KR20230029017A - Method for determining price based on responsibility of food distribution and system thereof - Google Patents

Abstract

The present invention relates to a technology for dynamically determining a price of food by predicting quality of the food changed during a distributing step capable of setting a quality predicting model depending on a history of a time-environmental factor by considering a quality indicator indicating a state of the food and an environmental factor involved in quality of the distributing step; receiving an environmental factor measured by the distributing step from a shipment time point of the food with a measured time and generating a transaction including distribution state information and a distribution entity of the food to be stored in a blockchain; calculating the quality of deterioration of the food depending on a quality predicting model based on the transaction in the blockchain for the food which is distributed and sold; and dynamically determining a price of the food based on the calculated quality and deterioration. The present invention can prevent forgery and alteration of the quality or a distribution history in principle.

Description

Method for determining price based on responsibility of food distribution and system

The present invention relates to a technology for dynamically determining the price of food by predicting the quality of the food that changes during the distribution process. It relates to a method, a recording medium recording the method, and a price determination system using the method.

Food is inevitably exposed to various temperature conditions as it passes through distribution channels from manufacturing to delivery to consumers. A change in condition or quality is very important. Because consumers indirectly purchase fresh food that has gone through various distribution processes, they cannot check the freshness information and hygiene status of the product before opening the package. Therefore, it is very important to provide information to consumers by predicting in advance how the freshness of food will be affected when exposed to various temperatures and how long the quality of eating at a certain level will be maintained under the storage temperature. It can be seen that it is a task.

For example, milk, eggs, or meat are popular fresh foods that are widely used as ingredients in food and cooking. However, after the pesticide egg scandal in Korea in 2017, consumers are anxious about the safety of eggs. The cause of this anxiety is that when food safety issues or accidents occur, scientifically reviewed and evaluated responses are not systematically conducted. Efforts are required to quickly deliver transparent and objective information based on science.

The prior art literature presented below attaches a sensor tag for detecting a change in the environment to a container or pallet on which a plurality of foods are loaded, and detects an environmental change occurring while transporting the food in real time using the sensor tag, Food quality monitoring technology is introduced to evaluate quality information in real time and provide it to consumers. However, these prior art documents only mention a general flow of providing quality information to consumers by transmitting a detected signal, but do not suggest a direction for predicting and utilizing food quality.

Therefore, there is a need to think about a technique that can accurately predict and provide information on the freshness or quality of food to consumers and differentiate and evaluate the value of the food based on this quality.

Korean Patent Publication No. 2012-0035737, "Food quality monitoring method"

The technical problem to be solved by the present invention is to overcome the limitation that quality-related factors measured in the conventional food distribution process provide only simple quality information, and to deliver to consumers despite the fixed price determined at the time of shipment from the production area of the food. It aims to solve the problem of unilaterally suffering damages due to distribution failure, by resolving the weakness that exists in the quality of the food being supplied, and only some of the various performers intervening in the distribution chain.

In order to solve the above technical problem, the method for determining the price of food according to an embodiment of the present invention includes (a) a distribution management system considering the quality index representing the state of food and environmental factors involved in the quality of the distribution process together. setting a quality prediction model according to the history of time-environment factors; (b) The distribution management system receives the environmental factors measured according to the distribution process from the time of shipment of the food together with the measurement time, and generates a transaction including information on the distribution status of the food and the distribution subject, thereby generating a block chain (block chain). chain); (c) calculating the quality and degree of deterioration of the food according to the quality prediction model by referring to the transaction in the blockchain for the food that can be distributed and sold by the distribution management system; and (d) dynamically determining the price of the food by referring to the calculated quality and the degree of deterioration by the distribution management system.

In the food price determination method according to an embodiment, the step (a) of setting a quality prediction model may include: (a1) selecting at least one quality indicator indicating a state of food; (a2) deriving a plurality of environmental factors involved in changes in the distribution process for the selected quality index, and pre-storing time-series measured value data of the quality index according to the change or combination of the environmental factors; and (a3) deriving a quality prediction model according to changes in time and environmental factors from the pre-stored time-series measurement value data for the selected quality index.

In the method for determining the price of food according to an embodiment, the step (b) of generating a transaction and storing it in a block chain is performed according to the distribution process from the time of shipment of the food using a sensor provided adjacent to (b1) the food. receiving the measured temperature and measurement time together; (b2) generating a transaction including distribution state information and a distribution subject of the food or the food by matching the input temperature and measurement time with the identifier of the food or the sensor; and (b3) inducing a plurality of nodes registered in the block chain to share the generated block by connecting the block including the transaction in the form of a hash chain and storing it in the block chain. there is.

In addition, in the method for determining the price of food according to an embodiment, in the step (b) of generating and storing a transaction in a block chain, an address of the block chain is set based on the identifier of the food or the sensor, By processing distribution information including production date, expiration date, quality index, temperature, and location information of the food with the distribution subject and processing it into a transaction, the quality information at the time of shipment of the food and the sequentially accumulated distribution history can be stored together. there is.

In the method for determining the price of food according to an embodiment, the step (c) of calculating the quality and degree of deterioration of the food may include: (c1) monitoring a distribution process to discover food that satisfies sellability conditions; (c2) extracting quality information that changes from the shipment status of the food for each distribution stage by reading a transaction in the blockchain for the found food; and (c3) calculating the quality and degree of deterioration of the food for each distribution stage using the quality prediction model.

In addition, in the method for determining the price of food according to an embodiment, the step (c) of calculating the quality and degree of deterioration of the food is performed by comparing each distribution step with the previous step in consideration of a time-series order (c4). Calculating the amount of change in quality and degree of deterioration, respectively; and (c5) matching and storing the amount of change in the quality and degree of deterioration for each distribution stage with the distribution subject of the corresponding stage.

In the method for determining the price of food according to an embodiment, the step (d) of dynamically determining the price of the food includes: (d1) reading the initial price set according to the quality of the food at the time of shipment or a distribution contract; ; and (d2) calculating a discounted price in consideration of the quality and the degree of deterioration calculated for the food based on the read initial price.

In addition, the method for determining the price of food according to an embodiment further includes preparing a first smart contract defining a condition for deterioration in quality and a discount action in advance, and calculating the quality and degree of deterioration of the food ( When the quality and degree of deterioration of the sellable food are calculated through step c), in the step (d) of dynamically determining the price of the food, with reference to the calculated quality and the degree of deterioration, the food is 1 The adjusted price can be calculated by checking whether the conditions for quality deterioration of the smart contract are satisfied, and applying a discount action corresponding to the defined conditions.

On the other hand, in the method for determining the price of food according to an embodiment, the step (d) of dynamically determining the price of food includes (d3) the amount of change in the quality and degree of deterioration of the food due to the distributor of the corresponding step for each distribution stage. Calculating a responsibility ratio within the entire degree of degradation by deriving ? and (d4) determining a discounted price to be borne by each distribution entity among the total discounted prices according to the calculated responsibility ratio.

In addition, the method for determining the price of food according to an embodiment further includes preparing in advance a second smart contract defining a condition for quality deterioration and a discount action according to a liability rate, and determining the quality and degree of deterioration of the food. When the quality and degree of deterioration of the sellable food are calculated through the step (c) of calculating, the step (d) of dynamically determining the price of the food may refer to the calculated quality and the degree of deterioration. It is possible to check whether the food satisfies the quality deterioration condition of the second smart contract, and apply a discount action according to the responsibility ratio corresponding to the defined condition to calculate the discount price to be borne by each distribution entity.

Furthermore, hereinafter, a computer-readable recording medium recording a program for executing the method of determining the price of food described above in a computer is provided.

In order to solve the above technical problem, a food price determination system according to an embodiment of the present invention, a communication unit that receives data measured according to the distribution process from the time of shipment of the food using a temperature sensor provided adjacent to the food ; a memory for storing a program for determining a price from the predicted quality of the food using the inputted data; and at least one processor for driving the program, wherein the program stored in the memory is configured to determine the history of the time-environmental factor in consideration of a quality index indicating a state of food and an environmental factor involved in the quality of a distribution process. set up a quality prediction model according to the standard, receive the measured environmental factors along with the measurement time, create a transaction including information on the distribution status of food and the subject of distribution, store it in the block chain, and distribute and sell it A command for calculating the quality and degree of deterioration of the food according to the quality prediction model by referring to transactions in the blockchain for possible food, and dynamically determining the price of the food by referring to the calculated quality and the degree of deterioration. include

In the food price determination system according to an embodiment, the program stored in the memory selects at least one quality indicator indicating the state of the food, and a plurality of environments involved in a change in the distribution process for the selected quality indicator. Factors are derived, and time-sequential measurement value data of quality indicators according to changes or combinations of the environmental factors are stored in advance, and quality according to changes in time and environmental factors is obtained from the previously stored time-series measurement value data for the selected quality indicators. It may include instructions for deriving a predictive model.

In the food price determination system according to an embodiment, the program stored in the memory receives the temperature and measurement time measured along the distribution process from the time of shipment of the food together, and converts the input temperature and measurement time to the food or A transaction including distribution state information and a distribution subject of the food matched with the identifier of the sensor is generated, and a block including the generated transaction is connected in the form of a hash chain and stored in a block chain, thereby storing the block It may include a command that induces a plurality of nodes registered in the chain to share.

In addition, in the food price determination system according to an embodiment, the program stored in the memory sets the address of the block chain based on the identifier of the food or the sensor, but the production date, expiration date, and quality of the food By matching distribution information including index, temperature, and location information with a distribution subject and processing it into a transaction, quality information at the time of shipment of the food and sequentially accumulated distribution history can be stored together.

In the food price determination system according to an embodiment, the program stored in the memory monitors a distribution process to find food that satisfies sellable conditions, reads a transaction in a block chain for the found food, and reads out a transaction in a block chain for each distribution step. and extracting quality information that changes from the shipment state of the food, and calculating the quality and degree of deterioration of the food for each distribution stage using the quality prediction model.

In addition, in the food price determination system according to an embodiment, the program stored in the memory calculates the amount of change in quality and degree of deterioration, respectively, through comparison with the immediately preceding stage for each distribution stage in consideration of a time-series sequence; The method may further include a command for matching and storing the amount of change in the quality and degree of deterioration for each distribution stage with a distribution entity at the corresponding stage.

In the food price determination system according to an embodiment, the program stored in the memory reads an initial price set according to the quality of the food at the time of shipment or a distribution contract, and determines the food based on the read initial price. An instruction for calculating a discounted price in consideration of the calculated quality and the degree of degradation may be included.

In addition, in the food price determination system according to an embodiment, the program stored in the memory derives the amount of change in the quality and degree of deterioration of the food due to the distributor at the corresponding step for each distribution step, and calculates the responsibility ratio within the total degree of deterioration. and determining a discounted price to be borne by each distribution entity among the total discounted prices according to the calculated responsibility ratio.

Furthermore, in the food price determination system according to an embodiment, the program stored in the memory further includes an instruction for preparing a third smart contract in advance, which defines a condition for quality deterioration, a sales price, and a discount action according to a responsibility ratio. and, when the quality and degree of deterioration of the salable food are calculated, refer to the calculated quality and degree of deterioration, but check whether the food satisfies the condition for the quality deterioration of the third smart contract. An adjusted selling price may be calculated by applying a discount action corresponding to , and a discount price to be borne by each distribution entity may be calculated by applying a discount action according to a responsibility ratio corresponding to a defined condition.

Embodiments of the present invention can provide a differential price determination method according to quality as well as quality information of food from quality-related factors measured in the food distribution process, and quality or distribution by managing distribution history through a block chain. Forgery and falsification of records can be fundamentally prevented, and the risk burden can be balanced fairly by objectively calculating the responsibility ratio of quality deterioration for various performers intervening in the distribution chain.

1 illustrates a food safety distribution platform for managing the role and performance history of each distribution subject in a food distribution environment in which embodiments of the present invention are implemented.
2 is a flowchart illustrating a price determination method considering the quality of food according to an embodiment of the present invention.
3 is a flowchart illustrating a process of setting a quality prediction model in more detail in the price determination method of FIG. 2 according to an embodiment of the present invention.
4 is a diagram for explaining a process of developing a quality prediction model adopted by embodiments of the present invention.
5 is a flowchart illustrating a process of generating and storing a transaction in a block chain in the price determination method of FIG. 2 according to an embodiment of the present invention in more detail.
6 and 7 are diagrams illustrating a process of storing data measured in a distribution management system in a block chain.
8 is a flowchart illustrating a process of calculating the quality and deterioration degree of food in more detail in the price determination method of FIG. 2 according to an embodiment of the present invention.
9 is a flowchart illustrating a process of dynamically determining a price of food in the price determination method of FIG. 2 according to an embodiment of the present invention in more detail.
10 is an exemplary view illustrating a process of bearing a discount amount in consideration of distribution responsibilities of each performer in dynamically determining the price of food.
11 is a block diagram showing a food price determination system according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, detailed descriptions of well-known functions or configurations that may obscure the gist of the present invention will be omitted in the following description and accompanying drawings. In addition, throughout the specification, 'including' a certain component means that other components may be further included, not excluding other components unless otherwise stated.

Also, terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The terms may be used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention.

Terms used in the present invention are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprise" or "comprise" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, but that one or more other features or It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless specifically defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in this application, they are not interpreted in an ideal or excessively formal meaning. .

1 illustrates a food safety distribution platform for managing the role and performance history of each distribution subject in a food distribution environment in which embodiments of the present invention are implemented.

Previously, it was explained that efforts are being made to grasp the state of food by utilizing various sensor devices introduced in the food distribution process. However, in the field, problems such as loss or manipulation of these measured values occur, and there are cases in which trust in food distribution information is lowered. Therefore, embodiments of the present invention provide a history of food shipped from a producer (food processor) 10 in the process of moving along a distribution chain by a distributor/transporter 20 in order to ensure safe food distribution. In order to accumulate and store information and to transparently manage the entire distribution process from reaching the seller (wholesale and retailer) 30 to selling to consumers, block chain technology was introduced. Furthermore, through the distribution management system 40 in charge of system operation, we intend to propose a technical means of dynamically determining the price of food at the point of minimum consumption by managing and querying the transactions recorded in the block chain. .

Therefore, the purpose of the distribution management system 40 utilizing the block chain is to prevent forgery and falsification of history information, and to provide data integrity core technology to be applied to a food safety distribution platform of real-time visibility. To this end, the intelligent food safety distribution framework configures block chain nodes in each producer (10), distributor (20), seller (30), and distribution management system (40) to share and manage distribution or history information. designed. In other words, by utilizing the blockchain distributed ledger, all participants in the network, such as producers 10, distributors 20, and sellers 30, can participate in verification work to share information, and food distribution information is consistent. security can be managed.

On the other hand, embodiments of the present invention described below propose a distribution management system that determines the price in consideration of the quality of food, but the price determination point in this case is not necessarily limited to the time point at which the food is delivered to the final consumer. Rather, it should be understood that it can also be used to determine an intermediate distribution price from the time the food is shipped by the producer to the time the food is delivered to at least one or more distributors or intermediaries that pass through in the distribution process. That is, the embodiments of the present invention should focus on the technical idea that a differentiated price can be determined in consideration of the quality of food at a specific point in the entire distribution process, and interpretation is limited to technology that simply determines or discounts the final consumer price. It shouldn't be. In addition, the expression 'consumer price' does not limitly refer to the price paid by the final consumer or individual consumer consuming food in a retail store or mart, and refers to the participants in the distribution process of food (distributors, intermediaries, sellers, It broadly means the price paid by general consumers, etc.).

2 is a flowchart illustrating a price determination method considering the quality of food according to an embodiment of the present invention, showing a series of calculations and processing processes implemented through a distribution management system. The distribution management system monitors a plurality of means of moving food (delivery/transportation means) identified in the distribution network, but observes the state indicator of the corresponding food using a sensor attached to each packaging unit of the food. In the embodiments of the present invention, 'temperature' is presented as a single condition indicator, and the quality prediction model introduced below is implemented to accurately predict the quality of food only with the temperature history. In addition, the food includes foods such as vegetables, fruits, fish, meat, eggs, and dairy products distributed in a fresh state, and eggs are exemplified here for detailed description of the technology, but are not limited thereto.

In step S210, the distribution management system sets a quality prediction model based on time-environmental factor history by considering both quality indicators representing the state of food and environmental factors involved in the quality of the distribution process. For the quality indicators selected for each food group, a DB for the actual exposure environment history (temperature/humidity/wind speed distribution in production, distribution, and sales) and a DB for experiment information under conditions reflecting it (steady temperature, alternate temperature, field) will be constructed. can In the case of the exemplified egg, the quality indicator may be at least one of specific gravity, pH, color, egg yolk coefficient, egg white coefficient, S-ovalbumin, and Haugh unit, and the quality according to the type of food Various indicators that can be specified may be utilized. For example, it was found that the experimental information DB could set eggs (haugh unit), kimchi (total acidity), sterilized milk (general bacteria), chicken (general bacteria), etc. as quality indicators. The environmental factor is to look at factors affecting freshness maintenance, and for example, in the case of eggs, it may be at least one of storage temperature, humidity, and wind speed.

In step S220, the distribution management system receives the environmental factors measured according to the distribution process from the time of shipment of the food together with the measurement time, and generates a transaction including information on the distribution status of the food and the distribution subject, thereby generating a block chain. (block chain). There are various indicators to determine the condition or quality of food, but measuring all of the various indicators indicating the freshness of food individually and figuring out the freshness from this is the highest accuracy when looking at the single goal of predicting freshness. can show However, in terms of cost and convenience, it is not realistic to have sensors corresponding to various indicators for each individual packaging unit in the distribution process of numerous foods. Therefore, in the embodiments of the present invention, only a single index 'temperature' is adopted, but accurate quality is obtained by utilizing not only the temperature measurement value at a single point in time but also the 'temperature history' that changes over time from the time of initial shipment. I was trying to predict. In this case, the measured environmental factors may be processed into comprehensive information about the distribution history together with the measurement time, and then converted into a transaction. These transactions, as components of the block chain, were introduced for the purpose of preventing forgery and falsification of data and transparent information sharing.

From an implementation point of view, embodiments of the present invention may apply a RAFT-consensus technique for selecting a miner using a representative election-based RAFT protocol. This is a fork-free consensus algorithm, which assumes that all nodes are honest and does not consider Byzantine Fault Tolerance, and is designed to operate when a majority (1/2n+1) of nodes are alive.

In step S230, the distribution management system calculates the quality and degree of deterioration of the food according to the quality prediction model by referring to transactions in the block chain for food that can be distributed and sold. Embodiments of the present invention seek to promote dynamic pricing for salable food. Therefore, while various foods in the distribution process were monitored, the quality of the food in a marketable state was determined. At this time, the quality and degree of deterioration of the food may be derived from time-series accumulated data by examining a plurality of transactions accumulated in the block chain through step S220. In this process, the quality prediction model defined in step S210 may be utilized. As described above, the quality prediction model proposed by the embodiments of the present invention predicts the quality of food through 'temperature', which is a single index, particularly 'temperature history' recorded over time. That is, for one fresh food, the freshness according to the time-temperature history is calculated. A more specific freshness prediction model will be described later in detail with reference to FIG. 4 .

In step S240, the distribution management system dynamically determines the price of the food by referring to the calculated quality and the degree of deterioration. That is, if the quality of the food according to the distribution process is predicted using the food quality prediction model in step S230, the discounted price is determined based on the quality prediction in consideration of the quality change (typically, quality deterioration). As a technical means, the embodiments of the present invention can manage distribution history through a block chain and determine the final selling price by utilizing a smart contract. At this time, the smart contract is a program that records predefined contract conditions in the blockchain and automatically executes actions according to the contract when the conditions are met. Induce price differentiation according to quality by reflecting the quality deterioration shown in the final price. From the implementation point of view, after storing the hash value of the quality history data collected from food distribution in the DB, it is possible to determine forgery or falsification using a smart contract, and automatically determine the price change according to the quality status at the point of sale of the food.

FIG. 3 is a flowchart illustrating a process of setting a quality prediction model (step S210) in more detail in the price determination method of FIG. 2 according to an embodiment of the present invention.

In step S211, at least one quality indicator indicating the state of food may be selected. In this process, it is preferable to select a preset number of quality indicators in order of relatively high dependence on temperature among a plurality of quality indicators representing the state of the food. For example, when the food is an egg, the quality indicator may be at least one of specific gravity, pH, color, yolk index, egg white index, S-ovalbumin, and Haugh unit. These quality indicators may vary depending on the type of food, but are not limited thereto.

In step S212, a plurality of environmental factors involved in changes in the distribution process for the quality index selected through step S211 are derived, and time-series measured value data of the quality index according to the change or combination of the environmental factors may be stored in advance. there is. In this process, the environmental factor may be derived from at least one of storage temperature, humidity, and wind speed, or a combination thereof, and a change in quality index according to the derived environmental factor may be recorded over time.

In step S213, a quality prediction model according to changes in time and environmental factors may be derived from the previously stored time-series measurement value data for the selected quality index. More specifically, using the time-series measured value data stored in advance for the previously selected quality indicator, the change of the quality indicator over time is set as a kinetic model, and the temperature dependence of the selected quality indicator ) as a polynomial model, it is possible to derive a quality prediction model according to changes in time and temperature. As described above, the embodiments of the present invention, in predicting the state or quality of food, do not measure the 'temperature' condition or other indicators at a single point in time, but rather measure the temperature that changes over time. We propose a quality prediction model using trends, that is, 'time-environment factor history' (more precisely, 'time-temperature history'). Hereinafter, a more detailed model development and implementation method will be described.

4 is a diagram for explaining a process of developing a quality prediction model adopted by embodiments of the present invention, and eggs are selected as an example of food, but the target food is not limited thereto as long as the technical idea remains the same. . In FIG. 4, in order to provide information on how the quality changes in advance through various distribution processes from production to delivery to consumers, dynamic prediction of the quality (freshness) of eggs and their changes according to temperature ( dynamic) A quality prediction model was presented.

Referring to FIG. 4, the quality prediction model proposed by the embodiments of the present invention first selects an objective quality index that sufficiently reflects the characteristics of each food in order to scientifically explain the quality change for each target food (step 1), and A database is built (step 2) through a quality change analysis (420) for each distribution environment investigation and environmental factors (410), and a mathematical model according to time-temperature history is developed (430) based on the built database ( Step 3), develop and verify a dynamic model that reflects the temperature conditions of the food distribution environment (step 4). Each step is sequentially described below.

(1) Step 1: Selection of quality prediction indicators

As introduced above, quality indexes for predicting egg quality include specific gravity, pH, color, yolk coefficient, egg white coefficient, S-ovalbumin, and Haugh unit. The heavy rain unit was selected as an example. Houth units are a standard internationally used indicator for evaluating the internal quality of eggs. The heavy rain index is a value calculated according to a certain formula by measuring the weight of the egg and the height of the egg white (rich egg white).

(2) Step 2: Building a database

Changes in heavy rain units, which are quality indicators selected for egg quality prediction, were evaluated by grade, storage temperature (steady temperature: 0℃, 5℃, 10℃, 20℃ and 30℃, alternating temperature: 10-20℃, 5-25℃, 10℃). -30 ℃), humidity (average 50%, 75%, 90%) and wind speed, etc. were reflected and a database containing multiple data was constructed.

(3) Step 3: Development of a mathematical model based on time-temperature history

To predict the change in the number of hours selected as an egg quality indicator, a kinetic model was used as the primary model and a polynomial model was used as the secondary model, The following Equations 1 and 2 were derived, respectively. In the following equations, t represents the elapsed time, and T represents the temperature of the food.

Gompertz, Hill and Wright, Logistic, and Baranyi and Roberts model can be used to express the change in heavy rain, which is one of the indicators of egg quality, and in Equation 1, the Baranyi and Roberts model is selected as an example. Thus, the change in heavy rainfall units over time was expressed. In addition, in Equation 1, the weight constant W considering the external flow velocity is combined and expressed. Here, the W value is composed of a ratio of a heat transfer coefficient in a distribution environment where a flow velocity is strongly generated and a heat transfer coefficient in a general distribution environment, and may have a value of 0.45 to 0.65 depending on the distribution environment. As an example, the average value of 0.55 was applied in the current model. Since the heavy rain unit decreases rapidly as the external flow rate increases, when weighted, the effect of the flow rate can be reflected in the quality model, which will improve the prediction accuracy of the model in a distribution environment where the actual temperature/flow rate varies. can

Next, the second model is to explain the effect of environmental conditions on the parameters analyzed in the first model, and a polynomial model was used to analyze the effect on temperature.

Equation 2 shows the temperature dependence of the heavy rain unit, and each coefficient was experimentally derived to minimize the difference between the experimental value and the predicted value.

(4) Step 4: Validate the model

Now, we tried to verify the accuracy and usability of the quality prediction model using various alternating temperature conditions (① 10-20℃, ② 10-30℃ ③ field verification experiment) that were not used in the model. For this verification, B _f (bias factor) and A _f (accuracy factor) were introduced as shown in Equations 3 and 4 to evaluate the prediction error.

Here, C _observed represents each data value experimentally observed, C _predicted represents a value predicted at the same time the data was observed, and n represents the number of observations.

The accuracy of the prediction model for the change in the number of eggs was analyzed and illustrated as shown in Table 1.

As a result of comparing A _f and B _f in a constant temperature environment, the prediction accuracy of the model was high, ranging from 1.021 to 1.037 and 0.989 to 0.999, respectively. This was found to match well. As can be seen from Table 1, A _f in a constant temperature environment and B _f appeared optimal, so a 3-order dynamic model was selected and expressed as in Equation 1. However, it is not necessarily limited to this order.

FIG. 5 is a flowchart illustrating in more detail a process of generating and storing a transaction in a block chain (step S220) in the price determination method of FIG. 2 according to an embodiment of the present invention.

In step S221, the measured temperature and measurement time from the time of shipment of the food according to the distribution process are received together using a sensor provided adjacent to the food. Then, in step S222, a transaction including distribution state information and a distribution subject of the food matching the identifier of the food or the sensor with the temperature and measurement time input through step S221 may be generated. Now, in step S223, a plurality of nodes registered in the block chain are induced to share the block by connecting the block including the transaction generated in step S222 in the form of a hash chain and storing it in the block chain. can As described above, in the series of processes (step S220) of generating a transaction and storing it in the block chain, the address of the block chain is set based on the identifier of the food or the sensor, but the production date, expiration date, By matching distribution information including quality index, temperature, and location information with a distribution subject and processing it into a transaction, the quality information at the time of shipment of the food and the sequentially accumulated distribution history can be stored together.

Referring to FIG. 6 illustrating a process of storing data measured in the distribution management system in the block chain, the food safety distribution system 610 determines the address of the block chain 620 based on the identifier (ID) of the sensor tag. and save quality/distribution history information in transactions, but show that blocks can be created through verification by node. To this end, it is possible to design a block chain storage method for unique key interfaces and addresses (Address, Related Key) values in the entire food distribution cycle. Through the illustrated structure, data integrity is guaranteed and reliable history management is possible by transacting quality/distribution/location information and storing them in the block chain.

7 is a diagram illustrating a process of storing data measured in a distribution management system in a block chain in more detail from an implementation point of view. After converting the history information on food distribution into JSON (JavaScript Object Notation) type, which is a text-based data exchange standard for exchanging and storing data, it is encoded in HEX to create a transaction. A block chain can be formed by generating and connecting blocks 625 in units of one minute from these transactions. Referring to FIG. 7, UTXO (Unspent Transaction Output), which is an unspent transaction output (balance), is illustrated, and the hash value (merkle root hash) of the transaction group is displayed in the block header when chaining the block 625. . This Merkle Root hash is a summary of all transaction details (from hundreds to thousands of transactions) in the block and processed into a small size, and exists in the block header. Furthermore, it has been exemplified that a RAFT-consensus technique for selecting a miner using a representative election-based RAFT protocol can be applied.

8 is a flowchart illustrating in detail the process of calculating the quality and degree of deterioration of food (step S230) in the price determination method of FIG. 2 according to an embodiment of the present invention.

In step S231, the distribution process may be monitored to discover food that satisfies marketable conditions. Since one of the objectives of the present invention is dynamic pricing, it is necessary to select sellable items from a variety of foods in the distribution process. Therefore, it is possible to discover foods that can be sold by inspecting the point at which the food arrives at the store or passes through a specific tag as a reference condition.

In step S232, a transaction in the block chain for the found food may be read, and quality information that changes from the shipping state of the food for each distribution stage may be extracted. That is, in this process, it is preferable to extract the quality information that changes from each transaction in order to observe the change in each distribution stage over time, rather than determining the first and final time points as one.

In step S233, the quality and degree of deterioration of the food may be calculated for each distribution stage using the previously set quality prediction model. Now, each extracted quality information is applied to the quality prediction model to calculate the quality and deterioration information of food for each distribution stage. Through this, it is possible to time-sequentially grasp how the quality deteriorates according to the distribution stage from the time of the first shipment.

Meanwhile, the process of calculating the quality and degree of deterioration of food (step S230) may further include the following additional steps. In step S234, the amount of change in quality and degree of deterioration is calculated for each distribution step by comparison with the previous step in consideration of the time-series sequence, and the amount of change in quality and degree of deterioration is calculated for each distribution step with the distribution subject of the corresponding step. It is desirable to match and save. Through this process, it is possible to more clearly identify the responsibilities of the distribution entity appearing in each distribution stage at the subsequent price determination stage.

9 is a flowchart illustrating a process of dynamically determining a price of food (step S240) in more detail in the price determination method of FIG. 2 according to an embodiment of the present invention.

In step S241, the quality at the time of shipment of the food or the initial price set according to the distribution contract may be read. Then, in step S242, a discounted price may be calculated in consideration of the calculated quality and the degree of deterioration of the food based on the initial price read through step S241. In other words, a strategy of discounting the selling price was introduced by determining the degree of deterioration in quality from the time of initial shipment. According to this dynamic pricing method, price differentiation according to quality can be achieved. Of course, the price at this time means the consumer price.

From an implementation point of view, in the embodiments of the present invention that utilizes a block chain to secure trust in distribution history, it is desirable to prepare and store the first smart contract defining the condition for quality deterioration and discount action in advance. If the quality and degree of deterioration of the sellable food are calculated through the previous process, it is checked whether the food satisfies the condition for the quality deterioration of the first smart contract by referring to the calculated quality and the degree of deterioration , the adjusted price can be calculated by applying the discount action corresponding to the defined condition. In other words, it is possible to implement dynamic prices according to quality through smart contracts.

On the other hand, if the price of food provided to consumers is determined in this way, it is necessary to determine who should bear the burden of the discounted price. For this, the following additional steps are required. Embodiments of the present invention adopt a strategy of determining a final price by determining a price based on prediction of quality, but sharing responsibility for quality deterioration for each distribution subject during overall distribution management.

Referring to FIG. 9 , in step S243 , it is possible to calculate the responsibility ratio within the total degree of deterioration by deriving the amount of change in the quality and degree of deterioration of food due to the distributor of the corresponding step for each distribution stage. Then, in step S244, it is possible to determine a discount price to be borne by each distribution entity among the total discounted prices according to the responsibility ratio calculated in step S243. In other words, after examining the responsibility for deterioration that occurred during the distribution process in individual stages, it is induced to bear the loss according to the discounted price by distributing the responsibility for each subject.

From an implementation point of view, in the embodiments of the present invention that utilizes a block chain to secure trust in distribution history, it is desirable to prepare and store the second smart contract in advance, which defines the discount action according to the condition for quality degradation and the liability rate. . If the quality and degree of deterioration of the food that can be sold are calculated through the previous process, referring to the calculated quality and the degree of deterioration, it is checked whether the food satisfies the condition for the quality deterioration of the second smart contract, , it is possible to calculate the discount price to be borne by each distribution entity by applying a discount action according to a responsibility ratio corresponding to a defined condition. In other words, it is possible to implement a pricing policy through smart contracts that each distribution entity bears responsibility for quality deterioration.

10 is an exemplary view illustrating a process of bearing a discount amount in consideration of distribution responsibilities of each performer in dynamically determining the price of food. The price decision system 40 in charge of system operation derives quality and degree of deterioration for each distribution stage by matching them with the performing entity through transactions in the block chain, and performs condition checks through predefined smart contracts 45. carry out As a result of the execution, when a condition is satisfied, a responsibility ratio may be derived as an action corresponding thereto, and a discount amount may be determined for each subject of the producer 10 , the distributor 20 , and the seller 30 . Referring to FIG. 10, a 10% liability rate for quality deterioration was calculated for the producer 10 and a discount of 50$ was imposed, and a 70% liability rate for quality deterioration was calculated for the distributor 20. It can be seen that a discount of 350$ has been imposed, and a discount of 100$ has been imposed on the vendor 30 as a 20% liability rate for quality deterioration has been calculated.

FIG. 11 is a block diagram showing a food price determination system 40 according to an embodiment of the present invention, and is a reconstruction of the above-described price determination method of FIG. 2 from the device configuration point of view. Therefore, in order to avoid duplication of description, the configuration of each component is briefly described in terms of operation and function of hardware. In addition, transportation means moving along the distribution network and sensors attached to individual products are omitted.

The communication unit 41 is a means for receiving data measured according to the distribution process from the time of shipment of the food using a temperature sensor provided adjacent to the food.

The memory 42 stores a program for determining a price from the predicted quality of the food using the data input through the communication unit 41 . The processor 43 may include at least one component as a means for driving the program, but a plurality of processors may be configured together according to performance needs for different purposes.

The program stored in the memory 42 sets a quality prediction model according to the history of time-environmental factors by considering both quality indicators representing the state of food and environmental factors involved in the quality of the distribution process, and measures the environmental factors It receives input along with the measurement time, generates a transaction including information on the distribution status of food and the subject of distribution, stores it in a block chain, and refers to the transaction in the block chain for food that can be distributed and sold to determine the quality of the food. and instructions for calculating the quality and degree of deterioration of the food according to the prediction model and dynamically determining the price of the food by referring to the calculated quality and degree of deterioration.

The program stored in the memory 42 selects at least one quality indicator representing the state of food, derives a plurality of environmental factors involved in a change in the distribution process for the selected quality indicator, and changes the environmental factor. or a command for pre-storing time-series measurement value data of quality indicators according to combinations and deriving a quality prediction model according to changes in time and environmental factors from the previously stored time-series measurement value data for the selected quality indicator. can

The program stored in the memory 42 receives the measured temperature and measurement time together from the time of shipment of the food along the distribution process, and matches the input temperature and measurement time with the identifier of the food or the sensor. A transaction including distribution state information and a distribution subject is created, and a block including the generated transaction is connected in the form of a hash chain and stored in the block chain, thereby enabling a plurality of nodes registered in the block chain to It may include a command that induces sharing.

In addition, the program stored in the memory 42 sets the address of the block chain based on the identifier of the food or the sensor, including the production date, expiration date, quality index, temperature, and location information of the food By matching the distribution information with the distribution subject and processing it into a transaction, the quality information at the time of shipment of the food and the sequentially accumulated distribution history can be stored together.

The program stored in the memory 42 monitors the distribution process to find food that satisfies sellable conditions, reads transactions in the block chain for the found food, and changes from the shipment status of the food for each distribution stage. It may include instructions for extracting quality information and calculating the quality and degree of deterioration of the food for each distribution stage using the quality prediction model.

In addition, the program stored in the memory 42 calculates the amount of change in quality and degree of deterioration, respectively, through comparison with the immediately preceding step for each distribution step in consideration of a time-series sequence, and calculates the quality and degree of deterioration for each distribution step. It may further include an instruction for matching and storing the amount of change with the distribution entity of the corresponding step.

The program stored in the memory 42 reads the quality at the time of shipment of the food or the initial price set according to the distribution contract, and calculates the quality and the degree of deterioration of the food based on the read initial price. It may include a command for calculating a discounted price taking into account.

In addition, the program stored in the memory 42 derives the amount of change in the quality and degree of deterioration of the food due to the distribution subject at each distribution stage, calculates the responsibility rate within the total degree of deterioration, and according to the calculated responsibility rate The controller may further include an instruction for determining a discounted price to be borne by each distribution entity among the total discounted prices.

Furthermore, the program stored in the memory 42 further includes instructions for preparing a third smart contract in advance, which defines a condition for deteriorating quality, a selling price, and a discount action according to a responsibility ratio, and provides quality and When the degree of deterioration is calculated, refer to the calculated quality and the degree of deterioration, but check whether the food satisfies the condition for the quality deterioration of the third smart contract, and adjust by applying a discount action corresponding to the defined condition. The discounted price to be borne by each distribution entity may be calculated by calculating the sold price and applying a discount action according to a responsibility ratio corresponding to the defined conditions.

According to the embodiments of the present invention described above, it is possible to provide a differential price determination method according to quality as well as quality information of food from quality-related factors measured in the food distribution process, and manage distribution history through a block chain. By doing so, forgery and falsification of quality or distribution history can be fundamentally prevented, and the risk burden can be fairly distributed by objectively calculating the responsibility ratio of quality deterioration for various performers intervening in the distribution chain.

Meanwhile, the embodiments of the present invention can be implemented as computer readable codes in a computer readable recording medium. The computer-readable recording medium includes all types of recording devices in which data that can be read by a computer system is stored.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. In addition, computer-readable recording media may be distributed to computer systems connected through a network, so that computer-readable codes may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the technical field to which the present invention belongs.

In the above, the present invention was examined focusing on various embodiments thereof. Those of ordinary skill in the art pertaining to the present invention will be able to understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the equivalent scope will be construed as being included in the present invention.

10: Producer/Food Processor
20: Distributor
30: Vendor
40: Pricing system/distribution management system
41: Communication department
42: memory
43: processor
45: Smart contract

Claims (20)

(a) setting a quality prediction model according to the history of time-environmental factors by the distribution management system in consideration of quality indicators representing the state of food and environmental factors involved in the quality of the distribution process;
(b) The distribution management system receives the environmental factors measured according to the distribution process from the time of shipment of the food together with the measurement time, and generates a transaction including information on the distribution status of the food and the distribution subject, thereby generating a block chain (block chain). chain);
(c) calculating the quality and degree of deterioration of the food according to the quality prediction model by referring to the transaction in the blockchain for the food that can be distributed and sold by the distribution management system; and
(d) dynamically determining, by the distribution management system, the price of the food with reference to the calculated quality and the degree of deterioration. According to claim 1,
In step (a),
(a1) selecting at least one quality index indicating the condition of the food;
(a2) deriving a plurality of environmental factors involved in changes in the distribution process for the selected quality index, and pre-storing time-series measured value data of the quality index according to the change or combination of the environmental factors; and
(a3) deriving a quality prediction model according to changes in time and environmental factors from the time-series measured value data previously stored for the selected quality index; According to claim 1,
In step (b),
(b1) receiving the measured temperature and measurement time along the distribution process from the time of shipment of the food using a sensor provided adjacent to the food;
(b2) generating a transaction including distribution state information and a distribution subject of the food or the food by matching the input temperature and measurement time with the identifier of the food or the sensor; and
(b3) inducing a plurality of nodes registered in the block chain to share by connecting the generated block including the transaction in the form of a hash chain and storing it in the block chain; pricing method. According to claim 3,
In step (b),
Based on the identifier of the food or the sensor, set the address of the blockchain, match the distribution information including the production date, expiration date, quality index, temperature, and location information of the food with the distribution subject and process it into a transaction , Food price determination method for storing together the quality information at the time of shipment of the food and the sequentially accumulated distribution history. According to claim 1,
In step (c),
(c1) monitoring the distribution process to discover foods that satisfy marketability conditions;
(c2) extracting quality information that changes from the shipment status of the food for each distribution stage by reading a transaction in the blockchain for the found food; and
(c3) calculating the quality and degree of deterioration of the food for each distribution stage using the quality prediction model; According to claim 5,
In step (c),
(c4) calculating the amount of change in quality and degree of deterioration for each distribution step by comparison with the previous step in consideration of the time-series order; and
(c5) matching and storing the amount of change in the quality and degree of deterioration for each distribution stage with a distribution entity at the corresponding stage; According to claim 1,
In step (d),
(d1) reading the quality of the food at the time of shipment or the initial price set according to the distribution contract; and
(d2) calculating a discounted price in consideration of the quality and the degree of deterioration calculated for the food based on the read initial price; According to claim 7,
Creating a first smart contract in advance that defines conditions for quality degradation and discount actions;
When the quality and degree of deterioration of the sellable food is calculated through the step (c), the step (d),
Checking whether the food satisfies the quality deterioration condition of the first smart contract by referring to the calculated quality and the degree of deterioration, and calculating an adjusted price by applying a discount action corresponding to a defined condition, How food is priced. According to claim 7,
In step (d),
(d3) deriving the amount of change in the quality and degree of deterioration of the food due to the distribution agent at each distribution stage to calculate the responsibility ratio within the total deterioration degree; and
(d4) determining a discount price to be borne by each distribution entity among the total discounted prices according to the calculated responsibility ratio; According to claim 9,
Creating a second smart contract in advance that defines a condition for quality degradation and a discount action according to a liability rate; further comprising;
When the quality and degree of deterioration of the sellable food is calculated through the step (c), the step (d),
With reference to the calculated quality and the degree of deterioration, it is checked whether the food satisfies the quality deterioration condition of the second smart contract, and a discount action according to the responsibility ratio corresponding to the defined condition is applied to the distribution subject. A method of pricing food that calculates the discounted price that must be borne by the individual. A computer-readable recording medium recording a program for executing the method of any one of claims 1 to 10 in a computer. a communication unit that receives data measured according to the distribution process from the time of shipment of the food by using a temperature sensor provided adjacent to the food;
a memory for storing a program for determining a price from the predicted quality of the food using the inputted data; and
At least one processor for driving the program; including,
Programs stored in the memory,
Quality indicators representing the state of food and environmental factors involved in the quality of the distribution process are considered together to set up a quality prediction model based on the history of time-environmental factors, and the measured environmental factors are input along with the measurement time to distribute food The quality and quality of the food according to the quality prediction model by generating and storing a transaction including state information and distribution subject in a block chain, and referring to a transaction in the block chain for food that can be distributed and sold. A food pricing system comprising an instruction for calculating a degree of deterioration and dynamically determining a price of the food by referring to the calculated quality and the degree of deterioration. According to claim 12,
Programs stored in the memory,
Selecting at least one quality index representing the state of food, deriving a plurality of environmental factors involved in changes in the distribution process for the selected quality index, and measuring quality indicators in time series according to changes or combinations of the environmental factors A food pricing system comprising pre-stored value data and an instruction for deriving a quality prediction model according to changes in time and environmental factors from the pre-stored time-series measurement value data for the selected quality index. According to claim 12,
Programs stored in the memory,
A transaction including the distribution state information and distribution subject of the food that receives the temperature and measurement time measured from the time of shipment of the food along with the distribution process, and matches the input temperature and measurement time with the identifier of the food or the sensor and a command for inducing a plurality of nodes registered in the block chain to share by connecting the block including the generated transaction in the form of a hash chain and storing it in the block chain. 's pricing system. 15. The method of claim 14,
Programs stored in the memory,
Based on the identifier of the food or the sensor, set the address of the blockchain, match the distribution information including the production date, expiration date, quality index, temperature, and location information of the food with the distribution subject and process it into a transaction , A food pricing system that stores quality information at the time of shipment of the food and sequentially accumulated distribution history together. According to claim 12,
Programs stored in the memory,
The distribution process is monitored to discover food that satisfies the sellable condition, the transaction in the block chain is read for the found food, and quality information that changes from the shipment status of the food for each distribution stage is extracted, respectively, and the quality prediction is performed. A food pricing system comprising instructions for calculating the quality and degree of deterioration of the food for each distribution stage using a model. 17. The method of claim 16,
Programs stored in the memory,
Considering the time-series order, the amount of change in quality and degree of deterioration is calculated for each distribution step by comparison with the previous step, and the amount of change in quality and degree of deterioration for each distribution step is matched with the distribution subject of the corresponding step and stored A pricing system for food, further comprising instructions. According to claim 12,
Programs stored in the memory,
A command for reading the quality at the time of shipment of the food or an initial price set according to a distribution contract, and calculating a discounted price based on the read initial price in consideration of the calculated quality and the degree of deterioration of the food Including, a pricing system for food. According to claim 18,
Programs stored in the memory,
For each distribution stage, the amount of change in the quality and degree of deterioration of the food due to the distribution entity at that stage is derived to calculate the responsibility rate within the total degree of deterioration, and according to the calculated liability rate, the discounted total price must be borne by each distribution entity. A system for determining the price of food, further comprising instructions for determining a discount price for each. According to claim 19,
Programs stored in the memory,
Further comprising an instruction for pre-writing a third smart contract defining a condition for quality deterioration, a sale price, and a discount action according to a liability rate;
When the quality and degree of deterioration of salable food are calculated, refer to the calculated quality and degree of deterioration, but check whether the food satisfies the condition for the quality deterioration of the third smart contract and respond to the defined condition. The food price determination system calculates the adjusted sales price by applying a discount action to calculate the discount price to be borne by each distribution entity by applying a discount action according to a responsibility ratio corresponding to a defined condition.

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