KR102547465B1 - Server, method, and program for predicting market price of harvest season by variety of agricultural products based on artificial intelligence - Google Patents

Abstract

This disclosure relates to a server for predicting a market price of a harvest season by a variety of agricultural products. The server includes: a memory storing cultivation conditions for each variety of agricultural products; a communication module which communicates with a first server which provides information on the cultivation status of each variety of the agricultural products and a second server which provides weather information in a cultivation area of the agricultural products; and a processor which calculates the expected market price of the agricultural products based on a plurality of artificial intelligence models.

Description

Server, method, and program for predicting market price of harvest season by variety of agricultural products based on artificial intelligence}

The present disclosure relates to a server and method for providing a market price prediction service for each variety of agricultural products.

Due to fluctuations in market prices of agricultural products, there are cases in which large profits cannot be obtained even after harvesting agricultural products.

It is expected that profits can be maximized by predicting market price changes of agricultural products, selling agricultural products, and deciding the type of agricultural products to be grown next.

However, conditions that affect the market price change of agricultural products are diverse, so these technologies have not been disclosed at present.

Republic of Korea Patent Registration No. 10-2384728, (2022.04.05)

Embodiments disclosed in the present disclosure are aimed at providing a market price prediction service for each variety of agricultural products.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

A server for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure for solving the above problems includes: a memory in which cultivation conditions for each variety of agricultural products are stored; a communication module that communicates with a first server providing cultivation status information for each variety of agricultural products and a second server providing meteorological information of a growing region of the agricultural products; and a processor for calculating an expected market price of the agricultural products based on a plurality of artificial intelligence models, wherein the processor includes the cultivation status information for each variety of the agricultural products received from the first server and the information on the cultivation status of the agricultural products received from the second server. A first artificial intelligence model that calculates an expected yield for each type of agricultural product based on weather information during the cultivation period of the agricultural product; a second artificial intelligence model for calculating an expected harvest price for each variety of agricultural products based on the expected yield calculated by the first artificial intelligence model and at least one influence condition on the price for each variety of agricultural products; and a processing module generating at least one of transaction recommendation information and cultivation recommendation information for the agricultural product based on the cultivation conditions and the calculated estimated price of the agricultural product.

In addition, the influence condition includes information on changes in at least one of interest rates, prices, real estate prices, tariffs, and the amount of imports for each type of agricultural product, and the second artificial intelligence model determines the expected yield and the amount of change in the influence condition. Based on this, it is possible to calculate the expected price.

In addition, the memory further stores the storage period and market price change after harvest for each variety of the agricultural products, and the processing module stores information on the expected harvest market price of the agricultural product variety cultivated by the user and the storage space of the agricultural product of the user. Based on this, it is possible to derive a post-harvest sales strategy for agricultural products grown by the user.

In addition, the communication module receives individual cultivation status information from the user's terminal, and the processing module calculates an expected harvest date of agricultural products grown by the user based on the received individual cultivation status information; Based on the expected market price of the agricultural product variety grown by the user and information on the storage space of the user's agricultural product, a harvest date when the user's agricultural product sales profit is maximized is calculated, and the calculated expected harvest date is calculated as At least one other cultivation method can be derived that allows adjustment to the harvest date.

In addition, the first artificial intelligence model is a model learned based on the yield of each type of agricultural product for each of a plurality of weather conditions during the growing period of the agricultural product, and the memory is configured for substitutable agricultural products for each type of agricultural product. Information is further stored, and the processing module calculates an error range of the expected harvest price for each type of agricultural product based on the expected yield calculated by the first artificial intelligence model for the replaceable agricultural product. can

In addition, when the calculated error range is out of a preset range, the processing module may correct the calculated expected harvest price for each variety of the agricultural product based on the expected yield calculated for the replaceable agricultural product. .

In addition, the cultivation conditions include a cultivation area and a plantation location, and the processing module derives a variety of agricultural products that can be cultivated by the user as cultivable agricultural products based on the user's plantation location, and the user determines the derived agricultural products. When cultivable agricultural products are cultivated, information on expected yield and expected profit according to the user's cultivation conditions may be calculated, and the cultivation recommendation information including the calculated result may be provided to the user terminal.

In addition, the processing module may determine the expected harvest price for each variety of the agricultural product, the harvest price for each variety of the agricultural product for at least one previous season, and the predicted weather information for the next season, based on the predicted weather information for the next season. Calculate the expected market price for each variety of agricultural products, derive at least one agricultural product variety that can be cultivated by the user based on the cultivation conditions of the user, and based on the expected market price for each variety of agricultural products calculated for the next season, A profit for each of the derived at least one agricultural product variety may be calculated, and the calculated result may be provided to the user terminal.

In addition, a method for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure for solving the above problems is a method performed by a server, and a first server that provides cultivation status information for each variety of agricultural products. Receiving cultivation status information for each variety of agricultural products from; Receiving weather information of a growing region of the agricultural product from a second server providing weather information of the growing region of the agricultural product; and calculating an expected market price of the agricultural product based on a plurality of artificial intelligence models, wherein the step of calculating the expected market price of the agricultural product comprises using a first artificial intelligence model to determine the cultivation status information for each variety of the agricultural product and the agricultural product. Calculating an expected yield for each type of agricultural product based on weather information during the cultivation period; Calculating, by a second artificial intelligence model, an expected harvest price for each variety of agricultural products based on the expected yield calculated by the first artificial intelligence model and at least one influence condition on the price for each variety of agricultural products; and generating at least one of transaction recommendation information and cultivation recommendation information for the agricultural product based on the cultivation conditions and the calculated expected price of the agricultural product.

In addition to this, a computer program stored in a computer readable recording medium for execution to implement the present disclosure may be further provided.

In addition to this, a computer readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the above-described problem solving means of the present disclosure, an effect of providing a market price prediction service for each variety of agricultural products is provided.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 and 2 are schematic diagrams of a system for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure.
3 is a block diagram of a server for providing a market price prediction service for each type of agricultural product according to an embodiment of the present disclosure.
4 is a flowchart of a method for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure.
5 is a diagram illustrating that a processing module derives transaction recommendation information and cultivation recommendation information for agricultural products using an artificial intelligence model.
6 is a diagram illustrating deriving a purchase offer price for a purchasing user.
7 is a diagram illustrating deriving a sales offer price for a sales user.
8 and 9 are diagrams illustrating predicted prices and actual prices for each distribution line and each size of agricultural products.
10 is a diagram illustrating deriving and providing a harvest date maximizing a user's profit based on individual cultivation status information received from a user terminal and user conditions.
11 is a diagram illustrating correction of the expected harvest market price of the agricultural product to be analyzed based on the yield of the agricultural product that can be replaced with the agricultural product to be analyzed.

Like reference numbers designate like elements throughout this disclosure. The present disclosure does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present disclosure belongs is omitted. The term 'unit, module, member, or block' used in the specification may be implemented as software or hardware, and according to embodiments, a plurality of 'units, modules, members, or blocks' may be implemented as one component, It is also possible that one 'part, module, member, block' includes a plurality of components.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case of being directly connected but also the case of being indirectly connected, and indirect connection includes being connected through a wireless communication network. do.

In addition, when a part "includes" a certain component, it means that it may further include other components without excluding other components unless otherwise stated.

Throughout the specification, when a member is said to be located “on” another member, this includes not only a case where a member is in contact with another member, but also a case where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

Expressions in the singular number include plural expressions unless the context clearly dictates otherwise.

In each step, the identification code is used for convenience of description, and the identification code does not explain the order of each step, and each step may be performed in a different order from the specified order unless a specific order is clearly described in context. there is.

Hereinafter, the working principle and embodiments of the present disclosure will be described with reference to the accompanying drawings.

In the present specification, the 'server for providing a market price prediction service for each variety of agricultural products according to the present disclosure' includes all of various devices capable of providing results to users by performing calculation processing. For example, a server for providing a market price prediction service for each variety of agricultural products according to the present disclosure may include a computer, a server device, and a portable terminal, or may be in any one form.

Here, the computer may include, for example, a laptop computer, a desktop computer, a laptop computer, a tablet PC, a slate PC, and the like equipped with a web browser.

The server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

The portable terminal is, for example, a wireless communication device that ensures portability and mobility, and includes a Personal Communication System (PCS), a Global System for Mobile communications (GSM), a Personal Digital Cellular (PDC), a Personal Handyphone System (PHS), and a PDA. (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smart phone (Smart Phone) ) and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-devices (HMDs). can include

Functions related to artificial intelligence according to the present disclosure are operated through a processor and a memory. A processor may consist of one or a plurality of processors. In this case, the one or more processors may be a general-purpose processor such as a CPU, an AP, or a digital signal processor (DSP), a graphics-only processor such as a GPU or a vision processing unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control input data to be processed according to predefined operating rules or artificial intelligence models stored in a memory. Alternatively, when one or more processors are processors dedicated to artificial intelligence, the processors dedicated to artificial intelligence may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

A predefined action rule or an artificial intelligence model is characterized in that it is created through learning. Here, being made through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, so that a predefined action rule or artificial intelligence model set to perform a desired characteristic (or purpose) is created. means burden. Such learning may be performed in the device itself in which artificial intelligence according to the present disclosure is performed, or through a separate server and/or system. Examples of the learning algorithm include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples.

An artificial intelligence model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and a neural network operation is performed through an operation between an operation result of a previous layer and a plurality of weight values. A plurality of weights possessed by a plurality of neural network layers may be optimized by a learning result of an artificial intelligence model. For example, a plurality of weights may be updated so that a loss value or a cost value obtained from an artificial intelligence model is reduced or minimized during a learning process. The artificial neural network may include a deep neural network (DNN), for example, a Convolutional Neural Network (CNN), a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), a Restricted Boltzmann Machine (RBM), A deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks, but is not limited to the above examples.

According to an exemplary embodiment of the present disclosure, a processor may implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network in which a machine learns by mimicking a human's biological neuron. The methodology of artificial intelligence includes supervised learning in which input data and output data are provided together as training data according to the learning method, so that the answer (output data) of the problem (input data) is determined, and only input data is provided without output data. In unsupervised learning, where the answer (output data) of the problem (input data) is not determined, and whenever an action is taken in the current state, a reward is given in the external environment. , it can be classified as reinforcement learning in which learning proceeds in the direction of maximizing this reward. In addition, the methodology of artificial intelligence may be classified according to the architecture, which is the structure of the learning model. The architecture of widely used deep learning technology is Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). , Transformers, and generative adversarial networks (GANs).

The device may include an artificial intelligence model. The artificial intelligence model may be one artificial intelligence model or may be implemented as a plurality of artificial intelligence models. Artificial intelligence models may be composed of neural networks (or artificial neural networks), and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to an overall model having a problem-solving ability by changing synaptic coupling strength through learning of artificial neurons (nodes) formed in a network by synaptic coupling. Neurons in a neural network may contain a combination of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. Illustratively, the device may include an input layer, a hidden layer, and an output layer. A neural network constituting the device can infer a result (output) to be predicted from an arbitrary input (input) by changing the weight of a neuron through learning.

The processor generates a neural network, trains or learns the neural network, performs an operation based on received input data, generates an information signal based on a result of the execution, or generates a neural network. Neural network models include GoogleNet, AlexNet, VGG Network, etc., CNN (Convolution Neural Network), R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network . For example, the neural network may include a deep neural network.

Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM(Boltzmann Machine), RBM(Restricted Boltzmann Machine), DBN(Depp Belief Network), DCN(Deep Convolutional Network), DN(Deconvolutional Network), DCIGN(Deep Convolutional Inverse Graphics Network), GAN(Generative Adversarial Network) ), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo State Network), DRN (Deep Residual Network), DNC (Differentiable Neural Computer), NTM (Neural Turning Machine), CN (Capsule Network), It will be appreciated by those skilled in the art that it may include any neural network, including but not limited to Kohonen Network (KN) and Attention Network (AN).

According to an exemplary embodiment of the present disclosure, the processor may include a Convolution Neural Network (CNN), a Region with Convolution Neural Network (R-CNN), a Region Proposal Network (RPN), a Recurrent Neural Network (RNN), such as GoogleNet, AlexNet, VGG Network, and the like. ), S-DNN (Stacking-based deep neural network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT for natural language processing, SP-BERT, MRC/QA, Text Analysis, Dialog System, GPT-3 , GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet Data intelligence for Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, Recommendation, and Data Creation. , but not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

1 and 2 are schematic diagrams of a system 10 for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure.

Referring to FIGS. 1 and 2 , the system 10 for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure includes a server 100, a first server 300, a second server 400 and a user. It includes the terminal 200.

However, in some embodiments, system 10 may include fewer or more components than those shown in FIG. 1 .

The server 100 refers to the server 100 for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure, and provides a service for predicting market prices for each variety of agricultural products based on an artificial intelligence model.

The first server 300 is a server 100 that provides cultivation status information for each variety of agricultural products, and refers to a server that collects agricultural product cultivation status information of users (producers) who grow agricultural products at predetermined times.

The second server 400 can be applied to any server providing weather information, and a server providing public data such as the Korea Meteorological Administration is applicable.

The server 100 calculates the expected market price of agricultural products using the cultivation status information of the user (producer) received from the first server 300 and the meteorological information received from the second server 400, and the user (producer, seller) ), agricultural product sales strategies, cultivation recommendation information, and agricultural product purchase strategies for users (consumers, buyers) can be provided.

FIG. 3 is a block diagram of a server 100 for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure.

4 is a flowchart of a method for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure.

Referring to FIG. 3 , a server 100 for providing a market price prediction service for each variety of agricultural products according to an embodiment of the present disclosure includes a processor 110, a communication module 130, and a memory 150, and the processor 110 may include at least one artificial intelligence model and a processing module 116 .

Specifically, the processor 110 includes a first artificial intelligence model 112, a second artificial intelligence model 114, and a processing module 116 that processes or reprocesses processing results of the artificial intelligence model.

However, in some embodiments, the server 100 may include fewer or more components than those shown in FIG. 3 .

The processor 110 may calculate the expected market price of agricultural products based on a plurality of artificial intelligence models, and may calculate trade recommendation information (sale recommendation information, purchase recommendation information) for agricultural products and expected harvest prices for each variety of agricultural products. there is.

In an embodiment of the present disclosure, an RNN model may be applied as an artificial intelligence model for predicting market prices of agricultural products.

The processor 110 performs the above-described operation using a memory 150 storing data for an algorithm or a program reproducing the algorithm for controlling the operation of components in the device, and the data stored in the memory 150. It may be implemented with at least one processor 110 that does. In this case, the memory 150 and the processor 110 may be implemented as separate chips. Alternatively, the memory 150 and the processor 110 may be implemented as a single chip.

In addition, the processor 110 may control any one or a combination of a plurality of components described above in order to implement various embodiments according to the present disclosure described in the following drawings on the present device.

In addition to operations related to the application program, the processor 110 may control general operations of the present device. The processor 110 may provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the components described above or by running an application program stored in the memory 150.

In addition, the processor 110 may control at least some of the components of the present device in order to drive an application program stored in the memory 150 . Furthermore, the processor 110 may combine and operate at least two or more of the components included in the device to drive the application program.

The communication module 130 may include one or more modules that connect the server 100 for predicting the market price of agricultural products for each variety of agricultural products to one or more networks.

The communication module 130 may include one or more components enabling communication with an external device, for example, a broadcast reception module, a wired communication module 130, a wireless communication module 130, and a short-distance communication module ( 130), and a location information module.

The wired communication module 130 includes not only various wired communication modules 130 such as a local area network (LAN) module, a wide area network (WAN) module, or a value added network (VAN) module. , USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service) various cable communication modules 130 can include

The wireless communication module 130 includes a WiFi module, a WiBro module, a global system for mobile communication (GSM), a code division multiple access (CDMA), a wideband code division multiple access (WCDMA), and a universal system (UMTS). It may include a wireless communication module 130 supporting various wireless communication schemes such as mobile telecommunications system (TDMA), Time Division Multiple Access (TDMA), Long Term Evolution (LTE), 4G, 5G, and 6G.

The wireless communication module 130 may include a wireless communication interface including an antenna and a transmitter for transmitting signals. In addition, the wireless communication module 130 may further include a signal conversion module that modulates a digital control signal output from the processor 110 through a wireless communication interface into an analog type wireless signal under the control of the processor 110. .

The short-range communication module 130 is for short-range communication, and includes Bluetooth®, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), and ZigBee. , Near Field Communication (NFC), Wireless-Fidelity (Wi-Fi), Wi-Fi Direct, and Wireless Universal Serial Bus (USB) technologies may be used to support short-range communication.

The memory 150 stores cultivation conditions for each variety of agricultural products.

In addition, the memory 150 stores information on substitutable agricultural products and substitutable foods for each breed.

The memory 150 may store data supporting various functions of the device. The memory 150 may store a plurality of application programs (application programs or applications) running in the device, data for operation of the device, and commands. At least some of these application programs may exist for basic functions of the device. Meanwhile, the application program may be stored in the memory 150, installed in the device, and driven by the processor 110 to perform an operation (or function).

The memory 150 may store data supporting various functions of the device and programs for operation of the processor 110, and input/output data (eg, music files, still images, moving images, etc.) This may be stored, and may store a plurality of application programs (application programs or applications) running in the device, data for operation of the device, and commands. At least some of these application programs may be downloaded from an external server through wireless communication.

The memory 150 may be a flash memory type, a hard disk type, a solid state disk type, a silicon disk drive type, or a multimedia card micro type. micro type), card-type memory (for example, SD or XD memory 150, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM It may include at least one type of storage medium among electrically erasable programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory 150, magnetic disk, and optical disk. In addition, the memory 150 is separate from the present device, but may be a database connected by wire or wirelessly.

In addition, the memory 150 may include a plurality of processes for the server 100 for providing a market price prediction service for each variety of agricultural products.

5 is a diagram illustrating that the processing module 116 derives transaction recommendation information and cultivation recommendation information for agricultural products using an artificial intelligence model.

It will be described with reference to FIG. 5 together.

The server 100 receives cultivation status information for each variety of agricultural products through the communication module 130 . (S100)

The server 100 receives weather information of the cultivation area through the communication module 130 . (S200)

The processor 110 calculates the expected yield for each type of agricultural product. (S300)

Referring to FIG. 5 , the processor 110 uses the first artificial intelligence model 112 to calculate the expected yield for each type of agricultural product based on information on the cultivation status of each type of agricultural product and meteorological information during the growing period of the agricultural product. can do.

In one embodiment, the memory 150 stores optimal meteorological conditions for obtaining the maximum yield for each variety of agricultural products, and the first artificial intelligence model 112 determines the amount of change in the optimal meteorological conditions. It is learned how the yield changes when inputted.

For example, the first artificial intelligence model 112 calculates the expected yield of the first agricultural product as the maximum yield in the corresponding season when the optimal weather condition for obtaining the maximum yield against the cultivation amount is input for the first agricultural product. In other words, as the input meteorological conditions differ more from the optimal meteorological conditions, the expected yield can be calculated less.

However, it does not mean that the expected yield is calculated to decrease in proportion to the difference in proportion to the optimal weather condition.

The first artificial intelligence model 112 may calculate an expected yield compared to the maximum yield by adding a weight for each weather condition for each variety of agricultural products.

For example, the first artificial intelligence model 112 may calculate that a specific weather condition has little effect on yield in a specific agricultural product type, and a specific weather condition has a large effect on yield even with a small difference.

In addition, the first artificial intelligence model 112 may calculate an expected harvest time for each variety of agricultural products based on the cultivation status information for each variety of agricultural products and meteorological information during the growing period of the agricultural products.

In the corresponding embodiment, the server 100 may receive the extent to which agricultural products have been cultivated so far from the first server 300 or the user terminal 200, and in some embodiments, the server 100 is the user (producer) terminal It is possible to receive an image of agricultural products being grown from and analyze the received image to directly generate cultivation status information of agricultural products.

The first artificial intelligence model 112 may calculate how much time remains until harvest of the corresponding agricultural product when information on the cultivation status and meteorological conditions during cultivation of the corresponding agricultural product are input.

In an embodiment of the present disclosure, the processor 110 may determine the cultivation period corresponding to the agricultural product by analyzing the cultivation status information of the agricultural product.

In this case, the cultivation period may refer to an actual cultivation period from the sowing of agricultural products until now, but may also be a cultivation period for standardization of agricultural products in the server 100. That is, the cultivation period determined/calculated by the processor 110 does not necessarily mean the actual number of cultivation days.

In an embodiment of the present disclosure, the cultivation period may mean a cultivation section, and the period from seeding to harvest is divided into a plurality of sections, and the processor 110 may determine which cultivation section the agricultural product currently corresponds to.

The first artificial intelligence model 112 may analyze an image of agricultural products photographed from the user terminal 200 in order to determine/calculate the growing period of agricultural products, and a detailed description thereof will be described later.

The processor 110 calculates the expected harvest price for each variety of agricultural products. (S400)

The second artificial intelligence model 114 may calculate the expected harvest price for each variety of agricultural products based on the expected yield calculated by the first artificial intelligence model 112 and at least one influence condition on the price for each variety of agricultural products. there is.

As described above, the first artificial intelligence model 112 may calculate the expected yield compared to the maximum yield of agricultural products based on weather conditions during cultivation of agricultural products.

The second artificial intelligence model 114 may calculate the predicted price and the expected market price of the harvest season so that the lower the expected yield, the higher, and may reflect at least one influencing condition in this process.

At this time, the influence condition includes information on changes in at least one of pests, temperatures, rainy season, interest rates, prices, real estate market prices, tariffs, and the amount of imports for each type of agricultural products.

The second artificial intelligence model 114 may calculate an expected price for each type of agricultural product based on the calculated expected yield and the amount of change in the influence condition.

For example, the second artificial intelligence model 114 may calculate a higher expected price for each type of agricultural products as interest rates, prices, real estate prices, and tariffs increase.

The memory 150 stores the dependence on the amount of import for each type of agricultural product.

For example, in the case of agricultural products that can be supplied domestically, there is no price volatility in response to changes in the amount of imports. As a specific example, if a war or natural disaster occurs in a country that produces most of a specific agricultural variety, the price of that agricultural variety increases worldwide.

The second artificial intelligence model 114 may calculate the expected price of agricultural products by type based on the current status of import amount by type of agricultural product, the expected amount of import, and the dependence on the amount of import by type of agricultural product.

The memory 150 stores the amount of import of each agricultural product, information on the importing country, and a plurality of keywords capable of predicting the amount of import, and the processing module 116 searches for keywords in news and SNS for a preset period of time, Based on the amount of each keyword searched, it is possible to calculate the expected import amount for each agricultural product variety.

At this time, the keyword includes a first keyword and a second keyword, the first keyword refers to a keyword related to an increase in the amount of import of a specific agricultural product variety, and the second keyword refers to a keyword related to a decrease in the amount of import of a specific agricultural product variety.

For example, the processor 110 detects a keyword associated with an abnormal temperature or war in a country where a specific agricultural product that imports more than a preset import amount is detected a preset number of times, based on the type of keyword and the number of appearances of the keyword. It is possible to adjust the country's expected production and calculate the expected import volume accordingly.

In conclusion, the processor 110 calculates the expected price by determining that when the expected production of a country that mainly produces a specific agricultural product decreases, the expected import amount also decreases accordingly, and affects the expected price of the agricultural product at the harvest time. can be corrected

The processor 110 generates at least one of trade recommendation information and cultivation recommendation information for agricultural products. (S500)

Specifically, the processing module 116 performs transaction recommendation information (sale recommendation information, purchase recommendation information) and cultivation recommendation information on agricultural products based on the cultivation conditions for each agricultural product variety and the calculated expected harvest price for each agricultural product variety. At least one piece of information can be created.

In an embodiment of the present disclosure, the cultivation conditions for each variety of agricultural products are selected from among cultivation area conditions, weather conditions related to cultivation, cultivation proficiency conditions of users related to cultivation, conditions of users' cultivation equipment related to cultivation, and storage space conditions of users related to cultivation. It may contain at least one condition.

In one embodiment, when the processor 110 provides information by outputting the expected yield calculated for each agricultural product and the expected price at the harvest season, the calculation results for other agricultural products in the same category or for alternative agricultural products are combined together. Can be printed out and provided.

6 is a diagram illustrating deriving a purchase offer price for a purchasing user.

Referring to FIG. 6 , the processor 110 provides information on the proposed purchase price of agricultural products to users (eg, buyers and consumers) as transaction recommendation information.

The processor 110 provides information on the estimated price for each harvest period calculated through the above process for each type of agricultural product as well as for each distribution line and size of the agricultural product, and also provides information on distribution costs so that the user can purchase agricultural products at a reasonable price. can make it

In one embodiment, the processor 110 may derive transaction recommendation information to purchase the corresponding agricultural product at the time when the price of the corresponding agricultural product drops the lowest based on information about the calculated expected price.

However, when information on a period in which a user (buyer) has to purchase is input, the processor 110 may derive transaction recommendation information by considering the information on the period as well.

For example, the processor 110 may generate transaction recommendation information by deriving t at which dP1/dt > dP2/dt is satisfied as the purchase time point.

7 is a diagram illustrating deriving a sales offer price for a sales user.

Referring to FIG. 7 , the processor 110 provides a user (eg, a producer) with information on a proposed sale price of agricultural products as transaction recommendation information.

The processor 110 may provide information on the expected price for each harvesting period calculated through the above process for each type of agricultural product as well as for each size of the agricultural product, and may provide information by comparing the price level for each type of business to be culled against the proposed sales price. .

8 and 9 are diagrams illustrating predicted prices and actual prices for each distribution line and each size of agricultural products.

The processor 110 may learn the artificial intelligence model by comparing the predicted price with the actual price and inputting the actual price of agricultural products, cultivation conditions, meteorological information during the cultivation process, and the amount of change in influence conditions as learning data.

The processor 110 may derive a post-harvest sales strategy (sale recommendation information) of the agricultural products grown by the user based on the expected market price of the agricultural products grown by the user and the growing conditions of the user.

Specifically, the processor may derive a post-harvest sales strategy for the agricultural products grown by the user based on the expected market price of the agricultural products cultivated by the user and information about the storage space of the agricultural products of the user.

The information on the storage space for agricultural products may include information on the size of the storage space for agricultural products possessed by the user, and when the information on the storage space for agricultural products is received from the user terminal 200, the processor 110 stores the agricultural products for each type. The available quantity and storage period can be calculated.

As an embodiment, the processor 110 may calculate at least one of a storable quantity and a storable period for the agricultural product varieties grown by the user based on information about the storage space of the user's agricultural products.

The processor 110 may derive a sales strategy capable of maximizing sales revenue of the user's agricultural products based on the expected harvest price calculated for the agricultural product variety being cultivated by the user and information about the storage space of the user's agricultural products.

For example, the expected harvest date of the first agricultural product is May 1, but the expected price per unit on May 1 of the first agricultural product is 100 won, the expected price per unit on May 2 is 110 won, and the expected price per unit on May 3 If the expected price per piece on May 4 is 112 won, and the expected price per piece on May 5 is 115 won, storing it for more than 4 days can maximize the user's first agricultural product sales revenue. there is a way

However, since the storable period is determined to maintain the freshness of the first agricultural product and the storable quantity of the storage space relative to the total amount grown by the user is determined, the processor 110 considers these information to develop an agricultural product sales strategy for the user. can be derived

For example, the processor 110 may calculate at least one of the daily yield of the user's agricultural product cultivation, the daily sales volume, and the storage amount in the storage space.

The processor 110 calculates the daily yield, daily sales volume, and daily storage amount of the agricultural product grown by the user during the harvesting period based on the expected yield of the agricultural product grown by the user, the expected price of the harvest period, and the user's growing conditions, so that the user terminal (200) can be provided.

10 is a diagram illustrating that a harvest date maximizing a user's profit is derived and provided based on individual cultivation status information received from the user terminal 200 and user conditions.

The communication module 130 may receive individual cultivation status information from the user terminal 200 .

The processor 110 calculates the expected harvest date of the agricultural products grown by the user based on the individual cultivation status information, and based on the expected harvest market price of the agricultural product varieties grown by the user and information about the user's storage space for the agricultural products, the user It is possible to calculate the harvest date when the profit from selling agricultural products is maximized.

The processor 110 may derive at least one other cultivation method so that the calculated expected harvest date may be adjusted to a harvest date that maximizes the calculated profit, and may provide the derived cultivation method to the user terminal 200 .

For example, if harvesting earlier than May 5 can increase sales revenue, the processor 110 may derive a cultivation method that advances the harvesting date.

Conversely, if harvesting later than May 5 can increase sales revenue, the processor 110 may derive a cultivation method that allows the harvesting date to be delayed.

As an embodiment, the processor 110 may generate individual cultivation status information and quality information of agricultural products by analyzing images of agricultural products photographed and received from the user terminal 200 .

Detailed information on the individual cultivation status information of agricultural products and the generation of quality information of agricultural products will be described later.

11 is a diagram illustrating correction of the expected harvest market price of the agricultural product to be analyzed based on the yield of the agricultural product that can be replaced with the agricultural product to be analyzed.

In an embodiment of the present disclosure, the first artificial intelligence model 112 may be a model learned based on the yield of each type of agricultural product for each of a plurality of weather conditions during the growing period of the agricultural product.

Referring to FIG. 11 , the memory 150 further stores information on agricultural products that can be replaced for each type of agricultural products.

The processor 110 may calculate an error range of expected harvest prices for each variety of agricultural products calculated fraudulently based on the expected yield calculated by the first artificial intelligence model 112 for replaceable agricultural products.

Referring to FIG. 11 , for example, when there is no agricultural product that can be substituted for a specific agricultural product, since there is no agricultural product to be purchased in place of the corresponding agricultural product, the accuracy of the calculated estimated price can be guaranteed.

However, in the case of agricultural product A, if the production of agricultural product A decreases and the price rises, consumers can purchase agricultural products B, E, or F, so other agricultural products can substitute for agricultural product A.

Therefore, if the yield of an agricultural product that can replace the corresponding agricultural product does not decrease together, an error may occur in the actual price of the corresponding agricultural product during the harvest period.

In an embodiment of the present disclosure, the processor 110 may use the first artificial intelligence model 112 to calculate an error range of the calculated expected price of the harvest season for each variety of agricultural products.

In this case, the processor 110 may correct the calculated expected harvest price for each variety of agricultural products based on the calculated expected yield of agricultural products when the calculated error range is out of a preset range.

In one embodiment, the memory 150 further stores a substitution matching degree for each of the agricultural products that can be replaced for each type of agricultural product.

In an embodiment of the present disclosure, agricultural products that can be replaced for each type of agricultural products refer to agricultural products that people can consume instead of the corresponding agricultural products, and the substitution matching degree may mean the degree to which substitution is possible.

In one embodiment, if the substitution matching degree is 100, it means agricultural products that can be completely replaced, and if the substitution matching degree is 0, it may mean that it cannot be replaced.

Substitutable agricultural products can be applied to agricultural products belonging to the same category, but do not necessarily belong to the same category.

In an embodiment of the present disclosure, substitute agricultural products and alternative matching degree are the result of consumers' selection, and thus, the result of a survey conducted by a large number of people may be used.

In one embodiment, the cultivation conditions may further include the user's agricultural product cultivation area and location of the farm.

The processor 110 may derive varieties of agricultural products that can be cultivated by the user as cultivated agricultural products based on the user's plantation location.

The processor 110 calculates information on the expected yield and expected profit according to the user's cultivation conditions when the user cultivates the derived cultivated agricultural product, and transmits cultivation recommendation information including the calculated result to the user terminal ( 200) can be provided/output.

The user can obtain information about the types of agricultural products that can be cultivated by the user as well as expected profits when the agricultural products are grown.

The processor 110 may calculate the expected revenue per unit area of the derived cultivated agricultural products and provide it to the user terminal 200 .

In one embodiment, the cultivation conditions may further include a user's cultivation skill level conditions and cultivation equipment conditions.

The processor 110 may calculate a matching score for each type of agricultural product for the user based on the user's plantation location, cultivation area, cultivation skill level condition, and cultivation equipment condition, and provide the calculated user matching score for each agricultural product type. .

The user matching score ranking does not mean the user's profit ranking, and the user matching score means agricultural products suitable for the user to grow. That is, it may refer to agricultural products that can be cultivated without requiring a user to exert a relatively large amount of force.

The processor 110 may reflect the cultivation equipment condition possessed by the user as a weight for the cultivation skill level condition of the user.

As an embodiment, the memory 150 may store cultivation skill level weights for each type of agricultural product and each type of cultivation equipment.

As an embodiment, the processor 110 may calculate information on expected yield and expected profit according to the user's cultivation conditions, including the user's cultivation skill level and cultivation equipment conditions, for each of the derived cultivated agricultural products.

In the case of performing such a process, since the user's cultivation skill and the cultivation equipment owned by the user are further considered, information on expected yield and expected profit can be more accurately calculated.

In one embodiment, the processor 110 determines the value of agricultural products for the next season based on the calculated expected harvest price for each variety of agricultural products, the harvest price for each variety of agricultural products for at least one previous season, and expected weather information for the next season. Estimated market prices for each variety can be calculated.

The processor 110 may calculate an expected profit for each agricultural product variety that can be cultivated by the user based on the expected market price for each agricultural product variety in the next season, and provide/output the calculated result to the user terminal 200 .

Below, the server 100 will explain the generation of individual cultivation status information and quality information of agricultural products.

In an embodiment of the present disclosure, the processor 110 may further include a third artificial intelligence model in which a method for calculating the quality of agricultural products is learned.

The third artificial intelligence model is learned based on a learning dataset in which quality information of corresponding agricultural products is labeled for each of a plurality of images of agricultural products.

A third artificial intelligence model may be a CNN model, and may form a bounding box of an agricultural product image during CNN learning.

The processor 110 may set criteria for forming a bounding box in an image of agricultural products for each type of agricultural products, and may perform image recognition through a bounding box having a predetermined ratio for recognition accuracy and processing.

For example, the size of the bounding box for each type of agricultural product may be set as in the following equation.

H < S1/S2 < K, (H, K: minimum and maximum range of bounding boxes for each type of agricultural product)

The third artificial intelligence model can identify at least one agricultural product within the image and determine the type of agricultural product identified.

When the type of agricultural products is identified, the third artificial intelligence model may generate defect information by analyzing images of agricultural products.

In an embodiment of the present disclosure, the defect information may mean the degree of defects included in agricultural products, and the degree of defects may be 0 when there are no defects in the image of agricultural products.

In the embodiments of the present disclosure, the defects of agricultural products are not limited to specific defects, and any defects that can reduce the quality of agricultural products, such as poor coloring, scratches, bruises, heat, disease, insect damage, nicks, etc., can be applied. do.

In addition, the third artificial intelligence model may calculate/generate color information on agricultural products by analyzing images of agricultural products.

In the process of selecting agricultural products, the external color of agricultural products is a very important condition in selecting agricultural products. In particular, looking at the color of the fruit, you can infer the nutrients in the fruit. For example, fruits such as grapes, cherries, strawberries, and apples contain a large amount of anthocyanin, which is colored red, purple, or blue depending on the concentration of hydrogen ions.

In addition, depending on the type of agricultural product, the degree of uniformity (uniformity) of the color of the entire area inside and outside of one agricultural product may be directly related to the quality of the agricultural product. At this time, the quality may include the taste and nutrients of the agricultural product.

The server 100 may receive information about the type and amount of agricultural products grown by the user from the user terminal 200 through the communication module 130 .

The processor 110 may calculate the number of samples required to generate the first quality information.

Since it is more effective to request the number of samples required according to the user's cultivation amount and cultivation scale than to analyze the entire agricultural product during cultivation or harvest by the user, in an embodiment of the present disclosure, the processor 110 collects the user's cultivation information. Based on this, the number of samples required is calculated.

In an embodiment of the present disclosure, a sample required for generating first quality information is referred to as a first agricultural product.

In one embodiment, the server 100 requests an input of at least one of the type, amount, and period of agricultural products being cultivated by the user.

In this case, as the cultivation amount, any numerical value capable of estimating the cultivation amount, such as seeding amount, yield, or cultivation area, can be applied.

The cultivation period means the period from sowing to the present, and the artificial intelligence model can be used in the process of determining/calculating the current cultivation period by analyzing the image of the first agricultural product, but information on the cultivation period is not necessarily required.

The processor 110 derives a shooting condition including at least one of a photographing distance, a photographing angle, and the number of photographed images of the first agricultural product according to the type of the first agricultural product.

The processor 110 provides the derived shooting conditions to the user terminal 200 .

The processor 110 calculates the number of samples (first agricultural products) necessary for generating the first quality information, and the number of captured images means how many images should be taken for one first agricultural product.

In order to accurately analyze agricultural products, this process is required because images of the entire area of agricultural products are required, and depending on the size of agricultural products, the shooting distance, shooting angle, and number of photographed images are required.

In one embodiment, the processor 110 may calculate the number of photographed images required for each type of agricultural product based on the following equation.

(L/N) x P

L: total surface area of the first agricultural product

N: standard area according to the type of first agricultural product

P: Weight of image importance by type of agricultural products

In some embodiments, the processor 110 may request a photographing such that the entire area of the agricultural product is included at a specific distance from the agricultural product, and a photographing angle may not be separately requested.

In some embodiments, the processor 110 may request a photographed image in which all agricultural products are photographed without deriving the number of photographed images.

As an embodiment, the processor 110 may derive a shooting condition when information on the type of agricultural products is received from the user terminal 200 . However, it is not limited thereto, and the processor 110 may recognize the type of agricultural products from the real-time photographed image of the user terminal 200 when the service application is operated, and derive a shooting condition based on the type and size of the recognized agricultural products. there is.

As an embodiment, the processor 110 may modify the shooting conditions even after the shooting conditions are provided to the user terminal 200 . For example, if the size of agricultural products is estimated based on the type of agricultural products and the required shooting distance is calculated, but as a result of analyzing the real-time photographed image of the user terminal 200, the size of the actual agricultural products is different, the processor 110 operates in real time. The shooting conditions can be modified based on the size of the produce in the shooting video.

The processor 110 may receive at least one image of first agricultural products in which the first agricultural products are photographed from the user terminal 200 .

The processor 110 may calculate defect information and first color information of the first agricultural product based on the image of the first agricultural product.

The processor 110 may generate first quality information representing the quality of the first agricultural product based on the defect information and the first color information of the first agricultural product.

In an embodiment of the present disclosure, quality information may be generated by including the size of agricultural products or considering the size of agricultural products.

The defect information may consist of the degree of defect or defect score for the first agricultural product, but may also be configured to include various types of information about the defect.

For example, the defect information may include at least one of a defect type, a defect size, a defect score according to a severity of the defect, and a defect size relative to the total area of the agricultural product.

The manager may visit the farmhouse of the user and directly check at least one second agricultural product to generate the second quality information.

As an embodiment, the server 100 may receive at least one of second color information and second sugar content values that a manager directly checks/measures for at least one second agricultural product at the user's farm site.

The processor 110 may generate second quality information representing the quality of at least one second agricultural product based on at least one of the second color information and the second sugar content value received from the manager terminal.

Since the first quality information generated based on the data directly measured by the user and the second quality information generated based on the data directly measured by the administrator may be different, the server 100 operating the platform may be different from each other at least once. Alternatively, a manager may be periodically dispatched to generate second quality information.

In one embodiment, the processor 110 may calculate a correction coefficient for the agricultural product of the user based on at least one second quality information.

Such a correction factor may be for standardizing quality measurement of agricultural products grown by users using platform services.

The processor 110 may generate final quality information by applying the correction coefficient to the first quality information generated after the correction coefficient is calculated.

In one embodiment, the second quality information may include a value of the second sugar content directly measured for at least one second agricultural product at the user's farm site.

In an embodiment of the present disclosure, the sugar content value includes at least one of a first sugar content value and a second sugar content value, and the first sugar content value is directly measured by the user using a sugar content meter owned by the user, and 2 The value of sugar content is directly measured by the manager using a sugar content meter after visiting the user's farm.

In one embodiment, the communication unit may receive the value of the first sugar content directly measured through the user's sugar content meter for the at least one first agricultural product.

At this time, it is not necessary to measure the sugar content of the first agricultural product, but it may be recommended to measure the sugar content of the first agricultural product for consistency of quality information. In some embodiments, the processor 110 may request to measure the sugar content of a predetermined number of agricultural products among agricultural products grown by the user.

The processor 110 may calculate the sugar level of the first agricultural product based on the type of the first agricultural product, the received first sugar content value, seasonal information of the first agricultural product, and the calculated first color information.

In one embodiment, when the first agricultural product is a harvested agricultural product, the processor 110 predicts the first agricultural product based on the type of the first agricultural product, the received value of the first sugar content, and the calculated first color information. A sugar level can be calculated.

In one embodiment, when the first agricultural product is an agricultural product being cultivated, the processor 110 includes the type of the first agricultural product, the received value of the first sugar content, seasonal information of the first agricultural product, and up to date of the first agricultural product. An expected sugar level of the first agricultural product may be calculated based on at least one of the cultivation period and the calculated first color information.

If the first agricultural product is an agricultural product that has been harvested, the sugar content may be almost confirmed, so the process is performed as above. The expected sugar level is calculated by adding the cultivation period up to.

The processor 110 may calculate a correction coefficient for the user's sugar content meter based on the second sugar content value when the first sugar content value and the second sugar content value differ by more than a threshold value.

The processor 110 may correct the first sugar content value by applying the correction coefficient to the received first sugar content value after the correction coefficient for the user's sugar content meter is calculated.

Since the value of sugar content measured may vary depending on the type of sugar content meter, the server 100 may standardize sugar content measurement through the above embodiment.

In one embodiment, the processor 110, when information (eg, brand, product name, etc.) on the sugar content meter used by the user is received from the user terminal 200, the correction coefficient for the user's sugar content meter based on the received information can be calculated.

In one embodiment, the processor 110 calculates the expected sugar content level for the harvested first agricultural product after the expected sugar content level is calculated for the first agricultural product being grown, the weather conditions during the cultivation period and the amount of change in the expected sugar content level can be trained by inputting it into an artificial intelligence model.

As this learning is repeated, the artificial intelligence model can more accurately calculate the expected sugar level for agricultural products under cultivation. As an embodiment, the processor 110 may input expected weather conditions together when calculating the expected sugar level for agricultural products being grown.

In an embodiment of the present disclosure, the storage unit may store color grades according to color information for each type of agricultural product.

The processor 110 may determine the color grade of the first agricultural product based on the calculated first color information, and generate first quality information based on at least one of defect information, color grade, and first sugar content value. can do.

In one embodiment, the processor 110 may receive acidity information on the first agricultural product from the user terminal 200, and the first agricultural product based on at least one of defect information, color grade, acidity information, and the value of the first sugar content. 1 Can generate quality information.

When the first agricultural product is an agricultural product being cultivated, the processor 110 may calculate individual cultivation status information and an expected harvest time of the agricultural product based on the type of the first agricultural product and the calculated first color information.

The processor 110 can grasp the cultivation status of agricultural products being grown by the user in more detail through this process, and accordingly, it is possible to derive and provide customized transaction recommendation information and cultivation recommendation information for the user.

In one embodiment, the processor 110 calculates defect information and first color information of the first agricultural product based on the image of the first agricultural product, and based on this, more accurately calculates the expected yield of the first agricultural product being grown by the user. can do.

For example, if agricultural products have more defects than expected and the generated quality information is evaluated badly, the amount of agricultural products discarded due to defects may increase, and the amount of agricultural products that do not satisfy the target quality may increase. This is because it can be judged by the decrease in yield.

Conversely, if there are fewer defects than expected and the generated quality information is evaluated well, this is because it can be judged as an increase in yield as well as quality of agricultural products.

The method according to one embodiment of the present disclosure described above may be implemented as a program (or application) to be executed in combination with the server 100, which is hardware, and stored in a medium.

The aforementioned program is C, C++, JAVA, machine language, etc. It may include a code coded in a computer language of. These codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related codes for which location (address address) of the computer's internal or external memory should be referenced for additional information or media required for the computer's processor to execute the functions. there is. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected through a network, and computer readable codes may be stored in a distributed manner.

Steps of a method or algorithm described in connection with an embodiment of the present disclosure may be implemented directly in hardware, implemented in a software module executed by hardware, or a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which this disclosure pertains.

In the above, the embodiments of the present disclosure have been described with reference to the accompanying drawings, but those skilled in the art to which the present disclosure pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present disclosure. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

10: A system for providing market price prediction service for each variety of agricultural products
100: A server providing market price prediction service for each type of agricultural products
110: processor
112: first artificial intelligence model
114: second artificial intelligence model
116: processing module
130: communication module
150: memory
200: user terminal
300: first server
400: second server

Claims (10)

A memory in which cultivation conditions for each type of agricultural product are stored;
a communication module that communicates with a first server providing cultivation status information for each variety of agricultural products and a second server providing meteorological information of a growing region of the agricultural products; and
Including a processor for calculating the expected market price of the agricultural products based on a plurality of artificial intelligence models,
the processor,
A first artificial intelligence that calculates an expected yield by type of the agricultural product based on the cultivation status information for each variety of the agricultural product received from the first server and the meteorological information during the growing period of the agricultural product received from the second server. Model;
a second artificial intelligence model for calculating an expected harvest price for each variety of agricultural products based on the expected yield calculated by the first artificial intelligence model and at least one influence condition on the price for each variety of agricultural products;
A processing module for generating at least one of transaction recommendation information and cultivation recommendation information for the agricultural product based on the cultivation conditions and the calculated estimated price of the agricultural product;
The cultivation conditions further include the user's cultivation location, cultivation area, cultivation proficiency conditions, and cultivation equipment conditions possessed by the user,
The processing module,
Based on the location of the plantation, the varieties of agricultural products that can be cultivated in the plantation area are derived as cultivated agricultural products,
When the user cultivates the derived cultivateable agricultural product, calculating information on expected yield and expected profit according to the user's cultivation conditions,
Calculate a matching score for each type of agricultural product for the user based on the plantation location, the cultivation area, the cultivation skill level condition, and the cultivation equipment condition,
Providing the cultivation recommendation information including the calculated result to the user's terminal;
When calculating information on the expected yield and expected profit according to the user's cultivation conditions, the user's cultivation equipment condition is reflected as a weight for the user's cultivation skill condition,
Deriving a post-harvest sales strategy for the agricultural products grown by the user based on the expected harvest market price of the agricultural product varieties grown by the user and information on the storage space of the agricultural products of the user,
Based on the individual cultivation status information received from the user's terminal, an expected harvest date of agricultural products grown by the user is calculated;
Calculate a harvest date when the user's agricultural product sales profit is maximized based on the expected market price of the agricultural product variety cultivated by the user and information about the storage space of the user's agricultural product;
Derive a cultivation method that allows the calculated expected harvest date to be adjusted to the calculated harvest date,
Through the second artificial intelligence model, the expected yield, pests corresponding to the impact conditions, temperature, rainy season, interest rate, price, real estate market price, tariff, and import amount by type of agricultural product, expected import amount by type of agricultural product, and the agricultural product Calculate the expected price by type of agricultural products according to the dependence on the amount of import by type of
Expected import amount for each type of agricultural products based on the amount of imported agricultural products pre-stored in the memory, information on the country of import, and keywords that can predict the amount of import searched by news and SNS for a predetermined period among keywords that can predict the amount of import Calculate,
The keyword includes a first keyword and a second keyword,
The first keyword is a keyword related to an increase in expected import amount for each type of agricultural product,
Characterized in that the second keyword is a keyword related to the decrease in expected import amount for each type of agricultural product,
A server that provides market price prediction service for each type of agricultural products.
delete delete delete delete delete delete According to claim 1,
The processing module,
Calculate the expected market price by type of the agricultural product in the next season based on the expected harvest price by type of the agricultural product, the harvest price by type of the agricultural product for at least one previous season, and the expected weather information for the next season do,
Based on the cultivation conditions of the user, at least one agricultural product variety that can be cultivated by the user is derived,
Based on the expected market price for each type of agricultural product calculated for the next season, calculating the profit for each of the derived at least one agricultural product variety,
Characterized in that providing the calculated result to the user terminal,
A server that provides market price prediction service for each type of agricultural products.
As a method performed by the server,
Receiving cultivation status information by variety of agricultural products from a first server providing cultivation status information by variety of agricultural products;
Receiving weather information of a growing region of the agricultural product from a second server providing weather information of the growing region of the agricultural product;
Calculating the expected market price of the agricultural products based on a plurality of artificial intelligence models,
The step of calculating the expected market price of the agricultural product,
Calculating, by a first artificial intelligence model, an expected yield for each variety of the agricultural product based on information on the cultivation status of each variety of the agricultural product and meteorological information during a growing period of the agricultural product;
Calculating, by a second artificial intelligence model, an expected harvest price for each variety of agricultural products based on the expected yield calculated by the first artificial intelligence model and at least one influence condition on the price for each variety of agricultural products; and
Generating at least one of transaction recommendation information and cultivation recommendation information for the agricultural product based on the cultivation conditions and the calculated expected price of the agricultural product,
The cultivation conditions further include the user's cultivation location, cultivation area, cultivation proficiency conditions, and cultivation equipment conditions possessed by the user,
The server,
Based on the location of the plantation, the varieties of agricultural products that can be cultivated in the plantation area are derived as cultivated agricultural products,
When the user cultivates the derived cultivateable agricultural product, calculating information on expected yield and expected profit according to the user's cultivation conditions,
Calculate a matching score for each type of agricultural product for the user based on the plantation location, the cultivation area, the cultivation skill level condition, and the cultivation equipment condition,
Providing the cultivation recommendation information including the calculated result to the user's terminal;
When calculating information on the expected yield and expected profit according to the user's cultivation conditions, the user's cultivation equipment condition is reflected as a weight for the user's cultivation skill condition,
Deriving a post-harvest sales strategy for the agricultural products grown by the user based on the expected harvest market price of the agricultural product varieties grown by the user and information on the storage space of the agricultural products of the user,
Based on the individual cultivation status information received from the user's terminal, an expected harvest date of agricultural products grown by the user is calculated;
Calculate a harvest date when the user's agricultural product sales profit is maximized based on the expected market price of the agricultural product variety cultivated by the user and information about the storage space of the user's agricultural product;
Derive a cultivation method that allows the calculated expected harvest date to be adjusted to the calculated harvest date,
Through the second artificial intelligence model, the expected yield, pests corresponding to the impact conditions, temperature, rainy season, interest rate, price, real estate market price, tariff, and import amount by type of agricultural product, expected import amount by type of agricultural product, and the agricultural product Calculate the expected price by type of agricultural products according to the dependence on the amount of import by type of
Based on the amount of imported agricultural products pre-stored in the memory, information on the importing country, and the amount of keywords that can predict the imported amount searched by news and SNS for a preset period among keywords that can predict the imported amount, the expected import amount for each type of agricultural products calculate,
The keyword includes a first keyword and a second keyword,
The first keyword is a keyword related to an increase in expected import amount for each type of agricultural product,
Characterized in that the second keyword is a keyword related to the decrease in expected import amount for each type of agricultural product,
How to provide market price prediction service for each harvest season of agricultural products.
A computer-readable recording medium in which a program for executing the method of claim 9 is stored in combination with a computer, which is hardware.

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