CN115760258A - Intelligent bid document generation method, system, computer device and storage medium - Google Patents
Intelligent bid document generation method, system, computer device and storage medium Download PDFInfo
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Abstract
The application relates to a bidding document intelligent generation method, a bidding document intelligent generation system, a computer device and a storage medium. Acquiring a bidding document to be processed, extracting operation element characteristics and generating an element vector of the bidding document to be processed; respectively calculating element vector similarity between the bidding document to be processed and each historical bidding document; screening out a target historical bidding document according to the calculation result; acquiring a historical bid winning file associated with a target historical bid winning file; extracting target operation elements of preset element types in the historical bid winning files; and importing the target operation element into a set bidding template, and automatically generating an electronic bidding document corresponding to the bidding document to be processed. The efficiency of writing the bidding document is improved, and the problem of information error or omission in manual report is avoided; and effective reference decision basis is provided for determining the final bid document for the bidder.
Description
Technical Field
The present application relates to the field of engineering bidding, and in particular, to a method, a system, a computer device and a storage medium for intelligent generation of a bid document.
Background
Bidding is an important market competition. The most important advantage is that the market competition principle of 'public, fair and fair' is fully embodied. A plurality of bidders obtain the best goods, projects or services at a relatively low price through bid-inviting purchase and fair competition; the economic benefit and the social benefit are improved, the quality of bidding projects is improved, the use efficiency of national capital is improved, and the investment and financing management system and the reform of the industry management system are promoted.
Particularly, in the construction industry, as the project volume is large, the period is long, the qualification requirement is high, and the project relates to a plurality of aspects such as materials, personnel, techniques and the like, the bidding is undoubtedly the most effective and economic means, and the method plays an increasingly important role in the development of daily business of each construction subject (a construction party, a supervision party and the like).
However, in the conventional bidding process, the bid document (or bid calling book) is still mostly created in a conventional offline manner or filled in online manner. No matter the traditional on-line production or on-line filling, the bidders are required to manually fill the bidding information of the bidding documents, and the filling content is more, so that the time and labor are consumed, errors or omissions are easy to occur, and the writing efficiency and quality of the bidding documents are influenced. Particularly, for enterprises with frequent bidding requirements, a scheme for quickly and accurately making bidding documents is urgently needed.
Disclosure of Invention
In order to solve the problems of low writing efficiency and poor quality of the current bid document making mode, the application provides a bid document intelligent generation method, a bid document intelligent generation system, a computer device and a storage medium.
In a first aspect, the bid document intelligent generation method provided by the application adopts the following technical scheme:
acquiring a bidding document to be processed;
extracting the operation element characteristics of the bidding document to be processed to generate an element vector of the bidding document to be processed;
respectively calculating element vector similarity between the bidding document to be processed and each historical bidding document;
screening out a target historical bidding document of which the calculation result meets a preset requirement according to the calculation result of the element vector similarity;
acquiring a history bid-winning file associated with the target history bid-winning file;
extracting target operation elements of preset element types in the historical bid winning files;
and importing the target operation element into a set bidding template, and automatically generating an electronic bidding document corresponding to the to-be-processed bidding document.
By adopting the technical scheme: determining a correlated historical bid-winning file by performing correlation analysis on the bid-winning file to be processed and the historical bid-winning file; extracting target operation elements based on the historical bid-winning file, and realizing automatic import and filling of related bid information in the set bid template; thereby realizing the automatic generation of the electronic bidding document. Therefore, the writing efficiency of the bidding document is improved, and the problem of information error or omission in manual reporting is avoided; and effective reference decision basis is provided for determining the final bid document for the bidder. The scheme has higher practical use value and can be widely popularized and used.
Optionally, the element vectors of the historical bidding documents are obtained by preprocessing in the following manner:
acquiring a plurality of historical bidding documents and corresponding historical bidding documents to form a bidding historical database;
extracting the operating elements in the bidding historical database to form a bidding operating element database;
calculating the word frequency of the operational elements corresponding to the bidding operational element database in each historical bidding document;
and constructing and obtaining the element vector of the historical bidding document based on the word frequency.
By adopting the technical scheme, the operation element database is generated based on the historical bidding document, then the element vector of the historical bidding document is constructed according to the word frequency of the historical bidding document, and a data relevance basis is provided for the relevance analysis of the to-be-processed bidding document and the historical bidding document.
Optionally, the constructing and obtaining the element vector of the historical bidding document based on the word frequency includes:
taking the word frequency of the operating elements in the historical bidding document as the vector value of the operating elements, and constructing to obtain the element vector corresponding to the historical bidding document;
and obtaining element vectors of the historical bidding documents according to all the historical bidding documents in the bidding historical database.
By adopting the technical scheme, the word frequency of the historical bidding document is used as the vector value of the operation element, and the construction of the element vector of the historical bidding document is realized.
Optionally, after extracting the business elements in the bid history database and before forming the bid business element database, the method further includes:
clustering the business elements by using a clustering algorithm;
and eliminating abnormal operating elements according to the clustering result so as to use normal operating elements to construct and form the bidding operating element database.
By adopting the technical scheme, the influence of abnormal data with low relevance in the extracted operational elements due to the defects of the extraction algorithm on the data quality of the bidding operational element database can be reduced.
Optionally, the operating elements are clustered by using a clustering algorithm; the step of eliminating abnormal operation elements according to the clustering result comprises the following steps:
utilizing a density-based clustering algorithm; aiming at the business elements, dividing all the business elements of the area meeting the set density into the same cluster; after the division is finished, judging the operation elements which do not belong to the cluster as abnormal operation elements; and eliminating the abnormal operation elements.
By adopting the technical scheme, the abnormal data is eliminated by using a density-based clustering algorithm.
Optionally, the obtaining of the plurality of historical bidding documents and the corresponding historical bidding documents includes:
and acquiring the plurality of historical bidding documents and the corresponding historical bidding documents which are disclosed by the network by utilizing a network crawler capturing technology.
By adopting the technical scheme, the effective acquisition of the historical bidding data is realized.
Optionally, the types of the business elements include business credit, construction technology, and quoted price cost; the preset element type includes a price quote cost.
By adopting the technical scheme, the method is beneficial to more effectively obtaining the related types of operation factors and can be better suitable for the field of building construction.
In a second aspect, the bid document intelligent generation system provided by the application adopts the following technical scheme:
intelligent bid document generation system includes:
the first acquisition module is used for acquiring the bidding document to be processed;
the processing module is used for extracting the operation element characteristics of the bidding document to be processed and generating an element vector of the bidding document to be processed;
the calculation module is used for respectively calculating the element vector similarity between the bidding document to be processed and each historical bidding document;
the screening module is used for screening out a target history bidding document of which the calculation result meets the preset requirement according to the calculation result of the element vector similarity;
the second acquisition module is used for acquiring the historical bid winning file associated with the target historical bid winning file;
the extraction module is used for extracting target operation elements of preset element types in the historical bid-winning files;
and the file generation module is used for importing the target operation element into a set bidding template and automatically generating an electronic bidding file corresponding to the to-be-processed bidding file.
By adopting the technical scheme, the functional module system capable of implementing the intelligent bid document generation method is provided.
In a third aspect, the present application provides a computer apparatus, which adopts the following technical solution:
the computer device comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, and the processor realizes the intelligent generation method of the bid document when executing the computer program.
By adopting the technical scheme, the computer device capable of implementing the intelligent bid document generation method is provided.
In a fourth aspect, the computer-readable storage medium provided by the present application adopts the following technical solutions:
a computer-readable storage medium storing a computer program; the computer program is executed by a processor the intelligent generation method of the bid document is realized.
By adopting the technical scheme, the carrier of the computer program of the intelligent bid document generation method is provided.
In summary, the present application includes at least the following advantageous technical effects:
the efficiency of writing the bidding document is improved, and the problem of information error or omission in manual report is avoided; and effective reference decision basis is provided for determining the final bid document for the bidder. The scheme has higher practical use value and can be widely popularized and used.
Drawings
FIG. 1 is a block flow diagram of a method for intelligent generation of bid documents according to an embodiment of the present application;
FIG. 2 is a flow diagram of an analytical model construction method in an embodiment of the present application;
FIG. 3 is a structural framework of a bid document intelligent generation system in an embodiment of the present application;
FIG. 4 is a structural frame of a computer device according to an embodiment of the present application;
description of reference numerals:
31. a first acquisition module; 32. a processing module; 33. a calculation module; 34. a screening module; 35. a second acquisition module; 36. an extraction module; 37. a file generation module; 41. a memory; 42 a processor; 43. a communication bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to fig. 1-4 and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application discloses an intelligent generation method of a bid document.
Referring to fig. 1, the bid document intelligent generation method includes the following steps:
s110: and acquiring the bidding document to be processed.
The bidding document intelligent generation method provided by the embodiment of the application can be implemented through a corresponding software system (such as a bidding document intelligent generation system), and the software system can be deployed on a server/computer device for use by a bidding user.
In alternative embodiments of the present application, the bidding user may include a construction party, a contractor, or other entity that may provide construction services.
The software system can acquire the bidding document to be processed in any existing mode.
For example, the software system may monitor bid inviting information issued by a relevant bid inviting website by using a web crawler technology, and after monitoring relevant bid inviting announcement, crawl relevant bid inviting files in time, and send the relevant bid inviting files to the bidding user side for the bidding user to confirm whether to bid. If the bidding requirement is confirmed, carrying out subsequent intelligent bid file generation processing; if it is determined that there is no bid need, it is discarded.
It should be understood that the bid document to be processed may be a bid document waiting for bidding in a bid valid period. The bidding document to be processed can be a bidding document automatically screened by a user, and can also be a bidding document which is actively pushed by a software system and meets the preset requirement.
Specifically, the bidding user may preset an interested subject (such as a concerned builder) or an interested field according to actual needs; the software system monitors the public bidding information according to a preset requirement; and if the bidding information is issued by a preset interested subject or accords with the preset interested field of the bidder, pushing the bidding information to the bidder in real time for confirmation. So as to better meet the personalized requirements of the bidding users. The bidding method has the advantages that the bidding method guarantees that the bidding notices are sent to bidding users with corresponding requirements at the first time of releasing the bidding notices, more consideration time is given to the bidding users or bidding documents are prepared, and the bid rate is improved; meanwhile, the major problem that the bidding users miss important bidding because the bidding users do not know the bidding announcement in time is avoided.
S120: and extracting the operation element characteristics of the bidding document to be processed to generate an element vector of the bidding document to be processed.
In the embodiment of the application, the types of the business elements comprise business credit worthiness, construction technology and quotation cost.
In the embodiment of the present application, the Term Frequency (Term Frequency) of the Term of the business element in the corresponding bid document is referred to as the business element feature. And generating element vectors of the bidding documents to be processed according to the word frequency of the related operating elements in the bidding documents to be processed.
The related business elements to be extracted from the bidding documents to be processed can be determined according to the business elements related to the element vectors of the historical bidding documents in the analysis model.
For example, the element vector form of each historical bidding document in the analysis model is (A11, A12, A13, …, A1 n), wherein n > 3, and represents the number of the business elements; a1j (j epsilon [1,n ]) represents the word frequency of the business element kj in the 1 st historical bid document. That is, the operation elements related to the element vector of the historical bid and bid document include k1, k2, k3, … and kn, so that it is determined that the relevant operation elements required to be extracted from the to-be-processed bid document are also k1, k2, k3, … and kn, and then the word frequencies of the operation elements k1, k2, k3, … and kn in the to-be-processed bid document are respectively counted to generate the element vector of the to-be-processed bid document.
S130: and respectively calculating element vector similarity between the bidding document to be processed and each historical bidding document.
And calculating the similarity of the element vectors of the bidding documents to be processed and the historical bidding documents in the analysis model according to the element vectors of the bidding documents to be processed and the element vectors of the historical bidding documents in the analysis model.
The similarity calculation method may adopt euclidean distance, cosine similarity, and the like. This is not a limitation.
Referring to fig. 2, the element vector of each historical bid document can be obtained by preprocessing as follows:
s210, a plurality of historical bidding documents and corresponding historical bidding documents which are disclosed by the network are obtained by utilizing a network crawler capturing technology, and a bidding historical database is formed.
In the embodiment of the present application, the historical bidding document corresponding to the historical bidding document includes a historical bidding document.
And S220, extracting the operating elements of the files in the bidding history database to form a bidding operating element database.
In the embodiment of the application, the extraction of the operational elements can be based on the segmentation recognition of the historical bidding documents, the word segmentation processing can be carried out on the text content in each segment, and the existing arbitrary word segmentation tools can be adopted, so that a plurality of words related to the corresponding type of operational elements can be obtained.
The identification of the segments of the historical bidding document can be realized by utilizing the segment identifiers of the document and the directory identifiers of each segment/each part.
For example, business credit, construction techniques, price quote will typically be 3 directories or subdirectories of the bid document, each directory or subdirectory being typically segmented as a directory row. The contents behind the current directory line and in front of the next directory line generally belong to the contents corresponding to the current directory line; the vocabulary related to the corresponding business elements can be obtained by performing word segmentation processing on the part of the content.
Of course, in alternative embodiments of the present application, any other manner that can achieve the extraction of the related vocabulary of the business elements may be fully adopted, and is not limited thereto.
The bidding and tendering operation element database stores a plurality of operation element vocabularies, and the operation element vocabularies can be sequentially ordered according to a certain sequence.
In an optional embodiment of the present application, in order to improve the data quality of the bidding operational element database, a clustering algorithm may be used to perform clustering processing on the operational elements of the bidding operational element database; and according to the clustering result, eliminating abnormal operating elements so as to use the normal operating elements to construct and form a bidding operating element database. Therefore, the influence on the data quality of the bidding operation element database due to the fact that abnormal data with low relevance exists in the extracted operation elements due to the defects of the extraction algorithm can be reduced.
And the elimination of the abnormal data can be processed according to the selected clustering algorithm and the clustering result.
Specifically, the abnormal data can be eliminated by using a clustering algorithm based on density: dividing all the operating elements in the area meeting the set density into the same cluster aiming at the operating elements of the bidding operating element database; after the division is completed, determining the business elements which do not belong to the cluster (are not divided into the cluster), and determining the business elements as abnormal business elements by indicating that the relevance between the business elements and other business elements is small; and the abnormal operation elements are removed.
Or, adopting K mean (such as K-means) clustering to realize the elimination of abnormal data:
1) Aiming at the business elements of the bidding business element database, randomly selecting 1 sample (one business element vocabulary represents one sample) from each business element type as an original cluster center (namely, how many business element types exist, how many cluster centers are set, and the clustering efficiency is improved);
2) Calculating the distance between the residual samples and the cluster center, and marking each sample as the class closest to the k cluster centers;
3) Recalculating the mean value of the sample points in each cluster, and taking the mean value as new k cluster centers;
4) Repeating 2) and 3) until the change of the cluster center is stable to form the final k clusters.
Calculating the distance from each point in the cluster to the center of the cluster based on the clustering result; taking a sample with the distance greater than a set distance threshold value as an abnormal sample, namely an abnormal operation factor; and taking the sample with the distance less than or equal to the set distance threshold value as a normal sample, namely a normal operation factor.
And S230, calculating the word frequency of the business element corresponding to the bidding business element database in each historical bidding document.
Assuming that the business elements corresponding to the bidding business element database include three, k1, k2 and k3, the word frequencies of k1, k2 and k3 in a certain historical bidding document are respectively counted to obtain the element vector of the historical bidding document.
And S240, constructing and obtaining an element vector of the historical bidding document based on the word frequency.
In the embodiment of the application, the element vector corresponding to the historical bidding document is constructed and obtained based on the word frequency of the operating element in the historical bidding document as the vector value of the operating element; therefore, the element vector of each historical bidding document is obtained according to all the historical bidding documents in the bidding historical database.
In an optional embodiment of the present application, all the obtained historical bidding documents may be further classified according to province/province level, and the historical bidding documents of each province are respectively processed to generate an element vector corresponding to the historical bidding documents of the provinces. Realizing an analysis model taking provinces as a unit to intelligently generate a corresponding electronic bidding document; thereby being suitable for the specific range use requirements of some bidding users and simultaneously reducing the data processing amount of the analysis model construction.
S140: and screening out target historical bidding documents with calculation results meeting preset requirements according to the calculation results of the element vector similarity.
Calculating the element vector similarity of the bidding document to be processed and the historical bidding document by using the Euclidean distance as an example, wherein the larger the Euclidean distance is, the smaller the similarity of the two documents is; setting a Euclidean distance threshold value as a preset requirement; when the calculation result is larger than the set Euclidean distance threshold value, judging that the preset requirement is not met; on the contrary, if the calculation result is within the set Euclidean distance threshold, the preset requirement is judged to be met; and taking the historical bidding document meeting the preset requirement as the target historical bidding document, and discarding the historical bidding document not meeting the preset requirement without processing. By adopting the screening mode, the screened target historical bid document can be guaranteed to have reference value, and the generated electronic bid document can be guaranteed to have effectiveness.
In an optional embodiment of the present application, when a plurality of target history bid documents are obtained by using the above-described screening method, one target history bid document with the minimum euclidean distance may be selected.
S150: and acquiring a history bid-winning file associated with the target history bid-winning file.
It should be understood that the target history bidding document is an item that has completed bidding, and thus there is typically a corresponding winning bid document.
S160: and extracting target operation elements of preset element types in the historical bid-winning files.
In the embodiment of the present application, the preset element type includes a quoted cost. The specific extraction method may adopt the above-mentioned method of extracting the operation elements, or any existing extraction method, which is not described herein again.
In the embodiment of the present application, the quoted cost includes, but is not limited to, the specific content of the human-machine information price, the bidding cost, and the like.
S170: and importing the target operation element into a set bidding template, and automatically generating an electronic bidding document corresponding to the bidding document to be processed.
Target operation elements related to the quoted price cost in the history bid-winning file are imported into a set bid template, wherein the set bid template can adopt templates of the history bid-winning file, so that the target operation elements can be imported quickly and accurately.
For other business element information needing to be filled in the set bidding template, such as business credit worthiness and construction technology, automatic import can be realized through the relevant information of the preset bidding user.
For example, business qualification information such as enterprise qualification (including qualification certificates, company licenses, etc.) of the bidding user, personnel qualification (including personnel information and various qualifications, practice certificates, etc.) for example, performance data (including related performance conditions, project data that has been completed and accepted for a job, etc.) prize winning data (including related organizations, certificates issued by enterprises, etc.) for example, financial reports (including various statements related to finance, for example), bidding records (including records, etc.) for example, is pre-filled in a relevant credit template; when an electronic bidding document needs to be generated, the pre-filled credit worthiness template is directly called to serve as a commercial credit worthiness part of the set bidding template, and the automatic filling of the content of the part is realized.
Similarly, the construction technology (such as construction organization scheme, construction design scheme, construction method, new process and other data) of the bidding user is filled in the relevant construction technology template in advance; when the electronic bidding document needs to be generated, the construction technology template which is filled in advance is directly called as a construction technology part for setting the bidding template, and the automatic filling of the content of the part is realized. Therefore, the automatic filling of the operation element contents of all parts of the whole set bidding template is realized, and the electronic bidding document is automatically generated.
In the embodiment of the application, the relevance analysis is carried out on the bidding document to be processed and the historical bidding document, so as to determine the relevant historical bidding document; extracting target operation elements based on the historical bid-winning file, and realizing automatic import and filling of related bid information in the set bid template; thereby realizing the automatic generation of the electronic bidding document. Therefore, the writing efficiency of the bidding document is improved, and the problem of information error or omission in manual reporting is avoided; meanwhile, through the analysis of the historical bidding documents, the intelligent analysis and the automatic filling of the quotation cost of the bidding documents to be processed are realized, and an effective reference decision basis is provided for bidders to determine the final bidding documents. The scheme has higher practical use value and can be widely popularized and used.
Based on the same design concept, the embodiment also discloses an intelligent generation system of the bid document.
Referring to fig. 3, the bid document intelligent generation system includes:
a first obtaining module 31, configured to obtain a to-be-processed bid document;
the processing module 32 is configured to extract the operation elements of the bidding document to be processed, and generate element vectors of the bidding document to be processed;
the calculating module 33 is configured to calculate element vector similarities between the bidding document to be processed and each historical bidding document;
the screening module 34 is configured to screen out a target history bidding document with a calculation result meeting a preset requirement according to the calculation result of the element vector similarity;
a second obtaining module 35, configured to obtain a history bid-winning file associated with the target history bid-winning file;
the extraction module 36 is used for extracting target operation elements of preset element types in the historical bid-winning files;
and a document generating module 37, configured to import the target operation element into the set bidding template, and automatically generate an electronic bidding document corresponding to the bid document to be processed.
In the embodiment of the present application, the first obtaining module 31 and the second obtaining module 35 may obtain data by using a web crawler technology. The processing module 32, the calculating module 33, the filtering module 34, the extracting module 36, the document generating module 37, etc. are used for realizing the processes of data processing, calculation, filtering, element extraction, and document generation, etc., and can be realized by a module such as a processor (e.g., a CPU).
In an optional embodiment of the present application, the bid document intelligent generation system includes, but is not limited to, the above modules, and corresponding modules can be flexibly added and combined according to actual situations. However, at least part or all of the steps of the intelligent bid document generation method can be implemented, for which reference is made to the description of the intelligent bid document generation method, which is not repeated herein.
The present application also provides a computer readable storage medium, which is stored with instructions that when loaded and executed by a processor, implement the above steps.
The computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present application provides a computer apparatus, including a memory 41 and a processor 42, and a communication bus 43 for implementing a communication connection between the memory 41 and the processor 42, where the memory 41 stores thereon a computer program that can be loaded by the processor 42 and execute the above method; when the processor 42 executes the computer program, at least a part of or all of the steps of the above-mentioned bidding document intelligent generation method can be controllably realized, please refer to the description of the above-mentioned bidding document intelligent generation method, which is not described herein again.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the foregoing function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above-described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The above embodiments are only used to describe the technical solutions of the present application in detail, but the above embodiments are only used to help understanding the method and the core idea of the present application, and should not be construed as limiting the present application. Those skilled in the art should also appreciate that various modifications and substitutions can be made without departing from the scope of the present disclosure.
Claims (10)
1. An intelligent bid document generation method is characterized in that: the intelligent generation method of the bid document comprises the following steps:
acquiring a bidding document to be processed;
extracting the operation element characteristics of the bidding document to be processed to generate an element vector of the bidding document to be processed;
respectively calculating element vector similarity between the bidding document to be processed and each historical bidding document;
screening out a target history bidding document of which the calculation result meets a preset requirement according to the calculation result of the element vector similarity;
acquiring a history bid-winning file associated with the target history bid-winning file;
extracting target operation elements of preset element types in the historical bid winning files;
and importing the target operation element into a set bidding template, and automatically generating an electronic bidding document corresponding to the to-be-processed bidding document.
2. The method of claim 1, wherein the element vector of each historical bid document is pre-processed as follows:
acquiring a plurality of historical bidding documents and corresponding historical bidding documents to form a bidding historical database;
extracting the operating elements in the bidding historical database to form a bidding operating element database;
calculating the word frequency of the operational elements corresponding to the bidding operational element database in each historical bidding document;
and constructing and obtaining the element vector of the historical bidding document based on the word frequency.
3. The intelligent generation method of bid document according to claim 2, wherein said constructing and obtaining element vectors of the historical bid documents based on the word frequency comprises:
the word frequency of the operating element in the historical bidding document is used as a vector value of the operating element, and the element vector corresponding to the historical bidding document is constructed;
and obtaining element vectors of the historical bidding documents according to all the historical bidding documents in the bidding historical database.
4. The intelligent bid document generation method according to claim 2, further comprising, after the extracting of the business elements in the bid history database and before the forming of the bid business element database:
clustering the business elements by using a clustering algorithm;
and eliminating abnormal operation elements according to the clustering result so as to use normal operation elements for constructing and forming the bidding operation element database.
5. The intelligent bid document generation method according to claim 4, wherein the business elements are clustered by using a clustering algorithm; the step of eliminating abnormal operation elements according to the clustering result comprises the following steps:
utilizing a density-based clustering algorithm; aiming at the business elements, dividing all the business elements of the area meeting the set density into the same cluster; after the division is finished, judging the operation elements which do not belong to the cluster as abnormal operation elements; and eliminating the abnormal operation elements.
6. The intelligent bid document generation method of claim 2, wherein the obtaining of the plurality of historical bid documents and the corresponding historical bid documents comprises:
and acquiring the plurality of historical bidding documents and the corresponding historical bidding documents which are disclosed by the network by utilizing a network crawler capturing technology.
7. The intelligent generating method of the bid document according to any one of claims 1 to 6, wherein the types of the business elements comprise business credit, construction technology, quoted cost; the preset element type includes a price quote cost.
8. An intelligent bid document generation system, comprising:
the first acquisition module is used for acquiring the bidding document to be processed;
the processing module is used for extracting the operation element characteristics of the bidding document to be processed and generating an element vector of the bidding document to be processed;
the calculation module is used for calculating element vector similarity between the bidding document to be processed and each historical bidding document;
the screening module is used for screening out a target history bidding document of which the calculation result meets the preset requirement according to the calculation result of the element vector similarity;
the second acquisition module is used for acquiring the historical bid winning file associated with the target historical bid winning file;
the extraction module is used for extracting target operation elements of preset element types in the historical bid-winning files;
and the file generation module is used for importing the target operation element into a set bidding template and automatically generating an electronic bidding file corresponding to the to-be-processed bidding file.
9. A computer arrangement comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, when executing the computer program, implementing the intelligent bid document generation method of any one of claims 1 to 7.
10. A computer-readable storage medium, wherein the computer storage medium stores a computer program; the computer program, when executed by a processor, implements a method for intelligent generation of a bid document according to any one of claims 1 to 7.
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Cited By (6)
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| CN116644724A (en) * | 2023-07-27 | 2023-08-25 | 太平金融科技服务(上海)有限公司深圳分公司 | Method, device, equipment and storage medium for generating bid |
| CN116720741A (en) * | 2023-08-07 | 2023-09-08 | 国义招标股份有限公司 | Evaluation method, system and storage medium applied to intelligent bidding |
| CN117131197A (en) * | 2023-10-27 | 2023-11-28 | 北京大学 | Method, device, equipment and storage medium for processing demand category of bidding document |
| CN117150000A (en) * | 2023-10-27 | 2023-12-01 | 北京大学 | Method, device, equipment and storage medium for generating bid |
| CN117541367A (en) * | 2024-01-08 | 2024-02-09 | 辽宁省网联数字科技产业有限公司 | Digital bidding document making and evaluating system based on artificial intelligence |
| CN117808441A (en) * | 2024-03-01 | 2024-04-02 | 江苏省港口集团有限公司 | Bid information checking method and system |
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| CN116644724A (en) * | 2023-07-27 | 2023-08-25 | 太平金融科技服务(上海)有限公司深圳分公司 | Method, device, equipment and storage medium for generating bid |
| CN116644724B (en) * | 2023-07-27 | 2024-03-26 | 太平金融科技服务(上海)有限公司深圳分公司 | Method, device, equipment and storage medium for generating bid |
| CN116720741A (en) * | 2023-08-07 | 2023-09-08 | 国义招标股份有限公司 | Evaluation method, system and storage medium applied to intelligent bidding |
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| CN117131197B (en) * | 2023-10-27 | 2024-01-12 | 北京大学 | Method, device, equipment and storage medium for processing demand category of bidding document |
| CN117150000B (en) * | 2023-10-27 | 2024-02-02 | 北京大学 | Method, device, equipment and storage medium for generating bid |
| CN117541367A (en) * | 2024-01-08 | 2024-02-09 | 辽宁省网联数字科技产业有限公司 | Digital bidding document making and evaluating system based on artificial intelligence |
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