KR102540269B1 - Supporting method for company and company supporting server performing the same - Google Patents

Abstract

The present invention relates to an enterprise support method capable of supporting enterprise operation and an enterprise support server performing the same.
The present invention includes an information collection unit 110 for collecting information for business operation; an information analysis unit 120 for analyzing and storing the information in a database 300; Disclosed is an enterprise support server 100 comprising an information matching unit 130 for matching matching information to a target enterprise among the information to the target enterprise.

Description

Company support method and company support server performing the same {Supporting method for company and company supporting server performing the same}

The present invention relates to an enterprise support method capable of supporting enterprise operation and an enterprise support server performing the same.

Fostering and supporting SMEs has a huge impact on the national economy in terms of employment, production, export, and creation of added value. The government is also aware of this and has established and implemented various policies to support SMEs.

However, in terms of actual SMEs, especially start-ups that have just started their business, it is very difficult to recognize the program that is applicable to them among the various policies poured out. In particular, in the case of various project-type tasks supervised by the central government, local governments, private organizations, etc., which are not supported by a regular support system, they are announced in various formats on more sites than are usually known, and since the support period is short, dedicated staff Monitoring is needed.

In order to overcome these problems, the government or the private sector provides search portal sites, but a large amount of information is delivered without proper filtering of the contents that meet the needs of each company, resulting in spamming or primitive search such as simple keyword search. Because it uses the authentication method, only some information that is excessively filtered is received, so the effectiveness of the existing service is not high.

Even if you have luckily identified a support system that suits you, preparing numerous documents to submit for support is a huge burden in time, material, and human resources for small and medium-sized businesses. In particular, in a small and medium-sized business environment with many changes in manpower, it is difficult to manage various submission documents with continuity, and the process of applying for support projects is also a part that takes time and effort. Each site takes a different format, so there is a problem that requires endless repetition of learning and trial and error until you get used to it.

An object of the present invention is to recognize the above problems and needs, and provide necessary information (eg, small and medium-sized business support information, financial products, etc.) It is an object of the present invention to provide a company support method capable of matching a target company or matching a target company and other companies capable of forming a consortium, and a company support server that performs the same.

In addition, an object of the present invention is to automate standardized office work such as data collection and data submission according to matched information to greatly reduce the business burden and operating cost of the company, and support for companies that perform it to provide a server.

The present invention has been created to achieve the object of the present invention as described above, and the information collection unit 110 for collecting information for business operation; an information analysis unit 120 for analyzing and storing the information in a database 300; Disclosed is an enterprise support server 100 comprising an information matching unit 130 for matching matching information to a target enterprise among the information to the target enterprise.

The information collection unit 110 may include a support information collection module that collects the information and a company information collection module that collects the company information by receiving company information from the terminal 200 associated with the target company. .

The matching information may include at least one of support information for corporate support projects, investment information for corporate investment, financial information for financial products, and consortium information for cooperation between companies.

The information matching unit 130 may match the matching information corresponding to the target company based on the company information.

The information collection unit 110 may include a website management module for managing a website for collecting the information, and a website scraping module for accessing and scraping the website.

The information analysis unit 120 may include a data conversion module for converting the information into standard data, and a data learning module for extracting and learning learning data for matching the matching information to the target company.

The information matching unit 130 may match the matching information based on the company information and derivative information derived from the company information.

The enterprise support server 100 may further include a data collection/submission unit 140 including a data collection module for collecting data necessary for the target company in order to utilize the matching information.

When it is necessary to submit the collected data, the data collection/submission unit 140 may additionally include a data submission module for accessing a website where the collected data is to be submitted and submitting the collected data. there is.

The data collection/submission unit 140 may further include a data verification module for verifying the collected data.

In another aspect, the present invention is a company support method performed in the company support server 100, comprising: an information collection step of collecting information for business operation; an information analysis step of analyzing and storing the information in a database (300); Disclosed is a company support method comprising an information matching step of matching matching information matched to a target company among the information to the target company.

The enterprise support method according to the present invention and the enterprise support server performing the same can provide necessary information (eg, small and medium business support information, financial products) to the enterprise at an appropriate time, and furthermore, a financial institution that is helpful to the target enterprise. There is an advantage in that it can match the target company with the target company, or match the target company with other companies that can form a consortium.

Through this, the present invention has the advantage of maximizing the efficiency of the operation of the company by matching appropriate information, financial institutions, or other companies optimized for the needs of the company, which is difficult to identify and select by the company itself.

In addition, the present invention has the advantage of greatly reducing the business burden and operating cost of the company by automating standardized office work such as data collection and data submission according to matched information.

1 is a conceptual diagram showing an enterprise support system according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating the enterprise support server of FIG. 1 .
FIG. 3 is a block diagram illustrating the information matching unit of FIG. 2 .
FIG. 4 is a flowchart illustrating an enterprise support method performed in the enterprise support server of FIG. 1 .

Hereinafter, the enterprise support system according to the present invention will be described with reference to the accompanying drawings.

As shown in FIGS. 1 to 3 , the enterprise support system according to the present invention includes an enterprise support server 100 for performing an enterprise support method and at least one connected to the enterprise support server 100 through a network. It includes a terminal 200 and a database 300 connected to the enterprise support server 100 .

The enterprise support system is a solution for supporting the business of a business, and may be, for example, a solution for supporting the operation of a small business.

The terminal 200 is a terminal associated with a target company to be supported through the company support method performed in the company support system, and corresponds to a computing device connected to the company support server 100 to be described later through a network, For example, it may be implemented as a desktop, laptop, tablet PC or smart phone, and may include a network interface for network connection with the enterprise support server 100 and a user input/output interface for user input/output.

For example, the terminal 200 may correspond to a mobile terminal and may be connected to the enterprise support server 100 through cellular communication or Wi-Fi communication.

As another example, the terminal 200 may correspond to a desktop and may be connected to the enterprise support server 100 through the Internet.

The enterprise support server 100 is a server that is connected to the terminal 200 through a network to perform tasks according to the request of the terminal 200 or transmits data to the terminal 200, and includes a processor, memory, storage device, It may include a network interface, a user interface input device, and a user interface output device.

The processor may include a program executor and a memory manager for performing a company support method described below.

The memory manager can manage memory areas in memory that are read or written by an executor.

The storage device may be implemented as a non-volatile memory such as a solid state disk (SSD) or a hard disk drive (HDD), and may be used to store data necessary for the UI/UX server 200.

The network interface includes an environment for connecting to the terminal 200 through a network, and may include, for example, an adapter for LAN (Local Area Network) communication.

The enterprise support server 100 includes an information collection unit 110 that collects information for business operation, an information analysis unit 120 that analyzes and stores the information in a database 300, and a support target among the information. It may include an information matching unit 130 for matching matching information matched to the target company to the target company.

The information collection unit 110 is a configuration for collecting information for business operation, and various configurations are possible.

For example, the information collection unit 110 includes a support information collection module that collects the information and a company information collection module that collects the company information by receiving company information from the terminal 200 associated with the target company. can do.

The support information collection module may be configured to collect information useful for business operation, and may be configured in various ways.

Here, the above information can be anything related to business operation, such as support information for corporate support projects, investment information for corporate investment, financial information for financial products, consortium information for cooperation between companies, and information about other companies. possible.

Preferably, the support information collection module is configured to collect as much information as possible by accessing various websites capable of collecting information.

The list of websites accessed by the support information collection module is dynamically managed and can be added or changed if necessary.

The support information collection module can maximize the collection of information scattered on various websites by utilizing web scraping, RPA (robotic process automation), REST API, and the like.

The information collected through the support information collection module may be stored and managed in the database 300 .

The corporate information collection module is configured to collect corporate information by receiving corporate information from the terminal 200 associated with the target company, and various configurations are possible.

Here, the company information may include all information related to the company, such as year of establishment, company size, financial status including assets, capital, and liabilities, certification status, affiliated industry group, affiliated region, and the like.

In addition, the company information may include information searched by the target company or keyword information input in advance.

In addition, the company information may include information related to a history of tasks performed or supported by the target company in the past.

Of course, the company information collection module, in addition to collecting company information by receiving company information from the terminal 200 associated with the target company, can also collect company information about the company using web scraping or the like.

The information analysis unit 120 is configured to analyze the collected information and store it in the database 300, and various configurations are possible.

In one embodiment, the information analysis unit 120 may include a data conversion module for converting collected information into structured data, and a data learning module for extracting and learning learning data for matching matching information to a target company. can

At this time, the data conversion module, when an image is included in the collected information, can extract and analyze text from the image.

In addition, when unstructured data (eg, request for proposal) is included among the collected information, the data conversion module may convert the unstructured data into structured data by text mining.

The data conversion module, in order to convert the unstructured data into structured data, goes through processes such as preprocessing, association analysis (Associate), cluster analysis (Summarize), classification (Categorize), etc. , based on this, meaningful concepts or characteristics can be extracted.

The structured data analyzed by the data conversion module may be stored and managed in the database 300 .

The data learning module is a configuration for extracting and learning learning data for matching matching information to a target company, and various configurations are possible.

Specifically, the data learning module may extract and learn learning data for matching matching information among the structured data converted by the data conversion module.

The information matching unit 130 can be configured in a variety of configurations to match matching information that is matched to a target company among the collected information to a target company.

Here, the matching information is information to be matched and recommended to a target company among the information collected by the above-described information collection unit 110, and includes support information on corporate support projects, investment information for corporate investment, and financial information on financial products. , and may include at least one of consortium information for inter-company cooperation.

At this time, the information matching unit 130 may match matching information based on company information on the target company.

Specifically, the information matching unit 130 may extract matching information by filtering collected information based on company information and transmit the extracted matching information to the terminal 200 associated with the target company.

In addition, the information matching unit 130 may be equipped with an artificial intelligence module to extract matching information step by step based on company information as well as derived information derived from company information.

Here, the derived information may be information obtained by identifying corporate needs or corporate intentions through corporate information.

In addition, the information matching unit 130 may adopt various recommendation algorithms to extract matching information optimized for a target company and recommend it to the target company.

For example, the information matching unit 130 may extract matching information using content-based filtering, a knowledge-based recommendation system, and the like, and recommend the matching information to target companies.

Furthermore, the information matching unit 130 may recommend authentication items necessary and suitable for the current situation of the target company based on company information (business history, field, certification holding, etc.).

Through this, the target company has the advantage of strengthening the authentication items that the company needs to reinforce in the current situation at an appropriate time to enhance corporate competitiveness and effectively utilizing the matching information recommended in a timely manner.

The information matching unit 130 may continuously match and recommend matching information to target companies according to the size, field, and time of the company.

Hereinafter, embodiments of utilizing information collected according to the type of matching information will be described in detail.

In the first embodiment, the matching information may be policy information or task information for supporting companies in the government, local governments, private organizations, and the like.

At this time, the information matching unit 130 may match matching information matched with the target company to the target company, and the target company may utilize the matched matching information for business operation.

However, in the case of policy information or task information for corporate support, it is announced in the form of a request for proposal, and screening through corporate support may be required.

In this case, the target company needs to submit data in the form of a proposal through the website in order to utilize the matched matching information.

The data that companies need to submit for application can be accompanied by numerous documents, and preparing these materials can act as a great burden for companies with small human resources.

In order to reduce the burden on companies, the company support server 100 according to the present invention additionally includes a data collection/submission unit 140 including a data collection module for collecting data necessary for a target company to utilize matching information. can include

Here, data can be understood as a concept that includes all of the various documents involved in utilizing matching information. For example, the data may include various documents such as the business registration certificate of the target company, organization chart, manpower status, certification, and intellectual property rights.

The data collection module is a configuration for collecting data necessary for the target company, and obtains the data from data separately stored in the database 300 or uses technology such as RPA to automatically access the website for issuing the data. It can be issued by connecting to .

The data collection module may manage the data so that changes and corrections are reflected when there is a change or correction to the collected data.

That is, in the case of a document having an expiration date (or issuance date), when the validity period expires, the data collection module can automatically connect to a website for issuing the document and obtain a new valid document.

In addition, the data collection module sends information about expiration dates, such as renewal date and expiration date, to the associated terminal 200 for reissuance, renewal, or data (eg, authentication) of which the validity period has arrived among the collected data. can provide

On the other hand, the data collection / submission unit 140, when it is necessary to submit the data collected through the data collection module, adds a data submission module that accesses the website where the collected data is to be submitted and submits the collected data. can be included with

For example, the data to be submitted may include all documents collected by the data collection module (documents stored in the database 300, documents issued through a website, documents prepared separately, etc.).

The data collection/submission unit 140 may list (table) the documents included in the collected data prior to data submission, visualize them, and provide them to the terminal 200 .

There is an advantage in intuitively checking prepared documents and incomplete documents among data to be submitted through the terminal 200 .

In addition, the data collection/submission unit 140 may further include a data verification module for verifying the collected data.

The data verification module, prior to data submission, verifies the prepared data and checks whether all the documents to be submitted are provided, and various configurations are possible.

For example, the data verification module may verify whether the prepared data is complete by utilizing RPA and text extraction technology (eg, OCR).

When it is determined that all the data is complete through the data verification module, the data collection/submission unit 140 can automatically access the web page where the collected data is to be submitted and submit (upload) the data. .

In the second embodiment, the matching information may be investment information for business investment.

At this time, the information collection unit 110 may collect corporate information on a company that is an investment target (investment company) and a company that wants to invest in the company (investment company).

The information matching unit 130 may match the investment target company and the investment company according to the company information of the investment company and the company information of the investment company, and mutually recommend each company to each other.

In the case of investment target companies, it is difficult to find optimized investment information, and investment companies also have problems in finding suitable investment destinations. From the point of view of companies, it is easy to commercialize in the early stage, and from the point of view of venture capital (VC), there is an advantage in that profits can be realized by investing in a company that is recommended for investment.

In the third embodiment, the matching information may be consortium information for cooperation between companies.

At this time, the information collection unit 110 may collect corporate information (business history, intellectual property rights, etc.) on various companies.

When companies carry out government-sponsored tasks, there are cases where one company performs independently, but there may be cases where two or more companies form a consortium and mutually collaborate.

However, it is very difficult to find a suitable company to form a consortium with other companies, and the success rate is very low.

However, the information matching unit 130 of the present invention may match companies that are suitable for forming a consortium because mutual technology complementation or cooperation is possible, and mutually recommend each company.

In a fourth embodiment, the matching information may be financial information about a financial product.

In this case, the information collection unit 110 may collect company information about various companies, company information about various financial institutions, and financial information about financial products of financial institutions.

If a company intends to utilize a financial product through a financial institution (for example, a guarantee institution such as Korea Technology Finance Corporation), it contacts each independent financial institution to find the most advantageous financial product and compares loan conditions through consultation. This can be a very cumbersome and time-consuming process.

By the way, the information matching unit 130 of the present invention may recommend a financial product of an optimal financial institution to the company by utilizing company information (including information necessary for determining a financial product) provided by the company.

At this time, the information matching unit 130 recommends a matching company to a financial institution providing a financial product, and as a result of the matching, if the detailed conditions (loan interest rate, period, etc.) of the financial product provided by the financial institution are changed, the changed finance Information can be provided back to the company.

Through this, even if a company does not contact each financial institution and compares financial products, it is possible to recommend the best financial institution's financial products, and furthermore, by providing corporate information to financial institutions, reverse auctions for financial institutions' financial products can be conducted. It has the advantage of being able to recommend financial products with the best conditions.

The present invention can be implemented as computer readable code on a computer readable recording medium, and the computer readable recording medium includes all types of recording devices storing data that can be read by a computer system. .

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. In addition, the computer-readable recording medium may be distributed to computer systems connected through a network, so that computer-readable codes may be stored and executed in a distributed manner.

In another aspect, the above-described enterprise support method performed by the enterprise support server 100, as shown in FIG. 4, includes an information collection step (S401) of collecting information for business operation, and a database by analyzing the information. It may include an information analysis step (S402) stored in 300, and an information matching step (S403) of matching matching information matched to a target company among the information to the target company.

The information collection step (S401) may include a support information collection step of collecting information and a company information collection step of collecting company information by receiving company information from the terminal 200 associated with the target company.

The information analysis step (S402) may include a data conversion step of converting the collected information into standard data, and a data learning step of extracting and learning learning data for matching the matching information to the target company.

In the information matching step (S403), matching information corresponding to a target company may be matched based on company information.

Furthermore, in the information matching step (S403), the matching information may be matched based on company information and derivative information derived from the company information.

Meanwhile, the company support method may further include a data collection step (S404) of collecting data necessary for the target company in order to utilize the matching information.

In addition, the company support method may further include a data submission step (S405) of accessing a website to which the collected data is to be submitted and submitting the collected data, when the collected data needs to be submitted.

At this time, the enterprise support method may further include a data verification step of verifying the collected data before the data submission step (S405).

Hereinafter, a company support method according to an embodiment of the present invention will be described in detail with reference to FIG. 5 .

5 illustrates an embodiment of a company support method for providing information on a request for proposal (RFP) for a project ordered by the government, local government, public institution, public organization or private sector to a company as matching information.

Referring to FIG. 5, in the information collection step (S401), the information collection unit 110 collects company information from the terminal 200 associated with the company, and corporate support information through various web page scrapings through the support information collection module. As such, various Requests for Proposals (RPFs) can be collected.

Here, corporate information is all information that can be collected related to the target company, and user-oriented information (foundation year, company size, financial status including assets, capital, liabilities, etc., certification status, industry group, region, target company It may be user propensity information that can infer the propensity or preferred field of a company (user), such as searched information, keyword information entered in advance, and information related to the history of tasks performed or supported by the target company in the past.

Next, in the information analysis step (S402), the information analysis unit 120 preprocesses (tokenizes, refines, normalizes) the user propensity information collected as company information and the RFP collected as company support information. etc.) can be converted into an analyzable form of formal data.

At this time, the collected user propensity information and request for proposal (RFP) may include unstructured data such as images and photos other than text. At this time, the information analysis unit 120 extracts text from unstructured data such as images and photos It can be pretreated in the same way.

Of course, various natural language processing methods may be applied to the information analysis unit 120.

For example, the information analysis unit 120 assigns an identifier (user-ID) to each user (company) to the collected user propensity information, and assigns an identifier (user-ID) to each user (company) for each task performed or supported in the past ( RFP-ID).

In addition, the information analysis unit 120 uses BoW (Bag of Word), Document-Term Matrix (DTM), Various word representation methods such as TF-IDF (Term Frequency-Inverse Document Frequency) can be applied.

For example, the information analysis unit 120 may express the words contained in the RFPs as a word matrix (RFP-word matrix) in order to digitize the letters included in the RFPs.

User propensity information and RFP analyzed in the form of structured data that can be analyzed by the information analysis unit 120 may be stored and managed in a database.

Next, in the information matching step (S403), the information matching unit 130 may perform text mining to infer the subject of the RFP and the user propensity information analyzed as the structured data.

The information matching unit 130 may utilize various text mining techniques, and may perform, for example, topic modeling.

More specifically, the information matching unit 130 may derive a subject distribution (user subject distribution) of users (company) from the TF-IDF derived using user propensity information.

In addition, the information matching unit 130 may apply a Latent Dirichlet Allocation (LDA) algorithm to the RFP-word matrix as a method of topic modeling in order to infer the RFP topic distribution using the RFP-word matrix. there is.

At this time, the information matching unit 130 may infer topics of the RFP based on a probability distribution through LDA model learning.

Next, the information matching unit 130 may match an RFP that is most similar (appropriate) to the subject distribution of the user (company) based on the similarity between the user subject distribution and the RFP subject distribution, and provide the user (company) with the matching RFP. there is.

At this time, the information matching unit 130 may apply various methods (eg, cosine similarity, Euclidean distance, Jacquard similarity, etc.) to calculate the similarity between user-topic distributed RFP topic distributions.

For example, the information matching unit 130 may calculate a cosine similarity to calculate a similarity between user topic distributions and RFP topic distributions.

The information matching unit 130 may provide the most optimized RFP information to the user (company) by filtering the similarity after calculating the similarity using the cosine similarity.

In the case of FIG. 5, a case of matching an RFP as company support information to a company using a content-based recommendation algorithm has been described as an example, but it goes without saying that the company support method according to the present invention can utilize various recommendation algorithms.

Since the above has only been described with respect to some of the preferred embodiments that can be implemented by the present invention, as noted, the scope of the present invention should not be construed as being limited to the above embodiments, and the scope of the present invention described above It will be said that the technical idea and the technical idea together with the root are all included in the scope of the present invention.

100: enterprise support server 200: terminal
300: database

Claims (11)

An information collection unit 110 that collects business support information for business operation;
an information analysis unit 120 for analyzing and storing the business support information in a database 300;
Includes an information matching unit 130 for matching with the target company in order to provide matching information matching with the target company to the target company among the company support information analyzed and stored by the information analysis unit 120 and
The information collection unit 110 includes a support information collection module that collects the company support information and a company information collection module that collects the company information by receiving company information from the terminal 200 associated with the target company, ,
The information matching unit 130 matches the matching information corresponding to the target company based on the company information,
The information analysis unit 120,
A data conversion module for converting the company support information into structured data, and a data learning module for extracting and learning learning data for matching the matching information to the target company,
The data conversion module, when unstructured data is included among the collected information, converts the unstructured data into structured data by text mining,
The data conversion module, in order to convert the unstructured data into structured data, preprocesses information, association analysis (Associate), cluster analysis (Cluster), summary (Summarize), and classification (Categorize) through the process of meaning. Extracting concepts or characteristics that exist,
The structured data analyzed by the data conversion module is stored and managed in the database 300,
The information matching unit 130 performs matching between the target company and the matching information based on learning by the data learning module,
The information matching unit 130 is equipped with an artificial intelligence module to extract matching information based on company information and derivative information derived based on the company information.
The matching information includes at least one of support information for corporate support projects, investment information for corporate investment, financial information for financial products, and consortium information for cooperation between enterprises,
The information collection unit 110 includes a website management module for managing a website for collecting the information, and a website scraping module for accessing and scraping the website,
Further comprising a data collection / submission unit 140 including a data collection module for collecting data necessary for the target company to utilize the matching information,
When it is necessary to submit the collected data, the data collection/submission unit 140 further includes a data submission module for accessing a website where the collected data is to be submitted and submitting the collected data,
The information collection unit 110 collects company information from the terminal 200 associated with the target company, collects request for proposals (RPFs) as company support information through web page scraping through the support information collection module,
The company information is information that can be collected in connection with the target company, and is user propensity information that can infer the propensity or preferred field of the target company,
The user propensity information includes the year of establishment, "financial status including company size, assets, capital, and liabilities", certification status, affiliated industry group, affiliated region, information searched by the target company, keyword information entered in advance, It includes information related to the history of tasks that the target company has performed or supported in the past,
The information analysis unit 120 pre-processes the user propensity information collected as company information and the RFP collected as company support information and converts them into an analyzable structured data form;
When the user propensity information and the request for proposal (RFP) include unstructured data of images and photos other than text, the information analysis unit 120 extracts and preprocesses the text from the unstructured data of the collected images and photos,
The information analysis unit 120 assigns an identifier (user-ID) to each target company to the collected user propensity information and assigns an identifier (RFP-ID) to each target company for each task performed or supported in the past,
The information analysis unit 120, in order to digitize the collected user propensity information and the letters included in the RFPs, into numbers that can be analyzed, the BoW (Bag of Word), document-term matrix (Document-Term Matrix) , DTM), TF-IDF (Term Frequency-Inverse Document Frequency) word expression method is applied,
The information analysis unit 120 expresses a word matrix (RFP-word matrix) for the RFP to digitize the letters included in the RFPs,
The user propensity information and the RFP analyzed in the form of structured data that can be analyzed by the information analysis unit 120 are stored and managed in the database 300,
The information matching unit 130 performs text mining including topic modeling for inferring the subject of the request for proposal (RFP) and the user propensity information analyzed as structured data,
The information matching unit 130 derives the subject distribution (user subject distribution) of the target company from the TF-IDF derived using the user propensity information,
The information matching unit 130 applies a Latent Dirichlet Allocation (LDA) algorithm to the RFP-word matrix to infer an RFP topic distribution using the RFP-word matrix,
The information matching unit 130 infers subjects of the request for proposal (RFP) based on a probability distribution through LDA model learning,
The information matching unit 130 matches a request for proposal (RFP) most similar to the subject distribution of the target company based on the degree of similarity between the user topic distribution and the RFP topic distribution, and provides it to the target company;
The information matching unit 130 applies any one of cosine similarity, Euclidean distance, and Jacquard similarity to calculate the similarity between the user topic distribution and the RFP topic distribution,
The information matching unit 130 calculates a degree of similarity between the user topic distribution and the RFP topic distribution, filters it, and provides the most optimized request for proposal (RFP) information to the target company. system. delete delete delete delete delete delete delete delete The method of claim 1,
The data collection / submission unit 140,
Enterprise support system, characterized in that it further comprises a data verification module for verifying the collected data. As an enterprise support method by the enterprise support system according to any one of claims 1 and 10,
an information collection step of collecting information for business operation by the information collection unit 110;
an information analysis step of analyzing the information by the information analysis unit 120 and storing it in a database 300;
In order to provide the target company with matching information that matches the target company to be supported among the information analyzed and stored by the information analysis unit 120, an information matching step of matching the target company,
The information analysis unit 120 includes a data conversion module for converting the information into standard data, and a data learning module for extracting and learning learning data for matching the matching information to the target company,
In the information collection step, the information collection unit 110 collects company information from the terminal 200 associated with the target company, and through the web page scraping through the support information collection module, the proposal request as company support information ( RPF) are collected,
In the information analysis step, the information analysis unit 120 pre-processes the user propensity information collected as company information and the RFP collected as company support information and converts them into an analyzable structured data form,
In the information matching step, the information matching unit 130 performs text mining including topic modeling for inferring the subject of the request for proposal (RFP) and the user tendency information analyzed as structured data. An enterprise support method, characterized in that performing.

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