2086 results about "Business requirements" patented technology

The invention relates to the technical field of artificial intelligence, in particular to an automatic construction method of an end-to-end agent based on graph structure semantic fusion. The method comprises the following steps: receiving business demand data input by a user; business target and demand constraint condition analysis is carried out on the business demand data, and a core workflow framework of the intelligent agent is generated; performing end-to-end execution path analysis on the core workflow framework of the intelligent agent to obtain an end-to-end workflow; constructing a dynamic evolution semantic map; and constructing an end-to-end call chain execution strategy based on the end-to-end workflow, and performing agent instance packaging and agent instance reinforcement learning enhancement processing according to the dynamic evolution semantic map, thereby automatically constructing an end-to-end agent. According to the invention, by fusing the graph structure knowledge and the generation capability of the large language model, an efficient, accurate and extensible agent automatic construction scheme is provided for various complex business scenes.

The invention relates to a dynamic data pipeline construction method based on artificial intelligence and multi-modal data processing, and belongs to the technical field of data fusion treatment and artificial intelligence. According to the method, a multi-modal deep learning model is adopted, semantic analysis is performed on business documents, user requirements and function specification instructions, and key indexes, data requirements and logic constraints are extracted; constructing a multi-modal knowledge graph, performing unified representation and feature extraction on structured and unstructured data resources, and performing embedded learning by using a graph neural network; based on a deep semantic matching model and a hybrid recommendation system, realizing accurate matching of business requirements and data resources; modeling generation of a data processing flow into a Markov decision process by adopting a reinforcement learning strategy, and automatically generating an optimized data processing workflow; the adaptability of the model to new business requirements is improved by utilizing a meta-learning algorithm; and dynamic scheduling and execution of tasks are realized through a workflow engine. According to the invention, the data processing efficiency can be improved.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention discloses a financial data generation method and system based on a rule configuration engine, and the method comprises the steps: converting a business demand into structured rule description through a semantic analysis engine according to the financial business demand, dynamically configuring a financial data processing rule through a context-aware rule template library, and generating financial data according to the financial data processing rule. Generating an executable rule configuration set; extracting related data from a plurality of heterogeneous financial data sources according to the rule configuration set, and mapping features of different data sources to a unified high-dimensional vector space by adopting a multi-source data fusion algorithm to generate a fused financial feature matrix; and inputting the fused financial feature matrix into a rule configuration engine, adopting a rule execution optimization model, dynamically adjusting a rule execution sequence and parameters, generating financial data, and performing noise addition processing on the financial data to ensure data privacy security. According to the embodiment of the invention, the financial data can be flexibly and efficiently generated, and the safety and efficiency of overall data processing are improved.

The invention relates to an intelligent recommendation method and device based on a knowledge graph and an intelligent recommendation system. The method comprises the following steps: acquiring a user query input by a user in a current session; determining a knowledge base corresponding to a business domain involved in the current session according to the user query; wherein the knowledge base comprises graph knowledge constructed by utilizing multi-source heterogeneous industry material data according to business requirements; determining a matched prompt word template in the current session according to the business domain involved in the current session and filling the prompt word; performing retrieval in the knowledge base to obtain context information; generating answers according to the context information and a user query input large language model, and generating recommended contents according to the answers and a prompt word template; according to the technical scheme, the real-time performance and accuracy of intelligent recommendation based on the knowledge graph are improved, the diversity and individuation degree of recommendation results are improved, and the knowledge service requirements of users are met.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

The invention discloses a power business scheduling method and system based on 5G communication grading and classification, and relates to the technical field of 5G slicing. According to the method, the prediction model and the real-time monitoring data are utilized to effectively predict the network flow and dynamically adjust the network resources according to different levels of power business requirements, so that the power business with high priority can quickly obtain necessary network support, the quality of the overall network service and the operation efficiency of the power business are improved, and the network flow is effectively predicted. And network resource allocation is optimized through the graph neural network, accurate analysis and response to power service grading are realized, predictability and stability of the network are enhanced, and more complex power service application is supported.

The invention discloses an intelligent demand structuring method and an intelligent demand structuring system, which are used for generating a structured demand document which can be used for subsequent software development based on business demand description of a natural language. For the simple requirement of a single module, one sentence input is adopted; for complex demands of multiple modules, a business modeling agent is called to carry out modeling understanding, natural language demands are converted into a business concept set, and the business concept set is further decomposed into specific structured demands by a demand structured agent. In the whole process, the user can adjust and refine the business software demand in real time through interaction with the business modeling agent and the demand structuring agent. According to the method, the structured demand document capable of being used for subsequent software development can be completed through natural language description, an efficient, flexible and intelligent new mode is provided for the structured demand of multi-field personalized service software and even software development, and the efficiency and quality of software development can be greatly improved.

The invention discloses a power enterprise intelligent collaborative office method and system based on artificial intelligence, and relates to the technical field of power informatization. According to the intelligent collaborative office method and system for the power enterprise based on artificial intelligence, request data is received from a user terminal through a deep learning model, a service demand type, an emergency degree and task complexity are identified, a service priority index is generated, classification and priority distribution are carried out, and based on the service priority, the user terminal is subjected to intelligent collaborative office. The method comprises the following steps of: matching associated tasks and resource requirements through a model, retrieving related historical data to optimize a resource configuration path, dynamically tracking task progress, execution state and resource consumption, identifying a bottleneck in an execution process and generating an optimization instruction, and finally, dynamically adjusting model parameters and resource configuration rules through an adaptive learning algorithm. And a closed-loop feedback mechanism is formed. Information flow and resource configuration are effectively integrated, the flexibility and response speed of collaborative office are improved, and the development requirements of smart grids and smart cities are met.

The invention discloses a management decision-making method and system based on a knowledge base construction technology. The method comprises the following steps: acquiring business demand information input by a target object; determining a target business type corresponding to the business demand information, and determining a target knowledge graph corresponding to the target business type from a preset knowledge base; performing semantic analysis and intention recognition on the business demand information by using a pre-trained demand analysis model to obtain a target demand feature corresponding to the business demand information; analyzing the target knowledge graph and the target demand features by using a graph neural network to obtain multi-dimensional decision suggestions for the service demand information; and feeding back the multi-dimensional decision suggestions to the target object. The technical problem that user experience is affected due to the fact that decision suggestions generated by a system in related business decision scenes are not highly matched with user requirements is solved.

The invention discloses a credit risk management method based on federal learning, and relates to the technical field of credit risk management. Firstly, multiple types of participants collect credit data, carry out preprocessing and privacy protection, and establish a federated learning architecture; secondly, constructing a federated learning model based on transverse, longitudinal and knowledge migration federated learning algorithms, selecting an algorithm by each participant according to data characteristics and business requirements, training the model in a local data set, regularly uploading parameters to a coordination server for aggregation to obtain global model parameters, and distributing the global model parameters for next training; performing multi-round iteration until the performance of the model is stable; and finally, evaluating the customer credit risk by using the trained model, and formulating a corresponding risk management strategy according to the customer default probability. The method breaks data islands, protects data privacy, adapts to various scenes, optimizes risk management, effectively improves the accuracy, safety and flexibility of credit risk management, and promotes the intelligent development of credit management in the financial field.

The invention discloses a computing node, a computing system, a control method, a control device, equipment and a medium, and relates to the technical field of computers, a plurality of acceleration cards are interconnected through an Ethernet switching module, and low-order channels of serializer / deserializer interfaces of the acceleration cards are connected with the Ethernet switching module through retimers. A high-order channel of at least one serializer / deserializer interface of each accelerator card is connected with the Ethernet switching module through a retimer, and the working mode of the retimer is configured through a first controller on a computing node according to the signal rate of the accelerator cards, so that a framework that communication interfaces between the accelerator cards are fully interconnected is realized. Any two acceleration cards can be directly connected, the communication bandwidth between the acceleration cards is improved, meanwhile, the number of the acceleration cards can be flexibly expanded, longitudinal expansion and transverse expansion of a computing system can be conveniently achieved, the problem that a traditional acceleration card interconnection scheme is not easy to expand is solved, and the method can adapt to service requirements and flexibly adjust working loads.

The invention belongs to the technical field of artificial intelligence model management, and discloses a multi-scene multi-base large model engine system. Comprising a front-end interaction layer, a service function layer, a core technology layer and a bottom supporting layer, the front-end interaction layer is used for providing an access mode, an interface protocol, authority management and log recording; the business function layer is used for meeting core business requirements of multiple scenes in the financial industry; the core technology layer comprises a multi-task instruction fine tuning platform, a large model automatic evaluation platform and a large model management platform; according to the method, the optimal model is dynamically matched, the task is efficiently executed, and through the multi-task instruction fine adjustment platform and the automatic evaluation platform, the model performance can be continuously optimized, the efficient utilization of model resources is realized, and meanwhile, the accuracy and response efficiency of service processing are improved.

The invention provides a test case generation and script optimization method and device based on machine learning, and the method comprises the steps: carrying out the training of a large model based on a Transform architecture through a public test case data set, calling the large model to generate a plurality of test cases meeting the business demands, recognizing the key operation steps in the test cases, and carrying out the script optimization of the test cases. Inserting check points at corresponding positions, converting the test script into an automatic test script described by a script language, and optimizing an execution sequence of the test script through a graph neural network; dynamically adjusting the structure of the test script through a deep reinforcement learning algorithm, generating a simulation test environment in combination with a GAN (Generative Adversarial Network), and verifying the test script. According to the method, the test case with high coverage rate and high quality can be automatically generated, the manual compiling cost is reduced, the automatic test script highly matched with the test case can be dynamically generated, and the execution efficiency is improved by optimizing the structure.

The invention relates to the field of knowledge graph construction, in particular to a customer archive knowledge graph construction method and system based on large model driving. The method comprises the following steps: encoding customer archives by using a large model technology, and fusing encoding results to generate entity twin vectors; constructing a knowledge graph by using structured data in the customer archive and the entity twin vector; the customer archive comprises an interaction log, and calculating an edge relation weight of the knowledge graph by using the interaction log; and adjusting the knowledge graph based on the edge relation weight. The method solves the problem that in the prior art, due to the fact that a traditional knowledge graph is difficult to accurately capture entity dynamic characteristics and implicit association, the constructed knowledge graph is difficult to meet the dynamic business requirements of real-time risk control, precision marketing and the like. And the interpretability and the reasoning ability of the knowledge graph are greatly improved.

The invention relates to the technical field of data processing, in particular to a cross-unit data management method, which comprises the following steps of: performing format standardization processing on data in a data pool of each organ and unit, converting the data into a uniform standard format, extracting semantic features to construct a semantic association network, and recording a semantic relationship of data items and a dynamic authorization and authority mapping mechanism. Then, according to the inquirer identity information, the unit attributes, the business requirements and the data sensitivity level, dynamically generating inquiry authorization and establishing an authority mapping table; and when a query condition is received, analyzing by utilizing a natural language processing technology, and generating a query route and optimizing a statement in combination with the semantic association network and the permission mapping table. And finally, calling data from each data pool according to the route, performing fusion processing, displaying the data to a querier in real time, and recording a query log. The invention aims to solve the problems of data semantic isolation, authority management staticization, low query efficiency and insufficient cross-unit data conflict resolution capability in the existing cross-unit data management.

The invention relates to the related technical field of integrated service management, in particular to a digital intelligent technology and software and hardware integrated service management method and platform and a storage medium, and the method comprises the steps: connecting an industrial internet, receiving integrated service requirements, deploying software and hardware based on specifications, fitting a service scene, setting a topological architecture conforming to the specifications, and collecting data to establish a performance model. According to the software and hardware matching topology architecture, a communication architecture is adopted, a monitoring mechanism is set, and abnormal response management is performed on each component and each service in the software and hardware matching topology architecture, so that the technical problems that a service scene is fixed and a static deployment mode is difficult to quickly respond to dynamically changing service requirements in a software and hardware integration process are solved; the technical effects that the industrial internet is connected, the integrated service demand information is received in real time, changes of service demands are flexibly coped with in the software and hardware configuration process, dynamic configuration and adjustment are carried out according to different service scenes and demands, service changes are rapidly responded, and the flexibility and adaptability of resource configuration are improved are achieved.

The invention provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention relates to the technical field of code generation, and provides a front-end code generation method and system based on a large language model and electronic equipment. According to the application, the business demand instruction is structured through the semantic analysis result corresponding to the business demand instruction to obtain the structured cue word, and the component library document carrying the component specification is set in the component-level cue word generator, so that the vectorized component library document is retrieved according to the semantic analysis result to generate the specification cue word, and the user experience is improved. Therefore, the structured cue word and the standard cue word are fused to obtain a fused cue word for code generation, and a front-end code corresponding to the business demand instruction is obtained, so that the large language model can be combined with the standard cue word to automatically adapt to fine-grained code specifications such as a file structure, a coding specification and a component dependence management rule; therefore, the problem that code specifications cannot be introduced into a large language model is solved, the number of times of secondary development and debugging is reduced, and the generation efficiency of front-end codes is improved.

The invention provides a business demand confirmation method and system based on a cloud platform, and relates to the technical field of cloud platforms, and the method comprises the steps: building a business process engine on the cloud platform according to a business scene and a business rule, carrying out the visual modeling of a business demand through the business process engine, and generating a standardized business demand model; deploying a demand verification service on the cloud platform, and through a formalized verification method of control flow analysis or data flow analysis and in combination with the business domain ontology model, performing automatic verification on the business demand logic represented in a formalized manner, and generating a demand verification report; and according to the demand verification report, judging whether the business demand logic has semantic contradictions or logic vulnerabilities, and when the semantic contradictions or logic vulnerabilities are detected, correcting the business demand logic which does not pass the verification in combination with the business domain ontology model. And deploying a demand collaboration service on the cloud platform, and publishing the corrected business demand logic in the form of a standardized interface for the collaboration party to subscribe.

The invention is suitable for the field of data management, and provides a front-end AI dialogue data management system and method, and the system comprises an asynchronous data flow management module; a state management module; a streaming data control module; a Markdown rendering engine is adopted; the beneficial effects of the invention are that the asynchronous data flow management module realizes high-efficiency synchronization of front-end and rear-end data, significantly reduces the data processing delay, and the state management module ensures the consistency of a user interface and rear-end data, optimizes the user experience, and improves the user experience. The built-in advanced Markdown rendering engine supports various self-defined grammars, so that the output content of the AI can be flexibly subjected to personalized rendering, modular design is adopted, customization and expansion can be performed according to service requirements, and the method is suitable for different application scenes.

The embodiment of the invention discloses a dynamic template generation method and device based on business requirements and a medium, and relates to the technical field of low code, and the method comprises the steps: obtaining a business requirement description text input by a user, analyzing the business requirement description text through a natural language processing technology, determining template configuration information corresponding to the business requirements, and generating a dynamic template according to the template configuration information; comprising plug-in identification, module identification and extension point logic; loading in a pre-constructed storage library through the plug-in identifier and the module identifier to obtain a demand plug-in and a demand module corresponding to the business demand; analyzing the extension point logic to determine an insertion position corresponding to the extension point logic; and dynamically combining the demand plug-in, the demand module and the extension point logic through a preset template rendering engine to generate a dynamic business code template corresponding to the business demand. The demand plug-in, the demand module and the extension point logic can be loaded and combined according to the user demand, the template is flexibly combined and adjusted, and the customized business demand is met.

The embodiment of the invention provides an RPA process generation method and device based on a large model, a medium and a product. The method comprises the following steps: receiving business demand information in a natural language form input by a user; based on a preset large model and a business capability knowledge base, analyzing the business demand information, and determining a business operation sequence matched with the business scene; based on a robot process automation (RPA) component knowledge base and a preset multi-modal visual understanding model, analyzing the service operation sequence into a single-step executable atomic operation sequence; and according to the atomic operation sequence and the large model, generating an executable code of the target RPA component. According to the scheme, a user is supported to input business process requirements in a natural language form, the requirements are automatically converted into the business process operation sequence based on a large model, and the corresponding RPA code is generated and executed, so that the understanding cost of the user is greatly reduced, the execution efficiency of the business process is improved, and the labor cost is reduced.

An AI-based training recommendation system (100) for personalized employee development, which includes the following: a module for creating user profiles that is configured to collect and analyze employee data, including job role, skills, learning history, performance indicators, and career aspirations; a module for real-time skills gap analysis that uses artificial intelligence to assess employee skills and identify areas for improvement; a training recommendation engine that uses machine learning algorithms to create personalized training suggestions based on employee data, company goals, and industry trends; a learning path optimization module configured to adaptively modify training recommendations based on employee progress, feedback, and evolving qualification requirements; an integration module for connecting to existing enterprise systems, including Learning Management Systems (LMS), HR platforms and performance management tools; a module for monitoring engagement and progress, designed to track employee participation in training, assess knowledge retention, and provide managers with insights; and an automated training planner configured to assign and prioritize training sessions based on employee availability and business requirements.

The invention provides a text hotspot clustering method based on a large language model. The method comprises the following steps: obtaining abstract output of a text from to-be-processed text data in a summarizing manner by utilizing the large language model; dividing the text abstract according to fields based on a large language model to obtain classification tags; performing element extraction on the text abstract based on a large language model to obtain element information; taking the text abstract as input, and using a vector embedding model to obtain semantic vector output; original text attributes, text abstracts, classification tags, element information and semantic vectors are stored in a vector library; a retrieval condition is formed according to business requirements, and a text candidate set is recalled from the vector library; and according to the text candidate set, obtaining a clustering result by utilizing a stage optimal clustering algorithm, taking the text abstract set of each cluster as input, obtaining a hotspot abstract summary based on a large language model, and integrating into a final hotspot result. According to the method, the efficiency and accuracy of large-scale dynamic text data analysis can be improved.

The invention relates to an intelligent scheduling system based on a large model and a scheduling method thereof, and belongs to the field of scheduling management, and the system comprises a business demand input module which is used for receiving, analyzing and converting business demands in an enterprise; the intelligent scheduling module is used for optimizing a scheduling scheme through a machine learning algorithm according to business requirements and human resource data; the scheduling issuing module is used for issuing a scheduling scheme and collecting employee feedback; and the continuous optimization module is used for continuously optimizing the scheduling scheme according to the feedback and the data analysis result. The method has the advantages that the scheduling efficiency is improved, the time and cost of manual intervention are greatly reduced through automatic and intelligent scheduling management, and the scheduling efficiency is improved.

The invention discloses a mine digital asset storage management method, which comprises the following steps of: extracting characteristics of mine data, dividing the mine data into hot data, warm data and cold data according to the characteristics, selecting different storage devices for the data, and dynamically adjusting migration of the data among the different storage devices based on access frequency and business requirements; future data storage requirements are predicted through a machine learning algorithm, and storage resources are scheduled; performing full life cycle management on the data, performing business value evaluation on the data, and adjusting storage priority and resource allocation based on the data value; carrying out full-life-cycle encryption on the data, and recording data operation through a block chain technology; energy consumption of the storage equipment is monitored in real time, and storage energy efficiency and carbon footprint management are adjusted. The problems of low storage efficiency, high storage cost and insufficient energy consumption optimization caused by data type diversity, access frequency difference and insufficient storage resource utilization in mine data management can be solved.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention particularly relates to a data governance method and system based on a large model. According to the data governance method based on the large model, data are automatically collected, and the data are preliminarily cleaned and preprocessed; carrying out deep analysis, mining and prediction on the data by utilizing the large model, and carrying out data conversion and formatting according to business requirements; a data quality standard, a data security policy and a data management process are formulated and executed according to service requirements, so that the accuracy, integrity and security of data are ensured; the data quality and the data flow are monitored in real time, and if a data quality problem is found, the data quality problem is fed back to the large model so that the large model can solve the data quality problem in time. According to the data governance method and system based on the large model, intelligentization and automation of data governance are achieved, the burden of manual operation is relieved, the data governance efficiency is improved, the data security and compliance are optimized, intelligent decision support is achieved, and the data value mining capacity is improved.

The invention discloses an enterprise information visual management system and method based on data sharing, and belongs to the technical field of enterprise information management.The method comprises the steps that by connecting business systems in an enterprise, user behavior data and business data are collected and preprocessed, and an internal data set is constructed; acquiring data from an external data source, and constructing an external data set through semantic analysis, screening and classification; internal and external data are integrated, a dynamic cleaning rule is formulated, multi-source fusion is realized through data association, data are stored in a classified manner according to business requirements, and a data warehouse with a layered architecture is constructed; meanwhile, a personalized data mart is generated based on user roles and permissions, user demands are automatically predicted, and analysis indexes and dimensions are pre-generated; and finally, constructing a visual analysis platform, generating a preliminary recommendation scheme in combination with the pre-generated content and the user operation track, fusing user-defined setting, and outputting a final visual result in real time, thereby realizing efficient management and visual presentation of enterprise information.