345 results about "Business knowledge" patented technology

The invention provides a construction method of a double-path collaborative decision network for multi-agent collaborative path optimization, and the method comprises the steps: obtaining multi-source heterogeneous real-time business data and historical business data, and constructing a standardized data set; converting the standardized data set into a dynamic environment input sequence; at least two domain agents perform full-process business logical reasoning based on the dynamic environment input sequence to generate a decision chain structure covering the full life cycle of the business; implementing full-flow path planning simulation based on the decision chain structure, and generating an initial path scheme including multi-resource collaborative scheduling; the environment perception intelligent agent puts the initial distribution path scheme into an actual logistics distribution scene for execution, and loads real-time environment change information to determine a logistics decision chain weight; and the logistics strategy agent optimizes the initial distribution path scheme based on the logistics decision chain weight to obtain a logistics business knowledge graph so as to construct a logistics double-path collaborative decision network.

The embodiment of the invention discloses an interactive intelligent analysis method and device based on a knowledge graph and a medium, and relates to the technical field of data processing.The method comprises the steps that a natural language query text input by a user is obtained, intention detection is conducted on the natural language query text, the corresponding intention fuzzy confidence degree is determined, and the knowledge graph is obtained according to the intention fuzzy confidence degree; matching a target query channel; when the target query channel is a guide query channel, performing path derivation on the natural language query text through a business knowledge graph constructed by pre-learning to generate a personalized analysis card sequence so as to obtain a target analysis dimension in the personalized analysis card sequence triggered by a user; and under the triggering of the target analysis dimension, switching the guide query channel into a precise query channel, and through the precise query channel, calling a pre-learned query mode knowledge base to generate a target query statement corresponding to the target analysis dimension, so as to determine a corresponding visual analysis result through the target query statement.

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.

Provided in the present application is a code generation method, comprising: a code development platform receives input information by a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information so as to obtain target business knowledge; then the code development platform combines the input information, the context information and the target business knowledge according to a prompt template, so as to obtain prompt information; and then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and presents to the user a code segment reasoned by the LLM. Thus, using the prompt information from the same data source in different processes for prompting the LLM can achieve complete sharing and complete alignment of the prompt information, thereby improving the accuracy of generating code in single time. Furthermore, using a unified prompt template to combine the context information and the business knowledge can make a code segment generated by the LLM more accurate, thereby improving the code acceptance rate in professional domains.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent collaborative data question-answering system and method based on a large model, and the system comprises an agent cluster module which is composed of five special agents, namely a data question-answering agent, a data extraction agent, a data analysis agent, a visualization agent and a quality examination agent, and achieves the task decomposition and collaborative execution through dynamic scheduling; the knowledge management module comprises a business knowledge base, a data element knowledge base and a user feedback base, and adopts a hierarchical knowledge fusion technology to provide domain knowledge support for the intelligent agent; and the supporting function module covers a front-end dialogue component and a verification and execution engine and is responsible for interactive interface rendering and result reliability verification. According to the method, the fine tuning requirement on the large model is remarkably reduced, the illusion of the large model is effectively intercepted through a dual verification mechanism, and the accuracy and reliability of question and answer results are improved.

The present invention relates to a knowledge graph construction method and system for complex relational business databases, including concept-level graph modeling, data-level graph modeling, and graph migration and integration. By accessing a business database or importing a model file, the database table structure and its constraint relationship of the business database are automatically analyzed, and a tree-like classification candidate concept list and candidate relationship list are formed according to certain rules, and a concept-level knowledge graph is quickly constructed. The classification and clustering algorithms are used to realize multi-layer classification and grouping of data records, and a data-level knowledge graph is quickly constructed. The constructed business knowledge graph is migrated and integrated, and it supports importing it into a graph database for storage and retrieval. The problem that the existing knowledge graph construction method fails to effectively consider the relevant characteristics of complex relational business databases is solved, the operation complexity is greatly reduced, and the efficiency of knowledge graph construction for complex relational business databases is improved.

The invention provides a system association recommendation method and system based on semantic and business feature fusion, and belongs to the technical field of text association analysis. Text segmentation is performed on a system file based on a regular expression, and term semantic features are extracted; constructing a system associated business knowledge graph based on the association requirements of the term content of the system file and the external business information, and storing graph data by using a Neo4j graph database; creating a clause-department, clause-post and clause-professional three-layer system business knowledge network, and calculating graph structure features of each layer of network clause entity based on a Node2Vec algorithm; fusing the semantic features of the clause entities and the graph structure feature vectors, and establishing an association relationship between the clause entities in the system association business knowledge graph based on a cosine similarity method; and realizing system clause association recommendation based on a Cypher query language of a Neo4j graph database. According to the method, on the premise that the calculation complexity is reduced and the external knowledge is integrated, the system text association recommendation is more accurate.

The invention discloses a water conservancy intelligent question-answering system and method based on knowledge enhancement and data driving, and aims at water conservancy business structured and unstructured data query, through loop optimization driven by positive and negative examples, precise classification of secondary intentions of water conservancy problems is realized, knowledge questions and answers, data query and professional water conservancy subclass questions and answers can be distinguished, and the system and the method can be applied to water conservancy business. And the semantic analysis efficiency is improved. Aiming at the problem of low query accuracy of retrieval enhancement generation in the field of water conservancy, a differential water conservancy knowledge base oriented to professional books, industrial standards and laws and regulations is constructed, local and networking information is processed through a multi-source knowledge fusion and conflict resolution mechanism, and the accuracy, interpretability and traceability of question and answer content are improved. Aiming at the problems of complex operation and low semantic query accuracy in query of massive water conservancy business data and monitoring data, multi-layer constraint Text-to-SQL conversion is performed based on water conservancy business knowledge, high-precision semantic query and automatic visual output of water conservancy structured data are realized, and query efficiency is improved.

The invention provides an intelligent question and answer method and device based on large model multi-source information fusion and electronic equipment, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like, and can be used for application scenes such as intelligent question and answer. According to the specific implementation scheme, pre-retrieval is conducted from a data table structure information base and a data table field information base according to a user problem, and a pre-retrieval result is obtained; retrieving from a business knowledge base, a business rule base and a question number template base according to the user question to obtain a knowledge enhancement result; according to the pre-retrieval result and the knowledge enhancement result, utilizing a query instruction generation model to obtain a query statement; and querying from the data table set according to the query instruction to generate an answer corresponding to the query word. The scheme can improve the professionality of intelligent questioning and answering, and is suitable for application scenes of various industries.

The invention relates to the field of discrete manufacturing intelligence, in particular to an intelligent decision-making and business collaboration method based on an OAG ontology and an LLM large model, which comprises the following steps: collecting historical data and business documents, and performing directional fine tuning on a basic large language model to form the LLM large model; the method comprises the following steps: analyzing discrete manufacturing scene original data and business documents, and constructing an OAG ontology library; receiving field data in real time through the LLM large model, updating the OAG ontology library, and performing feedback optimization on the LLM large model to form bidirectional feedback; integrating real-time data and historical data, inputting the data into an LLM large model, converting the data into business knowledge through layering of an OAG ontology library, and generating a main and standby decision scheme; and establishing an agent federated center, issuing a decision scheme, collecting execution data and feeding back an LLM large model, and realizing cross-scene collaboration and full-process data closed loop. According to the invention, through a technical path of a whole-process data closed loop, a core pain point that an existing LLM does not understand a business is effectively solved, and whole-link value conversion of discrete manufacturing data is realized.

The invention relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

The invention relates to an auxiliary verbal skill self-adaptive generation method fusing a knowledge graph and reinforcement learning, and belongs to the technical field of artificial intelligence and service compliance management crossing. The method comprises the following steps: collecting service data, extracting entities, attributes and relationships among the entities through natural language processing, constructing a knowledge graph marked with compliance attributes, and establishing an updating mechanism; determining a verbal skill generation main body according to an actual service scene, and integrating a conversation context, a client real-time state and a business target construction environment state; the verbal skill generation subject retrieves associated business knowledge according to the current environment state, and after the seat selects adaptive verbal skill interaction, verbal skill generation strategy parameters are updated through deep reinforcement learning; and constructing a multi-dimensional effect evaluation method based on the business data, feeding back an evaluation result to an iteration link, and optimizing reward calculation and knowledge graph association logic. Dynamic self-adaption of verbal skills in multiple service scenes is realized, and customer demand solving efficiency and business conversion quality are improved.

The invention discloses a water conservancy project knowledge graph large model construction method and device, and the method comprises the steps: constructing a water conservancy project typical business domain knowledge graph based on a water conservancy project management business knowledge base through employing a multi-business knowledge fusion water conservancy knowledge graph data layer, concept layer and instance layer construction method; aiming at the characteristics of complex water conservancy business knowledge structure and abundant potential association, the completeness and accuracy of a water conservancy typical business domain knowledge graph are improved through a self-iteration water conservancy business knowledge graph complementation technology based on a large language model; in order to efficiently combine the advantages of a knowledge graph and a large model in the aspects of knowledge representation and knowledge analysis, a retrieval enhancement generation technology based on the knowledge graph is adopted, entities, relationships and attributes of the entities and the relationships are automatically extracted from structured and unstructured knowledge bases, and a water conservancy project knowledge graph large model is created; and multi-target collaborative scheme reasoning application of flood control, water supply, power generation and the like of water conservancy projects is supported.

The invention discloses a data processing method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence, first type memory information is obtained according to the current session context with a user, a model can be ensured to understand the background of a current problem, the problem background is supplemented by using second type memory information, namely the historical session record of the user, and the user experience is improved. According to the method, the service knowledge information related to the question is retrieved, namely the target knowledge information, the target model generates the comprehensive reply according to the information, the context of the instant session is considered, and the personalized preference of the historical session and the current service rule are combined, so that the generated reply information is more comprehensive, and the reply efficiency is improved. Therefore, incoherence or inaccuracy caused by forgetting important details is avoided, and the technical effects of improving the accuracy of problem solving and enhancing individuation and satisfaction of user experience are achieved.

The invention relates to the technical field of data analysis, and discloses a feedback generation method and device based on a business knowledge graph, equipment and a medium, and the method comprises the steps: collecting multi-source business data, cleaning and segmenting the multi-source business data to form standardized text fragments, and carrying out the entity recognition and relation extraction of the text fragments to construct the business knowledge graph; receiving a natural language query instruction, extracting query intention features and key entity references, mapping the key entity references into the business knowledge graph, and performing association traversal under the guidance of the query intention features to obtain associated knowledge sub-graphs; and serializing the associated knowledge sub-graph into a knowledge background context, and jointly inputting the knowledge background context and a natural language query instruction into a pre-training language generation model to generate natural language feedback information. The method can be applied to business scenes such as financial science and technology, medical health and the like, semantic structuring is carried out on text knowledge, reasoning is carried out in knowledge association in combination with query semantics, accurate question answering is achieved, and knowledge retrieval efficiency is improved.

The invention provides a code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information to obtain target business knowledge, and then the code development platform splices the input information, the context information and the target business knowledge according to a prompt template to obtain prompt information, then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and a code snippet generated by LLM reasoning is presented to a user. The LLM is prompted by using the prompt information of the same data source in different processes, complete sharing and complete alignment of the prompt information are realized, the accuracy of single code generation is improved, and context information and business knowledge are assembled through a unified prompt template, so that code snippets generated by the LLM are more accurate. Therefore, the code acceptance rate in the professional field is improved.

The invention relates to an alarm rule automatic generation and closed loop optimization method and system, and relates to the field of computer system monitoring and testing. The method comprises the following steps: acquiring and cleaning a cluster to generate a standardized enhanced alarm event table based on multi-source heterogeneous data fusion; performing feature extraction on the enhanced alarm event table, constructing a feature vector, performing feature importance analysis on the feature vector by using a tree model, and outputting the feature importance analysis; according to statistical distribution of features in the structured feature vector, automatically generating a rule for identifying an alarm scene in combination with business knowledge; converting the alarm scene rule into a structured format, performing logic conversion through a test case generation engine, and outputting an automatic test case; and executing the automated test case and verifying the accuracy of the automated test case so as to dynamically optimize and adjust the alarm scene rule. According to the method, automatic rule generation and closed-loop optimization of a complex alarm scene can be realized, the alarm test efficiency and the scene recognition accuracy are remarkably improved, and the manual intervention requirement is reduced.

The invention provides an electric power customer service semantic analysis method and device based on an AI large model and a medium, and the method comprises the steps: carrying out the slicing of a received electric power consultation text based on a natural language, and obtaining a plurality of consultation text segments; performing semantic relationship analysis on each consultation text fragment based on the semantic understanding ability of the AI large model, and constructing a semantic relationship graph according to the semantic relationship between words in each consultation text fragment; determining a target power business scene associated with each consultation text fragment based on the semantic relationship graph and the power business knowledge graph; performing cross-fragment semantic integration on each consultation text fragment based on the logic relation between the consultation text fragments to obtain a cross-fragment semantic integration text; and outputting a reply text based on the cross-fragment semantic integration text and the target power business scene. According to the invention, the customer consultation can be comprehensively and accurately processed, the efficiency and accuracy of the electric power consultation service are remarkably improved, and thus the service experience of the user is improved.

The invention relates to a business knowledge base construction method, device and equipment for patrol operation and maintenance of a power production environment and a medium. The method comprises the following steps: acquiring an electric power inspection task, and encoding according to the electric power inspection task to obtain a task semantic vector; obtaining a preset knowledge base matrix and a state transition probability matrix; calculating a matching score of the task semantic vector and each state node; selecting state nodes corresponding to the first K highest matching scores as target state nodes, wherein K is a positive integer greater than or equal to 1; extracting a path taking the target state node as a starting point according to the state transition probability matrix to obtain a task matching path; wherein all the task matching paths form a task matching path set; and calculating a risk score of each task matching path to obtain a path risk score, and according to the path risk score, generating an auxiliary decision-making suggestion of the electric power patrol task. By adopting the method, the intelligent management capability of the operation state of the power equipment can be improved.

The invention discloses a customer complaint work order automatic circulation system and method based on composite intention disassembly. The system receives and preprocesses the multi-mode customer complaint data and initializes a service circulation context; business intention identification and business key slot position extraction are carried out; disassembling the composite business intention, and constructing an ordered work order execution queue of a directed acyclic graph structure through cyclic dependence verification; evaluating the work order circulation action with the maximum expected cumulative income by using a reinforcement learning model; when business knowledge query is involved, compliance evidence fragments are retrieved, sorted and output; and generating a candidate reply and a business operation instruction by the fine-tuned large language model, outputting the candidate reply after business compliance verification, and calling an API (Application Program Interface) of an underlying business system to execute entity business handling operation in a cross-system manner. The problems of data interaction and state synchronization among multi-source heterogeneous systems are solved, the automatic execution efficiency of the composite work order at the bottom layer node is improved, and the system unauthorized and dirty data risks caused by an uncertain instruction are effectively avoided.

The invention relates to the technical field of data processing, in particular to an intelligent verbal skill generation method and device based on multi-modal knowledge. An intelligent verbal skill generation device based on multi-modal knowledge comprises a multi-modal knowledge unification layer, a hierarchical knowledge retrieval layer and a template guide verbal skill generation layer, and data interaction is carried out among the layers through standardized API interfaces. The multi-modal knowledge unification layer uniformly converts different formats of business knowledge such as Word, PDF, Excel and pictures into standardized vector representation by using a multi-modal encoder in a specific field, and constructs structured unified knowledge storage; the hierarchical knowledge retrieval layer realizes accurate mapping from dialogues to knowledge; and the template guide verbal skill generation layer generates personalized verbal skill which conforms to business rules and can meet emotional requirements of customers in a single generation process through a retrieval enhancement generation technology. The problem of unification of knowledge representation is solved; the problem of balance between retrieval efficiency and accuracy is solved; and the problems of individuation and specialization of verbal skill generation are solved.

The invention discloses a data quality improvement method driven by service cognition in data, particularly relates to the technical field of data quality management, and is used for solving the problems of poor data traceability ambiguity consistency and evaluation distortion caused by existing service evolution. According to the method, a unified business knowledge base is constructed by integrating business cognition rules of multiple business fields, the difference between new business logic and existing business logic is analyzed based on the knowledge base, and business indexes in historical data are subjected to mapping configuration and version management; a local neighborhood analysis method is utilized to calculate structure entropy in a local data distribution area, a data consistency evaluation result is generated, finally, data meeting a business cognition rule is written into a data table, and meanwhile data traceability information and business logic version information are stored in an associated mode. Therefore, the problems of data traceability ambiguity and quality evaluation distortion in the dynamic evolution of the service logic of the existing data medium station are effectively solved.

The invention provides an intelligent document understanding method and system combining natural language processing and deep learning, and relates to the technical field of natural language processing and information management. Fusing the semantic pre-annotation result and the original text feature of the document, inputting the fused semantic pre-annotation result and the original text feature of the document into a cross-modal semantic enhancement model to obtain a document enhanced semantic representation, performing bidirectional semantic interaction with a dynamic business knowledge network based on the document enhanced semantic representation, generating an associated interaction result, and constructing a structured semantic asset; finally, the structured semantic assets are input into an intelligent document application engine, semantic feedback data are collected to optimize model parameters, document understanding accuracy and practicability can be improved, and diversified business requirements are met.

The invention provides a data marking method. The method can be applied to the technical fields of big data and artificial intelligence. The method comprises the steps of obtaining multiple pieces of multi-modal data, preprocessing the multiple pieces of multi-modal data, and generating multiple pieces of preprocessed text data; and performing vector conversion on the plurality of pieces of preprocessed text data to generate a plurality of pieces of feature vector data. Density clustering is carried out on the multiple pieces of feature vector data, a data sets are generated, and each data set comprises a first data label. Semantic clustering is conducted on the a first data labels through semantic analysis, and b second data labels are generated. And presetting a business knowledge graph, and performing knowledge fusion on the business knowledge graph and the b second data tags to generate b target data tags for data marking. The invention further provides a data marking device and equipment, a storage medium and a program product.

The invention provides an RAG retrieval enhanced SQL generation method based on a multi-dimensional knowledge base, and the method comprises the steps: respectively constructing a technical metadata knowledge base, a business knowledge base and an experience knowledge base based on a target database; respectively slicing knowledge contents of the technical metadata knowledge base, the business knowledge base and the experience knowledge base; receiving a query request of a user, and simultaneously performing parallel retrieval from the technical metadata knowledge base, the business knowledge base and the experience knowledge base to obtain a retrieval result; a large language model is called based on the retrieval result, SQL is output in a staged SQL generation mode, and the large language model adopts a multi-model collaborative architecture and adopts a gradient lifting algorithm for iterative optimization. Through the organic combination of the technical metadata knowledge base, the business knowledge knowledge base and the experience knowledge knowledge base, a comprehensive database query knowledge system is constructed, and the problem that knowledge coverage is not comprehensive in a traditional method is effectively solved.

The invention discloses a flood intelligent scheduling decision-making method and device based on a scheduling service knowledge graph. The method comprises the following steps: establishing a flood dispatching business knowledge graph; determining a flood scheduling research and judgment rule, establishing a flood scheduling rule base based on the flood scheduling research and judgment rule and the flood scheduling business knowledge graph, and storing rules in the flood scheduling rule base in an attribute vector form in a flood scheduling business knowledge graph embedding layer; mapping the knowledge and the rules to a knowledge graph and instantiating the knowledge and the rules to construct a flood dispatching centralized control business model; according to the method, the functions of a flood dispatching service knowledge graph, a rule base and a flood dispatching centralized control service model are integrated, a flood dispatching system is constructed, and flood dispatching auxiliary decision making is achieved based on knowledge graph mode reasoning and scheme matching. And guidance is provided for intelligence of related instruction operation processes such as flood discharge information release, flood scheduling, flood prevention consultation and the like.

The embodiment of the invention provides a business data processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: generating a query semantic vector according to a data query problem; performing table data query operation in the business knowledge base according to the query semantic vector to obtain data table information; generating an initial cue word according to the data table information; obtaining a to-be-executed structured query statement according to the initial cue word, the data query problem, the business database and the fine-tuned proprietary large model; inputting the to-be-executed structured query statement into a preset executor, and when the execution result of the preset executor executing the to-be-executed structured query statement is successful, executing query operation from the business database to obtain preliminary result data; and performing presentation processing according to the preliminary result data to obtain to-be-presented data. Conversion from a natural language data query problem to a structured data query problem is realized, and the technical effect of enhancing the accuracy of a query result is achieved.

The invention belongs to the field of artificial intelligence and big data analysis, and particularly relates to an AI-based drive type data analysis method and system.The method comprises the steps that S1, on the basis of natural language query input by a user, candidate SQL and / or DSL statements are generated through a pre-trained big language model; s2, generating a parallel query plan and optimizing the parallel query plan based on the candidate SQL and / or DSL statements and the metadata knowledge graph; s3, extracting authorization data; s4, fusing the extracted data to generate an analysis feature wide table, and identifying key features, abnormal modes and text abstracts in the analysis feature wide table; s5, executing controlled retrieval in the business knowledge graph based on the key features and the abnormal mode, and generating a security evidence sub-graph; s6, inputting the text abstract of the analysis feature wide table and the security evidence sub-graph into an AI inference engine, and outputting a structured report; and S7, displaying the structured report. The problems of high technical threshold and weak safety and interpretability in a data analysis process are solved.

The invention discloses a test case automatic generation method and device, and relates to the technical field of software testing, and the method comprises the steps: S1, constructing a system behavior graph and a business knowledge graph; s2, integrating the system behavior graph and the business knowledge graph into a multi-layer behavior graph; s3, acquiring test demand data; s4, performing retrieval in the multi-layer behavior map according to the demand data to obtain a retrieval result; s5, fusing the demand data and the retrieval result, and generating an enhancement prompt; s6, generating a test case according to the enhancement prompt; a system behavior graph and a business knowledge graph are independently constructed, and a double-graph technical architecture of cooperative work is adopted, so that double guarantee of technical correctness and business rationality is realized; complete contexts can be obtained from two dimensions of technology implementation and business rules simultaneously through a double-atlas collaborative retrieval technology; high-level business requirements, UI design and specific implementation logic of a system bottom layer are effectively connected, and real efficient and full-automatic test case generation is achieved.

The invention provides a lightweight configurable cue word labeling method and system for a Web system, and relates to the technical field of Web front-end development and man-machine interaction, and the system comprises a front-end labeling plug-in module, a cue word content service module, a cue word management module and an intelligent recommendation module. The front-end labeling plug-in captures text content selected by the user by monitoring a global shortcut key, and allows the user to add prompt words of a text, a rich text or a link type; the prompt word content service module provides an API (Application Program Interface) to realize persistent storage of data; the prompt word management module provides a unified management interface for an administrator; and the intelligent recommendation module automatically judges the prompt word display priority through a candidate recall and sorting algorithm based on the user click behavior data, and continuously optimizes the recommendation result by using an online learning mechanism. The method is integrated to an existing Web system in a non-intrusive mode, business core codes do not need to be modified, the use threshold of the system is remarkably lowered, and precipitation and sharing of business knowledge are promoted.