KR102453611B1 - Agricultural product price prediction and production adjustment system and method therefor - Google Patents

Abstract

The present invention relates to a production adjustment system using agricultural product price prediction information. According to the present invention, a production adjustment system using agricultural product price prediction information includes: an inflow calculation part; a production history information generation part; a price prediction part; a production plan calculation part; a production plan transmission part; an agricultural production revenue compensation part; and an agricultural product recommendation part. Big data information can be created by collecting production history and price information of agricultural products. The inflow of agricultural products can be corrected based on an inflow rate for each agricultural wholesale market.

Description

Production adjustment system and method using agricultural product price prediction information

The present invention relates to a production adjustment system using agricultural product price prediction information, and to a system for determining agricultural product prices by analyzing an inflow amount and production history of agricultural products, and providing agricultural product production guide information based on the determined price.

Agricultural product price prediction provides information on the transaction price and quantity of agricultural products brought into a specific wholesale center for agricultural products, such as the Agricultural Observation Center of the Korea Rural Economic Research Institute, for the last 3 months, or provides information on the wholesale price and quantity for the last 10 days. However, for the actual wholesale price and import quantity information for the last 10 days, only 2-3 pieces of information from a week ago can be checked when searching for the information with today's date.

As such, the existing agricultural product price prediction system only provides information on the previous agricultural product price trend or import quantity trend, and cannot predict the agricultural product price tomorrow or a week later or provide predicted price information online in real time.

In particular, as for agricultural products, it is difficult for all processes from sowing to harvest to proceed seamlessly according to the initial plan, and unlike the production of industrial products, force majeure situations that cannot be controlled artificially occur frequently. It can be said that the distribution center of agricultural products plays a large role in determining when to sell agricultural products according to supply and demand. However, since agricultural product prices fluctuate in real time, it is not easy to predict the price of agricultural products tomorrow or a week later to determine when to ship agricultural products.

Conventional agricultural product production area information is collected monthly through a telephone survey by monitor agents (local Agricultural Cooperatives, Agricultural Technology Center staff, etc.) commissioned by item and region, and is used to identify production and transaction trends in production areas. Because it is a work, it cannot be collected quickly and accurately, so there is a limit to using the collected agricultural product production area information as basic information to understand the production and transaction trends of the production area.

In addition, although the agricultural observation information support service has increased in importance since 1999, the agricultural observation information support system that performs this simply stores or provides a database of survey results and provides analysis services using the stored survey results. There is a limit to what you can't do.

In order to improve this limitation, the prior art Korean Patent Publication No. 10-1764147 collects wholesale prices for a certain period to determine the optimal time to ship agricultural products and analyzes the wholesale prices with a plurality of mixed regression models with different regression coefficients. Disclosed is an online agricultural product price prediction method and system for calculating at least one wholesale price from tomorrow to one week later. A method for predicting price and sales volume is disclosed.

However, this prior art has a problem that the price prediction system can be applied only to crops that have been harvested, although it is possible to predict the price and determine the shipping time by collecting only the wholesale price within a set period.

Therefore, a production adjustment system using agricultural product price prediction information that can predict the price of agricultural products by analyzing the wholesale price, inflow, and sales volume of agricultural products, and further establish a production plan for agricultural products based on the predicted price, and guide research is needed on

An object of the present invention is to generate big data information by collecting production history and price information for each item of agricultural products, and correcting the inflow of agricultural products based on the inflow ratio for each agricultural wholesale market, so that a more accurate It is to provide a production adjustment system using agricultural product price prediction information that can calculate the agricultural product inflow.

In addition, by predicting the meridian price of agricultural products and establishing a production plan using big data information and inflow data, agricultural product producers can maximize their sales profits and consumers can make rational decisions about production using agricultural product price prediction information. It aims to provide a coordination system.

Objects of the present invention are not limited to the objects mentioned above, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

The production adjustment system using agricultural product price prediction information according to an embodiment of the present invention includes an inflow calculation unit for extracting agricultural product inflow data flowing into the agricultural wholesale market, and production history information by collecting production information for a preset period A production history information generating unit that generates production history big data that A production plan calculation unit for calculating the agricultural production production plan, an agricultural product meridian price collection unit for collecting the actual meridian price of the agricultural product, a production plan amount transmission unit for transmitting the agricultural product production plan amount to a preset user terminal of the agricultural product grower, the agricultural product cultivation In the case where the business entity cultivates the agricultural products by modifying the production planning amount initially planned in response to the production planning amount calculated by the production plan calculation unit, compensation is made to the agricultural product grower in response to the difference in production profit from the initially planned production plan amount Agricultural product recommendation unit that provides recommended agricultural products to the agricultural product grower using the agricultural product meridian price for each year collected by the agricultural product production profit compensation unit and the agricultural product meridian price collection unit that supports the amount or recovers the amount of additional revenue, The production history information generation unit divides the inflow data collected by the inflow amount calculation unit by agricultural products and agricultural wholesale markets, and generates the production history big data on a daily, weekly and annual basis, and the inflow amount calculation unit includes: The nationwide inflow is calculated by sampling the inflow of agricultural products flowing into the set agricultural wholesale market, and the predicted inflow of the agricultural products is calculated by reflecting the correction value according to the inflow ratio for the predetermined period in the predetermined agricultural wholesale market, The inflow calculation unit calculates an average value of the agricultural product inflow ratio during a first preset period to the preset agricultural wholesale market and an estimated value corresponding to a change pattern of the agricultural product inflow ratio introduced during the preset second period, respectively, and When the estimated value is equal to or greater than the average value, according to [Equation 1] below calculating the predicted inflow amount by adding the average value to the ratio of the estimated value and the difference value of the average value;

[Equation 1]

When the estimated value is less than the average value, the predicted inflow is calculated by subtracting the ratio of the difference value from the average value according to Equation 2 below,

[Equation 2]

Here, I _exp is the predicted inflow amount, I _aver is the average value of the inflow rate of agricultural products inflow during the first preset period, and I _assu is the estimated value corresponding to the change pattern of the inflow ratio of agricultural products inflow during the second preset period, respectively. box.

The inflow calculation unit may include an average value of the inflow ratio of agricultural products introduced to the preset agricultural wholesale market for a first period and an estimated value corresponding to a change pattern of the inflow ratio of agricultural products introduced during the second period and an estimated value for the estimated value Each of the error values from the average value is calculated, and when the calculated error value is within a preset reference range, a preset constant (C) is reflected in the average value according to Equation 3 below, and the calculated error value When this preset reference range is exceeded, the predicted inflow amount is calculated by reflecting the preset constant (C) to the average value according to Equation 4 below, and then multiplying it by a correction factor (α),

[Equation 3]

Here, I _exp is the predicted inflow, and C is the constant.

When the average value is greater than the estimated value, the constant (C) has a negative sign, and when the average value is less than the estimated value, the constant (C) has a positive sign,

[Equation 4]

Here, I _exp is the predicted inflow, α is the correction factor, and C is the constant.

The price prediction unit predicts the meridian price of agricultural products based on the agricultural product inflow data and the production history big data, but predicts the price using a program command and an FFNN algorithm, and the FFNN algorithm predicts the meridian price of the agricultural product The variable input for this purpose is implemented to output the predicted price of agricultural products through a preset hidden layer, and a weight wn is assigned to each variable input for predicting the meridian price of the agricultural product, and the weight wn is the group value. It is updated every set period, and the production plan calculation unit calculates the production plan for the current year of the agricultural products according to the following [Equation 5] in response to the predicted meridian price and the actual meridian price of last year collected by the agricultural meridian price collection unit. calculate,

[Equation 5]

Here, O _p is the production plan for the current year, O _l is last year’s production, AC _p is the predicted meridian price, and AC _l is the actual meridian price of last year.

The production plan calculation unit calculates the recommended cultivation amount for each farm based on the production plan amount for the year according to the following [Equation 6],

[Equation 6]

Here, F _p is the recommended production amount for each farm, and a _i is the last year’s production of farm i.

The agricultural product production profit compensation unit calculates the expected profit using the initial production plan input by the agricultural grower and the previous year's meridian price according to the following [Equation 7], and uses the current year's production plan amount and the current year's actual meridian price. Calculating the actual profit, in response to the difference between the expected profit and the actual profit, support the compensation amount or recover the amount of additional revenue to the agricultural grower,

[Equation 7]

Here, R is the refund amount, AC _a is the current meridian price, and O _i is the initial production plan.

The agricultural production profit compensation unit, the initial production planning amount, means the production plan input from the agricultural producer before the production recommendation amount for each farm reflecting the production plan for the year is delivered, and calculated in [Equation 7] When the refund amount is calculated as a negative number, an additional revenue amount is recovered from the agricultural product grower, and when the refund amount is calculated as a positive number, a compensation amount is supported to the agricultural product grower, and the agricultural product production profit compensation unit, When the average profit of the farms that do not implement the recommended production amount (F _p ) by each farm is greater than the average of the profits of the farms that have implemented the recommended production amount (F _p ) by each farm, the following [Equation 8] and the following [Equation 9] ], additionally pay the profit compensation calculated in accordance with the above production plan to the farmhouse,

[Equation 8]

Here, R _p is the profit reserve, and α is the profit reserve coefficient.

[Equation 9]

(However, when B _aver≤ G _aver , α=0 is calculated)

Here, B _aver means the average profit of the farms that do not implement the production plan, and G _aver means the average profit of the farms that implement the production plan.

The agricultural product recommendation unit, with respect to the meridian price for each agricultural product collected by the agricultural product meridian price collection unit, recommends agricultural products with a high rate of increase in the agricultural product meridian price by year and agricultural products whose supply is insufficient compared to the demand to the agricultural product growers. Production adjustment system using agricultural product price prediction information.

According to the present invention, by collecting the production history and price information of agricultural products by item, generating big data information, and correcting the inflow of agricultural products based on the inflow ratio for each agricultural wholesale market, more accurate agricultural products in consideration of the change pattern of the regional wholesale market It is possible to provide a production adjustment system using agricultural product price prediction information that can calculate the inflow.

In addition, by predicting the meridian price of agricultural products and establishing a production plan using big data information and inflow data, agricultural product producers can maximize their sales profits and consumers can make rational decisions about production using agricultural product price prediction information. A coordination system may be provided.

1 is a diagram illustrating a block diagram of a production adjustment system using agricultural product price prediction information according to an embodiment of the present invention.
2 is a view for explaining a method of predicting an inflow of a production adjustment system using agricultural product price prediction information according to an embodiment of the present invention.
3 is a view for explaining another inflow prediction method of the production adjustment system using agricultural product price prediction information according to an embodiment of the present invention.
4 is a diagram for explaining a price prediction algorithm of a production adjustment system using agricultural product price prediction information according to an embodiment of the present invention.
5 is a flowchart illustrating a production adjustment system using agricultural product price prediction information according to an embodiment of the present invention.

Specific details including the problems to be solved for the present invention as described above, means for solving the problems, and the effects of the invention are included in the examples and drawings to be described below. Advantages and features of the present invention, and methods of achieving them, will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings.

The scope of the present invention is not limited to the embodiments described below, and various modifications may be made by those of ordinary skill in the art within the scope not departing from the technical gist of the present invention.

Hereinafter, the production adjustment system using the agricultural product price prediction information according to the present invention will be described in detail with reference to FIGS. 1 to 5 attached thereto.

First, FIG. 1 is a diagram showing a block diagram of a production adjustment system using agricultural product price prediction information according to an embodiment of the present invention, and FIG. 2 is a production adjustment using agricultural product price prediction information according to an embodiment of the present invention. It is a view for explaining an inflow prediction method of the system, and FIG. 3 is a diagram for explaining another inflow prediction method of the production adjustment system using agricultural product price prediction information according to an embodiment of the present invention, FIG. 4 is the present invention It is a view for explaining a price prediction algorithm of a production adjustment system using agricultural product price prediction information according to an embodiment of the present invention It is a flowchart.

Referring to FIG. 1 , the production adjustment system using agricultural product price prediction information according to an embodiment of the present invention includes an inflow calculation unit 110 , a production history information generation unit 120 , a price prediction unit 130 , and a production plan calculation. It may include a unit 140 , an agricultural product meridian price collection unit 150 , a production planning amount transmission unit 160 , a agricultural product production profit compensation unit 170 , and an agricultural product recommendation unit 180 .

The inflow calculation unit 110 extracts agricultural products inflow data flowing into the agricultural wholesale market, and the production history information generation unit 120 collects production information for a preset period and includes production history information. Data is generated, and the price prediction unit 130 predicts the meridian price of agricultural products based on the agricultural product inflow data and the production history big data, and the production plan calculation unit 140 is based on the predicted price. In this way, you can create an agricultural production plan.

For example, the production history information generating unit 120 divides the inflow data collected by the inflow amount calculating unit 110 by item of agricultural products and agricultural wholesale market, and in daily, weekly, monthly or annual units according to the setting. The production history big data may be generated.

In addition, the inflow calculation unit 120 samples the inflow of agricultural products flowing into the preset agricultural wholesale market and calculates the nationwide inflow, but reflects the correction value according to the inflow ratio for a preset period in the preset wholesale market. Thus, it is possible to calculate the predicted inflow of the agricultural products.

<Example 1>

In more detail, the inflow calculation unit 110 corresponds to the change pattern of the average value of the agricultural products inflow ratio introduced to the preset agricultural wholesale market during the first period and the change pattern of the agricultural goods inflow ratio in the second preset period. Calculate the estimated value to be calculated, and if the estimated value is equal to or greater than the average value, the predicted inflow is calculated by adding the ratio of the difference between the estimated value and the average value to the average value by the following [Equation 1], and the estimated value is When it is less than the average value, the predicted inflow amount may be calculated by subtracting the ratio of the difference value from the average value by Equation 2 below.

[Equation 1]

Here, I _exp is the predicted inflow amount, I _aver is the average value of the inflow rate of agricultural products inflow during the first preset period, and I _assu is the estimated value corresponding to the change pattern of the inflow ratio of agricultural products inflow during the second preset period, respectively. box.

[Equation 2]

Here, I _exp is the predicted inflow amount, I _aver is the average value of the inflow rate of agricultural products inflow during the first preset period, and I _assu is the estimated value corresponding to the change pattern of the inflow ratio of agricultural products inflow during the second preset period, respectively. box.

For example, referring to FIG. 2 , the inflow calculation unit 110 samples the collected nationwide inflow data (S210), and the inflow rate of potatoes for 3 months of 'A wholesale market' is 20%, 25%, In the case of 30%, an average value of 25 of the inflow ratio of potatoes may be calculated, and an estimated value of the inflow ratio of potatoes calculated in consideration of the change pattern of the inflow ratio of potatoes 35 may be calculated (S220).

At this time, the sign of the difference between the estimated value and the average is determined (S230), and when the difference has a positive sign (S240), the predicted inflow is the average value of 25 and the difference between the estimated value and the average value. The predicted inflow amount can be calculated as 29 by summing the ratio (eg, {(35-25)/25}*(35-25)=4).

Conversely, when the difference value has a negative sign (S250), the predicted inflow amount may be calculated by subtracting the ratio of the difference value from the average value.

For example, if the inflow ratio of potatoes for 3 months in 'Wholesale Market A' is 30%, 25%, and 20%, the average value of the inflow ratio of potatoes 25 is calculated, and the change pattern of the inflow ratio of potatoes is considered It is possible to calculate (S220) the estimated value 15 of the inflow ratio of the calculated potato.

Accordingly, the predicted inflow amount is calculated by adding the average value 20 to the ratio of the difference between the estimated value and the average value (ex. {(25-15)/25}*(25-15)=4) to obtain the predicted inflow amount 21 can be calculated as

When using the above calculation method, the correction value is reflected (summed or subtracted) based on the average value, but the correction value is reflected differently depending on the degree of difference between the average value and the estimated value. The value may be corrected, and when the deviation from the estimated value is relatively large, the large value may be corrected.

For example, if the inflow rate of potatoes in 'Wholesale Market A' for 3 months is 20%, 25%, and 30% (case 1) and 15%, 25%, and 35% (case 2), respectively, , case 1 and case 2 both have an average value of 25, but case 1 has an estimated value of 35 and case 2 has an estimated value of 45, so the predicted inflow in case 1 is 25+{(25-15)/25}*(25-15) ) = 29, and the predicted inflow in case 2 is 25+{(45-25)/25}*(45-25)=41.

Therefore, the numerical value is corrected by reflecting the estimated value based on the average value, but as the inflow ratio for a preset period increases or decreases abruptly, a value more biased to the estimated value is derived, so that a more reasonable predicted inflow can be calculated.

<Example 2>

In another embodiment, the inflow calculation unit 110 is a change pattern of the average value of the inflow ratio of agricultural products introduced to the preset agricultural wholesale market for a first period, and the inflow ratio of agricultural products introduced during the second predetermined period. The predicted inflow amount may be calculated by calculating an error value of an estimated value corresponding to and an average value of the estimated value.

When the calculated error value is within a preset reference range (less than 0.5), a preset constant C is added to the average value as shown in [Equation 3] below, and the calculated error value exceeds the preset reference range In this case, as shown in Equation 4 below, it is possible to calculate the national agricultural product inflow by reflecting the constant C and then multiplying it by a correction factor α.

In this case, when the average value is greater than the estimated value, the constant C may have a negative sign, and when the average value is less than the estimated value, the constant C may have a positive sign.

[Equation 3]

Here, I _exp is the predicted inflow, and C is the constant.

[Equation 4]

Here, I _exp is the predicted inflow, α is the correction factor, and C is the constant.

For example, referring to FIG. 3 , the inflow calculation unit 110 samples the collected nationwide inflow data ( S310 ), and the inflow rate of sweet potatoes for 3 months in 'B wholesale market' is 5%, 6%, 10 %, the average value of the inflow ratio of sweet potatoes is calculated as 7, and the estimated value of the inflow ratio of sweet potatoes calculated in consideration of the change pattern of the annual inflow ratio of sweet potatoes in the previous year of 'B wholesale market' is 12, The constant C may be calculated as 3.5, which is 50% of the average value, and an error value of the estimated value from the average value may be calculated as 0.42 (S320).

At this time, it is determined whether the calculated error value satisfies the reference range (S330), and when the error value satisfies the reference range (S340), a constant C (ex. 50% of the average value) is added to the average value. However, since the average value is smaller than the estimated value, the sign of the constant C has a positive sign.

Therefore, the predicted inflow ratio for agricultural products sweet potato of the 'B wholesale market' is calculated as 10.5 by reflecting the constant C +3.5 to the average value 7, and the predicted inflow rate is calculated by converting the calculated predicted inflow ratio. can

Conversely, when the error value exceeds the reference range (S350), the correction coefficient α may correct the error value exceeding the reference range by multiplying the average value by a coefficient for correcting the average value (eg 1.5).

For example, if the inflow ratio of sweet potatoes for 3 months in 'Wholesale B market' is 5%, 15%, and 25%, the average value of the inflow ratio of sweet potatoes is calculated as 15, and the annual sweet potato in the previous year of 'Wholesale B market' The estimated value of the inflow ratio of the sweet potato calculated in consideration of the change pattern of the inflow ratio of (S320) can be done.

At this time, it is determined whether the calculated error value satisfies the reference range (S330), and when the error value is out of the reference range (S350), a constant C (ex. 50% of the average value) is added to the average value However, since the average value is smaller than the estimated value, the sign of the constant C has a positive sign.

Accordingly, the predicted inflow ratio for the agricultural sweet potato of the 'wholesale market B' can be calculated by adding the average value 15 to the constant C +3.5 and multiplying it by a correction factor of 1.5 to calculate the predicted inflow amount as 27.75.

Meanwhile, the price prediction unit 140 may predict the meridian price of agricultural products based on the agricultural product inflow data and the production history big data, but predict the price using a program command and an FFNN algorithm.

Referring to FIG. 4 , the FFNN (Feed Forward Neural Network) is implemented such that a variable input for price prediction of the agricultural product passes through a hidden layer to output the predicted price of the agricultural product, and the input variable passes through the hidden layer. The predicted price may be calculated by reflecting the weight w _n assigned to each variable.

For example, the hidden layer may be configured in plurality, and the weight w _n may be updated at a preset period, so that the accuracy of the predicted price may be improved.

<Example 3>

Referring to FIG. 5 , the production adjustment system 100 using the agricultural product price prediction information collects agricultural product inflow data by wholesale market and item (S100), and generates big data of production history for a preset period (S200) step, predicting the price of agricultural products using the inflow data and the production history big data (S300), and establishing the production plan of the agricultural products based on the predicted price (S400). It is possible to predict the price, and adjust the production plan of the agricultural products by reflecting the predicted price.

Referring back to FIG. 1 , the production plan calculation unit 150 may calculate the agricultural production plan amount based on the meridian price predicted by the price prediction unit.

In more detail, the production plan calculation unit 150 calculates the production plan amount of the agricultural product for the current year in response to the predicted meridian price and the actual meridian price of last year collected by the agricultural product meridian price collection unit [Equation 5] ] can be calculated according to

[Equation 5]

Here, O _p is the production plan for the current year, O _l is last year's production, AC _p is the predicted meridian price, and AC _l is the actual meridian price of last year.

For example, last year's production (O _l ) of strawberries among the agricultural products was 200,000 tons, and the predicted meridian price (AC _p ) of this year's strawberry meridians predicted by the price prediction unit 130 was predicted to be 11,000 won per 1 kg, and last year's actual meridians If the price (AC _l ) is 10,000 won, the production plan for the year (O _p ) can be calculated as 220,000 tons.

As described above, based on the production plan amount (O _p ) for the year calculated through the production plan calculation unit 140 , the recommended cultivation amount for each farm can be calculated according to the following [Equation 6].

[Equation 6]

Here, F _p may mean the recommended production amount for each farm, and a _i may mean the last year's production of farm i.

That is, the production recommendation amount (F _p ) of the current year of the i farmhouse can be provided by using the production planned amount for the current year (O _p ) and last year's production amount (O ₁ ).

As an example, if last year's production of onions (O _l ) is 1,576,756 tons, the production plan for the current year (O _p ) is 1,354,154 tons, and last year's onion production (a _i ) of farm i is 23 tons, The recommended production amount (F _p ) can be recommended to be about 19.75 tons.

The agricultural product meridian price collection unit 150 may collect the actual meridian price of the agricultural product.

Here, the actual meridian price of the agricultural product may be collected from the outside including data such as price information of the nationwide wholesale market of the agricultural product item and the agricultural product production condition survey analysis.

The production plan transmission unit 160 may transmit the agricultural product production plan calculated by the production plan calculation unit 140 to a preset user terminal of the agricultural product grower.

Here, the preset terminal of the agricultural product grower may mean a device capable of wireless communication, such as a PC, a laptop computer, a smart phone, a tablet PC, etc. capable of receiving a signal of a production adjustment system using the agricultural product price prediction information. .

The agricultural product production profit compensation unit 170, when the agricultural products grower cultivates the agricultural products by correcting the production planning amount initially planned in response to the production plan amount calculated by the production plan calculation unit 140 for the year In response to the difference in production revenue from the initially planned production plan, compensation may be provided to the agricultural grower or the amount of additional revenue may be returned.

Here, the agricultural product production profit compensation unit 170, according to the following [Equation 7], can calculate the expected profit using the initial production plan input by the agricultural producer and the previous year's meridian price.

In addition, the actual profit is calculated using the production plan for the current year and the actual meridian price for the year, and the compensation amount support or additional profit amount can be returned to the agricultural grower in response to the difference between the expected profit and the actual profit. .

[Equation 7]

Here, R is the refund amount, AC _a is the meridian price of the current year, and O _i may mean the initial production plan.

As an example, the agricultural product production profit compensation unit 170 confirms that the actual meridian price (AC l) of last year's actual meridian price (AC _l ) is 9,845 won per 1 kg of the sesame leaf producing farm using the production adjustment system 100 using the agricultural product price prediction information. Although the planned production amount (O _i ) was planned to be 0.8 tons, the recommended production amount (F _p ) for each farmhouse reflecting the production planned amount (O _p ) for the current year calculated by the production plan calculation unit 140 is recommended to be 0.5 tons, The predicted meridian price (AC _p ) of the perilla leaves is calculated as 9,445 won per 1 kg, and when the meridian price (AC _a ) of the current year is confirmed to be 9,637 won, about 1,120 won can be supported.

At this time, the agricultural production profit compensation unit 170, the initial production planning amount (O _i ), the production recommendation amount (F _p ) for each farm reflecting the production plan amount (O _p ) of the year is delivered to the agricultural grower It may refer to the production plan input from the agricultural grower before becoming.

On the other hand, when the refund amount calculated in [Equation 7] is calculated as a negative number, the refund amount may be recovered from the agricultural grower.

Here, when the refund amount is calculated as a negative number, since it is calculated as a negative number when more revenue is generated than when the agricultural grower previously planned the initial production plan amount (O _i ) The refund amount calculated by the compensation unit 170 may be returned.

In addition, when the refund amount is calculated as a positive number, the compensation amount may be supported to the agricultural producer.

That is, when the refund amount is calculated as a positive number, the agricultural product grower is calculated as a positive number when a deficit occurs compared to when it was produced with the previously planned initial production amount (O _i ), so the agricultural product production profit compensation unit The refund amount calculated in (170) may be supported.

On the other hand, the refund amount calculated through [Equation 7] can be adjusted by adding more weight to the basic salary of the refund amount, and the part that recovers or supports the refund amount to the agricultural grower is a profit structure model such as insurance, An external company related to compensation support or a compensation support team in the production adjustment system 100 using the agricultural product price prediction information may be operated to support or refund the refund amount to the agricultural product grower.

On the other hand, the agricultural product production profit compensation unit 170, the average of the profit of the farmhouse that does not implement the production recommendation amount (F _p ) by the farmhouse is greater than the average profit of the farmhouse that has implemented the production recommendation amount (F _p ) by each farmhouse In this case, the profit compensation calculated according to the following [Equation 8] and [Equation 9] may be additionally paid to the farmhouse that has implemented the production plan.

[Equation 8]

Here, R _p is the profit reserve, and α is the profit reserve coefficient.

[Equation 9]

G_aver인 경우, α=0으로 산출)(However, B _aver

In case of G _aver , calculated as α=0)

Here, B _aver means the average profit of farms that do not implement the production plan, and G _aver means the average profit of farms that implement the production plan.

For example, before the production planned amount (O _p ) of the current year is delivered in farm A that grows garlic, the initial production plan (O _i ) is planned to be 800 kg, and the actual meridian price (AC _a ) of the current year is 3,400 won per 1 kg was, but

If 600 kg is recommended according to the production recommendation amount (F _p ) for each farmhouse in response to the production plan amount (O _p ) for the current year, and the predicted meridian price (AC _p ) is predicted to be 3,600 won, the profit of the farm A is the initial Since a loss of KRW 560,000 has occurred compared to the planned production amount (O _i ), a refund amount (R) of 140,000 can be provided according to [Equation 7], but the production recommendation amount (F _p ) for each farm was delivered but not implemented When the average profit of one farmhouse B is higher than the average revenue of farmhouse A, the profit reserve (R _p ) of the farmhouse A can be calculated according to Equation 8. Here, the average profit of farmhouse B produces 800 kg, which is the same production plan as the initial production plan (O _i ) of farmhouse A, so that the average profit of 2,720,000 won is calculated, and the average profit of farmhouse A is calculated as 2,160,000 won , the profit compensation coefficient (α) is calculated to be about 31,111 won, the total profit compensation received from the farm A (R _p ) may be 171,111 won.

The crop recommendation unit 180, with respect to the meridian price for each agricultural product collected by the agricultural product meridian price collection unit 150, the agricultural products with a high rate of increase in the agricultural product meridian price by year and the crops whose supply is insufficient compared to the demand. You can recommend it to a contractor.

Here, the crop recommendation unit 180 calculates a recommendation ranking score according to the following [Equation 10] by using the rate of increase in the agricultural meridian price increase by year and the ratio of crops whose supply is insufficient to recommend to the agricultural growers. can

[Equation 10]

Here, R _r is the crop recommendation ranking score, P _r is the crop price increase rate, and G _s is the unsatisfactory amount.

For example, compared to the previous year and last year, the price increase rate of crops was +5% for green pepper, -1.2% for garlic, +2% for sesame leaves, +1.8% for onion, +12% for strawberry, +2.8% for paprika, +2.8% for leek. +2.1%, lettuce +8%, spinach +2%, pumpkin +1.2% , according to [Equation 10], lettuce 18.4 points, strawberries 17.4 points, green pepper 6.5 points, paprika 2.8 points, leek 2.1 points, perilla leaves and spinach 2 points, onions 1.8 points, pumpkin 1.2 points, garlic -1.2 points It is calculated, and the calculated crop recommendation ranking score (R _r ) may be recommended to the crop growers in the order of the highest.

In addition, the crop recommendation unit 180, by reflecting the calculated crop recommendation ranking score (R _r ) further includes information on the crop items, cultivation conditions, cultivation method, etc. of the crops selected by the crop growers by the agricultural growers. can be provided to

Through the above process, the production adjustment system using the agricultural product price prediction information predicts the price and adjusts the production of agricultural products using the inflow amount calculated by the inflow calculation unit, but the price prediction unit collects the actual meridian price of the previous year Thus, the meridian price of the current year can be predicted and calculated. In addition, the production plan calculation unit collects last year's production for preset agricultural products, and calculates the production plan for the current year using the actual meridian price of last year and the meridian price of the current year collected by the agricultural product meridian price collection unit. have. At this time, it is possible to transmit the production plan and the predicted meridian price for the year to the agricultural producer using the production plan transmission unit. In addition, a loss occurs depending on the initial production plan amount of the agricultural grower and the production recommendation amount for each farm, or the average profit of the non-fulfilling farmhouse is more In high cases, a profit reserve may be provided to agricultural growers who have fulfilled the production recommendations for each farm in the current year. In addition, after the cultivation of agricultural products is finished, it is possible to recommend crops with a high rate of increase in the meridian price of agricultural products by year to the agricultural growers, and crops whose supply is insufficient compared to the demand.

According to an embodiment of the present invention, by collecting the production history and price information of agricultural products by item, generating big data information, and correcting the inflow of agricultural products based on the inflow ratio for each agricultural wholesale market, the change pattern of the regional wholesale market is taken into account. Thus, it is possible to provide a production adjustment system using agricultural product price prediction information that can calculate more accurate agricultural product inflows.

In addition, by predicting the meridian price of agricultural products and establishing a production plan using big data information and inflow data, agricultural product producers can maximize their sales profits and consumers can make rational decisions about production using agricultural product price prediction information. A coordination system may be provided.

In addition, the control method of the production adjustment system using agricultural product price prediction information according to an embodiment of the present invention may be recorded in a computer-readable medium including program instructions for performing various computer-implemented operations. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The medium may be those specially designed and configured for the present invention or those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

As described above, although one embodiment of the present invention has been described with reference to the limited examples and drawings, one embodiment of the present invention is not limited to the above-described embodiment, which is common knowledge in the field to which the present invention pertains. Various modifications and variations are possible from such a base material. Therefore, one embodiment of the present invention should be understood only by the claims described below, and all equivalents or equivalent modifications thereof will fall within the scope of the spirit of the present invention.

100: Production adjustment system using agricultural product price prediction information
110: inflow calculation unit
120: production history information generation unit
130: price prediction unit
140: production plan calculation unit
150: Agricultural products meridian price collection unit
160: production plan transmission unit
170: Agricultural Products Production Profit Compensation Department
180: Agricultural Products Recommendation Department

Claims (1)

an inflow calculation unit for extracting agricultural product inflow data flowing into the agricultural wholesale market;
a production history information generation unit that collects production information for a preset period and generates production history big data including production history information;
a price prediction unit for predicting the meridian price of agricultural products based on the agricultural product inflow data and the production history big data;
a production plan calculation unit for calculating a production plan for agricultural products based on the meridian price predicted by the price prediction unit;
Agricultural products meridian price collection unit for collecting the actual meridian price of the agricultural products;
a production planning amount transmitting unit for transmitting the agricultural production production planning amount to a preset user terminal of an agricultural product grower;
When the agricultural product grower cultivates the agricultural products by modifying the production planning amount initially planned in response to the production planning amount calculated by the production plan calculation unit, the agricultural product is grown in response to the difference in production profit from the initially planned production planning amount Agricultural products production profit compensation department to support the compensation amount to the business operator or to recover the additional profit amount; and
Agricultural products recommendation unit for providing recommended agricultural products to the agricultural product growers by using the agricultural product meridian price by year collected by the agricultural product meridian price collection unit;
including,
The production history information generation unit,
The inflow data collected by the inflow calculation unit is divided by agricultural products and agricultural wholesale markets, and the production history big data is generated on a daily, weekly and annual basis,
The inflow calculation unit,
The nationwide inflow is calculated by sampling the inflow of agricultural products flowing into the preset agricultural wholesale market, and the predicted inflow of the agricultural products is calculated by reflecting the correction value according to the inflow ratio for the preset period in the preset agricultural wholesale market, and ,
The inflow calculation unit,
Calculating the average value of the agricultural product inflow ratio introduced during the first preset period to the preset agricultural wholesale market and the estimated value corresponding to the change pattern of the agricultural product inflow ratio flowing in the preset second period, respectively,
When the estimated value is equal to or greater than the average value, the predicted inflow is calculated by adding the ratio of the difference between the estimated value and the average value to the average value according to Equation 1 below,
[Equation 1]

When the estimated value is less than the average value, the predicted inflow is calculated by subtracting the ratio of the difference value from the average value according to Equation 2 below,
[Equation 2]

Here, I _exp is the predicted inflow amount, I _aver is the average value of the inflow rate of agricultural products inflow during the first preset period, and I _assu is the estimated value corresponding to the change pattern of the inflow ratio of agricultural products inflow during the second preset period, respectively. box.
The inflow calculation unit,
The error value between the average value of the inflow ratio of agricultural products introduced into the preset agricultural wholesale market during the first period and the estimated value corresponding to the change pattern of the inflow ratio of agricultural products introduced during the second period and the average value of the estimated value to calculate each,
When the calculated error value is within a preset reference range, a preset constant (C) is reflected in the average value according to [Equation 3] below,
When the calculated error value exceeds the preset reference range, after reflecting the preset constant (C) to the average value according to [Equation 4] below, multiply the correction coefficient (α) to calculate the predicted inflow, but ,
[Equation 3]

Here, I _exp is the predicted inflow, and C is the constant.
When the average value is greater than the estimated value, the constant (C) has a negative sign,
When the average value is less than the estimated value, the constant (C) has a positive sign,
[Equation 4]

Here, I _exp is the predicted inflow, α is the correction factor, and C is the constant.
The price prediction unit,
Predict the meridian price of agricultural products based on the agricultural product inflow data and the production history big data, and predict the price using a program command and FFNN algorithm,
The FFNN algorithm is implemented to output the predicted price of the agricultural product through a hidden layer in which the variable input for predicting the meridian price of the agricultural product is preset,
Each weight (wn) is assigned to the variable input for predicting the meridian price of the agricultural product, and the weight (wn) is updated every preset period,
The production plan calculation unit,
In response to the predicted meridian price and the actual meridian price of last year collected by the agricultural meridian price collection unit, the production planned amount of the agricultural product for the current year is calculated according to the following [Equation 5],
[Equation 5]

Here, O _p is the production plan for the current year, O _l is last year’s production, AC _p is the predicted meridian price, and AC _l is the actual meridian price of last year.
The production plan calculation unit,
Based on the production plan for the year, the recommended cultivation amount for each farm is calculated according to the following [Equation 6],
[Equation 6]

Here, F _p is the recommended production amount for each farm, and a _i is the last year’s production of farm i.
The agricultural production profit compensation unit,
According to the following [Equation 7], the expected profit is calculated using the initial production plan input by the agricultural producer and the meridian price of the previous year,
Calculate the actual profit using the production plan for the year and the actual meridian price for the year,
In response to the difference between the expected profit and the actual profit, the compensation amount is supported or the amount of additional revenue is returned to the agricultural grower,
[Equation 7]

Here, R is the refund amount, AC _a is the current meridian price, and O _i is the initial production plan.
The agricultural production profit compensation unit,
The initial production plan means the production plan input from the agricultural grower before the production recommendation amount for each farm reflecting the production plan for the year is delivered,
When the refund amount calculated in [Equation 7] is calculated as a negative number, the amount of additional revenue is recovered from the agricultural grower,
If the refund amount is calculated as a positive number, the compensation amount is supported to the agricultural grower,
The agricultural production profit compensation unit,
When the average profit of the farms that do not implement the recommended production amount (F _p ) is greater than the average profit of the farms that have implemented the recommended production amount (F _p ) for each farm, the following [Equation 8] and the following [Equation 8] 9] additionally pays the profit compensation calculated according to the above production plan to the farmhouse,
[Equation 8]

Here, R _p is the profit reserve, and α is the profit reserve coefficient.
[Equation 9]

(However, B _aver

In case of G _aver , calculated as α=0)
Here, B _aver means the average profit of the farms that do not implement the production plan, and G _aver means the average profit of the farms that implement the production plan.
The agricultural product recommendation department,
With respect to the meridian price for each agricultural product collected by the agricultural product meridian price collection unit, agricultural product price prediction information, characterized in that it recommends to the agricultural product grower the agricultural product with a high rate of increase in the agricultural product meridian price by year and the agricultural product whose supply is insufficient compared to the demand production coordination system using

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