KR102202168B1 - Method and apparatus for obtaining agricultural public data based on bigdata - Google Patents

Abstract

The present invention discloses a method of predicting demand for agricultural products based on big data by an agricultural product management server. In particular, provided is the method, which receives weather forecast information related to a plurality of agricultural damage factors for a first period after present time from a weather forecast server, obtains information on the sales volume, sale price and the plurality of agricultural damage factors of the agricultural products from the present time to a second, and obtains the search amount of the agricultural products and keywords related to the agricultural products from a media server, analyzes the consumption trend of the agricultural product, and predicts the price and demand of the agricultural products at a time point after a third period based on the weather forecast information, information on the agricultural damage factors, and the consumption trend of the agricultural products.

Description

A method for collecting agricultural public data based on big data and a device therefor {METHOD AND APPARATUS FOR OBTAINING AGRICULTURAL PUBLIC DATA BASED ON BIGDATA}

The present invention relates to a method for collecting public agricultural data based on big data and an apparatus therefor, and more particularly, using an algorithm optimized for public data related to agriculture such as weather forecast data, natural disaster data, and pest data. It relates to a method for collecting and analyzing the collected public data, and an apparatus therefor.

Korean agriculture has become the foundation for economic development through productivity improvement, such as succeeding in self-sufficiency in rice through the Green Revolution. Due to the recent decrease in the food self-sufficiency rate due to the opening of the agricultural product market, stagnant agricultural income due to increased production costs, a decrease in cultivated land, and an increase in the ratio of the elderly population amid continuous decline in the population of farm households, maintenance of agricultural manpower and production possibilities are uncertain in the future. There are also positive aspects, such as an increase in agricultural professional managers.

The world is currently suffering from environmental pollution due to abuse of fertilizers and herbicides, vulnerability to extreme weather and diseases due to intensive cultivation of single crops, rapidly increasing world population, additional demand for food due to changes in dietary habits, and depletion of resources such as oil and water. As the sense of crisis for future food is growing and the demand for food quality and safety, and for a variety of foods is increasing due to the increase of the middle class and the elderly, developed countries are aware of the various public interest functions of agriculture and continue to support agriculture. Are doing.

In the era of the 4th industrial revolution, big changes are expected around the world, and agriculture is no exception.

Developed countries are making efforts to apply ICT technology to agriculture, inspire productivity and efficiency, and use it as a future growth engine.Korea also recognizes the need for investment in agriculture, establishes related policies, and makes efforts to formulate a budget, but is still in its infancy. It is true that there is a technology gap with advanced countries without leaving the country.

Although it is recognized that agriculture has a pluralistic function, there is still a lack of public consensus on the need for financial support, and a long-term macro plan was established in one control tower to standardize and build a platform, and to provide continuous and systematic support for farmers. It is necessary to prepare a plan.

Korean Patent Application Publication No. 10-2019-0134963 (Name: IoT-based agricultural product storage management system, 2018.07.25)

The present invention is to provide a method and apparatus for collecting public agricultural data based on big data.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. I will be able to.

In the method for predicting the demand of agricultural products by the agricultural products management server according to an embodiment of the present invention based on big data, weather prediction information related to a plurality of agricultural damage factors during a first period after the current point is received from the weather prediction server. And, obtaining information on the sales volume, sales price, and the plurality of agricultural damage factors of the agricultural product from the present point to the second period, obtaining the search amount of the agricultural product and keywords related to the agricultural product from a media server, It is possible to analyze the consumption trend of agricultural products, and predict the price and demand of the agricultural product at a time point after the third period based on the weather forecast information, information on the agricultural damage factors, and the consumption trend of the agricultural product.

In this case, the plurality of agricultural damage factors may include damage factors related to feng shui, flooding, forest fires, heat waves, and drought.

In addition, the scale of agricultural damage caused by each of the plurality of agricultural damage factors before the second period is obtained, different weights are set for each of the plurality of agricultural damage factors based on the agricultural damage scale, and the different weights It may further include predicting the production amount of the agricultural product based on.

In addition, predicting the price and demand of the agricultural product may include shipment of the agricultural product after the third period based on the consumption trend of the agricultural product, the predicted production amount, and the sales price and sales volume of the agricultural product before the second period. It may include determining whether and how much to ship.

In addition, analyzing the consumption trend of the agricultural products includes assigning different weights to each of the media media that are based on the search for the agricultural products or obtaining the keywords related to the agricultural products, and the agricultural products based on the different weights. It may include analyzing the trend of consumption of.

In addition, to obtain disease and pest information related to the agricultural product, estimate the damage scale and duration of damage to the agricultural product due to the disease and pest information related to the disease and pest information based on the disease and pest information, and minimize the size and duration of damage caused by the pest It may further include determining preventive measures for.

In addition, the disease and pest information may include the type of the disease and the pest, the expected number of the disease and the pest, and the type of damage caused by the disease and the pest to the agricultural product.

In addition, it is possible to determine the purchase time of the agricultural product and the sale of the agricultural product based on the price and demand of the agricultural product.

In addition, predicting the price and demand of the agricultural product may be characterized by further considering the amount of imported agricultural products and the price of the imported agricultural products.

In the agricultural products management server that predicts the demand for agricultural products based on big data according to the present invention, the weather prediction information related to a plurality of agricultural damage factors for a first period after the current point is received from the weather prediction server, and A data collection unit for acquiring information on the sales volume, sales price and the plurality of agricultural damage factors of the agricultural product up to two periods, and acquiring the search amount of the agricultural product and keywords related to the agricultural product from a media server; Based on the search volume of the agricultural product and the keyword related to the agricultural product, the consumption trend of the agricultural product is analyzed, and based on the weather forecast information, the information on the agricultural damage factors, and the consumption trend of the agricultural product, a third period A model construction unit that predicts the price and demand of the agricultural product at a later point in time; And a service providing unit for transmitting the price and demand of the agricultural product to a terminal device.

According to the present invention, efficient agricultural product production is possible by predicting the demand for the agricultural product using public agricultural data collected based on big data, estimating the required amount of agricultural product production, and establishing an agricultural plan accordingly.

The effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those of ordinary skill in the art from the following description. will be.

1 is a diagram illustrating a system for predicting demand for agricultural products according to the present invention.
2 to 3 are diagrams for explaining an embodiment of predicting the production amount of agricultural products according to the present invention.
4 to 5 are views for explaining an embodiment of predicting the demand for agricultural products according to the present invention.
6 to 8 are diagrams for explaining the configuration of devices according to the present invention.

In order to clarify the features and advantages of the problem solving means of the present invention, the present invention will be described in more detail with reference to specific embodiments of the present invention shown in the accompanying drawings.

However, in the following description and the accompanying drawings, detailed descriptions of known functions or configurations that may obscure the subject matter of the present invention will be omitted. In addition, it should be noted that the same components are indicated by the same reference numerals as possible throughout the drawings.

The terms or words used in the following description and drawings should not be construed as being limited to their usual or dictionary meanings, and the inventor may appropriately define the concept of terms for describing his or her invention in the best way. It should be interpreted as meaning and concept consistent with the technical idea of the present invention based on the principle that Therefore, the embodiments described in the present specification and the configurations shown in the drawings are only the most preferred embodiments of the present invention, and do not represent all the technical ideas of the present invention, so various alternatives that can be substituted for them at the time of application It should be understood that there may be equivalents and variations.

In addition, terms including ordinal numbers such as first and second are used to describe various elements, and are only used for the purpose of distinguishing one element from other elements, and to limit the elements. Not used. For example, without departing from the scope of the present invention, a second component may be referred to as a first component, and similarly, a first component may be referred to as a second component.

In addition, terms used in the present specification are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In addition, terms such as "comprises" or "have" described herein are intended to designate the presence of features, numbers, steps, actions, components, parts, or a combination thereof described in the specification, but one or a combination thereof. It is to be understood that the above other features, or the possibility of the presence or addition of numbers, steps, actions, components, parts, or combinations thereof, are not preliminarily excluded.

In addition, terms such as "unit", "group", and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware or software or a combination of hardware and software. In addition, "a or an", "one", "the" and similar related words are different from this specification in the context of describing the present invention (especially in the context of the following claims). Unless otherwise indicated or clearly contradicted by context, it may be used in a sense encompassing both the singular and the plural.

In addition to the above terms, specific terms used in the following description are provided to aid understanding of the present invention, and the use of these specific terms may be changed in other forms without departing from the technical spirit of the present invention.

In addition, embodiments within the scope of the present invention include computer-readable media having or transferring computer-executable instructions or data structures stored in the computer-readable media. Such computer-readable media may be any available media accessible by a general purpose or special purpose computer system. By way of example, such computer readable media may be in the form of RAM, ROM, EPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage, or computer executable instructions, computer readable instructions, or data structures. It may include, but is not limited to, physical storage media such as any other media that may be used to store or deliver certain program code means, and may be accessed by a general purpose or special purpose computer system. .

In addition, the present invention relates to a personal computer, a laptop computer, a handheld device, a multiprocessor system, a microprocessor-based or programmable consumer electronics, a network PC, a minicomputer, a mainframe computer, a mobile phone, a PDA, a pager. It can be implemented in a network computing environment having various types of computer system configurations including (pager) and the like. The invention may also be practiced in a distributed system environment where both local and remote computer systems linked through a network with a wired data link, a wireless data link, or a combination of wired and wireless data links perform tasks. In a distributed system environment, program modules may be located in local and remote memory storage devices.

In addition, it will be appreciated that each block of the process flow diagrams and combinations of the flow chart diagrams may be executed by computer program instructions. Since these computer program instructions can be mounted on the processor of a general purpose computer, special purpose computer or other programmable data processing equipment, the instructions executed by the processor of the computer or other programmable data processing equipment are described in the flowchart block(s). It creates a means to perform functions. These computer program instructions can also be stored in computer-usable or computer-readable memory that can be directed to a computer or other programmable data processing equipment to implement a function in a particular way, so that the computer-usable or computer-readable memory It is also possible to produce an article of manufacture containing instruction means for performing the functions described in the flowchart block(s). Computer program instructions can also be mounted on a computer or other programmable data processing equipment, so that a series of operating steps are performed on a computer or other programmable data processing equipment to create a computer-executable process to create a computer or other programmable data processing equipment. It is also possible for instructions to perform processing equipment to provide steps for executing the functions described in the flowchart block(s).

In addition, each block may represent a module, segment, or part of code that contains one or more executable instructions for executing the specified logical function(s). In addition, it should be noted that in some alternative execution examples, functions mentioned in blocks may occur out of order. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order depending on the corresponding function.

In describing the embodiments of the present disclosure in detail, examples of a specific system will be the main target, but the main subject matter to be claimed in this specification is the scope disclosed in the present specification to other communication systems and services having a similar technical background. It can be applied within a range that does not deviate greatly, and this will be possible at the judgment of a person skilled in the art.

Now, a method for predicting demand for agricultural products according to an embodiment of the present invention will be described in earnest.

1, the agricultural product demand prediction system according to an embodiment of the present invention includes a communication network 100, a terminal device 200, an agricultural product management server 300, a media server 400, and a weather prediction server 500. Can include.

Here, the terminal device 200 may be interlocked with the agricultural product management server 300 through the communication network 100. In this case, the communication network 100 may be a wireless communication network. For example, the wireless communication network may be a wireless communication network using LTE (Long Term Evolution), 5G (5 Generation), and WiFi.

The terminal device 200 of the present invention refers to a device of a user capable of transmitting and receiving various data via the communication network 100 according to a user's manipulation. The terminal device 200 may perform voice or data communication through the communication network 100, and may transmit and receive information through the agricultural product management server 300 and the communication network 100. For this, the terminal device 200 of the present invention may include a memory for storing programs and protocols for transmitting and receiving data with the agricultural product management server 300, and a microprocessor for calculating and controlling by executing various programs.

The terminal device 200 preferentially accesses an application (hereinafter,'app') providing device (not shown) connected to the communication network 100, for example, an app store, and provides an app for a clothing recommendation service from the corresponding app store. It can be received and installed.

In addition, such a terminal device 200 may be implemented in various forms. For example, the terminal device 200 described herein is a mobile terminal such as a smart phone, a tablet PC, a personal digital assistant (PDA), a portable multimedia player (PMP), and an MP3 player. Of course, a fixed terminal such as a smart TV or a desktop computer may be used.

In addition, the terminal device 200 of the present invention can not be listed all because the deformation of the portable device is very diverse according to the convergence trend of the digital device, but a unit of the same level as the above-mentioned units is used in the present invention. Any device that can be used as the terminal device 200 according to the present invention and can download and install an app related to the clothing recommendation service according to the present invention can be used as the terminal device 200 according to an embodiment of the present invention. have.

The agricultural product management server 300 transmits and receives various data and information to and from the terminal device 200, the media server 400, and the weather prediction server 500 for providing a big data-based agricultural product demand prediction service according to an embodiment of the present invention. And, it is a server device for predicting the production amount and demand of agricultural products based on the various data and information.

The media server 400 is a device for transmitting and receiving various data and information with the agricultural product management server 300 in order to provide a big data-based agricultural product demand prediction service according to an embodiment of the present invention. In particular, the media server 400 is linked with various SNS (Social Network Service), portal sites, and media media, and receives information on agricultural products from various SNS, portal sites, and media media. It can be delivered to the management server 300.

The weather forecast server 500 is a device for transmitting and receiving various data and information with the agricultural product management server 300 in order to provide a big data-based agricultural product demand prediction service according to an embodiment of the present invention. In particular, the meteorological prediction server 500 may transmit information on weather forecasting from a current point in time to a certain point in the future to the agricultural product management server 300.

On the other hand, in the embodiment according to the present invention described later,'agricultural product' may be used to include all grains, vegetables, fruits, eggs, special crops, and flowers, and'agricultural products' described in the embodiments of the present invention are grains. , Vegetables, fruits, eggs, specialty crops, and flowers.

2 to 3 are examples for explaining a method for predicting the production amount of agricultural products according to an embodiment of the present invention.

Referring to FIG. 2, the agricultural product management server 300 may receive weather prediction information from the weather prediction server 500 to a period of several months from the current point in time (S205). In this case, the weather prediction information may be information related to agricultural damage factors that affect the production of agricultural products. For example, the agricultural damage factors may include damage factors related to feng shui, flooding, forest fires, heat waves, and drought, as shown in FIG. 3, and predicted typhoons related to the damage factors in weather forecast information It may include the time of occurrence, the number of expected typhoons and the expected size of the corresponding typhoon, the monthly average wind direction, the monthly average wind speed, the monthly average precipitation, the monthly average humidity, and the monthly average temperature.

The agricultural product management server 300 may acquire information related to the sales volume, sales price, and agricultural damage factors of the agricultural product for the last year or several years (S210). Meanwhile, such information may be stored in the storage module 330 of the agricultural product management server 300.

As shown in FIG. 3, the agricultural product management server 300 acquires the scale of agricultural damage for each of the agricultural damage factors, and a first weight different for each of the agricultural damage factors based on the scale of agricultural damage Can be set (S215). In this case, the first weight may be set based on the size of agricultural damage. Referring to FIG. 3, since the size of agricultural damage due to feng shui damage is the largest, the largest first weight may be assigned. On the other hand, damage to wild animals, damage from flooding, and damage from forest fires are similar in scale to damage to agriculture, and thus the same first weight may be given.

For example, when the difference between the damage scales of each damage factor is less than a certain amount (eg, 1 billion won), the first weight between the damage scales may be set to be the same. However, the difference between the damage factors to which the same first weight is assigned should be less than a certain level. For example, if the amount of damage to agriculture of the wild animal damage factor is 118 instead of 111, the difference is less than 10 from the damage from flooding, but by more than 10 from the damage from forest fires. The same first weight may not be given.

In this case, the same first weight is assigned to all three factors, and then a weight of the first weight * X (for example, 1.1) is additionally assigned to the wild animal damage factor having the largest agricultural damage scale. Can make a difference. Similarly, referring to FIG. 3, heat damage damage and drought damage may be given the same first weight.

Meanwhile, the wild animal damage factor may not be included in the weather prediction information. Accordingly, the agricultural product management server 300 may extract the wild animal damage related information stored in the storage module 330 and set the first weights by further considering the wild animal damage factor based on this.

Meanwhile, the information related to the damage factor of wild animals may include the number of wild animals that may eat the agricultural product in the agricultural product production area or damage the agricultural product production area, and the probability of descending to the agricultural product production area.

The agricultural product management server 300 may predict the production amount of agricultural products based on the weather prediction information and weights of agricultural damage factors (S220). To this end, the agricultural products management server 300 may group weather prediction information related to each agricultural damage factor. For example, the predicted typhoon occurrence time, the expected number of typhoon occurrences, and the expected size of the typhoon, the monthly average precipitation, the monthly average wind direction, and the monthly average wind speed may be grouped into the first group. In addition, the expected typhoon occurrence time, the expected number of typhoon occurrences, the expected size of the corresponding typhoon, and the monthly average precipitation related to the flood damage may be grouped into a second group. In addition, it is possible to group the average monthly humidity and monthly average temperature related to forest fires and heat waves into a third group. In addition, the drought-related, monthly average precipitation, monthly average humidity, and monthly average temperature may be grouped into a fourth group. That is, the same weather prediction information may be commonly included in different groups.

The agricultural products management server 300 may apply the first weight of the damage factors corresponding to each group and add each of the weather prediction information of each group to which the first weight is applied. For example, assume that a first weight of 10 is given to a feng shui damage factor, a first weight of 5 to flood damage, wild animal damage, and wildfire damage, and a first weight of 2.5 to heat damage and drought damage.

For example, since the monthly average precipitation is included in the first group, the second group, and the fourth group, weather prediction information may be reprocessed by applying a weight of (10+5+2.5) to the monthly average precipitation. As another example, since the monthly average humidity is included in the third group (wildfire and heatwave) and the fourth group, weather prediction information may be reprocessed by applying a weight of (5+2.5+2.5) to the monthly average humidity.

The agricultural product management server 300 may predict the production amount of the agricultural product based on the reprocessed weather forecast information, the sales volume of the agricultural product over the last year or several years, and the sale price.

On the other hand, referring to FIG. 3, a plunge in agricultural product prices (oversupply, increased imports) can be seen as an agricultural damage factor. This is, in order to predict the demand and price of agricultural products to be described later, the amount of imported agricultural products and the price of the imported agricultural products are determined, and the demand and price of the agricultural products determined based on this are compared with the output of the corresponding agricultural products according to FIG. The degree to which the excess and imported products affect the domestic production of the agricultural product may be calculated, and the first weight according to the amount of agricultural damage may be applied to the degree of the price collapse of the estimated agricultural product based on the calculation in the process of FIG. 2.

That is, the embodiment of the present invention calculates the production amount of agricultural products by grafting machine learning technology, and calculates the production amount of the agricultural product again based on the calculated production amount of the agricultural product and the import price and the amount of import of the corresponding agricultural product. It can be calculated accurately.

Now, a method of predicting the demand and price of agricultural products according to an embodiment of the present invention will be described with reference to FIGS. 4 to 5.

Referring to FIG. 4, the agricultural product management server 300 may obtain an expected production amount of agricultural products as described based on FIG. 2.

The agricultural product management server 300 may receive information about the search volume for which the agricultural product was searched for from the media server 400 and related keywords and/or related keywords, etc. I can. In addition, the agricultural product management server 300 may receive information on the number of times and the exposure time of the agricultural product in video media media such as a video reproduction site and a TV medium (S405).

The agricultural product management server 300 may classify a search volume for which a corresponding agricultural product is searched by media, and classify the related search word and/or related keyword by media. In addition, the number of times and times the agricultural product is exposed to the video media medium may be classified by media.

In addition, the agricultural product management server 300 may assign a second weight to each media (S410). For example, the agricultural products management server 300 may assign a second weight to each media based on the influence of video media, SNS, and various portal sites, the number of users, the number of subscribers, the number of views, viewership, and topicality. For example, in a TV program with a topic of ``best'' within the last 1 month (for example, a TV program with an airing time of 1 hour), a dish or recipe using the agricultural product, information on the distribution of the agricultural product is 30 minutes If the exposure is abnormal, the second weight may be assigned the highest second weight among candidate weights.

In particular, when setting the second weight for the related search word and/or the related keyword, the second weight may be set according to the type of the related search word and/or the related keyword. For example, the related search word and/or the related keyword is classified into'very positive','positive','negative' and'very negative', and the second weight is 2 for'very positive' and'positive' A second weight of 1.5 may be given to', a second weight of 0.5 to'negative', and a second weight of 0.25 to'very negative'.

The agricultural product management server 300 applies the second weights assigned to each media to the search volume of the agricultural product, the keyword related to the agricultural product, the number of times and the exposure time of the agricultural product, etc. You can analyze the trend of consumption of

In addition, the agricultural product management server 300 can calculate the expected demand and expected price of agricultural products at a specific time based on the analyzed consumption trend, the expected production amount of the agricultural product, the sales price and sales volume of the agricultural product over the past year or years. Yes (S420). Meanwhile, the estimated price of agricultural products at a specific time may be calculated as shown in FIG. 5.

Meanwhile, the agricultural product management server 300 may further consider the amount of imported agricultural products and the price of the imported agricultural products, as described above, in order to calculate the expected demand and expected price of the agricultural product. At this time, the agricultural product management server 300 may calculate the expected demand and expected price of agricultural products by further taking into account the import amount and the imported agricultural product price of the corresponding agricultural product for the past year or the past several years, and the expected import amount and the import price of the corresponding agricultural product this year. Meanwhile, information on the amount of imported agricultural products and the price of imported agricultural products for the past year or the past several years, and the expected import amount and imported prices of the corresponding agricultural products this year may be stored in the storage module 330 of the agricultural product management server 300. For example, in step S420, the price and demand of agricultural products at a specific point in time may be predicted, and the price and demand to be reduced at a specific point in time may be predicted again by considering the amount and price of imported agricultural products.

At this time, the price and demand of the relevant agricultural product at a specific point in time can be determined based on the amount of imported agricultural products and the price of imported agricultural products in the last year or the last several years as the ratio of the sales volume of imported agricultural products and the sales volume and prices of domestic agricultural products. In addition, taking into account the degree of promotion of domestic agricultural products on the media medium (e.g., topicality of the media, number of users, influence, number of subscribers, views, viewership, exposure time, etc.), the sales volume of the imported agricultural products and the domestic agricultural products After re-adjusting the sales volume ratio and price of, the price and demand of the relevant agricultural product can be predicted based on the readjusted sales volume of imported agricultural products and the sales volume ratio and price of domestic agricultural products.

On the other hand, the agricultural product management server 300 may determine whether or not to ship the agricultural product at a specific time based on the price and demand of the agricultural product predicted in step S420, and to this end, it is possible to obtain information on pests and diseases related to the agricultural product. (S425).

Such disease and pest information may be received from the agricultural product management server 300 and a disease and pest providing server (not shown). The disease and pest information may include the pests that may occur at the present time, the types of the pests, the expected number of individuals, and the types of damage to the agricultural products. In addition, the pest and pest information may include information on when the pest can occur. For example, the information on pests and pests may include possible pests and pests at multiple points in time, the type of pests, the expected number of individuals, and the type of damage to the agricultural product.

In addition, the agricultural product management server 300 may estimate the size and duration of damage to the agricultural product due to the corresponding disease and pest information based on the corresponding disease and pest information. In addition, it is possible to determine a preventive measure for minimizing the size and duration of damage caused by the corresponding disease and pest, and transmit it to the terminal device 200.

The agricultural product management server 300 may determine the shipment timing and quantity of the agricultural product based on the consumption trend of the agricultural product, the predicted production amount of the agricultural product, the sale price and the sales volume of the agricultural product in the previous year or the last several years. That is, the agricultural product management server 300 can determine whether or not to ship the agricultural product at a specific time based on the consumption trend of the agricultural product, the predicted production amount of the agricultural product, the sales price and the sales volume of the agricultural product in the previous year or the last several years. There is (S430). In other words, it is possible to determine the time of shipment of the agricultural product and the amount of shipment at the time of shipment.

At this time, the agricultural product management server 300 may use disease and pest information when determining whether or not to ship the agricultural product and the amount to be shipped. For example, the agricultural product management server 300 may determine a shipment time and a shipment amount at each shipment time so that all agricultural products can be shipped before the pests and diseases occur based on the pest and pest information.

The agricultural product management server 300 may transmit information about the predicted price, demand, and production amount prediction result, and shipment time and shipment amount at each shipment to the terminal device 200.

Meanwhile, the method for predicting demand and price of agricultural products according to an exemplary embodiment of the present invention can be used not only by producers who produce agricultural products, but also by wholesalers and retailers who want to purchase agricultural products. Accordingly, the agricultural product management server 300 may recommend a purchase time and a sale time of the agricultural product based on the price and demand prediction result of the agricultural product for such wholesale and retail traders.

Referring to FIG. 5, for example, the dotted line represents the estimated price of agricultural products at each time point. The agricultural product management server 300 recommends purchasing agricultural products in July and October when the expected price of the agricultural product rises from the low to the high, and recommends selling the agricultural products in August and November when the agricultural product is consumed. can do. In general, since it is highly likely that the expiration date of agricultural products is not long, the agricultural product management server 300 refers to the time when the expected price declines and then rebounds (for example, July and October in FIG. 5) as the time of recommendation for purchasing agricultural products. It may be determined, and a time point (for example, August, November in FIG. 5) that has passed one month from that time point may be determined as a time point for recommending agricultural product sales. The agricultural product management server 300 may transmit information on the purchase time and sale time to the terminal device 200.

Now, a detailed method of acquiring disease and pest related information in step S425 described above will be described.

The agricultural product management server 300 may acquire N first images for a specific part of the agricultural product from an external device or a storage module of the agricultural product management server 300. The agricultural product management server 300 may divide each of the N first images into a plurality of first areas. In this case, the plurality of first regions for each of the N first images may be randomly divided, and thus, the sizes and arrangements of the plurality of first regions for each of the N first images coincide. You may not. In addition, the first images here may be images in which a specific part of the agricultural product has been photographed in a normal state in which no damage is caused by a disease or a pest. The agricultural product management server 300 may extract first color information by analyzing a plurality of first regions for each of the N first images for each region.

The agricultural product management server 300 extracts a vertex of each of the N first images, matches a plurality of corresponding first areas based on the extracted vertices, and calculates an error between the matched first areas. have. In addition, the agricultural product management server 300 may set a plurality of second areas in which the plurality of first areas are reset based on an error between the first areas.

In other words, N first images randomly divided In this case, each of the plurality of second regions may be set to the same size, but may be set to different sizes. In addition, when different sizes are set, it may be difficult to properly analyze color information of images having different resolutions and sizes if the absolute size is set for each of the plurality of second areas.

Accordingly, the agricultural product management server 300 may set a ratio for each of the plurality of second areas, and apply the ratio to each image to classify the plurality of second areas. In addition, the first images and the second images to be described later may be composed of only images having the same resolution and size, which may have the same resolution and size as the third image captured through the camera of the smart farm. In this case, the agricultural product management server 300 may set the plurality of second areas as an absolute size for each of the plurality of second areas based on the resolution and size.

The agricultural product management server 300 may acquire more than N M first images from an external device or a storage module of the agricultural product management server 300. In this case, some or all of the N first images may be included in the M first images.

The agricultural products management server 300 divides each of the M first images into a plurality of second areas based on settings related to a plurality of second areas, and a plurality of second areas of each of the M first images Second color information for each of them may be extracted (S230). The agricultural product management server 300 may store the extracted second color information as information on a specific part of the agricultural product that has not been damaged by a disease or pest, in a storage module.

The agricultural product management server 300 may acquire X second images from an external device or a storage module of the agricultural product management server 300. In this case, the second image may refer to images in which a specific part of the agricultural product has been damaged by a disease or a pest. Further, X may be more or less than M, or may be the same number.

The agricultural product management server 300 may classify each of the X second images into a plurality of second areas based on settings regarding a plurality of second areas. The agricultural product management server 300 may extract third color information for each of the plurality of second areas for each of the X second images. The agricultural products management server 300 may compare the second color information stored in the storage module with the extracted third color information for each second area, and determine one or more second areas having an error range equal to or greater than a predetermined threshold value. In addition, the agricultural product management server 300 may store third color information for one or more second areas in a storage module of the agricultural product management server 300. In this case, the third color information may include an average value, a mode value, and a distribution map for each of the one or more second areas. In other words, the agricultural products management server 300 includes the average color value, mode color value, distribution of color values, etc. for a specific second area for each of the X second images in the third color information, and the agricultural product management server ( 300) can be stored in the storage module.

In addition, when obtaining the X second images, the agricultural product management server 300 may collect disease and pest damage information of each of the X second images together. In this case, the information on the damage of the disease and pest may include the type of the disease and the pest, the number of the number of the disease and the pest, and the degree of damage to agricultural products due to the disease and the pest. When storing the third color information, the agricultural product management server 300 may include the type of the pest, the number of individuals, and the degree of damage to the agricultural product due to the pest. In general, the degree of damage will increase in proportion to the number of pests. In addition, it can be considered that the larger the number of diseases and pests, the wider the range in which the error value is more than a certain value in each second zone.

Accordingly, based on the third color information of one or more second zones, it is possible to determine a range in which a disease or pest has occurred in each second zone. And, based on the one or more second zones included in one second image and the third color information of the one or more second zones, the number of individuals and the degree of damage may be estimated according to the extent of the occurrence of the disease. .

For example, based on the third color information of one or more second zones, a portion with an error range of a certain or more in one or more second zones (that is, a portion that is determined to have occurred) is a whole second image. If the error range shown in is 40% of the portion that is more than a certain amount, it can be considered that 40% of the number of pests and the degree of damage in the corresponding second image occurred in the specific one or more second zones. Therefore, when storing the third color information of one or more specific second zones, the agricultural product management server 300 may store the number of diseases and pests and the degree of damage together based on the above description.

For example, if the number of pests and pests occurring in the entire second image is 100,000, it is estimated that the number of pests and pests occurring in the specific one or more second zones is 40,000, and When storing the third color information for the area, the number of the number of the pests and the type of pests may be linked together and stored.

The agricultural product management server 300 may receive a third image in which a specific part of the agricultural product is photographed through a camera installed in the smart farm. The agricultural product management server 300 may divide the third image into a plurality of second areas based on settings regarding a plurality of second areas.

The agricultural product management server 300 extracts fourth color information for each of the plurality of second areas of the third image, and converts the fourth color information to third color information stored in a storage module of the agricultural product management server 300. In comparison, it can be determined whether a disease or a pest has occurred in a specific part of the agricultural product included in the third image. At this time, when the fourth color information for each of the plurality of second zones has a matching rate of a certain or more for any one of the color values included in the third color information, the agricultural product management server 300 It can be determined that the number of corresponding pests and the degree of damage occurred in the second area of the third image.

On the other hand, when determining the degree of damage caused by pests and pests to a specific part of the agricultural product through the third image, among the plurality of second areas of the third image, the degree to which pests and diseases appear to have occurred within the area is less than 5%. Even if the second zones have a matching rate of more than a certain level with the third color information, they can be excluded from determining the number of pests and the degree of damage. Therefore, it is possible to determine the degree of damage caused by pests and pests in a specific area of the agricultural product based on the number of second areas with a degree of 5% or more, among the plurality of second areas of the third image. have.

At this time, the agricultural product management server 300 may determine what kind of disease and pest is the disease and the type of the disease in order to accurately determine the degree of damage caused by the disease and the pest for a specific part of the agricultural product.

However, the third color information is stored for a plurality of diseases and pests, and the agricultural product management server 300 extracts the fourth color information for each of the plurality of second areas of the third image and compares it with the third color information. In this case, when the third color information for which the matching rate is higher than a certain level is for a plurality of pests, it may be difficult to determine which pest is causing damage to a specific part of the agricultural product.

In this case, the agricultural product management server 300 may select a plurality of pests corresponding to the third color information for which the matching rate is greater than or equal to a predetermined value as candidate pests. The agricultural product management server 300 may select one of candidate diseases and pests based on the date and time when the third image was captured, the temperature and humidity of the smart farm at the corresponding date and time, and the soil component.

The agricultural product management server 300 compares the third color information on the selected candidate pests with the extracted fourth color information again, and estimates the number of individuals and the degree of damage of the pests occurring in a specific part of the agricultural product in the same manner as described above. , This information may be transmitted to the terminal device 200.

Now, a method for cultivating agricultural products using Internet of Things (IoT) technology will be described using information on agricultural products obtained by the above-described methods.

The agricultural product management server 300 may extract information on agricultural products being cultivated in the smart farm 400. In this case, the information on the agricultural product may be information on the variety of the agricultural product and the growing environment optimized for the agricultural product (eg, temperature and humidity, illuminance, contrast, lighting color, necessary pesticides, plant hormones, etc.).

In addition, the agricultural product management server 300 may set a target production amount for each variety and size of agricultural products grown in the smart farm 400. Here, although there is only one kind of agricultural product that can be produced in the smart farm 400, there may be various varieties of the agricultural product. For example, even if the agricultural product produced by the smart farm 400 is'strawberry', the smart farm 400 can grow various varieties such as'Maehyang','Yeobong','Red Pearl' and'Akihime'. I can.

Accordingly, the agricultural product management server 300 may set a target production amount for each variety of agricultural products grown in the smart farm 400. In addition, since the size of agricultural products (for example,'large','medium','small') may affect the sales volume and sales price, a target production amount according to the size of the agricultural product can be set. In other words, the agricultural product management server 300 may set a target production amount for each variety and/or size of agricultural products grown in the smart farm 400.

The agricultural product management server 300 measures the production environment of the smart farm 400 through sensors installed in the smart farm 400 in order to cultivate agricultural products according to the target production amount for each variety and/or size of agricultural products (S215), In order to produce agricultural products that meet the target production amount for each variety and/or size of agricultural products, the production environment of the smart farm 400 may be controlled (S220). In addition, the agricultural product management server 300 can transmit the production environment and the controlled production environment of the smart farm 400 in real time to the terminal device 200 in real time, such as the target size/target production rate for each type of agricultural product, and the expected achievement rate. have.

For example, temperature and humidity sensors may be installed on the smart farm 400. In addition, the agricultural product management server 300 may measure the temperature and humidity in the smart farm 400 through the temperature and humidity sensor. In addition, the agricultural product management server 300 sets a target temperature and humidity for cultivating agricultural products through information on agricultural products, and controls the temperature of the smart farm 400 through a lighting device and a heating device installed in the smart farm 400. And control the humidity of the smart farm 400 through a humidification device.

For example, a CO ₂ sensor may be installed in the smart farm 400. And, the agricultural product management server 300 may measure the concentration of CO _{2 in the} smart farm 400 through the CO ₂ sensor. In addition, the agricultural product management server 300 may set a CO ₂ concentration suitable for cultivation of agricultural products through information on agricultural products. In addition, when the agricultural product management server 300 determines that the CO ₂ concentration in the smart farm 400 is greater than or equal to the set CO ₂ concentration, the smart farm 400 through an oxygen supply device or a CO ₂ control device installed in the smart farm 400 The concentration of CO ₂ in it can be adjusted.

For example, a soil sensor may be installed on the smart farm 400. The agricultural product management server 300 may acquire soil information of a cultivation area in which each soil sensor is installed through a soil sensor. In addition, the agricultural product management server 300 enables nutrients to be provided to a cultivation area in which a corresponding soil sensor is installed based on soil information.

At this time, in order to provide nutrients, pesticides, nutrients, and/or plant hormones may be targeted to the cultivation area and sprayed through a composition sprayer installed under the ceiling of the smart farm 400.

Here, the plant hormone is a hormone for promoting/inhibiting the growth/differentiation of agricultural products. For example, agricultural growth hormones may include auxin and gibberellin, and differentiation hormones may include cytokinin and florigen. In addition, the growth inhibitory hormone may include ethylene and apsis acid.

Meanwhile, the spraying of the plant hormone may be sprayed by controlling the concentration of the plant hormone according to the size of the agricultural product and the number of flowers included in each cultivation area of the agricultural product.

Now, we will explain how to measure the size of agricultural products and the number of flowers. A plurality of cameras can be installed in each cultivation area. At this time, each camera is installed at a predetermined interval from a portion where agricultural products are grown, and a predetermined interval may be initially set. When the camera shoots agricultural products, two measuring lines can be made up and down based on the center line of the camera. At this time, a line is drawn based on the intersection point where each measurement line meets the agricultural product, and the length from the point where the line and the center line meet to the camera can be measured.

And, by using the measured length and the angle between the camera and each measurement line, it is possible to measure the vertical length of agricultural products.

In addition, each camera may photograph an agricultural product at a first viewpoint, generate a first image, and measure and store distances between both end points of the agricultural product and the camera at the first viewpoint. Then, according to the passage of time, a second image is generated by photographing agricultural products again at the second viewpoint. In addition, the difference between the ratio of agricultural products in the first image and the ratio of agricultural products in the second image is calculated, and the circumference length of the agricultural product based on the difference value and distances between both end points of the agricultural product and the camera at the first time point. Can be measured.

In other words, if each camera is continuously photographing agricultural products from the same point of view over time, each camera will generate an image of the same area at the same point of time. As the size of the agricultural product increases, the proportion of agricultural products in the image It will increase. Therefore, if you know the size of agricultural products at a specific point in the past, and measure the difference in the ratio of agricultural products in the images after time passes, the size of the agricultural products at the second point in time can be measured. In addition, the number and size of flowers of agricultural products can be measured through images captured by each camera.

The agricultural product management server 300 determines the amount and concentration of plant hormones to be sprayed into each cultivation area based on the target production amount for each kind/size of agricultural products and the size and number of flowers of the agricultural product obtained through the method of FIG. Can be adjusted. For example, usually spraying with the concentrations of giverillin and cytokinin at a ratio of 50:50, and if the flower is more than the first value based on the current state, the concentration of giverelin and cytokinin is reduced to 80:20. You can spray according to the ratio of. In addition, when the flower is less than the second value, the concentration of giverelin and cytokinin may be adjusted in a ratio of 30:70 to spray. At this time, the first value and the second value may be set by the agricultural product management server 300 based on a target production amount for each variety/size of agricultural products and a time point in the growth cycle of the agricultural product.

Meanwhile, when the size of the agricultural product measured in each cultivation area grows to a third value or more, in order to slow the growth rate of the agricultural product, the agricultural product management server 300 sprays apsis acid and/or ethylene into the cultivation area at a certain concentration. can do. Here, the third value may be set by the agricultural product management server 300 based on the target production amount for each variety/size and where the current time point in the growth cycle of the agricultural product corresponds.

Meanwhile, a lighting device for irradiating light on agricultural products may be installed in the smart farm 400. Regardless of the day or night, by providing the optimal contrast, illumination, and light color for the agricultural product, the environment in which agricultural products can grow best can be created.

To this end, the agricultural product management server 300 may acquire target illumination and light color for each time corresponding to the agricultural product based on information on the agricultural product, and control the lighting device based on the current time, target illumination and light color.

Specifically, the lighting device installed in the smart farm 400 may include a light emitting light, a light color adjusting device for adjusting the light color of the emitted light, and an opening and closing device for adjusting the contrast. The light color adjusting device is installed under the light, and the opening and closing device is installed under the light color adjusting device.

In this case, the illumination may emit white or daylight color. The light color control device is composed of a plurality of light color plates made of various colors. The agricultural product management server 300 controls the light color plate having a light color closest to the light color corresponding to the agricultural product from among the plurality of light color plates to be positioned directly below the lighting, and the remaining light color plates are emitted from the lighting. It is controlled to be located at a distance that does not affect the light, so that the light color of the lighting can be controlled.

On the other hand, the light color closest to the light color corresponding to agricultural products may be determined based on the RGB value. That is, the agricultural product management server 300 acquires information about the light color by time from the information on the agricultural product as an RGB value, and locates the light color plate having the RGB value closest to the RGB value of the information on the agricultural product under the lighting. Can be controlled to do.

On the other hand, recently, when a specific music is played and played to agricultural products, there are many research results on experiments in which the growth of the agricultural product increases rapidly. Therefore, the agricultural product management server 300 can reproduce sound waves such as music having a specific frequency or white noise through the speaker installed in the smart farm 400,

For example, the agricultural product management server 300 may obtain information on a target frequency and time for promoting the growth of the agricultural product from information on the agricultural product. The agricultural products management server 300 selects the sound wave closest to the target frequency among a plurality of sound wave information including previously stored music and white noise, and the selected closest sound wave at the time acquired from the information on the agricultural product. Can be controlled to be played through the speaker. On the other hand, the agricultural product management server 300 is outside the audible frequency range that is inaudible to the human ear, but if there is a frequency that helps to promote the growth of the agricultural product, the frequency of the sound wave reproduced from the speaker is used to promote the growth of the agricultural product. You can adjust the frequency of the sound wave to match the frequency that helps.

Agricultural products grown inside the smart farm 400 may be planted and produced in a plurality of cultivation areas. In this case, the plurality of cultivation areas may be movable. The plurality of cultivation areas may have a sufficient depth of soil for the products to be grown.

For example, in order to cultivate'strawberries', the depth of the soil must be '25 cm' or more, and thus, the depth of the cultivation area may be at least 25 cm or more, preferably 30 cm or more.

In addition, the plurality of cultivation areas may have different areas. However, in order to facilitate the movement and to have the same distance between the horizontal rails, the vertical lengths of the plurality of cultivation regions are the same, but the areas of the plurality of cultivation regions may be different from each other by adjusting only the horizontal length.

On the other hand, agricultural products can be planted in different cultivation areas according to the variety and target size. For example, it is possible to plant so that those with the same variety and target size of agricultural products can be cultivated in the same cultivation area. In addition, agricultural products with a large variety and size of target production can be planted in the largest cultivation area, but if the target production of agricultural products is not met even if planted in the largest cultivation area, the remaining target production is planted in the small cultivation area. I can. In other words, the varieties of agricultural products planted in each cultivation area may be distributed based on a target size and a target production amount.

Meanwhile, a plurality of cultivation areas may be installed on the horizontal moving rail for each line. In other words, each cultivation area may move in a horizontal direction (for example, in a horizontal direction) according to the movement of a horizontal moving rail in which each cultivation area is installed.

On the other hand, when the cultivation area moves in the rightmost direction according to the horizontal movement rail and is moved above the vertical movement rail, it can be moved in the vertical direction (eg, upward and downward) according to the movement of the vertical movement rail. By controlling the lateral movement rail and the vertical movement rail, each cultivation area can move to a different position. At this time, the horizontal movement rails and the vertical movement rail may be moved under the control of the agricultural product management server 300. That is, the agricultural product management server 300 controls the horizontal and vertical rails to move, so that each cultivation area can be moved.

For example, the cultivation area located in the line of the first horizontal movable rail may move to the vertical movable rail according to the movement of the horizontal movable rail, and then move to the line of the second horizontal movable rail according to the movement of the vertical movable rail. In addition, it is possible to move to a specific position of the second horizontal movable rail according to the movement of the second lateral movable rail.

Through the movement of the above-described cultivation area, agricultural products can be cultivated to meet the target size and target production amount according to the variety of agricultural products. For example, in view of the current growth status of the varieties of agricultural products cultivated in the first cultivation area, it is expected that the target size and/or the target production will not be achieved, whereas the varieties of agricultural products cultivated in the second cultivation area are the target. If the size and/or the target production amount is expected to be exceeded, the agricultural product management server 300 controls the horizontal and vertical rails to move so that the positions of the first and second cultivation areas can be changed. I can.

Through this, by continuously moving the cultivation area to a location with an appropriate environment so that agricultural products that can meet the target size and/or target production can be produced according to the target size and/or target production amount for each agricultural product variety, It can have the effect of producing agricultural products.

Meanwhile, the agricultural product management server 300 may be linked with a plurality of smart farms 400. In this case, the agricultural product management server 300 categorizes the above-described agricultural products by target production amount and/or target size for each variety, and a growth process of agricultural products produced in a plurality of smart farms 400 (for example, It is possible to collect data on the size and cultivation of agricultural products), and control the cultivation of agricultural products produced in the entire plurality of smart farms 400 through this.

For example, when there are a plurality of smart farms 400 producing'strawberries', data on the cultivation status of'strawberries' grown in the plurality of smart farms 400 are received in real time through IoT sensors. And, based on these data, the size and/or production amount of each variety of'strawberries' produced in each of the plurality of smart farms 400 is adjusted, and accordingly, the agricultural product management server 300 is configured to provide a plurality of smart farms ( 400) It is possible to control the temperature and humidity, CO ₂ concentration, illuminance, light color, composition components such as pesticides, and/or sound waves for each of them.

Through this, it is possible to maximize the production efficiency and profit efficiency of a plurality of smart farms 400, and there is a situation in which the production amount is inevitably reduced due to unexpected device failure, disease and pest occurrence in any one smart farm 400. Even when it occurs, the overall production amount of agricultural products produced in the plurality of smart farms 400 can be kept constant, so that stable supply of agricultural products and stable price maintenance can be achieved.

6 to 8 are block diagrams showing the configuration of an apparatus for implementing the embodiment of the present invention.

6 is a block diagram illustrating a configuration of a terminal device 200 for implementing the above-described embodiment of the present invention.

Referring to FIG. 6, the terminal device 200 may include a control module 210, a communication module 220, a storage module 230, an output module 240, an input module 250, and the like.

The input module 250 receives various information such as number and character information, and transmits a signal input in connection with setting various functions and controlling the functions of the terminal device 200 to the control module 250. In addition, the input module 250 may include at least one of a keypad and a touch pad that generate an input signal according to a user's touch or manipulation. In this case, the input module 250 is configured in the form of a single touch panel (or touch screen) together with the output module 240 to simultaneously perform input and display functions. In addition, the input module 250 may use all types of input means that may be developed in the future, in addition to input devices such as a keyboard, keypad, mouse, and joystick. Particularly, the input module 250 according to the present invention senses input information input from a user and transmits it to the control module 210.

The output module 240 displays information about a series of operation states and operation results that occur while performing a function of the terminal device 200. In addition, the output module 240 may display a menu of the terminal device 200 and user data input by a user. Here, the output module 240 is a liquid crystal display (LCD), an ultra-thin liquid crystal display (TFT-LCD, Thin Film Transistor LCD), a light emitting diode (LED), an organic light emitting diode (OLED). OrganicLED), an active organic light emitting diode (AMOLED, Active Matrix OLED), a retina display, a flexible display, and a three-dimensional display. In this case, when the output module 330 is configured in the form of a touch screen, the output module 240 may perform some or all of the functions of the input module 250.

The storage module 230 is a device for storing data, includes a main memory device and an auxiliary memory device, and stores an application program required for functional operation of the terminal device 200. The storage module 230 may largely include a program area and a data area. Here, when the terminal device 200 activates each function in response to a user's request, the terminal device 200 provides each function by executing the corresponding application programs under the control of the control module 210.

The communication module 220 may transmit and receive data with the agricultural product management server 300 through the communication network 100. In addition, the communication module 220 is an RF transmission means for up-converting and amplifying a frequency of a transmitted signal, an RF receiving means for low-noise amplifying and down-converting a received signal, and for processing a communication protocol according to a specific communication method. And data processing means. The communication module 220 may include at least one of a wireless communication module (not shown) and a wired communication module (not shown). In addition, the wireless communication module is a configuration for transmitting and receiving data according to a wireless communication method, and when the terminal device 200 uses wireless communication, any one of a wireless network communication module, a wireless LAN communication module, and a wireless fan communication module is used. Accordingly, data can be transmitted and received with the agricultural product management server 300. Here, the communication module 220 may include a plurality of communication modules. When a plurality of communication modules are included in the communication module 220, one communication module may perform communication in a Personal Area Network (PAN) method including Bluetooth.

In addition, the other communication module may communicate with the agricultural product management server 300 through the communication network 100. Here, the other communication module may use a wireless communication method such as WLAN (Wireless LAN), Wi-Fi, Wibro, Wimax, and High Speed Downlink Packet Access (HSDPA).

The control module 210 may be an operating system (OS) and a process device that drives each component.

Accordingly, the control module 210 of the terminal device 200 controls the transmission of the signal received through the input module 250 to the agricultural products management server 300 through the communication module 220, and controls the communication module 220 The beacon, Wi-Fi, a base station signal received through, or information transmitted from the agricultural products management server 300 may be controlled to be exposed through the output module 240.

In addition, the control module 210 of the terminal device 200 may control the overall operation process of the terminal device 200 according to an embodiment of the present invention. In other words, the entire operation process of the terminal device 200 according to the embodiments of the present invention described above based on FIGS. 1 to 5 may be controlled by the control module 210.

7 is a block diagram showing the configuration of an agricultural product management server 300 for implementing embodiments of the present invention.

Referring to FIG. 7, the agricultural product management server 300 may include a control module 310, a communication module 320, and a storage module 330.

The communication module 320 is for communicating with the terminal device 200, the media server 400, and the weather prediction server 500, and the communication network for performing the function of the communication module 320 is WLAN (Wireless LAN) and Wi-Fi. It is preferable to use wireless communication methods such as (Wi-Fi), Wibro, Wimax, and High Speed Downlink Packet Access (HSDPA), but is not limited thereto, and Ethernet according to the system implementation method. , xDSL (ADSL, VDSL), HFC (Hybrid Fiber Coaxial Cable), FTTC (Fiber to The Curb), FTTH (Fiber To The Home) can also be used.

The storage module 330 is a device for storing data, includes a main memory device and an auxiliary memory device, and stores an application program required for functional operation of the agricultural product management server 300. The storage module 330 may largely include a program area and a data area. Here, when the agricultural products management server 300 activates each function in response to the request of the terminal device 200, the agricultural products management server 300 executes the corresponding application programs under the control of the control module 310 to provide each function.

The control module 310 may be an operating system (OS) and a process device that drives each component.

Accordingly, the control module 310 of the agricultural product management server 300 may control the overall operation process of the agricultural product management server 300 according to an embodiment of the present invention. In other words, the overall operation process of the agricultural product management server 300 according to the embodiments of the present invention described above based on FIGS. 1 to 5 may be controlled by the control module 310.

In particular, referring to FIG. 8, the control module 310 of the agricultural product management server 300 may include a data collection unit 311, a model construction unit 313, and a service providing unit 315.

The data collection unit 311, from the media server 400, the weather prediction server 500, and the storage module 330, collects various types of data necessary to perform the above-described embodiments of the present invention based on FIGS. To collect. The data collection unit 311 may interwork with the communication module 320 in order to collect data from external devices such as the media server 400 and the weather prediction server 500.

The model construction unit 313 processes and analyzes the data collected by the data collection unit 311 to provide consumption trends, prices, demands, production volumes, purchase/sell times, shipment times/shipments, etc. Based on 5, statistics and estimation information necessary to perform the above-described embodiments of the present invention are generated.

The service providing unit 315 is interlocked with the communication module 320 to transmit statistics and estimation information generated by the model building unit 313 to the terminal device 200.

As explained above, the specification includes details of a number of specific implementations, but these should not be construed as limiting to the scope of any invention or claimable, but rather, which may be specific to a particular embodiment of a particular invention. It should be understood as a description of features. Certain features described herein in the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination. Furthermore, although features operate in a particular combination and may be initially described as so claimed, one or more features from a claimed combination may in some cases be excluded from the combination, and the claimed combination may be a subcombination. Or sub-combination variations.

Likewise, although operations are depicted in the drawings in a specific order, it should not be understood that such operations must be performed in that particular order or sequential order shown, or that all illustrated operations must be performed in order to obtain a desired result. In certain cases, multitasking and parallel processing can be advantageous. In addition, separation of the various system components of the above-described embodiments should not be understood as requiring such separation in all embodiments, and the program components and systems described are generally integrated together into a single software product or packaged in multiple software products. You should understand that you can.

Specific embodiments of the subject matter described herein have been described. Other embodiments are within the scope of the following claims. For example, the operations recited in the claims may be performed in a different order while still achieving desirable results. As an example, the process depicted in the accompanying drawings does not necessarily require that particular depicted order or sequential order to obtain desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

The present description presents the best mode of the present invention, and provides examples for describing the present invention and for enabling those skilled in the art to make and use the present invention. The thus written specification does not limit the present invention to the specific terms presented. Accordingly, although the present invention has been described in detail with reference to the above-described examples, those of ordinary skill in the art can make modifications, changes, and modifications to these examples without departing from the scope of the present invention.

Therefore, the scope of the present invention should not be determined by the described embodiments, but should be determined by the claims.

The present invention relates to a method for collecting public agricultural data based on big data and an apparatus therefor, and more particularly, using an algorithm optimized for public data related to agriculture such as weather forecast data, natural disaster data, and pest data. It relates to a method for collecting and analyzing the collected public data, and an apparatus therefor.

According to the present invention, efficient agricultural product production is possible by predicting the demand for the agricultural product using public agricultural data collected based on big data, estimating the required amount of agricultural product production, and establishing an agricultural plan accordingly.

Therefore, the present invention can contribute to the development of the overall agricultural and fishery industry through a method for predicting demand for agricultural products based on big data and a device for the same, and because the possibility of marketing or sales is sufficient, as well as a degree that can be implemented clearly There is a possibility.

100: communication network 200: terminal device 300: agricultural product management server 400: media server 500: weather prediction server

Claims (10)

In the method for the agricultural product management server to predict the demand for agricultural products based on big data,
Receive weather prediction information related to a plurality of agricultural damage factors during a first period after the current point from the weather prediction server,
Obtaining information on the sales volume, sales price and the plurality of agricultural damage factors of the agricultural product from the present point to the second period,
By acquiring the search amount of the agricultural product and the keyword related to the agricultural product from a media server, analyzing the consumption trend of the agricultural product,
Predicting the price and demand of the agricultural product at a point in time after the third period, based on the weather forecast information, the information on the agricultural damage factors, and the consumption trend of the agricultural product,
Analyzing the consumption trend of the agricultural products,
Assigning different weights to each of the media media based on the search for the agricultural product or acquiring the keyword related to the agricultural product,
Including analyzing the consumption trend of the agricultural products based on the different weights,
Acquire information on diseases and pests related to the agricultural products,
Based on the disease and pest information, estimate the size and duration of damage to the agricultural products due to the disease and pest related to the disease and pest information,
Further comprising determining a preventive measure for minimizing the scale and duration of damage caused by the disease and pest,
The disease and pest information includes the type of the disease and the pest, the number of predicted individuals of the disease and the pest, and the type of damage caused by the disease and the agricultural product,
Determine the purchase time of the agricultural product and the sale of the agricultural product based on the price and demand of the agricultural product,
Predicting the price and demand of the agricultural product,
Further consider the amount of imported agricultural products and the price of the imported agricultural products,
The plurality of agricultural damage factors include damage factors related to feng shui, flooding, wildfire, heatwave and drought and damage factors related to wild animals,
Acquiring the scale of agricultural damage by each of the plurality of agricultural damage factors before the second period,
Different weights are set for each of the plurality of agricultural damage factors based on the agricultural damage scale,
Further comprising predicting the production amount of the agricultural product based on the different weights,
The scale of agricultural damage includes information on the amount of money in units of billions for each of the plurality of agricultural damage factors,
Among the plurality of agricultural damage factors, the same weights are set for the agricultural damage factors in which the difference between the amount information according to the amount of farm damage is less than a predetermined value,
If there are three or more agricultural damage factors set with the same weight, and the difference between the agricultural damage factor having the lowest amount information and the agricultural damage factor having the highest amount information is more than the predetermined value, agricultural damage having the highest amount information The weight for the element is set to 1.1 times the same weight,
Disease and pest information related to the above agricultural products,
Acquiring a plurality of first images of a specific part of the agricultural product,
Based on the plurality of first images, a plurality of second images including first color information of the specific region when the agricultural product is not damaged by disease and pests, and that the specific region of the agricultural product has been damaged by disease or insect damage Is obtained, extracts second color information of the specific region included in the plurality of second images, compares the first color information and the second color information, and stores image values when a disease or pest has occurred And, obtained based on the comparison of the third image received through the camera installed in the farm with the image value,
How to forecast agricultural demand. delete delete The method of claim 1,
Predicting the price and demand of the agricultural product,
Including determining whether or not to ship the agricultural product after the third period and the amount of shipment based on the consumption trend of the agricultural product, the predicted production amount, and the sales price and sales amount of the agricultural product before the second period,
How to forecast agricultural demand. delete delete delete delete delete delete

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