for agricultural products comprises
Step one, constructing a new supply chain model of agricultural products according to data in a new supply chain;
the new supply chain comprises a central producer, a seller and an information service provider;
the central producer uploads 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 a new supply chain of agricultural products 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, constructing a response model of the market demand based on the new supply chain model;
step three, determining a mathematical model of the central manufacturer income according to the response model of the market demand;
and step four, processing the mathematical model of the central manufacturer income by adopting a reverse induction method to obtain the maximum income of the central manufacturer.
Further, in the second step, a response model of the market demand is constructed according to the selling price of the agricultural product, the freshness of the agricultural product, the green degree of the agricultural product and the credible factors of the green degree of the agricultural product, which are established by the seller in the new supply chain model.
Further, the response model of the market demand is as follows:
Qi＝1-pi+θi(t)+λig
in the formula, QiIs the market demand in i mode; i is a mode of whether members in the new supply chain of the agricultural product adopt the emerging information service, I is { N, I }, and N represents a mode that the members in the new supply chain do not adopt the emerging information service, which is called an N mode; i represents a mode that all members in the new supply chain adopt emerging information services, and is called as an I mode; p is a radical ofiIs the selling price of the seller in the i mode; lambda [ alpha ]iA confidence factor that is the greenness of the agricultural product; g isThe greenness of the agricultural product; thetai(t) is the freshness of the produce in i mode; t is the production date of the agricultural products.
Further, in step three, the determination process of the mathematical model of the central manufacturer profit is as follows: firstly, acquiring market demands in the I mode; secondly, determining a mathematical model of the income of the central manufacturer according to the market demand in the I mode and first related information data provided by the information service provider, wherein the first related information data comprises wholesale prices of competitive products of agricultural products, unit production cost of the central manufacturer and unit investment cost of the emerging information service of the central manufacturer.
Further, in the fourth step, a reverse induction method is adopted to process the mathematical model of the income of the central producer, and the process of obtaining the maximum income of the central producer is as follows:
firstly, obtaining the optimal wholesale price of the central producer by adopting a reverse induction method according to the unit production cost of the central producer, the unit sales cost of the seller, the freshness of the agricultural product when a misrepresentation action exists, the greenness of the agricultural product and the credible factor of the greenness; the misrepresentation behavior refers to misrepresentation of the freshness of the agricultural products by a central manufacturer;
and then obtaining the maximum profit of the central producer according to the optimal wholesale price of the central producer, the market demand and a mathematical model of the profit of the central producer.
Further, the optimal wholesale price of the central producer is:
Figure BDA0002619804080000031
the maximum revenue of the central producer is:
Figure BDA0002619804080000032
in the formula, wI*Optimal wholesale prices for central producers;
Figure BDA0002619804080000033
maximum revenue for the central producer; c. CwUnit production cost for central manufacturers; c. CrA unit sales cost for the vendor; θ (t) is the freshness of the agricultural product when no lie exists; phi is a fixed coefficient; lambda [ alpha ]IA confidence factor that is the greenness of the agricultural product; g is the greenness of the agricultural product; c. CorA unit investment cost for the vendor for emerging information services; c. CowA unit investment cost for the central producer for emerging information services.
Further, the third step further includes: determining a mathematical model of the seller's revenue according to the response model of the market demand; and then, processing the mathematical model of the seller income by adopting a reverse induction method to obtain the maximum income of the seller.
Further, the process of determining the mathematical model of the seller revenue is as follows: firstly, acquiring the market demand in the I mode; secondly, obtaining a mathematical model of the seller income according to the market demand in the I mode and second related information data provided by the information service provider; the second relevant information data includes a selling price of a competing agricultural commodity for the agricultural commodity, a selling cost of the seller and a unit investment cost of the seller for the emerging information service.
Further, the process of processing the mathematical model of the seller's profit by using the inverse induction method to obtain the maximum profit of the seller is as follows:
firstly, obtaining the optimal wholesale price of the central manufacturer according to the obtained unit production cost of the central manufacturer, the unit sales cost of the seller, the freshness of the agricultural product when the misreport action exists, the credible factor of the green degree of the agricultural product and the green degree of the agricultural product by adopting a reverse induction method; the misrepresentation behavior refers to misrepresentation of the freshness of the agricultural products by a central manufacturer; the optimal selling price of the seller is as follows:
Figure BDA0002619804080000041
then, obtaining the maximum profit of the seller according to the optimal wholesale price of the central producer, the market demand and a mathematical model of the profit of the seller;
the maximum profit for the vendor is:
Figure BDA0002619804080000042
in the formula, pN*The optimal selling price of the seller;
Figure BDA0002619804080000043
maximum revenue for the vendor; c. CwUnit production cost for central manufacturers; c. CrA unit sales cost for the vendor; θ (t) is the freshness of the agricultural product when no lie exists; phi is a fixed coefficient; lambda [ alpha ]IA confidence factor that is the greenness of the agricultural product; g is the greenness of the agricultural product; c. CorA unit investment cost for the vendor for emerging information services; c. CowA unit investment cost for the central producer for emerging information services.
In order to achieve the technical object, the present invention further provides an agricultural product new supply chain 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 new supply chain 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 a block chain and big data fusion emerging information service technology is adopted, so that 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 can be effectively avoided.
When a central producer 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 producer can be reduced, the trust among participants can be enhanced, and the trust of consumers on agricultural products can be improved.
The method comprises the steps of determining market demand, central producer income and seller income based on a block chain and a emerging information service technology of big data fusion, then obtaining the maximum income of the central producer and the maximum income of the seller by adopting a reverse induction method, and respectively making the optimal wholesale price of the central producer and the optimal sale price of the seller according to the maximum income of the central producer and the maximum income of the seller. The maximum earnings of a central production and a seller can be respectively obtained through the pricing method, and the optimal wholesale price and the optimal sale price can be effectively determined according to the respective maximum earnings, so that 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 manufacturer and the sale income can be maximized.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. Wherein:
FIG. 1 is a block chain and big data based agricultural product new supply chain system diagram of the present invention;
FIG. 2 is a block chain and big data based model diagram of a new supply chain for agricultural products according to the present invention;
FIG. 3 is a flow chart of the method for pricing agricultural products in the new supply chain of agricultural products based on block chains and big data according to the present invention;
FIG. 3-1 is a flow chart of mathematical model construction of the central producer revenue and the seller revenue under the N-mode of the present invention;
FIG. 3-2 is a flow chart of the mathematical model construction of the central producer revenue and the seller revenue in the I mode of the present invention;
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 determining the central producer maximum profit and the seller maximum profit in the I mode.
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 small agricultural production is the main strength of agricultural operation entities in China, and in order to improve the agricultural production efficiency and promote the income increase of farmers, a novel agricultural operation mode is advocated at present, and comprises organization forms such as professional households, home-farmer cooperative societies, agricultural industrialization leading enterprises and the like.
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 will necessarily cause changes in the network structure of the supply chain, which in turn will affect the relationships between users in the supply chain and ultimately the pricing strategy of the supply chain. Therefore, it is necessary to analyze and construct a new agricultural product supply chain network structure under the action of big data and block chain fusion.
Through analyzing the big data and the effects of the blockchain on the agricultural product demand prediction and safety tracing processes, a new agricultural product supply chain system based on the blockchain and the big data is constructed, as shown in fig. 1, the new 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 new supply chain including a central manufacturer and a seller based on the block chain and the big data in the application; the related information in the new supply chain of the agricultural product not only comprises production data and production operation data provided by the central producer, but also comprises 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 end 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 a block chain and big data based agricultural product new supply chain pricing method based on a block chain and big data based agricultural product new supply chain system shown in fig. 1, and is shown in fig. 3.
The method comprises the following steps:
step one, constructing a new supply chain model of agricultural products:
and S101, analyzing the members in the new supply chain system of the agricultural products, and dividing according to the properties.
Specifically, based on the block chain and big data-based agricultural product new supply chain system shown in fig. 1, both wholesalers and retailers participating in the agricultural product new supply chain system belong to the category of sales, and both small farmers and central producers belong to the category of producers, and according to the attributes of each member in the agricultural product new supply chain system, the agricultural product new supply chain is regarded as a model consisting of the central producer, the seller, the information service provider and the consumer, and the agricultural product new 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 manufacturerwCost per investment for emerging information services cow。
The platform provided by the information service provider is used for providing the needed information in the agricultural product new supply chain based on the block chain and the big data fusion technology, namely the agricultural product new supply chain based on the emerging information serviceThe information data of (1); 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 sellerrAnd unit investment cost c for emerging information servicesor；
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 new agricultural product supply chain more conveniently, certain assumptions are made on each member in the new agricultural product supply chain model:
in order to facilitate better and stable analysis on the new agricultural product supply chain, assumptions are made on users in the new agricultural product supply chain and the correlation condition of the users on 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 green innovative technology of 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 new 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 new supply chain system of actual agricultural products, because the central producer controls the fresh-living degree information and the green degree information of the agricultural products, in order to encourage the sellers to increase the ordering amount, the central producer may misrepresent the production date and the green degree of the agricultural products, that is, the central producer may misrepresent the information of the agricultural products in order to sell more agricultural products, that is, the central producer lives in misrepresentation behavior. When a central producer 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 producer 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 a new agricultural product supply chain;
in a new supply chain of agricultural products, a central manufacturer and a seller both have certain investment cost, and the unit production cost provided by the central manufacturer is cwAnd 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 new supply chain of agricultural products, based on the utility function theory, when a consumer purchases a product, the consumer can purchase the product only when the real value of the product is larger than the perception value of the consumer.
In the application, the safety change of the perception of the consumer brought by tracing is reflected by the green degree g of the product, and after the actual agricultural product is seen, the real perception value of the affected consumer meets a certain requirement, namely when U is greater than 0, the consumer can purchase the green agricultural product.
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:
Figure BDA0002619804080000091
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 a new supply chain of the agricultural product adopt emerging information services, I is { N, I }, and N represents a mode that the members do not adopt the emerging information services, and is called an N mode; i represents a mode that members in an agricultural supply chain all adopt emerging information services, and is called as an I mode; .
piIs the selling price of the seller in the i mode; when in I mode, θi(t) is expressed as theta (t), namely theta (t) is the freshness of the agricultural product in the I mode, 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 under the N mode, 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 a new supply chain of agricultural products do not adopt emerging information services based on big data and block chains, and when a central producer is supposed to lie information related to the agricultural products, a market demand function in the N mode is QN＝1-pN+θ(γt)+λNg, order lot size Q between central producer and seller in new supply chain of agricultural productsN＝1-pN+θ(γt)+λNg, the credibility factor of the green degree of the agricultural product is lambda at the momentNWherein the lie production date is gamma t, and the lie factor gamma belongs to [0,1 ]]。
After members in a new supply chain of agricultural products adopt emerging information services based on big data and block chains, the market demand function in the I mode is QI＝1-pI+θ(γt)+λIg, order lot size Q between central producer and seller in new supply chain of agricultural productsI＝1-pI+θ(γt)+λIg. At this time, the green credibility factor of the agricultural product is lambdaI. 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 productNBelow a confidence factor lambdaII.e. λN<λI. Wherein, the credibility factor is obtained by statistical analysis.
Step three, respectively constructing mathematical models of the central producer income and the seller income in different modes according to a response model of market demands;
first, a mathematical model of the central producer's revenue and the vendor's revenue in the N-mode, where the central producer and vendor do not employ the emerging information services, is constructed, as shown in FIG. 3-1.
The central producer is used as a leader of the supply chain to decide the wholesale price of the agricultural products, provide information such as production date, green degree and the like of the agricultural products, and the seller is used as a follower of the supply chain to decide the 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 modeO；
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 QNThe product of (1). But this results in a part of the product not being sold and rotten, i.e. the actual sales volume of the seller should be equal to the sales volume Q of the central producer without the misrepresentationo＝1-pN+θ(t)+λNg。
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 sale 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:
Figure BDA0002619804080000101
wherein the content of the first and second substances,
Figure BDA0002619804080000102
the revenue for the central producer in the N mode; w is aNIs the wholesale price of the central producer in the N mode; c. CwUnit sales cost for central manufacturers; qOOrder lot between the central producer and the seller when the central producer does not lie under 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, according to the selling price of the seller, the wholesale price of the central producer and the unit selling cost of the seller, under the N mode, the central producer does not have the market demand (order batch) Q under the misrepresentation actionOAnd market demand (order batch) Q in N-modeNA mathematical model of the vendor revenue is determined.
In this embodiment, the mathematical model of the vendor revenue is:
Figure BDA0002619804080000103
wherein the content of the first and second substances,
Figure BDA0002619804080000111
as revenue to the vendor in I mode; w is aNIs the wholesale price of the central producer in the N mode; c. CrUnit sales cost for the lower vendor; qNThe 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, a mathematical model of the central producer's profit and the seller's profit under the I-mode of the emerging information services adopted by the central producer and the seller is constructed, as shown in FIG. 3-2.
S311, acquiring order batch between the central producer and the seller in the I mode, namely the market demand Q in the I modeI；
S312, obtaining a mathematical model of the income of a central producer based on the market demand of a new supply chain of agricultural products 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 productIUnit production cost c of the central producerwAnd unit investment cost c of central producer for emerging information servicesow(ii) a Considering the influence of unit production cost on the income, therefore, a fixed coefficient is introduced to improve the income of a central producer; preferably, the fixed coefficient is an optimization coefficient phi;
s313, obtaining a mathematical model of the seller income based on the market demand of the new agricultural product supply chain and second relevant information provided by the platform of the information service provider; here, the information service providerThe second related information is provided as the selling price p of the competitive productIUnit sales cost c of the sellerrAnd the unit investment cost c of the seller with respect to emerging information servicesor(ii) a Considering the influence of unit sales cost on the profit, a fixed coefficient is introduced to improve the profit of a seller; preferably, the fixed coefficient is an optimization coefficient phi.
It should be noted that, there is no chronological relationship between step S312 and step S313, and step S312 may be executed first, and then step S313 may be executed; step S313 may be executed first, and then step S312 may be executed; or step S312 and step S313 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.
In this embodiment, the specific process of obtaining the mathematical model of the central producer profit based on the market demand of the new supply chain of agricultural products and the first related information provided by the information service provider in step S312 is as follows:
s3121, the information service provider obtains wholesale price of agricultural product of competitor of central producer, namely wholesale price w of competitive productIThe central manufacturer will make the unit production cost cwAnd unit investment cost c for emerging information servicesowSending the information to an information service provider;
s3122, obtaining wholesale price w of competitive products under mode I through platform of information service providerIUnit production cost c of the central producerwAnd unit investment cost c of central producer for emerging information servicesow；
S3123, wholesale price w of information service provider through competitive productsIUnit production cost c of the central producerwAnd optimizing the coefficient, unit investment cost c of the central producer for emerging information servicesowAnd market demand (order batch) QIDetermining central producer revenue
Figure BDA0002619804080000121
In this example, the mathematical model of the central producer revenue is:
Figure BDA0002619804080000122
in the formula (I), the compound is shown in the specification,
Figure BDA0002619804080000123
is the income of the central producer in the I mode; w is aIWholesale pricing for competitive products in mode I; phi is an optimization coefficient; c. CwProduction costs for central manufacturers; c. CowThe unit investment cost of the central producer for the emerging information services.
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In this embodiment, the specific process of obtaining the mathematical model of the seller profit based on the market demand of the new supply chain of agricultural products and the second related information provided by the information service provider in step S313 is as follows:
s3131, the information service provider obtains the selling price of the agricultural product of the competitor of the seller, namely the selling price p of the competitive productiThe seller sells its unit sales cost crCost per investment c of the vendor for emerging information servicesorSending the information to an information service provider;
s3132, obtaining the selling price p of competitive products in mode I through the platform of the information service providerIUnit sales cost c of the sellerrCost per investment c of the vendor for emerging information servicesor；
S3133, the information service provider selling price p by competing productsiThe unit sales cost c of the sellerrAnd optimizing coefficients, unit investment cost c of the vendor for emerging information servicesorDetermining seller revenue with market demand (order lot size)
Figure BDA0002619804080000126
In this embodiment, the mathematical model of the vendor revenue is:
Figure BDA0002619804080000124
in the formula (I), the compound is shown in the specification,
Figure BDA0002619804080000125
is the revenue of the seller in mode I; p is a radical ofIIs the selling price of competitive products in mode I; phi is an optimization coefficient; c. CrA unit sales cost for the vendor; c. CorThe unit investment cost for the vendor for emerging information services.
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Processing the income of the central producer and the income of the seller under different modes by a reverse induction method, and respectively determining the maximum income of the central producer and the maximum income of the seller under different modes;
firstly, determining the maximum profit of the central producer and the maximum profit of the seller under the N mode that the central producer and the seller do not adopt emerging information services, as shown in fig. 4;
under the N mode that the central producer and the seller do not adopt emerging information service, the income of the central producer and the income of the seller are processed based on the reverse induction method, and the selling price of the seller and the selling price of agricultural products are pN(wN)＝[cr+θ(t)+wN+λNg+1]2; then p is addedN(wN) Substituting into formula (3) to obtain
Figure BDA0002619804080000131
Obviously, there is an optimal wholesale price w for the agricultural productN*Can make central producer profit
Figure BDA0002619804080000132
And (4) maximizing.
In meeting the central manufacturer's income
Figure BDA0002619804080000133
In the case of maximization, the optimal wholesale price of the central producer is wN*The optimal selling price of the corresponding seller is pN*。
S401, according to unit sale cost c of central producerwThe unit sales cost c of the sellerrThe 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 lambdaNAnd green g to get the optimal wholesale price of the central producer.
In this example, the central producer's optimal wholesale price wN*Comprises the following steps:
Figure BDA0002619804080000134
it should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
S402, according to the unit sale cost c of the central manufacturerwThe unit sales cost c of the sellerrThe 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 lambdaNAnd green g to obtain the optimum selling price of the seller.
In the present embodiment, the optimum selling price p of the sellerN*Comprises the following steps:
Figure BDA0002619804080000135
it should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
S403, determining the maximum profit of the central manufacturer through the optimal wholesale price of the central manufacturer; the maximum profit of the seller is determined by the optimal selling price of the seller.
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, the maximum profit of the center manufacturer is known by substituting equation (8) into equation (3), as shown in equation (9).
Figure BDA0002619804080000141
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
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 (7) into equation (4) yields the maximum profit for the vendor, as shown in equation (10).
Figure BDA0002619804080000142
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
According to 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 are increased with the increase of theta (gamma t), but the maximum profit of the seller is reduced. In addition, with the green degree credibility factor lambda of agricultural productsNThe optimal wholesale price, the optimal sale price and the maximum profit of the user are increased.
The proof procedure for the above analysis:
as can be obtained by equations (7) and (8),
Figure BDA0002619804080000143
Figure BDA0002619804080000144
Figure BDA0002619804080000145
Figure BDA0002619804080000146
from QO*＝[1-cr-cw-θ(t)+2θ(γt)+λNg]/4、Q*＝[1-cr-cw+3θ(t)-2θ(γt)+λNg]The method can obtain the product of/4,
Figure BDA0002619804080000151
according to the formulas (7) and (8)
Figure BDA0002619804080000152
Analysis reveals that the central producer's misrepresentation will harm the retailer's interests. In addition, the credibility of the green color information of the agricultural products will influence the income of participants and the pricing strategy. That is, the higher the confidence factor for the green color of an agricultural product, the higher the member revenue in the new supply chain of the agricultural product.
Secondly, determining the maximum profit of the central producer and the maximum profit of the seller under the I mode of emerging information service adopted by the central producer and the seller, as shown in fig. 5;
and under the I mode of emerging information service, the central producer and the seller process the market demand, the income of the seller and the income of the central producer through a reverse induction method to obtain the optimal selling price, the maximum income of the central producer and the maximum income of the seller.
S411, according to unit production cost c of central manufacturerwThe unit sales cost c of the sellerrFreshness theta (t) of agricultural products and credibility factor lambdaIGreenness g, unit investment cost c of the central producer for emerging information servicesowUnit investment cost c of the seller for emerging information servicesorAnd optimizing the coefficient phi to obtain the optimal selling price of the seller.
In this embodiment, in mode I, the optimum selling price of the seller is
Figure BDA0002619804080000153
S412, according to the unit production cost c of the central manufacturerwThe unit sales cost c of the sellerrFreshness theta (t) of agricultural product when no misrepresentation exists, and credibility factor lambdaIGreenness g, unit investment cost c of the central producer for emerging information servicesowUnit investment cost c of the seller for emerging information servicesorAnd optimizing the coefficient phi to obtain the optimal wholesale price of the central manufacturer.
In this embodiment, in mode I, the central producer's optimal wholesale price wI*Is composed of
Figure BDA0002619804080000154
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
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.
S413, according to unit sale cost c of central producerrThe unit sales cost c of the sellerwFreshness theta (t) of agricultural products and credibility factor lambdaIGreenness g, unit investment cost c of the central producer for emerging information servicesowUnit investment cost c of the seller for emerging information servicesorAnd the optimization factor phi yields the market demand (optimal order lot) between the central producer and the seller.
In this embodiment, in mode I, the market demand (optimal order lot size) is
Figure BDA0002619804080000161
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
And S414, 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 of the central producer is determined according to the market demand (optimal order lot) and the optimal wholesale price of the central producer.
In this embodiment, in mode I, the maximum revenue for the central manufacturer is
Figure BDA0002619804080000162
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 of the seller is determined according to the market demand (optimal order lot) and the optimal selling price of the seller.
In this embodiment, in mode I, the maximum profit for the vendor is
Figure BDA0002619804080000163
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 wholesale 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 QI*>0, thereby obtaining the following relationship.
cow+cor<θ(t)-crs-cws-φcr-φcw+λIg+1 (16)
As can be seen from equation (16), the cost c of the emerging information services based on big data, blockchainow+corPhi 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, apply the value in the new information to cost optimization and efficiency improvement of the new supply chain of agricultural products, and reduce the cost optimization coefficient phi of the emerging information service.
Through the formulas (11), (12), (14) and (15), the green value is related to the wholesale price of the central producer, the selling price of the seller, the maximum profit of the central producer and the maximum profit of the seller:
Figure BDA0002619804080000171
Figure BDA0002619804080000172
Figure BDA0002619804080000173
Figure BDA0002619804080000174
wherein M is 1-cor-cow-crs-cws+θ(t)-φcr-φcw+λIg.
From (17) and (18): 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 participants.
And the credible factor lambda of green color of agricultural productsIThe optimum selling price (17) of the agricultural product is more sensitive than the optimum wholesale price (18), and the revenue (19) of the central producer is more sensitive than the revenue (20) of the 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:
Figure BDA0002619804080000181
Figure BDA0002619804080000182
Figure BDA0002619804080000183
Figure BDA0002619804080000184
wherein M is 1-cor-cow-crs-cws+θ(t)-φcr-φcw+λIg。
As can be seen from the above equation, in mode I, as the cost optimization coefficient of emerging information services increases, the retail price of green agricultural products will increase, with the change in the optimal wholesale price being related to the sales cost between the retailer and the central producer (i.e., c)w-cr). When c is going tow-cr>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 manufacturer and the sale price of the seller is obtained as follows:
Figure BDA0002619804080000185
Figure BDA0002619804080000186
Figure BDA0002619804080000187
Figure BDA0002619804080000188
wherein M is 1-cor-cow-crs-cws+θ(t)-φcr-φcw+λIg。
As can be seen, the cost of information services c is emerging with central manufacturersowThe 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 corThe 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 input on emerging information services. And the seller invests the overflow effect, the income of the central producer can be increased, and in order to maximize the income, the wholesaler incentivizes the retailers to order more agricultural products by setting lower wholesale prices.
Through equations (11), (12), (14) and (15), the relationship between the emerging information service cost and the maximum profit of the central manufacturer and the maximum profit of the seller is obtained as follows:
Figure BDA0002619804080000191
Figure BDA0002619804080000192
Figure BDA0002619804080000193
Figure BDA0002619804080000194
wherein M is 1-cor-cow-crs-cws+θ(t)-φcr-φcw+λIg。
It can be seen that the central producer is emerging for information service cost (c)ow) With distributors, central producersThe gains are inversely related. In addition, retailer emerging information service costs (c)or) But also the profit of the seller and the central producer. This indicates that when a central manufacturer or a vendor who invests in the emerging information services needs to negotiate with a third-party information service company about the emerging information service cost, the investment of the emerging information service cost should be controlled as much as possible, and 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.
The embodiment of the device is as follows:
the invention also provides a pricing device for the new supply chain of agricultural products, 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 pricing method for the new supply chain of agricultural products. Since the agricultural product new supply chain pricing method is described in detail in the method embodiment, the detailed description is omitted here.
Compared with the prior art, each user in the supply chain participates in a new agricultural product supply chain, and an information technology of block chain and big data fusion is adopted, so that a central manufacturer can be effectively prevented from misreading the information of the agricultural products in order to sell more agricultural products, namely the central manufacturer lives in misreading behaviors.
When a central producer 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 producer can be reduced, the trust among participants can be enhanced, and the trust of consumers on agricultural products can be improved.
Based on the fact that a new pricing mechanism is needed for the new agricultural product supply chain, market operation can be well adapted, therefore, the application correspondingly provides a new agricultural product supply chain pricing method, the method is based on an information technology of block chain and big data fusion, market requirements, central producer profits and seller profits are determined, then a reverse induction method is adopted, the maximum profits of the central producer and the maximum profits of the seller are obtained, and the optimal wholesale price of the central producer and the optimal sale price of the seller are respectively made according to the maximum profits of the central producer and the maximum profits of the seller. Therefore, the agricultural product new supply chain pricing method can be well suitable for the market, market requirements are met, and central manufacturers and sellers can obtain the maximum benefits.
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 (10)

1. A new supply chain pricing method for agricultural products is characterized by comprising
Step one, constructing a new supply chain model of agricultural products according to data in a new supply chain;
the new supply chain comprises a central producer, a seller and an information service provider;
the central producer uploads 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 a new supply chain of agricultural products 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, constructing a response model of the market demand based on the new supply chain model;
step three, determining a mathematical model of the central manufacturer income according to the response model of the market demand;
and step four, processing the mathematical model of the central manufacturer income by adopting a reverse induction method to obtain the maximum income of the central manufacturer.
2. The agricultural product new supply chain pricing method according to claim 1, wherein in step two, the response model of the market demand is constructed according to the selling price of the agricultural product, the freshness of the agricultural product, the green degree of the agricultural product and the credible factors of the green degree of the agricultural product established by the seller in a new supply chain model.
3. The agricultural product new-supply-chain pricing method of claim 2, wherein the response model of market demand is:
Qi＝1-pi+θi(t)+λig
in the formula, QiIs the market demand in i mode; i is a mode of whether members in the new supply chain of the agricultural product adopt the emerging information service, I is { N, I }, and N represents a mode that the members in the new supply chain do not adopt the emerging information service, which is called an N mode; i represents a mode that all members in the new supply chain adopt emerging information services, and is called as an I mode; p is a radical ofiIs the selling price of the seller in the i mode; lambda [ alpha ]iA confidence factor that is the greenness of the agricultural product; g is the greenness of the agricultural product; thetai(t) is the freshness of the produce in i mode; t is the production date of the agricultural products.
4. The agricultural product new supply chain pricing method of claim 3, wherein in step three, the determination process of the mathematical model of the central producer revenue is: firstly, acquiring market demands in the I mode; secondly, determining a mathematical model of the income of the central manufacturer according to the market demand in the I mode and first related information data provided by the information service provider, wherein the first related information data comprises wholesale prices of competitive products of agricultural products, unit production cost of the central manufacturer and unit investment cost of the emerging information service of the central manufacturer.
5. The agricultural product new supply chain pricing method according to claim 4, wherein in the fourth step, a reverse induction method is adopted to process the mathematical model of the income of the central producer, and the process of obtaining the maximum income of the central producer comprises the following steps:
firstly, obtaining the optimal wholesale price of the central producer by adopting a reverse induction method according to the unit production cost of the central producer, the unit sales cost of the seller, the freshness of the agricultural product when a misrepresentation action exists, the greenness of the agricultural product and the credible factor of the greenness; the misrepresentation behavior refers to misrepresentation of the freshness of the agricultural products by a central manufacturer;
and then obtaining the maximum profit of the central producer according to the optimal wholesale price of the central producer, the market demand and a mathematical model of the profit of the central producer.
6. The agricultural product new supply chain pricing method of claim 5, wherein the central producer's optimal wholesale price is:
Figure FDA0002619804070000021
the maximum revenue of the central producer is:
Figure FDA0002619804070000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002619804070000034
optimal wholesale prices for central producers;
Figure FDA0002619804070000032
maximum revenue for the central producer; c. CwUnit production cost for central manufacturers; c. CrA unit sales cost for the vendor; θ (t) is the freshness of the agricultural product when no lie exists; phi is a fixed coefficient; lambda [ alpha ]IA confidence factor that is the greenness of the agricultural product; g is the greenness of the agricultural product; c. CorA unit investment cost for the vendor for emerging information services; c. CowA unit investment cost for the central producer for emerging information services.
7. The agricultural product new supply chain pricing method according to claim 3, characterized in that step three further comprises: determining a mathematical model of the seller's revenue according to the response model of the market demand; and then, processing the mathematical model of the seller income by adopting a reverse induction method to obtain the maximum income of the seller.
8. The agricultural product new-supply-chain pricing method of claim 7, wherein the determination of the mathematical model of vendor revenue is by: firstly, acquiring the market demand in the I mode; secondly, obtaining a mathematical model of the seller income according to the market demand in the I mode and second related information data provided by the information service provider; the second relevant information data includes a selling price of a competing agricultural commodity for the agricultural commodity, a selling cost of the seller and a unit investment cost of the seller for the emerging information service.
9. The agricultural product new-supply-chain pricing method of claim 8, wherein the mathematical model of the seller's revenue is processed by inverse induction, and the process of obtaining the seller's maximum revenue is:
firstly, obtaining the optimal wholesale price of the central manufacturer according to the obtained unit production cost of the central manufacturer, the unit sales cost of the seller, the freshness of the agricultural product when the misreport action exists, the credible factor of the green degree of the agricultural product and the green degree of the agricultural product by adopting a reverse induction method; the misrepresentation behavior refers to misrepresentation of the freshness of the agricultural products by a central manufacturer; the optimal selling price of the seller is as follows:
Figure FDA0002619804070000033
then, obtaining the maximum profit of the seller according to the optimal wholesale price of the central producer, the market demand and a mathematical model of the profit of the seller;
the maximum profit for the vendor is:
Figure FDA0002619804070000041
in the formula, pN*The optimal selling price of the seller;
Figure FDA0002619804070000042
maximum revenue for the vendor; c. CwUnit production cost for central manufacturers; c. CrA unit sales cost for the vendor; θ (t) is the freshness of the agricultural product when no lie exists; phi is a fixed coefficient; lambda [ alpha ]IA confidence factor that is the greenness of the agricultural product; g is the greenness of the agricultural product; c. CorA unit investment cost for the vendor for emerging information services; c. CowA unit investment cost for the central producer for emerging information services.
10. An agricultural product new supply chain pricing device, comprising a memory, a processor and a program stored in the memory and executable on the processor, the processor when executing the program implementing an agricultural product new supply chain pricing method according to any of claims 1-9.
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CN113469830B (en) * 2021-06-29 2024-06-11 浙江数秦科技有限公司 Agricultural product bidding transaction system based on blockchain and growth model
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