KR20210023216A - System and method for farm service based on big data - Google Patents

Abstract

According to a first aspect of the present invention, a big-data based system for a farm service comprises: an information storage unit storing essential information inputted by a user, who needs to use the farm, and a farmer, who supports the user using each terminal, request information requested by the user and farmer, and evaluation information on farms and travel destinations; a type classification unit forming at least one user group having a common type of multiple pieces of detailed farm information corresponding to the essential information and the request information among the users and at least one farm group having a common type of multiple pieces of detailed farm information corresponding to the farm information among the farm owners using the essential information and requested information; a type recommendation unit recommending the user and farmer using request information of the user and the farm detailed information type of the farmers when the user requests the farm; a farm selection unit providing information on farm recommended based on a recommendation rate calculated based on the degree of agreement between the request information according to the priority of the user, who requested the farm owner, and the detailed farm type information of the farm owners and satisfaction; and a travel course recommendation unit providing local travel information and creating and recommending a travel course by using travel request information according to the priority requested by the user based on the selected farm.

Description

Big data-based farm service system and method {SYSTEM AND METHOD FOR FARM SERVICE BASED ON BIG DATA}

The present invention relates to a big data-based farm service system and method, specifically, when a user wants a weekend farm or farm experience, a farmer is recommended to the user by reflecting the location information and farm information selected by the user and the farmer. , By analyzing the request and farm information according to the user's priority and recommending the optimal farm, it is possible to improve the satisfaction of users and farmers, and to recommend surrounding tourist information and ideal travel courses to users who want to use the farm. It relates to a big data-based farm service system and method.

The farm service includes a weekend farm that sells land to users who need a farm, an experience farm that provides experiences such as cultivation, care, harvesting, sowing, and threshing, and a stay-type weekend farm where you can experience farming while staying. Includes Kleingarten.

There are not many ways to effectively promote or advertise weekend farms for weekend farms, except for low-cost weekend farms operated by agricultural technology centers, agricultural cooperatives, and local governments, and weekend farms are mainly introduced on the homepages of local governments.

Although the farm experience is also in high demand, the route through which detailed information can be communicated or known is limited.

Therefore, it is mainly made up of daycare centers and kindergartens, which mainly do farm experience, but it is true that there is a personal demand, but it is difficult to personally visit the farm and experience it.

Importantly, although the government or local governments have attempted to revitalize weekend farms through urban and rural connection projects, actual publicity seems to be insufficient.

Even in public data portals, it is difficult to find information on weekend farms so that the state of weekend farms nationwide cannot be found.

Although the convergence tourism service industry has recently attracted attention based on the 6th industry, information to provide such services is limited, and even though experiential tourism is increasing, rural tourism is less than 1.4% of the total domestic tourism demand. little.

KR 10-1282743 B1 (2013.07.05.announcement)

The present invention was conceived to solve various conventional problems as described above, and when a user wants to experience a weekend farm or a farm, a farmer is recommended to the user by reflecting the location information and farm information selected by the user and the farmer. However, it is possible to improve the satisfaction of users and farmers by analyzing requests and farm information according to the user's priority and recommending the best farm, and recommending surrounding tourist information and ideal travel courses to users who want to use the farm. Its purpose is to provide a farm service system and method based on big data that can be used.

Another object of the present invention is to improve the reliability between users and farmers by effectively connecting users and farmers with matching detailed information types through a type recommendation unit, and to improve the quality of service by recommending an ideal travel course to users through a tour course recommendation unit. It is to provide a farm service system and method based on big data that can be improved.

In order to achieve the above objects, the big data-based farm service system according to the first aspect of the present invention includes essential information input by a user who needs to use a farm and a farmer supporting the user using each terminal, An information storage unit for storing request information requested from the user and the farm owner, and evaluation information on farms and travel destinations; At least one user group in which the types of the essential information and the plurality of detailed farm information corresponding to the request information are common among the users using the essential information and the request information, and a plurality of details corresponding to the farm information among the farm owners A type classification unit for forming at least one farm group in which types of farm information are common; When the user requests a farm, a type recommendation unit recommending the user and the farm owner using the request information of the user and the farm detailed information type of the farm owners; Among the farm owners recommended by the type recommendation unit, it is recommended based on a recommendation rate calculated based on the degree of agreement and satisfaction between the request information according to the priority of the user who requested the farmer and the corresponding detailed farm type information A farm selection unit that provides information on the farm and allows the user to select a farm; And a travel course recommendation unit that provides local travel information using travel request information according to the priority requested by the user based on the selected farm, and generates and recommends a travel course, wherein the essential information input by the farm user is It includes age information, gender information, and residence location, and the farm information of the user and the farmer includes at least one farm information, and the at least one farm information includes cost, scenery, agricultural equipment, toilet, water supply facility, parking lot, Includes at least one of access road, farm operation information, and evaluation information, and the travel request information includes at least one travel request information, and the at least one travel request information includes travel period, keyword, theme, transportation, and evaluation information. It characterized in that it includes at least one.

At this time, the farm information includes any one of cost information, location, scenery, toilet, parking facility, agricultural equipment, water supply facility information, and the type recommendation unit, when the user requests the farm of the farmer, the user and the farmer By comparing the types of and reflecting the farm with a high recommendation rate, the farm owner is recommended, and the recommendation result recommended by the type recommendation unit is changed according to the farm information and request information of the user and the farm owner.

In addition, the information storage unit may include a storage module for storing essential information, request information, and travel request information of the user and the farmer; An evaluation module for evaluating reviews and satisfaction for the use of the farm after the user uses the farm, and for evaluating reviews and satisfaction for the use of tourist attractions and restaurants after traveling to nearby tourist destinations; And a public data module connected to the Korea Tourism Organization server, the local government server, and the public application program map server to provide metadata.

In addition, the type classification unit may include a detailed information type generation module configured to set a range value for each of the detailed farm information and generate detailed farm information types for each of the range values; The detailed information of the request information entered by the user is extracted from the detailed information types of the users that are included in the detailed information types generated by the farm detailed information type generation module, and classified among users whose extracted user detailed information types all match, A user detailed information classification module for classifying users with matching essential information of the users; And the detailed information of the farm information input by the farm owner extracts the detailed information type of the farm owner included among the detailed information types generated by the detailed information type generation module, and classifies the farm owners in which all the extracted farm owner detailed information types match. It includes; a farmer detailed information classification module for classifying the farm owners matching the essential information of the farmers as a target.

In addition, the type recommendation unit may include: a detailed information type recommendation module for comparing and recommending request information according to the user's priority and the detailed farm information type of the farmer; A recommendation rate calculation module for calculating a detailed recommendation rate recommended by the detailed information type recommendation unit; A recommendation point calculation module for calculating a recommendation point by multiplying a recommendation rate for each detailed information type calculated by the recommendation rate calculation module and a weight for each preset detailed information type through a recommendation rate according to a preset priority for each information type; And a farm recommendation module for recommending a farm according to the recommendation point calculation result.

In addition, the farm selection unit, a farm information providing module for providing information on the farm owner and the farm recommended by the type recommendation unit; An evaluation information providing module that provides pre-evaluation information on the recommended farm owner and farm; And a selection module through which a user can select a farm based on the information.

In addition, the travel course recommendation unit may include a filtering module for sorting according to priority based on request information requested from the user's terminal; A travel course generation module that calculates a travel time and generates a travel course; And a travel course recommendation module for recommending the generated travel course to the user.

On the other hand, the big data-based farm service method according to the second aspect of the present invention includes essential information input by a user who needs a farm and the farm owner using each terminal, request information requested from the user and the farm owner, and farm evaluation information. , An information storage step of storing the travel request information and the travel evaluation information requested by the user in the information storage unit; Using the essential information and request information (farm information) stored in the information storage step, at least one group of farmers in which the essential information and a plurality of detailed information types corresponding to the request information are common among the users, is assigned to a type classification unit. Type classification step formed by; A type recommendation step of recommending the user and the farm owner by a type recommendation unit using the request information requested by the user according to the priority and the farm detailed information type of the farm owner when the user requests the farm of the farmer; Among the farm owners recommended by the type recommendation unit, the farm owner and the farm recommended based on the recommendation rate calculated based on the degree of correspondence between the request information according to the user priority requesting the farm owner and the detailed type information of the corresponding farm owners. A farm selection step in which a user receives and selects farm information by a farm selection unit based on the information on the farm; And a travel course recommendation step of providing surrounding tourism information by a travel course recommendation unit based on the travel request information and the farm inputted by the user through the terminal, generating and recommending a travel course; and, the essential information of the user It includes age information, gender information, and residence location, and the farm information of the user and the farmer includes at least one detailed information, and the at least one detailed information (farm information) includes location information, scenery, toilets, parking facilities, It is characterized by including at least one of agricultural equipment, water supply facility information, and evaluation information.

In this case, in the type classification step of generating the at least one farm owner group, the farm detailed information type generation module sets a range value for each detailed information of the farm information, and generates a detailed information type for each range value. step; The user detailed information classification module extracts the user detailed information type including the detailed information of the request information according to the priority entered by the user among the detailed information types generated by the farm detailed information type generation module, and the extracted user detailed information A user detailed information classification step of classifying users whose types all match, but classifying users whose essential information matches the targets; And the farmer's detailed information classification module extracts the farmer's detailed information type included among the detailed information types generated by the detailed information type generation module in which the detailed information of the farm information input by the farmer, and the extracted farmer's detailed information types all match. It includes; but classified between the farm owners, the farmer detailed information classification step of classifying the farm owners matching the essential information of the farm owners as a target.

In addition, the type recommendation step of recommending the farm owner to the user may include a detailed information type recommendation step in which the detailed information type recommendation module recommends the user's detailed information type and the farmer's detailed information type to each other; A recommendation rate calculation step of calculating, by a recommendation rate calculator, a recommendation rate based on a preset recommendation rate for each detailed information type generated by the detailed information type recommendation module; A recommendation point calculation step in which a recommendation point calculation module calculates a recommendation point by multiplying a recommendation rate for each detailed information type calculated by the recommendation rate calculation module and a weight for each preset detailed information type; And a farm recommendation step of recommending a farm owner according to the calculation result; and the farm recommendation step of recommending to the user terminal includes essential information and request of the farm owner whose recommendation point calculated by the recommendation point calculation module is greater than a preset point. It is done to provide information.

에 의해 이루어지며, 상기 수학식에서, T는 이용자에 의해 입력된 여행기간, N_p는 관심 여행지 총 수, P_place(i)는 i번째 여행지 관람시간, T_time(i)은 i번째 여행지 이동 거리에 대한 이동시간을 나타내는 것을 특징으로 한다.In addition, the travel course recommendation step of recommending the travel course to the user may include receiving metadata from a Korea Tourism Organization server, a local government server, and a public application program map server; Filtering the travel information requested by the user according to priority based on the farm selected by the user; Generating a travel course by calculating a travel time according to the user's entire travel period and viewing time of the travel destination; Recommending the generated travel course to a user; And after the trip, writing a review and evaluating the satisfaction level; Including, the step of filtering the trip course is Equation

In the above equation, T is the travel period input by the user, N _p is the total number of tourist destinations of interest, P _place (i) is the i-th travel time, and T _time (i) is the i-th travel distance. Characterized in that it represents the travel time for.

According to the above-described problem solving means, the present invention has the following effects.

In the present invention, when a farm user requests a farm owner, it is possible to more effectively recommend users and farmers by reflecting farm information selected by the user and the farm owner. By recommending the farmer who owns the farm corresponding to the nearby location based on the user's residence location, not only the user can quickly find the best farmer, but also has the effect of improving the satisfaction between the user and the farmer through accurate recommendations. .

In addition, the present invention provides a user with matching essential information and detailed user information types, by providing information of a farmer selected from among farmers recommended in the past through the type recommendation unit by users matching the essential information and the user detailed information types. By checking the farm owner selected by another user whose information and user detailed information types match, there is an effect of improving the quality of service by improving the reliability between the user and the farmer.

In addition, the present invention provides meta-information provided by public institutions to farm users, and recommends the ideal travel course based on the farm selected by the user through the travel course recommendation unit according to the priority selected by the user, thereby increasing travel efficiency and enjoyment. There is an effect that can increase the degree of satisfaction and satisfaction.

1 is a diagram schematically showing a farm service system based on big data according to a first aspect of the present invention.
FIG. 2 is a diagram schematically illustrating a configuration of an information storage unit in FIG. 1.
3 is a diagram schematically illustrating a configuration of a type classification unit in FIG. 1.
4 is a diagram schematically showing the configuration of a type recommendation unit in FIG. 1.
5 is a diagram schematically showing the configuration of a farm selection unit in FIG. 1.
6 is a diagram schematically showing the configuration of a travel course recommendation unit in FIG. 1.
7 is a diagram showing step-by-step a big data-based farm service method according to the second aspect of the present invention.

Hereinafter, a preferred embodiment of a big data-based farm service system and method according to the present invention will be described in detail with reference to the accompanying drawings. For reference, terms and words used in the specification and claims should not be construed as being limited to their usual or dictionary meanings, and the inventors appropriately explain the concept of terms in order to explain their own invention in the best way. Based on the principle that it can be defined, it should be interpreted as a meaning and concept consistent with the technical idea of the present invention. In addition, 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.

1 is a diagram schematically showing a farm service system based on big data according to a first aspect of the present invention.

The big data-based farm service system according to the present invention, as shown in FIG. 1, is an information storage unit 10, a type classification unit 20, a type recommendation unit 30, a farm selection unit 40, and a tour course recommendation. Includes part 50.

Such a big data-based farm service system stores and processes various types of information input by the farm owner and the user using a terminal, respectively, as will be described later.

FIG. 2 is a diagram schematically illustrating a configuration of an information storage unit in FIG. 1.

The information storage unit 10 includes essential information input by a user who needs to use a farm and a farm owner supporting the user using each terminal, request information requested from the user and the farmer, and evaluation information on the farm and travel destination. As to store, as shown in FIG. 2, it is configured to include a storage module 11, an evaluation module 12, and a public data module 13.

The storage module 11 stores the information on the server when a user who needs to use the farm inputs user information and request information using a terminal, and the farm owner supporting the farm uses the terminal to provide information on the farm owner and the farm. When you enter information about it, this information is saved on the server.

The evaluation module 12 stores this information in the server when the farm user inputs evaluation information and tourist attraction evaluation information on the farm after using the farm.

The public data module 13 receives metadata from the Korea Tourism Organization server, the local government server, and the public application program map server.

At this time, the essential information of the user includes age information, gender information, and residence information.

In addition, the essential information of the farmer includes information on age information and gender information.

Meanwhile, the age information of the essential information is automatically updated by the information storage unit 10 according to the passage of time.

In addition, the request information includes information on cost information, location, scenery, toilets, parking facilities, agricultural equipment, and water facilities according to user priority. This request information becomes important information when recommending a farmer to a user.

In addition, the evaluation information is information in which the user indicates satisfaction according to each request information in numerical values from 1 to 5 for farm information and tourist destination information.

Accordingly, the type recommendation unit 30, which will be described later, calculates the recommendation rate based on the information requested by the user according to the priority based on the location of the residence, when the user requests the required farm, and sends the farmer to the user based on the calculation result. I recommend you.

The recommendation result recommended by the type recommendation unit 30 may be changed according to a user's request.

3 is a diagram schematically illustrating a configuration of a type classification unit in FIG. 1.

The type classification unit 20 classifies users and farmers whose information is matched based on at least one of essential information and request information stored in the information storage unit 10.

By classifying and grouping users having the same information, the type classification unit 20 can provide the grouped users with farm owners selected in the past by users belonging to the same group, and the quality of service can be improved by providing the grouped users. have.

As shown in FIG. 3, the type classification unit 20 includes a detailed information type generation module 21, a user detailed information classification module 22, and a farmer detailed information classification module 23.

The detailed information type generation module 21 sets a range value for each detailed information of the request information, and generates detailed information types for each range value.

For example, the range value of the detailed information of the weekend farm request information is shown in Table 1 below, but is not limited thereto.

[Table 1]

The user detailed information classification module 22 extracts a user detailed information type in which the detailed information of the request information input by the user is included among the detailed information types generated by the detailed information type generation module 21, and the extracted user detailed information Users with matching types are categorized among users, but users with matching essential information are categorized as targets.

Referring to Table 1 above, for example, if the requested information entered by the user is based on priority, location information is road 00, Yeonseo-myeon, Sejong-si, 60,000 won per account, farm equipment available, toilet available, scenery 3, user The detailed information classification module 22 extracts location information detailed information type 1, cost information detailed information type 2, agricultural equipment detailed information type 2, toilet detailed information type 1, and landscape information detailed information type 2 as user detailed information type. , The extracted user detailed information types are classified among matching users.

Accordingly, the user detailed information classification module 22 classifies users, but classifies users whose essential information and detailed information types all match.

The farmer's detailed information classification module 23 extracts the farmer's detailed information type including the detailed information of the request information input by the farmer among the detailed information types generated by the detailed information type generation module 21, and extracts the extracted farmer's detailed information type. All of them are categorized by matching farm owners, but the farm owners with matching essential information are categorized as targets.

Referring to Table 1 above, for example, if the requested information entered by the farmer is the location information of Geumnam-myeon, Sejong-si, Road No. 00, the cost information is 60,000 won per account, no agricultural equipment, no toilet, and landscape 4, the farmer's detailed information is classified. The module 23 extracts location information detailed information type 2, cost information detailed information type 2, agricultural equipment detailed information type 1, toilet detailed information type 2, and landscape detailed information type 1 as a farmer's detailed information type, and the extracted farmer Classify among farmers whose detailed information types match.

4 is a diagram schematically showing the configuration of a type recommendation unit in FIG. 1.

When a user requests a farmer, the type recommendation unit 30 recommends the user requesting the farmer by comparing the information of the farmers based on the information of the users classified by the type classification unit 20.

This type recommendation unit 30 includes a detailed information type recommendation module 31, a recommendation rate calculation module 32, a recommendation point calculation module 33, and a farm recommendation module 34, as shown in FIG. 4.

The detailed information type recommendation module 31 compares and recommends the detailed information types of the user and the detailed information types of the farmer.

In other words, it is recommended by comparing the corresponding detailed information types. For example, referring to Table 2 below, the cost information detailed information type of the user detailed information type and the cost information detailed information type of the farmer detailed information type are compared.

At this time, the user detailed information types are also compared with the farmer detailed information types of another farmer 2.

The recommendation rate calculation module 32 calculates a recommendation rate through a preset recommendation rate for each detailed information type recommended by the detailed information type recommendation module 31.

For example, the preset matching rate is 100% if the detailed information type is the same, 80% if the detailed information type has one type gap, 50% if there are two type gaps, and 20% if there are three type gaps. However, it is not limited thereto.

Accordingly, referring to Table 2 below, it is possible to check the recommendation rates of the detailed user information types and the farm detailed information types of the farmer.

The recommendation point calculation module 33 calculates a recommendation point by multiplying the recommendation rate for each detailed information type calculated by the recommendation rate calculation module 32 and a preset weight for each detailed information type.

For example, according to the priority set by the user, the weight for each preset type of detailed information is 1st priority 30 points, 2nd priority 30 points, 3rd priority 20 points, 4th priority 10 points, 5th priority 10 points, and recommendation points. The recommended points calculated by the calculation module are shown in Table 2 below.

Here, the weight is changed according to the user's priority.

The recommendation point calculation module 33 adds up the calculated recommendation points. As shown in Table 2 below, the farmer's recommended point is 86 points.

[Table 2]

The farm recommendation module 34 recommends a farm according to the calculation result of the recommendation point calculated as described above.

5 is a diagram schematically showing the configuration of a farm selection unit in FIG. 1.

The farm selection unit 40 is, among the farm owners recommended by the type recommendation unit 30, the degree of correspondence and satisfaction between the request information according to the priority of the user who requested the farm and the detailed farm type information of the corresponding farm owners. Provides information on the recommended farm based on the recommendation rate calculated based on and the user selects the farm. As shown in FIG. 5, the farm information providing module 41, the evaluation information providing module 42 and the selection It comprises a module 43.

The farm information providing module 41 provides farm owner essential information and farm information to the user's terminal in the order of farm owners with a high recommendation rate among users and farm owners recommended by the type recommendation unit 30.

In other words, the farm selection unit 40 provides essential information and request information of the farmer whose recommendation points summed in the recommendation point calculation module 33 are greater than or equal to a preset point.

For example, if the preset point is 80, the farm selection unit 40 provides essential information and request information of the farmer.

In addition, the farm selection unit 40, to a user whose essential information and user detailed information types match, and a farmer selected from among the farm owners recommended in the past through the type recommendation unit by users whose essential information and user detailed information types match, Farmers whose recommended points are more than preset points can be provided together.

For example, if the user has been recommended by another user whose required information and the user's detailed information types match in the past and the selected farm owner is the above example, the farm selection unit will provide the user with the required information of the past farmer and the recommended farm owner. Provide requested information.

The evaluation information providing module 42 provides pre-evaluation information on the recommended farm owner and the farm together.

Accordingly, the user finally selects the farmer in the selection module 43 after receiving essential information, request information, and evaluation information of the farmers.

6 is a diagram schematically showing the configuration of a travel course recommendation unit in FIG. 1.

The travel course recommendation unit 50 includes a filtering module 51, a travel course generation module 52, and a travel course recommendation module 53, as shown in FIG. 6.

으로 계산된다.The filtering module 51 performs primary filtering based on the keyword and theme requested by the user, centering on the farm selected by the farm selection unit 40, and the secondary filtering is an equation

Is calculated as.

In the above equation, T is the travel period input by the user, N _p is the total number of destinations of interest, P _place (i) is the viewing time of the i-th destination, and T _time (i) is the travel time for the i-th travel distance. Show.

For example, referring to Table 3, the user selects a happy farm (36.56 127.29) located in Yeonseo-myeon, Sejong-si as an experience farm, and enters the keywords'lake park' and'museum' as keywords, and theme. If'cultural tourism' and'historical tourism' are entered as, only tourism corresponding to this input information among tourist attractions in the database is filtered.

[Table 3]

The first filtered information is sorted according to its priority. Priority can be calculated according to the satisfaction level of tourist attractions, and places with a satisfaction level of 3.0 or less are excluded.

In the secondary data filtering step, possible courses within the travel period input by the user are calculated by calculating the GPS distance of the tourist attraction sorted in the data. In the secondary data filtering step, the following equation is used.

[Equation]

In the above equation, T denotes the travel period entered by the user, N _p is the total number of destinations of interest, P _place (i) is the viewing time of the i-th destination, and T _time (i) is moved to a value considering the travel distance. Means time.

For example, in Table 3, if tourist destinations are listed by priority according to satisfaction, Sejong Library (A), Sejong Lake Park (B), National Tax Museum (C), Sejong City Folk Museum (D) will be in order, and the rest will be Excluded. The optimal travel time is calculated through this as shown in Table 4 below.

[Table 4]

In Table 4 above, 2 courses and 5 courses exceeding 4 hours are excluded, and the travel course creation module 52 generates 1, 3, 4, 6 courses, and the travel course recommendation module 53 generates the user for 4 hours. Recommend travel destinations and routes that can be viewed.

Hereinafter, a big data-based farm service method according to the second aspect of the present invention will be described.

7 is a diagram showing step-by-step a big data-based farm service method according to the second aspect of the present invention.

Big data-based farm service method according to the present invention, as shown in Fig.7, information storage step (S1), type classification step (S2), type recommendation step (S3), farm selection step (S4) and travel course recommendation It includes step S5.

First, in the information storage step (S1), the information storage unit 10 requires the user and the farmer who need the farm to enter the required information using their respective terminals, the requested information (farm detailed information) and the farm Save the evaluation information for.

In the type classification step (S2), the type classification unit 20 classifies users and farmers whose information is matched based on at least one of essential information and request information stored in the information storage unit 10.

At this time, the type classification step (S2) includes a detailed information type generation step, a user detailed information classification step, and a farmer detailed information classification step.

In the detailed information type generation step, the detailed information type generation module 21 sets a range value for each detailed information of the request information, and generates detailed information types for each of the range values.

In the user detailed information classification step, the user detailed information classification module 22 extracts a user detailed information type including the detailed information of the request information input by the user from among the detailed information types generated by the detailed information type generation module 21. And, the extracted user detailed information types are classified among users who all match, but the users whose essential information matches are classified as targets.

In the farmer detailed information classification step, the detailed information of the request information input by the farmer by the farmer detailed information classification module 23 is included among the detailed information types generated by the detailed information type generation module 21. Extracted, and classified among the farmers whose detailed information types of the extracted applicants all match, and classified as the target of the farmers whose essential information of the farmers matched.

In the type recommendation step (S3), when the user requests the farmer, the type recommendation unit 30 compares the information of the farmers with the information of the farmers based on the information of the user classified in the type classification unit 20 by the user requesting the farmer. I recommend you.

At this time, the type recommendation step (S3) includes a detailed information type recommendation step, a recommendation rate calculation step, a recommendation point calculation step, and a farm recommendation step.

In the detailed information type recommendation step, the detailed information type recommendation module 31 compares the user detailed information type and the farmer detailed information type and recommends them.

In the recommendation rate calculation step, the recommendation rate is calculated through a preset recommendation rate for each detailed information type recommended by the detailed information type recommendation module 31.

In the recommendation point calculation step, the recommendation point calculation module 33 calculates a recommendation point by multiplying the recommendation rate for each detailed information type calculated by the recommendation rate calculation module 32 and a preset weight for each detailed information type.

In the farm selection step (S4), the farm selection unit 40 provides essential information and request information of the farm owner to the user's terminal in the order of the farm owner with a high recommendation rate among users and farm owners recommended by the type recommendation unit 30. .

In other words, the farm selection step (S4) provides essential information and request information of the farmer whose recommendation points added in the calculation step are greater than or equal to a preset point.

In the travel course recommendation step (S5), after sorting the data filtered through the first filtering module according to priority, and then sorting the data again through the second filtering, the travel course generation module 52 is the filtered data. To generate an optimal travel course, the travel course recommendation module 53 recommends the generated travel course to the user and provides information on each tourist attraction.

In this way, the travel course recommendation step (S5) of recommending the travel course to the user may include receiving metadata from a Korea Tourism Organization server, a local government server, and a public application program map server; Filtering the travel information requested by the user according to priority based on the farm selected by the user; Generating a travel course by calculating a travel time according to the user's entire travel period and viewing time of the travel destination; Recommending the generated travel course to a user; And after the trip, writing a review and evaluating satisfaction.

에 의해 이루어지며, 상기 수학식에서, T는 이용자에 의해 입력된 여행기간, N_p는 관심 여행지 총 수, P_place(i)는 i번째 여행지 관람시간, T_time(i)은 i번째 여행지 이동 거리에 대한 이동시간을 나타낸다.At this time, the step of filtering the travel course is Equation

In the above equation, T is the travel period input by the user, N _p is the total number of tourist destinations of interest, P _place (i) is the i-th travel time, and T _time (i) is the i-th travel distance. It represents the travel time for.

The present invention described above is not limited by the above-described embodiments and the accompanying drawings, and various substitutions, modifications, and changes are possible within the scope of the technical spirit of the present invention. It will be obvious to those of ordinary skill in the following.

10: information storage unit 20: type classification unit
30: type recommendation department 40: farm selection department
50: Travel Course Recommendation Department

Claims (11)

An information storage unit for storing essential information input by a user who needs to use a farm and a farm owner supporting the user using respective terminals, request information requested from the user and the farm owner, and evaluation information on farms and travel destinations;
At least one user group in which the types of the essential information and the plurality of detailed farm information corresponding to the request information are common among the users using the essential information and the request information, and a plurality of details corresponding to the farm information among the farm owners A type classification unit for forming at least one farm group in which types of farm information are common;
When the user requests a farm, a type recommendation unit recommending the user and the farm owner using the request information of the user and the farm detailed information type of the farm owners;
Among the farm owners recommended by the type recommendation unit, it is recommended based on a recommendation rate calculated based on the degree of agreement and satisfaction between the request information according to the priority of the user who requested the farmer and the corresponding detailed farm type information A farm selection unit that provides information on the farm and allows the user to select a farm; And
Including; a travel course recommendation unit that provides local travel information and creates and recommends a tour course by using travel request information according to the priority requested by the user based on the selected farm; and
The essential information input by the farm user includes age information, gender information, and residence location, and the farm information of the user and the farmer includes at least one farm information, and the at least one farm information includes cost, scenery, and farming. It includes at least one of equipment, toilets, water supply facilities, parking lots, access roads, farm operation information and evaluation information,
The travel request information includes at least one travel request information, and the at least one travel request information includes at least one of travel period, keyword, theme, transportation, and evaluation information. .
The method according to claim 1,
The farm information includes any one of cost information, location, scenery, toilet, parking facility, agricultural equipment, water supply facility information,
The type recommendation unit, when a user requests a farm of the farmer, compares the type of the user and the farmer and recommends the farmer by reflecting the farm with a high recommendation rate,
The recommendation result recommended by the type recommendation unit is changed according to the farm information and request information of the user and the farmer.
The method according to claim 1,
The information storage unit,
A storage module for storing essential information, request information, and travel request information of the user and the farmer;
An evaluation module for evaluating reviews and satisfaction for the use of the farm after the user uses the farm, and for evaluating reviews and satisfaction for the use of tourist attractions and restaurants after traveling to nearby tourist destinations; And
A big data based farm service system comprising: a public data module connected to a Korea Tourism Organization server, a local government server, and a public application program map server to receive metadata.
The method according to claim 1,
The type classification unit,
A detailed information type generation module for setting a range value for each of the detailed farm information and generating detailed farm information types for each of the range values;
The detailed information of the request information entered by the user is extracted from the detailed information types of the users that are included in the detailed information types generated by the farm detailed information type generation module, and classified among users whose extracted user detailed information types all match, A user detailed information classification module for classifying users with matching essential information of the users; And
The detailed information of the farm information input by the farmer is extracted from the detailed information types of the farmers, which are included among the detailed information types generated by the detailed information type generation module, and classified among farmers whose all the extracted farmer detailed information types match, A farm service system based on big data, comprising: a farm owner detailed information classification module for classifying farm owners with matching essential information of the farm owners.
The method according to claim 1,
The type recommendation unit,
A detailed information type recommendation module for comparing the request information according to the user's priority and the detailed farm information type of the farm owner to recommend each other;
A recommendation rate calculation module for calculating a detailed recommendation rate recommended by the detailed information type recommendation unit;
A recommendation point calculation module for calculating a recommendation point by multiplying a recommendation rate for each detailed information type calculated by the recommendation rate calculation module and a weight for each preset detailed information type through a recommendation rate according to a preset priority for each information type; And
And a farm recommendation module for recommending a farm according to the calculation result of the recommendation point.
The method according to claim 1,
The farm selection unit,
A farm information providing module that provides information on farm owners and farms recommended by the type recommendation unit;
An evaluation information providing module that provides pre-evaluation information on the recommended farm owner and farm; And
A big data based farm service system comprising: a selection module through which a user can select a farm based on the information.
The method according to claim 1,
Travel Course Recommendation Department,
A filtering module that sorts according to priority based on request information requested from the user's terminal;
A travel course generation module that calculates a travel time and generates a travel course; And
And a travel course recommendation module for recommending the generated travel course to the user.
As a method of servicing using a big data-based farm service system according to any one of claims 1 to 7,
Essential information that the user who needs the farm and the farm owner input using their respective terminals, the request information and farm evaluation information requested by the user and the farmer, the travel request information and the travel evaluation information requested by the user are stored in the information storage unit. Storing information storage step;
Using the essential information and request information (farm information) stored in the information storage step, at least one group of farmers in which the essential information and a plurality of detailed information types corresponding to the request information are common among the users, is assigned to a type classification unit. Type classification step formed by;
A type recommendation step of recommending the user and the farm owner by a type recommendation unit using the request information requested by the user according to the priority and the farm detailed information type of the farm owner when the user requests the farm of the farmer;
Among the farm owners recommended by the type recommendation unit, the farm owner and the farm recommended based on the recommendation rate calculated based on the degree of correspondence between the request information according to the user priority requesting the farm owner and the detailed type information of the corresponding farm owners. A farm selection step in which a user receives and selects farm information by a farm selection unit based on the information on the farm; And
Including; a travel course recommendation step of providing surrounding tourism information by a travel course recommendation unit based on the travel request information and the farm inputted by the user through the terminal, generating and recommending a travel course; and
The essential information of the user includes age information, gender information, and residence location, the farm information of the user and the farmer includes at least one detailed information, and the at least one detailed information (farm information) includes location information, scenery , Toilet, parking facility, agricultural equipment, water supply facility information, big data-based farm service method comprising at least one of the evaluation information.
The method of claim 8,
The type classification step of generating the at least one farm owner group,
A detailed information type generation step in which the farm detailed information type generation module sets a range value for each detailed information of the farm information, and generates detailed information types for each of the range values;
The user detailed information classification module extracts the user detailed information type including the detailed information of the request information according to the priority entered by the user among the detailed information types generated by the farm detailed information type generation module, and the extracted user detailed information A user detailed information classification step of classifying users whose types all match, but classifying users whose essential information matches the targets; And
The farmer's detailed information classification module extracts the farmer's detailed information type included among the detailed information types generated by the detailed information type generation module in which the detailed information of the farm information input by the farmer, and the extracted farmer's detailed information types all match. A farm service method based on big data, comprising: a step of classifying detailed information of farm owners for classifying the farm owners, but classifying the farm owners with matching essential information of the farm owners.
The method of claim 8,
The type recommendation step of recommending the farm owner to the user,
A detailed information type recommendation step in which the detailed information type recommendation module recommends the user's detailed information type and the farmer's detailed information type to each other;
A recommendation rate calculation step of calculating, by a recommendation rate calculator, a recommendation rate based on a preset recommendation rate for each detailed information type generated by the detailed information type recommendation module;
A recommendation point calculation step in which a recommendation point calculation module calculates a recommendation point by multiplying a recommendation rate for each detailed information type calculated by the recommendation rate calculation module and a weight for each preset detailed information type; And
Including; a farm recommendation step of recommending the farmer according to the calculation result,
The farm recommendation step recommended to the user terminal,
Big data-based farm service method, characterized in that providing essential information and request information of a farmer whose recommendation point calculated by the recommendation point calculation module is more than a preset point.
The method of claim 8,
The travel course recommendation step of recommending the travel course to the user,
Receiving metadata from a Korea Tourism Organization server, a local government server, and a public application program map server;
Filtering the travel information requested by the user according to priority based on the farm selected by the user;
Generating a travel course by calculating a travel time according to the user's entire travel period and the travel destination viewing time;
Recommending the generated travel course to a user; And
Including; after the trip, writing a review and evaluating satisfaction
Filtering the travel course may be achieved by Equation

Is made by
In the above equation, T is the travel period input by the user, N _p is the total number of destinations of interest, P _place (i) is the viewing time of the i-th destination, and T _time (i) is the travel time for the i-th travel distance. User and farmer service method using big data, characterized in that it represents.

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