CN113868507A - Bidding information acquisition method and device combining RPA and AI and electronic equipment - Google Patents
Bidding information acquisition method and device combining RPA and AI and electronic equipment Download PDFInfo
- Publication number
- CN113868507A CN113868507A CN202111058012.6A CN202111058012A CN113868507A CN 113868507 A CN113868507 A CN 113868507A CN 202111058012 A CN202111058012 A CN 202111058012A CN 113868507 A CN113868507 A CN 113868507A
- Authority
- CN
- China
- Prior art keywords
- information
- bid
- bidding
- rpa
- query
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000005516 engineering process Methods 0.000 claims abstract description 44
- 238000006243 chemical reaction Methods 0.000 claims abstract description 24
- 238000004801 process automation Methods 0.000 claims abstract description 7
- 238000012795 verification Methods 0.000 claims description 53
- 238000003058 natural language processing Methods 0.000 claims description 45
- 238000004590 computer program Methods 0.000 claims description 17
- 238000012015 optical character recognition Methods 0.000 claims description 11
- 230000008676 import Effects 0.000 abstract description 10
- 238000010586 diagram Methods 0.000 description 11
- 238000007726 management method Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 5
- 238000013473 artificial intelligence Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000013479 data entry Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/08—Auctions
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to a bidding information acquisition method, device and electronic equipment combining RPA and AI, relating to the technical field of RPA and AI, wherein the method is applied to a robot process automation RPA system, and the specific disclosed technical scheme is as follows: acquiring keywords to be inquired and inquiry conditions; acquiring bidding information corresponding to the keywords meeting the query conditions from the bidding purchasing navigation network according to the keywords and the query conditions; carrying out format conversion on the bidding information by adopting an NLP technology to obtain the bidding information in a preset format; and importing the bidding information in the preset format into a preset database. From this, through adopting the RPA system to replace artifical the acquisition and input bid information, greatly reduced the cost of labor, improved work efficiency, and through adopting the mode that RPA and AI combine to obtain bid information and with the preset form under bid information import preset database, guaranteed the accuracy of the bid information of acquisition and import in the preset database.
Description
Technical Field
The application relates to the technical field of robot process automation and artificial intelligence, in particular to a bidding information acquisition method and device combining RPA and AI and an electronic device.
Background
Robot Process Automation (RPA) is a Process task that simulates human operations on a computer through specific robot software and automatically executes according to rules.
Artificial Intelligence (Artificial Intelligence), abbreviated in english as AI. The method is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence, a field of research that includes robotics, speech recognition, image recognition, natural language processing, and expert systems.
At present, bidding information needs to be acquired and imported into a preset system in many scenes. For example, in order to avoid situations of insufficient budget, excessive budget, inaccurate project cycle definition, and the like during bidding, a property company can maximize the project profit, generally obtain historical bidding information of the property industry as a reference, and import the historical bidding information into an internal system of the property company. In the related art, the bid information required for searching the bid website is usually manually input to a preset system, and then the search result is manually input to the preset system. This kind of mode through artifical acquisition and input information of tendering, under the condition that the quantity of information of tendering is a lot of, owing to need carry out a large amount of data entry work, this has just led to work efficiency low, and the cost of labor is high, and the mode of artifical acquisition and input information of tendering is difficult to guarantee the accuracy of the information of tendering that acquires and input.
Disclosure of Invention
The application provides a bid and offer information acquisition method, device and electronic equipment combining RPA and AI, and aims to solve the technical problems of low efficiency, high labor cost and poor accuracy in the prior art of acquiring and inputting bid and offer information.
The embodiment of the first aspect of the present disclosure provides a bid information obtaining method combining an RPA and an AI, which is applied to a robot process automation RPA system, and the method includes: acquiring keywords to be inquired and inquiry conditions; acquiring bidding information corresponding to the keywords meeting the query conditions from a bidding procurement navigation network according to the keywords and the query conditions; carrying out format conversion on the bidding information by adopting a Natural Language Processing (NLP) technology to obtain the bidding information in a preset format; and importing the bidding information in the preset format into a preset database.
The embodiment of the second aspect of the present disclosure provides a bid information obtaining apparatus combining an RPA and an AI, which is applied to a robot process automation RPA system, and the apparatus includes: the first acquisition module is used for acquiring keywords to be inquired and inquiry conditions; the second acquisition module is used for acquiring bidding information corresponding to the keywords meeting the query conditions from a bidding purchasing navigation network according to the keywords and the query conditions; the conversion module is used for converting the format of the bidding information by adopting a Natural Language Processing (NLP) technology so as to obtain the bidding information in a preset format; and the importing module is used for importing the bidding information in the preset format into a preset database.
An embodiment of a third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method according to the embodiment of the first aspect of the present disclosure.
A fourth aspect of the present disclosure is directed to a non-transitory computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, implements the method according to the first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the method according to the first aspect of the present disclosure.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
after the RPA system acquires the keywords to be queried and the query conditions, the bidding information corresponding to the keywords meeting the query conditions is acquired from the bidding purchasing navigation network according to the keywords and the query conditions, format conversion is performed on the bidding information by adopting an NLP technology to acquire the bidding information in a preset format, and the bidding information in the preset format is further imported into a preset database.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow diagram of a bid information acquisition method incorporating RPA and AI according to one embodiment of the present application;
FIG. 2 is a flow diagram of a bid information acquisition method incorporating RPA and AI according to another embodiment of the present application;
FIG. 3 is an exemplary diagram of a query page according to one embodiment of the present application;
FIG. 4 is an exemplary diagram of first bid information in an original format and bid information in a preset format according to one embodiment of the present application;
FIG. 5 is an exemplary diagram of a detail page according to one embodiment of the present application;
FIG. 6 is a schematic flow chart diagram of a bid information acquisition method in conjunction with RPA and AI according to another embodiment of the present application;
FIG. 7 is an exemplary diagram of an account login page according to one embodiment of the present application;
fig. 8 is a schematic structural diagram of a bid information acquisition apparatus that combines RPA and AI according to an embodiment of the present application; and
FIG. 9 is a schematic structural diagram of an electronic device according to one embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
In the description of the present application, it is to be understood that the term "plurality" means two or more; the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
It can be understood that, in the manner of manually acquiring and inputting bid submitting information in the related art, under the condition that the amount of bid submitting information is large, a large amount of data input work is required, which leads to low work efficiency and high labor cost, and the manner of manually acquiring and inputting bid submitting information is difficult to ensure the accuracy of the acquired and inputted bid submitting information.
The method for acquiring the bidding information and importing the bidding information into the preset database by using the RPA system is a main mode for acquiring and inputting the bidding information.
Specifically, through the combination of the RPA and the AI, the RPA system is realized to acquire bidding information instead of manpower and import the bidding information into a preset database. The RPA system can work continuously for 7 × 24 hours as long as data exists, so that the labor cost can be greatly reduced, the working efficiency is improved, and the accuracy of obtaining and importing the bidding information in the preset database is ensured by obtaining the bidding information in a mode of combining the RPA and the AI and importing the bidding information in the preset format into the preset database.
For the purpose of clearly explaining the embodiments of the present invention, terms referred to in the embodiments of the present invention will be explained first.
In the description of the present invention, the term "bid procurement navigation web" refers to a website providing a bid procurement service, and the bid procurement navigation web in the embodiment of the present application may be any website capable of providing a bid procurement service, which is not limited in the present application.
In the description of the present invention, the term "bid information" refers to any information related to bidding and bidding, such as a project name, a province or region where the project is located, a release time, a winning company name, a winning amount, an affiliated industry, a contract amount, a contract signing date, a purchaser, a contact, an area, a property cost unit price, a development unit, a purchase unit, and the like.
In the description of the present invention, the term "keyword" refers to a search keyword used in query in a bid inviting procurement navigation network, and may be arbitrarily set as required, for example, when bid inviting and bidding information of a property industry needs to be acquired, the keyword may be "property management", "sanitation and safety", "logistics service", and the like.
In the description of the present invention, the term "query condition" refers to a condition that defines a query result when a query is made in a bid procurement navigation network, and may include a time range, a city range, and the like, for example. For example, if the query condition in the bid procurement navigation network includes a time range of "2020-01-01 to 2020-12-31", i.e., 01/2020 to 12/31/2020, only the query results satisfying the time range are included in the query results.
In the description of the present invention, the term "format conversion" refers to converting data from one display format or storage format to another display format or storage format. For example, storing data on a web page into an Excel table can be regarded as performing format conversion on the data on the web page, and storing data in one Excel table into another Excel table with a different form style can be regarded as performing format conversion on the data in the Excel table. The preset format in the embodiment of the invention refers to a storage format which needs to be met by bidding information when the bidding information is imported into a preset database; the original format refers to a storage format of data directly derived from a webpage.
In the description of the present invention, the term "database" is also referred to as a data management system, and is a data set in which processed data is stored together in a manner that allows multiple users to share the data and reduces redundancy as much as possible. The preset database in the embodiment of the present invention refers to a database into which bidding information needs to be imported, for example, a database of an internal system of an enterprise, and after the bidding information is imported into the database, the enterprise can directly present data in the database on a web page of the internal system through the internal system.
The bid information acquisition method, apparatus and electronic device in conjunction with RPA and AI are described below with reference to specific embodiments.
Fig. 1 is a flowchart of a bid information acquisition method in conjunction with an RPA and an AI according to an embodiment of the present application, as shown in fig. 1, the method including:
Note that, the bidding information acquisition method according to the embodiment of the present application, which combines the RPA and the AI, is executed by the RPA system. In an exemplary embodiment, the RPA system may be configured to execute the method at a specific time, or the RPA system may be configured to execute the method continuously throughout the day, which is not limited in this application.
In an exemplary embodiment, at least one keyword and a corresponding query condition that need to be queried may be stored in a preset database, so that the RPA system may obtain the keyword and the query condition to be queried from the preset database through an interface with the preset database. By acquiring the keywords to be queried and the query conditions in the manner, the keywords and the query conditions stored in the database can be maintained when the keywords and/or the query conditions to be queried need to be added, deleted or modified, and the flexibility is high.
It should be noted that, the RPA system may obtain one or more keywords from a preset database each time to perform one round of query according to the one or more keywords, and the number of keywords obtained by the RPA system in each round of query is not limited in the present application. For example, the preset database includes keywords "property management", "cleaning", "security", and "logistics service" required for querying bidding information in the property industry, and the RPA system may obtain one of the keywords each time for a subsequent round of query, or directly obtain each keyword in the property industry for a subsequent round of query.
The query condition may include at least one of a time range of bidding information to be queried, a city range to be queried, and the like.
And 102, acquiring bidding information corresponding to the keywords meeting the query conditions from the bidding purchase navigation network according to the keywords and the query conditions.
The bid procurement navigation network refers to a website for providing bid procurement services. It should be noted that, the bid procurement navigation network in the embodiment of the present application may be any website capable of providing a bid procurement service, and the present application is not limited to this.
The bidding information corresponding to the keyword may be bidding information corresponding to at least one of the following items: the corresponding project name includes a project of a keyword, a project of an affiliated industry matched with the keyword, a project of a keyword included in corresponding bid information, a project of a keyword included in corresponding attachment including detailed bid information, and the like.
The bid and bid information may include any information related to bid and bid of the project, such as the project name (also referred to as the project title), province, region, release time, bid-winning company name, bid amount, industry under consideration, contract amount, contract signing date, buyer, contact address, area, property cost unit price, development unit, and purchasing unit of the project.
In an exemplary embodiment, the bid procurement navigation network includes a query page, and the RPA system may first access the query page of the bid procurement navigation network according to the website of the bid procurement navigation network, and input the keyword to be queried and the query condition on the query page to obtain the bid information corresponding to the keyword satisfying the query condition.
For example, assuming that the keyword is "property management", the query condition includes a time range "2020-01-01 to 2020-12-31", and the query city is "city a", the RPA system may input the keyword "property management" in the keyword input position of the query page, input the time range "2020-01-01 to 2020-12-31" in the time range input position, input the query city "city a" in the query city input position, and further start the query through a control having a start query process on the query page to obtain bid and ask information corresponding to an item containing the keyword "property management" in the item name in the city a within a time range of 01/m/2020 to 12/m/31/m/2020.
The preset database is a database in which bidding information needs to be imported. For example, the preset database may be a database of an internal system of an enterprise, and the enterprise may directly present data in the database on a web page of the internal system through the internal system. And the preset format is a storage format which needs to be met by the bidding information when the bidding information is imported into a preset database.
In an exemplary embodiment, after acquiring bidding information corresponding to a keyword meeting a query condition from the bidding procurement navigation network, the RPA system may firstly adopt an NLP technique to understand the bidding information, extract the bidding information to be imported into a preset database, and store the bids in a preset format, thereby acquiring the bidding information in the preset format.
In an exemplary embodiment, after obtaining the bidding information, the RPA system may further determine whether the obtained bidding information belongs to a preset industry, and if so, perform format conversion on the bidding information to obtain the bidding information in a preset format. When the specific judgment is carried out, the bidding information can be analyzed by adopting an NLP technology so as to determine whether the bidding information belongs to the preset industry. I.e. before step 103, may further include:
and (3) determining that the bidding information belongs to the preset industry by adopting an NLP technology.
The preset industry can be set according to needs, and the application is not limited to this. For example, the preset industry may be set as a property industry, a building industry, and the like.
Specifically, when the NLP technology is adopted for judgment, the NLP technology can be adopted to analyze the bidding information to obtain a field containing the industry belonging to the field in the bidding information, and whether the bidding information is the preset industry is judged according to the field, and if so, the bidding information is determined to belong to the preset industry.
After the bidding information is obtained, the fact that the bidding information belongs to the preset industry is firstly determined, format conversion is carried out on the bidding information, the bidding information in the preset format is obtained, the fact that the bidding information in the preset industry is obtained is achieved, and the preset industry can be set according to needs, so that the bidding information in the preset format can be obtained for any industry by the method in the embodiment of the application, and the universality is high.
And 104, importing the bidding information in the preset format into a preset database.
In an exemplary embodiment, after the RPA system acquires the bidding information in the preset format, the RPA system may import the bidding information in the preset format into the preset database through an interface between the RPA system and the preset database.
Because the RPA system is adopted to replace manual acquisition of the bidding information, the RPA system can continuously work all day long as data exists, so that the labor cost can be greatly reduced, the work efficiency is improved, and after the acquired bidding information is converted into a preset format, the bidding information in the preset format can be imported into a preset database through an interface, and the efficiency of data entry of the preset database is further improved. In addition, the bidding information is acquired in a mode of combining the RPA and the AI, and the bidding information in the preset format is imported into the preset database, so that the accuracy of acquiring and importing the bidding information in the preset database is guaranteed.
In an exemplary embodiment, after the RPA system imports the bidding information in a preset format into a preset database, a prompt message may be sent to the user, for example, an email, a short message, an instant message, or the like is sent to a terminal used by the user, so as to notify the user that the import of the bidding information is completed.
In addition, after the RPA system imports the bidding information in the preset format into the preset database, the RPA system may record the keyword and the query condition corresponding to the bidding information imported into the preset database, so that when the RPA system acquires the bidding information in the next round, the RPA system does not acquire the bidding information according to the recorded keyword and query condition.
Note that the bid information corresponding to the keyword satisfying the query condition acquired from the bid procurement navigation network by the RPA system may include bid information corresponding to items for which a winning bid result is already made and bid information corresponding to items for which a winning bid result is not made. In the embodiment of the present application, after the bid information corresponding to the keyword satisfying the query condition is obtained from the bid procurement navigation network, the bid information corresponding to the item with the bid-winning result can be obtained from the bid information, and further, steps 103 and 104 are performed only on the bid information corresponding to the item with the bid-winning result, that is, only the bid information corresponding to the item with the bid-winning result in the preset format is imported into the preset database. And the bidding information corresponding to the items without the bid-winning result can be sent to the terminal where the user is located, so that the bidding information corresponding to the items without the bid-winning result can be manually processed. Therefore, the RPA system can timely find the bidding information corresponding to the bidding project and timely send the bidding information to the user, so that the user can timely check the bidding information corresponding to the bidding project, and the timeliness of information acquisition is improved.
In the embodiment of the application, after the RPA system acquires the keyword and the query condition to be queried, the bidding information corresponding to the keyword meeting the query condition is acquired from the bidding purchasing navigation network according to the keyword and the query condition, format conversion is performed on the bidding information by adopting an NLP technology to acquire the bidding information in the preset format, and the bidding information in the preset format is imported into a preset database.
The method for obtaining bid-winning information provided in the embodiment of the present application is further described below with reference to fig. 2.
Fig. 2 is a flowchart of a bid information acquisition method in conjunction with an RPA and an AI according to another embodiment of the present application, as shown in fig. 2, the method including:
In step 203, the first bid information in the original format is derived.
In an exemplary embodiment, the bid inviting procurement navigation network may include a query page, and the RPA system may first access the query page of the bid inviting procurement navigation network according to the website of the bid inviting procurement navigation network, and input the keyword to be queried and the query condition on the query page, so as to obtain bid inviting information corresponding to the keyword satisfying the query condition, and export the bid inviting information. The format of the derived bid-posting information is related to the setting of the bid-posting purchase navigation network. In the embodiment of the present invention, bidding information that can be directly derived from the bidding procurement navigation network is referred to as first bidding information, and a format in which the first bidding information is derived is referred to as an original format.
It should be noted that, when the RPA system inputs the keyword and the query condition to be queried on the query page, the RPA system may adaptively adjust the input mode of the keyword and the query condition according to the specific setting of the bid procurement navigation network, for example, the keyword or the query condition is input on the query page, or the keyword and the option corresponding to the query condition to be queried are selected on the query page.
In addition, the query condition may include at least one of a time range of bidding information to be queried, a city to be queried, and a type of information, a search manner, a search mode, and the like shown in fig. 3.
For example, referring to the query page shown in fig. 3, the query page includes a keyword input box and an option or input box corresponding to the query condition. Assuming that a keyword to be queried is "property management", a query condition includes a time range of "2020-01-01 to 2020-12-31", and a query city is "a city", the RPA system may input the keyword "property management" in a keyword input box 31 of a query page, input a time range of "2020-01-01 to 2020-12-31" in a time range input box 33, select "a city" through a selection box 32 under a search area term, and further perform a query through a control (an immediate search control in fig. 3) having an open query process on the query page to obtain bid information corresponding to "property management" in a city in a time range of 31 days of 01/2020/01 to 2020/12/31.
It should be noted that the query page shown in fig. 3 is only an illustrative illustration, and should not be construed as a limitation to the technical solution of the present application.
It is understood that the first bid information derived from the bid procurement navigation network may not be comprehensive, for example, the first bid information may include only the item name, province, region, release time, winning company name and winning amount of the item. In an exemplary embodiment, more comprehensive bidding information may also be obtained in the following manner in step 204.
And 204, acquiring corresponding detail pages aiming at the items respectively, wherein the detail pages comprise second bidding information corresponding to the corresponding items.
The first bidding information corresponds to at least one item, that is, the first bidding information may be bidding information corresponding to the at least one item. In an exemplary embodiment, for each item, a detail page corresponding to each item may also be obtained, where the detail page corresponding to each item includes the second bid information corresponding to the corresponding item. Wherein the second bidding information may include: the project corresponds to at least one of the affiliated industry, contract amount, contract signing date, buyer, contact information, area, property cost unit price, development unit, purchasing unit and the like.
And step 205, performing format conversion on the first bidding information and the second bidding information by adopting an NLP technology to obtain bidding information in a preset format.
In an exemplary embodiment, the NLP technology may be adopted to perform format conversion on the first bid information in the preset format to obtain the first bid information in the preset format, and the NLP technology may be adopted to perform format conversion on the second bid information of the corresponding item included in the detail page corresponding to each item to obtain the second bid information in the preset format, where the first bid information in the preset format and the second bid information in the preset format together constitute the bid information in the preset format.
In an exemplary embodiment, for the derived first bid information in the original format, format conversion may be performed to obtain the first bid information in a preset format in the following manner: analyzing the first bidding information in the original format by adopting an NLP technology to obtain first sub-bidding information corresponding to each project, wherein the first sub-bidding information comprises at least one item of the project name, province, region, release time, bid-winning enterprise name and bid-winning amount of the corresponding project; and storing the first sub bid information respectively corresponding to each project in a preset format to obtain the first bid information in the preset format.
In an exemplary embodiment, for a detail page corresponding to each item, the detail page may be parsed by using NLP technology to extract second sub-bidding information corresponding to the item from the detail page, where the second sub-bidding information includes at least one of the following information: the industry to which the project corresponds, contract amount, contract signing date, buyer, contact, area, property cost unit price, development unit and purchasing unit, and then the second sub bid information corresponding to each project is stored in a preset format, so that the second bid information in the preset format can be obtained.
Referring to the table shown in fig. 4, the table format shown at the upper side of fig. 4 is the original format of the first bid information derived by the RPA system, and the table format shown at the lower side of fig. 4 is a format that the bid information needs to satisfy when the bid information is imported into a preset database, that is, a preset format.
After the RPA system acquires the first bid information corresponding to the "property management" keyword satisfying the query condition from the bid navigation network, the RPA system may derive the first bid information in the original format as shown in the upper table of fig. 4, and analyze the first bid information in the original format to acquire the first sub bid information corresponding to 2 items corresponding to sequence numbers 1 and 2, where the first sub bid information corresponding to each item includes an item title, a province, a region, a release date, a winning enterprise, and a winning amount. The RPA system may store the first sub bid information corresponding to each item in the corresponding position in the table shown on the lower side of fig. 4 in accordance with the correspondence relationship shown by the arrow shown in fig. 4.
Further, since the bid information derived by the RPA system may not be comprehensive, the RPA system may also obtain a detail page as shown in fig. 5 for each item. Further, the RPA system may analyze the detail page using the NLP technique to extract second sub bid information corresponding to the item from the detail page, for example, an industry to which the item belongs may be extracted from a 41 position of the detail page, a contract amount may be extracted from a 42 position of the detail page, a contract date may be extracted from a 43 position of the detail page, a buyer may be extracted from a 44 position of the detail page, the second sub bid information may be stored in a corresponding position in the table shown in the lower side of fig. 4, first sub bid information corresponding to each item and second sub bid information corresponding to each item may be stored in a corresponding position in the table shown in the lower side of fig. 4, and then bid information in a preset format may be obtained.
In an exemplary embodiment, the RPA system may further determine whether the first bidding information and the second bidding information belong to a preset industry after acquiring the first bidding information and the second bidding information, and if so, perform format conversion on the first bidding information and the second bidding information to acquire the bidding information in a preset format. Specifically, when the first bidding information and the second bidding information in the detail page in the original format are determined, the NLP technology may be adopted to analyze the first bidding information and the second bidding information in the original format, so as to determine whether the first bidding information and the second bidding information belong to the preset industry. That is, before step 205, it may further include:
and determining that the first bidding information and the second bidding information belong to a preset industry by adopting an NLP technology.
The preset industry can be set according to needs, and the application is not limited to this. For example, the preset industry may be set as a property industry, a building industry, and the like.
Specifically, when the NLP technology is adopted for the judgment, the NLP technology may be adopted to analyze the first bidding information or the second bidding information to obtain a field containing an industry to which the first bidding information or the second bidding information belongs, and judge whether the first bidding information or the second bidding information is a preset industry according to the field, and if so, determine that the first bidding information or the second bidding information belongs to the preset industry.
After the bidding information is obtained, the fact that the bidding information belongs to the preset industry is firstly determined, format conversion is carried out on the bidding information, the bidding information in the preset format is obtained, the fact that the bidding information in the preset industry is obtained is achieved, and the preset industry can be set according to needs, so that the bidding information in the preset format can be obtained for any industry by the method in the embodiment of the application, and the universality is high.
In the embodiment of the application, after the RPA system acquires the keyword to be queried and the query condition, the keyword and the query condition are input into a query page to query first bid information corresponding to the keyword meeting the query condition, the first bid information in an original format is derived, corresponding detail pages are respectively acquired aiming at each item, the detail pages comprise second bid information corresponding to the corresponding item, format conversion is carried out on the first bid information and the second bid information by adopting an NLP technology to acquire the bid information in a preset format, and then the bank flow data in the preset format is led into a preset database, so that the RPA system is adopted to replace manual acquisition and entry of the bid information, the labor cost is greatly reduced, the working efficiency is improved, and the bid information is acquired by adopting a mode of combining RPA and AI and the bid information in the preset format is led into the preset database, the accuracy of acquiring and importing the bidding information in the preset database is ensured.
It is understood that, in a possible implementation form, the RPA system may need to log in a user account before obtaining bid information corresponding to a keyword satisfying a query condition from a bid procurement navigation network according to the keyword and the query condition, and the method for obtaining bid information in combination with RPA and AI provided in the embodiment of the present application is further described below with reference to fig. 6.
Fig. 6 is a flowchart of a bid information acquisition method in conjunction with an RPA and an AI according to another embodiment of the present application, as shown in fig. 6, the method including:
In an exemplary embodiment, the bid procurement navigation web may include an account login page. The PRA system may access an account login page of the bid procurement navigation network according to the acquired website information of the bid procurement navigation network, and input account information and corresponding password information on the account login page to log in the user account.
The website information of the bidding purchase navigation network, the account information of the user account, and the corresponding password information may be sent to the RPA system by the user in advance, or may be obtained by the RPA system in other manners, which is not limited in this application.
In an exemplary embodiment, before logging in the user account, it may further need to input a verification code according to the verification code picture, and accordingly, after step 603, further includes:
judging whether a verification code picture exists on an account login page or not;
when the verification code picture exists on the account login page, identifying characters in the verification code picture by adopting an Optical Character Recognition (OCR) technology;
and determining the verification code corresponding to the character by adopting NLP technology.
The verification code picture may be a picture containing numbers and operators.
It will be appreciated that when the account login page requires the input of the verification code according to the verification code picture, the corresponding verification code identifier or text description is usually included near the verification code input box, for example, a text typeface of "input verification code" may be included above the verification code input box. Then, in an exemplary embodiment, the RPA system may analyze the account login page by using an NLP technique, and when the account login page includes the "verification code" character, it may determine that a verification code picture exists on the account login page, and if the account login page does not include the "verification code" character, it may determine that the verification code picture does not exist on the account login page.
In an exemplary embodiment, when it is determined that the verification code picture does not exist on the account login page, the RPA system may input account information and corresponding password information on the account login page, and may further directly log in the user account.
When the RPA system determines that the verification code picture exists on the account login page, the RPA system may use an OCR technology to identify the verification code picture to identify characters in the verification code picture, and use an NLP technology to calculate a verification code corresponding to the characters, for example, a character in the verification code picture is "7 + 7? "the verification code corresponding to the character is calculated to be 14 by adopting NLP technology, and then account information, corresponding password information and verification code are input in the account login page to log in the user account. That is, step 604 may be replaced with: and inputting account information, corresponding password information and a verification code on the account login page to log in the user account.
Referring to the account login page shown in fig. 7, when the RPA system determines that the verification code picture exists on the account login page, the RPA system may identify the verification code picture by using OCR technology to identify the character "7 + 7? "and calculate the corresponding verification code of the character as 14 by using NLP technology, and then can input account information in the account input box of the account login page shown in fig. 7, input corresponding password information in the password input box, and input verification code in the verification code input box to log in the user account.
Whether a verification code picture exists on an account login page or not is determined through an RPA system, when the verification code picture exists on the account login page, characters in the verification code on the verification code picture are identified by adopting an OCR (optical character recognition) technology, a verification code corresponding to the characters is determined by adopting an NLP (non line segment) technology, account information, corresponding password information and the verification code are input on the account login page to log in a user account, and the user account is guaranteed to be correctly logged in when the verification code picture exists on the account login page.
And 606, converting the format of the bidding information by adopting an NLP technology to acquire the bidding information in a preset format.
The specific implementation process and principle of steps 605-607 can refer to the description of the above embodiments, and will not be described herein again.
In the embodiment of the application, after the RPA system acquires the keyword to be queried and the query condition, website information of a bid purchasing navigation network, account information of a user account and corresponding password information are acquired, an account login page is accessed according to the website information of the bid purchasing navigation network, the account information and the corresponding password information are input on the account login page to log in the user account, bid inviting information corresponding to the keyword meeting the query condition is acquired from the bid purchasing navigation network according to the keyword and the query condition, format conversion is carried out on the bid inviting information by adopting an NLP technology to acquire the bid inviting information in a preset format, and then the bank flow data in the preset format is led into a preset database, so that the manual acquisition and the entry of the bid inviting information are replaced by the RPA system, the labor cost is greatly reduced, and the working efficiency is improved, and the bidding information is acquired by combining the RPA and the AI, and the bidding information in the preset format is imported into the preset database, so that the accuracy of acquiring and importing the bidding information in the preset database is ensured.
In order to implement the above embodiments, the present application also proposes a bid information acquisition apparatus combining RPA and AI. Fig. 8 is a schematic structural diagram of a bid information acquiring apparatus combining RPA and AI according to an embodiment of the present application, and as shown in fig. 8, the bid information acquiring apparatus 800 combining RPA and AI is applied to an RPA system, and includes: a first obtaining module 801, a second obtaining module 802, a converting module 803, and an importing module 804, wherein,
a first obtaining module 801, configured to obtain a keyword to be queried and a query condition;
a second obtaining module 802, configured to obtain bid information corresponding to the keyword meeting the query condition from the bid procurement navigation network according to the keyword and the query condition;
a conversion module 803, configured to perform format conversion on the bid information by using an NLP technique to obtain the bid information in a preset format;
an importing module 804 is configured to import the bidding information in the preset format into a preset database.
In one embodiment of the present application, the bid procurement navigation web includes a query page;
the second obtaining module 802 includes:
the first input unit is used for inputting the keywords and the query conditions into a query page so as to query first bid information corresponding to the keywords meeting the query conditions;
and the deriving unit is used for deriving the first bidding information in the original format.
In one embodiment of the present application, the first bid information corresponds to at least one item;
the second obtaining module 802 further includes:
and the acquisition unit is used for respectively acquiring corresponding detail pages aiming at each item, and the detail pages comprise second bid-offering information corresponding to the corresponding items.
In an embodiment of the present application, the apparatus 800 further includes:
and the first determining module is used for determining that the bidding information belongs to the preset industry by adopting an NLP technology.
In one embodiment of the present application, the bid procurement navigation web includes an account login page;
wherein, the apparatus 800 further comprises:
the third acquisition module is used for acquiring website information of the bid purchasing navigation network, account information of the user account and corresponding password information;
the access module is used for accessing an account login page according to the website information of the bid purchasing navigation network;
and the input module is used for inputting account information and corresponding password information on the account login page so as to log in the user account.
In an embodiment of the present application, the apparatus 800 further includes:
the judging module is used for judging whether the verification code picture exists on the account login page or not;
the recognition module is used for recognizing characters in the verification code picture by adopting an OCR technology when the verification code picture exists on the account login page;
the second determining module is used for determining the verification code corresponding to the character by adopting an NLP technology;
and the input module is used for inputting account information, corresponding password information and verification codes on the account login page so as to log in the user account.
It should be noted that the foregoing explanation on the embodiment of the method for obtaining bid information in combination with RPA and AI also applies to the apparatus for obtaining bid information in combination with RPA and AI of this embodiment, and details that are not disclosed in the embodiment of the apparatus for obtaining bid information in combination with RPA and AI of this application are not repeated here.
To sum up, the apparatus for acquiring bid information combining RPA and AI according to the embodiment of the present application acquires a keyword to be queried and a query condition, acquires bid information corresponding to the keyword satisfying the query condition from a bid procurement navigation network according to the keyword and the query condition, performs format conversion on the bid information by using NLP technology to acquire the bid information in a preset format, and then imports the bid information in the preset format into a preset database.
In order to implement the foregoing embodiments, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the electronic device implements the method for acquiring bid information in combination with RPA and AI according to any one of the foregoing method embodiments.
To achieve the above embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a bid information obtaining method combining RPA and AI as described in any of the foregoing method embodiments.
To achieve the above embodiments, the present disclosure further provides a computer program product, which when being executed by an instruction processor, implements the bid information obtaining method combining RPA and AI according to any one of the method embodiments.
FIG. 9 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure. The electronic device 12 shown in fig. 9 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 9, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the memory 28 and the processing unit 16.
Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.
Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via the Network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the memory 28, for example, implementing the methods mentioned in the foregoing embodiments.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (15)
1. A bid information acquisition method combining RPA and AI, which is applied to a robot process automation RPA system, the method comprises:
acquiring keywords to be inquired and inquiry conditions;
acquiring bidding information corresponding to the keywords meeting the query conditions from a bidding procurement navigation network according to the keywords and the query conditions;
carrying out format conversion on the bidding information by adopting a Natural Language Processing (NLP) technology to obtain the bidding information in a preset format;
and importing the bidding information in the preset format into a preset database.
2. The RPA and AI combined bid information acquisition method of claim 1, wherein the bid procurement navigation web includes a query page;
the acquiring bid information corresponding to the keyword meeting the query condition from a bid procurement navigation network according to the keyword and the query condition includes:
inputting the keywords and the query conditions into the query page to query first bidding information corresponding to the keywords meeting the query conditions;
deriving the first bid information in an original format.
3. The RPA and AI combined bid information acquisition method according to claim 2, wherein the first bid information corresponds to at least one item;
wherein, according to the keyword and the query condition, obtaining bidding information corresponding to the keyword meeting the query condition from a bidding procurement navigation network, further comprises:
and respectively acquiring corresponding detail pages aiming at the items, wherein the detail pages comprise second bid-offering information corresponding to the corresponding items.
4. The RPA and AI combined bid information acquisition method according to any one of claims 1 to 3, wherein after acquiring bid information corresponding to the keyword satisfying the query condition from a bid procurement navigation network based on the keyword and the query condition, the method further comprises:
and determining that the bidding information belongs to a preset industry by adopting the NLP technology.
5. The RPA and AI combined bid information acquisition method according to any one of claims 1-3, wherein the bid procurement navigation web includes an account login page;
before obtaining bidding information corresponding to the keyword meeting the query condition from a bidding procurement navigation network according to the keyword and the query condition, the method further comprises the following steps:
acquiring website information of the invitation purchase navigation network, account information of a user account and corresponding password information;
accessing the account login page according to the website information of the bid purchasing navigation network;
and inputting the account information and the corresponding password information on the account login page so as to log in the user account.
6. The RPA and AI combined bid information acquisition method according to claim 5, wherein after accessing the account login page according to the website information of the bid procurement navigation network, the method further comprises:
judging whether a verification code picture exists on the account login page or not;
when a verification code picture exists on the account login page, identifying characters in the verification code picture by adopting an Optical Character Recognition (OCR) technology;
determining a verification code corresponding to the character by adopting an NLP technology;
the inputting the account information and the corresponding password information on the account login page to log in the user account includes:
and inputting the account information, the corresponding password information and the verification code on the account login page so as to log in the user account.
7. A bid information acquisition device combining RPA and AI, applied to a robot process automation RPA system, comprising:
the first acquisition module is used for acquiring keywords to be inquired and inquiry conditions;
the second acquisition module is used for acquiring bidding information corresponding to the keywords meeting the query conditions from a bidding purchasing navigation network according to the keywords and the query conditions;
the conversion module is used for converting the format of the bidding information by adopting a Natural Language Processing (NLP) technology so as to obtain the bidding information in a preset format;
and the importing module is used for importing the bidding information in the preset format into a preset database.
8. The RPA and AI combined bid information acquisition device of claim 7 wherein the bid procurement navigation web includes a query page;
wherein the second obtaining module includes:
a first input unit, configured to input the keyword and the query condition into the query page to query for first bid information corresponding to the keyword satisfying the query condition;
and the deriving unit is used for deriving the first bidding information in an original format.
9. The RPA and AI combined bid information acquisition apparatus according to claim 8, wherein the first bid information corresponds to at least one item;
wherein, the second obtaining module further comprises:
and the acquisition unit is used for respectively acquiring corresponding detail pages aiming at the items, and the detail pages comprise second bid information corresponding to the corresponding items.
10. The RPA and AI combined bid information acquisition apparatus according to any one of claims 7-9, characterized by further comprising:
and the first determining module is used for determining that the bidding information belongs to the preset industry by adopting the NLP technology.
11. The RPA and AI combined bid information acquisition apparatus according to any one of claims 7-9, wherein the bid procurement navigation web includes an account login page;
wherein, the device still includes:
the third acquisition module is used for acquiring the website information of the invitation purchase navigation network, the account information of the user account and the corresponding password information;
the access module is used for accessing the account login page according to the website information of the bid purchasing navigation network;
and the input module is used for inputting the account information and the corresponding password information on the account login page so as to log in the user account.
12. The RPA and AI combined bid information acquisition apparatus of claim 11, further comprising:
the judging module is used for judging whether the verification code picture exists on the account login page or not;
the identification module is used for identifying characters in the verification code picture by adopting an Optical Character Recognition (OCR) technology when the verification code picture exists on the account login page;
the second determining module is used for determining the verification code corresponding to the character by adopting an NLP technology;
the input module is used for inputting the account information, the corresponding password information and the verification code on the account login page so as to log in the user account.
13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1-6 when executing the computer program.
14. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.
15. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111058012.6A CN113868507A (en) | 2021-09-09 | 2021-09-09 | Bidding information acquisition method and device combining RPA and AI and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111058012.6A CN113868507A (en) | 2021-09-09 | 2021-09-09 | Bidding information acquisition method and device combining RPA and AI and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113868507A true CN113868507A (en) | 2021-12-31 |
Family
ID=78995248
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111058012.6A Pending CN113868507A (en) | 2021-09-09 | 2021-09-09 | Bidding information acquisition method and device combining RPA and AI and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113868507A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730121A (en) * | 2022-11-14 | 2023-03-03 | 百思特管理咨询有限公司 | Bidding information capture method based on software robot |
| CN116308775A (en) * | 2022-12-26 | 2023-06-23 | 广州悦秀智讯科技信息咨询有限公司 | RPA-based multi-system bill automatic processing method and system |
| WO2023159778A1 (en) * | 2022-02-24 | 2023-08-31 | 来也科技(北京)有限公司 | Bidding document acquisition method and apparatus combining rpa and ai |
-
2021
- 2021-09-09 CN CN202111058012.6A patent/CN113868507A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023159778A1 (en) * | 2022-02-24 | 2023-08-31 | 来也科技(北京)有限公司 | Bidding document acquisition method and apparatus combining rpa and ai |
| CN115730121A (en) * | 2022-11-14 | 2023-03-03 | 百思特管理咨询有限公司 | Bidding information capture method based on software robot |
| CN116308775A (en) * | 2022-12-26 | 2023-06-23 | 广州悦秀智讯科技信息咨询有限公司 | RPA-based multi-system bill automatic processing method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113868507A (en) | Bidding information acquisition method and device combining RPA and AI and electronic equipment | |
| CN104951468A (en) | Data searching and processing method and system | |
| CN111553137B (en) | Report generation method and device, storage medium and computer equipment | |
| CN113342976A (en) | Method, device, storage medium and equipment for automatically acquiring and processing data | |
| CN113239173B (en) | Question-answer data processing method and device, storage medium and electronic equipment | |
| CN109582906A (en) | Determination method, apparatus, equipment and the storage medium of data reliability | |
| CN112331348A (en) | Analysis method and system for set marking, data, project management and non-programming modeling | |
| CN111291936B (en) | Product life cycle prediction model generation method and device and electronic equipment | |
| CN109711849B (en) | Ether house address portrait generation method and device, electronic equipment and storage medium | |
| CN109636303B (en) | Storage method and system for semi-automatically extracting and structuring document information | |
| CN115221380A (en) | Method, system and platform for managing urban construction files in batches | |
| CN111444368B (en) | Method and device for constructing user portrait, computer equipment and storage medium | |
| CN113157853A (en) | Problem mining method and device, electronic equipment and storage medium | |
| CN112836150A (en) | Identification method, system, equipment and medium for tracing code of medicine | |
| CN111695077A (en) | Asset information pushing method, terminal equipment and readable storage medium | |
| CN114385921B (en) | Bidding recommendation method, system, equipment and storage medium | |
| CN113641908B (en) | Course pushing method, course pushing device, server and computer storage medium | |
| CN115408598A (en) | Information processing method, apparatus, device, storage medium, and program product | |
| CN111507366B (en) | Training method of recommendation probability model, intelligent completion method and related device | |
| CN113065935A (en) | Data processing method, device, equipment and storage medium | |
| CN112949963A (en) | Employee service quality evaluation method and device, storage medium and intelligent equipment | |
| CN118037318B (en) | AI purchasing business analysis device and method based on supply chain management | |
| CN113836384A (en) | Method and device for acquiring bid-winning information by combining RPA and AI and electronic equipment | |
| CN115204812A (en) | Intelligent administrative examination and approval method, device and medium based on browser plug-in | |
| CN114519170A (en) | Data processing method, system, electronic device and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |