CN115841365A - Model selection and quotation method, system, equipment and medium based on natural language processing - Google Patents
Model selection and quotation method, system, equipment and medium based on natural language processing Download PDFInfo
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Abstract
The application provides a model selection and quotation method, system, equipment and medium based on natural language processing, and the system comprises: the document preprocessing module is used for converting the format of the document to be processed; the information extraction module is used for extracting information of the document to be processed to obtain various kinds of demand information of the product; the information summarizing module is used for filtering, classifying and summarizing various kinds of demand information and associated data to obtain product demand information, customer information and enterprise information; the product recommendation module is used for recommending candidate schemes consisting of a plurality of products according to the product demand information, the customer information and the enterprise information; the quotation rule module is used for acquiring the boundary conditions of product quotation according to quotation rules formulated by enterprises, and determining the model selection and quotation of the product by combining the candidate schemes and the boundary conditions of the product quotation; the service background module generates a preset quotation list for product type selection and quotation and outputs the quotation list.
Description
Technical Field
The application relates to the field of intelligent product quotation, in particular to a type selection and quotation method, system, equipment and medium based on natural language processing.
Background
For the traditional production and manufacturing industry, product quotation is an essential link in the product sale process. With the continuous growth of enterprises, the product lines of the enterprises are continuously increased, and the low-efficiency manual quotation mode gradually becomes the bottleneck of the product sale link.
For some large enterprises, the types and models of products produced and sold are numerous, and few products are thousands of products and many products are tens of thousands of products. It takes much labor and time to select a suitable product from a large number of similar product models and complete the price quote. In addition, the product quotation process, while certainly labor and time consuming, is not always traded for sales revenue. With the expansion of corporate sales services, less than half of the annual quotations made may be able to form sales orders. Finally, since the product sales process often does not have a fixed time, the product pricing process sometimes occurs on weekends, holidays, and even in the middle of the night. This situation takes up the rest time of the staff and cannot respond to the customer's demand quickly.
However, previous alternative quotation systems lack the ability to process requirement documents in different formats and can only provide fixed templates or product lists for selection by the customer. For a wide variety of product requirement documents, different customers may provide product requirement documents in different layouts, different formats, and even different forms (paper documents, electronic documents, photographs). For these product requirement documents, the reading process can only be performed manually in the past. Sometimes a business has to hire a twenty-three person team to specifically process all customers' demand documents and complete their quote.
Therefore, how to improve the automation degree and efficiency of product selection and quotation is a problem to be solved.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present application provides a method, system, device and medium for selecting and quoting based on natural language processing to solve one of the above-mentioned technical problems.
In a first aspect, the present application provides a natural language processing-based type selection and quotation system, including:
the document preprocessing module is used for converting the document format of the document to be processed into a preset digital document format;
the information extraction module is used for extracting information from the document to be processed based on a natural language processing mode to obtain various kinds of demand information of the product;
the enterprise internal database is used for providing associated data of enterprise internal operation;
the information summarizing module is used for filtering, classifying and summarizing the various types of demand information and the associated data to obtain product demand information, customer information and enterprise information associated with product type selection and quotation;
the product recommendation module is used for recommending candidate schemes consisting of a plurality of products according to the product demand information, the client information and the enterprise information, and the candidate scheme corresponding to each group of products comprises a candidate product list, product models and product quantity;
the quotation rule module is used for acquiring the boundary conditions of product quotation according to quotation rules formulated by enterprises, and determining the model selection and quotation of the product by combining the candidate schemes and the boundary conditions of the product quotation;
and the service background module is used for generating a preset quotation list by the model selection and quotation of the product and outputting the preset quotation list.
In an embodiment of the application, the document preprocessing module converts a paper document or an image document into a preset digital document format through optical character recognition.
In an embodiment of the present application, the information extraction module further includes:
the entity identification unit is used for carrying out named entity identification on each character of the document to be processed to obtain named entities of various types of the requirement information;
the entity resolution unit is used for carrying out entity resolution on the candidate reference vectors of which the named entities are in the coreference relationship to obtain the named entities subjected to resolution of various types of demand information;
and the relation extraction unit is used for extracting the relation of the semantic relation between the digested named entities to obtain a relation extraction result of various types of demand information of the product represented by the triples.
In an embodiment of the present application, the internal database of the enterprise provides the information summarizing module with associated data of internal operation of the enterprise, where the associated data includes basic information of the client, historical purchasing records of the client, inventory conditions of the current enterprise, production scheduling conditions of the current enterprise, sales promotion strategies of the current enterprise, historical quotations and historical sales details of related products, details of technical indexes of the products, production costs of the products, and the like.
In an embodiment of the application, the product recommendation system is configured to generate a candidate solution composed of multiple products according to a product demand of a client, a historical purchase of the client, a product sales record, a current inventory of an enterprise, a current production situation of the enterprise, and a technical index of the product, where the candidate solution corresponding to each group of products includes a candidate product list, a product model, and a product quantity.
In an embodiment of the application, the service background module further includes at least one of: a system login function, a system authority management function, a service notification function, a document verification function and a quotation rule management function.
In an embodiment of the present application, the service notification includes at least one of: mobile phone short message notification, wechat public number notification, and email notification.
In a second aspect, the present application further provides a natural language processing-based type selection and quotation method, including:
converting the document format of the document to be processed into a preset digital document format;
extracting information of the document to be processed based on a natural language processing mode to obtain various kinds of requirement information of the product;
acquiring associated data of internal operation of an enterprise;
filtering, classifying and summarizing the various types of demand information and the associated data to obtain product demand information, customer information and enterprise information associated with product type selection and quotation;
recommending candidate schemes consisting of a plurality of products according to the product demand information, the customer information and the enterprise information, wherein the candidate schemes corresponding to each group of products comprise a candidate product list, product models and product quantity;
obtaining a boundary condition of product quotation according to a quotation rule formulated by an enterprise, and determining the model selection and quotation of the product by combining the candidate scheme and the boundary condition of the product quotation;
and generating a preset quotation sheet by the model selection and quotation of the product and outputting the preset quotation sheet.
In a third aspect, the present application also provides an electronic device comprising a processor, a memory, and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is used for executing the computer program stored in the memory to realize the natural language processing based type selection and quotation method according to any one of the embodiments.
In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, where the computer program is used to make a computer execute the natural language processing based typing and quotation method according to any one of the above embodiments.
The beneficial effect of this application: the application provides a model selection and quotation method, system, equipment and medium based on natural language processing (1) an intelligent model selection and quotation system based on Natural Language Processing (NLP) technology, which enables enterprise users to conveniently and quickly analyze and arrange customer requirement documents, automatically completes product model selection and quotation operations, greatly improves product model selection and quotation efficiency, and has the obvious characteristics of rapidness and high efficiency.
(2) The intelligent model selection and quotation system based on the Natural Language Processing (NLP) technology automatically completes demand analysis, product model selection and product quotation, and finally automatically generates a quotation according to a quotation format template, so that the intervention of human resources is reduced to the maximum extent, and the intelligent model selection and quotation system has the advantages of high automation degree and labor saving.
(3) An intelligent type selection and quotation system based on Natural Language Processing (NLP) technology has the characteristic of capability of processing different typesetting, different formats and different forms (paper documents, electronic documents and photos) of product requirement documents.
Drawings
FIG. 1 is a schematic diagram of an implementation environment of a natural language processing based model selection and quotation system according to an embodiment of the present application;
FIG. 2 is a block diagram of a natural language processing based typing and quotation system provided in an embodiment of the present application;
FIG. 3 is a functional block diagram of a natural language processing based typing and quotation system provided in an embodiment of the present application;
FIG. 4 is a flow diagram of a natural language processing based typing and quotation method provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following embodiments of the present application are described by specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure of the present application. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present application, and the drawings only show the components related to the present application and are not drawn according to the number, shape and size of the components in actual implementation, and the type, number and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of the embodiments of the present application, however, it will be apparent to one skilled in the art that the embodiments of the present application may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form rather than in detail in order to avoid obscuring the embodiments of the present application.
To make the objects, technical solutions and advantages of the present application more clear, the following detailed description of the embodiments of the present application will be made with reference to the accompanying drawings.
Please refer to fig. 1, which is a schematic diagram illustrating an application environment of a model selection and quotation method based on natural language processing according to an embodiment of the present application. As shown in fig. 1, the enforcement environment application network architecture may include a server 01 (server cluster) and a terminal cluster (i.e., client cluster). The terminal cluster may include one or more terminals, and the number of terminals will not be limited herein. As shown in fig. 1, the ue specifically includes a user terminal 100a, a user terminal 100b, a user terminal 100c, a user terminal …, and a user terminal 100n. As shown in fig. 1, the user terminal 100a, the user terminal 100b, the user terminals 100c, …, and the user terminal 100n may respectively perform a network connection with the server 10, so that each user terminal may perform data interaction with the server 10 through the network connection. Here, the specific connection mode of the network connection is not limited, and for example, the connection mode may be directly or indirectly connected through wired communication, or may be directly or indirectly connected through wireless communication.
As shown in fig. 1, the server 01 in the embodiment of the present application may be a server corresponding to a terminal. The server 01 may be an independent physical server, a server cluster or a distributed device configured by a plurality of physical servers, or a cloud server providing cloud computing services. For convenience of understanding, the user terminal may perform model selection and price quotation based on natural language processing by collecting the document to be processed and sending the collected document to the server 01. The model selection and quotation method based on natural language processing can be carried out in any equipment such as a server, a server cluster or a cloud computing service cluster. For example, the server has both the model selection and price quotation functions for the natural language based processing.
Please refer to fig. 2, which is a block diagram of a natural language processing based type selection and quotation system according to an embodiment of the present application, and is detailed as follows:
the document preprocessing module 21 is configured to convert a document format of a document to be processed into a preset digital document format;
specifically, the document preprocessing module converts a paper document or an image document into a preset digital document format through optical character recognition, for example, firstly, classifying a required document to be processed according to the form of the required document; the method mainly comprises the following steps of (1) mainly dividing the method into two categories of structured documents and unstructured documents; for a structured document, analysis software corresponding to a file format can be directly used for processing, for example, in a Python environment, a Word document can be processed by Python-docx, a PDF document can be processed by PyMuPDF, and an Excel document can be processed by xlwings; for an unstructured document, a paper document and a document photo thereof are converted into a digital text format by an Optical Character Recognition (OCR) technology and then are subjected to subsequent processing.
The information extraction module 22 is used for extracting information of the document to be processed based on a natural language processing mode to obtain various kinds of requirement information of products;
wherein, the information extraction module further comprises:
the entity identification unit is used for carrying out named entity identification on each character of the document to be processed to obtain named entities of various types of the requirement information;
for example, there are various ways to extract entities from documents to be processed, including extraction based on preset rules and extraction by machine learning. The entity extraction based on the preset rule can firstly establish a named entity list in advance, and then sequentially extract character information of keywords, key features or key positions from the obtained document file to be processed as named entities according to entity names in the named entity list; the extraction through the machine learning mode can utilize the pre-labeled corpus to train the language model, so that the language model learns the probability of a certain word or the probability of the word as a component of the named entity, a candidate field is calculated to serve as the probability value of the named entity, and if the probability value of the named entity is larger than a threshold value, the named entity is extracted to serve as the named entity.
The entity resolution unit is used for carrying out entity resolution on the candidate reference vectors of which the named entities are in the coreference relationship to obtain the named entities subjected to resolution of various types of demand information;
in particular, entity disambiguation is performed, the purpose of which is that for the same entity name, the content of its expression is completely different in different file environments. Specifically, entity disambiguation may employ a clustering-based entity disambiguation method or an entity disambiguation method based on entity links, where the clustering-based entity disambiguation method refers to that a target entity list is not given, entity named items are disambiguated in a clustering manner, all named items pointing to the same target entity are clustered to the same category by a disambiguation system, and each category in a clustering result corresponds to one target entity; the entity disambiguation method based on entity link refers to that a target entity list is given, and entity designation items are linked with corresponding entities in the target entity list to realize disambiguation. And then, pairwise generating candidate entity pairs (the entity surfaces are subjected to Cartesian products) for the extracted entities according to the sequence of the left entity and the right entity to prepare for subsequent relation extraction.
For another example, a character code of each word in the text is obtained; splicing each character code in the text with at least one adjacent character code to obtain a candidate reference vector; splicing each candidate index vector with the rest candidate index vectors pairwise to obtain candidate common-index tensors; judging whether the candidate index vectors corresponding to the candidate coreference tensor are in coreference relation or not, and finishing coreference resolution according to the coreference relation, wherein the coreference resolution method can obtain whether the phrases at any positions and other phrases are in coreference relation or not, and on one hand, the syntactic structure and semantic structure of the input text do not need to be analyzed, and the named entity does not need to be analyzed; on the other hand, the method can be embedded into other tasks and models processed by various natural languages, and has wide application range; so as to quickly and accurately complete the coreference resolution.
And the relation extraction unit is used for extracting the relation of the semantic relation between the digested named entities to obtain a relation extraction result of various types of demand information of the product represented by the triples.
After the entity identification and the entity disambiguation, the relationships among the named entities can be extracted, wherein the relationship extraction refers to identifying semantic relationships among the named entities, and the relationship extraction method comprises sentence-level relationship extraction, corpus-level relationship extraction, limited domain relationship extraction, open domain relationship extraction and the like. In this embodiment, there may be various relationships between named entities, such as classification relationships, proximity relationships, membership relationships, affiliation relationships, attribute relationships, hierarchical relationships, and the like, and there may be various specific relationship names in each relationship, which indicate the relationship between the entities.
Specifically, natural Language Processing (NLP) technology is used for extracting various types of requirement information related to products from text paragraphs and spreadsheets output by a requirement document preprocessing module; the method comprises three steps of entity identification, entity disambiguation and relationship extraction; particularly, the information extraction module can adopt a cross-mode general document pre-training model ERNIE-Layout opened by Baidu corporation to process various non-structured documents and acquire the client requirement information.
An internal enterprise database 23 for providing data associated with internal operations of the enterprise;
specifically, the internal database of the enterprise provides the information summarizing module with associated data of internal operation of the enterprise, where the associated data includes information such as basic information of the client, historical purchasing records of the client, inventory conditions of the current enterprise, production and production scheduling conditions of the current enterprise, sales promotion strategies of the current enterprise, historical quotations and historical sales details of related products, details of technical indexes of the products, and production costs of the products.
For example, the information summarizing module is used for providing related information stored in the enterprise, wherein the related information comprises part of customer information and all enterprise information; the data sources of the internal database of the enterprise are provided by an enterprise research and development system, an enterprise production system and an enterprise sales system, such as an ERP system.
The information summarizing module 24 is used for filtering, classifying and summarizing the various types of demand information and the associated data to obtain product demand information, customer information and enterprise information associated with product type selection and quotation;
the information summarizing module is used for filtering, summarizing and arranging all relevant information used for product type selection and quotation; it contains product demand information, customer information and enterprise information; the product demand information comprises various technical parameter indexes which need to be achieved by the product, the potential product purchasing quantity of a client, the possible purchasing time of the product and other product additional requirements put forward by the client; the client information comprises the company type of the client, the company scale of the client, the past historical purchase record, the historical refund record and refund period of the client, the rating of the client in the enterprise and the like; in particular, the above customer information may affect the final price quoted for the product; the enterprise information comprises the current inventory condition of the enterprise, the current production scheduling condition of the enterprise, the current sold product catalog of the enterprise, the technical index details of all products produced by the enterprise, the current sales promotion strategy of the enterprise, the historical sales details of all products and the current generation cost of all products; the enterprise information directly influences the model selection and the price quotation of the product.
The product recommending module 25 is configured to recommend candidate schemes composed of multiple products according to the product demand information, the customer information, and the enterprise information, where the candidate scheme corresponding to each group of products includes a candidate product list, a product model, and a product quantity;
the product recommendation system is used for generating candidate schemes composed of multiple products according to product requirements of customers, historical customer purchases, product sales records, current enterprise inventory, current enterprise production conditions and technical indexes of the products, and the candidate schemes corresponding to each group of products comprise a candidate product list, product models and product quantity.
Please refer to fig. 3, which is a schematic block diagram of a model selection and quotation system based on natural language processing according to an embodiment of the present application;
the product recommendation system, namely the product recommendation module 25 in fig. 2, generates a plurality of product candidate schemes to the enterprise quotation rule engine according to the product requirements of the client, the historical purchase and product sale records of the client, the current inventory and production conditions of the enterprise, the technical indexes of the products and other information; each product candidate scheme comprises a candidate product list, a corresponding product model and a product quantity; the technical index requirements of the products are main filtering conditions of product type selection, then the quantity requirements and the current inventory condition of the products, the time requirements of the products and the current production scheduling condition, additional requirements and historical purchasing and selling records of the products are comprehensively considered, and finally a plurality of product candidate schemes are generated and at least comprise a high cost performance scheme, a high performance scheme, a client preference scheme and the like.
A quotation rule module 26, configured to obtain a boundary condition of product quotation according to a quotation rule formulated by an enterprise, and determine model selection and quotation of a product by combining the candidate solution and the boundary condition of the product quotation;
namely, the enterprise quotation rule engine in fig. 3 is used for completing model selection and pricing of a final product according to various summary information and results of a product recommendation system; the method completes product quotation according to quotation rules set by enterprise managers; the rules engine may determine the boundary conditions for product offers to avoid the system producing price-extreme results that violate the laws.
Specifically, according to quotation rules formulated by enterprise managers, boundary conditions of product quotation are determined, and quotation results which violate the laws and are extreme in price are avoided.
And the service background module 27 is configured to generate a preset quotation list for the model selection and quotation of the product, and output the preset quotation list.
Wherein the service background module further comprises at least one of the following: the system comprises a system login function, a system authority management function, a service notification function, a document auditing function and a quotation rule management function. The service notification includes at least one of: mobile phone short message notification, wechat public number notification, and email notification.
The business background module is used for generating and outputting quotations and also provides a system management function and a management interface thereof; the generation of the quotation automatically completes the generation work of the quotation by filling a preset quotation format template; the system management function and the interface comprise a system login function and an interface thereof, a system authority management function and an interface thereof, a service notification function, a document verification function and an interface thereof, a quotation rule management function and an interface thereof and the like; the service notification comprises a short message notification of a mobile phone, a public number notification of WeChat, an email notification and the like.
In this embodiment, first, an intelligent model selection and quotation system of Natural Language Processing (NLP) technology allows enterprise users to conveniently and quickly analyze and arrange customer requirement documents, automatically complete product model selection and quotation operations, greatly improve product model selection and quotation efficiency, and have obvious characteristics of rapidness and high efficiency.
Secondly, the intelligent model selection and quotation system based on the Natural Language Processing (NLP) technology automatically completes demand analysis, product model selection and product quotation, and finally automatically generates quotation sheets according to quotation sheet format templates, so that the intervention of human resources is reduced to the maximum extent, and the intelligent model selection and quotation system has the advantages of high automation degree and labor saving.
Third, an intelligent typing and quotation system based on Natural Language Processing (NLP) technology that features the ability to process different types, formats, and formats of product demand documents (paper documents, electronic documents, photographs).
Please refer to fig. 4, which is a schematic flow chart of a model selection and quotation method based on natural language processing according to an embodiment of the present application, wherein the model selection and quotation method based on natural language processing includes:
step S401, converting the document format of the document to be processed into a preset digital document format;
s402, extracting information of the document to be processed based on a natural language processing mode to obtain various kinds of demand information of products;
step S403, acquiring associated data of internal operation of the enterprise;
it should be noted that step S403 is not executed in sequence with step S401 and step S402, and step S403 may be executed before step S401 and step S402, or processed simultaneously.
Step S404, filtering, classifying and summarizing the various types of demand information and the associated data to obtain product demand information, customer information and enterprise information associated with product type selection and quotation;
step S405, recommending candidate schemes composed of a plurality of products according to the product demand information, the customer information and the enterprise information, wherein the candidate scheme corresponding to each group of products comprises a candidate product list, product models and product quantity;
step S406, acquiring a boundary condition of product quotation according to a quotation rule formulated by an enterprise, and determining model selection and quotation of a product by combining the candidate scheme and the boundary condition of the product quotation;
and step S407, generating a preset quotation sheet by the model selection and the quotation of the product, and outputting the preset quotation sheet.
In this embodiment, the model selection and quotation method based on natural language processing corresponds to the model selection and quotation method based on natural language processing, and specific functions and technical effects can be obtained by referring to the system embodiment, which is not described herein again.
Referring to fig. 5, an embodiment of the present application further provides an electronic device 500, which includes a processor 501, a memory 502, and a communication bus 503;
a communication bus 503 is used to connect the processor 501 and the memory 502;
the processor 501 is adapted to execute the computer program stored in the memory 502 to implement the method according to one or more of the first embodiment described above.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, the computer program being used for causing a computer to execute the method according to any one of the above-mentioned embodiments.
Embodiments of the present application also provide a non-volatile readable storage medium, where one or more modules (programs) are stored in the storage medium, and when the one or more modules are applied to a device, the one or more modules may cause the device to execute instructions (instructions) included in an embodiment of the present application.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or a combination of any of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. 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 involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.
Claims (10)
1. A model selection and quotation system based on natural language processing, comprising:
the document preprocessing module is used for converting the document format of the document to be processed into a preset digital document format;
the information extraction module is used for extracting information of the document to be processed based on a natural language processing mode to obtain various kinds of requirement information of the product;
the enterprise internal database is used for providing associated data of enterprise internal operation;
the information summarizing module is used for filtering, classifying and summarizing the various types of demand information and the associated data to obtain product demand information, customer information and enterprise information associated with product type selection and quotation;
the product recommendation module is used for recommending candidate schemes consisting of a plurality of products according to the product demand information, the customer information and the enterprise information, and the candidate scheme corresponding to each group of products comprises a candidate product list, product models and product quantity;
the quotation rule module is used for acquiring the boundary conditions of product quotation according to quotation rules formulated by enterprises, and determining the model selection and quotation of the product by combining the candidate schemes and the boundary conditions of the product quotation;
and the service background module is used for generating a preset quotation list by the model selection and quotation of the product and outputting the preset quotation list.
2. The system of claim 1, wherein the document preprocessing module converts a paper document or an image document into a preset digital document format through optical character recognition.
3. The system of claim 1, wherein the information extraction module further comprises:
the entity identification unit is used for carrying out named entity identification on each character of the document to be processed to obtain named entities of various types of the requirement information;
the entity resolution unit is used for performing entity resolution on the candidate reference vectors of which the named entities are in the coreference relationship to obtain the named entities subjected to resolution of various types of demand information;
and the relation extraction unit is used for extracting the relation of the semantic relation between the digested named entities to obtain a relation extraction result of various types of demand information of the product represented by the triples.
4. The system of claim 1, wherein the internal database of the enterprise provides the information summarizing module with relevant data of internal operation of the enterprise, and the relevant data includes basic information of the client, historical purchase records of the client, inventory condition of the current enterprise, production scheduling condition of the current enterprise, promotion strategy of the current enterprise, historical quotations and historical sales details of related products, technical index details of the products, production cost of the products, and the like.
5. The system of claim 1, wherein the product recommendation system is configured to generate candidate solutions composed of a plurality of products according to product requirements of the customer, historical purchases of the customer, product sales records, current inventory of the enterprise, current production conditions of the enterprise, and technical indicators of the products, and the candidate solution corresponding to each group of products includes a candidate product list, a product model, and a product quantity.
6. The system of any of claims 1 to 5, wherein the service back-office module further comprises at least one of: a system login function, a system authority management function, a service notification function, a document verification function and a quotation rule management function.
7. The method of any of claims 7, wherein the service notification comprises at least one of: mobile phone short message notification, wechat public number notification, and email notification.
8. A model selection and quotation method based on natural language processing is characterized by comprising the following steps:
converting the document format of the document to be processed into a preset digital document format;
extracting information of the document to be processed based on a natural language processing mode to obtain various kinds of requirement information of the product;
acquiring associated data of internal operation of an enterprise;
filtering, classifying and summarizing the various types of demand information and the associated data to obtain product demand information, customer information and enterprise information associated with product selection and quotation;
recommending candidate schemes consisting of a plurality of products according to the product demand information, the customer information and the enterprise information, wherein the candidate schemes corresponding to each group of products comprise a candidate product list, product models and product quantity;
obtaining a boundary condition of product quotation according to a quotation rule formulated by an enterprise, and determining the model selection and quotation of the product by combining the candidate scheme and the boundary condition of the product quotation;
and generating a preset quotation sheet by the type selection and quotation of the product, and outputting the preset quotation sheet.
9. An electronic device comprising a processor, a memory, and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is configured to execute a computer program stored in the memory to implement the method of claim 8.
10. A computer-readable storage medium, having stored thereon a computer program for causing a computer to execute the method of claim 8.
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