166 results about "Needs analysis" patented technology

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention relates to the technical field of structured retrieval, and discloses a retrieval enhancement generation method and system based on LLM structured index and vector hybrid retrieval, and the retrieval enhancement generation method based on LLM structured index and vector hybrid retrieval comprises the steps of processing and analyzing a non-structural document, generating a hierarchical tree data extraction with document logic, and generating a hierarchical tree data extraction with document logic. The method comprises the following steps: obtaining a query demand, analyzing and identifying the query demand, self-matching any one or a combined retrieval strategy to quickly and accurately obtain a retrieval result in the complete logic positioning information, optimizing hierarchical tree data by adopting context perception, and establishing the complete logic positioning information in the hierarchical tree data through a reasoning path generation method. According to the method, through structured indexing, accurate positioning of specific chapters of the document, improvement of retrieval accuracy and support of multi-step logical reasoning, the long tail problem which cannot be processed by traditional RAG is solved, a clear and transparent retrieval path can be provided, and the system credibility is enhanced.

The invention relates to the technical field of artificial intelligence, and discloses a contract generation system and method based on multiple agents, and the system comprises a user interaction module which is used for receiving a contract demand inputted by a user and displaying a generation result; the knowledge base module is used for storing and retrieving legal terms, industry specifications and contract templates in a distributed manner; the agent cluster module comprises a demand analysis agent, a law expert agent, an industry expert agent, a risk assessment agent, a language optimization agent and a coordination control agent, the agents interact through a standardized communication protocol, and a coordination strategy is dynamically adjusted based on contract complexity calculated in real time; and full-process automation of contract generation is realized. Through cooperative work of multiple agents, the efficiency and quality of contract generation are improved, the law compliance and industry suitability of the contract are ensured, the risk in the contract generation process is reduced, and the user experience is improved.

The invention discloses an interdisciplinary PBL teaching plan intelligent generation system based on knowledge graph and multi-Agent cooperation, and the system comprises an interdisciplinary knowledge base construction module which constructs knowledge graphs of mathematics, physics and computer subjects through semantic vectorization and entity alignment; the retrieval enhancement module is used for dynamically supplementing knowledge blind areas based on an RAG mechanism; the teaching demand analysis module is used for analyzing teacher input and generating a teaching target matrix; the structured modeling module is used for decomposing tasks and designing a teaching framework according to a three-dimensional target system (cognition, ability and subject literacy); the teaching plan generation module is used for generating subject contents through multi-Agent collaboration; and the teaching plan optimization module verifies interdisciplinary consistency and outputs a standardized teaching plan. According to the method, the knowledge graph and multi-Agent dynamic weight distribution are creatively fused, deep integration of interdisciplinary knowledge is realized, teaching plan generation efficiency and scientificity are remarkably improved, and intelligent lesson preparation support is provided for teachers.

The invention provides a whole-process intelligent management system for stem cell culture medium development, and belongs to the technical field of intelligent management. According to the method, artificial intelligence, big data analysis and knowledge graph technologies are fused, and the whole process from demand analysis, formula intelligent design and virtual simulation, small-scale culture verification and iterative optimization, pilot scale-up process optimization to quality compliance and tracing is covered. All the modules are coordinated through the central intelligent decision engine, the research and development efficiency of the culture medium can be remarkably improved, the development cost can be greatly reduced, and batch consistency and quality stability of the culture medium are ensured by reducing trial and error and optimizing in real time. Meanwhile, a full-chain electronic tracing function is provided, the requirements of laws and regulations for compliance are comprehensively met, and the problems that traditional research and development are low in efficiency, high in cost, unstable in batch and the like are effectively solved.

The invention discloses an intelligent furniture design system based on AI big data, and relates to the technical field of artificial intelligence, and the system comprises a data collection module which is used for collecting user behavior data, environment data and market data; the data processing module is used for carrying out cleaning, feature extraction and fusion on the collected data to generate structured design input parameters, and the structured design input parameters comprise user preference vectors and spatial constraint parameters; according to the intelligent furniture design system, accurate analysis of design requirements is achieved by comprehensively collecting user behaviors, environment and market data, the problem that design requirement analysis is incomplete due to the fact that a traditional data collection means is single is solved, the system adopts an advanced data processing technology, and the design requirement analysis accuracy is improved. Valuable design input parameters are effectively extracted and fused, a solid foundation is provided for personalized design, and a design generation and optimization module based on a deep learning model can automatically generate and optimize a furniture design scheme.

The present disclosure discloses a system (106) and a method (400) for energy management in a network (108). The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention relates to the technical field of computers, and discloses an application development method based on a large model technology, and the method comprises the steps: 1, carrying out the demand analysis, and converting a fuzzy business description into an executable technical scheme through natural language understanding and a structured modeling technology; 2, carrying out architecture design and planning, and constructing a code-free and low-code dual-mode collaborative architecture; 3, establishing a visual arrangement engine, and establishing a dual-mode development interface of a code-free development mode and a low-code development mode; step 4, aiming at different requirements of a code-free scene and a low-code scene, a dynamic adaptation strategy is adopted, and differentiated resource management of lightweight adaptation and elastic expansion is carried out; and step 5, performing field adaptation, and obtaining the industry exclusive capability according to the general model and the field knowledge through code-free rapid adaptation and low-code deep customization. By means of the scheme, the development threshold can be lowered, the efficiency can be improved, and the accuracy of cross-industry application is improved.

The invention provides an intelligent recommendation method and system for industrial design products and a storage medium, and relates to the technical field of product recommendations, and the method comprises the steps: obtaining voice data of a user, and carrying out the comprehensive analysis to obtain a keyword selection coefficient; selecting keywords in a mode of carrying out threshold comparison on keyword selection coefficients, and searching in an industrial design product library by taking the selected keywords as indexes to obtain a first recommended product set; and analyzing a matching condition between the expected price and the material of the user and the price and the material of the product to obtain a second recommended product set, analyzing user interaction data to obtain an interaction score, and recommending the industrial design product with the maximum interaction score in the second recommended product set to the user. According to the method, through deep demand analysis and research on the matching condition of the demand and the recommendation result, the correlation of the recommendation result and the user satisfaction degree are improved.

The invention provides a centralized quantum key relay network and a key distribution method, relates to the technical field of quantum communication, and comprises four steps of relay request initiation and demand analysis, path constraint and decision, cooperative key generation and verification, and monitoring and processing in a transmission process. Through the quantum digital signature, the quantum security MAC check code and the post quantum cryptography signature mechanism, the security of the system facing the quantum computing threat is greatly improved, and meanwhile, the dynamic path selection combining the aging level, the multi-factor dynamic scoring model and the elastic target time window is adopted; according to the method, the optimal path and the target time can be dynamically adjusted according to the timeliness requirement, the path condition and the network state of the request, the path selection is more flexible, different timeliness requirements are met, the integrity and legality of the secret key are ensured by adopting a three-level verification mode, and meanwhile, when the network has a fault, the safety of the network is ensured. A standby path can be automatically switched and a problem node can be isolated.

The invention discloses an electric power knowledge retrieval system based on a large language model, and relates to the technical field of data processing, and the system comprises a training data collection module which is used for collecting professional data and cross-domain general data in the electric power field; the model training module is used for performing internal and external balance training on the hierarchical memory network architecture based on the training data and constructing a large electric power knowledge retrieval model; the retrieval instruction identification module is used for receiving a retrieval demand of a user side and analyzing and generating a disassembly user demand; and the electric power knowledge retrieval module is used for receiving disassembly user requirements and inputting the electric power knowledge retrieval large model to perform electric power knowledge retrieval. The technical problems that an existing power knowledge retrieval system is low in efficiency and poor in accuracy when processing complex documents and cross-domain knowledge are solved, and the technical effect that the power knowledge retrieval efficiency and accuracy are improved through a hierarchical memory network architecture based on a large language model and internal and external balance training is achieved.

The invention discloses an artificial intelligence-based tourism strategy automatic generation method, which belongs to the technical field of tourism strategy generation, and comprises the following steps: S1, tourism demand analysis, S2, dynamic physical strength and emotion prediction, S3, elastic scenic spot sorting and alternative generation, S4, cross-platform transaction, and S5, preference and strategy optimization. According to the method, a multi-modal model is utilized to analyze hidden information in texts, pictures and voices, for example, demands for barrier-free facilities are recognized from playing with the elderly, meanwhile, physiological data of intelligent equipment are accessed, and factors such as weather are combined to predict daily physical strength and emotion, so that the user experience is improved. The generated strategy is accurately matched with the real demand and the physical state of the user, the stroke discomfort caused by the demand understanding deviation is avoided, and the integrating degree of the playing experience of the user is improved.

The invention discloses a helicopter transmission system digital engineering overall technology analysis method, and relates to the field of helicopter transmission system digital engineering, and the method comprises the steps: building a helicopter transmission system overall design digital environment; demand capturing is carried out around all benefit critical people and facing scenes; obtaining preliminary design requirements, node parameters, interface requirements and overall requirements of the helicopter transmission system; carrying out helicopter transmission system-component-part demand analysis, and establishing a demand model, a function model, a logic model and a physical model; establishing a design model, a manufacturing model, an assembly model and a test model; and establishing a comprehensive database, and storing the model established in each stage into the comprehensive database to realize numerical model multiplexing and complete digital design closed loop. The problems that a traditional analysis method is low in efficiency, low in integration level, short in simulation evaluation and long in closed loop iteration period are solved.

The invention discloses an intelligent demand analysis and man-hour evaluation method and system, and belongs to the technical field of computer vision and artificial intelligence. Analyzing the multi-modal demand document containing the text, the picture and the table to generate structured demand data; based on the structured demand data and the system module description, performing module-level splitting on the demand to generate a module task set; retrieving an associated system implementation context for each task in the module task set based on a multi-dimensional knowledge base; fusing the structured demand data, the module task set and the system implementation context to generate a fine-grained technical task; based on a historical case index, calculating reference working hours for the fine-grained technical task; and cross-module cooperative correction and personalized correction based on the ability of the task executor are sequentially performed on the reference working hours to obtain final evaluation working hours, so that automatic deep analysis and accurate and personalized working hour evaluation of software requirements can be realized.

The invention discloses an intelligent control method for collaborative operation of a concrete leveling robot, and the method comprises the steps: obtaining the position state updating data of multiple devices according to an initial operation region division result, carrying out the dynamic distribution of tasks in combination with the workload and mobility of each device, and determining the responsible region and operation sequence of each device; according to the operation rhythm control parameters, operation area boundary data between adjacent devices are obtained, the elevation deviation between adjacent areas is analyzed according to the boundary elevation connection requirement, and the boundary processing priority and the adjustment scheme are determined; according to the boundary processing priority, obtaining operation progress difference data between the devices, and if the progress of a certain device is lagged, dynamically updating a task allocation strategy through data interaction frequency adjustment to obtain an optimized device operation path; and according to the updated region boundary data, obtaining the latest state information of multi-device cooperation, aiming at a region seamless connection target, adjusting the operation rhythm between the devices, and determining a final elevation consistency control parameter.

Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which automatically manages appointment scheduling. The analytic server receives a customer request to schedule an appointment. The analytic server determines the required data from both customer and service provider for making the appointment. The analytic server retrieves customer data comprising requested service attributes, user preferences, users attributes from internal database and external data source. The analytic server retrieves service providers' data comprising provider service attributes, providers' attributes from internal database and external data sources. The analytic server accesses external data source by web crawling various websites. The analytic server executes an artificial intelligence model to predict user preferences and needs. The analytic server determines potential service providers best matching the customer's input or predicted preferences. The analytic server generates an appointment for each matching service provider and transmits an electronic message comprising the appointments to customer device.

A computer-implemented method for verifying a software-based behavior planner of an automated driving function. The method includes: providing a verification environment model to limit the state space of the behavior planner according to a specifiable traffic scene; providing a formal requirement as a criterion for the correctness of decisions of the behavior planner; generating a model checker representation of the behavior planner taking into account the provided verification environment model; analyzing the model checker representation using a model checking procedure with respect to the formal requirement. The verification environment model is used to determine a physically meaningful parameter interval for at least one location parameter and / or movement parameter of the participants of the given traffic scene. The model checking procedure systematically samples the parameter interval and thus checks the behavior planner for a representative selection of the possible temporal and spatial developments of the given traffic scene.

The embodiment of the invention relates to a script analysis method and device, equipment and a storage medium. The method comprises the following steps: generating script structure data of a script to be analyzed; performing cross-session state analysis on a target entity node in the script structure data to obtain visual effect identification data; and performing visual effect demand analysis on the to-be-analyzed script according to the script structure data and the visual effect identification data to obtain a visual effect analysis result. According to the technical scheme provided by the embodiment of the invention, flexible, universal, accurate and efficient automatic script analysis can be realized.

A knowledge graph construction method based on ontology and large language model belongs to the field of artificial intelligence and knowledge graph construction, comprising the following steps: first, initial ontology construction for knowledge extraction task: step (1.1) domain requirement analysis and scope definition; step (1.2) ontology reuse; step (1.3) concept classification and hierarchical structure construction; step (1.4) relationship definition and attribute construction; step (1.5) attribute system design; second, ontology extension method based on large language model, comprising the following substeps: step (2.1) LLM automatically generates an extension candidate set; step (2.2) consistency verification of extension candidates; step (2.3) update domain ontology; third, automatic extraction of triples combined with adaptive chain thinking mechanism; fourth, triple fusion and knowledge graph construction oriented to heterogeneous data. The application effectively improves the structural standardization and semantic consistency of knowledge graph construction.

The application belongs to the technical field of hydraulic systems, and discloses a helicopter hydraulic system joint simulation method based on multi-discipline, wherein step one is to acquire hydraulic system demand; step two is to analyze the hydraulic system demand to obtain a hydraulic system use case diagram; step three is to perform function analysis to obtain an activity diagram, a sequence diagram and a black box state diagram; step four is to perform system design synthesis based on the function analysis to obtain a white box state diagram and build a hydraulic system function logic model based on Rhapsody; step five is to build a hydraulic system physical performance model based on AMESim based on the hydraulic system physical function model based on Rhapsody; the cabin display panel of the hydraulic system is simulated in AMESim based on the sequence diagram; step six is to build a main tail rotor dynamics three-dimensional visual model based on CAT IA; step seven is to combine the hydraulic system function logic model, the complete hydraulic system physical prototype model and the main tail rotor dynamics three-dimensional visual model; and step eight is to set simulation conditions and perform joint simulation.

The invention discloses an electronic equipment design method and system based on a digital product performance test, and belongs to the technical field of electronic engineering and computer science, and the method comprises the steps: collecting the demands of a user for electronic equipment, carrying out the recognition, judgment and analysis of the demands of the user, generating a three-dimensional model, and forming an electronic equipment model; based on the requirements of product performance testing, analyzing and carrying out testing process planning, establishing an electronic equipment twin production environment, introducing multi-physics field influence factors, deploying a testing environment, completing product performance testing, and evaluating a testing result; comparing the electronic equipment test result with user requirements, judging whether the electronic equipment test result meets the user requirements or not, if not, establishing a reinforcement learning framework and an optimization program to generate an optimization result according to the to-be-optimized performance of the product, and importing the optimization result into the electronic equipment model to form update of electronic equipment model design. According to the method, efficient optimization of electronic equipment model design is realized.

The invention discloses an experimental data analysis method, device and equipment and a storage medium, and relates to the field of data processing, and the method comprises the steps: carrying out the demand analysis of an experimental data file and a user analysis demand based on a large language model, and determining an experimental task specification which at least comprises an experimental type; according to the experiment type and the experiment data file, generating a standardized data table and a data portrait; determining an experimental data analysis plan according to the experimental task specifications and the data portraits; executing an experimental data analysis plan, and performing data processing on the standardized data table to obtain cartographic data and a statistical analysis result; based on a decoupling mechanism of drawing code generation and execution, generating experimental chart data according to the drawing data; and generating an experimental data analysis report according to the experimental chart data and the statistical analysis result. According to the scheme, end-to-end experimental data analysis from data input to report generation is realized, and the accuracy of experimental data analysis is improved.

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.

The invention relates to the technical field of early warning methods, in particular to a slope stability early warning method based on InSAR and sensor fusion. Comprising the steps of monitoring area evaluation and demand analysis, technical scheme design, multi-source data acquisition, InSAR data processing, data analysis and fusion, three-level response mechanism setting, early warning model development and early warning system release. According to the method, potential risks can be responded accurately in time, and engineering safety accident risks are reduced; and multi-dimensional early warning judgment logic is provided, and different requirements from a simple threshold value to a complex mechanism are covered. The limitation of a single method can be reduced by combined use of models; and early warning information is ensured to effectively reach responsible personnel, and closed-loop management is formed. Misoperation is prevented through the multi-stage process design, and the information arrival rate is enhanced through multi-channel pushing.

The invention relates to the technical field of computing power networks, in particular to an intelligent scheduling method and system for a computing power network. The method comprises the following steps: collecting computing power node data, network link data and task demand data of each node in a computing power network, and generating a standardized data sample; inputting the standardized data sample into a demand analysis model to complete a task demand portrait; combining the task demand level with the real-time resource state of the computing power network, constructing a multi-target optimization scheduling model, and solving the model to obtain an optimal scheduling scheme adaptive to the task and the network state; according to the optimal scheduling scheme, computing power resource allocation and task issuing are completed; the system comprises a data acquisition module, a task demand analysis module, an intelligent scheduling decision module and an execution module. Through the above mode, systematic consideration of the computing power resource state, the task demand characteristics and the dynamic adaptation adjustment is realized, and accurate dynamic matching of the computing power resource and the task demand can be realized.

This invention belongs to the field of virtual assisted teaching technology, and particularly relates to an intelligent virtual assisted teaching knowledge needs analysis method. It includes the following steps: extracting teaching keywords to establish a keyword database; extracting teaching corpora; defining element features, completing feature annotation, and completing element identification; extracting element pairs, establishing element classification features, extracting element relationships, and completing classification; establishing a historical assisted teaching knowledge graph; acquiring historical information, extracting historical teaching data, and outputting an element attention output matrix based on a multi-head self-attention module; element path and element extraction; this invention achieves intelligent and efficient chemical knowledge needs analysis based on historical data of training subjects, and is used to provide training subjects with recommendations for teaching content and teaching needs, as well as an automatic planning method for teaching courses.

This invention discloses a multi-scenario service data retrieval demand analysis method, belonging to the field of service data retrieval technology. The method includes statistically analyzing three types of data demands: basic vehicle owner information, vehicle information, and service record information. It analyzes the changing trends of service decision-making correlations among these three types of demands in historical data, comprehensively obtaining the correlation decision weight values ​​influencing vehicle service plan formulation. The scenario-based demand information is divided into four categories: market research, customer service, marketing promotion, and after-sales service. Based on the changing trends of the correlation influence between market research scenario demands and correlation decision weight values, a first-type weight influence coefficient is obtained to determine the positioning and promotion area of ​​new vehicle models. Then, based on the changing trends of marketing promotion scenario demands and correlation decision weight values, a second-type weight influence coefficient is obtained to formulate service promotion plans. Finally, combined with service record information, personalized service and repair demand plans are formulated. This invention can improve the accuracy of on-demand service data retrieval.

The system according to the embodiment aims to provide real-time customized advice when planning a project. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an update unit. The reception unit inputs project goals or requirements. The analysis unit analyzes the information input by the reception unit and provides customized advice. The provision unit provides the advice provided by the analysis unit to a user. The update unit updates the advice provided by the provision unit based on user feedback.

The invention relates to a three-dimensional dynamic simulation model construction method for an aero-engine machining production line, and the method comprises the steps: firstly carrying out the demand analysis and machining data statistics, and determining a simulation target and production data; then, constructing a three-dimensional static layout of the production line, performing dynamic feature configuration on the equipment model, and endowing the equipment model with behaviors such as processing, transmission and the like; thirdly, defining logic chains such as equipment state control and task scheduling, communicating all elements of the production line, and accurately reflecting operation logic; integrating the model into a simulation environment, running and checking, and checking interference and logic errors; finally, simulation result analysis is conducted according to the productivity, the equipment utilization rate and the logistics distance parameters, three-dimensional simulation of the whole process of the aero-engine machining production line from static layout to dynamic operation is achieved, and production line design optimization, bottleneck recognition and efficiency improvement can be effectively assisted.

The invention discloses a cascade reservoir outflow analysis and simulation method based on a GraphDB knowledge graph, and the method comprises the steps: carrying out the relation recognition and knowledge reasoning of related entities of reservoir outflow through a GraphDB graph database constructed based on a large amount of data, mining, and determining the influence of each entity on the outflow flow, thereby carrying out the accurate outflow simulation based on the knowledge graph. Comprising the following steps: demand analysis and data collection; carrying out data modeling; integrating and loading data; constructing a knowledge graph; knowledge reasoning; querying and analyzing; and displaying and applying a result. The cascade reservoir outflow analysis and simulation method based on the GraphDB knowledge graph not only can visualize the complex relation between the input and output of the cascade reservoir and the upstream and downstream water levels and the like, provides convenience for knowledge sharing cooperation of different departments and institutions, but also can analyze and simulate the outflow of the cascade reservoir on the basis of a large amount of historical data. Historical decisions or optimization strategies under different scenes are quickly derived, and data-driven decision support is provided for flood control and disaster reduction of a reservoir, benefit-making water supply and the like.