KR20210061652A - Heavy equipment recommendation system using metadata relation based on ontology - Google Patents

Abstract

An objective of the present invention is to provide a heavy equipment recommendation system using ontology-based metadata relation which can recommend heavy equipment suitable for a condition suggested by a heavy equipment user. According to one embodiment of the present invention, the heavy equipment recommendation system using ontology-based metadata relation comprises: an information storage unit storing location information received by a GPS terminal installed on heavy construction equipment, required information inputted by using a terminal of a user and a terminal of a heavy equipment provider, group information classifying the provider and the user into a group, and evaluation information of the provider and the user; an information request unit calculating and normalizing the weight in accordance with requested information if the user requests a search based on information stored in the information storage unit; an ontology building unit building metadata ontology of the provider, the user, and heavy equipment based on data of the information request unit and the information storage unit; and a heavy equipment recommendation unit analyzing, weight-converting and recommending requested information based on the metadata ontology.

Description

Heavy equipment recommendation system utilizing ontology-based metadata association {HEAVY EQUIPMENT RECOMMENDATION SYSTEM USING METADATA RELATION BASED ON ONTOLOGY}

The present invention relates to a heavy equipment recommendation system utilizing ontology-based metadata correlation, and more particularly, to a heavy equipment recommendation system using ontology-based metadata correlation capable of recommending heavy equipment suitable for conditions suggested by heavy equipment users. .

Due to the continuous oversupply of heavy equipment, the utilization rate is usually estimated to be around 50%, and taking this into account, about 50% of the number of registered units is in suspension.

Looking at the utilization rate for the past 10 years, the average annual average is 50-60%, and the government is seeking a fundamental solution through supply and demand control to improve the chronic heavy equipment shutdown problem that has persisted to this day.

Due to this, competition in the heavy equipment rental market is getting fiercer day by day, and the government has created a construction equipment rental payment guarantee system to protect workers in the heavy equipment rental business, as there are cases in which the rental price is not paid even if they are lent. Article 68-3)

From the perspective of construction companies that want to rent heavy equipment, there have been many difficulties in introducing heavy equipment to the construction site because the procurement of heavy equipment through heavy equipment brokerages was not smooth.

In order to find a suitable heavy equipment owner for a construction site, it was necessary to inquire with the heavy equipment owner, and even if a suitable heavy equipment was found, it was difficult to calculate the rental period and amount.

Recently, in order to solve this problem as a prior art,'heavy equipment rental service method and system' has been published in Publication No. 10-2015-0100063.

According to the above invention, a heavy equipment consumer and a heavy equipment provider can be connected to more effectively extract and rent heavy equipment that can be quickly moved to a place of use until a desired time by the heavy equipment consumer.

However, since the above service cannot provide real-time heavy equipment information based on GPS information, it is difficult to rent heavy equipment in a short distance, so it is difficult to rent heavy equipment quickly, and there is a limitation in that it is not possible to provide personalized service according to user preference. .

In addition, in order to solve this problem,'GPS-based construction management system and its method' was recently published in registration number 10-1876899 as a prior art.

The above invention relates to a GPS-based construction management system, by monitoring the operating status of construction machinery in a construction site based on real-time location information transmitted from a GPS terminal and receiving the progress of the excavation process to analyze and provide construction management information. In addition, it can improve the efficiency of construction management.

However, the service is a construction management system based on GPS information, and is not an invention designed to solve the problem of rental of heavy equipment. Therefore, it is necessary to check the location of heavy equipment based on GPS in real time, and to recommend and rent heavy equipment more efficiently based on the user's location.

Korean Patent Publication No. 10-2015-0100063 Korean Patent Registration No. 10-1876899

An object of the present invention is to provide a heavy equipment recommendation system utilizing ontology-based metadata association that can recommend heavy equipment suitable for conditions suggested by heavy equipment users.

In addition, an object of the present invention is to provide a heavy equipment recommendation system utilizing an ontology-based metadata association that can improve the efficiency of rental of heavy equipment by receiving GPS location information of heavy construction equipment in real time based on an ontology.

In addition, the present invention provides a semantic search function by combining related information in data search and recommendation by constructing a metadata ontology, and a heavy equipment utilizing the ontology-based metadata association that can provide a recommendation service tailored to user preference. It aims to provide a recommendation system.

In addition, an object of the present invention is to provide a heavy equipment recommendation system utilizing an ontology-based metadata association that connects a real-time construction heavy equipment provider and a user based on the user's location to extract the requested heavy equipment and efficiently rent it.

In addition, according to the present invention, a provider who has suffered from an oversupply of heavy equipment can increase the efficiency of management by supplying the heavy equipment in an inactive state to users who need it in real time, and the user can immediately receive the necessary heavy equipment around a short distance. Therefore, the object of the present invention is to provide a heavy equipment recommendation system that utilizes ontology-based metadata relevance that can reduce not only time but also cost.

In addition, the present invention analyzes the user's usage pattern, stores heavy equipment according to the user's preferred use, and connects it with the provider ontology to effectively recommend and rent user-customized heavy equipment. It aims to provide a system.

In addition, the present invention provides a heavy equipment recommendation system that utilizes ontology-based metadata association that enables the provider to receive real-time information on the location and driving distance of heavy equipment, and to increase the efficiency of heavy equipment management by storing the information in a database. It aims to provide.

In addition, the present invention can increase the accuracy of information provision to users by sharing photos of the state of heavy equipment, information on heavy equipment pilots, and information on operation history with the user, and to the provider, the user's heavy equipment usage information and evaluation information The purpose of this is to provide a heavy equipment recommendation system that utilizes ontology-based metadata association that enables users to check their usage patterns and operational status by sharing.

The effects that can be achieved by the heavy equipment recommendation system according to an embodiment of the present invention are not limited to those mentioned above, and can be extended to not only heavy equipment but also agricultural machinery and trucks, and effects not mentioned are described below. From will be able to be clearly understood by a person skilled in the art.

To achieve this purpose, the heavy equipment recommendation system utilizing the correlation between the ontology-based metadata is the location information received from the GPS terminal installed in the heavy construction equipment, the essential information input using the terminal of the heavy equipment provider and the user's terminal, and the provider. Group information for classifying users into groups, an information storage unit that stores evaluation information of the provider and users, and when the user makes a search request based on the information stored in the information storage unit, the weight is calculated and normalized according to the requested information. An ontology building unit that builds metadata ontologies of heavy equipment, users, and providers based on the data of the information request unit, the information storage unit and the information request unit, and analyzes, weights, and recommends requested information based on the metadata ontology. Includes a heavy equipment recommendation department.

According to the present invention as described above, there is an advantage in that it is possible to recommend heavy equipment suitable for conditions suggested by heavy equipment users.

In addition, according to the present invention, there is an advantage in that it is possible to improve the efficiency of rental of heavy equipment by receiving GPS location information of heavy construction equipment in real time based on an ontology.

In addition, according to the present invention, it is possible to provide a semantic search function by combining related information in data search and recommendation by constructing a metadata ontology, and to provide a recommendation service suitable for user preference.

In addition, according to the present invention, there is an advantage that it is possible to effectively rent by extracting the requested heavy equipment by connecting a user with a real-time construction heavy equipment provider based on the user's location.

In addition, according to the present invention, providers who have suffered from an oversupply of heavy equipment can increase the efficiency of management by supplying heavy equipment that is in an idle state to users who need it in real time, and users will be immediately supplied with necessary heavy equipment based on a short distance. Therefore, there is an advantage that not only time but also cost can be saved.

In addition, according to the present invention, there is an advantage that it is possible to effectively recommend and rent a user-customized heavy equipment by analyzing a user's usage pattern, storing heavy equipment according to a user's preferred use, and connecting it to a provider ontology.

In addition, according to the present invention, there is an advantage that the provider can receive information on the location and driving distance of the heavy equipment in real time, and by storing the information in a database, the efficiency of heavy equipment management can be increased.

In addition, according to the present invention, it is possible to increase the accuracy of information provision to users by sharing photos of the state of heavy equipment, information on heavy equipment pilots, and information on operation history with the user, and to the provider, the user's heavy equipment usage information and evaluation information It has the advantage of being able to check the user's usage pattern and operation status by sharing the data.

1 is a block diagram of a heavy equipment recommendation system utilizing correlations between ontology-based metadata according to an embodiment of the present invention.
2 is a diagram showing relationship knowledge between metadata ontology according to an embodiment of the present invention.
3 is a diagram showing a heavy equipment metadata ontology according to an embodiment of the present invention.
4 is a diagram showing a user metadata ontology according to an embodiment of the present invention.
5 is a diagram showing a provider metadata ontology according to an embodiment of the present invention.
6 is a diagram showing a user's preference, similar use, and relationship ontology during a search according to an embodiment of the present invention.
7 is a diagram showing a user preferred heavy equipment relationship ontology during a search according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein.

In addition, the singular expression used in the present invention includes a plurality of expressions unless the context clearly indicates otherwise. In the present invention, terms such as “consisting of” or “comprising” should not be construed as necessarily including all of the various elements or various steps described in the invention, and some of the elements or some steps are included. It should be interpreted that it may not be, or may further include additional components or steps.

Among the terms used in this specification, “ontology” aims to clearly define the concept of a resource that is an object of an information system and describe it in detail so that more accurate information can be found. Ontology is a tool that can implement a semantic web, and is a tool that can semantically connect knowledge concepts and is expressed using languages such as RDF, OWL, and SWRL.

Among the terms used in this specification, “metadata” is a combination of related information in the data and semantically describes one piece of data. It helps users to search for the data they are looking for.

1 is a configuration diagram of a heavy equipment recommendation system utilizing correlations between ontology-based metadata according to an embodiment of the present invention.

Referring to FIG. 1, the heavy equipment recommendation system utilizing the correlation between ontology-based metadata includes an information storage unit 110, an information request unit 120, an ontology construction unit 130, and a heavy equipment recommendation unit 140.

Here, a GPS terminal is installed in the heavy construction equipment, and the user and the heavy equipment provider access the heavy equipment recommendation system through the terminal to transmit and receive information, and the user can receive a recommendation for the requested heavy equipment based on a real-time location.

Heavy equipment provider terminals and heavy equipment user terminals consist of a personal computer, a terminal equipped with a memory means such as a smart phone, a tablet PC, a personal information terminal or a web pad, and a mobile communication function equipped with a microprocessor and capable of computing. In addition, various applications can be installed to provide various services to heavy equipment providers and users.

The information storage unit 110 includes real-time location information received from a GPS terminal of heavy construction equipment, essential information input using a terminal of a heavy equipment provider and a terminal of a user, group information for classifying the provider and user into groups, and the provider. It stores the user's evaluation information.

The information storage unit 110 includes an information input module 111, a group classification module 112, and an evaluation module 113.

Here, the information input module 111 may be connected to a user through a user terminal. Accordingly, the information input module 111 stores location information received from a GPS terminal, and essential information input by using a terminal of a heavy equipment provider and a terminal of a user.

The group classification module 112 classifies the inputted information into groups using metadata association.

In one embodiment, the group classification module 112 may classify a user group for recommending heavy equipment requested by the user.

User groups can be broadly classified into general construction, specialized construction, and others, and the general construction business can be divided into construction work, civil engineering work, civil engineering construction work, landscaping work, and industrial environment facility work. Construction business is classified into 29 types, including interior construction business, earthwork business, masonry business, wet waterproofing work, and painting work. Other means a group that does not belong to the general construction industry and the specialized construction industry.

In another embodiment, the group classification module 112 may classify the heavy equipment group for recommendation using the association between the heavy equipment metadata of the heavy equipment module 131.

First, the group classification module 112 may apply a probabilistic method to derive a correlation between heavy equipment metadata. Using heavy equipment with multiple uses, it is possible to derive similarities between the uses to which conditional probability is applied. The conditional probability equation for deriving the similarity between uses is as follows [Equation 1].

[Equation 1]

Mx: the number of heavy equipment that has use x among all heavy equipment,

My: number of heavy equipment that has use y among all heavy equipment,

P: Relative probability of heavy equipment y with respect to reduced heavy equipment x in the heavy equipment model

The use of the heavy equipment is as shown in [Table 1] below.

[Table 1]

If you refer to the data of the Korea Construction Equipment Association, there are a total of 27 types of heavy equipment according to the use as above. In this embodiment, the heavy equipment is classified into 27 heavy equipment for recommendation of heavy equipment as described above.

The group classification module 112 may calculate a similarity value between uses by obtaining relative probabilities for each use of heavy equipment. Based on the calculated value, use 6 and use 7 are connected in a similar use relationship. [Equation 2] below is an equation for connecting each use.

[Equation 2]

m: mean value of conditional probabilities,

Each heavy equipment calculated for a similar purpose in Equation 2 above is connected through an isSimilarTo relationship, and can be used for similar heavy equipment search and recommendation through similar heavy equipment.

In another embodiment, the group classification module 112 may classify a provider group to recommend heavy equipment requested by a user.

The provider group can be broadly classified into construction equipment management, general construction equipment rental, and individual construction equipment rental. In the provider model, there is hasID that represents the ID of the provider. Provider is expressed based on the hasID attribute and the relationship defined in [Table 2] below.

[Table 2] below defines the concept and relationship of objects in order to connect with the heavy equipment model based on the main considerations.

[Table 2]

The evaluation module 113 stores satisfaction evaluated by heavy equipment providers and users.

When the user makes a search request based on the information stored in the information storage unit 110, the information request unit 120 calculates and normalizes a weight according to the requested information.

That is, the information requesting unit 120 can determine a user's group using the real-time location of the user and the real-time location of the heavy equipment based on the heavy equipment metadata ontology for recommending heavy equipment, and search for heavy equipment according to the group's heavy equipment preference. have. In addition, users can search for heavy equipment by receiving user preferences and recommend them to users.

The information request unit 120 includes an information retrieval module 121, a weight calculation module 122, and a normalization module 123.

The information retrieval module 121 searches for the location of heavy equipment in real time based on the location of the user, but after determining the user group, the information search module 121 may search for the location-based heavy equipment according to the use preference of the group and similar uses connected through an isSimilarTo relationship.

When the user's information is entered, the group is determined according to the rules of Semantic Web Rule Language (SWRL) (Horrocks et al., 2004), and a query is generated and recommended based on the preferred use of the group to which the user belongs and the similar use of the preferred use. You can search for heavy equipment.

The weight calculation module 122 calculates a weight for recommending heavy equipment, but each heavy equipment has a different weight according to the searched condition, and the weight for recommendation of the searched heavy equipment is calculated together with a rating value input to the heavy equipment metadata ontology. To determine the weight of the heavy equipment.

Here, the heavy equipment searched based on two uses similar to the preferred use of the user group and the preferred use may be a list of candidate heavy equipment for recommending heavy equipment. The weight calculation of the candidate heavy equipment list searched in the user location-based heavy equipment search is as shown in [Equation 3] below.

[Equation 3]

Mxy: heavy equipment y, searched for use x,

: 용도 x로 검색된 중장비의 평가

: Evaluation of heavy equipment searched for use x

Gz: weight by application

The weight of the use Gz is as shown in [Equation 4] below.

[Equation 4]

The list of heavy equipment searched based on the group's preferred use and uses similar to the preferred use has a weight value according to [Equation 3] and [Equation 4], and the value of the weight is the ranking of the heavy equipment when the heavy equipment is recommended to the user. Decide.

For example, when users select heavy equipment, the main considerations include heavy equipment name, usage, rental period, price, and keywords. The user can search for the heavy equipment the user wants based on the metadata considered when selecting the heavy equipment. Users can enter major metadata and search for heavy equipment in the heavy equipment metadata ontology based on the entered metadata.

In addition, the weight calculation module 122 may calculate the weight of the searched heavy equipment based on user preference. At this time, the searched heavy equipment list is a candidate heavy equipment to be recommended to a user. Each heavy equipment list has a weight value for recommendation. The calculation of the searched heavy equipment based on user preference may be defined as in [Equation 5].

[Equation 5]

n: Heavy equipment name, usage, rental period, price, number of keywords selected as the user's preferred metadata,

Mk: Heavy equipment searched by user's preference

In [Equation 5], the weight value of the heavy equipment Mk retrieved by the user's preference can be calculated as the sum of the weights of the heavy equipment metadata that the user prefers in the metadata connected to the heavy equipment Mk.

The normalization module 123 may perform normalization as a process for recommending heavy equipment.

That is, the weights of metadata used for calculating the weight of Mk in [Equation 5] may be normalized to 1 to 2 in order to have the same range in the corresponding heavy equipment through the normalization module 123. Each metadata normalization equation is shown in [Equation 6].

[Equation 6]

m: Meta data that the user considers when choosing heavy equipment,

Mk: Heavy equipment searched by user's preference

Mmk: Heavy equipment Mk retrieved by metadata m

이고, Lm은 메타 데이터 m으로 검색된 중장비 리스트 M에서 각각의 메타 데이터의 가중치 값이다.For example,

And Lm is a weight value of each meta data in the heavy equipment list M searched with meta data m.

The ontology building unit 130 builds metadata ontology of heavy equipment, users, and providers based on the data of the information storage unit 110 and the information request unit 120. This ontology building unit 130 is composed of a heavy equipment module 131, a user module 132, and a provider module 133.

The heavy equipment module 131 manages metadata, describes the heavy equipment, and defines characteristics of the heavy equipment. The heavy equipment module 131 manages heavy equipment metadata, which will be described in detail as follows.

The heavy equipment metadata describes the heavy equipment in question, indicates the characteristics of the heavy equipment well, and the well-defined heavy equipment metadata facilitates the management of the heavy equipment. Heavy equipment metadata includes ID, type, price, range, evaluation, real-time location information, rental period, usage history, failure history, payment guarantee, pilot, appearance, etc. Appears to be a consideration. In addition, the type, purpose, and appearance of heavy equipment can be used as metadata for searching and recommending heavy equipment.

The user module 132 may build a user metadata ontology using user metadata.

The user module 132 may be used as an ontology model by connecting with a heavy equipment model for personalized recommendation of heavy equipment to a user. Users can select from the metadata the main considerations they consider for heavy equipment recommendations. You can recommend heavy equipment based on location by selecting the name of the heavy equipment, the rental period, and determining the remaining priorities. [Table 3] below defines the concept and relationship of objects to connect with the heavy equipment model based on this.

[Table 3]

The provider module 133 may build a provider metadata ontology using provider metadata. The provider module 133 may be used as an ontology model in connection with a heavy equipment model for heavy equipment management and personalized recommendation of heavy equipment.

Providers can input major considerations that users consider to provide heavy equipment through the terminal. At this time, the main considerations are information such as the name of the heavy equipment, specifications, history information, and manager.

The heavy equipment recommendation unit 140 analyzes, weights, and recommends requested information based on the metadata ontology. The heavy equipment recommendation unit 140 includes an analysis module 141 and a recommendation module 142.

The analysis module 141 may provide candidate heavy equipment by performing analysis based on normalized values and prioritizing.

Here, the analysis module 141 analyzes the recommended heavy equipment based on the weight calculated by [Equation 5] through the weight calculation module 122 together with the recommendation result based on the metadata selected by the user, and analyzes each meta. You can change the weight of the data.

Accordingly, the analysis module 142 may perform weight calculation again. The application to change each weight is as shown in [Equation 7]. A new weight can be obtained by multiplying each metadata by a weight based on the contribution of the result recommended by each metadata.

[Equation 7]

In the above [Equation 7], the contribution multiplied by the weight of the metadata is MW. The contribution that affects the recommendation result ranges from 1 to 2 companies.

The recommendation module 142 may recommend candidate heavy equipment by reflecting the priority according to the analysis result by the analysis module 141.

The recommendation module 142 may search not only metadata selected by the user but also metadata not input by using similar metadata defined in the heavy equipment metadata ontology. Heavy equipment may be ranked according to the searched user's preference, and users may receive recommendations according to the heavy equipment for which the ranking is determined.

2 is a diagram showing relationship knowledge between metadata ontology according to an embodiment of the present invention.

Referring to FIG. 2, in this embodiment, in order to recommend heavy equipment to a user, a heavy equipment metadata ontology is constructed based on heavy equipment metadata, and similarity between heavy equipment metadata is generated and used for heavy equipment recommendation. A figure showing the relationship between such metadata is shown in FIG. 2.

Metadata is data that describes data and is used for data search, storage, and management purposes. In particular, in the case of search, it is being developed to enable semantic search by applying it to an ontology that can infer through the association between terms in a specific domain.

The heavy equipment module 131 manages heavy equipment metadata, describes the heavy equipment, and defines characteristics of the heavy equipment. The heavy equipment module 131 manages heavy equipment metadata and will be described in detail as follows.

Heavy equipment metadata describes the heavy equipment in question, indicates the characteristics of heavy equipment well, and well-defined heavy equipment metadata facilitates the management of heavy equipment. Heavy equipment metadata includes ID, type, price, range, evaluation, real-time location information, rental period, usage history, failure history, payment guarantee, pilot, appearance, etc.In particular, location information is important to users when recommending heavy equipment. Appears to be a consideration. In addition, the type, purpose, and appearance of heavy equipment can be used as metadata for searching and recommending heavy equipment.

In this embodiment, in the recommendation of heavy equipment, a metadata ontology can be constructed using types, uses, shapes, ranges, evaluations, real-time location information, usage history, failure history, and operation areas, etc., in which user preferences are well reflected. Through this, personalized services can be provided to users.

Here, the ontology development 101 methodology can be applied to construct a heavy equipment metadata knowledge base having an ontology structure. The Ontology Development 101 methodology is a methodology proposed by the team at Stanford University's Knowledge Systems laboratory, and consists of a total of 7 steps as shown in [Table 4].

[Table 4]

3 is a diagram showing a heavy equipment metadata ontology according to an embodiment of the present invention.

Referring to FIG. 3, it is an ontology model of a heavy equipment module expressed based on metadata such as heavy equipment type, price, location, and range. In FIG. 3, the relationship is represented by a black arrow, and the relationship with other classes is expressed.

The properties of an object are represented by dotted arrows, and the properties of each class are expressed. Through the heavy equipment metadata model, heavy equipment can be searched through relationships and attributes linked to metadata such as type, price, range, and location.

[Table 5] below describes the concept of ontology objects for constructing heavy equipment metadata ontology. Concepts of each object are expressed as a connection relationship with concepts of other related objects, and the heavy equipment metadata enables searching and recommending heavy equipment based on the relationship connected to each concept of each object.

[Table 5]

[Table 6] below shows the relationship between heavy equipment metadata expressed in the concept of an object and each metadata. The concepts of other objects connected to the machine concept are the main metadata of heavy equipment, and the connection with the machine concept can be connected by the relationship as shown in [Table 3].

[Table 6]

The connection between the machine concept and the person concept differs according to the owner, manager, and pilot related to the heavy equipment, and in the person concept, the pilot can be connected to the isemployedas relationship in charge of the operation of the heavy equipment. Also, the concept of Use can be linked to the isSimilarTo relationship to express similar uses.

Other metadata of heavy equipment can be expressed as attributes. Heavy equipment metadata expressed as attributes are shown in [Table 7].

[Table 7]

The level can be used to search for heavy equipment by considering the real-time location in the user's basic information. The evaluation score is metadata expressing the evaluation of heavy equipment of users using the heavy equipment in numerical terms and can be considered when determining the ranking of the heavy equipment to be recommended.

Concepts of other objects connected to the concept of heavy equipment have properties that contain information about the concepts of each object. The properties of each concept are shown in [Table 8].

[Table 8]

The Person concept has the names of people related to heavy equipment as an attribute. Users can search for owners, managers, and pilots through the attribute of the Person concept, and search for heavy equipment and receive recommendations through the attribute of the Machine concept connected thereto. The hasDescription attribute linked to the specification concept is a description of the specifications of the heavy equipment, and the user can search for the heavy equipment through search, and the heavy equipment can be recommended through the specifications that the user prefers.

4 is a diagram showing a user metadata ontology according to an embodiment of the present invention.

Referring to FIG. 4, in the user model, there are hasID representing the user's ID and hasPosition property representing the location. User is expressed based on the hasID and hasPostion properties and the relationship defined in [Table 9], and the connection of the concepts of each object is as shown in FIG. 4.

[Table 9]

5 is a diagram showing a provider metadata ontology according to an embodiment of the present invention.

Referring to FIG. 5, the provider group can be roughly classified into a construction equipment management business, a general construction equipment rental business, and an individual construction equipment rental business. In the provider model, there is hasID that represents the ID of the provider. The provider is expressed based on the hasID attribute and the relationship defined in <Figure 8>, and the connection of the concepts of each object is as shown in FIG. 5.

6 is a diagram showing a user's preference, similar use, and relationship ontology during a search according to an embodiment of the present invention.

6, the information request unit 120 determines a group of users using the real-time location of the user and the real-time location of the heavy equipment based on the heavy equipment metadata ontology for recommending heavy equipment, and selects the heavy equipment according to the group's preference for heavy equipment. You can search. In addition, users can search for heavy equipment by receiving user preferences and recommend them to users.

The search request module 121 searches for the location of the heavy equipment in real time based on the location of the user, but after determining the user group, it may search for the location-based heavy equipment according to the group's usage preference and similar uses connected through an isSimilarTo relationship.

When the user's information is entered, the group is determined according to the rules of Semantic Web Rule Language (SWRL) (Horrocks et al., 2004), and a query is generated and recommended based on the preferred use of the group to which the user belongs and the similar use of the preferred use. You can search for heavy equipment.

The query for user location-based heavy equipment search may search for heavy equipment through a connected relationship between concepts as shown in FIG. 6.

You can search for the preferred use defined through the group_Prefer_Use relationship of the UserGroup concept, and search for other uses connected with the isSimilarTo relationship as a similar use to the preferred use, and search for heavy equipment connected to each use. Relationships with similar uses may allow up to two links in total.

For example, if the user group's preferred use is “counter balance forklift”, then “reach type forklift” connected for similar use and “hinged bucket” connected with “reach type forklift” are derived. Can be searched.

7 is a diagram showing a user preferred heavy equipment relationship ontology during a search according to an embodiment of the present invention.

Referring to FIG. 7, the connection of the relationship with the main concepts in the heavy equipment model is shown. In FIG. 7, reference number (1) shows the connection when searching for connected heavy equipment based on the preferred heavy equipment metadata input by the user. Reference number (2) shows the connection when searching for heavy equipment by determining the uses of the heavy equipment metadata for similar purposes.

As described above, although the present invention has been described by the limited embodiments and drawings, the present invention is not limited to the above embodiments, which is, if one of ordinary skill in the field to which the present invention belongs, various modifications and Transformation is possible. Therefore, the idea of the present invention should be grasped only by the claims set forth below, and all equivalent or equivalent modifications thereof will be said to belong to the scope of the idea of the present invention.

110: information storage unit
111: information input module
112: Group classification module
113: evaluation module
120: information request unit
121: information retrieval module
122: weight calculation module
123: normalization module
130: Ontology construction unit
131: heavy equipment module
132: user module
133: provider module
140: heavy equipment recommendation department
141: analysis module
142: recommended module

Claims (5)

It stores the location information received from the GPS terminal installed in the heavy construction equipment, essential information input using the terminal of the heavy equipment provider and the terminal of the user, group information for classifying the provider and user into groups, and evaluation information of the provider and user. An information storage unit;
An information request unit that calculates and normalizes weights according to the requested information when the user makes a search request based on information stored in the information storage unit;
An ontology building unit for constructing metadata ontology of heavy equipment, users, and providers based on the data of the information storage unit and the information request unit; And
It characterized in that it comprises a heavy equipment recommendation unit that analyzes, weights, and recommends the requested information based on the metadata ontology.
Heavy equipment recommendation system utilizing the correlation between ontology-based metadata.
The method of claim 1,
The information storage unit
An information input module for storing location information received from a GPS terminal and essential information input using a terminal of a heavy equipment provider and a terminal of a user;
A group classification module for classifying the input information into groups using metadata association; And
It characterized in that it comprises an evaluation module for storing the satisfaction evaluated by the heavy equipment provider and the user
Heavy equipment recommendation system utilizing the correlation between ontology-based metadata.
The method of claim 1,
The information request unit
A search request module that searches for a location of heavy equipment received from a real-time GPS terminal based on a user's location, determines a user group, and then searches for a location-based heavy equipment according to the group's use preference and similar uses connected in a similar relationship;
The heavy equipment has different weights according to the searched condition, and a weight for recommendation of the searched heavy equipment is calculated together with a rating value input to the heavy equipment metadata ontology to determine a weight of the heavy equipment;
And a normalization module that normalizes the weights of metadata used for calculating the weight of the heavy equipment to have the same range in the heavy equipment.
Heavy equipment recommendation system utilizing the correlation between ontology-based metadata.
The method of claim 1,
The ontology building unit
A heavy equipment module for managing information on ID, type, price, range, evaluation, real-time location information, rental period, use history, failure history, payment guarantee, pilot, and appearance of the heavy equipment metadata together with attribute information;
A user module for configuring one ontology model by connecting with a heavy equipment model for personalized recommendation of heavy equipment to a user; And
And a provider module that connects with the provider when the provider inputs heavy equipment name, specifications, history information, and manager information to form one ontology model.
Heavy equipment recommendation system utilizing the correlation between ontology-based metadata.
The method of claim 1,
The heavy equipment recommendation unit
An analysis module that analyzes a user group, a provider group, and a preferred purpose based on the location of the user using metadata correlation; And
In accordance with the user's search request, characterized in that it comprises a recommendation module for recommending heavy equipment by utilizing metadata association
Heavy equipment recommendation system utilizing the correlation between ontology-based metadata.

Images