KR20190143791A - Method, apparatus and program for matching procurement bidding information - Google Patents

Abstract

Disclosed is a method for matching financing bidding information performed by a computing device, which comprises the steps of: obtaining applicant information; collecting financing bidding information; extracting the financing bidding information matched with the applicant information; and providing the extracted financing bidding information.

Description

METHOD, APPARATUS AND PROGRAM FOR MATCHING PROCUREMENT BIDDING INFORMATION}

The present invention relates to a procurement bidding information matching method, apparatus, and program.

Artificial intelligence technologies include machine learning (Machine Learning) technology, and deep learning technology, which is widely used for analyzing images, especially machine learning technology.

Deep learning is a set of machine learning algorithms that attempts a high level of abstraction (summarizing key content or functions in large amounts of data or complex data) through a combination of several nonlinear transformations. Is defined. Deep learning can be seen as a field of machine learning that teaches computers how people think in a large framework.

Block chain technology is a decentralized data storage technology that stores data in blocks, connects them in a chain, and replicates and stores them simultaneously on numerous computers. Also called a public trading ledger, it sends the transaction history to all users participating in a transaction without keeping transaction records on a centralized server, and every transaction participant can share and contrast information to forge or alter data. Characterized in that there is no.

MICE is an abbreviation of English letters such as meetings, incentive trips, conventions, exhibitions and events. In a narrow sense, MICE is a promising industry focused on international conferences and exhibitions. In the broader sense, it means a convergence industry that includes participant-centered reward tourism and mega events.

Registered Patent Publication No. 10-0339055, Registered on May 21, 2002

The problem to be solved by the present invention is to provide a procurement bidding information matching method, apparatus and program.

Problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

Procurement bid information matching method according to an aspect of the present invention for solving the above problems, the step of obtaining applicant information (S101), collecting procurement bidding information (S102), procurement bid matching the applicant information Extracting the information (S103) and providing the extracted procurement bidding information (S104).

In addition, the step (S101) may include a step (S105) of acquiring evaluation information of the applicant and a step (S106) of acquiring resource information of the applicant.

In addition, the step (S103), the step (S107) of evaluating the selectability of the applicant for each of the collected procurement bidding information based on the evaluation information of the applicant and the collected based on the resource information of the applicant It may include the step (S108) of evaluating the performance of the applicant for each procurement bidding information.

In addition, the step (S107), the step (S109) of determining whether the applicant can support the collected procurement bidding information, and the determination result, the selection of the applicant for each of the one or more supportable procurement bidding information It may include the step of calculating the probability (S110).

In addition, the step S103 may include extracting one or more procurement bidding information for which the calculated selection probability exceeds a preset reference value (S111) and for each of the one or more procurement bidding information extracted in the step (S111). Calculating an expected return (S112) and determining priorities for each of the one or more procurement bidding information extracted in the step (S111) based on the selection probability and the expected return (S113), Step S104 may include providing one or more procurement bidding information to the applicant based on the priority (S114).

In addition, the step (S108), the step of determining whether or not duplicate bidding and the number of duplicate bids (S115) and calculating the extended resources of the applicant based on the resource information of the applicant and the duplicate bid multiples (S116) In addition, the step (S114) may include the step (S117) of providing the applicant with the number of procurement bidding information that can be supported by the applicant based on the enlarged resource.

In addition, the step (S102) includes the step of collecting the MICE procurement bidding information (S118), the step (S103), the procurement bidding items included in the MICE procurement bidding information based on the evaluation information of the applicant And extracting one or more procurement tender items that can be supported by the applicant (S119), and the step (S104) may include providing information on the extracted one or more procurement tender items (S120). Can be.

In addition, the step (S120), collecting information on one or more companies that can perform one or more procurement bidding items that the applicant can not support among the procurement bidding items included in the MICE procurement bidding information (S121), Providing information about the one or more companies (S122), generating information for jointly bidding on the MICE procurement bidding information based on at least some of the one or more companies and the applicant's information (S123) and the generation It may include the step (S124) for transmitting the joint bidding information.

According to an aspect of the present invention, there is provided a procurement bidding information providing apparatus including a memory for storing one or more instructions and a processor for executing the one or more instructions stored in the memory. By executing the above instructions, the step of acquiring applicant information, collecting procurement bidding information, extracting procurement bidding information matching the applicant information, and providing the extracted procurement bidding information are performed.

In accordance with an aspect of the present invention for solving the above problems is provided a computer program stored in a computer-readable recording medium that can be combined with a computer that is hardware, to perform the procurement bidding information matching method according to the disclosed embodiment.

Other specific details of the invention are included in the detailed description and drawings.

According to the disclosed embodiment, there is an effect that a service that can automatically extract and provide the appropriate procurement bidding information to the applicant is provided.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a flowchart illustrating a procurement bidding information matching method according to an exemplary embodiment.
2 is a flowchart illustrating a method of obtaining applicant information according to an exemplary embodiment.
3 is a flowchart illustrating a method of extracting procurement bidding information according to an exemplary embodiment.
4 is a flowchart illustrating an example of evaluating a candidate's selection possibility.
5 is a flowchart illustrating an example of extracting and providing procurement bidding information.
6 is a flowchart illustrating a method of evaluating feasibility according to an embodiment.
7 is a flowchart illustrating a method of matching MICE procurement bidding information according to an exemplary embodiment.
8 is a flowchart illustrating a method of providing co-bidding information according to an exemplary embodiment.
9 is a block diagram of an apparatus according to an embodiment.

Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but can be embodied in various different forms, and the present embodiments only make the disclosure of the present invention complete, and those of ordinary skill in the art that It is provided to fully inform the skilled worker of the scope of the invention, which is defined only by the scope of the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, "comprises" and / or "comprising" does not exclude the presence or addition of one or more other components in addition to the mentioned components. Like reference numerals refer to like elements throughout, and "and / or" includes each and all combinations of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, these components are of course not limited by these terms. These terms are only used to distinguish one component from another. Therefore, of course, the first component mentioned below may be a second component within the technical spirit of the present invention.

Unless otherwise defined, all terms used in the present specification (including technical and scientific terms) may be used in a sense that can be commonly understood by those skilled in the art. In addition, terms that are defined in a commonly used dictionary are not ideally or excessively interpreted unless they are specifically defined clearly.

As used herein, the term "part" or "module" refers to a hardware component such as software, FPGA, or ASIC, and the "part" or "module" plays certain roles. However, "part" or "module" is not meant to be limited to software or hardware. The “unit” or “module” may be configured to be in an addressable storage medium or may be configured to play one or more processors. Thus, as an example, a "part" or "module" may include components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Functions provided within components and "parts" or "modules" may be combined into smaller numbers of components and "parts" or "modules" or into additional components and "parts" or "modules". Can be further separated.

In the present specification, the computing device refers to any kind of hardware device including at least one processor, and may be understood as a meaning encompassing a software configuration that operates on the hardware device according to an embodiment. For example, a computing device may be understood as including, but not limited to, a smartphone, a tablet PC, a computer, and a user client and an application running on each device.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in connection with FIGS. 1 to 8 is described as being performed by a computing device, but the subject of each step is not limited thereto, and at least some of the steps may be performed in different devices according to embodiments. It may be.

1 is a flowchart illustrating a procurement bidding information matching method according to an exemplary embodiment.

In step S101, the computing device obtains applicant information.

In one embodiment, the applicant information may include information about a company (applicant) that performs procurement bidding, but is not limited thereto. In this specification, the term 'company' may be understood to encompass not only a business such as a sole proprietor or a corporation, but also a general individual, and does not limit a particular subject.

In step S102, the computing device collects procurement bidding information.

In one embodiment, the procurement bidding information may mean, but is not limited to, the tender notice distributed by the government for procurement. Distributors of procurement bidding information may include, but are not limited to, public enterprises, private companies, and individuals.

The kind of bidding announcement may also include various kinds such as spot procurement and service procurement, and is not limited to a specific kind. For example, a bid notice may be a bid notice for procuring and supplying a specific product, or a bid notice for procuring and supplying a research service or a professional service business.

In step S103, the computing device extracts procurement bidding information that matches the applicant information.

In one embodiment, the computing device may extract and provide procurement bidding information suitable for each applicant according to the applicant information.

For example, the computing device obtains various applicant information such as career, business history, sales, performance, business area, past procurement history of each applicant, and analyzes it to extract procurement bidding information that can be easily performed by each applicant. And may be provided.

According to an embodiment, an artificial intelligence model for extracting a fitness by comparing applicant information and procurement bidding information may be utilized. In this specification, an artificial intelligence model is understood as meaning including a model trained through machine learning.

For example, the artificial intelligence model may mean a model trained using existing procurement bidding information and big data including information on a company selected according to each procurement bidding information as learning data. In addition, the information on the procurement performance of the selected companies is included in the training data, and information on whether each company actually performed the procurement business is also collected as big data, which can be used to train the AI model. .

A detailed method of extracting and providing procurement bidding information corresponding to applicant information will be described later.

In step S104, the computing device provides the extracted procurement bidding information to the applicant.

In one embodiment, the procurement bidding information provided to the applicant may include information for bidding on the procurement. According to an embodiment, the computing device automatically supports the extracted procurement bidding information or automatically creates at least some or all of the proposals for supporting the extracted procurement bidding information according to a selection input or a preset content from the applicant. It can also be provided to applicants.

For example, the computing device may obtain the form of the proposal based on the procurement bidding information, and prepare the proposal using the applicant information. According to an embodiment, the computing device extracts candidate information suitable for procurement bidding information (that is, an applicant's advantage corresponding to the procurement bidding information) using an artificial intelligence model, and automatically proposes a proposal based on the extracted applicant information. I can write it.

As another example, the computing device may assist the applicant in preparing a proposal by extracting candidate information suitable for procurement bidding information using the artificial intelligence model and providing the candidate to the applicant.

2 is a flowchart illustrating a method of obtaining applicant information according to an exemplary embodiment.

In operation S101, the computing device may acquire evaluation information of the applicant (S105).

For example, the applicant's evaluation information may include information for quantitative and qualitative evaluation of the applicant, such as the applicant's career information, performance information, business performance information.

In addition, the computing device may acquire resource information of the applicant (S106).

For example, the applicant's resource information includes information on the applicant's production capacity, infrastructure, inventory, manpower information, current procurement projects, and how much resources he / she has to perform each procurement task. can do.

3 is a flowchart illustrating a method of extracting procurement bidding information according to an exemplary embodiment.

In operation S103, the computing device may evaluate the selectability of the applicant for each of the collected procurement bidding information based on the evaluation information of the applicant (S107).

A detailed method of evaluating the applicability of applicants will be described later with reference to FIG. 4.

In addition, the computing device may evaluate the performance of the applicant for each of the collected procurement bidding information based on the volunteer information of the applicant (S108).

For example, based on the applicant's resource information, the computing device determines whether or not the applicant has resources to perform procurement when the bid for each procurement bidding information is successful. Assess the likelihood

4 is a flowchart illustrating an example of evaluating a candidate's selection possibility.

In the above-described step (S107), the computing device may determine whether the applicant can support the collected procurement bidding information (S109).

For example, each procurement bidding information may include information on qualifications for bidding on the procurement project. The computing device may determine whether the support is possible by comparing the qualification conditions included in the procurement bidding information with the applicant information, and extract only the procurement bidding information that the applicant can support.

In addition, as a result of the determination, the selection probability of the applicant for each of the at least one procurement bidding information that can be supported may be calculated (S110).

For example, satisfying the qualifications included in each procurement bidding information may not mean that the bidding for the procurement bidding information is successful (ie, selected). Accordingly, the computing device may calculate a probability that the candidate can be selected for each procurement bidding information by analyzing and comparing the information included in the procurement bidding information with the applicant information.

Various AI models can be used to calculate the probability. For example, as a model trained based on machine learning, an artificial intelligence model capable of inputting applicant information and procurement bidding information and outputting a selection probability may be utilized.

In one embodiment, the artificial intelligence model may extract a parameter for calculating a selection probability from each applicant information and procurement bidding information through natural language processing. The computing device may obtain the probability of selection as an output thereof by inputting the extracted parameters into the artificial intelligence model. The AI model may be trained using procurement bidding information that has been previously terminated and big data including information on applicants selected corresponding to the procurement bidding information. According to an embodiment, the learned model may include parameters obtained by preprocessing the big data.

In another embodiment, a model from which parameters can be extracted may be separately learned and used, or after the computing device guesses a parameter, provides a speculative result to the applicant, and then inputs the selected parameter into the artificial intelligence model. Probability of selection may be calculated.

5 is a flowchart illustrating an example of extracting and providing procurement bidding information.

In operation S103, the computing device may extract one or more procurement bidding information in which the calculated selection probability exceeds a preset reference value in operation S111.

In one embodiment, the reference value may be set automatically or may be preset by the volunteer. For example, the computing device may extract only procurement bidding information having a selection probability of 70% or more by the applicant's selection. The computing device may receive a reference value as a numerical value by selection of an applicant, or may receive an input value through a predetermined standard such as high, medium, or low through a user interface.

In addition, the computing device may calculate an expected profit for each of the one or more procurement bidding information extracted in the step S111 (S112).

According to an embodiment of the present disclosure, the computing device may calculate an expected value of sales according to each procurement bidding information, and calculate a cost of an applicant for performing this. Based on this, the computing device may calculate an expected return for each procurement bidding information.

According to an embodiment, when the applicant needs to provide the service according to the procurement bidding information, the computing device may calculate the size of the expected service and provide the service to the applicant. In addition, the computing device may obtain information about the personnel to perform the respective services, and calculate the cost in consideration of the size of the service and the wages of the respective personnel.

In addition, the computing device may determine the priority of each of the one or more procurement bidding information extracted in the step S111 based on the selection probability and the expected profit in operation S113.

For example, a computing device may assign higher priorities with higher selection probabilities and higher expected returns.

According to an embodiment of the present disclosure, the computing device may calculate an expected value obtained by multiplying the expected profit and the selection probability for each procurement bidding information. The computing device may give higher priority as the expected value is higher. Priority may be given as a ranking, or as a score, or a ranking and a score may be given together.

In addition, the computing device may provide one or more procurement bidding information to the applicant based on the priority (S114).

For example, the computing device may provide the applicant with a predetermined number of procurement bidding information in ascending order of priority. The amount of procurement bidding information provided may be provided on the basis of the number, provided on the basis of the expected profit, may be provided on the basis of the cost required to perform each procurement, and the resources required to perform each procurement. It may be provided based on the amount of.

For example, if there are currently 100 resources for the applicant to perform procurement, the computing device may provide procurement bidding information such that the sum of resources necessary for performing the procurement bidding information provided is 100 or less.

As another example, the computing device may provide procurement bidding information in consideration of duplicate bidding. For example, not all procurement bidding information may be selected, so procurement bidding information exceeding each applicant's resources may be provided. In this case, it is impossible to carry out when all are selected, it is possible to select only as many procurement projects as can be performed based on the priority if all selected.

6 is a flowchart illustrating a method of evaluating feasibility according to an embodiment.

In the above-described step (S108), the computing device may determine whether the applicant's double bidding and the duplicate bid multiple (S115).

For example, the computing device may determine how many times procurement bidding information for the candidate is provided to the applicant.

In addition, the computing device may calculate the extended resource of the applicant based on the volunteer information of the applicant and the overlapped bid multiple (S116).

For example, the computing device may calculate the enlarged resource of the applicant by multiplying the number of duplicate bids by the resource information of the applicant.

In operation S114, the computing device may provide the applicant with the number of procurement bidding information that the applicant can support based on the enlarged resource in operation S117.

For example, if the multiple of multiple bidding is three times, the computing device may provide procurement bidding information corresponding to three times the resources of the applicant.

In one embodiment, the computing device may provide procurement bidding information based on the expanded resource, but if the required resource of the single procurement bidding information exceeds the applicant's resource, the procurement bidding information may not be provided to the applicant. That is, the computing device may provide procurement bidding information that requires resources less than the resources of the applicant, but may provide a number of procurement bidding information such that the required resources of the entire procurement bidding information are less than or equal to the extended resources of the applicant.

In one embodiment, each procurement bidding information and corresponding candidate information may be managed in a blockchain, thereby preventing the applicant from bidding on procurement based on false information. For example, support information, selection information, and performance information that the applicant bids on procurement information are all managed through the blockchain, and if the procurement cannot be performed normally due to false information, the information is also stored through the blockchain. It can be managed to penalize future support.

In addition, since each procurement bidding information and support, selection, and performance information are managed transparently through the blockchain, it is possible to review and verify each business performance information stored in the blockchain after the fact.

The type of procurement bidding information according to the disclosed embodiment is not limited, but according to an embodiment, procurement bidding information may mean MICE procurement bidding information. MICE is an abbreviation of English letters such as meetings, incentive trips, conventions, exhibitions and events. In a narrow sense, MICE is a promising industry focused on international conferences and exhibitions. In the broader sense, it means a convergence industry that includes participant-centered reward tourism and mega events.

7 is a flowchart illustrating a method of matching MICE procurement bidding information according to an exemplary embodiment.

In operation S102, the computing device may collect MICE procurement bidding information S118.

In one embodiment, the MICE procurement bidding information may include various procurement bidding items. For example, the MICE procurement bidding information may include procurement bidding items for meetings, tourism, shopping, transportation (air, bus, etc.), lodging, and exhibition.

In operation S103, the computing device may extract one or more procurement bidding items that can be supported by the applicant from among procurement bidding items included in the MICE procurement bidding information based on the evaluation information of the applicant (S119).

For example, the computing device may extract procurement bidding items that the applicant can support (ie, qualify for the support) based on the applicant information. According to an embodiment of the present disclosure, the computing device may extract procurement bidding items having a selection probability of more than a predetermined reference value.

In operation S104, the computing device may provide information on the extracted one or more procurement bidding items in operation S120.

8 is a flowchart illustrating a method of providing co-bidding information according to an exemplary embodiment.

In the above-described step (S120), the computing device may collect information about one or more companies that can perform one or more procurement bidding items that the applicant cannot support among the procurement bidding items included in the MICE procurement bidding information (S121). Can be.

In addition, the computing device may provide information about the one or more companies (S122).

In addition, the computing device may generate information for co-bidding the MICE procurement bidding information based on at least some of the one or more companies and the applicant's information (S123).

For example, in the case of MICE procurement bidding information, various items are included together, and it may be difficult for one company to perform all of them. Accordingly, the computing device collects information about other companies that can perform procurement bidding items that the applicant cannot perform, and provides the applicants with at least some of the companies and the applicants to jointly support the MICE procurement bidding information. can do.

According to an embodiment of the present disclosure, the computing device may extract companies capable of performing all items included in the MICE procurement bidding information, and collect information to generate information for co-bidding.

In addition, the computing device may transmit the generated joint bidding information (S124).

The co-bidding may be performed according to the selection input of each company and the supporter, or may be automatically grouped and matched by the computing device according to an embodiment.

For example, the computing device selects the companies that can perform the procurement bidding items included in the MICE procurement bidding information among the companies subscribed to the procurement bidding information matching service according to the disclosed embodiment, and proposals written by the respective companies. Can be automatically collected, and based on the generated co-bidding information, it can be transmitted to the reception server.

9 is a block diagram of an apparatus according to an embodiment.

The processor 102 may include a connection passage (eg, a bus, etc.) that transmits and receives signals with one or more cores (not shown) and graphics processor (not shown) and / or other components. .

The processor 102 according to one embodiment executes one or more instructions stored in the memory 104 to perform the method described in connection with FIGS. 1-8.

For example, the processor 102 obtains applicant information by collecting one or more instructions stored in a memory, collects procurement bidding information, extracts procurement bidding information matching the applicant information, and extracts the procurement bidding information. Can be provided.

Meanwhile, the processor 102 may include random access memory (RAM) and read-only memory (ROM) for temporarily and / or permanently storing a signal (or data) processed in the processor 102. , Not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processor, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102. Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, in a software module executed by hardware, or by a combination thereof. The software module may be a random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art.

The components of the present invention may be implemented in a program (or an application) and stored in a medium to be executed in combination with a computer which is hardware. The components of the present invention may be implemented as software programming or software elements, and likewise, embodiments may include various algorithms implemented in combinations of data structures, processes, routines or other programming constructs, such as C, C ++. It may be implemented in a programming or scripting language such as Java, an assembler, or the like. The functional aspects may be implemented with an algorithm running on one or more processors.

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those skilled in the art to which the present invention pertains may realize the present invention in other specific forms without changing the technical spirit or essential features thereof. I can understand that. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.

100: computing device
102: processor
104: memory

Claims (7)

A procurement bidding information matching method performed by a computing device,
Acquiring applicant information (S101);
Collecting procurement bidding information (S102);
Extracting procurement bidding information matching the applicant information (S103); And
Providing the extracted one or more procurement bidding information (S104); And
Managing the applicant information and the one or more procurement bidding information through a blockchain; Including,
The step (S101),
Obtaining evaluation information of the applicant (S105); And
Acquiring resource information of the applicant, wherein the resource information includes information on an amount of resources for which the applicant can perform each procurement (step S106); Including,
The step (S103),
Evaluating selectivity of the applicant for each of the collected procurement bidding information based on the evaluation information of the applicant (S107); And
Evaluating (S108) the feasibility of the applicant for each of the collected procurement bidding information based on the volunteer information of the applicant; Including,
The step (S107),
Extracting a parameter for calculating a selection probability from the collected procurement bidding information and the applicant information through natural language processing; And
The selected probability is extracted by inputting the extracted parameters into the learned AI model using the procured bidding information that has been previously terminated and big data including information on the applicants selected in correspondence with the procurement bidding information as learning data. Comprising;
The step (S108),
Determining whether or not a duplicate bid and duplicate bid multiples (S115);
Calculating an enlarged resource of the supporter based on the resource information of the supporter and the overlapped bid multiple, and calculating the enlarged resource by multiplying the overlapped bid multiple by an amount of the supporter's resources; And
Providing the number of procurement bidding information that the applicant can support based on the expanded resource to the applicant, wherein the required resource of each procurement bidding information is less than or equal to the resource of the applicant, and the total procurement bidding information provided to the applicant (S117), so that the sum of required resources of the is less than or equal to the expanded resource of the applicant; Including,
Procurement bidding information provided in the step (S117),
And procurement bidding information corresponding to a value obtained by multiplying the overlapped bid multiple by the amount of resources that the applicant can perform each procurement.
How to Match Procurement Bid Information. The method of claim 1,
The step (S107),
Determining whether the applicant can support the collected procurement bidding information (S109); And
As a result of the determination, calculating the selection probability of the applicant for each of the at least one procurement bidding information that can be supported (S110),
How to Match Procurement Bid Information. The method of claim 2,
The step (S103),
Extracting one or more procurement bidding information for which the calculated selection probability exceeds a preset reference value (S111);
Calculating an expected return for each of the one or more procurement bidding information extracted in the step (S111) (S112); And
Determining priorities for each of the one or more procurement bidding information extracted in the step S111 based on the selection probability and the expected profit; Including,
The step (S104),
Providing one or more procurement bidding information to the applicant based on the priority (S114); Including,
How to Match Procurement Bid Information. The method of claim 1,
The step (S102),
Collecting the MICE procurement bidding information (S118); Including,
The step (S103),
Extracting one or more procurement bidding items that the supporter can support from procurement bidding items included in the MICE procurement bidding information based on the applicant's evaluation information (S119); Including,
The step (S104),
Providing information on the extracted one or more procurement bidding items (S120); Including,
How to Match Procurement Bid Information. The method of claim 4, wherein
The step (S120),
Collecting information on one or more companies that can support one or more procurement bidding items that the applicant cannot support among procurement bidding items included in the MICE procurement bidding information (S121);
Providing information on the one or more companies (S122);
Generating information for jointly bidding on the MICE procurement bidding information based on at least some of the one or more companies and the applicant's information (S123); And
Transmitting the generated joint bidding information (S124); Including,
How to Match Procurement Bid Information. Memory for storing one or more instructions; And
A processor for executing the one or more instructions stored in the memory;
The processor executes the one or more instructions,
Performing the method of claim 1,
Procurement bidding information matching device. A computer program, coupled to a computer, which is hardware, stored on a recording medium readable by a computer so as to perform the method of claim 1.

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