KR102612337B1 - 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 the price of food by predicting the quality of food that changes during the distribution process, taking into account quality indicators indicating the state of the food and environmental factors involved in the quality of the distribution process. Establish a quality prediction model based on the history of environmental factors, and input environmental factors measured according to the distribution process from the point of shipment of the food along with the measurement time to create a transaction that includes information on the distribution status of the food and the distribution entity. The quality and degree of deterioration of the food are calculated according to the quality prediction model by referring to transactions within the block chain for food that can be distributed and sold, and the quality and degree of deterioration of the food are calculated by referring to the calculated quality and degree of deterioration. The price of is determined dynamically.

Description

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

The present invention relates to a technology that predicts the quality of food that changes during the distribution process and dynamically determines the price of food. In particular, the distribution management system predicts the real-time quality of food and determines the price by taking into account the responsibility of the distribution entity. 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 goes through the distribution channel from being manufactured to being delivered to consumers. In the case of fresh food, where maintaining freshness has an absolute influence on the value of the product, the freshness due to these temperatures changes over time. Changes in status or quality are very important. Because consumers indirectly purchase fresh food that has gone through various distribution processes, they cannot check the freshness information and sanitary conditions of the product before opening the package. Therefore, it is very important to provide information to consumers by predicting in advance how freshness will be affected when food is exposed to various temperatures and how long a certain level of edible quality will be maintained at the storage temperature. You can see 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-laced egg scandal in Korea in 2017, consumers have become anxious about the safety of eggs. The cause of this anxiety is that when food safety issues or accidents occur, responses that are reviewed and evaluated on a scientific basis are not systematically implemented. Therefore, efforts are required to quickly deliver transparent and objective information based on science.

The prior art literature presented below attaches a sensor tag that detects environmental changes to a container or pallet loaded with a large number of foods, and detects environmental changes that occur during transportation of food in real time using the sensor tag. We are introducing food quality monitoring technology to evaluate quality information in real time and provide it to consumers. However, these prior art documents only mention the general flow of providing quality information to consumers by transmitting detected signals, and do not suggest a direction for predicting and utilizing food quality.

Therefore, there is a need to think about techniques that can accurately predict and provide information about the freshness or quality of food to consumers and at the same time 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 that the present invention aims to solve is to overcome the limitation that quality-related factors measured in the conventional food distribution process provide only simple quality information, and to deliver food to consumers despite the fixed price determined at the time of shipment from the production site. We aim to resolve the weakness that exists in the quality of food being distributed and resolve the problem in which only some of the various performing entities involved in the distribution chain unilaterally bear the losses resulting from distribution failure.

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 that considers quality indicators indicating the state of the food and environmental factors involved in the quality of the distribution process; Setting up a quality prediction model according to the history of time-environment factors; (b) The distribution management system receives environmental factors measured according to the distribution process from the point of shipment of the food along with the measurement time and generates a transaction containing the distribution status information of the food and the distribution entity, thereby creating a block chain. chain); (c) the distribution management system calculating the quality and degree of deterioration of the food according to the quality prediction model by referring to transactions in the blockchain for distributed and sellable food; and (d) the distribution management system dynamically determining the price of the food with reference to the calculated quality and degree of deterioration.

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

In the method for determining the price of food according to an embodiment, step (b) of generating a transaction and storing it in a blockchain includes (b1) using a sensor provided adjacent to the food to follow the distribution process from the time of shipment of the food. Step of inputting the measured temperature and measurement time together; (b2) generating a transaction including distribution status information and a distribution entity of the food matching the input temperature and measurement time with the identifier of the food or the sensor; and (b3) linking the blocks containing the generated transactions in the form of a hash chain and storing them in the blockchain, thereby inducing a plurality of nodes registered in the blockchain to share them. there is.

In addition, in the method for determining the price of food according to an embodiment, step (b) of creating a transaction and storing it in the blockchain sets the address of the blockchain based on the identifier of the food or the sensor, By processing distribution information, including the food production date, expiration date, quality index, temperature, and location information, with the distribution entity 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. there is.

In the method for determining the price of food according to one embodiment, the step (c) of calculating the quality and degree of deterioration of the food includes: (c1) monitoring the distribution process to discover food that satisfies the conditions for sale; (c2) reading transactions in the blockchain for the discovered food and extracting quality information that changes from the shipment status of the food at each stage of distribution; and (c3) calculating the quality and degree of deterioration of the food for each stage of distribution using the quality prediction model.

In addition, in the method for determining the price of food according to one embodiment, the step (c) of calculating the quality and degree of deterioration of the food is performed by comparing the distribution stage with the previous stage in consideration of the time series order (c4). Calculating the amount of change in quality and degree of deterioration, respectively; and (c5) matching and storing the change in quality and degree of deterioration at each stage of distribution with the distribution entity at that 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 or distribution contract at the time of shipment of the food. ; 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 the step of creating a first smart contract defining conditions and discount actions for quality deterioration in advance, and calculating the quality and degree of deterioration of the food ( When the quality and degree of deterioration of a salable food are calculated through step c), the step (d) of dynamically determining the price of the food refers to the calculated quality and degree of deterioration to determine whether the food is 1 It is possible to check whether the conditions for quality deterioration of the smart contract are satisfied and calculate the adjusted price by applying a discount action corresponding to the defined conditions.

Meanwhile, 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 (d3) the amount of change in the quality and degree of deterioration of the food due to the distribution entity at the corresponding stage in each distribution stage. Deriving a liability ratio within the overall degree of deterioration; and (d4) determining a discount price to be borne by each distribution entity among the total discounted prices according to the calculated liability ratio.

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

Furthermore, the following provides a computer-readable recording medium on which a program for executing the above-described food price determination method on a computer is recorded.

In order to solve the above technical problem, the food price determination system according to an embodiment of the present invention includes a communication unit that receives data measured according to the distribution process from the point of shipment of the food using a temperature sensor provided adjacent to the food. ; a memory storing a program that determines a price from the quality of the food predicted using the input data; and at least one processor that drives the program; wherein the program stored in the memory considers quality indicators indicating the state of the food and environmental factors involved in the quality of the distribution process and determines the history of time-environment factors. Establish a quality prediction model according to the requirements, input the measured environmental factors along with the measurement time, create a transaction including information on the distribution status of the food and the distribution entity, store it in the block chain, distribute it, and sell it. A command to calculate 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 to dynamically determine the price of the food by referring to the calculated quality and degree of deterioration. Includes.

In the food pricing system according to one embodiment, the program stored in the memory selects at least one quality indicator representing the state of the food, and a plurality of environments involved in changes in the distribution process for the selected quality indicator. Factors are derived and time-series 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 time-series measurement value data previously stored for the selected quality indicators. May include commands for deriving a prediction model.

In the food price determination system according to one embodiment, the program stored in the memory receives the temperature and measurement time measured according to the distribution process from the time of shipment of the food, and applies the input temperature and measurement time to the food or A transaction containing the distribution status information and the distribution entity of the food matched with the identifier of the sensor is created, and the block containing the generated transaction is connected in the form of a hash chain and stored in the blockchain to store the block. It may contain commands that induce multiple nodes registered in the chain to share.

In addition, in the food price determination system according to one embodiment, the program stored in the memory sets the address of the blockchain based on the identifier of the food or the sensor, but determines the production date, expiration date, and quality of the food. By matching distribution information including index, temperature, and location information with the distribution entity 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 one embodiment, the program stored in the memory monitors the distribution process to discover food that satisfies the conditions for sale, and reads transactions in the blockchain for the discovered food to determine each stage of distribution. It may include instructions for extracting changing quality information from the shipment status of the food and calculating the quality and degree of deterioration of the food at each stage of distribution using the quality prediction model.

In addition, in the food price determination system according to one embodiment, the program stored in the memory calculates the amount of change in quality and degree of deterioration for each distribution stage through comparison with the previous stage in consideration of the time series order, and It may further include a command for matching and storing the amount of change in quality and degree of deterioration at each stage of distribution with the distribution entity at that stage.

In the food price determination system according to one embodiment, the program stored in the memory reads the initial price set according to the quality or distribution contract at the time of shipment of the food, and sets the initial price for the food based on the read initial price. It may include a command for calculating a discounted price in consideration of the calculated quality and degree of deterioration.

In addition, in the food price determination system according to one embodiment, the program stored in the memory calculates the liability ratio within the overall degree of deterioration by deriving the amount of change in the quality and degree of deterioration of the food due to the distribution entity at each stage of distribution. It may further include instructions for determining a discount price to be borne by each distribution entity among the total discounted prices according to the calculated liability ratio.

Furthermore, in the food pricing system according to one embodiment, the program stored in the memory further includes instructions for pre-creating a third smart contract that defines a discount action according to conditions for quality deterioration, sales price, and liability ratio. And, if the quality and degree of deterioration of the food available for sale are calculated, refer to the calculated quality and degree of deterioration, but conditions defined by checking whether the food satisfies the conditions for quality deterioration of the third smart contract. The adjusted sales price can be calculated by applying the discount action corresponding to , and the discount price to be borne by each distribution entity can be calculated by applying the discount action according to the responsibility ratio corresponding to the defined conditions.

Embodiments of the present invention can provide not only food quality information from quality-related factors measured in the food distribution process, but also a differential price determination method according to quality, and can provide quality and distribution by managing distribution history through blockchain. It is possible to fundamentally prevent forgery and falsification of records, and the risk burden can be fairly distributed by objectively calculating the ratio of responsibility for quality deterioration to various performing entities involved in the distribution chain.

Figure 1 illustrates a food safety distribution platform for managing the role and performance history of each distribution entity in a food distribution environment in which embodiments of the present invention are implemented.
Figure 2 is a flowchart illustrating a method for determining the price considering the quality of food according to an embodiment of the present invention.
Figure 3 is a flowchart illustrating in more detail the process of setting a quality prediction model in the price determination method of Figure 2 according to an embodiment of the present invention.
Figure 4 is a diagram for explaining the process of developing a quality prediction model adopted by embodiments of the present invention.
Figure 5 is a flowchart illustrating in more detail the process of creating a transaction and storing it in a blockchain in the price determination method of Figure 2 according to an embodiment of the present invention.
Figures 6 and 7 are diagrams illustrating the process of storing data measured in a distribution management system in a blockchain.
Figure 8 is a flowchart illustrating in more detail the process of calculating the quality and degree of deterioration of food in the price determination method of Figure 2 according to an embodiment of the present invention.
Figure 9 is a flowchart illustrating in more detail the process of dynamically determining the price of food in the price determination method of Figure 2 according to an embodiment of the present invention.
Figure 10 is an example diagram to explain the process of dynamically determining the price of food and burdening the discount amount by considering the distribution responsibility of each performing entity.
Figure 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 known functions or configurations that may obscure the gist of the present invention are omitted in the following description and attached drawings. In addition, throughout the specification, 'including' a certain component does not mean excluding other components unless specifically stated to the contrary, but rather means that other components may be further included.

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

The 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 the present application, terms such as “comprise” or “comprise” are intended to designate the presence of the described features, numbers, steps, operations, components, parts, or combinations thereof, but are not intended to indicate the presence of one or more other features or It should be understood that this does not exclude in advance the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless specifically defined differently, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. 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 technology, and unless clearly defined in the present application, should not be interpreted as having an ideal or excessively formal meaning. .

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

Previously, it was explained that efforts are being made to determine the condition of food using various sensor devices introduced in the food distribution process. However, problems such as loss or manipulation of these measurement values occur in the field, and there are cases where trust in food distribution information is reduced due to this. Therefore, in order to ensure food safety distribution, embodiments of the present invention provide information on the history of food shipped from the producer (food processing company) 10 as it moves along the distribution chain by the distributor/transporter 20. Block chain technology was introduced to accumulate and store information and transparently manage the entire distribution process from sales to sellers (wholesalers and retailers) (30) to consumers. Furthermore, we would like to propose a technical means to dynamically determine the price of food at the minimum consumption point by managing and querying transactions recorded in this blockchain through the distribution management system 40, which is in charge of system operation. .

Therefore, the purpose of the distribution management system 40 using blockchain is to prevent forgery and falsification of history information and provide data integrity core technology to be applied to a food safety distribution platform with real-time visibility. To this end, the intelligent food safety distribution framework configures blockchain nodes in each of the producers (10), distributors (20), sellers (30), and distribution management systems (40) to share and manage distribution and history information. It was designed. In other words, by utilizing the blockchain's distributed ledger, all participants in the network, including producers (10), distributors (20), and sellers (30), can participate in verification work to share information, and food distribution information is consistent. It can be managed securely.

Meanwhile, embodiments of the present invention described below propose a distribution management system that determines the price in consideration of the quality of the food, but the price determination point in this case is not necessarily limited to the point in time when the food is delivered to the final consumer. Rather, it should be understood that it can also be used to determine the intermediate distribution price from the time the food is shipped by the producer to the time the food is delivered to at least one distributor or middleman passing through during the distribution process. In other words, the embodiments of the present invention should focus on the technical idea that differentiated prices can be determined considering the quality of food at a specific point in the entire distribution process, and are limited to the technology for simply determining or discounting the final consumer price. It shouldn't be. In addition, the expression 'consumer price' does not refer only to the price paid by final consumers or individual consumers who consume food at retail stores or marts, and refers to the parties involved in the food distribution process (distributors, middlemen, sellers, It broadly refers to the price paid by general consumers (may include general consumers, etc.).

Figure 2 is a flowchart illustrating a method for determining a price 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 multiple means of food movement (delivery/transportation means) identified in the distribution network, and observes the condition indicators of the food using sensors attached to each food packaging unit. In the embodiments of the present invention, 'temperature' was presented as a single condition indicator, and the quality prediction model introduced below was implemented to accurately predict the quality only from the temperature history of such food. In addition, food includes foods such as vegetables, fruits, fish, meat, eggs, and dairy products that are distributed in a fresh state. Here, eggs are exemplified for specific description of the technology, but the technology is not limited thereto.

In step S210, the distribution management system sets a quality prediction model according to the history of time-environment factors by considering quality indicators indicating the state of the food and environmental factors involved in the quality of the distribution process. For the quality indicators selected for each food group, we can build a DB on the environmental history (temperature/humidity/wind speed distribution at production, distribution, and sales sites) that is actually exposed, and a DB on experimental information under conditions that reflect this (constant temperature, variable temperature, on-site). You can. In the case of the illustrated egg, the quality indicator may be at least one of specific gravity, pH, color, yolk coefficient, egg white coefficient, S-ovalbumin, and Haugh unit, and the quality can be determined depending on the type of food. A variety of specific indicators can be used. For example, it was discovered that the experimental information DB can set eggs (haugh unit), kimchi (total acidity), pasteurized milk (general bacteria), chicken (general bacteria), etc. as quality indicators. Environmental factors are used to examine factors that affect maintaining freshness. For example, in the case of eggs, they can be at least one of storage temperature, humidity, and wind speed.

In step S220, the distribution management system receives environmental factors measured according to the distribution process from the point of shipment of the food along with the measurement time, generates a transaction including the distribution status information of the food and the distribution entity, and sends it to the blockchain. Save it in (block chain). There are various indicators to determine the condition or quality of food. Considering the single goal of predicting freshness, measuring all of the various indicators indicating the freshness of food individually and determining freshness from these measures has the highest accuracy. It can be shown. However, in the distribution process of numerous foods, it is not realistic from the viewpoint of cost and convenience to equip each individual packaging unit with sensors corresponding to various indicators. Therefore, in the embodiments of the present invention, only 'temperature', a single indicator, is adopted, but accurate quality is ensured 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 wanted to predict. At this time, the environmental factors measured in this way can be processed into comprehensive information about the distribution history along with the measurement time and then converted into a transaction. These transactions are a component of the blockchain and were introduced for the purpose of preventing forgery and falsification of data and transparent information sharing.

From an implementation perspective, embodiments of the present invention can apply RAFT-Consensus technology to elect miners using the RAFT protocol based on representative election. This is a consensus algorithm without fork. It assumes that all nodes are honest, 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 blockchain for distributed and sellable food. Embodiments of the present invention seek to promote dynamic price determination for salable food. Therefore, we wanted to monitor various foods in the distribution process and determine the quality of foods that were ready for sale. At this time, the quality and degree of deterioration of food can be derived from time-series accumulated data by looking at a number of transactions accumulated in the blockchain through step S220. In this process, the quality prediction model defined in step S210 can be utilized. As described above, the quality prediction model proposed by embodiments of the present invention predicts the quality of food through a single indicator, 'temperature', especially 'temperature history' recorded over time. In other words, the freshness of one fresh food is calculated according to the time-temperature history. A more specific freshness prediction model will be described in detail later with reference to FIG. 4.

In step S240, the distribution management system dynamically determines the price of the food with reference to the calculated quality and degree of deterioration. In other words, if the quality of 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 by taking into account quality changes (usually quality deterioration). As a technical means, embodiments of the present invention can manage distribution history through blockchain and determine the final sales price by utilizing smart contracts. At this time, a smart contract is a program that records predefined contract conditions on the blockchain and automatically executes actions according to the contract when the conditions are met. It utilizes distribution and quality data based on the trust of the blockchain network to improve the distribution process. The quality deterioration shown in is reflected in the final price to induce price differentiation according to quality. From an implementation perspective, the hash value of quality history data collected from food distribution can be stored in a database, then use a smart contract to determine whether there has been forgery or alteration, and price changes can be automatically determined based on the quality status at the time of food sale.

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

In step S211, at least one quality indicator indicating the state of the food can be selected. In this process, it is desirable to select a preset number of quality indicators in order of relative dependence on temperature among a plurality of quality indicators indicating 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, egg yolk coefficient, egg white coefficient, S-ovalbumin, and Haugh unit. These quality indicators may vary depending on the type of food, but are not limited to this.

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

In step S213, a quality prediction model according to changes in time and environmental factors can be derived from the time-series measurement data previously stored for the selected quality indicator. More specifically, the change in the quality indicator over time is set as a dynamic model using the time-series measurement data previously stored for the previously selected quality indicator, and the temperature dependence of the selected quality indicator is set. ) by setting it as a polynomial model, a quality prediction model according to changes in time and temperature can be derived. As described above, embodiments of the present invention do not simply measure 'temperature' conditions or other indicators at a single point in time when predicting the condition or quality of food, but rather measure the temperature that changes over time. We propose a quality prediction model that uses trends, that is, 'time-environment factor history' (more precisely, 'time-temperature history'). Below, we describe more specific model development and implementation methods.

Figure 4 is a diagram to explain the process of developing a quality prediction model adopted by embodiments of the present invention. Eggs were selected as an example of a food, but the target food is not limited to this as long as the technical idea remains the same. . In Figure 4, a dynamic (freshness) prediction of egg quality (freshness) and its changes according to temperature is provided in advance to provide information in advance on how the quality changes as eggs go through various distribution processes from production to delivery to consumers. dynamic) quality prediction model was presented.

Referring to Figure 4, the quality prediction model proposed by embodiments of the present invention first selects objective quality indicators that sufficiently reflect the characteristics of individual foods in order to scientifically explain changes in quality for each target food (step 1), and Build a database (step 2) through a distribution environment survey and quality change analysis (420) for environmental factors (410), and develop a mathematical model according to time-temperature history (430) based on the constructed database ( Step 3), develop and verify a dynamic model that reflects the variable temperature conditions of the food distribution environment (step 4). Below, each step is explained sequentially.

(1) Step 1: Selection of quality prediction indicators

As previously introduced, quality indices for predicting egg quality include specific gravity, pH, color, yolk coefficient, egg white coefficient, S-ovalbumin, Haugh unit, etc. As an example, a heavy rainfall unit was selected. The rain unit is an internationally used indicator as a standard method 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 (thick egg white). Eggs with a high heavy rain unit have convex whites and good cohesion, so they do not spread when broken.

(2) Step 2: Database construction

Changes in rain units, a quality indicator selected to predict egg quality, were graded and stored at storage temperature (normal temperature: 0℃, 5℃, 10℃, 20℃ and 30℃, alternating temperature: 10-20℃, 5-25℃, 10℃). A database containing a large number of data was constructed by reflecting independent variables such as -30℃), humidity (average 50%, 75%, 90%), and wind speed.

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

To predict changes in the rainfall unit 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 equations below, t represents the elapsed time, and T represents the temperature of the food.

To express changes in heavy rainfall units, which are one of the indicators of egg quality, Gompertz, Hill and Wright, Logistic, and Baranyi and Roberts models can be used, and in Equation 1, the Baranyi and Roberts model is selected as an example. Thus, the change in heavy rain units over time was expressed. In addition, in Equation 1, it is expressed by combining the weight constant W considering the external flow rate. Here, the W value is composed of the ratio of the heat transfer coefficient in a distribution environment in which a strong flow rate occurs and the heat transfer coefficient in a general distribution environment, and can have a value of 0.45-0.65 depending on the distribution environment. As an example, the average value of 0.55 was applied in the current model. Since the unit of heavy rain decreases faster as the external flow rate increases, the influence of flow rate can be reflected in the quality model by assigning weights, which will improve the model's prediction accuracy in a distribution environment where actual temperature/flow rate varies widely. You can.

Next, the secondary model is intended to explain the influence of environmental conditions on the parameters analyzed in the primary model, and a polynomial model was used to analyze the influence on temperature.

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

(4) Step 4: Model verification

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

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

The accuracy of the model for predicting changes in egg rainfall units was analyzed and shown in Table 1.

As a result of comparing A _f and B _f in a constant temperature environment, the model's prediction accuracy was found to be high, ranging from 1.021 to 1.037 and 0.989 to 0.999, respectively. Even in a variable temperature environment, the actual and predicted values were in the range of 1.025 to 1.036 and 0.993 to 1.036, respectively. This appeared to be in good agreement. As can be seen in Table 1, A _f in a constant temperature environment and B _f were found to be optimal, so a third-order dynamic model was selected and expressed as Equation 1. However, it is not necessarily limited to this order.

Figure 5 is a flowchart illustrating in more detail the process of creating a transaction and storing it in a blockchain (step S220) in the price determination method of Figure 2 according to an embodiment of the present invention.

In step S221, the temperature and measurement time measured according to the distribution process from the time of shipment of the food are input together using a sensor provided adjacent to the food. Then, in step S222, a transaction including distribution status information and a distribution entity of the food may be created by matching the temperature and measurement time input through step S221 with the identifier of the food or the sensor. Now, in step S223, the block containing the transaction created through step S222 is connected in the form of a hash chain and stored in the blockchain to induce a plurality of nodes registered in the blockchain to share it. You can. As described above, a series of processes (step S220) of creating a transaction and storing it in the blockchain sets the address of the blockchain based on the identifier of the food or the sensor, including the production date of the food, expiration date, By matching distribution information including quality index, temperature, and location information with the distribution entity 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.

Referring to Figure 6, which illustrates the process of storing data measured in the distribution management system in the blockchain, the food safety distribution system 610 determines the address of the blockchain 620 based on the identifier (ID) of the sensor tag. It shows that blocks can be created by creating and storing quality/distribution history information in transactions, but verifying each node. To this end, it is possible to design a blockchain storage method for unique identifier (Unique Key) interface and address (Related Key) values at each stage throughout the entire food distribution business cycle. Through the illustrated structure, quality/distribution/location information is transacted and stored in the blockchain, thereby ensuring data integrity and enabling reliable history management.

Figure 7 is a diagram illustrating in more detail the process of storing data measured in a distribution management system in a blockchain from an implementation perspective. History information about food distribution is converted into JSON (JavaScript Object Notation) type, a text-based data exchange standard created to exchange and store data, and then encoded with HEX to create a transaction. A block chain can be formed by creating and connecting blocks 625 in units of 1 minute from these transactions. Referring to Figure 7, UTXO (Unspent Transaction Output), which is an unspent transaction output (balance), is illustrated, and when chaining block 625, the hash value (merkle root hash) of the transaction group is displayed in the block header. . This Merkle Root hash summarizes all transaction details in the block (several hundred to thousands of transactions) and processes them into a small size, which exists within the block header. Furthermore, it was demonstrated that RAFT-consensus technology, which elects miners using the RAFT protocol based on representative election, can be applied.

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

In step S231, the distribution process can be monitored to discover food that satisfies the conditions for sale. Since one of the purposes of the present invention is dynamic price determination, it is necessary to select those that can be sold among various foods in the distribution process. Therefore, it is possible to discover food that can be sold by examining the time when the food arrives at the store or passes a specific tag as a standard condition.

In step S232, the transaction in the blockchain for the discovered food can be read to extract changing quality information from the shipment status of the food at each stage of distribution. In other words, in this process, rather than judging from the first shipment time to the final time as one, it is desirable to extract each changing quality information from the transaction in order to observe trends in each stage of distribution over time.

In step S233, the quality and degree of deterioration of the food can be calculated for each stage of distribution using a previously set quality prediction model. Now, each extracted quality information is applied to the quality prediction model to calculate food quality and deterioration information at each stage of distribution. Through this, it is possible to understand in time series how quality deteriorates depending on the distribution stage from the time of 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, considering the time-serial order, the amount of change in quality and degree of deterioration is calculated for each stage of distribution through comparison with the previous stage, and the amount of change in quality and degree of deterioration is calculated for each stage of distribution with the distribution entity of the corresponding stage. It is desirable to match and save. Through this process, it is possible to more clearly identify the responsibilities of distribution entities at each distribution stage in the subsequent price determination stage.

Figure 9 is a flowchart illustrating in more detail the process (step S240) of dynamically determining the price of food in the price determination method of Figure 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 can be read. Then, in step S242, a discounted price can be calculated by considering the quality and the degree of deterioration calculated for the food based on the initial price read through step S241. In other words, a strategy was introduced to discount the sales price by determining the degree to which quality had deteriorated compared to 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 refers to the consumer price.

From an implementation perspective, it is desirable that embodiments of the present invention that utilize blockchain to ensure trust in distribution history create and store a first smart contract defining conditions for quality deterioration and discount actions in advance. If the quality and degree of deterioration of the food available for sale have been calculated through the previous process, check whether the food satisfies the conditions for quality deterioration of the first smart contract with reference to the calculated quality and degree of deterioration, and , the adjusted price can be calculated by applying a discount action corresponding to the defined conditions. In other words, it is possible to implement dynamic prices according to quality through smart contracts.

On the other hand, once the price of food provided to consumers has been determined, 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 the price based on quality prediction and determining the final price by dividing responsibility for quality deterioration by each distribution entity during overall distribution management.

Referring to FIG. 9, in step S243, the amount of change in the quality and degree of deterioration of the food due to the distribution entity at that stage can be derived for each stage of distribution to calculate the responsibility ratio within the overall degree of deterioration. Then, in step S244, the discount price to be borne by each distribution entity among the total discounted prices can be determined according to the liability ratio calculated in step S243. In other words, after examining the responsibility for deterioration that occurred during the distribution process at each stage, it is distributed pro rata to each entity and encouraged to bear the loss due to the discounted price.

From an implementation perspective, in embodiments of the present invention that utilizes blockchain to ensure trust in distribution history, it is desirable to create and store a second smart contract in advance that defines a discount action according to conditions and responsibility ratio for quality deterioration. . If the quality and degree of deterioration of the food available for sale have been calculated through the previous process, check whether the food satisfies the conditions for quality deterioration of the second smart contract with reference to the calculated quality and degree of deterioration, and , the discount price that must be borne by each distribution entity can be calculated by applying a discount action according to the liability ratio corresponding to the defined conditions. In other words, it is possible to implement a pricing policy that requires each distributor to bear responsibility for quality deterioration through a smart contract.

Figure 10 is an example diagram to explain the process of dynamically determining the price of food and burdening the discount amount by considering the distribution responsibility of each performing entity. The pricing system (40), which is in charge of system operation, derives the quality and degree of deterioration at each stage of distribution by matching them with the performing entity through transactions within the blockchain, and conditions are checked through a predefined smart contract (45). Perform. As a result of the performance, if the conditions are met, the responsibility ratio is derived as a corresponding action, and the discount amount can be determined for each entity of the producer (10), distributor (20), and 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 confirmed that a discount of $350 was imposed, and a 20% liability rate for quality deterioration was calculated for the seller (30), and a discount of $100 was imposed.

Figure 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 price determination method of Figure 2 described above in terms of device configuration. Therefore, here, to avoid duplication of explanation, the configuration of each component will be outlined from the perspective of hardware operation and function. In addition, transportation means moving along the distribution network and sensors attached to individual products were omitted.

The communication unit 41 is a means of 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 that determines the price from the quality of the food predicted using the data input through the communication unit 41. The processor 43 may include at least one component as a means of 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 considers quality indicators indicating the state of the food and environmental factors involved in the quality of the distribution process, sets up a quality prediction model according to the history of time-environment factors, and measures the environmental factors. It receives input along with the measurement time, generates a transaction including information on the distribution status of the food and the distribution entity, and stores it in the block chain, and refers to the transaction in the block chain for food that can be distributed and sold to ensure the quality of the food. Calculates the quality and degree of deterioration of the food according to a prediction model, and includes instructions for dynamically determining the price of the food with reference to the calculated quality and degree of deterioration.

The program stored in the memory 42 selects at least one quality indicator indicating the state of the food, derives a plurality of environmental factors involved in changes in the distribution process for the selected quality indicator, and changes the environmental factors. Or, it may store in advance the time-series measurement value data of the quality index according to the combination, and may include instructions for deriving a quality prediction model according to changes in time and environmental factors from the time-series measurement value data previously stored for the selected quality index. You can.

The program stored in the memory 42 receives the temperature and measurement time measured according to the distribution process from the time of shipment of the food, and matches the input temperature and measurement time with the identifier of the food or the sensor. It creates a transaction including distribution status information and the distribution entity, and connects the blocks containing the generated transaction in the form of a hash chain and stores it in the blockchain, allowing a plurality of nodes registered in the blockchain to It may contain commands that encourage 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, and includes the production date, expiration date, quality index, temperature, and location information of the food. By matching distribution information with the distribution entity 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.

The program stored in the memory 42 monitors the distribution process, discovers food that satisfies the conditions for sale, reads transactions in the blockchain for the discovered food, and changes the shipment status of the food at each stage of distribution. It may include instructions for extracting quality information and calculating the quality and degree of deterioration of the food at each stage of distribution 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 for each stage of distribution through comparison with the previous stage in consideration of the time-serial order, and calculates the amount of change in quality and degree of deterioration for each stage of distribution. A command for matching and storing the amount of change with the distribution subject of the corresponding stage may be further included.

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 for the food based on the read initial price. It may include a command that calculates a discounted price by taking into account the price.

In addition, the program stored in the memory 42 calculates the liability ratio within the overall degree of deterioration by deriving the amount of change in the quality and degree of deterioration of the food due to the distribution entity at that stage at each stage of distribution, and according to the calculated liability ratio. It may further include a command for determining a discount price to be borne by each distribution entity among the total discounted price.

Furthermore, the program stored in the memory 42 further includes instructions for pre-creating a third smart contract that defines a discount action according to conditions for quality deterioration, sales price, and liability ratio, and determines the quality and quality of food available for sale. When the degree of deterioration is calculated, refer to the calculated quality and degree of deterioration, check whether the food satisfies the conditions for quality deterioration of the third smart contract, and adjust by applying a discount action corresponding to the defined condition. The sales price can be calculated, and the discount price to be borne by each distribution entity can be calculated by applying a discount action according to the responsibility ratio corresponding to the defined conditions.

According to the above-described embodiments of the present invention, it is possible to provide not only food quality information from quality-related factors measured in the food distribution process, but also a differential price determination method according to quality, and manage distribution history through blockchain. By doing so, it is possible to fundamentally prevent forgery and falsification of quality or distribution history, and the risk burden can be fairly distributed by objectively calculating the ratio of responsibility for quality deterioration to the various performing entities involved in the distribution chain.

Meanwhile, embodiments of the present invention can be implemented as computer-readable code on a computer-readable recording medium. Computer-readable recording media include all types of recording devices that store data that can be read by a computer system.

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

In the above, the present invention was examined focusing on its various embodiments. Those skilled in the art will 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 restrictive perspective. The scope of the present invention is indicated in the claims rather than the foregoing description, and all differences within the equivalent scope should be construed as being included in the present invention.

10: Producer/Food Processing Company
20: Distributor
30: Vendor
40: Pricing system/distribution management system
41: Department of Communications
42: memory
43: processor
45: Smart Contract

Claims (20)

(a) a step where the distribution management system considers quality indicators representing the state of food and environmental factors involved in the quality of the distribution process together to establish a quality prediction model according to the history of time-environmental factors;
(b) The distribution management system receives environmental factors measured according to the distribution process from the point of shipment of the food along with the measurement time, generates a transaction including the distribution status information of the food and the distribution entity, and ships the food. A step of storing point-in-time quality information and sequentially accumulated distribution history together in a block chain;
(c) 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 blockchain for food that is distributed and available for sale, and takes responsibility for the distribution entity at each distribution stage. Matching the amount of change in the quality and degree of deterioration at each stage of distribution from the transaction with the distribution entity at that stage in order to identify it; and
(d) The distribution management system dynamically determines the price of the food with reference to the calculated quality and degree of deterioration, and uses a smart contract that defines a discount action according to the conditions and responsibility ratio for quality deterioration for each stage of distribution. A method for determining the price of food, including a step of determining a discount price to be borne by each distribution entity according to the liability ratio within the degree of deterioration caused by the distribution entity at the relevant stage. According to claim 1,
In step (a),
(a1) selecting at least one quality indicator indicating the state of the food;
(a2) deriving a plurality of environmental factors involved in changes in the distribution process for the selected quality indicator and pre-storing time-series measurement value data of the quality indicator according to changes or combinations of the environmental factors; and
(a3) deriving a quality prediction model according to changes in time and environmental factors from the time-series measurement data previously stored for the selected quality indicator. Method for determining the price of food, including. According to claim 1,
In step (b),
(b1) receiving the temperature and measurement time measured 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 status information and a distribution entity of the food matching the input temperature and measurement time with the identifier of the food or the sensor; and
(b3) connecting the blocks containing the generated transactions in the form of a hash chain and storing them in the blockchain, thereby inducing a plurality of nodes registered in the blockchain to share them; food, including; How to determine the price of . According to claim 3,
In step (b),
The address of the blockchain is set based on the identifier of the food or the sensor, and the distribution information including the production date, expiration date, quality index, temperature, and location information of the food is matched with the distribution entity and processed into a transaction. ,
History information about food distribution is converted to JSON (JavaScript Object Notation) type and encoded to create a transaction, the hash value of the transaction group is displayed in the block header to chain blocks, and consensus is based on representative election. A method of determining the price of food that operates according to an algorithm. According to claim 1,
In step (c),
(c1) monitoring the distribution process to discover food that satisfies marketable conditions;
(c2) reading transactions in the blockchain for the discovered food and extracting quality information that changes from the shipment status of the food at each stage of distribution; and
(c3) calculating the quality and degree of deterioration of the food at each stage of distribution 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 stage by comparing it with the previous stage in consideration of the time series order; and
(c5) matching and storing the amount of change in quality and degree of deterioration at each stage of distribution with the distribution entity at that stage; a method for determining the price of food, further comprising a. According to claim 1,
In step (d),
(d1) reading the quality at the time of shipment of the food or the initial price set according to the distribution contract; and
(d2) calculating a discounted price in consideration of the quality and degree of deterioration calculated for the food based on the read initial price. delete According to claim 7,
In step (d),
(d3) calculating the liability ratio within the overall degree of deterioration by deriving the amount of change in the quality and degree of deterioration of the food due to the distribution entity at each stage of distribution; and
(d4) determining a discount price to be borne by each distribution entity among the total discounted prices according to the calculated liability ratio; a method for determining the price of food, further comprising a. According to clause 9,
Further comprising: preparing the smart contract in advance,
When the quality and degree of deterioration of salable food are calculated through step (c), step (d) is,
Referring to the calculated quality and degree of deterioration, it is checked whether the food satisfies the conditions for quality deterioration of the smart contract, and a discount action according to the responsibility ratio corresponding to the defined condition is applied to each distribution entity. A method of determining the price of food, which calculates the discounted price that must be given. A computer-readable recording medium recording a program for executing the method of any one of claims 1 to 7, 9, and 10 on a computer. a communication unit that receives data measured from the point of shipment of the food according to the distribution process using a temperature sensor provided adjacent to the food;
a memory storing a program that determines a price from the quality of the food predicted using the input data; and
Including at least one processor that runs the program,
The program stored in the memory is,
By considering quality indicators indicating the condition of the food and environmental factors involved in the quality of the distribution process, a quality prediction model based on the history of time-environment factors is set up, and the measured environmental factors are input along with the measurement time to distribute the food. By creating a transaction including status information and distribution entity, quality information at the time of shipment of the food and sequentially accumulated distribution history are stored in a block chain, and blocks for food that can be distributed and sold Calculate the quality and degree of deterioration of the food according to the quality prediction model by referring to transactions in the chain, and calculate the amount of change in quality and degree of deterioration from the transaction to each stage of distribution in order to determine the responsibility of the distribution entity at each distribution stage. It matches with the distribution entity at the corresponding stage and dynamically determines the price of the food with reference to the calculated quality and degree of deterioration, but uses a smart contract that defines a discount action according to the conditions and responsibility ratio for quality deterioration at each stage of distribution. A food price determination system including a command for determining a discount price to be borne by each distribution entity according to the liability ratio within the degree of deterioration caused by the distribution entity at the relevant stage. According to claim 12,
The program stored in the memory is,
Select at least one quality indicator that represents the state of the food, derive a plurality of environmental factors involved in changes in the distribution process for the selected quality indicator, and measure the quality indicator in a time series according to changes or combinations of the environmental factors. A food price determination system that stores value data in advance and includes instructions for deriving a quality prediction model according to changes in time and environmental factors from the time-series measurement data previously stored for the selected quality index. According to claim 12,
The program stored in the memory is,
A transaction that includes the distribution status information and distribution entity of the food by receiving the temperature and measurement time measured according to the distribution process from the time of shipment of the food, and matching the entered temperature and measurement time with the identifier of the food or the sensor. and a command that induces a plurality of nodes registered in the block chain to share the food by connecting the block containing the generated transaction in the form of a hash chain and storing it in the block chain. pricing system. According to claim 14,
The program stored in the memory is,
The address of the blockchain is set based on the identifier of the food or the sensor, and the distribution information including the production date, expiration date, quality index, temperature, and location information of the food is matched with the distribution entity and processed into a transaction. ,
History information about food distribution is converted to JSON (JavaScript Object Notation) type and encoded to create a transaction, the hash value of the transaction group is displayed in the block header to chain blocks, and consensus is based on representative election. A food pricing system that operates according to an algorithm. According to claim 12,
The program stored in the memory is,
Monitor the distribution process to discover food that satisfies the conditions for sale, read transactions in the blockchain for the discovered food, extract changing quality information from the shipment status of the food at each stage of distribution, and predict the quality. A food price determination system comprising instructions for calculating the quality and degree of deterioration of the food for each stage of distribution using a model. According to claim 16,
The program stored in the memory is,
Considering the time series order, the amount of change in quality and degree of deterioration is calculated for each stage of distribution through comparison with the previous stage, and the amount of change in quality and degree of deterioration for each stage of distribution is matched and stored with the distribution entity of the corresponding stage. A pricing system for food, further comprising instructions. According to claim 12,
The program stored in the memory is,
An instruction for reading the quality at the time of shipment of the food or the initial price set according to the distribution contract, and calculating a discounted price in consideration of the quality and the degree of deterioration calculated for the food based on the read initial price. Including, pricing system for food. According to claim 18,
The program stored in the memory is,
At each stage of distribution, 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 liability ratio within the overall degree of deterioration, and according to the calculated liability ratio, the above-mentioned discounted total price must be borne by each distribution entity. A food price determination system further comprising instructions for determining each discount price. According to claim 19,
The program stored in the memory is,
It further includes instructions for pre-writing the smart contract,
When the quality and degree of deterioration of food available for sale are calculated, refer to the calculated quality and degree of deterioration, but check whether the food satisfies the conditions for quality deterioration of the smart contract and provide a discount corresponding to the defined conditions. A food price determination system that calculates the adjusted sales price by applying an action and calculates the discount price to be borne by each distribution entity by applying a discount action according to the responsibility ratio corresponding to defined conditions.