Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art to a certain extent.
Therefore, an object of the embodiments of the present invention is to provide an artificial intelligence-based bid evaluation method, which improves the efficiency and accuracy of bid evaluation.
It is another object of embodiments of the present invention to provide an artificial intelligence based bid evaluation system.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the invention comprises the following steps:
In one aspect, the embodiment of the invention provides an artificial intelligence-based bid evaluation method, which comprises the following steps:
Acquiring enterprise basic data of a plurality of first bidders, and performing text recognition extraction on the enterprise basic data to obtain enterprise key information of each first bidder;
Acquiring a preliminary examination condition preset by a target bidding party, and screening each first bidding party according to the enterprise key information and the preliminary examination condition to obtain a second bidding party;
Acquiring an electronic bidding document of the second bidding party, and performing text analysis and word segmentation on the electronic bidding document to obtain structured bidding data;
Acquiring preset bidding conditions of the target bidding party, determining bidding keywords according to the bidding conditions, and further generating a bidding party index library of the second bidding party according to the bidding keywords and the structured bidding data;
And acquiring a bid evaluation quantization rule preset by the target bidding party, and determining a bid evaluation result of the second bidding party according to the bid evaluation quantization rule and the bidding party index library.
Further, in one embodiment of the present invention, the step of obtaining enterprise basic data of a plurality of first bidders, and performing text recognition extraction on the enterprise basic data to obtain enterprise key information of each first bidder specifically includes:
Acquiring enterprise basic data of each first bidding party through a crawler technology, wherein the enterprise basic data comprises enterprise registration information, enterprise credit information and enterprise project case information;
text recognition is carried out on the enterprise basic data to obtain first text information;
And carrying out text extraction on the first text information according to a preset target keyword to obtain enterprise key information of each first bidding party.
Further, in one embodiment of the present invention, the step of screening each of the first bidders according to the enterprise key information and the preliminary examination condition to obtain a second bidder specifically includes:
Determining a plurality of hard screening indexes and a plurality of soft screening indexes according to the preliminary examination conditions;
Matching the enterprise key information with the hard screening index, and determining the first bidding party conforming to the hard screening index as a bidding party to be screened;
And matching the enterprise key information of the bidder to be screened with the soft screening index, and determining that the soft screening index which accords with the enterprise key information reaches the preset number as the second bidder.
Further, in one embodiment of the present invention, the step of performing text parsing and word segmentation on the electronic bid document to obtain structured bid data specifically includes:
Text analysis is carried out on the electronic bidding document to obtain unstructured bidding data, wherein the unstructured bidding data comprises enterprise qualification data, enterprise performance data, system authentication data, personnel team data, financial condition data and construction scheme data;
and performing word segmentation processing on the unstructured bidding data to obtain the structured bidding data, wherein the structured bidding data comprises two-dimensional table structures of enterprise qualification data, enterprise performance data, system authentication data, staff team data, financial condition data and construction scheme data.
Further, in one embodiment of the present invention, the step of obtaining a bidding condition preset by the target bidding party, determining a bidding keyword according to the bidding condition, and further generating a bidding party index base of the second bidding party according to the bidding keyword and the structured bidding data specifically includes:
Acquiring the bidding conditions, wherein the bidding conditions comprise enterprise qualification requirements, enterprise performance requirements, system authentication requirements, personnel team requirements, financial condition requirements and construction scheme requirements;
word segmentation processing and word frequency statistics are carried out on the bidding conditions, so that a plurality of bidding keywords are obtained;
searching the structured bidding data according to the bidding keywords to obtain the two-dimensional table structure with the bidding keywords, and obtaining corresponding context content;
determining a label name, a label classification and a label description according to the bidding keywords, determining a bidding party identifier and a label value according to the context content, and generating a bidding party index label according to the label name, the label classification, the label description, the bidding party identifier and the label value;
and generating a bidding party index base according to the bidding party index labels corresponding to the bidding keywords.
Further, in one embodiment of the present invention, the step of determining the bid evaluation result of the second bidder according to the evaluation quantization rule and the bidder index library specifically includes:
determining a bid quotation scoring rule and a bid index scoring rule according to the rating quantization rule;
determining a bid price of the second bidding party according to the construction scheme data, acquiring a bid evaluation reference price preset by the target bidding party, and determining a bid price according to the bid price, the bid evaluation reference price and the bid price rule;
determining a plurality of bidding side index labels according to the bidding side index library, and determining bidding index scores according to the bidding side index labels and the bidding index score rules;
And determining the bidding total score of the second bidding party according to the bidding price score and the bidding index score, and determining the bidding evaluation result according to the bidding total score.
Further, in an embodiment of the present invention, the evaluation method further includes the steps of:
And ordering the bidding total scores of the plurality of second bidders in a descending order to obtain a bidder score sequence, further generating an auxiliary bid evaluation report according to the bidder score sequence, and pushing the auxiliary bid evaluation report to a preset reviewer.
In another aspect, an embodiment of the present invention provides an artificial intelligence-based bid evaluation system, including:
The key information extraction module is used for acquiring enterprise basic data of a plurality of first bidders, and performing text recognition extraction on the enterprise basic data to acquire enterprise key information of each first bidder;
the bidding party screening module is used for acquiring preliminary examination conditions preset by the target bidding party, and screening each first bidding party according to the enterprise key information and the preliminary examination conditions to obtain a second bidding party;
The bidding document conversion module is used for acquiring an electronic bidding document of the second bidding party, and carrying out text analysis and word segmentation on the electronic bidding document to obtain structured bidding data;
The bidding party index library generation module is used for acquiring bidding conditions preset by the target bidding party, determining bidding keywords according to the bidding conditions, and further generating a bidding party index library of the second bidding party according to the bidding keywords and the structured bidding data;
And the bid evaluation module is used for acquiring a bid evaluation rule preset by the target bidding party and determining a bid evaluation result of the second bidding party according to the bid evaluation rule and the bidding party index base.
In another aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for implementing a connection communication between the processor and the memory, where the program, when executed by the processor, implements an artificial intelligence based scoring method as described above.
In another aspect, an embodiment of the present invention further provides a storage medium, where the storage medium is a computer readable storage medium, and the storage medium stores one or more programs, and the one or more programs may be executed by one or more processors, so as to implement an artificial intelligence based rating method as described above.
The advantages and benefits of the invention 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 invention.
According to the embodiment of the invention, enterprise basic data of a plurality of first bidding parties are acquired, text recognition and extraction are carried out on the enterprise basic data to obtain enterprise key information of each first bidding party, then preliminary examination conditions preset by a target bidding party are acquired, each first bidding party is screened according to the enterprise key information and the preliminary examination conditions to obtain a second bidding party, then an electronic bidding file of the second bidding party is acquired, text analysis and word segmentation processing are carried out on the electronic bidding file to obtain structured bidding data, then the bidding conditions preset by the target bidding party are acquired, bidding key words are determined according to the bidding conditions, a bidding party index base of the second bidding party is generated according to the bidding key words and the structured bidding data, then bid evaluation rules preset by the target bidding party are acquired, and bid evaluation results of the second bidding party are determined according to the bid evaluation rules and the bidding party index base. According to the embodiment of the invention, the enterprise key information is acquired through text recognition and extraction, the primary screening is carried out to obtain the second bidding party, then text analysis and word segmentation processing are carried out on the electronic bidding document of the second bidding party, and the bidding party index library is generated by combining the bidding key words, so that the bidding evaluation result can be automatically generated according to the bidding party index library and a preset evaluation quantization rule, and the bidding evaluation efficiency and accuracy are improved.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. It should be noted that although functional block division is performed in a system diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the system diagram or the sequence in the flowchart. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
In the description of the present application, the plurality means two or more, and if the description is made to the first and second for the purpose of distinguishing technical features, it should not be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the precedence of the indicated technical features. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
The bid evaluation method based on the artificial intelligence provided by the embodiment of the application can be applied to a terminal, a server and software running in the terminal or the server. In some embodiments, the terminal may be a smart phone, tablet, notebook, desktop, set-top box, or the like; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like; the software may be an application or the like implementing an artificial intelligence based rating method, but is not limited to the above form.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In the embodiments of the present application, when related processing is performed according to user information, user behavior data, user history data, user location information, and other data related to user identity or characteristics, permission or consent of the user is obtained first, and the collection, use, processing, and the like of the data comply with related laws and regulations and standards of related countries and regions. In addition, when the embodiment of the application needs to acquire the sensitive personal information of the user, the independent permission or independent consent of the user is acquired through popup or jump to a confirmation page and the like, and after the independent permission or independent consent of the user is definitely acquired, the necessary relevant data of the user for enabling the embodiment of the application to normally operate is acquired.
In order to improve the efficiency of bidding work and meet the high-speed development requirement of the communication industry, along with the continuous increase of the requirement of purchasing links, how to quickly and accurately complete bid evaluation becomes a problem to be solved. The embodiment of the invention provides a method for assisting a review expert in electronic bid evaluation by utilizing the combination of a crawler technology and an AI technology, identifying enterprise information and a bid file through means of data acquisition, AI identification, big data analysis and the like, and automatically outputting a bid scoring result.
Referring to fig. 1, a flowchart of steps of an artificial intelligence-based bid evaluation method according to an embodiment of the present invention is shown, and referring to fig. 1, the embodiment of the present invention provides an artificial intelligence-based bid evaluation method, which specifically includes the following steps:
S101, acquiring enterprise basic data of a plurality of first bidders, and performing text recognition extraction on the enterprise basic data to obtain enterprise key information of each first bidder.
Specifically, the embodiment of the invention utilizes the web crawler technology to grasp enterprise basic data such as enterprise qualification certificates, enterprise credit investigation, project cases and the like of bidders from appointed websites such as enterprise official networks, industrial and commercial information inquiry platforms, enterprise inquiry and the like to a local database, and extracts enterprise key information so as to facilitate the subsequent preliminary examination of the bidders.
Referring to fig. 2, further as an alternative implementation manner, a step flow chart of step S101 provided by the embodiment of the present invention is shown in fig. 2, where a step of obtaining enterprise basic data of a plurality of first bidders, performing text recognition extraction on the enterprise basic data, and obtaining enterprise key information of each first bidder specifically includes:
s1011, acquiring enterprise basic data of each first bidding party through a crawler technology, wherein the enterprise basic data comprises enterprise registration information, enterprise credit information and enterprise project case information;
s1012, carrying out text recognition on the enterprise basic data to obtain first text information;
and S1013, performing text extraction on the first text information according to the preset target keywords to obtain enterprise key information of each first bidding party.
Specifically, the collection configuration module is used for configuring the examination elements in the bidding process, and the configuration is as follows:
1) Collecting target sites: enterprise network address, business information inquiry platform address, human and social security, enterprise Cha Cha, etc.;
2) Collecting keywords: keywords such as enterprise name, unified social authentication code, etc.
The required key data such as texts, pictures and tables are captured through the web crawler technology, wherein the key data comprises information of enterprise public, enterprise registration information of industrial and commercial websites, enterprise information collected by enterprise investigation, credit information of enterprise credit service platform enterprises and the like.
If the acquired enterprise basic data comprises pictures and/or tables, converting the pictures and/or tables into text contents by using an AI technology, and carrying out text recognition and keyword extraction on the text contents to obtain enterprise key information.
In some alternative embodiments, the obtained business key information may be compared with corresponding information in the electronic bidding document to identify whether the material provided by the bidder is authentic.
S102, obtaining preliminary examination conditions preset by the target bidding party, and screening each first bidding party according to the enterprise key information and the preliminary examination conditions to obtain a second bidding party.
Specifically, the embodiment of the invention is provided with preliminary examination conditions in advance for preliminary screening of the first bidder, and the screened second bidder only needs to analyze the subsequent electronic bidding documents, so that the bidding evaluation efficiency is further improved.
Referring to fig. 3, as a step flow chart of step S102 provided by the embodiment of the present invention, further as an alternative implementation manner, the step of screening each first bidder according to the enterprise key information and the preliminary examination condition to obtain the second bidder specifically includes:
S1021, determining a plurality of hard screening indexes and a plurality of soft screening indexes according to the preliminary examination conditions;
S1022, matching the enterprise key information with the hard screening index, and determining the first bidding party conforming to the hard screening index as the bidding party to be screened;
s1023, matching the enterprise key information of the bidders to be screened with the soft screening indexes, and determining that the bidders to be screened, which meet the soft screening indexes and reach the preset number, are second bidders.
Specifically, the preliminary examination conditions in the embodiment of the invention include a hard screening index and a soft screening index, wherein the hard screening index is an index which must be satisfied, and the soft screening index is an index which needs to satisfy a sufficient number. Firstly, matching enterprise key information with a hard screening index, and filtering out a first bidding party which does not meet the hard screening index; and then matching the enterprise key information of the bidder to be screened with the soft screening index, and taking the bidder to be screened as a second bidder when the soft screening index met by a certain bidder to be screened reaches the preset quantity.
In some alternative embodiments, AI identification is used to determine whether the bidder qualifies for bidding based on crawler content, and the report is reviewed in a preliminary review for review team reference.
S103, acquiring an electronic bidding document of the second bidding party, and performing text analysis and word segmentation processing on the electronic bidding document to obtain structured bidding data.
Specifically, the electronic bidding documents provided by the bidder are unstructured data, so that in order to facilitate the analysis of the subsequent bidding documents, the electronic bidding documents need to be converted into structured bidding data, and various contents of the electronic bidding documents are stored in a two-dimensional table. And analyzing the text in the mark book by using an AI recognition technology, and extracting and warehousing the content by using a big data word segmentation technology to finish the conversion of unstructured data into structured data.
Referring to fig. 4, which is a step flowchart of step S103 provided by the embodiment of the present invention, referring to fig. 4, further as an alternative implementation manner, the steps of performing text parsing and word segmentation on the electronic bid file to obtain structured bid data specifically include:
s1031, carrying out text analysis on the electronic bidding document to obtain unstructured bidding data, wherein the unstructured bidding data comprises enterprise qualification data, enterprise performance data, system authentication data, personnel team data, financial condition data and construction scheme data;
S1032, word segmentation processing is carried out on the unstructured bidding data to obtain structured bidding data, wherein the structured bidding data comprises two-dimensional table structures of enterprise qualification data, enterprise performance data, system authentication data, personnel team data, financial condition data and construction scheme data.
Specifically, the text analysis of the electronic bidding document mainly comprises the processes of text recognition, semantic analysis and content classification, wherein the text recognition and the semantic analysis can adopt the existing artificial intelligent model, and the embodiment of the invention is not described herein; classifying text content of the electronic bidding document based on the identified semantics to obtain enterprise qualification data, enterprise performance data, system authentication data, personnel team data, financial condition data and construction scheme data, wherein the enterprise qualification data comprises certificate information analysis such as enterprise certificates, enterprise qualification, environment quality authentication and the like, the enterprise performance data comprises data analysis such as contract pictures and tables, the financial condition data comprises financial data analysis provided by analysis, the personnel team data comprises data analysis and warehousing such as personnel information, social security, social authentication and the like, and the construction scheme data comprises information analysis and warehousing such as project construction scheme, quotation and the like.
The unstructured bidding data obtained by text analysis is subjected to word segmentation, and various word segmentation algorithms can be adopted, for example, in some embodiments, a dictionary-based word segmentation algorithm can be adopted, sentences are segmented into words according to a dictionary, and then the best combination mode of the words is searched; in some embodiments, word segmentation algorithm based on words may be used, where the sentence is divided into individual words, and then the words are combined into words, so as to find an optimal combination mode. And after word segmentation processing is completed, two-dimensional table structures of enterprise qualification data, enterprise performance data, system authentication data, personnel team data, financial condition data and construction scheme data, namely structured bidding data, can be obtained.
S104, acquiring preset bidding conditions of the target bidding party, determining bidding keywords according to the bidding conditions, and further generating a bidding party index library of the second bidding party according to the bidding keywords and the structured bidding data.
Specifically, a bidder index library is established according to the bidding conditions and the architecture bloom bidding data analysis, and bidder index labels are generated one by one.
Referring to fig. 5, as a step flow chart of step S104 provided by the embodiment of the present invention, referring to fig. 5, further as an optional implementation manner, a step of obtaining preset bidding conditions of a target bidding party, determining bidding keywords according to the bidding conditions, and further generating a bidding party index base of a second bidding party according to the bidding keywords and structured bidding data is specifically included:
S1041, acquiring bidding conditions, wherein the bidding conditions comprise enterprise qualification requirements, enterprise performance requirements, system authentication requirements, personnel team requirements, financial condition requirements and construction scheme requirements;
S1042, performing word segmentation processing and word frequency statistics on bidding conditions to obtain a plurality of bidding keywords;
S1043, searching the structured bidding data according to the bidding keywords to obtain a two-dimensional table structure with the bidding keywords, and obtaining corresponding context content;
S1044, determining a label name, a label classification and a label description according to the bidding keywords, determining a bidding party identifier and a label value according to the context content, and generating a bidding party index label according to the label name, the label classification, the label description, the bidding party identifier and the label value;
s1045, generating a bidding side index library according to the bidding side index labels corresponding to the bidding keywords.
Specifically, firstly, keywords in bidding conditions are found out according to a word segmentation algorithm, then word frequency statistics is carried out on the keywords, the keywords are automatically added into a bidding keyword dictionary library after exceeding a preset alarm value, and meanwhile, the bidding keyword dictionary library can be preset with default bidding keywords; and carrying out full-text retrieval on the structured bidding data according to the bidding keywords, for the content with the bidding keywords, extracting the context content of the bidding keywords as evidence for storage, automatically identifying whether the part of context content meets the corresponding bidding conditions based on semantic analysis, and marking with 0 or 1 to obtain the bidding party index label.
S105, acquiring a bid evaluation quantization rule preset by the target bidding party, and determining a bid evaluation result of the second bidding party according to the bid evaluation quantization rule and the bidding party index base.
Specifically, the big data analysis technology is utilized to perform association analysis on the content of the bid file identified by the AI according to the bid evaluation rule, and the bid evaluation result of the second bidding party is output.
Referring to fig. 6, a flowchart of a step of step S105 provided by the embodiment of the present invention is shown, and referring to fig. 6, further as an alternative implementation, the step of determining a bid evaluation result of a second bidder according to a bid evaluation rule and a bidder index base specifically includes:
s1051, determining a bid quotation scoring rule and a bid index scoring rule according to the bid evaluation quantification rule;
s1052, determining the bidding price of the second bidding party according to the construction scheme data, acquiring a bid evaluation reference price preset by the target bidding party, and further determining the bidding price according to the bidding price, the bid evaluation reference price and the bidding price rule;
s1053, determining a plurality of bidding side index labels according to the bidding side index library, and determining bidding index scores according to the bidding side index labels and bidding index score rules;
s1054, determining the bidding total score of the second bidding party according to the bidding price score and the bidding index score, and determining the bidding evaluation result according to the bidding total score.
Specifically, according to the bid evaluation quantization rule, the bid total score of each second bidder is automatically calculated as follows:
Bid price score = (bid evaluation reference price/bid price) ×price weight×100, wherein the price weight is determined by a bid price rule;
Bidding index score = Σ (index 1 score+index 2 score+ … … +index n score), wherein indexes 1 to n are indexes meeting bidding conditions in a bidding party index base, and the scores of the indexes are determined through a bidding index score rule;
Total bid score = bid quote score + bid index score.
The bid evaluation result is determined according to the bid total scores of the second bidders, for example, the second bidder with the highest bid total score is directly selected as the winning bidder, and for example, a plurality of second bidders with bid total scores exceeding a threshold value or being ranked at the top are selected and pushed to the reviewer for further review.
Fig. 7 is a flowchart showing another step of the method for evaluating a label based on artificial intelligence according to an embodiment of the present invention, and referring to fig. 7, further as an alternative implementation manner, the method for evaluating a label further includes the following steps:
S106, ordering the bidding total scores of the second bidders in a descending order to obtain a bidder score sequence, further generating an auxiliary bid evaluation report according to the bidder score sequence, and pushing the auxiliary bid evaluation report to a preset reviewer.
Specifically, the embodiment of the invention identifies and compares pictures and documents from a crawler, electronic bidding texts provided by bidders and the like one by one according to bidding conditions, and outputs analysis reports to bidders which do not meet bidding requirements; and automatically outputting a scoring report as a reference of expert review according to a scoring method of the detailed review.
As shown in fig. 8, which is a schematic instruction diagram of the evaluation method based on artificial intelligence according to the embodiment of the present invention, it can be understood that the implementation process of one specific embodiment of the present invention is as follows:
1) Establishing bidding conditions, initial review conditions, detailed review scoring formulas and the like of the project;
2) Acquiring enterprise basic data through a web crawler technology, and performing preliminary examination on a bidding party;
3) Acquiring an electronic bidding document of a bidder;
4) Analyzing an electronic bidding document, carrying out key data storage by combining project bidding conditions and detailed review computing elements, establishing a bidder database, carrying out statistics on keywords by utilizing a word segmentation algorithm, automatically adding a dictionary database (the dictionary database can be manually set with the keywords in advance) exceeding a preset threshold value, carrying out full text retrieval on the keywords in the dictionary database, carrying out extraction on the context of a paragraph where the keywords are located as credentials, automatically identifying whether the keywords meet the conditions, marking with 0 or 1, and constructing a bidder index database;
5) Performing association analysis on the content of the bid file identified by the AI according to the bid evaluation quantization rule by using a big data analysis technology;
6) And outputting a scoring condition and an evaluation report.
The method steps of the embodiments of the present invention are described above. It can be understood that in the embodiment of the invention, the enterprise key information is obtained through text recognition and extraction, and preliminary screening is performed to obtain the second bidder, then text analysis and word segmentation processing are performed on the electronic bidding document of the second bidder, and the bidder index library is generated by combining the bidding key words, so that the bidding evaluation result can be automatically generated according to the bidder index library and the preset evaluation rule, and the efficiency and accuracy of the bidding evaluation are improved.
Compared with the prior art, the embodiment of the invention has the following advantages:
1. Automatic classifying data: classifying and integrating data related to bid evaluation, performing multidimensional unfolding analysis, and establishing a bidding party index library around bid evaluation conditions of bidding parties to ensure accuracy of bid evaluation; the web crawler technology, the AI technology and the big data analysis are introduced to provide an auxiliary means for the electronic bid evaluation process in engineering bidding.
2. Automatic data conversion: under the assistance of an AI technology, intelligent analysis and certificate validity analysis are carried out on bidding documents of bidders, and text data are automatically converted into structured data; and establishing a bidder index library for a bidder by utilizing a big data technology, and establishing a bidder view for the subsequent bid evaluation, so as to realize intelligent recommendation.
3. Establishing an integrity system: analyzing the qualification analysis, financial statement, certificate and the like of the bidding documents of the bidder, crawling with the public websites, and establishing the honest documents of the bidder; false information in the electronic bidding documents which possibly exist is eliminated, and non-conforming bidders are eliminated.
4. The whole process is automatically reviewed, human factor intervention is prevented, fairness and fairness are achieved, and the time of the bid evaluation process is saved.
Referring to fig. 9, a schematic structural diagram of an artificial intelligence-based bid evaluation system according to an embodiment of the present invention is provided, and referring to fig. 9, an artificial intelligence-based bid evaluation system is provided according to an embodiment of the present invention, including:
The key information extraction module is used for acquiring enterprise basic data of a plurality of first bidders, and performing text recognition extraction on the enterprise basic data to obtain enterprise key information of each first bidder;
the bidding party screening module is used for acquiring preliminary examination conditions preset by the target bidding party, and screening each first bidding party according to the enterprise key information and the preliminary examination conditions to obtain a second bidding party;
the bidding document conversion module is used for acquiring an electronic bidding document of a second bidding party, and carrying out text analysis and word segmentation processing on the electronic bidding document to obtain structured bidding data;
The bidding party index library generation module is used for acquiring bidding conditions preset by a target bidding party, determining bidding keywords according to the bidding conditions, and further generating a bidding party index library of a second bidding party according to the bidding keywords and the structured bidding data;
And the bid evaluation module is used for acquiring a bid evaluation quantization rule preset by the target bidding party and determining a bid evaluation result of the second bidding party according to the bid evaluation quantization rule and the bidding party index library.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
The embodiment of the invention also provides electronic equipment, which comprises: the system comprises a memory, a processor, a program stored on the memory and capable of running on the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the program realizes the artificial intelligence-based marking method when being executed by the processor. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
Referring to fig. 10, a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention is shown in fig. 10, where the embodiment of the present invention provides an electronic device, including:
The processor 1001 may be implemented by using a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. to execute related programs to implement the technical solution provided by the embodiments of the present invention;
Memory 1002 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). The memory 1002 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present disclosure are implemented by software or firmware, relevant program codes are stored in the memory 1002, and the processor 1001 invokes an artificial intelligence-based rating method for executing the embodiments of the present disclosure;
an input/output interface 1003 for implementing information input and output;
The communication interface 1004 is configured to implement communication interaction between the present device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);
A bus 1005 for transferring information between the various components of the device (e.g., the processor 1001, memory 1002, input/output interface 1003, and communication interface 1004);
Wherein the processor 1001, the memory 1002, the input/output interface 1003, and the communication interface 1004 realize communication connection between each other inside the device through the bus 1005.
The embodiment of the invention also provides a storage medium which is a computer readable storage medium and is used for computer readable storage, the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors so as to realize the artificial intelligence-based marking method.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the method shown in fig. 1.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the present invention has been described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features described above may be integrated in a single physical device and/or software module or one or more of the functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above-described method of the various embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing 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). In addition, the computer-readable medium may even be paper or other suitable medium upon which the program described above is printed, as the program described above may 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 is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means 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 invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.