449 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 invention relates to the technical field of archive information query, and discloses an AI-based archive information resource intelligent sharing method and system, and the method comprises the steps: firstly constructing a multi-dimensional data module according to a business data type, and the multi-dimensional data module comprises an archive system database, a high-dimensional document vector database and an archive business knowledge graph; and performing deep analysis on user query through a natural language processing tool to form a structured keyword list. Then, performing semantic analysis on the keyword list by utilizing a pre-trained archive information resource understanding large model, and generating an enhanced query text; the query text is recalled from the data modules of the multiple dimensions, candidate answers are obtained, and the candidate answers are fused and optimized through the general large language model. And finally, returning the optimal answer to the user. By introducing the AI technology, the accuracy and comprehensiveness of file information resource sharing are improved, the retrieval result is optimized, and the retrieval efficiency is improved.

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.

The invention discloses an automatic driving platform for a long-distance pipe network heat supply system based on a large model and a knowledge graph, and the platform comprises an infrastructure layer which is used for connecting an actual heat source, a long-distance pipe network and a multi-stage pump station heat supply facility of the long-distance pipe network heat supply system into the platform; the data processing layer comprises a data acquisition unit and a multi-source data fusion unit; the digital twinborn layer is used for establishing a digital twinborn model of the long-distance pipe network heat supply system; the large language model-knowledge graph layer comprises a graph construction unit which is used for constructing each business knowledge graph; the system also comprises a large language model construction unit which is used for constructing and training large language models of all businesses and processing automatic driving related businesses of the long-distance pipe network heat supply system. The business application decision-making layer comprises a business application unit which is used for providing a business application module of the long-distance pipe network heat supply system; and the intelligent decision-making unit is used for carrying out simulation analysis according to the digital twin model and calling each business large model to output a corresponding business decision-making result.

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 discloses a language transformation method and device based on retrieval enhancement and a storage medium, and relates to the technical field of language transformation.The method comprises the steps that in response to a query request, query information is determined based on the query request; performing intention recognition on the query information, and determining a target cue word template from a preset cue word template library; performing question recall on the query information to obtain target data; and performing language conversion through the target data and the target cue word template to generate a target structured query language. The problem that an original technical scheme is insufficient in generalization in a specific field is solved by retrieving business knowledge and industry common knowledge related to the problem through intention recognition, and the problem that the model effect cannot be improved by performing fine adjustment in the specific field due to the limitation of computing resources is solved through problem recall. And the language conversion efficiency and accuracy are improved.

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 invention discloses a policy question reply method, device and equipment and a computer program product, and relates to the technical field of natural language processing, and the policy question reply method comprises the steps: receiving a policy consultation question; based on a pre-generated business knowledge base, replying the policy consultation question to generate a policy question reply result, the business knowledge base being obtained based on a pre-generated business knowledge decision tree, and the business knowledge decision tree being generated through a preset agent according to the initial business knowledge question-answer pair. In the application, the business knowledge base is obtained based on the pre-generated business knowledge decision-making tree, the business knowledge decision-making tree is generated through the preset agent according to the initial business knowledge question-answer pair, and when the business knowledge base is constructed, the knowledge text does not need to be sliced, so that the problem that an existing knowledge base question-answer system is relatively low in accuracy is solved; the accuracy of information retrieval of the business knowledge base is improved, and then the accuracy of policy question reply is improved.

The invention provides an energy information query method, system and device based on a large language model.The method comprises the steps that service key data are obtained and preprocessed, and then a service key database is constructed; based on a large language model, extracting entities, attributes, relationships and concepts in the business key database, and constructing a business knowledge graph; integrating the business knowledge graph into the big language model to obtain a business big language model; and in response to interaction information sent by the user, identifying the intention of the user by adopting an interaction big language model, querying target information matched with the intention of the user in the business big language model, and feeding back the queried target information to the user, so as to solve the defects in the aspects of real-time updating processing and quality evaluation of the energy data, and improve the quality of the energy data. And the reliability and timeliness of a query result are difficult to guarantee.

The invention discloses an AB experiment generation and optimization method based on an AI model and a local vector database. The AB experiment generation and optimization method comprises the following steps: constructing a local experiment database comprising a business knowledge base, a data mart and historical experiment data; carrying out vectorization processing on the business knowledge base, the data marts and the historical experimental data, and storing generated vectors in a local vector database; a large-scale language AI model is utilized to carry out a prompt project, full-process automation of AB experiments is realized, and the full-process automation comprises automatic experiment creation, automatic effect estimation and automatic result analysis. According to the method, the experimental design scheme can be automatically analyzed and generated through training and fine tuning by utilizing the large language AI model, and the fine tuning process enables the large language AI model to adapt to a specific business scene and accurately identify key factors influencing user behaviors, so that intelligent suggestions are provided for experimental design.

The invention provides a customer service interaction method, an interaction device, equipment, a storage medium and a program product, and relates to the technical field of computers. The customer service interaction method based on the large language model comprises the following steps: performing intention scene recognition on a received user question; identifying that the user question belongs to a specified intention scene, and determining a corresponding domain knowledge base matched with the user question; performing enhanced retrieval on the knowledge base in the corresponding field based on the user question to obtain a business knowledge fragment for enhancing the user question; and inputting the business knowledge fragments into a large language model, so that the large language model performs filtering type question reply processing based on the business knowledge fragments, and outputs reply information of the user question based on a processing result. According to the technical scheme, the real intention of the user in continuous interaction can be deeply analyzed, the content which the user wants to consult can be more accurately grasped, and the consulting requirement of the user is better met.

A power equipment data intelligent interaction method, system, device and medium, comprising: retrieving database table information based on query information input by a user to obtain database table information; performing power equipment knowledge base retrieval based on the query information to obtain business knowledge calculation logic and a context learning example; creating prompts based on database table information, business knowledge calculation logic and context learning examples, and inputting the prompts into the large model to generate database statements; running the database statement and judging whether the statement succeeds or not, and if yes, outputting a query result; otherwise, error correction is carried out, and corrected database statements are obtained and run again; according to database table information retrieval, extraction of table field links, table main and foreign keys and table association relationships can be carried out on user query, the accuracy and performance of model generation database languages are improved, the data query threshold is lowered, and the data utilization efficiency is improved; the power equipment knowledge base can realize flexibility and expandability of business knowledge injection.

The invention discloses a data quality problem deep analysis and traceability system and method, and relates to the technical field of data processing, and the system comprises a data collection module, a data monitoring module, a knowledge graph construction module, a machine learning analysis module and a traceability analysis module. According to the system, the data is closely connected with the business knowledge, the source and the processing flow through the data association network constructed based on the knowledge graph technology, when a data quality problem occurs, the source and the propagation path of problem data can be rapidly tracked in the complex knowledge graph, and meanwhile, the data quality is improved. According to the abnormal data association analysis driven by machine learning, the internal relation between the seemingly isolated abnormal data can be mined, the whole view of the data quality problem is presented from the global perspective, the defect that the potential systematic problem is ignored due to the fact that only single-point anomaly is processed is avoided, and the data quality problem can be fundamentally solved.

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 provides an expert knowledge-driven large-model customized data processing method and related equipment. The method comprises the following steps: inputting acquired business data into a trained large model capable of dynamically customizing an expert sub-model; in the large model, based on the business data, determining a knowledge embedding vector; based on the knowledge embedding vector, determining the weight of each adapter expert, and customizing an expert sub-model; and processing the business data based on the expert sub-model. According to the embodiment of the invention, the business knowledge hierarchical structure is constructed by using the multi-dimensional business information and the expert knowledge based on the large-scale business data. In combination with a large-model efficient fine tuning technology and a mixed expert strategy, dynamic expert group adaptation is realized under the guidance of business data knowledge, so that different expert models are customized for different business data, automatic adaptation under multiple scenes is realized, and more professional data processing is realized.

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.