KR20220116889A - Strategic information service provision system for bidding strategy establishment and service provision method thereof - Google Patents

Abstract

The present invention relates to a system for providing a strategy information service for establishing a bidding strategy and a method for providing the service. The system for providing a strategy information service for establishing a bidding strategy comprises: a data server (10) providing bidding order data and business information data on the web; a service server (20) networked with the data server (10) to collect big data by searching and indexing bidding information data and general business information data, calculating and learning the collected big data, and inferring and providing bidding strategy establishment data and business recommendation data; and a user terminal (50) networked with the service server (20) to receive bidding information and competitor business information. Accordingly, when a user calls the service, a strategy information service for establishing a bidding strategy and a personalized business recommendation service can be provided.

Description

The strategic information service provision system for bidding strategy establishment and service provision method thereof

The present invention relates to a bidding strategy information provision service method, and more specifically, low cost and easy and simple competitor prediction, score prediction during cooperation, contention rate prediction, optimal bid rate prediction, and major review through information analysis of ordering organizations and competitors. It relates to a system for providing strategic information for establishing a bidding strategy and a service provision method for automatically generating a list and automated bidding PMS, and supporting the establishment of a bidding strategy by making it possible to use various information intuitively.

In general, the representative of a small and medium-sized business or the person in charge of management and public procurement is to establish a bidding strategy and management strategy in the public procurement project. need to figure out

In addition, in order to establish bidding and management strategies, major business types and major ordering agencies of competitors, total contract amount and monthly contract trend compared to the previous year, basic status and contract trend analysis, current activities and dynamics, partner company analysis and cooperation contract business history analysis , it is necessary to analyze the bidding pattern for each project participation method by each ordering organization.

Electronic bidding is a method that allows you to participate in the selection of construction and service companies and purchase of goods through the Internet without having to visit the bidding site and submit bidding documents. It will be done over the internet.

The advantage of such electronic bidding is that transparency and fairness of bidding are guaranteed, and work efficiency can be increased and the cost of bidding can be reduced through the simplification and automation of the bidding process.

Bid analysis is required to bid in electronic bidding, and the system provided for public procurement by the bidding ordering agency provides only one-dimensional simple data with content composition based on institutional or corporate standards. Because the content is provided based on a specific business standard rather than in a form that is easy to establish a strategy through have.

In the case of the existing service for establishing a bid strategy, it is inconvenient to use in the form of a complicated menu configuration and complicated data configuration, and this also has a problem in that simple primary processing data based on raw data for single bid information is provided.

In the prior art of analyzing a bid price in order to bid in an electronic bid, a method for providing bid analysis information on a network has been proposed in Korean Patent Laid-Open No. 10-2007-0114972.

This prior art collects a plurality of preliminary prices calculated from a plurality of open bid announcement events, an arrangement of a plurality of preliminary prices, the number of times a bid participant selects a number in which the preliminary prices are arbitrarily arranged, and a scheduled price, etc., as basic data, Objective and customer-oriented bid analysis information that can increase the likelihood of successful bids to a large number of bidders, etc. A configuration that can be provided is disclosed.

In this method of providing bid analysis information, analyzing the bid price using statistics is merely a prediction because any specific target is not specified, and the accuracy is extremely low. There is a problem in that it is impossible to calculate the exact preliminary price, and because most of them use a method of extracting a random number using a simple randomization program without analyzing the existing wicket result data, there is a problem that the bid is successful with the probability of winning the lottery. .

The present invention for electronic bidding participation that can increase the prediction rate by taking into account the current status of overall statistics and predicting, analyzing the propensity of the ordering party and each announcement case, and analyzing the competitor's propensity with recent bid results. It is possible to provide a method of providing a bidding information analysis service based on the big data weighted moving average method

KR 10-2007-0114972 No. A1 (2007. 12. 05.) KR 10-2018-0062546 A1 (2018. 06. 11.) KR 10-2187797 B1 (2020. 12. 07.)

The present invention recommends a personalized customized business with business support through an artificial intelligence help bot, and supports the business and announcement document review process. In addition, by supporting the bid preparation and competitor analysis process, information such as the number of contracts, contract amount, and average bid rate is efficiently visualized and provided.

The present invention reconstructs the information needed by companies and job seekers through the combination and tertiary processing of various data such as bidding information, corporate credit information, corporate sales information, and corporate salary information into UI/UX from the user's point of view to provide users with It provides a variety of strategic information content visually for In addition, it provides high value-added strategy information and forecast information through artificial intelligence grafting, and aims to provide user satisfaction by reflecting appropriate pricing policies for each level of complex strategic content.

In addition, the present invention provides member companies (artificial intelligence RPA) through secondary processing and complex processing of various datasets such as company-specific bid (successful bid) information, credit information, financial information, average salary information, and task performance history. Combination information for strategic analysis for successful bidding of companies), complex comparison information for the purpose of establishing stable and efficient corporate growth strategies, and furthermore, AI recommendation algorithm of artificial intelligence and help bots are combined to provide strategic information, forecast information and personalized convenience The purpose of this is to provide the high value-added bid information data service provided.

The present invention predicts competitors by deriving a company that conducts businesses of a similar size and type to the company that prefers businesses of a similar nature based on a specific business,

When collaborating with a specific company, predict the score for cooperation by predicting the cooperation score based on the price score and technology score trend of the specific company for the last 3 months. Predicts the contention rate based on the contention rate trend of businesses with high personality similarity within the institution, and predicts the optimal bid rate by analyzing and inferring the bid rate patterns of the expected competitors selected by the user in relation to the business. Through text mining of AI-based request for proposal, it automatically generates a review list by summarizing and excerpting major review subjects that affect whether or not to participate in the bid, and automatically generates basic workbook, information collection and analysis in the review of proposal request By supporting the automated bidding project management system (PMS, Project Management System) by will be.

The purpose of the present invention and its technical problems are not limited to the technical problems described above. Accordingly, other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description.

Technical characteristics for achieving the intended purpose of the present invention is a system for providing a strategy information service for establishing a bidding strategy, comprising: a data server for providing bidding order data and business information data on the web; a service server that is networked with the data server to search and index bidding information data and general business information data to collect big data, calculate and learn the collected big data, and infer and provide bidding strategy establishment data and business recommendation data; Including a user terminal that is networked to the service server to receive bidding information and competitor business information, and being able to provide a strategy information service for establishing a bidding strategy and a personalized business recommendation service when a user calls a service. .

The data server includes: a data server of a bidding ordering organization that provides bidding information data and general business information data; a data search server comprising a data server of a related institution and a portal site, wherein the service server is networked with the data server to search, index, and store bidding information data and general business information data; a big data server having a big data platform for analyzing and processing bidding information data collected from the web page of the data search server and general business information of competitors and storing the analyzed data; Including an artificial intelligence server that has an artificial intelligence module and learns the analysis data of the big data server to infer bid strategy establishment data and business recommendation data, strategy information service and personalized business for establishing bid strategy when a user calls a service Including being able to provide a recommendation service.

The data search server 21 is networked with the data server of the bid ordering organization, and has a relational database (RDB) to retrieve bid information data and general business information data from the web page of the bid ordering organization data server. and a first data search server for indexing and storing; A second data retrieval server that is networked with the data server and portal site of the relevant institution and that has a web crawler to search for, index, and store the recruitment activity information and general business information of the competitor from the web page of the data server of the relevant institution do.

The artificial intelligence module of the artificial intelligence server includes a competitor prediction module that derives a competitor similar to the user's business based on big data obtained from the first data search server, and a big data obtained from the first data search server. A cooperation score prediction module for predicting a competitor's cooperation score with An optimal bid rate prediction module that predicts the user's optimal bid rate based on the big data that becomes A major review list automatic generation module, an automated bidding PMS module supporting automatic generation of basic workbooks, information collection and analysis based on big data obtained from the first data search server, and the big data obtained from the second data search server It operates based on the interaction of data and the user's history and includes a business recommendation module that recommends AI personalized personalized business.

The competitor prediction module includes deriving a company that performs businesses of a size and type similar to that of the corresponding company that prefers businesses of a similar nature based on a specific business based on big data obtained from the first data search server. .

The cooperation score prediction module 231 predicts the cooperation score based on the price score and technology score trend of the last 3 months of the specific company when cooperating with a specific company based on the big data obtained from the first data search server. include

Based on the big data obtained from the first data search server, the contention rate prediction module is based on the contention trend of businesses with high keyword similarity and personality similarity and businesses with high personality similarity within the same ordering organization based on a specific business. It involves predicting the contention rate.

The optimal bidding rate prediction module predicts the optimal bidding rate by complexly analyzing and inferring the bidding rate patterns of expected competitors selected by the user in relation to the business based on the big data obtained from the first data search server. include

The automatic generation of the main review list module summarizes and extracts the main items to be reviewed through text mining of the artificial intelligence-based proposal request based on the data obtained from the first data search server, and determines the main matters affecting whether to participate in the bid Includes automatic extraction and support.

The automated bidding PMS module includes supporting automatic generation of basic workbooks, information collection and analysis so that big data obtained from the first data search server can smoothly manage the process from review of proposal request to bidding participation.

A method of providing a bid strategy establishment strategy information service by a strategy information service providing system for bid strategy establishment includes the steps of setting bid information data and business information data of a bid ordering organization and related organizations to an open API (S100); a step (S110) of searching and indexing the bid information data and business information data of the bid ordering organization and related organizations by a relational database (RDB) and a crawler; Entering and storing the searched and indexed bid information data into a big data platform (S120); performing calculation and learning for bidding strategy information and recommended business information based on the collected big data (S130); A step of performing an operation for adjusting the personalization application weight for each influence factor (S140); Requesting bidding data information and business information through a user terminal (S150); and receiving bidding strategy information and business information by the artificial intelligence module (S160).

The step (S100) of searching and indexing the bidding information data of the bidding ordering institution and related institutions by the RDB and web crawler is business characteristic information, contract pattern information of the bidding ordering institution, and statistical status of the contract partner of the bidding ordering institution. , at least any one or more of business types and major customers of competitors, comparison of current status of competitors with the previous year, current activities and dynamics of competitors, analysis information on partner companies of competitors, and analysis information on bidding patterns of competitors.

The step of performing calculation and learning for bidding strategy information and recommended business information based on the collected big data (S130) further includes calculation and learning for adjusting the personalized application weight for each influence factor.

The step of requesting bid data information and business information through the user terminal (S150) includes inputting a keyword including a search and analysis target company name, company search, agency search, bid analysis, strategic information support, and business keyword. .

The step (S160) of receiving the bidding strategy information and business information by the artificial intelligence module predicts competitors according to the step (S130) of performing calculations and learning for the bidding strategy information and recommended business information based on the collected big data. Cooperation score prediction, contention rate prediction, optimal bid rate prediction, automatic generation of major review lists, project recommendation, analysis and summary of the bid ordering institution's announcement and proposal request to automatically generate and provide a PMS (Project Management System) include

The step of receiving the bidding strategy information and the business information by the artificial intelligence module ( S160 ) includes providing a recommended business list according to the list registered by the user as a business of interest.

The business characteristic information includes those provided by business information such as business type, performance, and technology restrictions for public announcement projects.

The contract pattern information of the bidding ordering agency includes those provided by the past history-based business type, competition rate by size, and bid rate analysis information of the ordering agency.

The statistical status of the contract partner of the bidding ordering agency includes that provided by contract information for each business type of each contract partner (competitor) based on the past history of the ordering agency.

The business type and main customer of the competitor include those provided by main customer information for each business type based on the competitor's past history, main business type, and contract information for each customer (ordering organization).

Competitor's status trend compared to the previous year includes information provided by the competitor's sales, credit, number of employees, average salary, technology, key business keywords, and monthly trend compared to the previous year.

The current activities and dynamics of the competitor include those provided by the competitor's recent trends, the latest news, and online information and issues of whether job postings are posted or not.

The partner company analysis information of the competitor includes information provided by major partner company information based on the competitor, cooperative projects and business keywords for each partner company, and average bid rate information during cooperation.

The bidding pattern analysis information of the competitor includes that which is provided by the bidding rate pattern information for each competitor's standard ordering organization, each business type, and each participation method (single/joint).

The PMS is a step (S200) of setting a project with high project cost conformance to the average amount of money in the first recommendation range based on the distribution of the amount of the contract amount of the existing project, the average value, the trend, and the preference for the size of the project (S200). Based on the preference of the business type of the service:goods:construction of one project, the step of recommending the high-weighted project type as a priority (S210), and deriving the preference for the detailed project type based on the keyword analysis of the announcement of the previously performed project a step of first recommending a business corresponding to the announcement name containing a high-ranking keyword (S220), a step of recommending a business with a high degree of agreement between the existing customer and the preferred customer type based on the customer and customer type preference of the existing business (S230), on the basis of the distance between the location of the company and the location of the business, including a step (S240) of first recommending the business of an institution adjacent to the location of the company (S240), and the steps (S200) to (S240) by artificial intelligence and a step (S250) of recommending a project based on the adjusted weight by adjusting the weight for each influence factor by the interaction of .

Adjusting the weight of each influence factor of artificial intelligence by analyzing the characteristics of the business in which the company participated in the bidding or won the project among the recommended projects and calculating the hit ratio for each influence factor in the steps (S200) to (S240) ( S260).

As described above, according to the present invention, data results that are visually provided including a graphic interface and text can obtain results beyond simple information retrieval.

The present invention is not limited to the standards of the bidding ordering agency, but by providing bidding information and recommended business information based on an objective standard applied to the case of a competitor, from the user's point of view, through the use of competitive company trend analysis information, corporate competitiveness is strengthened, and the By confirming the detailed performance and scale of public procurement projects, supporting the establishment of corporate operation strategies through analysis of cash flow information of other companies, and supporting strategic analysis of public procurement projects through analysis of bidding information, the time required for establishing a bidding strategy is reduced. It has the effect of shortening, resolving difficulties, and improving the order rate.

1 is a system block diagram showing the external API linkage and crawling relationship of the present invention.
2 is a block diagram of an artificial intelligence module of the present invention;
3 is a service provision flow chart of the present invention;
4 is a PMS automatic generation flow chart of the present invention;
Figure 5 (a) is a view showing an example of visually providing the UI / UX-based strategy information content of the present invention, (b) is a view showing an example of a conventional service provision form
6 is an example of a menu screen for inquiring business (announcement) information according to the present invention
7 is an example of a menu screen in which bidding ordering organization information is analyzed according to the present invention;
8 is an example of another menu screen in which bidding ordering organization information is analyzed according to the present invention;
9 is an example of another menu screen in which bidding ordering organization information is analyzed according to the present invention;
10 is an example of another menu screen in which bidding ordering organization information is analyzed according to the present invention;
11 is an example of another menu screen in which bidding ordering organization information is analyzed according to the present invention;
12 is an example of another menu screen in which bidding ordering organization information is analyzed according to the present invention;
13 is an example of a screen in which artificial intelligence is applied to big data according to the present invention and business conditions are analyzed
14 is an example of providing a bid recommendation list by applying artificial intelligence to big data according to the present invention.
15 is an example in which artificial intelligence is applied to big data according to the present invention to summarize and analyze a bidding document for bidding conditions and restrictions, and to provide an analysis summary information check list
16 is an example of providing an announcement summary information and a request for proposal by applying artificial intelligence to big data according to the present invention.
17 is an example of analysis results of bid preparation process management and strategy information collection by a help bot by applying artificial intelligence to big data according to the present invention.

The features and advantages of the present invention will be more clearly understood by the embodiments described by the accompanying drawings.

The application of the present invention is not limited by the configuration and arrangement of components described in the embodiments of the present invention or shown in the drawings. The present invention is capable of being embodied in other embodiments and of being carried out in various ways. Also, the expressions and predicates used in the embodiments with respect to terms such as the orientation of devices or elements are used only to simplify the description of the present invention, and do not indicate or imply that the related devices or elements simply have to have a specific orientation. . For example, terms such as “first” and “second” are used in the embodiments and claims describing the present invention, but such terms are not intended to indicate or imply relative importance or spirit.

In addition, the terms or words used in the present specification and claims should not be construed as being limited to conventional or dictionary meanings, and in order for the inventor to explain the terms and concepts of the invention in the best way, it is consistent with the technical idea of the present invention. It should be interpreted as described based on the meaning and concept of

Therefore, the present invention is not limited to the presented embodiment, and within the equivalent scope of the technical spirit of the present invention and the technical spirit described in the claims to be described below by those of ordinary skill in the technical field to which the present invention belongs. Various modifications and changes are possible in

Although the present invention has been described mainly in terms of preferred embodiments with reference to the accompanying drawings, it will be apparent to those skilled in the art that many various and obvious modifications can be made therefrom without departing from the scope of the present invention. Accordingly, the scope of the present invention should be construed by the appended claims to cover many such modifications.

Prior to describing an embodiment of the present invention, the bid ordering organization and related organizations refer to the following companies.

That is, the bid ordering institution is an institution in which public data is registered and provides the public data as an open API, including public institutions, national institutions, autonomous administrative institutions, educational institutions, educational administrative institutions, committees, legislative institutions, and constitutional institutions. In addition, a related institution means an institution that has relevant data necessary for strategy establishment and insight acquisition.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a system block diagram showing an external API linkage and crawling relationship of the present invention.

Referring to FIG. 1 , a system for providing a strategy information service for establishing a bidding strategy includes a data server 10 , a service server 20 , and a user terminal 30 .

The data server 10 is composed of a data server 110 of a bid ordering institution and a data server 120 of a related institution and a portal site, and the data server 110 of the bid ordering institution provides bid order data and business information on the web. Data is provided as an open API, and the data server 120 of related organizations and portal sites provides general business information as an open API.

The service server 20 is networked with the data server 110 of the bidding ordering institution and the data server 120 of the related institutions and portal sites to search and index the bidding information data and the competitor's general business information data to collect big data, , calculates and learns the collected big data, and infers and provides bidding strategy establishment data and business recommendation data, and is composed of a data search server 21 , a big data server 22 , and an artificial intelligence server 23 .

The data search server 21 includes a first data server 210 and a second data server 220 .

The first data server 210 is networked with the data server 110 of the bid ordering organization, and has an RDB to search and index data related to bid information and general business information data from the web page of the bid ordering organization data server. It is stored and provided to the big data server 22 .

The RDB collects strategic information and business information for establishing a bidding strategy by connecting with an open API to a bidding ordering institution and related institutions.

Accordingly, RDB uses a unique identifier to connect two or more databases or tables so that each item and its properties can be linked with all other items and their properties. Create a powerful and flexible database that allows each other field to be linked.

The RDB is a database that expresses data in the form of a simple table, and has a structure that can facilitate communication between users and programmers rather than a hierarchical structure.

The second data server 220 is networked with the data server 120 and the portal site of the relevant institution, and is provided with a web crawler to provide general information such as recruitment activity information and general business information of a competitor from the web page of the data server of the relevant institution. The business information is searched, indexed, stored, and provided to the big data server 22 .

The competitor's general business information data includes contract business and task performance as keywords, such as total sales, credit rating, number of patents, venture business certification, Innobiz certification, R&D task performance, contract amount, contract statistics, number of employees and annual salary, etc. It includes the company's annual business operation pattern and quantitative information of the company.

The web crawler is a type of bot or software agent, usually starting from a list of URLs called seeds, recognizing all hyperlinks on the page and updating the URL list, and the updated URL list is recursively Revisit and collect data.

These web crawlers are software used by sites operating search engines to automatically search and index various information on the web. By repeatedly finding and synthesizing new web pages, using the findings to find and indexing new information, a vast amount of data can be searched.

Accordingly, bidding strategy establishment information and project recommendation-related information obtained from a plurality of bidding ordering organizations and related organizations can be freely accessed as a table of horizontal and vertical items. Users can freely divide and combine tables, and add or change tables without being affected by other changes.

The big data platform of the big data server 22 automatically preprocesses, mash-up and It is reconstructed by analyzing and processing bidding information data and competitor's general business information so that it can be easily used for big data analysis and big data business information content through complex calculations. It continuously supplements data for artificial intelligence learning from RDB and web crawler so that it can be processed by the artificial intelligence server 230 .

The big data platform is based on the company's average successful bid rate information, etc., based on the company's overall average score, price score, technology average score, annual contract pattern and successful bid rate trend pattern, major customer ranking, contract statistics with the customer, and Includes analysis of the successful bid rate of the company, the contract business trend of the company, and customer (ordering agency) information.

In addition, it is possible to analyze major customer rankings, contract statistics with the customer, the successful bid rate with the customer, the contract business trend of the company according to the contract trend by institution, and information of the customer (ordering institution).

In addition, it is possible to analyze the business statistics and additional information of each company in the ranking of major partners of the company.

In addition, it makes it easy to analyze contract statistics, successful bid rates, average scores, and patterns according to the case where the company is a sole supplier or a joint supplier.

In addition, a graph comparing total sales, credit rating, number of patents, venture business certification, Innobiz certification, R&D task execution, number of CHD employees and full-time relationship, average salary, contract amount, contract TN, contract amount and employee salaries trends, etc. can be displayed visually.

In addition, it enables the user to recommend the optimal business by big data analysis and artificial intelligence according to the list of business interests registered by the user.

In addition, as a summary analysis of the bidding document for bidding conditions and restrictions, it shows an analysis summary information checklist, and enables to automatically generate a bidding PMS of the corresponding project announcement according to the list check result.

In addition, the status of user companies and management companies based on big data, such as bid promotion results, project order statistics, and bid lists, are visually provided for easy analysis.

In addition, by supporting detailed information that automatically generates work schedules by AI, collecting/analyzing and automating suppliers and competitors, predicting the expected overall score when working with a selected partner, forecasting the competitor, predicting the competitor's overall score and bid rate It supports the management of the bidding preparation process and the collection and analysis of strategic information by supporting forecasting and information on collecting issues, news, and recruitment activities of competitors.

The artificial intelligence server 23 has an artificial intelligence module to learn the analysis data of the big data server 22 to infer the bidding strategy establishment data and the business recommendation data.

Figure 2 shows the configuration of the artificial intelligence module of the present invention.

2, the artificial intelligence module includes a competitor prediction module 230, a cooperation score prediction module 231, a contention rate prediction module 232, an optimal bid rate prediction module 233, and a main review list automatic generation module ( 234 ), an automated bidding PMS module 235 , and a business recommendation module 236 .

The competitor prediction module 230 is based on the big data acquired from the first data search server 210, a company that prefers businesses of a similar nature based on a specific business, and performs businesses of a size and type similar to that of the corresponding company. to derive

The cooperation score prediction module 231 is based on the big data obtained from the first data search server 210, when cooperating with a specific company, the cooperation score based on the price score and technology score trend of the specific company for the last 3 months predict

The contention rate prediction module 232 is based on the big data obtained from the first data search server 210. Based on a specific business, the business with high keyword similarity and personality similarity and the business with high personality similarity within the same ordering organization The contention rate is predicted based on the contention trend of the players.

The optimal bid rate prediction module 233 is based on the big data acquired from the first data search server 210 by complexly analyzing and inferring the bid rate patterns of the expected competitors selected by the user in relation to the business. Predict the optimal bid rate.

The main review list automatic generation module 234 summarizes and extracts the main review target contents through text mining of the artificial intelligence-based proposal request form based on the data obtained from the first data search server 210, and determines whether or not to participate in the bidding. It automatically extracts and supports the main factors that have an impact.

The automated bidding PMS module 235 automatically generates basic work schedules, collects information, and analyzes the big data obtained from the first data search server 210 to smoothly manage the process from the review of the proposal request to the participation in the bid. Support.

The business recommendation module 236 operates based on the interaction of the user's history with the big data acquired from the second data search server 220 and recommends an AI personalized personalized business.

The user terminal 30 includes a user terminal that is networked to the service server to receive bid information and competitor business information, and when a user calls a service, a strategy information service for establishing a bid strategy and a personalized business recommendation service can be provided. there will be

The communication device is a wireless communication device that guarantees portability and mobility, and includes a smartphone, a smartpad, a Personal Communication System (PCS), a Global System for Mobicommunication (GSM), a Personal Digital Cellular (PDC), and a PHS. (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) It may include all types of handheld-based wireless communication devices such as terminals and tablet PCs.

This user terminal 30 is provided with an application such as an AI help bot (Artificial Intelligence helpbot) to apply for a service to the service server 10 of the present invention, and strategy information for establishing a bidding strategy provided from the service server 20 receive service.

The strategy information service providing system for establishing a bidding strategy configured as above is that the bidding information data of the bidding ordering institution and related institutions are searched and indexed by a relational database (RDB) and a crawler, and then in the big data platform. The service of the present invention is provided by a user terminal that is processed and stored, and has an application such as an AI bot (Artificial Intelligence bot).

3 shows a service provision flow chart of the present invention.

The bidding information data required for analysis for providing the service of the strategy information service providing system for establishing the bidding strategy as described above is acquired by using the API (application programming interface) linkage of the bidding ordering institution and web crawling.

Bid information data and business information data of the bid ordering organization and related organizations are disclosed by the open API (S100).

.

The data search server 21 , that is, the RDB and the web crawler of the first data search server 210 and the second data search server 220 , obtains bidding information data from the bidding ordering organization server 110 and the related organization server 120 . And business information data is searched and indexed and stored (S110).

Through this external API linkage and crawling, bidding information such as order plan, pre-standards, bid announcement, open bid results, successful bid information, contract status, etc. General data such as competitor's national pension subscription information, patent registration information, national R&D project list, project performance information, venture company registration information, Innobiz registration information, recruitment activity information, sales, credit rating, etc. It is possible to predict the business development trends of competitors by acquiring

In addition, by supporting the process of reviewing business and public notice documents, and supporting bid preparation and competitor analysis processes, information such as the number of contracts, contract amount, and average bid rate can be efficiently visualized and provided.

Bid information data and business information data searched and indexed by the first data search server 210 and the second data search server 220 are input to the big data platform of the big data server 22 and stored (S120).

The artificial intelligence server 23 performs calculations and learning for bidding strategy information and recommended business information based on the big data collected in the big data server 22 (S130).

Depending on the size of the contract amount of the existing business, the proportion of the business type of the existing business, the keywords for the announcement of the existing business, the customers and customer types of the existing business, and the distance between the location of the company and the business location Calculation and learning are performed to adjust the personalized application weight for each influence factor (S140).

When bidding data information and business information are requested through the user terminal 30 (S150), bidding strategy information and business information are provided from the artificial intelligence server 23 (160).

The step (S100) of searching and indexing the bidding information data of the bidding ordering institution and related institutions by the RDB and web crawler is business characteristic information, contract pattern information of the bidding ordering institution, statistical status of the contracting party of the bidding ordering institution, business of competitors It includes at least any one or more of types and major customers, competitor's status trend compared to the previous year, competitor's current activities and dynamics, competitor's partner company analysis information, and competitor's bidding pattern analysis information.

The step of performing calculation and learning for bidding strategy information and recommended business information based on the collected big data (S130) further includes calculation and learning for adjusting the personalized application weight for each influence factor.

The above influencing factors are the size according to the contract amount of the existing business, the proportion of the business type of the existing business, the keyword for the announcement of the existing business, the customer and customer type of the existing business, and the location and business location of the company distance, etc.

The step of requesting bid data information and business information through the user terminal (S150) includes inputting a keyword including a search and analysis target company name, company search, agency search, bid analysis, strategic information support, and business keyword. .

The step (S160) of receiving the bidding strategy information and business information by the artificial intelligence module predicts competitors according to the step (S130) of performing calculations and learning for the bidding strategy information and recommended business information based on the collected big data. Cooperation score prediction, contention rate prediction, optimal bid rate prediction, automatic generation of major review lists, project recommendation, analysis and summary of the bid ordering institution's announcement and proposal request to automatically generate and provide a PMS (Project Management System) include

In the step of receiving the bidding strategy information and business information by the artificial intelligence module (S160), a recommended business list is provided according to the list registered by the user as a business of interest.

The above project characteristic information is provided by project information such as project type, performance, region, and whether or not technology is restricted for the public notice project.

The contract pattern information of the bidding ordering agency is provided by the bidding ordering agency's past history-based business type, competition rate by size, and bid rate analysis information.

The statistical status of the contracting party of the bidding ordering agency is provided by the contract information for each business type of the bid ordering agency based on the past history of the contracting party (competitor) and the contracting party.

The business type and main customer of the above competitor are provided based on the competitor's past history based on the main customer information by business type, main business type, and contract information by customer (ordering institution).

Competitor's Status Trend Year-over-year comparison is provided by competitor's sales, credit, number of employees, average annual salary, technology, key business keywords, and monthly trends compared to the previous year.

The competitor's current activities and dynamics are provided by the competitor's latest trends, the latest news, and online information and issues of whether job postings are posted or not.

The partner company analysis information of the competitor is provided by competitor-based major partner company information, cooperative business and business keywords by partner company, and average bid rate information during cooperation.

The bidding pattern analysis information of the competitor is provided by the bidding rate pattern information by the competitor's standard ordering organization, business type, and participation method (single/joint).

4 shows a PMS automatic generation flow chart of the present invention.

The PMS sets the project with high project cost conformity in the average amount of money in the first recommendation range based on the distribution, average value, trend, and preference for project size of the contract amount of the existing project (S200), and Based on the business type preference of the business service:goods:construction, it is recommended in priority from the high weight business type (S210).

Then, based on the keyword analysis of the public announcement of the previously performed business, preference for detailed business type is derived, and the business corresponding to the announcement name including the high-ranking keyword is first recommended (S220).

In addition, based on the customer and customer type preference of the existing business, a business having a high degree of agreement between the existing customer and the preferred customer type is recommended (S230).

In addition, based on the distance between the location of the company and the location of the business, the business of the institution adjacent to the location of the company is first recommended ( S240 ).

In addition, the weight of each influence factor by the interaction of the steps (S200) to (S240) is adjusted as a percentage by artificial intelligence, and a project is recommended based on the adjusted weight (S250).

In addition, the weight of each influence factor of artificial intelligence is adjusted by calculating the hit ratio for each influence factor in steps S200 to S240 by analyzing the characteristics of the business in which the company participated in the bidding or won the order among the recommended projects. (S260).

The hit ratio (A) for each influencing factor in the steps (S200) to (S240) is derived by the following equation.

[Equation]

A = No. of bidding (orders) projects / No. of recommended projects

When recommending a business by the business recommendation algorithm, the evaluation value of the project is required. If the average movement trend is on the rise, the evaluation value is equal to or higher than the average value of the 1st priority section, the closer to the average value, the 1st (higher ranking) , and if the average movement trend is downward, the closer to the average value in the project range equal to or lower than the average value of the 1st priority section, the closer to the 1st order (higher priority).

The evaluation value is calculated in such a way that, with the 1st priority being the highest, the evaluation value is calculated to be lower as it goes down. If P1 is the detection of the project cost section in which 70% of the number of projects is distributed, then the average value of P1 = the sum of the project expenses in P1 / the number of projects in P1, and the absolute value of the difference between the project cost of the recommended project and the average value of P1 is close to 0. the higher the score.

In addition, when recommending a business, the evaluation value for the business is calculated by analyzing the keyword ranking searched by the corresponding company.

In the case of a business that includes keywords with overlap among keywords of the company, the evaluation value is calculated in the same order as the keywords of the corresponding company. That is, the evaluation value is calculated so that the first rank is the highest point and the evaluation value becomes lower as the rank goes down.

The keyword hit evaluation value calculation formula analyzes the keywords of the business name of the existing contracted business, detects the sequence of duplicate keywords, and gives points. In addition, by assigning weight to duplicate keywords (the ratio of the above keyword ranking to the total number of keywords removed), the company's final keyword ranking and score are given. extra points)

According to the present invention as described above, it is possible to intuitively grasp a lot of information at a glance by visually provided data, and it is possible to reduce the manpower and cost required for establishing a bidding strategy through quick collection and analysis.

Of course, more diverse data sets may be included in addition to the illustrated examples, and thus more diverse content and insight may be added.

5 is a diagram comparing an example in which the UI is visually displayed according to the present invention and an example in which the conventional UIRK is displayed. An example of visually providing various strategic information contents for users by reconfiguring the information needed by companies and job seekers through the combination of data and tertiary processing into UI/UX from the user's point of view and an example of a service type provided in the past is shown, and (b) shows an example of a conventional service provision form.

Referring to this figure, the UI of the conventional service is complicated and unintuitive and inconvenient to use, but the UI according to the present invention provides a data result that is visually provided including a graphic interface and text simply and intuitively A lot of information can be grasped at a glance by the data obtained, and the manpower and cost required to establish a bidding strategy can be reduced through quick collection and analysis.

6 shows an example in which a menu screen for inquiring business (announcement) information is configured according to the present invention.

This figure shows an example of searching for similar projects based on the project cost range by inputting big data as a keyword of interest.

7 shows an example of a menu screen in which bidding ordering organization information is analyzed according to the present invention.

This drawing summarizes and aggregates the contract status and average successful bid rate (%) of the relevant organizations based on the order, and visually shows the monthly contract trends (patterns) such as the number of contracts, amount, and successful bid rate (%).

8 shows a detailed list in order of the top 1 to 3 ranks of the main contract partner companies by business type based on the bidding ordering agency and the contract amount by business type of all contract partner companies based on the ordering agency according to the present invention.

9 is an example of another menu screen that analyzes bidding ordering agency information according to the present invention, and visually shows information comparing the status information of competing companies, key business keywords, monthly down payment and the number of employees compared to the previous year.

10 is an example of another menu screen that analyzes bidding ordering agency information according to the present invention, summarizing the contract status and average successful bid rate (%) based on company standards, and the number and amount of monthly contracts related to monthly contract trends based on company standards , visually showing the successful bid rate (%).

11 is an example of another menu screen in which bidding ordering agency information is analyzed according to the present invention, and summarizes the company-based major ordering agency 1 to 3 ranking information, and visually shows the contract trends for each company by month.

12 is an example of another menu screen that analyzes bidding ordering organization information according to the present invention, and shows summary information of the top 5 supplier rankings of the corresponding company based on the company and detailed list information of the current contract with the partner company based on the company.

13 is an example of a screen where artificial intelligence is applied to big data according to the present invention and the company status is analyzed. It visually shows a graph comparing the trend of full-time relationship, average annual salary, contract amount, contract TN, contract amount, and employee salaries.

14 is an example of providing a bidding recommendation list by applying artificial intelligence to big data according to the present invention. According to the interest business list registered by the user as a business of interest, it shows and recommends an optimal business by big data analysis and artificial intelligence. have.

15 is an artificial intelligence applied to big data according to the present invention to summarize and analyze the bidding documents for bidding conditions and restrictions, show the analysis summary information check list, and according to the list check result, the bidding PMS of the corresponding business announcement Let it be created automatically.

16 is an example of providing an announcement summary information and a request for proposal by applying artificial intelligence to big data according to the present invention. is provided visually for easy analysis.

17 is an example of analysis results of bid preparation process management and strategy information collection by a help bot by applying artificial intelligence to big data according to the present invention. bid preparation by supporting the expected overall score prediction and competitor prediction when working with a selected partner company, the expected competitor's overall score and bid rate prediction, and information about the expected competitor's issues, news, and recruitment activities It supports process management and strategic information collection and analysis.

So far, the present invention has been focused on preferred embodiments.

The embodiments described in this specification and the configurations shown in the drawings relate to one most preferred embodiment of the present invention, and do not represent all of the technical spirit of the present invention, so various equivalents and modifications that can be substituted for them are It should be understood that there may be examples.

Therefore, the present invention is not limited to the presented embodiment, and within the equivalent scope of the technical spirit of the present invention and the technical spirit described in the claims to be described below by those of ordinary skill in the technical field to which the present invention belongs. There may be embodiments in which various modifications and changes are possible.

10: data server
110: data server of the bidding ordering institution 120: data server of the related institution
20: service server
21: Data Retrieval Server
210: first data search server 220: second data search server
22: Big data server
23: artificial intelligence server
230: competitor prediction module 231: cooperation score prediction module
232: contention rate prediction module 233: optimal bid rate prediction module
234: auto-generation of main review list module 235: automated bidding PMS module
236: recommended business module

Claims (6)

In the system for providing strategic information service for establishing a bidding strategy,
a data server 10 that provides bid order data and business information data on the web;
Networking with the data server 10 to search and index bidding information data and general business information data to collect big data, to calculate and learn the collected big data, and to infer and provide bidding strategy establishment data and business recommendation data a service server 20;
Including a user terminal 50 that is networked to the service server 20 to receive bid information and competitor business information to provide a strategy information service for establishing a bid strategy and a personalized business recommendation service when a user calls a service A system for providing strategic information services for establishing a bidding strategy, including being
The method according to claim 1,
The data server 10,
A system for providing a strategy information service for establishing a bidding strategy, including a data server 110 of a bidding ordering institution that provides bidding information data and general business information data, and a data server 120 of a related institution and a portal site.
The method according to claim 1,
The service server 20,
A first data search server 210 that is networked with the data server 110 of the bid ordering organization and stores the bid information data and general business information data from the web page of the bid ordering organization data server by having an RDB. Wow;
A second data search that is networked with the data server 120 and the portal site of the relevant institution, and is provided with a web crawler to search, index, and store the recruitment activity information and general business information of the competitor from the web page of the data server of the relevant institution a data retrieval server 21 including a server 220;
a big data server 22 having a big data platform for analyzing and processing the bidding information data collected from the web page of the data search server 21 and general business information of competitors and storing the analyzed data;
A system for providing a strategy information service for establishing a bidding strategy, comprising an artificial intelligence server (23) having an artificial intelligence module and learning analysis data of the big data server to infer bidding strategy establishment data and business recommendation data.
The method according to claim 1,
The artificial intelligence module of the artificial intelligence server 23,
a competitor prediction module 230 for deriving a competitor similar to the user's business based on the big data obtained from the first data search server 210;
A cooperation score prediction module 231 for predicting a cooperation score of a competitor based on the big data obtained from the first data search server 210;
a contention rate prediction module 232 for predicting a contention rate of a competitor based on big data acquired from the first data search server 210;
an optimal bid rate prediction module 233 for predicting the user's optimal bid rate based on the big data obtained from the first data search server 210;
A major review list automatic generation module 234 for automatically extracting and supporting major matters affecting whether to participate in a bid based on data obtained from the first data search server 210;
An automated bidding PMS module 235 that supports automatic generation of basic workbooks, information collection and analysis based on big data acquired from the first data search server 210;
Strategic information for establishing a bidding strategy including a business recommendation module 236 that operates based on the interaction between the user's history and the big data acquired from the second data search server 220 and recommends an AI-personalized customized business. service delivery system.
In the method of providing a bid strategy establishment strategy information service by a strategy information service providing system for bid strategy establishment,
setting the bid information data and business information data of the bid ordering organization and the related organizations as an open API (S100);
Business characteristics information from the bidding ordering agency and related organizations, contract pattern information of the bidding ordering agency, statistical status of the contracting party of the bidding ordering agency, business types and major customers of competitors, current status trends of competitors compared with the previous year, current activities and dynamics of competitors, A step of searching and indexing bidding information data and business information data including at least one of competitor's partner company analysis information and competitor's bidding pattern analysis information using a relational database (RDB) and a crawler (S110) and;
Entering and storing the searched and indexed bid information data into a big data platform (S120);
performing calculations for bidding strategy information and recommended business information based on the collected big data (S130);
Depending on the size of the contract amount of the existing business, the proportion of the business type of the existing business, the keywords for the publicity of the previously performed business, the customers and customer types of the existing business, and the distance between the location of the company and the business location performing an operation for adjusting the personalized application weight for each influence factor (S140);
Requesting bidding data information and business information through the user terminal (S150) and;
Competitor prediction, cooperation score prediction, contention rate prediction, optimal bid rate prediction, major review list automatically generated, business recommendation according to the step (S130) of performing calculations for bidding strategy information and recommended business information based on the collected big data A bidding comprising the step (S160) of automatically generating a PMS (Project Management System) based on the analysis and summary announcement of the bidding ordering institution and the request for proposal, and receiving a list of recommended projects according to the list registered as a business of interest by the user (S160) Strategies How to provide strategic information services.
5. The method according to claim 4,
The PMS is
Based on the distribution, average value, trend, and preference for project size of the contract amount of the existing project, setting a project with high project cost conformance in the average amount in the first recommendation range (S200);
Based on the preference of the business type of the existing business service: Goods: Corporation, the step of recommending from the high weight business type to the priority (S210),
Step (S220) of deriving preference for detailed business types based on the analysis of keywords in public notices of previously performed projects and preferentially recommending projects corresponding to the publicity names including high-ranking keywords (S220);
Recommending a business with a high degree of agreement between the existing customer and the preferred customer type based on the customer and customer type preference of the existing business (S230);
Including a step (S240) of preferentially recommending the business of an institution adjacent to the location of the company based on the distance between the location of the company and the location of the business,
Bid strategy establishment strategy information comprising the step (S250) of recommending a project based on the adjusted weight by adjusting the weight for each influence factor by the interaction of the steps (S200) to (S240) by artificial intelligence (S250) HOW TO PROVIDE SERVICES.

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