WO2024022354A1 - 结合rpa及ai实现ia的对象推荐方法、装置及存储介质 - Google Patents

结合rpa及ai实现ia的对象推荐方法、装置及存储介质 Download PDF

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WO2024022354A1
WO2024022354A1 PCT/CN2023/109169 CN2023109169W WO2024022354A1 WO 2024022354 A1 WO2024022354 A1 WO 2024022354A1 CN 2023109169 W CN2023109169 W CN 2023109169W WO 2024022354 A1 WO2024022354 A1 WO 2024022354A1
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bidding
document
information
knowledge representation
knowledge
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PCT/CN2023/109169
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English (en)
French (fr)
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于孟萱
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北京来也网络科技有限公司
来也科技(北京)有限公司
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Publication of WO2024022354A1 publication Critical patent/WO2024022354A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/16—File or folder operations, e.g. details of user interfaces specifically adapted to file systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/11—File system administration, e.g. details of archiving or snapshots
    • G06F16/116—Details of conversion of file system types or formats
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models

Definitions

  • This application relates to the field of computer technology, and in particular to an object recommendation method, device and storage medium that combines RPA and AI to implement IA.
  • Robotic Process Automation uses specific "robot software” to simulate human operations on a computer and automatically execute process tasks according to rules.
  • AI Artificial Intelligence
  • Intelligent Automation is a general term for a series of technologies from robotic process automation to artificial intelligence. It combines RPA with Optical Character Recognition (OCR), Intelligent Character Recognition (ICR), process mining ( Process Mining), Deep Learning (Deep Learning, DL), Machine Learning (ML), Natural Language Processing (NLP), Speech Recognition (Automatic Speech Recognition, ASR), Speech Synthesis (Text To Speech, TTS), Computer Vision (CV) and other AI technologies are combined to create end-to-end business processes that can think, learn and adapt, covering process discovery, process automation, and automatic and continuous data collection Collect and understand the meaning of data, and use data to manage and optimize the entire process of business processes.
  • OCR Optical Character Recognition
  • ICR Intelligent Character Recognition
  • process mining Process Mining
  • Deep Learning Deep Learning
  • ML Machine Learning
  • NLP Natural Language Processing
  • Speech Recognition Automatic Speech Recognition, ASR
  • Speech Synthesis Text To Speech, TTS
  • Computer Vision Computer Vision
  • terminal technology has become increasingly mature, which has improved the convenience of users' production and life.
  • the terminal can rely on historical user preference information or interaction information between products and user products to recommend user preference information to the user.
  • bidding application scenarios relying only on historical user preference information or interactive information will lead to low accuracy in object recommendation due to the lack of consideration of bidding background knowledge.
  • the embodiments of this application provide an object recommendation method, device and storage medium that combines RPA and AI to implement IA, so as to solve problems existing in related technologies.
  • embodiments of this application provide an object recommendation method that combines RPA and AI, including:
  • the target knowledge representation model is used to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and the second knowledge representation information corresponding to at least one bidding document in the bidding document collection is obtained.
  • the first knowledge graph is connected with the bidding document and the bidding document collection. correspond;
  • Using a matching network model respectively obtain a matching degree set between the first knowledge representation information and at least one second knowledge representation information, and determine the target bidding document in at least one bidding document based on the matching degree set;
  • the method before using the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, the method further includes:
  • Triplet extraction technology is used to obtain the triplet information set corresponding to the bidding document and the bidding document set;
  • the first knowledge graph corresponding to the bidding document and the bidding document set is established.
  • triplet extraction technology is used to obtain the triplet information set corresponding to the bidding document and the bid document set, including:
  • the stuttering word segmentation model is used to segment the first triplet information set and the second triplet information set to obtain the processed first triplet information set and the processed second triplet information set;
  • the entity boundary string review process is performed on the processed first triplet information set and the processed second triplet information set to obtain a triplet information set corresponding to the bidding document and the bidding document set.
  • the method further includes:
  • vector space mapping is performed on at least one knowledge pair in the knowledge graph to establish a target knowledge representation model.
  • the method further includes:
  • the target knowledge representation model is determined in the initial knowledge representation model set, and the target knowledge representation model is a distributed knowledge reasoning model.
  • the method before using the matching network model to obtain the matching degree set of the first knowledge representation information and at least one second knowledge representation information, the method further includes:
  • the user representation learning module and the bidding attribute information are used to adjust the first knowledge representation information to obtain the adjusted first knowledge representation information
  • determining a target bid document in at least one bid document based on a set of matching degrees includes:
  • the bidding document corresponding to the highest matching degree is obtained, and the bidding document is determined as the target bidding document.
  • embodiments of the present application provide an object recommendation device that combines RPA and AI, including:
  • the document acquisition unit is used to obtain bidding documents and bidding document collections through the robotic process automation RPA system;
  • the information acquisition unit is configured to use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and obtain the second knowledge representation information corresponding to at least one bidding document in the bidding document set, and the first knowledge graph and Correspondence between bidding documents and bidding document collections;
  • a document determination unit configured to use a matching network model to respectively obtain a matching degree set between the first knowledge representation information and at least one second knowledge representation information, and determine a target bidding document in at least one bidding document based on the matching degree set;
  • the object recommendation unit is used to recommend target bidding objects corresponding to the target bidding document.
  • the device further includes a target document acquisition unit, a collection extraction unit and a map creation unit,
  • the target document acquisition unit is used to obtain bidding documents and bid document collections in the target document format through the robotic process automation RPA system;
  • the set extraction unit is used to obtain the triplet information set corresponding to the bidding document and the bidding document set using triplet extraction technology;
  • the graph creation unit is used to create a first knowledge graph corresponding to the bidding document and the bidding document set based on the triplet information set.
  • the collection extraction unit includes a collection acquisition subunit, a word segmentation processing subunit and an audit processing subunit,
  • the set acquisition subunit is used to obtain the first triplet information set corresponding to the bidding document and the second triplet information set corresponding to the bidding document set using triplet extraction technology;
  • the word segmentation processing subunit is used to segment the first triplet information set and the second triplet information set using the stuttering word segmentation model to obtain the processed first triplet information set and the processed second triplet information set.
  • the review processing subunit is used to conduct entity boundary string review processing on the processed first triplet information set and the processed second triplet information set to obtain triplets corresponding to the bidding document and the bidding document set. Collection of information.
  • the device further includes a collection acquisition unit, a map acquisition unit and a model establishment unit,
  • the collection acquisition unit is used to obtain the bidding training document collection
  • the graph acquisition unit is used to obtain the knowledge graph corresponding to the bidding training document collection
  • the model building unit is used to perform vector space mapping on at least one knowledge pair in the knowledge graph based on distributed representation learning and reasoning technology, and establish a target knowledge representation model.
  • the device further includes a parameter acquisition unit and a model determination unit,
  • the parameter acquisition unit is used to acquire the model performance parameters corresponding to each initial knowledge representation model in the initial knowledge representation model set;
  • the model determination unit is used to determine the target knowledge representation model in the initial knowledge representation model set based on the model performance parameters, and the target The knowledge representation model is a distributed knowledge reasoning model.
  • the device further includes an attribute information acquisition unit, an information adjustment unit and a collection traversal unit,
  • the attribute information acquisition unit is used to obtain the bidding attribute information corresponding to the bidding document and the bidding attribute information corresponding to any bidding document in the bidding document collection;
  • the information adjustment unit is used to adjust the first knowledge representation information using the user representation learning module and the bidding attribute information to obtain the adjusted first knowledge representation information;
  • the information adjustment unit is also used to adjust the second knowledge representation information corresponding to any bidding document using the user representation learning module and the bid attribute information to obtain the adjusted second knowledge representation information;
  • the set traversal unit is used to traverse the second knowledge representation information set to obtain the adjusted second knowledge representation information set.
  • the document determination unit includes a matching degree acquisition subunit and a document matching subunit
  • the matching degree acquisition subunit is used to obtain the highest matching degree in the matching degree set
  • the document matching subunit is used to obtain the bidding document corresponding to the highest matching degree from at least one bidding document, and determine the bidding document as the target bidding document.
  • embodiments of the present application provide a terminal that combines RPA and AI.
  • the terminal includes a memory and a processor.
  • the memory and the processor communicate with each other through an internal connection path, the memory is used to store instructions, the processor is used to execute the instructions stored in the memory, and when the processor executes the instructions stored in the memory, the processor The method in any embodiment of the first aspect is executed.
  • embodiments of the present application provide a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program.
  • the method in any embodiment of the first aspect is executed. .
  • embodiments of the present application provide a computer program product, including a computer program that, when executed by a processor, implements the method in any embodiment of the first aspect.
  • the robotic process automation RPA system is used to obtain the bidding document and the bidding document collection;
  • the target knowledge representation model is used to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and the bidding document collection is obtained
  • the second knowledge representation information corresponding to at least one bidding document in the first knowledge graph corresponds to the bidding document and the bidding document set;
  • a matching network model is used to obtain the matching between the first knowledge representation information and at least one second knowledge representation information. degree set, and determine the target bid document in at least one bid document based on the matching degree set; recommend the target bid object corresponding to the target bid document.
  • the target knowledge representation model is used to obtain the knowledge representation information corresponding to the bidding documents and bidding documents on the first knowledge graph, and recommendations are made based on the knowledge representation information corresponding to the bidding documents and bidding documents.
  • the bidding documents can be improved Matching with the target bidding document can improve the accuracy of object recommendation.
  • Figure 1 shows a schematic background diagram of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 2 shows a schematic background architecture diagram of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 3 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 4 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 5 shows a schematic flow chart of a format conversion according to an embodiment of the present application
  • Figure 6 shows a schematic diagram showing a first knowledge graph according to an embodiment of the present application
  • Figure 7 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 8 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 9 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application
  • Figure 10 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 11 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 12 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 13 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 14 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application
  • Figure 15 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to one embodiment of the present application
  • Figure 16 shows a structural block diagram of a terminal according to an embodiment of the present application.
  • RPA refers to the use of specific "robot software” to simulate human operations on a computer and automatically execute process tasks according to rules.
  • the RPA system contains at least three components: development tools, operation tools and control center.
  • the intelligent automation platform can realize the seamless integration of RPA, Intelligent Document Processing (IDP), Conversational AI (CoAI), Process Mining and other capabilities, and has the capabilities of "business understanding", “ The five major categories of functions, “Process Creation”, “Run Anywhere", “Centralized Management and Control”, and “Human-Machine Collaboration”, enable enterprises to realize end-to-end intelligent automation of business processes, replace manual operations, further improve business efficiency, and accelerate digital transformation.
  • Process Creator is a programming tool for process development, which performs specific steps such as interface automation, AI recognition, and data reading and writing in the process.
  • Process Creator allows you to easily assemble automated processes that meet business needs in a flowchart and low-code manner by dragging and dropping each step with the mouse.
  • Runs can be started manually on demand, or automatically when specific trigger conditions are met. Tasks can be arranged and the process can be traced back.
  • Robot Commander is a platform for unified management of multiple process robots within an enterprise. It can quickly issue tasks in batches and provide process robots with the data, credentials, files, etc. required for operation. You can also monitor the running status of the process robot in real time or review its history.
  • the intelligent automation platform also provides artificial intelligence (Artificial Intelligence, AI) capabilities specially designed for RPA. These AI capabilities also constitute the fourth component of the intelligent automation platform, called the intelligent document processing platform.
  • AI artificial intelligence
  • the intelligent document processing platform is a processing platform based on deep learning algorithms such as Optical Character Recognition (OCR) and Natural Language Processing (NLP). It provides document identification, classification, feature extraction, verification, Comparison, error correction and other functions realize the automation of daily document processing work in enterprises.
  • OCR Optical Character Recognition
  • NLP Natural Language Processing
  • biding document refers to the offer invitation document issued by the tenderer to potential bidders and informing them of project requirements, bidding activity rules, contract conditions and other information, which is the main basis for project bidding activities.
  • the term "tender documents” refers to the responsive documents prepared by the bidder in response to the requirements of the bidding documents.
  • tender subject refers to the subject for whom tender documents are prepared as required by the tender documents.
  • knowledge representation model refers to a model used to represent semantic information of images, texts, speech, etc. as low-dimensional dense entity vectors.
  • Types of knowledge representation models include but are not limited to distance model (Structured Embedding, SE), single layer neural network model (Single Layer Model, SLM), energy model (Semantic Matching Energy, SME), transfer distance model (Translational Distance Model), Bilinear function based models, tensor neural network model (Neural Tensor Network, NTN), matrix decomposition model, translation model, etc.
  • knowledge representation information refers to the low-dimensional dense entity vector information corresponding to the semantic information obtained using the knowledge representation model.
  • knowledge graph refers to a semantic network with entities and concepts as nodes and semantic relationships as edges.
  • Knowledge graph known as knowledge domain visualization or knowledge domain mapping map in the library and information industry, is a series of different graphics showing the knowledge development process and structural relationship. It uses visualization technology to describe knowledge resources and their carriers, and mine, analyze, and construct Hexian knowledge and their interconnections.
  • matching network model refers to the implementation of mapping objects into an embedding space that also encapsulates the label distribution, and then using different architectures to project test objects into the same embedding space, Then use cosine similarity to measure similarity and implement a model for classification and detection effects.
  • document format refers to a special encoding method of text information used by computers in order to store text information.
  • Document formats include but are not limited to text txt format, HTML format, word format, Portable Document Format (PDF) format, etc.
  • triple extraction technology refers to a technology used to jointly extract triple information of entities + relationships, including extraction of multiple relationships between entities.
  • triple refers to a set of the form ((x, y), z) (that is, a triple is an even pair whose first projection is also an even pair). ), often abbreviated as (x, y, z). Triplets are a concept in data structure, a common basic course in computer science. It is mainly a compression method used to store sparse matrices.
  • triplet information is information used to identify entities and relationships between entities. This triplet information does not specifically refer to a certain fixed information. For example, when the triplet information corresponding to at least one piece of data changes, the triplet information can also change accordingly.
  • Entity generally refers to something that can exist independently and is the basis of all attributes and the origin of all things. Entity is the most basic element in the knowledge graph.
  • stuttering word segmentation model refers to a model used for word segmentation processing of Chinese text. This stuttering word segmentation model can perform word segmentation, part-of-speech tagging, keyword extraction and other functions for Chinese text, and supports custom dictionaries.
  • word segmentation processing refers to the process of recombining continuous word sequences into word sequences according to certain specifications.
  • entity boundary string audit processing refers to a process for auditing whether triplet information is information of the instruction category.
  • model performance parameters refers to configuration variables internal to the model. Model performance parameters can be obtained using data estimation or data learning. Model performance parameters are required when making model predictions. Model performance parameter values define model functionality. Model performance parameters are usually saved as part of the learned model. Model performance parameters can be estimated using optimization algorithms, which are efficient searches for possible values of parameters.
  • distributed representation learning inference technology refers to a technology that represents entity vectors in a low-dimensional dense vector space and then performs calculation and reasoning.
  • the user can use the object recommendation application to recommend objects based on the data input by the user.
  • Figure 1 shows a schematic background diagram of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application.
  • the user can click on the object recommendation application of the terminal.
  • the terminal detects that the user clicks on the object recommendation application, the terminal can display the object recommendation interface. Users can input bidding documents based on the object recommendation interface. Then, when the terminal detects that the user clicks the object recommendation button, the terminal can recommend objects based on the bidding document input by the user.
  • FIG. 2 shows a schematic background architecture diagram of an object recommendation method combining RPA and AI according to one embodiment of the present application.
  • the terminal 11 can upload the bidding documents issued by the user to the server 13 through the network 12.
  • the server 13 can determine the bidding document that matches the bidding document based on historical user preference information, and send the bidding document that matches the bidding document to the terminal 11 through the network 12.
  • the terminal receives
  • the server 13 sends a bidding document the terminal can display the bidding document on the display interface.
  • a bidding scenario in a bidding scenario, it is necessary to understand the interactive information between past winning bidders and projects, attribute information of enterprises and projects, and background knowledge of enterprises and projects, including enterprise qualifications, enterprise investment amounts, etc., so as to Recommend bidding information improved by users.
  • the terminal when the terminal makes user recommendations, it only historical user preference information and interaction information, resulting in low accuracy of user recommendations by the terminal.
  • the terminal includes but is not limited to: wearable devices, handheld devices, personal computers, tablet computers, vehicle-mounted devices, smart phones, computing devices or other processing devices connected to wireless modems, etc.
  • Terminal equipment can be called different names in different networks, such as: user equipment, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication Equipment, User Agent or User Device, Cellular Telephone, Cordless Telephone, Personal Digital Processing (personal digital assistant, PDA), fifth generation mobile communications technology (5th generation mobile networks, 5G) network or terminal equipment in future evolution networks, etc.
  • An operating system can be installed on the terminal.
  • the operating system refers to an operating system that can run in the terminal. It is a program that manages and controls terminal hardware and terminal applications. It is an indispensable system application in the terminal.
  • the operating system includes but is not limited to Android system, IOS system, Windows phone (WP) system and Ubuntu mobile operating system.
  • Figure 3 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to an embodiment of the present application. As shown in Figure 3, the method may include the following steps:
  • Step S101 Obtain the bidding documents and bidding document collection through the robotic process automation RPA system;
  • a bid document collection refers to a collection of at least one bid document.
  • the bidding document collection does not specifically refer to a certain bidding document collection.
  • the collection of bidding documents can also change accordingly.
  • the bid document set may also change accordingly.
  • the terminal can obtain the bidding documents and bidding document collection through the robotic process automation RPA system.
  • Step S102 Use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and obtain the second knowledge representation information corresponding to at least one bidding document in the bidding document set;
  • the target knowledge representation model refers to a trained model used to obtain knowledge representation information on the first knowledge graph.
  • the target knowledge representation model does not specifically refer to a fixed model.
  • the target knowledge representation model may change.
  • the terminal obtains a model modification instruction for the target knowledge representation model, the target knowledge representation model may also change.
  • the first knowledge graph refers to the knowledge graph corresponding to the bidding document and the bidding document collection.
  • the first knowledge graph does not specifically refer to a fixed graph.
  • the first knowledge graph can change.
  • the bidding document collection changes, the first knowledge graph may also change.
  • the first knowledge representation information refers to the knowledge representation information corresponding to the bidding document.
  • the first knowledge representation information does not specifically refer to certain fixed information.
  • the first knowledge representation information may change.
  • the first knowledge representation information may also change.
  • the second knowledge representation information refers to the knowledge representation information corresponding to the bidding document.
  • the second knowledge representation information does not specifically refer to certain fixed information. For example, when the bidding document changes, the second knowledge representation information may change. When the first knowledge graph changes, the second knowledge representation information may also change.
  • the terminal can use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph. Moreover, the terminal can also obtain the second knowledge representation information corresponding to at least one of the bidding documents.
  • Step S103 Use the matching network model to obtain the matching degree set between the first knowledge representation information and at least one second knowledge representation information, and determine the target bidding document in at least one bidding document based on the matching degree set;
  • the matching degree refers to the matching degree between the first knowledge representation information and the second knowledge representation information.
  • the matching degree does not refer to a fixed matching degree.
  • the matching degree may change.
  • the matching degree may also change.
  • the matching degree set refers to a set formed by at least one matching degree between the first knowledge representation information and at least one second knowledge representation information.
  • the matching degree set does not specifically refer to a fixed set. For example, when the amount of second knowledge representation information changes, the matching degree set may change. When the matching degree changes, the matching degree set may also change.
  • the target bidding document refers to a bidding document obtained in the bidding document collection based on the matching degree set that matches the bidding document.
  • the target bid document does not refer to a fixed document.
  • the target bid document can change when the set of bid documents changes.
  • the target bidding document can also change.
  • the terminal can adopt a matching network model to obtain the matching between the first knowledge representation information and the at least one second knowledge representation information respectively. degree collection. Furthermore, the terminal may determine the target bidding document in at least one bidding document based on the matching degree set.
  • Step S104 Recommend target bidding objects corresponding to the target bidding document.
  • the target bidding object refers to the bidding object corresponding to the target bidding document.
  • the target bidding object does not specifically refer to a fixed object. For example, when the target bid document changes, the target bid object can change.
  • the terminal can recommend the target bidding object corresponding to the target bidding document.
  • the robotic process automation RPA system is used to obtain the bidding document and the bidding document collection;
  • the target knowledge representation model is used to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and at least one of the bidding document collection is obtained.
  • the target knowledge representation model is used to obtain the knowledge representation information corresponding to the bidding documents and bidding documents on the first knowledge graph, and recommendations are made based on the knowledge representation information corresponding to the bidding documents and bidding documents.
  • the bidding documents can be improved Matching with the target bidding document can improve the accuracy of object recommendation.
  • Figure 4 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to one embodiment of the present application. As shown in Figure 4, the method may include the following steps:
  • Step S201 Obtain the bidding documents and bidding document collection through the robotic process automation RPA system
  • the terminal when the terminal obtains the bidding document and the bidding document set based on RPA, the terminal can crawl the bidding document and the bidding document set in the information extraction source through the RPA system, as well as the document format corresponding to the bidding document and at least one of the bidding document set.
  • the document format corresponding to a bidding document when the terminal obtains the bidding document and the bidding document set based on RPA, the terminal can crawl the bidding document and the bidding document set in the information extraction source through the RPA system, as well as the document format corresponding to the bidding document and at least one of the bidding document set.
  • the document format corresponding to a bidding document is not limited to a bidding document.
  • the information extraction source refers to the source of bidding documents and bidding document collections.
  • the information extraction source does not specifically refer to a certain fixed information.
  • Information extraction sources include but are not limited to public resource trading websites, industry core bidding websites, regional bidding websites, etc.
  • the number of information extraction sources is multiple, which can ensure full coverage of information data and keep track of the latest bidding documents and bidding document collections at any time.
  • the terminal when the terminal captures the bidding documents and bidding document collections in the information extraction source through the RPA system, the terminal can capture the web page through the website address in the information extraction source.
  • the terminal can extract the bidding document and the bidding document collection from the web page source code, as well as the document format corresponding to the bidding document and the document format corresponding to at least one bidding document in the bidding document collection.
  • the terminal when the terminal captures the web page of the industry's core bidding website, the terminal can extract the word format bidding document corresponding to the industry's core bidding website from the web page source code of the industry's core bidding website.
  • the terminal when the terminal captures the regional bidding website, the terminal can also extract the PDF format bidding document corresponding to the regional bidding website from the web page source code of the regional bidding website.
  • the terminal can obtain the bidding documents and bidding document collection through the robotic process automation RPA system.
  • Step S202 Obtain the bidding document and bidding document collection in the target document format through the robotic process automation RPA system;
  • the target document format refers to the document format corresponding to the acquired bidding document selected by the RPA system and the document format corresponding to at least one bidding document in the bidding document set.
  • the target document format does not feature a fixed format. For example, when the document format selected by the RPA system changes, the target document format can change accordingly. When the terminal obtains the format modification instruction for the target document format, the target document format may also change.
  • the terminal when the terminal obtains the bidding document and the bidding document set through the Robotic Process Automation RPA system, if the terminal determines that the document format corresponding to the bidding document and the document format corresponding to at least one bidding document in the bidding document set are not target documents format, the terminal can perform format conversion on the bidding documents and bidding document collections through the RPA system, thereby obtaining the bidding documents and bidding document collections in the target document format.
  • the terminal when the terminal performs format conversion on the bidding documents and bidding document collections through the RPA system, the terminal can use the built-in document format conversion tool of the RPA system to perform format conversion on the bidding documents. For example, when the terminal obtains a bidding document in word format, the terminal can use the python win32 library to call the word underlying macro language (Visual Basic for Applications, VBA) to convert the bidding document in word format into a bidding document in PDF format.
  • VBA Visual Basic for Applications
  • the target document format set by the terminal is PDF format
  • the terminal captures the bidding document in the information extraction source through the RPA system, it will capture the bidding document A in word format.
  • the terminal can use the built-in document format conversion tool of the RPA system to convert the bidding document A Perform format conversion and convert it into a PDF format bidding document, as shown in Figure 5.
  • the terminal can obtain the bidding document and bidding document collection in the target document format through the Robotic Process Automation RPA system.
  • Step S203 Use triple extraction technology to obtain the triplet information set corresponding to the bidding document and the bid document set;
  • a triplet information set refers to a set aggregated from at least one triplet of information.
  • the triplet information set does not specifically refer to a fixed set.
  • the triplet information set can change.
  • the bid document set changes, the triplet information set can also change.
  • the triple information included in the triple information set includes but is not limited to project_type, project_tender_announcement_release_date, project_tender_company, project_funding source, project_ownership_place, project _Tendering_Scope, Project_Duration, Project_Construction_Scale, Project_Investment Amount, Project_Enterprise Qualification, Project_Personnel_Qualification, Project_Enterprise Credit_Registration_Requirements, Project_Performance_Qualification, Tendering_Project, Bidding_company, project_quote, bidding_project, bidding_company, project_credit, project_enterprise_performance, this_bid evaluation_score, winning_company, project_whether_bid_winning, project_drop_rate, Projects_Awards etc.
  • the triple extraction technology used by the terminal includes but is not limited to using multi-head selection technology and multi-round question and answer technology. , sub-module extraction technology, etc.
  • the terminal when the terminal uses multi-head selection technology to obtain the bidding document and the triplet information set corresponding to the bidding document set, the terminal can use conditional random field (Conditional Random Field, CRF) to solve the entity recognition task and extract the relationship.
  • CRF Conditional Random Field
  • the terminal when the terminal uses multi-round question and answer technology to obtain the bidding document and the triplet information set corresponding to the bidding document set, the terminal can treat entity-relationship extraction as a multi-round question and answer task by constructing questions for entities and relationships.
  • the model uses text information (text to be extracted) to answer the question template and then obtain entity-relationships.
  • question and answer tasks span-based answer extraction can be used.
  • the terminal when the terminal uses module-by-module extraction technology to obtain the bidding document and the triplet information set corresponding to the bidding document set, the terminal can divide the entity-relationship extraction into two modules. First, the head entity (Head-Entity extraction, HE) is extracted, and then the tail entity-relation (Tail-Entity and Relation, TER) is jointly extracted, which can reduce entity overlap.
  • the head entity Head-Entity extraction, HE
  • the tail entity-relation Tiil-Entity and Relation, TER
  • the terminal when the terminal uses triple extraction technology to obtain the bidding document and the triple information set corresponding to the bidding document set, the terminal may use triple extraction technology to obtain the first triple information set corresponding to the bidding document and The second set of triplet information corresponding to the bid document set. Furthermore, the terminal can use the stuttering word segmentation model to segment the first triplet information set and the second triplet information set to obtain the processed first triplet information set and the processed second triplet information set. . Finally, the terminal can perform entity boundary string review processing on the processed first triplet information set and the processed second triplet information set, thereby obtaining a triplet information set corresponding to the bidding document and the bidding document set. .
  • the terminal can remove random characters in the triplet information set, retain only preset types of triplet information, such as numbers, English, Chinese characters, etc., and remove repeated triplet information in the triplet information set.
  • the information quality of the triplet information set can be improved, and the accuracy of object recommendation can be improved.
  • the first triplet information set refers to the triplet information set corresponding to the bidding document.
  • the first triplet information set does not specifically refer to a fixed set. For example, when the bidding document changes, the first triplet information set may change. When the triplet information changes, the first triplet information set may also change.
  • the second triplet information set refers to the triplet information set corresponding to the bidding document set.
  • the second triplet information set does not specifically refer to a fixed set. For example, when the set of bidding documents changes, the second set of triplet information may change. When the triplet information changes, the second triplet information set may also change.
  • the terminal when the terminal uses the stuttering word segmentation model to segment the first triplet information set and the second triplet information set, the terminal can use the part-of-speech tagging tool pos_seg to segment the first triplet information set and the second triplet information set.
  • the triplet information in the two-triplet information set is marked. Furthermore, by comparing the performance of the stuttering word segmentation model and the part-of-speech tagging tool, useless noise entities in the first triplet information set and the second triplet information set can be removed.
  • the terminal can reduce noise in the triplet information set by performing entity boundary string review processing on the processed first triplet information set and the processed second triplet information set, Improve the quality of triplet information collection. For example, when the triplet information obtained by the terminal is "Network A_Province B_Electric Power Co., Ltd._Operation_Status_Yes", if the terminal sets “Network A_Province B_Electric Power Co., Ltd.” to The required triplet information, "the_operating_status_is" is irrelevant information, the terminal can pass the entity boundary string Review and process to remove irrelevant information.
  • the terminal when the terminal uses triple extraction technology to obtain the bidding document and the triple information set corresponding to the bid document set, the terminal can also remove the number of times by removing invalid features and sparse features in the triple information set. Triplet information corresponding to objects that are lower or have not conducted bidding activities.
  • the terminal can use the triplet extraction technology to obtain the triplet information set corresponding to the bidding document and the bidding document set.
  • Step S204 Based on the triplet information set, establish a first knowledge graph corresponding to the bidding document and the bidding document set;
  • the terminal when the terminal establishes the first knowledge graph corresponding to the bidding document and the bidding document set based on the triplet information set, the terminal can use the target deep learning model to obtain the triplet corresponding to the target entity in the triplet information set. group information subset, and merge the triplet information in the triplet information subset, and the terminal can construct the first knowledge graph corresponding to the data set.
  • the target deep learning model refers to a model in which the terminal obtains a subset of triplet information corresponding to the target entity.
  • the target deep learning model does not specifically refer to a fixed deep learning model. For example, when the type of deep learning model changes, the target deep learning model can also change accordingly. When the model name of the deep learning model changes, the target deep learning model can also change accordingly.
  • the target entity refers to entity information that the terminal needs to use when constructing the first knowledge graph.
  • the target entity information does not specifically refer to certain fixed information. For example, when the triplet information set changes, the target entity information can also change. When the entity information changes, the target entity information can also change.
  • the first knowledge graph information refers to information corresponding to the first knowledge graph.
  • the first knowledge graph information does not specifically refer to certain fixed information.
  • the first knowledge graph information may also change.
  • the target bidding attribute information changes, the first knowledge graph information may also change.
  • the target entity information changes, the first knowledge graph information may also change.
  • FIG. 6 shows a schematic diagram of a first knowledge graph according to an embodiment of the present application.
  • “Tender Document E2” is the winning document of "Tender Document E1”
  • the person in charge of "Tender Document E2” is "Character G2”
  • “Tender Document E2” comes from “Organization F2”
  • “Tender Document E1” comes from “Organization F1”
  • the executive officer of "Tender Document E1” is "Character G1”.
  • the terminal can establish the first knowledge graph corresponding to the bidding document and the bidding document set based on the triplet information set.
  • Step S205 Obtain the model performance parameters corresponding to each initial knowledge representation model in the initial knowledge representation model set
  • the initial knowledge representation model refers to an untrained knowledge representation model.
  • This initial knowledge representation model does not characterize a fixed model. For example, when the model performance parameters of the initial knowledge representation model change, the initial knowledge representation model can change.
  • the terminal obtains a model modification instruction for the initial knowledge representation model, the initial knowledge representation model may also change.
  • the initial knowledge representation model set refers to a set aggregated from at least one initial knowledge representation model.
  • the initial knowledge representation model set does not specifically refer to a fixed set. For example, when the initial knowledge representation model changes, the set of initial knowledge representation models can change. When the number of initial knowledge representation models changes, the set of initial knowledge representation models can also change.
  • the terminal can obtain the model performance parameters corresponding to each initial knowledge representation model in the initial knowledge representation model set.
  • Step S206 Based on the model performance parameters, determine the target knowledge representation model in the initial knowledge representation model set;
  • the terminal when the terminal determines the target knowledge representation model in the initial knowledge representation model set based on the model performance parameters, the terminal evaluates the learning efficiency and learning effect of the initial knowledge representation model based on the model performance parameters corresponding to the initial knowledge representation model. Furthermore, the terminal can select the target knowledge representation model to be used based on the learning efficiency and learning effect corresponding to at least one initial knowledge representation model in the initial knowledge representation model set.
  • the initial knowledge representation model when the initial knowledge representation model is a transfer distance model, the initial knowledge representation model can be a Translating Embeddings for Modeling Multi-relational Data (TransE) model, a Flexible Translation (TransF) model, and a learning entity and relationship Embedding (Learning Entity and Relation Embeddings, TransR) model, Hyperplane Translation (Translating on Hyperplanes, TransH) model, Knowledge-driven Behavior Understanding (Human Activity Understanding, HAKE) model.
  • the initial knowledge representation model when the initial knowledge representation model is a bilinear model, the initial knowledge representation model can be a semantic matching model such as RESCAL, DistMult, ComplEx, HolE, SimplE, Analogy, etc.
  • the terminal After evaluating the learning efficiency and learning effect of these initial knowledge representation models, the terminal can The TransE model is selected as the target knowledge representation model.
  • the terminal can determine the target knowledge representation model in the initial knowledge representation model set based on the model performance parameters.
  • Step S207 Use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and obtain the second knowledge representation information corresponding to at least one bidding document in the bidding document;
  • the terminal when the terminal uses the target knowledge representation model to obtain knowledge representation information on the first knowledge graph, the terminal can use the target knowledge representation model and map the symbolic representation in the triplet information based on the distributed representation learning reasoning method. to vector space and numerical representation. Therefore, the disaster of dimensionality can be reduced, while the implicit association between entities and relationships in triple information can be captured, and the calculation is direct and the training speed is fast.
  • the terminal when the terminal obtains the triplet information "State Grid_Jiangxi province_Electric Power", the terminal can represent the triplet information as knowledge representation information (0, 1, 2) based on the distributed representation learning inference method.
  • the terminal can use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph. Moreover, the terminal can also obtain the second knowledge representation information corresponding to at least one of the bidding documents.
  • Step S208 Use the matching network model to obtain the matching degree set between the first knowledge representation information and at least one second knowledge representation information, and determine the target bidding document in at least one bidding document based on the matching degree set;
  • the terminal when the terminal adopts a matching network model to obtain a set of matching degrees between the first knowledge representation information and at least one second knowledge representation information, the terminal may use the first knowledge representation information as fixed data of the matching network model. , sequentially input the second knowledge representation information in the second knowledge representation information set to the matching network model. Furthermore, the terminal can respectively obtain a set of matching degrees between the first knowledge representation information and at least one second knowledge representation information.
  • the terminal when the terminal adopts the matching network model to respectively obtain the matching degree set between the first knowledge representation information and at least one second knowledge representation information, the terminal can also refer to the attention mechanism to improve the matching degree set acquisition. accuracy.
  • the attention mechanism refers to a special structure embedded in the machine learning model, which is used to automatically learn and calculate the contribution of input data to output data.
  • the attention mechanism is not specific to a fixed structure. For example, when the terminal obtains modification instructions for the attention mechanism, the attention mechanism can change.
  • the terminal can adopt a matching network model to obtain the matching between the first knowledge representation information and the at least one second knowledge representation information respectively. degree collection. Furthermore, the terminal may determine the target bidding document in at least one bidding document based on the matching degree set.
  • Step S209 Recommend target bidding objects corresponding to the target bidding document.
  • the terminal when the terminal determines a target bid document in at least one bid document based on a set of matching degrees, the terminal may determine at least two target bid documents. Furthermore, the terminal can display the at least two target bidding documents in sequence on the display page.
  • the terminal may sort at least two matching degrees in the matching degree set in order of size. Furthermore, the terminal can select the bidding documents corresponding to the top 10 matching degrees as the target bidding documents. Therefore, the terminal can display the 10 target bidding documents in order on the display page.
  • the terminal when the terminal recommends the target bidding object corresponding to the target bidding document, the terminal can also provide interpretable and referenceable basis for the target bidding object through the recommendation explanation module, thereby improving the accuracy of object recommendation. It can make the recommendation results highly interpretable.
  • the recommendation explanation module refers to a module for letting the user understand why the target bid document is recommended to him through an easy-to-understand explanation.
  • the recommended interpretation module does not refer to a fixed module. For example, when the target bid document changes, the recommendation interpretation module can change. When the terminal obtains a module modification instruction for the recommended interpretation module, the recommended interpretation module may also change.
  • the terminal can recommend the target bidding object corresponding to the target bidding document.
  • the robotic process automation RPA system is used to obtain the bidding documents and bidding document collections in the target document format. Therefore, the matching of the RPA system to the bidding documents and bidding document collections in different document formats can be improved, thereby improving the object push accuracy of recommendations.
  • triple extraction technology is used to obtain the triple information set corresponding to the bidding document and the bidding document set. Based on the triple information set, the first knowledge graph corresponding to the bidding document and the bidding document set is established, so the first knowledge can be improved The accuracy of map acquisition can improve the accuracy of bidding object recommendation.
  • the model performance parameters corresponding to each initial knowledge representation model in the initial knowledge representation model set are obtained. Based on the model performance parameters, the target knowledge representation model is determined in the initial knowledge representation model set. Therefore, the required target is selected from the initial knowledge representation model set.
  • Knowledge representation model can improve the accuracy of object recommendation.
  • Figure 7 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to an embodiment of the present application. As shown in Figure 7, the method may include the following steps:
  • Step S301 Obtain the bidding documents and bidding document collection through the robotic process automation RPA system;
  • Step S302 Obtain the bidding training document collection
  • the bidding training documents refer to documents used to train the knowledge representation model so that the knowledge representation model has the expression of prior knowledge.
  • This bidding training document does not specifically refer to a fixed document.
  • the bidding training document can change.
  • the bidding training document and the bidding document collection change the bidding training document can also change.
  • the bidding training document set refers to a collection of at least one bidding training document.
  • the collection of bidding training documents does not specifically refer to a fixed collection.
  • the bidding training document set may change.
  • the bidding training document set can also change.
  • the terminal can obtain the bidding training document collection.
  • Step S303 Obtain the knowledge graph corresponding to the bidding training document collection
  • the terminal when the terminal obtains the bidding training document set, the terminal can obtain the bidding attribute information and entity information corresponding to the bidding training document set. Furthermore, the terminal can obtain the knowledge graph information corresponding to the bidding attribute information and entity information through the robotic process automation RPA system and optical character recognition OCR model. Finally, the terminal can display the knowledge graph corresponding to the knowledge graph information.
  • the bidding attribute information refers to the attribute information corresponding to the bidding training document collection.
  • the bidding attribute information includes but is not limited to bidding information, bidding information, industry information, regional information, etc.
  • the bidding attribute information corresponding to "Tendering Document M" in the bidding training document collection is bidding information.
  • the bidding attribute information corresponding to "Bidding Document N" is bidding information.
  • the entity information refers to information about entities corresponding to the bidding training document set.
  • Entity information includes but is not limited to object attribute information, data attribute information, relationship attribute information, and so on.
  • object attribute information refers to entity names or attribute names that abstract and describe similar entities.
  • Object attribute information includes but is not limited to people, institutions, time, posts, accounts, etc.
  • Data attribute information refers to the attribute information corresponding to the entity itself.
  • the attribute information corresponding to the entity "person D" includes but is not limited to name D1, previous name D2, age D3, etc.
  • Relational attribute information refers to the mutual relationship information between entities.
  • the knowledge graph information refers to the information used in constructing the knowledge graph.
  • the knowledge graph information does not specifically refer to a certain fixed information. For example, when entity information changes, the knowledge graph information can change. When the bidding attribute information changes, the knowledge graph information can also change.
  • the terminal can obtain the knowledge map corresponding to the bidding training document collection.
  • Step S304 Based on distributed representation learning and reasoning technology, perform vector space mapping on at least one knowledge pair in the knowledge graph, and establish a target knowledge representation model;
  • knowledge refers to entity relationship information contained in the knowledge graph.
  • This knowledge does not refer to a fixed knowledge.
  • this knowledge can change when the knowledge graph changes.
  • this knowledge can also change.
  • the terminal when the terminal performs vector space mapping on at least one knowledge pair in the knowledge graph, the terminal can obtain the mapping function corresponding to the entity relationship in the knowledge graph based on distributed representation learning reasoning technology. Furthermore, the terminal can map the entity relationship to the vector space and represent the entity relationship in numerical form. For example, the knowledge "winning the bid” can be mapped to 25, and the knowledge "bidding" can be mapped to the index value 68.
  • the terminal can perform vector space mapping on at least one knowledge pair in the knowledge graph based on distributed representation learning reasoning technology. Furthermore, the terminal can establish a target knowledge representation model.
  • Step S305 Use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and obtain Second knowledge representation information corresponding to at least one bidding document in the bidding document;
  • Step S306 Use the matching network model to obtain matching degree sets between the first knowledge representation information and at least one second knowledge representation information respectively;
  • Step S307 Obtain the highest matching degree in the matching degree set
  • the terminal when the terminal obtains that the matching degree between the first knowledge representation information and the second knowledge representation information B1 is 30, and the matching degree between the first knowledge representation information and the second knowledge representation information B2 is 50, the first knowledge representation information The matching degree between the information and the second knowledge representation information B3 is 70, then the terminal can obtain that the matching degree between the first knowledge representation information and the second knowledge representation information B3 is the highest matching degree in the matching degree set.
  • the terminal can obtain the highest matching degree in the matching degree set.
  • Step S308 Obtain the bid document corresponding to the highest matching degree from at least one bid document, and determine the bid document as the target bid document;
  • the terminal when the terminal obtains that the matching degree between the first knowledge representation information and the second knowledge representation information B3 is the highest matching degree in the matching degree set, the terminal can obtain the bidding document b3 corresponding to the highest matching degree from at least one bidding document. . Furthermore, the terminal can determine the bid document b3 as the target bid document.
  • the terminal can obtain the bidding document corresponding to the highest matching degree from at least one bidding document, and determine the bidding document as the target bidding document.
  • Step S309 Recommend target bidding objects corresponding to the target bidding document.
  • the knowledge graph corresponding to the bidding training document set is obtained, and based on the distributed representation learning reasoning technology, vector space mapping is performed on at least one knowledge pair in the knowledge graph to establish the target knowledge Representation model, therefore can improve the accuracy of establishing the target knowledge representation model, which in turn can improve the accuracy of user recommendations.
  • the bidding document corresponding to the highest matching degree is obtained from at least one bidding document, and the bidding document is determined as the target bidding document. Therefore, the bidding document with the highest matching degree is determined as the target Tender documents can improve the accuracy of determining target tender documents.
  • Figure 8 shows a flow chart of an object recommendation method that combines RPA and AI to implement IA according to an embodiment of the present application. As shown in Figure 8, the method may include the following steps:
  • Step S401 Obtain the bidding documents and bidding document collection through the robotic process automation RPA system;
  • Step S402 Use the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph, and obtain the second knowledge representation information corresponding to at least one bidding document in the bidding document;
  • Step S403 Obtain the bidding attribute information corresponding to the bidding document and the bidding attribute information corresponding to any bidding document in the bidding document collection;
  • the bidding attribute information refers to the attribute information corresponding to the bidding document.
  • the bidding attribute information does not specifically refer to a certain fixed information.
  • the bidding attribute information includes but is not limited to bidding information, industry information, regional information, etc.
  • the bidding attribute information refers to the attribute information corresponding to the bidding document.
  • the bidding attribute information includes but is not limited to bidding information, industry information, regional information, etc.
  • the terminal can obtain the bidding attribute information and the bidding document collection corresponding to the bidding document.
  • Bid attribute information corresponding to any bidding document.
  • Step S404 Use the user representation learning module and the bidding attribute information to adjust the first knowledge representation information to obtain the adjusted first knowledge representation information;
  • the user representation learning module refers to a module for automatically learning effective features from original input data and converting input information into effective feature representations.
  • the user said that the learning module does not refer to a fixed module.
  • the user representation learning module adopts representation forms including but not limited to local representation, distribution representation, etc.
  • the local representation refers to the form expressed as a one-hot vector. This discrete representation has good interpretability, but the dimensionality of the one-hot vector is very high and cannot be expanded and each vector Orthogonal similarity cannot be calculated.
  • the distribution representation can generally be represented as a low-dimensional continuous dense vector, which has much stronger representation ability and the similarity is easy to calculate.
  • the terminal when the terminal obtains the bidding attribute information corresponding to the bidding document, the terminal can use the user representation learning module to fuse and multiply the feature representation corresponding to the bidding attribute information and the first knowledge representation information. Furthermore, the terminal can obtain the adjusted first knowledge representation information.
  • the terminal can use the user representation learning module and the bidding attribute information to adjust the first knowledge representation information to obtain the adjusted first knowledge representation information.
  • Step S405 Use the user representation learning module and the bid attribute information to adjust the second knowledge representation information corresponding to any bid document to obtain the adjusted second knowledge representation information;
  • the terminal when the terminal obtains the bid attribute information corresponding to the bid document, the terminal can use the user representation learning module to fuse and multiply the feature representation corresponding to the bid attribute information and the second knowledge representation information. Furthermore, the terminal can obtain the adjusted second knowledge representation information.
  • the terminal can use the user representation learning module and the bid attribute information to adjust the second knowledge representation information corresponding to any bid document, The adjusted second knowledge representation information is obtained.
  • Step S406 Traverse the second knowledge representation information set to obtain the adjusted second knowledge representation information set;
  • the terminal can traverse the second knowledge representation information set to obtain the adjusted second knowledge representation information set.
  • Step S407 Use the matching network model to obtain the matching degree set between the adjusted first knowledge representation information and the adjusted at least one second knowledge representation information, and determine the target bid in at least one bid document based on the matching degree set. document;
  • Step S408 Recommend target bidding objects corresponding to the target bidding document.
  • the user representation learning module and the bidding attribute information are used to adjust the first knowledge representation information, and we obtain The adjusted first knowledge representation information traverses the second knowledge representation information set to obtain the adjusted second knowledge representation information set; therefore, by using the multi-model fusion learning method to perform representation learning and fine-tuning of the knowledge representation information, it can improve Accuracy of object recommendations.
  • a matching network model is used to respectively obtain the matching degree set between the adjusted first knowledge representation information and the adjusted at least one second knowledge representation information, and determine the target bidding document in at least one bidding document based on the matching degree set. , recommend the target bidding objects corresponding to the target bidding documents. Therefore, by making recommendations based on the adjusted knowledge representation information, the terminal can fully consider the bidding background knowledge when recommending objects, thereby improving the accuracy of object recommendation.
  • FIG. 9 is a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the object recommendation device that combines RPA and AI to implement IA can be implemented as all or part of the device through software, hardware, or a combination of both.
  • the object recommendation device 9000 that combines RPA and AI to implement IA includes a document acquisition unit 9001, an information acquisition unit 9002, a document determination unit 9003 and an object recommendation unit 9004, where:
  • the document acquisition unit 9001 is used to obtain bidding documents and bidding document collections through the robotic process automation RPA system;
  • the information acquisition unit 9002 is used to obtain the first knowledge representation information corresponding to the bidding document on the first knowledge graph using the target knowledge representation model, and obtain the second knowledge representation information corresponding to at least one bidding document in the bidding document set, the first knowledge graph Corresponds to bidding documents and bidding document collections;
  • the document determination unit 9003 is configured to use a matching network model to respectively obtain a matching degree set between the first knowledge representation information and at least one second knowledge representation information, and determine the target bidding document in at least one bidding document based on the matching degree set;
  • the object recommendation unit 9004 is used to recommend target bidding objects corresponding to the target bidding document.
  • Figure 10 shows an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the object recommendation device 9000 also includes a target document acquisition unit 9005, a collection extraction unit 9006 and a graph creation unit 9007, which are used to obtain the first knowledge corresponding to the bidding document on the first knowledge graph using the target knowledge representation model.
  • the target document acquisition unit 9005 is used to obtain bidding documents and bid document collections in the target document format through the robotic process automation RPA system;
  • the set extraction unit 9006 is used to obtain the triplet information set corresponding to the bidding document and the bidding document set using triplet extraction technology;
  • the graph creation unit 9007 is configured to create a first knowledge graph corresponding to the bidding document and the bidding document set based on the triplet information set.
  • FIG. 11 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the set extraction unit 9006 includes a set acquisition sub-unit 9106, a word segmentation processing sub-unit 9206 and an audit processing sub-unit 9306.
  • the set extraction unit 9006 is used to use triple extraction technology to obtain the bidding documents and the corresponding bidding document sets. When triplet information is collected:
  • the set acquisition subunit 9106 is used to obtain the first triplet information set corresponding to the bidding document and the second triplet information set corresponding to the bidding document set using triplet extraction technology;
  • the word segmentation processing subunit 9206 is used to perform word segmentation processing on the first triplet information set and the second triplet information set using the stuttering word segmentation model to obtain the processed first triplet information set and the processed second triplet information set. Tuple information collection;
  • the audit processing subunit 9306 is used to perform entity boundary string audit processing on the processed first triplet information set and the processed second triplet information set to obtain the triplet corresponding to the bidding document and the bidding document set. Group information collection.
  • FIG. 12 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the object recommendation device 9000 also includes a collection acquisition unit 9008, a map acquisition unit 9009 and a model establishment unit 9010, which are used to obtain the first knowledge representation information corresponding to the bidding document using the target knowledge representation model, and obtain the first knowledge representation information corresponding to the bidding document.
  • At least one bid document corresponds to the second knowledge representation information before:
  • the collection acquisition unit 9008 is used to obtain the bidding training document collection
  • the graph acquisition unit 9009 is used to acquire the knowledge graph corresponding to the bidding training document collection
  • the model building unit 9010 is used to perform vector space mapping on at least one knowledge pair in the knowledge graph based on distributed representation learning and reasoning technology, and establish a target knowledge representation model.
  • FIG. 13 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the object recommendation device 9000 also includes a parameter acquisition unit 9011 and a model determination unit 9012, which are used to obtain the first knowledge representation information corresponding to the bidding document using the target knowledge representation model, and obtain the corresponding information of at least one bidding document in the bidding document.
  • the second knowledge represents the information before:
  • the parameter acquisition unit 9011 is used to acquire the model performance parameters corresponding to each initial knowledge representation model in the initial knowledge representation model set;
  • the model determination unit 9012 is used to determine the target knowledge representation model in the initial knowledge representation model set based on the model performance parameters, where the target knowledge representation model is a distributed knowledge reasoning model.
  • FIG. 14 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the object recommendation device 9000 also includes an attribute information acquisition unit 9013, an information adjustment unit 9014 and a set traversal unit 9015, which are used to acquire the first knowledge representation information and at least one second knowledge representation information using a matching network model.
  • the matching degree set Before the matching degree set:
  • the attribute information acquisition unit 9013 is used to obtain the bidding attribute information corresponding to the bidding document and the bidding attribute information corresponding to any bidding document in the bidding document collection;
  • the information adjustment unit 9014 is used to adjust the first knowledge representation information using the user representation learning module and bidding attribute information to obtain the adjusted first knowledge representation information;
  • the information adjustment unit 9014 is also configured to use the user representation learning module and the bid attribute information to adjust the second knowledge representation information corresponding to any bid document to obtain the adjusted second knowledge representation information;
  • the set traversal unit 9015 is used to traverse the second knowledge representation information set to obtain the adjusted second knowledge representation information set.
  • FIG. 15 shows a schematic structural diagram of an object recommendation device that combines RPA and AI to implement IA according to an embodiment of the present application.
  • the document determination unit 9003 also includes a matching degree acquisition subunit 9103 and a document matching subunit 9203.
  • the document determination unit 9003 is used to determine the target bid document in at least one bid document based on the matching degree set:
  • Matching degree acquisition subunit 9103 is used to obtain the highest matching degree in the matching degree set
  • the document matching subunit 9203 is used to obtain the bidding document corresponding to the highest matching degree from at least one bidding document, and determine the bidding document as the target bidding document.
  • the document acquisition unit obtains the bidding documents and the bid document collection through the robotic process automation RPA system; the information acquisition unit uses the target knowledge representation model to obtain the first knowledge representation information corresponding to the bidding documents on the first knowledge graph. , obtain the second knowledge representation information corresponding to at least one bidding document in the bidding document, and the first knowledge graph corresponds to the bidding document and the bidding document set; the document determination unit adopts a matching network model to obtain the first knowledge representation information and at least one second knowledge representation respectively.
  • the knowledge represents a set of matching degrees between information, and determines a target bidding document in at least one bidding document based on the matching degree set; the object recommendation unit recommends a target bidding object corresponding to the target bidding document.
  • the terminal can fully Taking into account the background knowledge of bidding can improve the accuracy of object recommendation.
  • Figure 16 shows a structural block diagram of a terminal according to an embodiment of the present application.
  • the terminal includes: a memory 1610 and a processor 1620.
  • the memory 1610 stores a computer program that can run on the processor 1620.
  • the processor 1620 executes the computer program, the object recommendation method in the above embodiment is implemented.
  • the number of memory 1610 and processor 1620 may be one or more.
  • the terminal also includes:
  • the communication interface 1630 is used to communicate with external devices and perform data interactive transmission.
  • the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc.
  • ISA Industry Standard Architecture
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, only one thick line is used in Figure 16, but it does not mean that there is only one bus or one type of bus.
  • the memory 1610, the processor 1620 and the communication interface 1630 are integrated on one chip, the memory 1610, the processor 1620 and the communication interface 1630 can communicate with each other through the internal interface.
  • Embodiments of the present application provide a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, the method provided in the embodiment of the present application is implemented.
  • An embodiment of the present application also provides a chip, which includes a processor for calling and running instructions stored in the memory, so that the communication device installed with the chip executes the method provided by the embodiment of the present application.
  • Embodiments of the present application also provide a chip, including: an input interface, an output interface, a processor and a memory.
  • the input interface, the output interface, the processor and the memory are connected through an internal connection path, and the processor is used to execute the code in the memory. , when the code is executed, the processor is used to execute the method provided by the application embodiment.
  • processor can be a central processing unit (CPU), or other general-purpose processor, digital signal processor (digital signal processing, DSP), application specific integrated circuit (application specific integrated circuit), ASIC), field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor can be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor that supports advanced RISC machines (ARM) architecture.
  • ARM advanced RISC machines
  • the above-mentioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory.
  • the memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.
  • non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (erasable PROM, EPROM), electrically removable memory.
  • ROM read-only memory
  • PROM programmable ROM
  • EPROM erasable programmable read-only memory
  • Erase programmable read-only memory electrically EPROM, EEPROM
  • Volatile memory may include random access memory (RAM), which acts as an external cache. By way of illustration, but not limitation, many forms of RAM are available.
  • static random access memory static random access memory
  • dynamic random access memory dynamic random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • double data rate synchronous dynamic random access Memory double data date SDRAM, DDR SDRAM
  • enhanced synchronous dynamic random access memory enhanced SDRAM, ESDRAM
  • synchronous link dynamic random access memory direct memory bus random access memory
  • direct rambus RAM direct rambus RAM
  • a computer program product includes one or more computer instructions.
  • the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • Computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • An embodiment of the present application also provides a computer program product, including a computer program that implements the method described in any of the foregoing embodiments when executed by a processor.
  • references to the terms “one embodiment,” “some embodiments,” “an example,” “specific examples,” or “some examples” or the like means that specific features are described in connection with the embodiment or example.
  • structures, materials or features are included in at least one embodiment or example of the present application.
  • the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
  • those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, features defined as “first” and “second” may explicitly or implicitly include at least one of these features. In the description of this application, “plurality” means two or more than two, unless otherwise explicitly and specifically limited.
  • logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered a sequenced list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium, For use by, or in combination with, instruction execution systems, devices or devices (such as computer-based systems, systems including processors or other systems that can fetch instructions from and execute instructions from the instruction execution system, device or device) or equipment.
  • various parts of the present application may be implemented in hardware, software, firmware, or a combination thereof.
  • various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the method in the above embodiment can be completed by instructing relevant hardware through a program.
  • the program can be stored in a computer-readable storage medium. When executed, the program includes one of the steps of the method embodiment or other steps. combination.
  • each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software function modules. If the above integrated modules are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium.
  • the storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

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Abstract

一种对象推荐方法包括：通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息，第一知识图谱与招标文档和投标文档集合对应；采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；对目标投标文档对应的目标投标对象进行推荐。

Description

结合RPA及AI实现IA的对象推荐方法、装置及存储介质 技术领域
本申请涉及计算机技术领域，尤其涉及一种结合RPA及AI实现IA的对象推荐方法、装置及存储介质。
背景技术
机器人流程自动化(Robotic Process Automation)简称RPA，是通过特定的“机器人软件”，模拟人在计算机上的操作，按规则自动执行流程任务。
人工智能(Artificial Intelligence，AI)是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门技术科学。
智能自动化(Intelligent Automation，IA)是一系列从机器人流程自动化到人工智能的技术总称，将RPA与光学字符识别(Optical Character Recognition，OCR)、智能字符识别(Intelligent Character Recognition，ICR)、流程挖掘(Process Mining)、深度学习(Deep Learning，DL)、机器学习(Machine Learning，ML)、自然语言处理(Natural Language Processing，NLP)、语音识别(Automatic Speech Recognition，ASR)、语音合成(Text To Speech，TTS)、计算机视觉(Computer Vision，CV)等多种AI技术相结合，以创建能够思考、学习及自适应的端到端的业务流程，涵盖从流程发现、流程自动化，到通过自动而持续的数据收集、理解数据的含义，使用数据来管理和优化业务流程的整个历程。
随着科学技术的发展，终端技术的日益成熟，提高了用户生产生活的便利性。终端应用场景中，终端可以依靠历史用户偏好信息或者商品以及用户商品之间的交互信息给用户推荐用户偏好的信息。然而，在招投标应用场景中，仅依靠历史用户偏好信息或者交互信息，会由于缺乏对招投标背景知识的考虑，从而导致对象推荐的准确性不高的问题。
发明内容
本申请实施例提供一种结合RPA及AI实现IA的对象推荐方法、装置及存储介质，以解决相关技术存在的问题。
第一方面，本申请实施例提供了一种结合RPA及AI的对象推荐方法，包括：
通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息，第一知识图谱与招标文档和投标文档集合对应；
采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；
对目标投标文档对应的目标投标对象进行推荐。
在一个实施方式中，在采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息之前，还包括：
通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合；
采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合；
基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱。
在一个实施方式中，采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合，包括：
采用三元组抽取技术获取招标文档对应的第一三元组信息集合和投标文档集合对应的第二三元组信息集合；
采用结巴分词模型对第一三元组信息集合和第二三元组信息集合进行分词处理，得到处理后的第一三元组信息集合和处理后的第二三元组信息集合；
对处理后的第一三元组信息集合和处理后的第二三元组信息集合进行实体边界字符串审核处理，得到与招标文档和投标文档集合对应的三元组信息集合。
在一个实施方式中，所述方法还包括：
获取招投标训练文档集合；
获取招投标训练文档集合对应的知识图谱；
基于分布式表示学习推理技术，对知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型。
在一个实施方式中，所述方法还包括：
获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数；
基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型，目标知识表示模型为分布式知识推理模型。
在一个实施方式中，在采用匹配网络模型，获取第一知识表示信息和至少一个第二知识表示信息的匹配度集合之前，所述方法还包括：
获取招标文档对应的招标属性信息和投标文档集合中任一投标文档对应的投标属性信息；
采用用户表示学习模块和招标属性信息对第一知识表示信息进行调整，得到调整后的第一知识表示信息；
采用用户表示学习模块和投标属性信息对任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息；
遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合。
在一个实施方式中，基于匹配度集合确定至少一个投标文档中的目标投标文档，包括：
获取匹配度集合中最高匹配度；
从至少一个投标文档中，获取最高匹配度对应的投标文档，并将投标文档确定为目标投标文档。
第二方面，本申请实施例提供了一种结合RPA及AI的对象推荐装置，包括：
文档获取单元，用于通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
信息获取单元，用于采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息，第一知识图谱与招标文档和投标文档集合对应；
文档确定单元，用于采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；
对象推荐单元，用于对目标投标文档对应的目标投标对象进行推荐。
在一个实施方式中，装置还包括目标文档获取单元、集合抽取单元和图谱建立单元，
目标文档获取单元，用于通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合；
集合抽取单元，用于采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合；
图谱建立单元，用于基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱。
在一个实施方式中，集合抽取单元包括集合获取子单元、分词处理子单元和审核处理子单元，
集合获取子单元，用于采用三元组抽取技术获取招标文档对应的第一三元组信息集合和投标文档集合对应的第二三元组信息集合；
分词处理子单元，用于采用结巴分词模型对第一三元组信息集合和第二三元组信息集合进行分词处理，得到处理后的第一三元组信息集合和处理后的第二三元组信息集合；
审核处理子单元，用于对处理后的第一三元组信息集合和处理后的第二三元组信息集合进行实体边界字符串审核处理，得到与招标文档和投标文档集合对应的三元组信息集合。
在一个实施方式中，装置还包括集合获取单元、图谱获取单元和模型建立单元，
集合获取单元，用于获取招投标训练文档集合；
图谱获取单元，用于获取招投标训练文档集合对应的知识图谱；
模型建立单元，用于基于分布式表示学习推理技术，对知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型。
在一个实施方式中，装置还包括参数获取单元和模型确定单元，
参数获取单元，用于获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数；
模型确定单元，用于基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型，目标 知识表示模型为分布式知识推理模型。
在一个实施方式中，装置还包括属性信息获取单元、信息调整单元和集合遍历单元，
属性信息获取单元，用于获取招标文档对应的招标属性信息和投标文档集合中任一投标文档对应的投标属性信息；
信息调整单元，用于采用用户表示学习模块和招标属性信息对第一知识表示信息进行调整，得到调整后的第一知识表示信息；
信息调整单元，还用于采用用户表示学习模块和投标属性信息对任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息；
集合遍历单元，用于遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合。
在一个实施方式中，文档确定单元包括匹配度获取子单元和文档匹配子单元，
匹配度获取子单元，用于获取匹配度集合中最高匹配度；
文档匹配子单元，用于从至少一个投标文档中，获取最高匹配度对应的投标文档，并将投标文档确定为目标投标文档。
第三方面，本申请实施例提供了一种结合RPA及AI的终端，该终端包括：存储器和处理器。其中，该存储器和该处理器通过内部连接通路互相通信，该存储器用于存储指令，该处理器用于执行该存储器存储的指令，并且当该处理器执行该存储器存储的指令时，使得该处理器执行上述第一方面任一种实施方式中的方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，计算机可读存储介质存储计算机程序，当计算机程序在计算机上运行时，上述第一方面任一种实施方式中的方法被执行。
第五方面，本申请实施例提供了一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现上述第一方面任一种实施方式中的方法。
在一个或者相关的实施例中，通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息，第一知识图谱与招标文档和投标文档集合对应；采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；对目标投标文档对应的目标投标对象进行推荐。因此，采用目标知识表示模型在第一知识图谱上获取招标文档和投标文档对应的知识表示信息，并基于招标文档和投标文档对应的知识表示信息进行推荐，在进行对象推荐时，可以提高招标文档和目标投标文档的匹配性，进而可以提高对象推荐的准确性。
上述概述仅仅是为了说明书的目的，并不意图以任何方式进行限制。除上述描述的示意性的方面、实施方式和特征之外，通过参考附图和以下的详细描述，本申请进一步的方面、实施方式和特征将会是容易明白的。
附图说明
在附图中，除非另外规定，否则贯穿多个附图相同的附图标记表示相同或相似的部件或元素。这些附图不一定是按照比例构建的。应该理解，这些附图仅描绘了根据本申请的一些实施方式，而不应将其视为是对本申请范围的限制。
图1示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的背景示意图；
图2示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的背景架构示意图；
图3示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图；
图4示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图；
图5示出本申请一个实施例的一种格式转换的流程示意图；
图6示出本申请一个实施例的一种第一知识图谱的展示示意图；
图7示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图；
图8示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图；
图9示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图10示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图11示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图12示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图13示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图14示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图15示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图；
图16示出根据本申请一实施例的一种终端的结构框图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，仅用于解释本申请，而不能理解为对本申请的限制。
在本申请的描述中，术语“多个”指两个或两个以上。
在本申请的描述中，术语“RPA”是指通过特定的“机器人软件”，模拟人在计算机上的操作，按规则自动执行流程任务。RPA系统至少包含三个组成部分：开发工具、运行工具和控制中心。智能自动化平台能够实现RPA、智能文档处理(Intelligent Document Processing，IDP)、对话式AI(Conversational AI，CoAI)、流程挖掘(Process Mining)等多项能力的无缝集成，具有“业务理解”、“流程创建”、“随处运行”、“集中管控”、“人机协同”这五大类功能，为企业实现业务流程端到端的智能自动化，代替人工的操作，进一步提高业务效率，加速数字化转型。
在智能自动化平台中，这RPA系统的三个组成部分分别被命名为流程创造者、流程机器人和机器人指挥官。其中，流程创造者是流程的开发的编程工具，在流程中进行界面自动化操作、AI识别、数据读写等具体步骤。流程创造者允许以流程图、低代码的方式，采用鼠标拖拽各个步骤，轻松组装符合业务需求的自动化流程。
RPA流程编写完毕后，部署在流程机器人之中。可以根据需要手动启动运行，或在满足特定触发条件时自动启动。任务可编排，过程可回溯。
机器人指挥官是对于企业内部的多个流程机器人进行统一管理的平台，可以快速批量下发任务，并为流程机器人提供运行时所需的数据、凭证、文件等。还可以实时监测流程机器人的运行状态，或回看其历史记录。
智能自动化平台中还提供了专门为RPA设计的人工智能(Artificial Intelligence，AI)能力，这些AI能力也构成了智能自动化平台的第四个组成部分，称之为智能文档处理平台。智能文档处理平台是基于光学字符识别(Optical Character Recognition，OCR)、自然语言处理(Natural Language Processing，NLP)等深度学习算法打造的处理平台，提供了文档的识别、分类、要素提取、校验、比对、纠错等功能，实现企业日常文档处理工作的自动化。
在本申请的描述中，术语“招标文档”指的是招标人向潜在投标人发出并告知项目需求、招标投标活动规则和合同条件等信息的要约邀请文档，是项目招标投标活动的主要依据。
在本申请的描述中，术语“投标文档”指的是投标人应招标文档要求编制的响应性文档。
在本申请的描述中，术语“投标对象”指的是应招标文档要求编制投标文档的对象。
在本申请的描述中，术语“知识表示模型”是指用于将图像、文本、语音等的语义信息表示为低维稠密的实体向量的模型。知识表示模型的类型包括但不限于距离模型(Structured Embedding,SE)、单层神经网络模型(Single Layer Model,SLM)、能量模型(Semantic Matching Energy,SME)、转移距离模型(Translational Distance Model)，双线性模型(Bilinear function based models)、张量神经网络模型(Neural Tensor Network,NTN)、矩阵分解模型、翻译模型等等。
在本申请的描述中，术语“知识表示信息”是指使用知识表示模型获取到的语义信息对应的低维稠密的实体向量信息。
在本申请的描述中，术语“知识图谱(knowledge graph)”是指以实体、概念作为节点，以语义关系作为边的语义网络。知识图谱，在图书情报界称为知识域可视化或知识领域映射地图，是显示知识发展进程与结构关系的一系列各种不同的图形，用可视化技术描述知识资源及其载体，挖掘、分析、构建和显 示知识及它们之间的相互联系。
在本申请的描述中，术语“匹配网络模型”是指用于实现将对象映射到一个嵌入空间，该空间也封装了标签分布，然后使用不同的体系结构将测试对象投影到同一嵌入空间中，接着使用余弦相似度来衡量相似度，实现分类和检测效果的模型。
在本申请的描述中，术语“文档格式”指的是电脑为了存储文本信息而使用的对文本信息的特殊编码方式。文档格式包括但不限于文本txt格式、HTML格式、word格式、便携式文档(Portable Document Format，PDF)格式等等。
在本申请的描述中，术语“三元组抽取技术”是指用于联合抽取实体+关系的三元组信息，包括实体间的多关系抽取的技术。
在本申请的描述中，术语“三元组”是指形如((x，y)，z)的集合(这就是说，三元组是这样的偶，其第一个射影亦是一个偶)，常简记为(x，y，z)。三元组是计算机专业的一门公共基础课程——数据结构里的概念，主要是用来存储稀疏矩阵的一种压缩方式。
在本申请的描述中，术语“三元组信息”是用于标识实体和实体间关系的信息。该三元组信息并不特指某一固定信息。例如当至少一个数据对应的三元组信息发生变化时，该三元组信息也可以相应变化。
在本申请的描述中，术语“实体”是指一般是指能够独立存在的、作为一切属性的基础和万物本原的东西。实体是知识图谱中最基本的元素。
在本申请的描述中，术语“结巴分词模型”是指用于对中文文本进行分词处理的模型。该结巴分词模型可以对中文文本进行分词、词性标注、关键词抽取等功能，并且支持自定义词典。
在本申请的描述中，术语“分词处理”是指将连续的字序列按照一定的规范重新组合成词序列的过程。
在本申请的描述中，术语“实体边界字符串审核处理”是指用于审核三元组信息是否为指令类别的信息的过程。
在本申请的描述中，术语“模型性能参数”是指模型内部的配置变量。模型性能参数可以用数据估计或数据学习得到。进行模型预测时需要模型性能参数。模型性能参数值可以定义模型功能。模型性能参数通常作为学习模型的一部分保存。可以使用优化算法估计模型性能参数，优化算法是对参数的可能值进行的一种有效搜索。
在本申请的描述中，术语“分布式表示学习推理技术”是指将实体向量表示在低维稠密向量空间中，然后进行计算和推理的技术。
随着科学技术的发展，终端技术的日益成熟，提高了用户生产生活的便利性。终端应用场景中，用户可以通过对象推荐应用程序对用户所输入的数据进行对象推荐。
根据一些实施例，图1示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的背景示意图。如图1所示，用户可以点击终端的对象推荐应用程序，当终端检测到用户点击对象推荐应用程序时，终端可以展示对象推荐界面。用户可以基于对象推荐界面，输入招标文档。接着，当终端检测到用户点击对象推荐按键时，终端可以针对用户所输入的招标文档进行对象推荐。
根据一些实施例，图2示出本申请一个实施例的一种结合RPA及AI的对象推荐方法的背景架构示意图。如图2所示，终端11可以通过网络12将用户发出的招标文档上传至服务器13。当服务器13接收到该招标文档时，服务器13可以基于历史用户偏好信息确定与该招标文档匹配的投标文档，并通过网络12将与该招标文档匹配的投标文档发送至终端11，当终端接收到服务器13发送的投标文档时，终端可以在显示界面上显示该投标文档。
在一些实施例中，在招投标场景中，需要理解过去中标企业及项目之间的交互信息、企业以及项目的属性信息、企业以及项目的背景知识，包含企业资质，企业投资金额等等，从而针对用户提高的招标信息进行推荐。但是，相关技术中，终端在进行用户推荐时，仅历史用户偏好信息以及交互信息，从而导致终端进行用户推荐的准确性不高。
易于理解的是，该终端包括但不限于：可穿戴设备、手持设备、个人电脑、平板电脑、车载设备、智能手机、计算设备或连接到无线调制解调器的其它处理设备等。在不同的网络中终端设备可以叫做不同的名称，例如：用户设备、接入终端、用户单元、用户站、移动站、移动台、远方站、远程终端、移动设备、用户终端、终端、无线通信设备、用户代理或用户装置、蜂窝电话、无绳电话、个人数字处理 (personal digital assistant，PDA)、第五代移动通信技术(5th generation mobile networks，5G)网络或未来演进网络中的终端设备等。该终端上可以安装操作系统，该操作系统是指可以运行在终端中的操作系统，是管理和控制终端硬件和终端应用的程序，是终端中不可或缺的系统应用。该操作系统包括但不限于安卓Android系统、IOS系统、Windows phone(WP)系统和Ubuntu移动版操作系统等。
参照下面的描述和附图，将清楚本申请的实施例的这些和其他方面。在这些描述和附图中，具体申请了本申请的实施例中的一些特定实施方式，来表示实施本申请的实施例的原理的一些方式，但是应当理解，本申请的实施例的范围不受此限制。相反，本申请的实施例包括落入所附加权利要求书的精神和内涵范围内的所有变化、修改和等同物。
以下结合附图描述根据本申请实施例的结合RPA及AI实现IA的对象推荐方法。
图3示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图，如图3所示，该方法可包括以下步骤：
步骤S101：通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
根据一些实施例，投标文档集合是指由至少一个投标文档汇聚而成的集体。该投标文档集合并不特指某一投标文档集合。例如当投标文档内容发生变化时，该投标文档集合也可以相应变化。例如，当投标文档集合中包括的投标文档数量发生变化时，该投标文档集合也可以相应变化。
易于理解的是，当终端进行对象推荐时，终端可以通过机器人流程自动化RPA系统，获取招标文档和投标文档集合。
步骤S102：采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息；
根据一些实施例，目标知识表示模型指的是训练好的用于在第一知识图谱上获取知识表示信息的模型。该目标知识表示模型并不特指某一固定模型。例如，当第一知识图谱发生变化时，该目标知识表示模型可以发生变化。当终端获取到针对目标知识表示模型的模型修改指令时，该目标知识表示模型也可以发生变化。
在一些实施例中，第一知识图谱是指与招标文档和投标文档集合对应的知识图谱。该第一知识图谱并不特指某一固定图谱。例如，当招标文档发生变化时，该第一知识图谱可以发生变化。当投标文档集合发生变化时，该第一知识图谱也可以发生变化。
在一些实施例中，第一知识表示信息指的是招标文档对应的知识表示信息。该第一知识表示信息并不特指某一固定信息。例如，当招标文档发生变化时，该第一知识表示信息可以发生变化。当第一知识图谱发生变化时，该第一知识表示信息也可以发生变化。
在一些实施例中，第二知识表示信息指的是投标文档对应的知识表示信息。该第二知识表示信息并不特指某一固定信息。例如，当投标文档发生变化时，该第二知识表示信息可以发生变化。当第一知识图谱发生变化时，该第二知识表示信息也可以发生变化。
易于理解的是，当终端获取到招标文档和投标文档集合时，终端可以采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息。并且，终端还可以获取投标文档中至少一个投标文档对应的第二知识表示信息。
步骤S103：采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；
根据一些实施例，匹配度指的是第一知识表示信息和第二知识表示信息之间的匹配度。该匹配度并不特指某一固定匹配度。例如，当第一知识表示信息发生变化时，该匹配度可以发生变化。当第二知识表示信息发生变化时，该匹配度也可以发生变化。
在一些实施例中，匹配度集合指的是由第一知识表示信息和至少一个第二知识表示信息之间的至少一个匹配度汇聚而成的集合。该匹配度集合并不特指某一固定集合。例如，当第二知识表示信息的数量发生变化时，该匹配度集合可以发生变化。当匹配度发生变化时，该匹配度集合也可以发生变化。
在一些实施例中，目标投标文档指的是基于匹配度集合在投标文档集合中获取到的与招标文档匹配的投标文档。该目标投标文档并不特指某一固定文档。例如，当投标文档集合发生变化时，该目标投标文档可以发生变化。当招标文档发生变化时，该目标投标文档也可以发生变化。
易于理解的是，当终端获取到第一知识表示信息和至少一个第二知识表示信息时，终端可以采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合。进而，终端可以基于匹配度集合确定至少一个投标文档中的目标投标文档。
步骤S104：对目标投标文档对应的目标投标对象进行推荐。
根据一些实施例，目标投标对象指的是目标投标文档对应的招标对象。该目标投标对象并不特指某一固定对象。例如，当目标投标文档发生变化时，该目标投标对象可以发生变化。
易于理解的是，当终端获取到目标投标文档时，终端可以对目标投标文档对应的目标投标对象进行推荐。
在本申请实施例中，通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息；采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；对目标投标文档对应的目标投标对象进行推荐。因此，采用目标知识表示模型在第一知识图谱上获取招标文档和投标文档对应的知识表示信息，并基于招标文档和投标文档对应的知识表示信息进行推荐，在进行对象推荐时，可以提高招标文档和目标投标文档的匹配性，进而可以提高对象推荐的准确性。
图4示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图，如图4所示，该方法可包括以下步骤：
步骤S201：通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
根据一些实施例，当终端基于RPA获取招标文档和投标文档集合时，终端可以通过RPA系统抓取信息抽取源中的招标文档和投标文档集合，以及招标文档对应的文档格式和投标文档集合中至少一个投标文档对应的文档格式。
在一些实施例中，信息抽取源指的是招标文档和投标文档集合的来源。该信息抽取源并不特指某一固定信息。信息抽取源包括但不限于公共资源交易网站、行业核心招投标网站、地区招投标网站等等。信息抽取源的个数为多个，可以确保信息数据全量覆盖，随时掌握最新招标文档和投标文档集合。
在一些实施例中，终端通过RPA系统抓取信息抽取源中的招标文档和投标文档集合时，终端可以通过信息抽取源中的网站地址抓取网页。当终端抓取到网页时，终端可以从网页源码中提取出招标文档和投标文档集合，以及招标文档对应的文档格式和投标文档集合中至少一个投标文档对应的文档格式。
例如，当终端抓取到行业核心招投标网站的网页时，终端可以从行业核心招投标网站的网页源码中提取出行业核心招投标网站对应的word格式的招标文档。当终端抓取到地区招投标网站时，终端也可以从地区招投标网站的网页源码中提取出地区招投标网站对应的PDF格式的投标文档。
易于理解的是，当终端进行对象推荐时，终端可以通过机器人流程自动化RPA系统，获取招标文档和投标文档集合。
步骤S202：通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合；
根据一些实施例，目标文档格式指的是RPA系统所选择的获取的招标文档对应的文档格式和投标文档集合中至少一个投标文档对应的文档格式。该目标文档格式并不特征某一固定格式。例如，当RPA系统所选择的文档格式发生变化时，该目标文档格式可以相应变化。当终端获取到针对目标文档格式的格式修改指令时，该目标文档格式也可以发生变化。
在一些实施例中，当终端通过机器人流程自动化RPA系统，获取招标文档和投标文档集合时，若终端判定该招标文档对应的文档格式和投标文档集合中至少一个投标文档对应的文档格式不是目标文档格式，则终端可以通过RPA系统对招标文档和投标文档集合进行格式转换，从而得到目标文档格式的招标文档和投标文档集合。
在一些实施例中，当终端通过RPA系统对招标文档和投标文档集合进行格式转换时，终端可以利用RPA系统内置的文档格式转换工具对招投标文档进行格式转换。例如，当终端获取到word格式的招标文档时，终端可以利用python win32库，调用word底层宏语言(Visual Basic for Applications，VBA)，将word格式的招标文档转换成PDF格式的招标文档。
例如，若终端设置的目标文档格式为PDF格式时，若终端通过RPA系统抓取信息抽取源中的招标文档时，抓取到word格式的招标文档A。终端可以利用RPA系统内置的文档格式转换工具对招标文档A 进行格式转换，转换为PDF格式的招标文档，如图5所示。
易于理解的是，当终端通过机器人流程自动化RPA系统，获取到招标文档和投标文档集合时，终端可以通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合。
步骤S203：采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合；
根据一些实施例，三元组信息集合指的是由至少一个三元组信息汇聚而成的集合。该三元组信息集合并不特指某一固定集合。例如，当招标文档发生变化时，该三元组信息集合可以发生变化。当投标文档集合发生变化时，该三元组信息集合也可以发生变化。
例如，三元组信息集合中包括的三元组信息包括但不限于项目_类型、项目_招标_公告_发布_日期、项目_招标_公司、项目_资金来源、项目_归属_地、项目_招标_范围、项目_工期、项目_建设_规模、项目_投资额、项目_企业资质、项目_人员_资质、项目_企业信用_登记_要求、项目_业绩_资格、招标_项目、招标_公司、项目_报价、投标_项目、投标_公司、项目_信用、项目_企业_业绩、本次_评标_得分、中标_公司、项目_是否_中标、项目_下浮_率、项目_奖项等等。
根据一些实施例，当终端采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合时，终端采用的三元组抽取技术包括但不限于采用多头选择技术、多轮问答技术、分模块抽取技术等等。
在一些实施例中，当终端采用多头选择技术获取招标文档和投标文档集合对应的三元组信息集合时，终端可以用条件随机场(Conditional Random Field，CRF)来解决实体识别任务，将关系抽取视为多头选择问题，从而实现识别每个实体潜在的多种关系的功能。
在一些实施例中，当终端采用多轮问答技术获取招标文档和投标文档集合对应的三元组信息集合时，终端可以将实体-关系抽取视为多轮问答任务，通过为实体和关系构造问题模型，利用文本信息(待抽取文本)来回答问题模版，进而得到实体-关系。在问答任务上，可以采用基于跨度(span)的答案抽取方式。
在一些实施例中，当终端采用分模块抽取技术获取招标文档和投标文档集合对应的三元组信息集合时，终端可以将实体-关系抽取分成两个模块。首先抽取头实体(Head-Entity extraction，HE)，之后联合抽取尾实体-关系(Tail-Entity and Relation，TER)，可以减少实体重叠情况。
根据一些实施例，当终端采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合时，终端可以采用三元组抽取技术获取招标文档对应的第一三元组信息集合和投标文档集合对应的第二三元组信息集合。进而，终端可以采用结巴分词模型对第一三元组信息集合和第二三元组信息集合进行分词处理，得到处理后的第一三元组信息集合和处理后的第二三元组信息集合。最终，终端可以对处理后的第一三元组信息集合和处理后的第二三元组信息集合进行实体边界字符串审核处理，从而得到与招标文档和投标文档集合对应的三元组信息集合。因此，终端可以除去三元组信息集合中的乱字符，仅保留预设类型的三元组信息，例如数字、英文、汉字等等，并除去三元组信息集合中的重复三元组信息。进而可以提高三元组信息集合的信息质量，可以提高对象推荐的准确性。
在一些实施例中，第一三元组信息集合指的是招标文档对应的三元组信息集合。该第一三元组信息集合并不特指某一固定集合。例如，当招标文档发生变化时，该第一三元组信息集合可以发生变化。当三元组信息发生变化时，该第一三元组信息集合也可以发生变化。
在一些实施例中，第二三元组信息集合指的是投标文档集合对应的三元组信息集合。该第二三元组信息集合并不特指某一固定集合。例如，当投标文档集合发生变化时，该第二三元组信息集合可以发生变化。当三元组信息发生变化时，该第二三元组信息集合也可以发生变化。
在一些实施例中，当终端采用结巴分词模型对第一三元组信息集合和第二三元组信息集合进行分词处理时，终端可以利用词性标注工具pos_seg对第一三元组信息集合和第二三元组信息集合中的三元组信息进行标注。进而，通过结巴分词模型和词性标注工具的性能比较，可以去除第一三元组信息集合和第二三元组信息集合中的无用噪声实体。
在一些实施例中，终端通过对处理后的第一三元组信息集合和处理后的第二三元组信息集合进行实体边界字符串审核处理，终端可以减少三元组信息集合中的噪声，提升三元组信息集合的质量。例如，当终端获取到的三元组信息为“A网_B省_电力_有限公司_的_经营_状态_是”时，若终端设置“A网_B省_电力_有限公司”为需要的三元组信息，“的_经营_状态_是”为无关信息，则终端可以通过实体边界字符串 审核处理，去除这些无关信息。
在一些实施例中，当终端采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合时，终端还可以通过去除三元组信息集合中的无效特征以及稀疏特征来去除次数较低，或者没有进行过招投标活动的对象对应的三元组信息。
易于理解的是，当终端获取到目标文档格式的招标文档和投标文档集合时，终端可以采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合。
步骤S204：基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱；
根据一些实施例，当终端基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱时，终端可以采用目标深度学习模型在三元组信息集合中获取目标实体对应的三元组信息子集，并对三元组信息子集中各三元组信息进行合并，终端可以构建数据集合对应的第一知识图谱。
在一些实施例中，目标深度学习模型是指终端获取目标实体对应的三元组信息子集的模型。该目标深度学习模型并不特指某一固定深度学习模型。例如当深度学习模型的类型发生变化时，该目标深度学习模型也可以相应变化。当深度学习模型的模型名称发生变化时，该目标深度学习模型也可以相应变化。
在一些实施例中，目标实体指的是终端构建第一知识图谱时需要用到的实体信息。该目标实体信息并不特指某一固定信息。例如，当三元组信息集合发生变化时，该目标实体信息也可以发生变化。当实体信息发生变化时，该目标实体信息也可以发生变化。
在一些实施例中，第一知识图谱信息指的是第一知识图谱对应的信息。该第一知识图谱信息并不特指某一固定信息。例如，当第一知识图谱发生变化时，该第一知识图谱信息也可以发生变化。当目标招投标属性信息发生变化时，该第一知识图谱信息也可以发生变化。当目标实体信息发生变化时，该第一知识图谱信息也可以发生变化。
例如，图6示出本申请一个实施例的一种第一知识图谱的展示示意图。如图6所示，“投标文档E2”为“招标文档E1”的中标文档，“投标文档E2”的负责人为“人物G2”，“投标文档E2”来源于“机构F2”；“招标文档E1”来源于“机构F1”，“招标文档E1”的执行官为“人物G1”。
易于理解的是，当终端获取到招标文档和投标文档集合对应的三元组信息集合时，终端可以基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱。
步骤S205：获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数；
根据一些实施例，初始知识表示模型指的是未经训练的知识表示模型。该初始知识表示模型并不特征某一固定模型。例如，当初始知识表示模型的模型性能参数发生变化时，该初始知识表示模型可以发生变化。当终端获取到针对初始知识表示模型的模型修改指令时，该初始知识表示模型也可以发生变化。
在一些实施例中，初始知识表示模型集合指的是由至少一个初始知识表示模型汇聚而成的集合。该初始知识表示模型集合并不特指某一固定集合。例如，当初始知识表示模型发生变化时，该初始知识表示模型集合可以发生变化。当初始知识表示模型的数量发生变化时，该初始知识表示模型集合也可以发生变化。
易于理解的是，当终端获取目标知识表示模型时，终端可以获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数。
步骤S206：基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型；
根据一些实施例，当终端基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型时，终端基于初始知识表示模型对应的模型性能参数评估该初始知识表示模型的学习效率和学习效果。进而，终端可以根据初始知识表示模型集合中至少一个初始知识表示模型对应的学习效率和学习效果选择所采用的目标知识表示模型。
例如，当初始知识表示模型为转移距离模型时，该初始知识表示模型可以为翻译嵌入(Translating Embeddings for Modeling Multi-relational Data，TransE)模型、灵活翻译(Flexible Translation，TransF)模型、学习实体和关系嵌入(Learning Entity and Relation Embeddings，TransR)模型、超平面翻译(Translating on Hyperplanes，TransH)模型、知识驱动的行为理解(Human Activity Understanding，HAKE)模型。当初始知识表示模型为双线性模型时，该初始知识表示模型可以为RESCAL、DistMult、ComplEx、HolE、SimplE、Analogy等语义匹配模型。对这些初始知识表示模型的学习效率和学习效果进行评估后，终端可 以选择采用TransE模型作为目标知识表示模型。
易于理解的是，当终端获取到初始知识表示模型集合中各初始知识表示模型对应的模型性能参数时，终端可以基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型。
步骤S207：采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档中至少一个投标文档对应的第二知识表示信息；
根据一些实施例，当终端采用目标知识表示模型在第一知识图谱上获取知识表示信息时，终端可以通过目标知识表示模型，基于分布式表示学习推理方法，将三元组信息中的符号表示映射到向量空间并且进行数值表示。因此可以减少维数灾难，同时可以捕捉三元组信息中实体和关系之间的隐式关联，并且计算直接、训练速度快。
例如，当终端获取到三元组信息“国网_江西省_电力”时，终端可以基于分布式表示学习推理方法，将三元组信息表示为知识表示信息(0，1，2)。
易于理解的是，当终端获取到目标知识表示模型时，终端可以采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息。并且，终端还可以获取投标文档中至少一个投标文档对应的第二知识表示信息。
步骤S208：采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；
根据一些实施例，当终端采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合时，终端可以将第一知识表示信息作为匹配网络模型的固定数据，依次将第二知识表示信息集合中的第二知识表示信息输入至匹配网络模型。进而，终端可以分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合。
在一些实施例中，终端在采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合时，终端还可以引用注意力机制来提高匹配度集合获取的准确性。
在一些实施例中，注意力机制指的是在机器学习模型中嵌入的一种特殊结构，用来自动学习和计算输入数据对输出数据的贡献大小的结构。该注意力机制并不特指某一固定结构。例如，当终端获取到针对注意力机制的修改指令时，该注意力机制可以发生变化。
易于理解的是，当终端获取到第一知识表示信息和至少一个第二知识表示信息时，终端可以采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合。进而，终端可以基于匹配度集合确定至少一个投标文档中的目标投标文档。
步骤S209：对目标投标文档对应的目标投标对象进行推荐。
根据一些实施例，当终端基于匹配度集合确定至少一个投标文档中的目标投标文档时，终端可以确定至少两个目标投标文档。进而，终端可以将该至少两个目标投标文档在展示页面中按先后顺序排列展示。
例如，终端可以按照大小顺序对匹配度集合中至少两个匹配度进行排序。进而，终端可以选择排名前10的匹配度对应的投标文档作为目标投标文档，从而，终端可以将这10个目标投标文档在展示页面中按先后顺序排列展示。
在一些实施例中，当终端对目标投标文档对应的目标投标对象进行推荐时，终端还可以通过推荐解释模块，为目标投标对象提供可解释可参考的依据，从而在提高对象推荐准确性的同时可以使推荐结果具备高解释度。
在一些实施例中，推荐解释模块指的是用于通过通俗易懂的解释让用户了解到为什么会推荐给他目标投标文档的模块。该推荐解释模块并不特指某一固定模块。例如，当目标投标文档发生变化时，该推荐解释模块可以发生变化。当终端获取到针对推荐解释模块的模块修改指令时，该推荐解释模块也可以发生变化。
易于理解的是，当终端获取到目标投标文档时，终端可以对目标投标文档对应的目标投标对象进行推荐。
在本申请实施例中，通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合，因此可以提高RPA系统对不同文档格式的招标文档和投标文档集合的匹配性，进而可以提高对象推 荐的准确性。其次，采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合，基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱，因此可以提高第一知识图谱获取的准确性，进而可以提高投标对象推荐的准确性。另外，获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数，基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型，因此通过从初始知识表示模型集合选择需要的目标知识表示模型，可以提高对象推荐的准确性。
图7示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图，如图7所示，该方法可包括以下步骤：
步骤S301：通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
具体过程如上所述，此处不再赘述。
步骤S302：获取招投标训练文档集合；
根据一些实施例，招投标训练文档指的是用于训练知识表示模型，使得知识表示模型具有先验知识的表达的文档。该招投标训练文档并不特指某一固定文档。例如，当知识表示模型发生变化时，该招投标训练文档可以发生变化。当招标文档和投标文档集合发生变化时，该招投标训练文档也可以发生变化。
在一些实施例中，招投标训练文档集合指的是由至少一个招投标训练文档汇聚而成的集合。该招投标训练文档集合并不特指某一固定集合。例如，当招投标训练文档发生变化时，该招投标训练文档集合可以发生变化。当知识表示模型发生变化时，该招投标训练文档集合也可以发生变化。
易于理解的是，当终端获取目标知识表示模型时，终端可以获取招投标训练文档集合。
步骤S303：获取招投标训练文档集合对应的知识图谱；
根据一些实施例，当终端获取到招投标训练文档集合时，终端可以获取到招投标训练文档集合对应的招投标属性信息和实体信息。进而，终端可以通过机器人流程自动化RPA系统和光学字符识别OCR模型，获取招投标属性信息和实体信息对应的知识图谱信息。最终，终端可以展示该知识图谱信息对应的知识图谱。
在一些实施例中，招投标属性信息是指招投标训练文档集合对应的属性信息。该招投标属性信息包括但不限于招标信息、投标信息、行业信息、区域信息等等。例如，招投标训练文档集合中“招标文档M”对应的招投标属性信息为招标信息。“投标文档N”对应的招投标属性信息为投标信息。
在一些实施例中，实体信息指的是投标训练文档集合对应的实体的信息。实体信息包括但不限于对象属性信息、数据属性信息、关系属性信息等等。其中，对象属性信息是指抽象和刻划同类实体的实体名或属性名。对象属性信息包括但不限于人物、机构、时间、帖子、账号等等。数据属性信息是指实体本身对应的属性信息。例如，实体“人物D”对应的属性信息包括但不限于姓名D1、曾用名D2、年龄D3等等。关系属性信息是指实体之间的相互关系信息。
在一些实施例中，知识图谱信息指的是构建知识图谱时用到的信息。该知识图谱信息并不特指某一固定信息。例如，当实体信息发生变化时，该知识图谱信息可以发生变化。当招投标属性信息发生变化时，该知识图谱信息也可以发生变化。
易于理解的是，当终端获取到招投标训练文档集合时，终端可以获取招投标训练文档集合对应的知识图谱。
步骤S304：基于分布式表示学习推理技术，对知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型；
根据一些实施例，知识指的是知识图谱中包含的实体关系信息。该知识并不特指某一固定知识。例如，当知识图谱发生变化时，该知识可以发生变化。当招投标训练文档集合发生变化时，该知识也可以发生变化。
在一些实施例中，当终端对知识图谱中至少一个知识对进行向量空间映射时，终端可以基于分布式表示学习推理技术，获取到知识图谱中实体关系对应的映射函数。进而，终端可以将实体关系映射至向量空间，并以数值的形式表示实体关系。例如，“中标”这个知识可以被映射至25，“投标”这个知识可以被映射至索引值68。
易于理解的是，当终端获取到招投标训练文档集合对应的知识图谱时，终端可以基于分布式表示学习推理技术，对知识图谱中至少一个知识对进行向量空间映射。进而，终端可以建立目标知识表示模型。
步骤S305：采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取 投标文档中至少一个投标文档对应的第二知识表示信息；
具体过程如上所述，此处不再赘述。
步骤S306：采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合；
具体过程如上所述，此处不再赘述。
步骤S307：获取匹配度集合中最高匹配度；
例如，当终端获取到第一知识表示信息和第二知识表示信息B1之间的匹配度为30，第一知识表示信息和第二知识表示信息B2之间的匹配度为50，第一知识表示信息和第二知识表示信息B3之间的匹配度为70，则终端可以获取到第一知识表示信息和第二知识表示信息B3之间匹配度为匹配度集合中最高匹配度。
易于理解的是，当终端获取到第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合时，终端可以获取匹配度集合中最高匹配度。
步骤S308：从至少一个投标文档中，获取最高匹配度对应的投标文档，并将投标文档确定为目标投标文档；
例如，当终端获取到第一知识表示信息和第二知识表示信息B3之间匹配度为匹配度集合中最高匹配度时，终端可以从至少一个投标文档中，获取最高匹配度对应的投标文档b3。进而，终端可以将投标文档b3确定为目标投标文档。
易于理解的是，当终端获取到匹配度集合中最高匹配度时，终端可以从至少一个投标文档中，获取最高匹配度对应的投标文档，并将投标文档确定为目标投标文档。
步骤S309：对目标投标文档对应的目标投标对象进行推荐。
具体过程如上所述，此处不再赘述。
在本申请实施例中，通过获取招投标训练文档集合，获取招投标训练文档集合对应的知识图谱，基于分布式表示学习推理技术，对知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型，因此可以提高目标知识表示模型建立的准确性，进而可以提高用户推荐的准确性。其次，通过获取匹配度集合中最高匹配度，从至少一个投标文档中，获取最高匹配度对应的投标文档，并将投标文档确定为目标投标文档，因此通过将匹配度最高的投标文档确定为目标投标文档，可以提高目标投标文档确定的准确性。
图8示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐方法的流程图，如图8所示，该方法可包括以下步骤：
步骤S401：通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
具体过程如上所述，此处不再赘述。
步骤S402：采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档中至少一个投标文档对应的第二知识表示信息；
具体过程如上所述，此处不再赘述。
步骤S403：获取招标文档对应的招标属性信息和投标文档集合中任一投标文档对应的投标属性信息；
根据一些实施例，招标属性信息指的是招标文档对应的属性信息。该招标属性信息并不特指某一固定信息。该招标属性信息包括但不限于招标信息、行业信息、区域信息等等。
在一些实施例中，投标属性信息指的是投标文档对应的属性信息。该投标属性信息包括但不限于投标信息、行业信息、区域信息等等。
易于理解的是，当终端获取到招标文档对应的第一知识表示信息，获取投标文档中至少一个投标文档对应的第二知识表示信息时，终端可以获取招标文档对应的招标属性信息和投标文档集合中任一投标文档对应的投标属性信息。
步骤S404：采用用户表示学习模块和招标属性信息对第一知识表示信息进行调整，得到调整后的第一知识表示信息；
根据一些实施例，用户表示学习模块指的是用于从原始输入数据中自动学习出有效的特征，并将输入信息转换为有效的特征表示的模块。该用户表示学习模块并不特指某一固定模块。该用户表示学习模块的采用的表示形式包括但不限于局部表示、分布表示等等。
在一些实施例中，局部表示指的是表示为one-hot向量的形式，这种离散的表示方式具有很好的解释性，但one-hot向量的维数很高，且不能扩展且各个向量之间正交无法计算相似性。
在一些实施例中，分布表示通常可以表示为低维的连续稠密向量，表示能力要强很多，相似度也很容易计算。
在一些实施例中，当终端获取到招标文档对应的招标属性信息时，终端可以采用用户表示学习模块，将招标属性信息对应的特征表示与第一知识表示信息进行融合加乘。进而，终端可以获取到调整后的第一知识表示信息。
易于理解的是，当终端获取到招标文档对应的招标属性信息时，终端可以采用用户表示学习模块和招标属性信息对第一知识表示信息进行调整，得到调整后的第一知识表示信息。
步骤S405：采用用户表示学习模块和投标属性信息对任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息；
根据一些实施例，当终端获取到投标文档对应的投标属性信息时，终端可以采用用户表示学习模块，将投标属性信息对应的特征表示与第二知识表示信息进行融合加乘。进而，终端可以获取到调整后的第二知识表示信息。
易于理解的是，当终端获取到投标文档集合中任一投标文档对应的投标属性信息时，终端可以采用用户表示学习模块和投标属性信息对任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息。
步骤S406：遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合；
易于理解的是，当终端获取到投标文档集合中任一投标文档对应的投标属性信息时，终端可以遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合。
步骤S407：采用匹配网络模型，分别获取调整后的第一知识表示信息和调整后的至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；
具体过程如上所述，此处不再赘述。
步骤S408：对目标投标文档对应的目标投标对象进行推荐。
具体过程如上所述，此处不再赘述。
在本申请实施例中，通过获取招标文档对应的招标属性信息和投标文档集合中任一投标文档对应的投标属性信息，采用用户表示学习模块和招标属性信息对第一知识表示信息进行调整，得到调整后的第一知识表示信息，遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合；因此通过采用多模型融合学习的方法，对知识表示信息进行表示学习以及微调，可以提高对象推荐的准确性。其次，采用匹配网络模型，分别获取调整后的第一知识表示信息和调整后的至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档，对目标投标文档对应的目标投标对象进行推荐，因此，通过基于调整后的知识表示信息进行推荐，终端在进行对象推荐时，可以充分考虑招投标背景知识，进而可以提高对象推荐的准确性。
下述为本申请装置实施例，可以用于执行本申请方法实施例。对于本申请装置实施例中未披露的细节，请参照本申请方法实施例。
请参见图9，其是本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图。该结合RPA及AI实现IA的对象推荐装置可以通过软件、硬件或者两者的结合实现成为装置的全部或一部分。该结合RPA及AI实现IA的对象推荐装置9000包括文档获取单元9001、信息获取单元9002、文档确定单元9003和对象推荐单元9004，其中：
文档获取单元9001，用于通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
信息获取单元9002，用于采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档集合中至少一个投标文档对应的第二知识表示信息，第一知识图谱与招标文档和投标文档集合对应；
文档确定单元9003，用于采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；
对象推荐单元9004，用于对目标投标文档对应的目标投标对象进行推荐。
在一个实施方式中，图10示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的 结构示意图。如图10所示，对象推荐装置9000还包括目标文档获取单元9005、集合抽取单元9006和图谱建立单元9007，用于在采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息之前：
目标文档获取单元9005，用于通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合；
集合抽取单元9006，用于采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合；
图谱建立单元9007，用于基于三元组信息集合，建立招标文档和投标文档集合对应的第一知识图谱。
在一个实施方式中，图11示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图。如图11所示，集合抽取单元9006包括集合获取子单元9106、分词处理子单元9206和审核处理子单元9306，集合抽取单元9006用于采用三元组抽取技术获取招标文档和投标文档集合对应的三元组信息集合时：
集合获取子单元9106，用于采用三元组抽取技术获取招标文档对应的第一三元组信息集合和投标文档集合对应的第二三元组信息集合；
分词处理子单元9206，用于采用结巴分词模型对第一三元组信息集合和第二三元组信息集合进行分词处理，得到处理后的第一三元组信息集合和处理后的第二三元组信息集合；
审核处理子单元9306，用于对处理后的第一三元组信息集合和处理后的第二三元组信息集合进行实体边界字符串审核处理，得到与招标文档和投标文档集合对应的三元组信息集合。
在一个实施方式中，图12示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图。如图12所示，对象推荐装置9000还包括集合获取单元9008、图谱获取单元9009和模型建立单元9010，用于在采用目标知识表示模型获取招标文档对应的第一知识表示信息，获取投标文档中至少一个投标文档对应的第二知识表示信息之前：
集合获取单元9008，用于获取招投标训练文档集合；
图谱获取单元9009，用于获取招投标训练文档集合对应的知识图谱；
模型建立单元9010，用于基于分布式表示学习推理技术，对知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型。
在一个实施方式中，图13示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图。如图13所示，对象推荐装置9000还包括参数获取单元9011和模型确定单元9012，用于在采用目标知识表示模型获取招标文档对应的第一知识表示信息，获取投标文档中至少一个投标文档对应的第二知识表示信息之前：
参数获取单元9011，用于获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数；
模型确定单元9012，用于基于模型性能参数，在初始知识表示模型集合中确定目标知识表示模型，目标知识表示模型为分布式知识推理模型。
在一个实施方式中，图14示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图。如图14所示，对象推荐装置9000还包括属性信息获取单元9013、信息调整单元9014和集合遍历单元9015，用于在采用匹配网络模型，获取第一知识表示信息和至少一个第二知识表示信息的匹配度集合之前：
属性信息获取单元9013，用于获取招标文档对应的招标属性信息和投标文档集合中任一投标文档对应的投标属性信息；
信息调整单元9014，用于采用用户表示学习模块和招标属性信息对第一知识表示信息进行调整，得到调整后的第一知识表示信息；
信息调整单元9014，还用于采用用户表示学习模块和投标属性信息对任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息；
集合遍历单元9015，用于遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合。
在一个实施方式中，图15示出本申请一个实施例的一种结合RPA及AI实现IA的对象推荐装置的结构示意图。如图15所示，文档确定单元9003还包括匹配度获取子单元9103和文档匹配子单元9203，文档确定单元9003用于基于匹配度集合确定至少一个投标文档中的目标投标文档时：
匹配度获取子单元9103，用于获取匹配度集合中最高匹配度；
文档匹配子单元9203，用于从至少一个投标文档中，获取最高匹配度对应的投标文档，并将投标文档确定为目标投标文档。
本申请实施例各装置中的各模块的功能可以参见上述方法中的对应描述，在此不再赘述。
在本申请实施例中，通过文档获取单元通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；信息获取单元采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取投标文档中至少一个投标文档对应的第二知识表示信息，第一知识图谱与招标文档和投标文档集合对应；文档确定单元采用匹配网络模型，分别获取第一知识表示信息和至少一个第二知识表示信息之间的匹配度集合，并基于匹配度集合确定至少一个投标文档中的目标投标文档；对象推荐单元对目标投标文档对应的目标投标对象进行推荐。因此，通过采用目标知识表示模型在第一知识图谱上获取招标文档和投标文档对应的知识表示信息，并基于招标文档和投标文档对应的知识表示信息进行推荐，终端在进行对象推荐时，可以充分考虑招投标背景知识，进而可以提高对象推荐的准确性。
图16示出根据本申请一实施例的一种终端的结构框图。如图16所示，该终端包括：存储器1610和处理器1620，存储器1610内存储有可在处理器1620上运行的计算机程序。处理器1620执行该计算机程序时实现上述实施例中的对象推荐方法。存储器1610和处理器1620的数量可以为一个或多个。
该终端还包括：
通信接口1630，用于与外界设备进行通信，进行数据交互传输。
如果存储器1610、处理器1620和通信接口1630独立实现，则存储器1610、处理器1620和通信接口1630可以通过总线相互连接并完成相互间的通信。该总线可以是工业标准体系结构(Industry Standard Architecture，ISA)总线、外部设备互连(Peripheral Component Interconnect，PCI)总线或扩展工业标准体系结构(Extended Industry Standard Architecture，EISA)总线等。该总线可以分为地址总线、数据总线、控制总线等。为便于表示，图16中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
在一个实施方式中，在具体实现上，如果存储器1610、处理器1620及通信接口1630集成在一块芯片上，则存储器1610、处理器1620及通信接口1630可以通过内部接口完成相互间的通信。
本申请实施例提供了一种计算机可读存储介质，其存储有计算机程序，该程序被处理器执行时实现本申请实施例中提供的方法。
本申请实施例还提供了一种芯片，该芯片包括，包括处理器，用于从存储器中调用并运行存储器中存储的指令，使得安装有芯片的通信设备执行本申请实施例提供的方法。
本申请实施例还提供了一种芯片，包括：输入接口、输出接口、处理器和存储器，输入接口、输出接口、处理器以及存储器之间通过内部连接通路相连，处理器用于执行存储器中的代码，当代码被执行时，处理器用于执行申请实施例提供的方法。
应理解的是，上述处理器可以是中央处理器(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(digital signal processing，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现场可编程门阵列(fieldprogrammablegate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者是任何常规的处理器等。值得说明的是，处理器可以是支持进阶精简指令集机器(advanced RISC machines，ARM)架构的处理器。
进一步地，在一个实施方式中，上述存储器可以包括只读存储器和随机存取存储器，还可以包括非易失性随机存取存储器。该存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以包括只读存储器(read-only memory，ROM)、可编程只读存储器(programmable ROM，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器可以包括随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用。例如，静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(dynamic random access memory，DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(double data date SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器 (direct rambus RAM，DR RAM)。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行计算机程序指令时，全部或部分地产生按照本申请的流程或功能。计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输。
本申请实施例还提供一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现前述任一实施例所述的方法。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包括于本申请的至少一个实施例或示例中。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或隐含地包括至少一个该特征。在本申请的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现特定逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分。并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。
应理解的是，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。上述实施例方法的全部或部分步骤是可以通过程序来指令相关的硬件完成，该程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本申请各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。上述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读存储介质中。该存储介质可以是只读存储器，磁盘或光盘等。
以上，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到其各种变化或替换，这些都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以权利要求的保护范围为准。

Claims (16)

  1. 一种结合RPA及AI实现IA的对象推荐方法，包括：
    通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
    采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信息，获取所述投标文档集合中至少一个投标文档对应的第二知识表示信息，所述第一知识图谱与所述招标文档和所述投标文档集合对应；
    采用匹配网络模型，分别获取所述第一知识表示信息和所述至少一个第二知识表示信息之间的匹配度集合，并基于所述匹配度集合确定所述至少一个投标文档中的目标投标文档；
    对所述目标投标文档对应的目标投标对象进行推荐。
  2. 根据权利要求1所述的方法，还包括：
    通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合；
    采用三元组抽取技术获取所述招标文档和所述投标文档集合对应的三元组信息集合；
    基于所述三元组信息集合，建立所述招标文档和所述投标文档集合对应的第一知识图谱。
  3. 根据权利要求2所述的方法，其中所述采用三元组抽取技术获取所述招标文档和所述投标文档集合对应的三元组信息集合，包括：
    采用三元组抽取技术获取招标文档对应的第一三元组信息集合和投标文档集合对应的第二三元组信息集合；
    采用结巴分词模型对所述第一三元组信息集合和所述第二三元组信息集合进行分词处理，得到处理后的第一三元组信息集合和处理后的第二三元组信息集合；
    对所述处理后的第一三元组信息集合和所述处理后的第二三元组信息集合进行实体边界字符串审核处理，得到与所述招标文档和所述投标文档集合对应的三元组信息集合。
  4. 根据权利要求1至3中任一项所述的方法，还包括：
    获取招投标训练文档集合；
    获取所述招投标训练文档集合对应的知识图谱；
    基于分布式表示学习推理技术，对所述知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型。
  5. 根据权利要求1至3中任一项所述的方法，还包括：
    获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数；
    基于所述模型性能参数，在所述初始知识表示模型集合中确定目标知识表示模型，所述目标知识表示模型为分布式知识推理模型。
  6. 根据权利要求1至5中任一项所述的方法，其中在所述采用匹配网络模型，获取所述第一知识表示信息和所述至少一个第二知识表示信息的匹配度集合之前，还包括：
    获取所述招标文档对应的招标属性信息和所述投标文档集合中任一投标文档对应的投标属性信息；
    采用用户表示学习模块和所述招标属性信息对所述第一知识表示信息进行调整，得到调整后的第一知识表示信息；
    采用所述用户表示学习模块和所述投标属性信息对所述任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息；
    遍历第二知识表示信息集合，得到调整后的第二知识表示信息集合。
  7. 根据权利要求1至6任一项所述的方法，其中所述基于所述匹配度集合确定所述至少一个投标文档中的目标投标文档，包括：
    获取所述匹配度集合中最高匹配度；
    从所述至少一个投标文档中，获取所述最高匹配度对应的投标文档，并将所述投标文档确定为所述目标投标文档。
  8. 一种结合RPA及AI实现IA的对象推荐装置，包括：
    文档获取单元，用于通过机器人流程自动化RPA系统，获取招标文档和投标文档集合；
    信息获取单元，用于采用目标知识表示模型在第一知识图谱上获取招标文档对应的第一知识表示信 息，获取所述投标文档集合中至少一个投标文档对应的第二知识表示信息，所述第一知识图谱与所述招标文档和所述投标文档集合对应；
    文档确定单元，用于采用匹配网络模型，分别获取所述第一知识表示信息和所述至少一个第二知识表示信息之间的匹配度集合，并基于所述匹配度集合确定所述至少一个投标文档中的目标投标文档；
    对象推荐单元，用于对所述目标投标文档对应的目标投标对象进行推荐。
  9. 根据权利要求8所述的装置，其中所述装置还包括目标文档获取单元、集合抽取单元和图谱建立单元，
    所述目标文档获取单元，用于通过机器人流程自动化RPA系统，获取目标文档格式的招标文档和投标文档集合；
    所述集合抽取单元，用于采用三元组抽取技术获取所述招标文档和所述投标文档集合对应的三元组信息集合；
    所述图谱建立单元，用于基于所述三元组信息集合，建立所述招标文档和所述投标文档集合对应的第一知识图谱。
  10. 根据权利要求9所述的装置，其中所述集合抽取单元包括集合获取子单元、分词处理子单元和审核处理子单元，
    所述集合获取子单元，用于采用三元组抽取技术获取招标文档对应的第一三元组信息集合和投标文档集合对应的第二三元组信息集合；
    所述分词处理子单元，用于采用结巴分词模型对所述第一三元组信息集合和所述第二三元组信息集合进行分词处理，得到处理后的第一三元组信息集合和处理后的第二三元组信息集合；
    所述审核处理子单元，用于对所述处理后的第一三元组信息集合和所述处理后的第二三元组信息集合进行实体边界字符串审核处理，得到与所述招标文档和所述投标文档集合对应的三元组信息集合。
  11. 根据权利要求8至10中任一项所述的装置，其中所述装置还包括集合获取单元、图谱获取单元和模型建立单元，
    所述集合获取单元，用于获取招投标训练文档集合；
    所述图谱获取单元，用于获取所述招投标训练文档集合对应的知识图谱；
    所述模型建立单元，用于基于分布式表示学习推理技术，对所述知识图谱中至少一个知识对进行向量空间映射，建立目标知识表示模型。
  12. 根据权利要求8至10中任一项所述的装置，其中所述装置还包括参数获取单元和模型确定单元，
    所述参数获取单元，用于获取初始知识表示模型集合中各初始知识表示模型对应的模型性能参数；
    所述模型确定单元，用于基于所述模型性能参数，在所述初始知识表示模型集合中确定目标知识表示模型，所述目标知识表示模型为分布式知识推理模型。
  13. 根据权利要求8至12中任一项所述的装置，其中所述装置还包括属性信息获取单元、信息调整单元和集合遍历单元，
    所述属性信息获取单元，用于获取所述招标文档对应的招标属性信息和所述投标文档集合中任一投标文档对应的投标属性信息；
    所述信息调整单元，用于采用用户表示学习模块和所述招标属性信息对所述第一知识表示信息进行调整，得到调整后的第一知识表示信息；
    所述信息调整单元，还用于采用所述用户表示学习模块和所述投标属性信息对所述任一投标文档对应的第二知识表示信息进行调整，得到调整后的第二知识表示信息；
    所述集合遍历单元，用于遍历所述第二知识表示信息集合，得到调整后的第二知识表示信息集合。
  14. 一种结合RPA及AI实现IA的终端，包括：处理器和存储器，所述存储器中存储有指令，所述指令由处理器加载并执行，以实现如权利要求1至7中任一项所述的方法。
  15. 一种计算机可读存储介质，所述计算机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至7中任一项所述的方法。
  16. 一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现根据权利要求1-7中任一项所述的方法。
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