119 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 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 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.

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 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 application discloses an education facility site selection and configuration optimization method and system, computer equipment and a medium. The method comprises the following steps: acquiring data of a target area and preprocessing; performing primary school education facility supply analysis and resident demand analysis based on township primary school education facility supply data and rural community life power supply data, and measuring the supply and demand accessibility of primary school education facilities; performing population flow measurement of different time scales based on the rural community life power supply data; performing K-means clustering analysis according to the supply and demand accessibility, the population monthly flow level and the population annual flow level, and obtaining clustering results; matching the clustering results with rural community units, identifying the layout characteristics and types of rural community primary school education facilities, and realizing targeted township primary school education facility site selection and configuration optimization in units of rural communities. The application can provide primary school education facility site selection and configuration optimization by replacing the previous population census data.

The invention provides a construction method and device for a divergent thinking chain data set and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a design demand inputted by a user through employing a tree structure, analyzing the design demand, and extracting key information from the design demand; randomly generating a plurality of different roles, and associating the key information based on the view angles of the different roles to generate a first divergent search word; performing dimension expansion on the first divergent search word according to a preset logical thinking mode to generate a second divergent search word; converging the second divergent search term into an executable search term through the constraint of the design requirement; according to the design requirement, the key information, the first divergent search term, the second divergent search term and the executable search term, a divergent thinking chain data set is constructed, and the divergent thinking chain data set is used for providing training data for a large language model. The problem that an output result is ordinary when a large language model faces a creative task is solved.

The invention relates to a simulation deduction scene construction method oriented to far-domain collaborative application, and the method comprises the following steps: S1, carrying out scene simulation demand analysis, and determining a simulation deduction target; s2, carrying out red party model capability requirements and application mode design: aiming at the proposed red and blue targets, in combination with a simulation deduction purpose, carrying out design of red party model elements, a red party collaborative application model and network topology, and proposing scene construction requirements for the number, the distribution potential, the movement and the confrontation strategy of the blue targets matched with the red party capability; s3, implementing a deduction scene, and performing deduction simulation verification and selection verification; and S4, carrying out simulation deduction and efficiency evaluation analysis. According to the invention, the design of each device and the collaborative model thereof in the simulation scene can be realized, and the pertinence of the construction and application of the far-field first-line collaborative simulation scene is improved.

This application relates to the field of requirements analysis technology, specifically providing an AI-based natural language requirements analysis method and system. The method includes the following steps: acquiring natural language requirements text, then using AI-based feature extraction technology to extract features from the natural language requirements text to obtain text domain features, user style features, and task features; determining the combination of language analysis modules and the parameters corresponding to each language analysis module in the combination based on the text domain features, user style features, and task features; determining the execution order of each language analysis module in the combination based on the text domain features and task features, then constructing a requirements analysis process based on the execution order and the combination of language analysis modules; and using the requirements analysis process to perform requirements analysis on the natural language requirements text to obtain requirements information. This method enables the analysis process to adapt to users' personalized expressions and accurately analyze different analysis tasks.

The invention relates to the field of electromagnetic parameter design of a phase-shifting transformer, in particular to an electromagnetic parameter design method of an ice suppression type phase-shifting transformer considering multi-factor coupling. According to the technical scheme, the method comprises the steps of collecting multi-factor constraint conditions, wherein the multi-factor constraint conditions comprise meteorological condition collection, line thermodynamic parameter collection and system electrical parameter collection; ice suppression power demand analysis based on thermal-electric coupling; designing the output capability of the ice suppression type phase-shifting transformer meeting the system constraint; reversely solving electromagnetic parameters of the phase-shifting transformer; verification and iterative optimization: substituting the designed electromagnetic parameters into an electromagnetic transient equation for simulation analysis, and verifying whether the electromagnetic transient characteristics of the electromagnetic parameters meet requirements or not when the system fails or is quickly adjusted; and if the verification is not passed, returning to adjust the designed electromagnetic parameters until all design targets are met. The method is suitable for electromagnetic parameter design of the ice suppression type phase-shifting transformer.

An object of the system according to the embodiment is to efficiently find out detailed needs and broad requests.SOLUTION: A system according to an embodiment includes a character generation unit, an opinion collection unit, a need analysis unit, and a request analysis unit. The character generation unit uses the generation AI to create a unique group of characters. The opinion collection component collects opinions from the characters created by the character creation component. The need analysis unit analyzes the opinions collected by the opinion collection unit to find out detailed needs. The request analysis unit analyzes the opinions collected by the needs analysis unit to find out a wide range of requests.SELECTED DRAWING: Figure 1

The invention provides an AI engine user demand analysis method for multi-source data input, relates to the field of artificial intelligence, and solves the technical problems that in the prior art, relevance and dynamic insight of user demands are insufficient, and user motivation is difficult to mine. The method comprises the following steps: acquiring multi-modal data from different data sources, and carrying out preprocessing and semantic fusion to generate a multi-modal data stream with unified representation; wherein the data source comprises a public network data source and an Internet of Things data source; based on the multi-modal data flow, through a graph neural network, constructing a dynamic demand graph with demand entities as nodes and association relationships as edges; based on the dynamic demand graph, identifying user demands and user motivations by using graph reasoning and causal discovery algorithms, and generating a structured demand analysis report; and based on real-time monitoring data, updating the dynamic demand map through an incremental learning algorithm, and carrying out continuous tracking of demand evolution.

The invention discloses an agent interaction memory cooperation method and device based on memory grading and a medium, and the method comprises the steps: obtaining the original interaction data of a user, and carrying out the context interaction analysis of the original interaction data, so as to obtain an interaction state variable; based on the interaction state variable, determining user interaction multiplexing state data through cross-session multiplexing of the user unique identifier; obtaining an iteratable shared knowledge base through high-frequency multiplexing knowledge precipitation according to the user interaction multiplexing state data; performing data interaction control on the interaction state variable, the user interaction multiplexing state data and the iteratable shared knowledge base to determine a directional calling state of the memory data; and based on the directional calling state of the memory data, through multi-level cache matching of agent demand analysis, obtaining agent interactive memory collaborative storage data. Through the method, the technical problem that an artificial intelligence interaction memory function cannot meet real-time interaction dynamic and personalized requirements in the prior art is solved.

The embodiment of the invention discloses an SPEC-based artificial intelligence document generation method and related equipment. The SPEC-based artificial intelligence document generation method comprises four SPEC stages including a demand analysis stage, an outline generation stage, a task planning stage, a content creation stage and the like. According to the SPEC-based artificial intelligence document generation method, through a standardized SPEC four-stage process and in combination with key technologies such as a cue word constraint mechanism, progress label analysis and format label analysis, the intellectualization of the creation process of documents, especially professional documents, is realized, and the creation efficiency and the document quality are remarkably improved; specifically, a document creation process is divided into four ordered stages including a demand analysis stage, an outline generation stage, a task planning stage and a content creation stage, so that staged management can be realized, parameters are finely controlled in each stage, and a document which is easier to meet user requirements is generated. The method is widely applied to the technical field of artificial intelligence.

The invention discloses an aircraft design demand analysis and intelligent design method and system based on generative artificial intelligence, and belongs to the technical field of aircraft design. The problems that in a traditional aircraft design process, demand analysis efficiency is low, semantics is fuzzy, and design scheme innovation is limited are solved. The method comprises the steps that a large language model is adopted to analyze design requirements in an aircraft design document to obtain design elements, then semantic disambiguation and feature extraction are conducted on the design elements, and a design requirement instruction is generated; generating an aircraft appearance image: generating the aircraft appearance image by using a generative artificial intelligence model according to the generated design demand instruction; and converting the aircraft appearance image into an aircraft aerodynamic shape by adopting a Unique3D method, and outputting an aircraft aerodynamic shape model. According to the method, the influence caused by data redundancy is effectively eliminated, the training difficulty of the design model is reduced, the design efficiency is improved, and the precision and innovativeness of aircraft design are improved.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.