48 results about "Business knowledge" patented technology

The application discloses a kind of collaborative robot system and service process for active power grid marketing, belong to artificial intelligence and electric power marketing service technical field.System includes: robot perception interaction end, cloud intelligent analysis hub and dynamic customer memory bank, robot perception interaction end deploys lightweight vision model, real-time identification customer emotion, body state and explicit feature, and initiatively trigger service;Cloud intelligent analysis hub uses power grid knowledge enhanced large language model, carries out depth demand analysis and individualized strategy generation to multi-modal data;Dynamic customer memory bank stores customer historical interaction record and portrait information in the form of knowledge graph, realizes the continuity and memory of service.The application is through edge small model+cloud big model collaborative architecture, general AI capability and power grid business knowledge are deeply fused, so that robot can actively identify customer demand, provide marketing service, improve customer experience and marketing conversion rate of electric power business hall.

This application provides a semantic modeling method for bank data access, relating to the field of data processing technology, including: receiving structured business files, determining the target character set and parsing to obtain header fields and data rows; performing normalized mapping on the header fields and generating field data profiles based on sample values; generating field semantic profiles using a banking business knowledge base; identifying fields with multiple candidate business expressions or inconsistent with the field data profiles, receiving user confirmation or correction to obtain confirmed field profiles; generating a target physical table structure based on physical column names and confirmed field profiles and writing it into data rows, saving the mapping relationship between the original field names, physical column names, confirmed field profiles, and user confirmation records; generating a question data schema for intelligent question data services to call based on the mapping relationship; and updating the banking business knowledge base based on user confirmation records and supplementary content.

This invention relates to the field of data analysis technology, and in particular to a query analysis method, apparatus, device, and storage medium based on a large language model. The method provides comprehensive and effective data support for enterprise decision-making. It achieves semantic fusion of multi-source heterogeneous data through a unified knowledge graph, breaking the limitations of traditional data silos and providing a comprehensive and accurate business knowledge foundation for the large language model, reducing model illusions from the source. It achieves differentiated analysis processing through task type classification, automatically scheduling different capability modules of the large language model according to user needs, balancing the efficiency of basic queries with the accuracy of in-depth analysis. It obtains the full business context through entity links, limiting the reasoning process of the large language model to the enterprise's real business scenarios, significantly improving the business relevance of the analysis results. It achieves in-depth diagnostic analysis through causal attribution, fully leveraging the logical reasoning capabilities of the large language model and overcoming the limitations of descriptive analysis.

This application provides a chaos testing method, apparatus, device, storage medium, and program product, which can be applied to the big data field. The method includes: modifying the business data of key nodes based on fault factors in a business knowledge graph to obtain multiple fault nodes. The business knowledge graph is obtained from historical business data, and the key nodes are determined based on their impact on the business. Fault factors include adjustment values ​​for logical judgment conditions and timing delay values. The method also involves extracting the relationships between nodes in the business knowledge graph, and using multiple fault nodes as starting points, traversing the business knowledge graph based on these relationships to obtain multiple fault chains. Finally, the method injects the faults in each of the multiple fault chains into a test system to obtain test results.

This specification provides a risk detection method, apparatus, system, and device. The method includes: acquiring structured configuration parameters of a target resource operation project to be detected, and target business knowledge of the business domain to which the target resource operation project belongs; using a preset large language model, performing semantic analysis processing on the structured configuration parameters based on the target business knowledge to obtain project configuration information corresponding to the target resource operation project; acquiring multi-dimensional configuration risk detection rules corresponding to the target resource operation project; using the preset large language model, performing multi-dimensional configuration risk detection processing on the target resource operation project based on the project configuration information and the multi-dimensional configuration risk detection rules; and determining whether there is a risk in executing the target resource operation project based on the multi-dimensional configuration risk detection processing results.

This invention discloses a blockchain-based engineering cost management system and method, relating to the field of engineering cost technology. It includes a chain storage stage and a weight adjustment stage. The chain storage stage includes the following steps: the management terminal inputs member information and initializes the weight value of each user terminal; the processing unit establishes a blockchain; the user terminal inputs engineering cost content; and the processing unit executes a dual authentication procedure. The dual authentication procedure performs biometric authentication on members and business knowledge authentication on the input engineering cost content. This invention utilizes the chain storage structure of blockchain to ensure the credibility of engineering cost at the audit level. The dual authentication procedure plus cross-validation procedure can ensure that the person operating the user terminal is the member themselves and can also confirm that the operator understands the internal business of the engineering cost team, improving the authenticity of the engineering cost content. The dynamically adjusted weight values ​​can adapt to different engineering cost teams.

The present application relates to nuclear power safety protection technical field, especially a kind of nuclear power plant pump body abnormality detection method and device.The method is: first, obtain pump body characteristic curve, based on the pump body characteristic curve, instrument arrangement and business knowledge determine the influence factor of the pump body;Then obtain the historical working data of pump body, establish training set by data analysis method;Based on training set, complete the training of regression model, reconstruction model and statistical model and obtain early warning threshold based on residual or statistical distribution;Finally, the current relevant data are input into the state reconstruction algorithm and statistical model or regression model, the statistical quantity or predicted value of pump in current state is obtained, whether there is abnormality is judged by comparing with early warning threshold.The present application selectively uses suitable model to judge the abnormality of pump body, simple and convenient.

The application provides an insurance business knowledge base intelligent optimization method, system, medium and electronic equipment. The insurance business knowledge base intelligent optimization method comprises: acquiring user question data; using a business model to answer the user question data to obtain a feedback signal, the feedback signal comprising user feedback and business expert feedback; based on the feedback signal, diagnosing the answer information to generate an optimization strategy; using the optimization strategy to dynamically optimize the business model to obtain an optimization execution result; verifying and feeding back the optimization execution result to obtain an insurance business knowledge base intelligent optimization result. The insurance business knowledge base intelligent optimization method of the application can realize intelligent and autonomous evolution of the insurance knowledge base.

This invention discloses a method, device, and computer program product for automated parsing and task aggregation of power outage maintenance orders. The method includes: Step S1, establishing a corresponding retrieval list and mapping relationship based on an equipment ledger containing work teams, substations, and line names; Step S2, performing structured parsing on the unstructured equipment description strings in the power outage maintenance order, and determining the substation corresponding to each piece of equipment based on the retrieval list and mapping relationship; Step S3, automatically aggregating the maintenance tasks corresponding to each piece of equipment to the corresponding grassroots work teams according to the mapping relationship, and outputting a structured task list. This invention achieves full-process automation of maintenance order parsing and task aggregation, significantly improving processing efficiency and judgment accuracy, solidifying implicit business knowledge, promoting the standardization of maintenance processes, and possessing good adaptability and scalability. It can effectively support the intelligent and digital management of power maintenance operations and ensure the safe operation of the power grid.

The application discloses an order production scheduling and real-time early warning management system, comprising an order scheduling module, a process tracking module, a progress early warning module and a resource board module, wherein the order scheduling module is configured to determine scheduling information according to order product information; the process tracking module is configured to track the current state of the product by associating the ERP, MES and PLM systems; the progress early warning module is configured to conduct multi-level comprehensive risk early warning according to the current date, the delivery date and the current state; and the resource board module is configured to summarize order product production information and conduct statistical analysis. The application also discloses an order production scheduling and real-time early warning management method. The application can deeply integrate specific business knowledge, realize full closed-loop management from intelligent scheduling to real-time early warning, fill the digital gap between the planning and execution layers, and improve the overall operation resilience, delivery reliability and customer satisfaction.

This application provides a data query method, apparatus, device, and medium based on natural language recognition, belonging to the field of data query technology. The method includes: obtaining extended keywords based on user natural language queries; obtaining pattern information and business knowledge based on the extended keywords, user natural language queries, and a vector database; generating target prompt words based on the pattern information, business knowledge, user natural language queries, and system prompt word templates; generating multiple candidate query statements through a target large language model based on the target prompt words and model generation parameters, and determining the optimal query statement; executing the optimal query statement in a database test environment, obtaining an execution result set and capturing abnormal information; validating the execution result set and obtaining a verification result; if the abnormal information is empty and the verification result is qualified, then generating a visualization chart based on the extended keywords, the optimal query statement, and the qualified result set. This application can improve the accuracy of power data queries.

The application relates to the cross field of artificial intelligence and energy information technology, and discloses an energy business large model semantic understanding and application method and system, aiming to solve the problems of weak professional understanding ability, poor system expansibility, high response delay and lack of explainability of general large models in energy scenarios. The method comprises the following steps: receiving a user natural language request through a front-end interface, inputting a DeepSeek large language model after text preprocessing to perform intent recognition and parameter extraction, and realizing accurate semantic analysis in combination with an energy business knowledge graph; and performing task routing according to an intent label and parameter integrity. Through the above scheme, the semantic understanding accuracy is significantly improved to 89.2%, 18 types of business automatic responses are supported, the one-time problem solving rate reaches 83.6%, the end-to-end response time is less than 2.1 seconds, and a continuous optimization closed loop of "use-feedback-evolution" is formed.

The application discloses an e-commerce API interface security vulnerability detection method based on a knowledge graph, which comprises the following steps: collecting API gateway logs and API document data of an e-commerce system, and constructing a business knowledge graph; obtaining an initial vulnerability detection rule library, and forming a rule knowledge graph; constructing a meta-knowledge graph; calling rules in the rule knowledge graph for matching detection; storing conflict events in the meta-knowledge graph; creating an embryo node, splitting a new rule node when the aggregation amount reaches a maturity threshold, and storing the new rule node as a birth event in the meta-knowledge graph; periodically calculating an effectiveness index, and storing the effectiveness index as a death event in the meta-knowledge graph when the effectiveness index is lower than an apoptosis threshold; analyzing the correlation mode between events, detecting API calling requests, and generating a vulnerability detection result. The application constructs business, rule and meta-knowledge graphs to realize API vulnerability detection self-evolution, and has the advantages of wide coverage, high accuracy and continuous optimization.

The application provides a business information pushing method and system based on a knowledge graph and a large language model, wherein the method comprises the following steps: acquiring a business knowledge graph of a target business and a corresponding business knowledge base; injecting the business knowledge base into a preset large language model to acquire a corresponding business guidance large language model; receiving question information of a client, acquiring corresponding graph query statements through the business guidance large language model; acquiring evidence subgraphs associated with the graph query statements based on the graph query statements; inputting the question information, the evidence subgraphs and text summary information corresponding to the evidence subgraphs as prompt contexts into the business guidance large language model to generate at least one piece of business information corresponding to the question information and push the business information to the client. Therefore, the application can effectively improve the reply generation accuracy of the large language model and improve the operation processing efficiency of the target business.

This application discloses a method, apparatus, device, and medium for optimizing a large-scale enterprise business knowledge generation model based on forgetting operations and knowledge reconstruction. It relates to the field of model forgetting technology, and includes: determining structured data based on target text containing enterprise business knowledge to construct an initial knowledge graph; determining the forgetting range corresponding to the forgetting operation in the initial knowledge graph to perform the forgetting operation and obtain a target knowledge graph; determining the target knowledge to be modified in the target text from the difference information between the graphs; generating a knowledge relevance distribution map based on the target knowledge and the large-scale enterprise business knowledge generation model to determine the target parameters in the large-scale model; and using the target knowledge and target parameters to determine the forgetting target to update the low-rank sparse adapter loaded at the target parameters in the large-scale enterprise business knowledge generation model, thereby completing the forgetting operation on the large-scale enterprise business knowledge generation model. This application enables efficient and accurate selective forgetting of knowledge by the large-scale model.

This invention discloses a Text2SQL generation method based on multi-source knowledge fusion and adaptive progressive processing. The method includes constructing an enhanced schema semantic library, a dynamic experience vector knowledge base, and a business domain knowledge graph to obtain a multi-source knowledge repository; constructing an adaptive progressive decision-making strategy based on the multi-source knowledge repository to obtain query processing paths from natural language query text; generating SQL candidate query statements by invoking a prompt word assembler and fusing it with a preset LLM large language model based on the query processing paths; performing SQL verification on the SQL candidate query statements; if the SQL verification passes, the final Text2SQL is used as a successful interaction case to dynamically update the dynamic experience vector knowledge base, and the confidence attribute is adjusted based on the experience value to update the business knowledge graph; if the SQL verification fails, an error message is sent and the natural language query text is re-entered. This invention solves the problem that existing methods cannot effectively achieve both accuracy and efficiency in obtaining SQL query statements.

The application is suitable for the technical field of DDD, and provides a middle platform construction method and device, terminal equipment and storage medium. Through repeated mathematical abstraction of specific field model itself concept, an empty flow table only including data categories and a plurality of empty configuration tables only including configuration items are designed according to business requirements, while the relationship characteristics between concepts are reserved, the dependence on specific business knowledge is avoided, and the specific business is not coupled. Then each configuration table is mapped to a value object through a software development kit developed in advance. Finally, all value objects are perfected according to business requirements through the software development kit, the complete life and use cycle of the value objects in DDD are managed, the configuration file meeting the business requirements is generated, the problem that DDD is too general and difficult to land is avoided, unlike the tool components of TMF, COLA and other technical layers, the complexity of the business layer is reduced, and the construction level of the middle platform is improved.

The application provides a smart query method, system, computer device and storage medium, the method comprises the following steps: acquiring data from a data source and constructing a data set based on preset rules and conditions; selecting corresponding data fields and records from the constructed data set based on the established business scenario to create an analysis table, and constructing a business knowledge base related to the business scenario; extracting metadata of the analysis table and the business knowledge base, establishing a first independent vector index based on the extracted metadata, and establishing a second independent vector index based on the field enumeration value in the analysis table; acquiring a user-selected business scenario and a user question under the business scenario, retrieving and generating a prompt word related to the user question based on the independent vector index; based on the user question and the prompt word related to the user question, generating an executable SQL through a large language model, and acquiring a query result based on the executable SQL.

This invention discloses an intelligent scheduling method, device, equipment, and medium for low-voltage operation and maintenance work orders. The method includes: processing multi-source operation and maintenance business data based on the Guangming Big Data Model to construct a corresponding business knowledge base; obtaining work orders to be scheduled; acquiring search results using a dual-channel retrieval method based on the business knowledge base; enhancing the search results and inputting them into the Guangming Big Data Model to obtain scheduling decision information; constructing a multi-factor constrained scheduling model based on the scheduling decision information; and executing the scheduling model through a scheduling intelligent agent constructed based on the Guangming Big Data Model to schedule the work orders to be scheduled. This achieves efficient and accurate intelligent scheduling of low-voltage operation and maintenance work orders, improving scheduling rationality and the utilization rate of operation and maintenance resources.

To provide a system. 【Solution means】 Means for providing an information reception device for a user to input project information, Means for storing the input project information in a storage device and performing collation, Means for automatically generating business documents using a template based on the stored information, Means for searching past business data and extracting similar examples, Means for generating an analysis report by combining current business information and past business knowledge, Means for providing the generated documents and reports to the user, Means for enabling information input at the site and confirmation of automatically generated documents using a portable information terminal, Means for analyzing past business data using a machine learning model and providing useful knowledge related to current business, Means for outputting information and presenting an analysis report via a human operation device, A system including the above.

This invention relates to the field of intelligent decision-making technology in emergency management, and provides an intelligent emergency decision support method based on a hybrid agent network. The method includes: constructing a decision knowledge space containing a knowledge system of the emergency domain; storing knowledge units in vector form to create a knowledge base supporting semantic retrieval; upon receiving disaster event information, a planning agent decomposes and orchestrates the decision task, generating a task orchestration scheme containing multiple sub-tasks and their dependencies; an analysis agent performs multi-dimensional analysis of the decision task according to the task orchestration scheme; a generation agent summarizes the analysis results; and, combined with a business knowledge system, generates disposal suggestions and a decision report. This invention achieves deep integration of business knowledge and AI capabilities, collaborative decision-making among multiple types of agents, and adaptive orchestration of the decision process, thereby providing decision-makers with comprehensive, accurate, and scientific decision support during the critical time following a disaster.

This invention provides a method, apparatus, and electronic device for updating the world model of a business network. The method includes: acquiring newly added business knowledge vectors and the current model; calculating the knowledge reconstruction error using a variational autoencoder; verifying the newly added knowledge vectors when the error is less than a threshold; and finally fusing the newly added knowledge vectors with the current model based on the verification results to obtain an updated world model. The method provided by this invention uses a variational autoencoder to perform distribution sensing on the newly added business knowledge vectors to calculate the reconstruction error, and, under the premise of controllable error, combines a logical verification mechanism to obtain the verification result. Finally, based on this result, a conflict-free fusion with the current world model is achieved. This not only adaptively quantifies the novelty and uncertainty of input data in open environments but also effectively solves the illusion problem of generative knowledge in large language models, ensuring that the business network can achieve efficient, accurate, and logically coherent continuous evolution when dealing with multi-source event streams.

This application provides a virtual interactive response system and method based on speech and semantic recognition, relating to the field of speech and semantic recognition technology. Responding to a acquired real-time speech stream, it extracts acoustic feature vectors, performs real-time speech recognition, and generates a first text sequence; it extracts word embedding features, determines nonlinear coupling weights, and generates a fused semantic vector; it performs sequence editing operations on the first text sequence based on the fused semantic vector to generate a second text sequence; it performs domain-knowledge-enhanced semantic parsing on the second text sequence to generate intent encoding and entity encoding; based on the intent encoding, it retrieves structured response data from a preset business knowledge graph; and it drives the virtual interactive object to perform corresponding action feedback based on the structured response data. This application can dynamically locate spoken language correction boundaries using acoustic paralinguistic features, and improves the robustness and accuracy of virtual interactive responses through closed-loop linkage of cross-modal information and semantic reconstruction.

This application relates to a method, apparatus, medium, and electronic device for quality inspection of natural resource asset inventory data, belonging to the field of data quality inspection technology. The method includes: parsing a quality inspection rule chain template file to obtain multiple actual rule operators and their corresponding execution order; determining the target plot data to be processed from all plot data to be inspected, and judging the operator type of the target rule operator; if the operator type is a rule operator related to business logic compliance checks, then a preset business knowledge graph is invoked, and based on the business knowledge graph and the target rule operator, a business logic compliance check is performed on the target plot data to obtain a first quality inspection result; if the operator type is a rule operator related to technical checks, then a technical check is performed on the target plot data to obtain a second quality inspection result. This application has the effect of improving the quality of natural resource asset inventory data inspection.

The application provides a language large model enabled scene-based metadata automatic generation method and system, and belongs to the technical field of data processing, and specifically comprises: a data preprocessing module responsible for generating business terms and definitions conforming to scene specifications, and establishing a preliminary semantic association network between business terms; an inference module responsible for automatically deriving the calculation formula, statistical dimension and calculation period of the business terms based on the preliminary semantic association network, and further deriving derived business knowledge of the business terms; a semantic mapping module responsible for establishing a precise mapping relationship between technical attribute information and business terms, forming integrated metadata; a verification module responsible for constructing a three-layer verification system of rules, semantics and logic, comparing and analyzing the generated metadata with historical versions, industry standards and business rules by using a language large model, and publishing the verified metadata to a metadata warehouse, thereby improving the application value of the extracted metadata results.

The invention belongs to the technical field of shale gas effusion early warning, and discloses a shale gas effusion early warning method and system based on an anti-interference multi-classification technology, the method predicts various abnormal working conditions of a gas well based on a Transform multi-classification model, and on the basis of oil pressure, casing pressure, instantaneous flow rate, downstream pressure and other production dynamic data, through a sliding window and difference calculation, the gas well pressure can be accurately measured. Constructing a time sequence feature, a statistical feature, a periodic feature and an event feature by a self-attention mechanism, and raising the dimension of original four-dimensional data to a 441-dimensional feature vector; based on the gas well static features, gas well static feature vectors are extracted; and the characteristics of production dynamic data and gas well static data are fused for reasoning, so that the generalization ability of the model is remarkably improved. Preprocessing and auxiliary judgment are carried out by applying a rule with relatively high certainty in business knowledge, so that the model effusion abnormity alarm performance is improved.

This invention belongs to the field of intelligent office automation and artificial intelligence technology, specifically relating to an automated office method based on multi-agent collaboration using a large language model. The method includes: task input and dual knowledge base access, task enhancement based on a business knowledge base, task executability determination and inference knowledge base compensation, step plan generation, ReAct-style GUI step execution, screenshot and log-driven step success detection and failure self-reflection and improvement, and task summary and knowledge accumulation. The method involves a management agent retrieving evidence from the business knowledge base and inference knowledge base to generate acceptable task specifications and step plans; a detection agent performs executability pre-checks before execution and verifies and diagnoses the step results before and after execution using screenshots and logs. This invention separates the management of business knowledge from the inference / execution strategy, reducing retrieval noise and improving strategy reuse and cross-scenario migration capabilities.

This application provides a simulation method, apparatus, device, storage medium, and program product for business training, which can be applied to fields such as artificial intelligence, speech recognition, deep learning, and fintech. The method includes: responding to a received simulation request from a target object; generating simulation outline information for target business simulation training based on the business type included in the simulation request, the target object's attribute information, and a pre-built business knowledge graph; determining business interaction information for interacting with the target object based on the simulation outline information; receiving business operation information and response information from the target object during the interaction with the target object based on the business interaction information, wherein the business operation information is obtained by the target object performing operations in the simulation business processing system in response to the business interaction information; and evaluating the target object's response information and business operation information in response to the business interaction information to obtain a simulation evaluation result.

This application provides a method, apparatus, device, and storage medium for detecting user call risk alarms, relating to the field of data processing technology. It is used to improve the efficiency and accuracy of abnormal business scenario detection and to issue timely abnormal alarms. The method includes: using round-based segmentation to clarify the dialogue structure in user call data, integrating business knowledge through multi-path recall, combining structured prompts to guide classification, and comparing the cumulative results with dynamic thresholds, thereby achieving accurate detection and timely alarms for abnormal business scenarios.

This invention relates to the field of intelligent decision-making technology, and discloses an intelligent decision-making method and system based on knowledge graphs. The method includes acquiring input data of a decision-making scenario, matching it with similar cases in a business knowledge base, extracting factual information, industry experience, and historical rules to form a set of knowledge nodes; analyzing the logical dependencies and temporal consistency of nodes to construct a complete logical chain; evaluating the contribution of nodes to filter key node sequences, converting them into sentence fragments and adjusting their coherence, detecting and correcting logical contradictions; integrating stable expression results with decision conclusions, and generating a complete explanatory text. This method can achieve interpretability and logical consistency in intelligent decision-making, meeting the multiple requirements of intelligent decision-making for transparency, credibility, and compliance.