KR102562844B1 - Server, method and program for providing forward and reverse auction services between buyers and sellers of agricultural products - Google Patents

Abstract

The present invention relates to a server for providing forward and reverse auction services between a buyer and a seller of an agricultural product which opens purchase bid information to a reverse auction server when the purchase bid information received from a buyer terminal satisfies a reverse auction condition and provides a forward auction service when the purchase bid information does not satisfy the reverse auction condition.

Description

Server, method and program for providing forward and reverse auction services between buyers and sellers of agricultural products}

The present disclosure relates to a server providing forward and reverse auction services between buyers and sellers of agricultural products.

In general, agricultural products are sold to buyers through direct auctions by sellers or wholesale market corporations through auctions.

In this way, a system that benefits both sellers and buyers by diversifying the standardized auction system for agricultural products is needed, but in the end, there is a problem that the balance is not balanced because it is concentrated on one system.

Accordingly, a technology capable of providing a balanced forward and reverse auction service is required, but a technology for providing such a service has not been disclosed at present.

Republic of Korea Patent Publication No. 10-2003-0084142

Embodiments disclosed in the present disclosure have an object to provide a server that provides forward and reverse auction services between buyers and sellers 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 providing a forward and reverse auction service between a buyer and a seller of agricultural products according to an embodiment of the present disclosure for solving the above problems includes a communication module communicating with a buyer terminal and a seller terminal; and when purchase bidding information for agricultural products is received from the buyer terminal, verifying whether or not the purchase bidding information satisfies conditions for disclosure to the first server for reverse auction, and if the purchase bidding information satisfies the conditions, The purchase bidding information is disclosed to the first server, a reverse auction is performed by matching the purchase bidding information received from the seller terminal, the reverse auction result is transmitted to the buyer terminal, and the purchase bidding information satisfies the condition. If not satisfied, a processor controlling the buyer terminal to be connected to a second server for a forward auction, wherein the first server transmits at least one seller terminal with respect to the purchase bidding information disclosed in order to provide the reverse auction service. To receive the sales bidding information from.

In addition, the condition includes an agricultural product type condition and an agricultural product price condition, and the processor determines if there is a seller selling the type of agricultural product included in the purchase bidding information among sellers registered in the first server. , When it is determined that the purchase bidding information satisfies the type condition of the agricultural product, and the desired purchase price included in the purchase bidding information satisfies the preset price condition, it may be determined that the price condition of the agricultural product is satisfied. .

In addition, the processor calculates an expected market price of the agricultural product based on the cultivation status information of each variety of the agricultural product and weather information of a growing region of the agricultural product, and determines the preset price condition based on the calculated expected market price. can

In addition, when the desired selling price and the desired purchasing price included in the first selling bid information received from the first seller terminal are within a preset price range, the processor may convert the first selling bidding information and the purchasing bidding information. can be matched.

In addition, when the desired selling price or the desired purchase price included in the first sale bidding information received from the first seller terminal is not within a preset price range, the processor performs a first sale to the first seller terminal. Information on the proposed price may be provided, or a first proposed purchase price may be provided to the terminal of the person wishing to purchase.

Also, the processor may calculate the first proposed sale price or the first proposed purchase price based on the expected market price calculated for the agricultural product and the desired sale price or the desired purchase price.

In addition, the server further includes a memory, and the processor, when a request for subscription to the reverse auction service is received from the seller terminal, at least one type of agricultural product grown by the seller input to the seller terminal , Receives and stores agricultural product sales information including grades and cultivation amounts in the memory, wherein the purchase bidding information includes at least one of the type, grade, and amount of agricultural products that the buyer wants to purchase, and the processor The agricultural product sales information stored in the memory is compared with the purchase bidding information, and based on the comparison result, a purchase bidding notification may be provided to a terminal of a seller corresponding to agricultural product sales information that satisfies the purchase bidding information among the sellers. .

In addition, when the desired purchase price included in the purchase bid information does not satisfy the price condition of the agricultural product, the processor calculates a second proposed purchase price that replaces the desired purchase price based on the calculated expected market price. and provide the calculated purchase offer price to the purchaser terminal.

In addition, the method for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure for solving the above problems is a method performed by a server, and purchase bidding information of agricultural products from a buyer terminal receiving; verifying whether the purchase bid information satisfies a condition for publishing to a first server for a reverse auction; disclosing the purchase bidding information to the first server when the purchase bidding information satisfies the condition; proceeding with a reverse auction by matching sales bidding information received from a seller terminal, and transmitting the reverse auction result to the buyer terminal; and connecting the buyer terminal to a second server for a forward auction when the purchase bidding information does not satisfy a condition between, wherein the first server provides the purchase bidding information disclosed above to provide the reverse auction service. To receive the sales bidding information from at least one seller terminal.

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 server providing forward and reverse auction services between buyers and sellers 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 is a schematic diagram of a system for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.
2 is a diagram illustrating a system for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.
3 is a block diagram of an apparatus for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.
4 is a flowchart of a method for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.
5 is a diagram illustrating purchase bid information received from a purchaser terminal.
6 is a diagram illustrating that purchase bid information received from a buyer terminal is disclosed to a first server and a notification is provided to a seller terminal.
7 shows details of already sold bids and purchase bids that can be placed on the seller's terminal.
8 is a diagram illustrating providing a function of modifying sales bidding details with respect to details of sales bidding through a seller terminal.
9 is a diagram illustrating that a processing module derives transaction recommendation information and cultivation recommendation information for agricultural products using an artificial intelligence model.
10 is a diagram illustrating the derivation of a purchase offer price for a buyer.
11 is a diagram illustrating the derivation of a selling offer price for a seller.
12 and 13 are diagrams illustrating predicted prices and actual prices for each distribution line and each size of agricultural products.
14 is a diagram illustrating deriving and providing a harvest date maximizing a seller's profit based on individual cultivation status information received from a user terminal and user conditions.
15 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 by 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 100 for providing forward and reverse auction services between buyers and sellers of agricultural products according to the present disclosure' includes various devices capable of providing results to users by performing calculation processing. For example, the server 100 that provides forward and reverse auction services between buyers and sellers of agricultural products according to the present disclosure may include a computer, a server 100 device, and a portable terminal, or may be of any one type. can

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 100 device is a server 100 that processes information by communicating with an external device, and includes an application server 100, a computing server 100, a database server 100, a file server 100, and a game server. (100), mail server 100, proxy server 100, web server 100, and the like.

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 the processor 110 and the memory 150. The processor 110 may include one or a plurality of processors 110 . At this time, one or a plurality of processors 110 may include a general-purpose processor 110 such as a CPU, AP, or Digital Signal Processor (DSP), a graphics-only processor 110 such as a GPU, a Vision Processing Unit (VPU), or an artificial processor such as an NPU. It may be an intelligence-only processor (110). One or more processors 110 control input data to be processed according to predefined operating rules or artificial intelligence models stored in the memory 150 . Alternatively, when one or more processors 110 are dedicated AI processors 110, the AI dedicated processor 110 may be designed as a hardware structure specialized for processing a specific AI 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 100 and/or system 10. 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, the processor 110 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 110 generates a neural network, trains or learns the neural network, performs an operation based on received input data, and generates an information signal based on the result of the operation. Neural network models include GoogleNet, AlexNet, VGG Network, etc., CNN (Convolution Neural Network), R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network) ), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restrcted Boltzman Machine (RBM), It may include, but is not limited to, various types of models such as Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, etc. The processor 110 is one for performing calculations according to the models of the neural network. It may include the above processor 110. 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 110 may include a Convolution Neural Network (CNN) such as GoogleNet, AlexNet, VGG Network, etc., a Region with Convolution Neural Network (R-CNN), a Region Proposal Network (RPN), an 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 (Restructed 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, Various artificial intelligence structures and algorithms such as 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, Data Creation, etc. may be used, but is not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

1 is a schematic diagram of a system 10 for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.

Referring to FIG. 1 , a system 10 for providing forward and reverse auction services between a buyer and a seller of agricultural products according to an embodiment of the present disclosure includes a server 100, a buyer terminal 400, a seller terminal 500, a second It includes a first server 200 and a second server 300 .

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

However, the configuration of the system 10 for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure is not limited to the configuration of FIG. 1, and as will be described later, in some embodiments, a third Components such as the server 600 and the fourth server 700 may be further included.

As shown in FIG. 1 , the server 100 may communicate with the buyer terminal 400 , the seller terminal 500 , the first server 200 and the second server 300 , respectively.

2 is a diagram illustrating a system 10 that provides forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.

Referring to FIG. 2 , a system 10 for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure will be briefly described.

The first buyer may place a bid on the purchase of agricultural products from the server 100 through the terminal.

At this time, when the server 100 discloses all purchase bids of all buyers to the server 100, purchase bids that do not meet various conditions (eg, desired price that does not meet the market price) are disclosed to sell agricultural products. It can confuse sellers.

Therefore, when the server 100 receives a purchase bid for agricultural products from the buyer terminal 400, the purchase bid information is disclosed to the server 100 to notify sellers of suitability (the server 100 discloses conditions). whether or not you are satisfied).

At this time, it is not limited only to the fact that the purchase bidding information does not match the desired price, and various causes such as the absence of a seller selling agricultural products that the buyer wants to purchase can be applied.

When the purchase bidding information of the first buyer satisfies the disclosure condition, the purchase bidding information is disclosed to the first server 200 that provides the reverse auction service, and a notification is provided to the terminal 600 of the seller using the service; As shown in FIG. 2 , the third seller can place a sales bid.

Then, when the first buyer satisfies the offer information of the third seller's sale bid, the server 100 concludes a reverse auction transaction.

In contrast, when the purchase bidding information received from the terminal 400 of the first purchaser does not satisfy the conditions for disclosure to the server 100, the server 100 provides the second server 300 that provides forward auction service. so that the terminal 400 of the first purchaser is connected.

The system 10 for providing forward and reverse auction services between a buyer and a seller of agricultural products according to an embodiment of the present disclosure is performed as the above-described process, and below, this process will be described in more detail with reference to other drawings. do.

3 is a block diagram of an apparatus for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.

A server 100 that provides forward and reverse auction services between buyers and sellers of agricultural products includes a processor 110, a communication module, and a memory 150.

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

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 elements included in the device to drive the application program.

Referring to FIG. 3 , the processor 110 may include a processing module 116 responsible for processing data in the processor 110, a first artificial intelligence model 112 and a second artificial intelligence model 114, Some embodiments may include more artificial intelligence models.

The communication module 130 communicates with the buyer terminal 400 and the seller terminal 500 . In addition, the communication module 130 may communicate with at least one of the first server 200 , the second server 300 , the third server 600 and the fourth server 700 .

The communication module 130 may include one or more modules that connect the server 100 providing forward and reverse auction services between buyers and sellers 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 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 the external server 100 through wireless communication.

The memory 150 is a flash memory type, a hard disk type, a solid state disk type, an SDD type (Silicon Disk Drive type), and a multimedia card micro type. (multimedia card micro type), card-type memory (eg SD or XD memory, 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, 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 providing forward and reverse auction services between buyers and sellers of agricultural products.

4 is a flowchart of a method for providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure.

Referring to FIG. 4 , a process in which the server 100 providing forward and reverse auction services between buyers and sellers of agricultural products according to an embodiment of the present disclosure provides services will be described in more detail.

The processor 110 receives purchase bidding information for purchase of agricultural products from the purchaser terminal 400 . (S100)

5 is a diagram illustrating purchase bid information received from the purchaser terminal 400 .

Referring to FIG. 5 , purchase bidding information may include at least one of agricultural product items (types), varieties, weight/number, desired price, price per Kg, product grade, desired delivery date, and request information.

Since the processor 110 needs to verify whether the purchase bidding information satisfies the conditions for disclosure to the first server 200 for reverse auction, it may request that essential information necessary for verification be input without fail.

Next, the processor 110 verifies whether the purchase bid information received at S100 satisfies conditions for disclosure to the first server 200 . (S200)

At this time, the first server 200 is for the reverse auction, and the second server 300 is for the forward auction.

The processor 110 may receive and receive at least one of the type, variety, and weight/number of agricultural products grown by the seller from the terminal 600 of the seller subscribing to the reverse auction service, and manage them as the corresponding seller's DB. .

In addition, the processor 110 may check the weight/number of agricultural products grown by the seller terminal 500 at predetermined intervals.

Through this configuration, when the purchase bid information is received from the purchaser terminal 400, the processor 110 checks whether there is a seller handling agricultural products included in the purchase bid information, so that the purchase bid information is transmitted to the first server. It can be verified whether the conditions for disclosure to (200) are satisfied.

The processor 110 discloses the purchase bid information received from the purchaser terminal 400 to the first server 200 when it satisfies conditions for disclosure to the first server 200 . (S300)

The processor 110 proceeds with a reverse auction by matching the sale bidding information received from the seller terminal 500 and transmits the reverse auction result to the buyer terminal 400 . (S400)

FIG. 6 is a diagram illustrating that the purchase bid information received from the buyer terminal 400 is disclosed to the first server 200 and a notification is provided to the seller terminal 500 .

As an embodiment, the processor 110 may provide a function allowing a person to add agricultural products of a specific category to items of interest from the terminal 600 of a seller using the service. Further, the processor 110, as shown in FIG. 6 , when the purchase bidding information for the corresponding agricultural product is disclosed to the first server 200, the processor 110 reports that the purchase bidding information has been generated by the terminal 600 of the seller who has added the corresponding agricultural product as an item of interest. Notifications can be provided.

6 to 8 illustrate providing a service from the server 100 to the seller terminal 500 through a service application, a direct transaction is a UI in which a seller directly deals with a buyer, and in an embodiment of the present disclosure, the server 100 The reverse auction service provided by ) through the first server 200 may correspond to the sales bidding part.

FIG. 7 displays details of already sold bids and purchase bids that can be placed on the seller terminal 500 .

Referring to FIG. 7 , the seller terminal 500 has selected the full mode in the sales bidding mode, and the offer price button is activated for a case where a sale bid has already been made by proposing a price (bottom) and a new purchase bid case. is exemplified.

The seller can use the reverse auction service by clicking the price proposal button, checking the purchase bidding information of the relevant case, and proposing an appropriate price.

8 is a diagram illustrating providing a function of modifying sales bidding details with respect to details of sales bidding through the seller terminal 500 .

When the seller proposes a price for the purchase bid information, but a purchase decision is not made for a certain period of time, the buyer may make a purchase decision by modifying the proposed price (sale bid information).

As described above, a reverse auction for the purchase bidding information may be performed in step S400, but this does not necessarily mean that the reverse auction is successful.

When the reverse auction for specific purchase bidding information is not concluded for a predetermined time or a predetermined number of times, the processor 110 may propose a correction of the estimate included in the purchase bidding information using a pre-learned artificial intelligence model.

In one embodiment, the processor 110 may calculate a reasonable market price for information on the corresponding agricultural product using an artificial intelligence model, and may propose a price to a buyer or seller based on the calculated market price.

Next to S200, if the purchase bid information does not satisfy the disclosure condition of the first server 200, the processor 110 may control the buyer terminal 400 to be connected to the second server 300 for forward auction. there is. (S500)

In one embodiment, the conditions for disclosure to the first server 200 include a type condition of agricultural products and a price condition of agricultural products.

The processor 110 determines that the purchase bidding information satisfies the type condition of the agricultural product when there exists a seller selling agricultural products of the type included in the purchase bidding information among the sellers registered in the first server 200; When the desired purchase price included in the purchase bidding information satisfies the preset price condition, it is determined that the price condition of the agricultural product is satisfied.

At this time, the processor 110 may calculate the preset condition using an artificial intelligence model.

As an embodiment, the processor 110 may calculate an expected market price of agricultural products based on the cultivation status information for each type of agricultural products and weather information of a growing region of the agricultural products.

The processor 110 may determine the preset price condition based on the calculated market price.

More detailed information on the calculation of the expected market price will be described later.

In an embodiment, the processor 110 may, when the desired selling price and the desired purchasing price included in the first selling bid information received from the first seller terminal 500 are within a preset price range, obtain the first selling bid information and It is possible to control purchase bidding information to be matched.

Through this configuration, it is possible to achieve an effect of automatically matching purchase bidding information with a seller who has set a predetermined price range.

In one embodiment, the processor 110, when receiving a subscription request for the reverse auction service from the seller terminal 500, inputs to the seller terminal 500, the type, grade, and amount of at least one agricultural product grown by the seller. Agricultural product sales information including is received and stored in the memory 150 as agricultural product sales information.

Through the above configuration, the processor 110 may manage which types of agricultural products are handled by sellers subscribing to the service. The processor 110 inquires the seller terminal 500 about whether there is a changed/updated history of agricultural product sales information at every preset time, and if there is a changed or updated history, it is received from the seller terminal 500 and stored in the memory 150 save to

When purchase bidding information is received from the buyer terminal 400, the processor 110 compares agricultural product sales information of sellers stored in the memory 150 with purchase bidding information, and satisfies the purchase bidding information among sellers based on the comparison result. Purchase bidding information may be provided to the seller's terminal 600 corresponding to agricultural product sales information.

In one embodiment, the processor 110 may, when the desired selling price and the desired purchase price included in the first selling bid information received from the first seller terminal 500 are not within a preset price range, the first seller terminal In step 500, information on the first proposed sales price may be provided or the first proposed purchase price may be provided to the terminal of the purchaser.

The processor 110 may calculate a proposed sale price based on the estimated market price calculated as follows and provide it to the seller, or may calculate a proposed purchase price and provide it to the buyer.

For example, if a seller wants to sell certain agricultural products at too high a price and the server 100 requests a price reduction without grounds, it may be difficult to persuade the seller and it may be accepted as a request for a price reduction based on facts. can

However, in the embodiment of the present disclosure, since it may be difficult to trade agricultural products at the corresponding price based on the expected market price calculated as follows, the seller calculates the proposed price and provides it to the seller terminal 500 to convince the seller according to the expected market price. It has the effect of being able to accept the selling offer price.

In addition, if the buyer wants to purchase agricultural products too cheaply and the server 100 requests to increase the purchase price without grounds, it may be difficult to persuade the buyer and it may be accepted as a request to raise the price based on facts. .

However, in the embodiment of the present disclosure, since it may be difficult to trade agricultural products at the corresponding price based on the expected market price calculated as follows, the purchase offer price is calculated and provided to the buyer terminal 400 to persuade the buyer according to the expected market price. It has the effect of being able to accept the purchase offer price.

As an embodiment, the processor 110 may calculate a purchase offer price based on a desired purchase price included in the purchase bid information received from the purchaser terminal 400 and an expected market price calculated for the corresponding agricultural product.

In one embodiment, the processor 110 may, when the desired selling price or the desired purchase price included in the first selling bid information received from the first seller terminal is not within a preset price range, the first seller terminal Information on the first proposed selling price may be provided as , or the first proposed purchasing price may be provided to the terminal of the purchaser.

In one embodiment, the processor 110 may calculate the first proposed sale price or the first proposed purchase price based on the expected market price calculated for the agricultural product and the desired sale price or the desired purchase price. there is.

At this time, various embodiments can be applied to the processor 110 calculating the specific purchase offer price. For example, by assigning a weight of 9 to the expected market price and assigning a weight of 1 to the desired purchase price, the purchase request of the buyer The purchase offer price may be calculated so that the price is partially reflected. However, since the buyer's desired purchase price may be too low, the processor 110 may calculate the proposed purchase price by considering the difference between the expected market price and the desired purchase price.

In an embodiment of the present disclosure, the server 100 not only provides a forward/reverse auction service, but also provides information by calculating an expected market price to calculate a purchase/sale proposal price, and generates information generated in the process of calculating the expected market price. It can be used as a platform that provides various services by providing the expected profit for each type of agricultural products to sellers who grow agricultural products.

In an embodiment of the present disclosure, in addition to providing the reverse and forward auction services described above, the server 100 may predict harvest prices for each variety of agricultural products in order to assist the reverse auction service, and in this process, one or more pre-learned AI models can be used.

The third server 600 is a server 100 that provides cultivation status information for each variety of agricultural products, and refers to a server 100 that collects agricultural product cultivation status information of a user (seller) who grows agricultural products at a predetermined time. .

The fourth server 700 can be applied to any server 100 that provides weather information, and the server 100 that provides 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 (seller) received from the third server 600 and the meteorological information received from the fourth server 700, and the user (producer, seller) ), agricultural product sales strategies, cultivation recommendation information, and agricultural product purchase strategies for users (consumers, buyers) can be provided.

Hereinafter, with reference to FIGS. 9 to 14 , a method for the server 100 to predict harvest prices for each variety of agricultural products will be described in more detail.

9 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. 9 together.

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

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

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

Referring to FIG. 9 , 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 third server 600 or the seller terminal 500, and in some embodiments, the server 100 may send the seller terminal 500 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 images of agricultural products taken from the seller terminal 500 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.

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.

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 the embodiment of the present disclosure, the cultivation conditions for each variety of agricultural products are among the cultivation area conditions, weather conditions related to cultivation, seller's cultivation proficiency conditions related to cultivation, seller's cultivation equipment conditions related to cultivation, and storage space conditions of sellers 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.

10 is a diagram illustrating the derivation of a purchase offer price for a buyer.

Referring to FIG. 10 , the processor 110 provides information on the proposed purchase price of agricultural products to the buyer as transaction recommendation information.

The processor 110 provides information on the expected 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 buyer 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 during which the purchaser 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.

11 is a diagram illustrating the derivation of a selling offer price for a seller.

Referring to FIG. 11 , the processor 110 provides the seller terminal 500 with information on the proposed selling 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. .

12 and 13 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 seller, based on the expected market price of agricultural products grown by the seller and the growing conditions of the seller.

Specifically, the processor may derive a sales strategy after harvesting the agricultural products grown by the seller, based on the estimated market price of agricultural products grown by the seller and information about the storage space of the agricultural products of the seller.

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 seller, and when the information on the storage space for agricultural products is received from the seller terminal 500, 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 seller based on information about the storage space of the seller.

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

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 seller's first agricultural product sales profit. there is a way

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

For example, the processor 110 may calculate at least one of a yield per day, a sales amount per day, and an amount stored in a storage space of the seller's agricultural product cultivation.

The processor 110 calculates the daily yield, daily sales volume, and daily storage amount of the agricultural products grown by the seller during the harvesting period based on the expected yield of the agricultural products grown by the seller, the expected price of the harvest period, and the seller's growing conditions, and the seller terminal (500).

FIG. 14 is a diagram illustrating that a harvest date maximizing a seller's profit is derived and provided based on individual cultivation status information received from the seller terminal 500 and user conditions.

The communication module 130 may receive individual cultivation status information from the seller terminal 500 .

The processor 110 calculates the expected harvest date of the agricultural products grown by the seller based on the individual cultivation status information, and based on the expected harvest market price of the agricultural product variety grown by the seller and information about the seller's storage space for the agricultural products, the seller 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 seller terminal 500 .

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 seller terminal 500 .

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 a cultivation area of the seller and a location of the cultivation site.

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

The processor 110 calculates information on the expected yield and expected profit according to the seller's cultivation conditions when the seller cultivates the derived cultivated agricultural product, and transmits the cultivation recommendation information including the calculated result to the seller terminal ( 500) can be provided/output.

Sellers can obtain information about the types of agricultural products they can grow as well as the expected profits when they grow the agricultural products.

The processor 110 may calculate the expected revenue per unit area of the derived cultivated agricultural products and provide it to the seller terminal 500 .

In one embodiment, the cultivation conditions may further include a seller'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 seller based on the seller's plantation location, cultivation area, cultivation skill level condition, and cultivation equipment condition, and provide the calculated seller matching score for each agricultural product type. .

The ranking of the seller matching score does not mean the seller's profit ranking, and the seller matching score means agricultural products suitable for the seller to grow. That is, it may refer to agricultural products that can be grown without requiring a seller to exert relatively great effort.

The processor 110 may reflect the seller's cultivation equipment condition as a weight for the seller's cultivation skill level condition.

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 seller's cultivation conditions, including the seller's cultivation skill level and cultivation equipment conditions, for each of the derived cultivated agricultural products.

When such a process is performed, since the cultivation skill of the seller and the cultivation equipment possessed by the seller 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 seller based on the expected market price for each agricultural product variety in the next season, and provide/output the calculated result to the seller terminal 500 .

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.

In one embodiment, the processor 110 stores the quality information calculated as follows as the quality information of the seller using the service, and matches the quality information of agricultural products included in the purchase bidding information received from the buyer terminal 400 using the same. So you can match buyers and sellers.

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 seller from the seller terminal 500 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 for the seller to request the number of samples required according to the seller's cultivation amount and cultivation scale than to analyze the entire agricultural product grown or harvested by the seller, in an embodiment of the present disclosure, the processor 110 provides the seller'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 input on at least one of the type, amount, and period of agricultural products grown by the seller.

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 seller terminal 500 .

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 photographing condition when information on the type of agricultural products is received from the seller terminal 500 . 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 seller terminal 500 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 seller terminal 500 . For example, if the size of agricultural products is estimated based on the type of agricultural products and the necessary shooting distance is calculated, but as a result of analyzing the real-time photographed image of the seller terminal 500, the size of the actual agricultural products is different, the processor 110 performs 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 seller terminal 500 .

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 seller's farmhouse site 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 a seller's farmhouse 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 data directly measured by the seller and the second quality information generated based on data measured directly by the manager 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 agricultural products of the seller based on at least one second quality information.

Such a correction factor may be for standardizing quality measurement of agricultural products grown by sellers 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 farmhouse of the seller.

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 seller using a sugar content meter owned by the seller. 2 The value of sugar content is directly measured by the manager using a sugar content meter after visiting the seller's farmhouse.

In one embodiment, the communication unit may receive a value of the first sugar content directly measured through a sugar content meter of the seller with respect to 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 seller.

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, the 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.

When the first sugar content value and the second sugar content value differ by more than a threshold value, the processor 110 may calculate a correction coefficient for the seller's sugar content meter based on the second sugar content 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 seller'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 seller is received from the seller terminal 500, the correction coefficient for the sugar content meter of the seller 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 seller terminal 500, 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 seller 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 seller.

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 seller. 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 an embodiment of the present disclosure described above may be implemented as a program (or application) to be executed in combination with a server, 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 code for determining where (address address) of the computer's internal or external memory the additional information or media required for the computer's processor to execute the functions should be referenced. 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 communication module of the computer 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 by 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: Auction service provision system for agricultural products
100: server
110: processor
112: first artificial intelligence model
114: second artificial intelligence model
116: processing module
130: communication module
150: memory
200: first server
300: second server
400: buyer terminal
500: seller terminal
600: third server
700: fourth server

Claims (10)

A communication module that communicates with the buyer's terminal and the seller's terminal;
When purchase bidding information including the type, grade, and quantity of agricultural products desired to be purchased by the buyer is received from the buyer terminal, the purchase bidding information is to be disclosed to the first server for reverse auction, so that the type conditions and prices of agricultural products are disclosed. verify whether the condition is satisfied,
When the purchase bidding information satisfies the type condition of the agricultural product and the price condition of the agricultural product, the purchase bidding information is disclosed to the first server;
proceeding with a reverse auction by matching sales bidding information received from the seller terminal, and delivering the reverse auction result to the buyer terminal;
When the purchase bidding information does not satisfy the condition, a processor for controlling the purchaser terminal to be connected to a second server for a forward auction;
The first server receives the sale bid information from at least one seller terminal with respect to the open purchase bid information to provide the reverse auction service;
the processor,
If there is a seller selling the type of agricultural products included in the purchase bidding information among the sellers registered in the first server, it is determined that the purchase bidding information satisfies the condition of the type of agricultural products;
When the desired purchase price included in the purchase bidding information satisfies a predetermined price condition, it is determined that the price condition of the agricultural product is satisfied;
Calculate the expected market price of the agricultural product based on the cultivation status information of each variety of the agricultural product and the weather information of the growing region of the agricultural product,
Determine the preset price condition based on the calculated expected market price;
When a subscription request for the reverse auction service is received from the seller terminal, agricultural product sales information including the type, grade, and cultivation amount of at least one agricultural product grown by the seller input to the seller terminal is received and stored in memory. and
comparing the agricultural product sales information stored in the memory with the purchase bidding information;
Based on the comparison result, a purchase bid notification is provided to a terminal of a seller corresponding to agricultural product sales information that satisfies the purchase bid information among the sellers,
Through the first artificial intelligence model based on the cultivation status information by variety of the agricultural product and weather information during the cultivation period of the agricultural product, the expected yield compared to the maximum yield is calculated by adding weights for each weather condition for each variety of the agricultural product. do,
Calculate the expected harvest price for each variety of agricultural products through a second artificial intelligence model based on at least one influence condition on the expected yield calculated by the first artificial intelligence model and the price for each variety of agricultural products,
Extracting defect information in the image of each type of agricultural product through a third artificial intelligence model based on the image of each type of agricultural product;
The above influence conditions are disease, temperature, rainy season, interest rate, price, real estate market price, tariff, and import amount of agricultural products by type,
The defect information is poor coloring, scratches, bruises, heat, disease, insect damage, and scratches,
A server that provides forward and reverse auction services between buyers and sellers of agricultural products.
delete delete According to claim 1,
the processor,
When the desired selling price and the desired purchasing price included in the first selling bid information received from the first seller terminal are within a preset price range,
Characterized in that the first selling bidding information and the purchasing bidding information are matched.
A server that provides forward and reverse auction services between buyers and sellers of agricultural products.
According to claim 4,
the processor,
When the desired selling price or the desired purchasing price included in the first selling bid information received from the first seller terminal is not within a preset price range;
Characterized in that information on the first proposed selling price is provided to the first seller terminal, or the first proposed purchase price is provided to the purchaser terminal.
A server that provides forward and reverse auction services between buyers and sellers of agricultural products.
According to claim 5,
the processor,
Characterized in that the first proposed sale price or the first proposed purchase price is calculated based on the expected market price and the desired sales price or the desired purchase price calculated for the agricultural product,
A server that provides forward and reverse auction services between buyers and sellers of agricultural products.
delete According to claim 1,
the processor,
If the desired purchase price included in the purchase bidding information does not satisfy the price condition of the agricultural product,
Calculating a second purchase proposal price that replaces the desired purchase price based on the calculated expected market price;
Characterized in that the calculated purchase offer price is provided to the purchaser terminal,
A server that provides forward and reverse auction services between buyers and sellers of agricultural products.
As a method performed by the server,
Receiving purchase bidding information including the type, grade, and amount of agricultural products the purchaser wants to purchase from the purchaser terminal;
verifying whether the purchase bidding information satisfies a type condition of agricultural products and a price condition of agricultural products to be disclosed to a first server for reverse auction;
disclosing the purchase bidding information to the first server when the purchase bidding information satisfies the agricultural product type condition and the agricultural product price condition;
proceeding with a reverse auction by matching sales bidding information received from a seller terminal, and transmitting the reverse auction result to the buyer terminal; and
connecting the buyer terminal to a second server for a forward auction when the purchase bidding information does not satisfy a condition between;
The first server receives the sale bid information from at least one seller terminal with respect to the open purchase bid information to provide the reverse auction service;
If there is a seller selling the type of agricultural product included in the purchase bidding information among the sellers registered in the first server, it is determined that the purchase bidding information satisfies the condition of the type of agricultural product;
When the desired purchase price included in the purchase bidding information satisfies a predetermined price condition, it is determined that the price condition of the agricultural product is satisfied;
Calculate the expected market price of the agricultural product based on the cultivation status information of each variety of the agricultural product and the weather information of the growing region of the agricultural product,
Determine the preset price condition based on the calculated expected market price;
When a subscription request for the reverse auction service is received from the seller terminal, agricultural product sales information including the type, grade, and cultivation amount of at least one agricultural product grown by the seller input to the seller terminal is received and stored in memory. and
comparing the agricultural product sales information stored in the memory with the purchase bidding information;
Based on the comparison result, a purchase bid notification is provided to a terminal of a seller corresponding to agricultural product sales information that satisfies the purchase bid information among the sellers,
Through the first artificial intelligence model based on the cultivation status information of each variety of the agricultural products and the weather information during the cultivation period of the agricultural products, the expected yield compared to the maximum yield is calculated by adding weights for each weather condition for each variety of the agricultural products. do,
Calculate the expected harvest price for each variety of agricultural products through a second artificial intelligence model based on at least one influence condition on the expected yield calculated by the first artificial intelligence model and the price for each variety of agricultural products,
Extracting defect information in the image of each type of agricultural product through a third artificial intelligence model based on the image of each type of agricultural product;
The above influence conditions are disease, temperature, rainy season, interest rate, price, real estate market price, tariff, and import amount of agricultural products by type,
The defect information is poor coloring, scratches, bruises, heat, disease, insect damage, and scratches,
A method of providing forward and reverse auction services between buyers and sellers 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.