689 results about "Modal analysis" patented technology

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention discloses a short video intelligent editing method and system based on multi-modal analysis, and relates to the technical field of video editing. The method is used for improving editing efficiency and visual experience and comprises the following steps: extracting lip motion features of a character, visual saliency features of a commodity and a voice emotion intensity value from a target short video stream to form multi-modal time sequence data; afterwards, the voice stream is recorded, a product keyword timestamp is extracted, the alignment degree is calculated through dynamic time warping in combination with a visual saliency peak value, and a preliminary editing point set is generated through weighted evaluation in combination with an emotional intensity value; constructing an editing decision optimization model based on deep reinforcement learning, taking the multi-modal features as state input, adjusting the retention probability of editing points through a joint reward function, and selecting an optimal transition mode; and the lip movement and voice synchronization error before and after the editing point and the emotional and visual continuity of the transition section are analyzed, the discontinuous region is smoothed, and the edited finished product is output, so that precise short video intelligent editing is realized.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention discloses a multi-knowledge-base scheduling routing method based on intention recognition and feedback optimization, and the method comprises the steps: S1, receiving multi-modal input from a user, carrying out the deep semantic analysis of the input content through natural language understanding and multi-modal analysis, and outputting a standardized intention label; s2, constructing a capability portrait for each knowledge base, and then generating a route matching score matrix according to a similarity relationship between the user intention tag and the capability portrait of each knowledge base; s3, screening out one or more most relevant candidate knowledge bases according to the scoring matrix and a set matching threshold value, and selecting a retrieval combination strategy to realize information recall; s4, information retrieval is executed based on the selected knowledge base, recall results and original user input are submitted to a large language model for content generation, and answers or suggestions are formed and output; and S5, user feedback collection and strategy optimization are carried out. According to the method, a more accurate, efficient and self-evolutionary knowledge base scheduling mechanism can be realized.

The invention relates to the field of artificial intelligence, and discloses an intelligent AI semantic annotation method and system based on multi-modal analysis, images, audios, texts and multi-modal data of a sensor are synchronously collected through AI, deep semantic features of all modals are extracted by using a deep learning model after preprocessing, vectors are generated, and the multi-modal data are subjected to semantic annotation; fusing into a semantic scene vector by means of a cross-modal attention mechanism, constructing a cross-modal association graph, and learning a causal logic path by using a graph neural network; constructing a spatiotemporal causal diagram by combining spatiotemporal information, and integrating an event time sequence and spatial dependence by using a time sequence diagram neural network to form a venation path; according to the method, the two knowledge maps are fused into a comprehensive knowledge map, preliminary annotations are generated through rule base reasoning, a high-precision result is output after optimization, the problems of semantic loss and annotation fragmentation caused by modal splitting and context missing in a traditional method can be effectively solved, meanwhile, the manual verification cost is reduced, and the integrity and efficiency of semantic annotation in a complex scene are improved.

The invention discloses a data governance agent system and an implementation method, and relates to the field of data processing, and the system comprises a data analysis module, a data storage module, a user response module, a quality monitoring and feedback module and a field large language model module. The data analysis module performs unified analysis modeling on structured and unstructured data; the data storage module adopts a graph database and a vector database to store knowledge data; the user response module supports natural language interaction and intelligent question and answer; the quality monitoring and feedback module optimizes data quality through closed-loop feedback; and the field large language model module constructs an optimization model adaptive to the vertical field. Through multi-modal analysis, knowledge graph construction, double-library collaborative storage and model dynamic optimization, a data barrier is broken through, manual dependence is reduced, the data treatment efficiency, quality and intelligent level are improved, and the method is suitable for specialized data treatment requirements of industries such as manufacturing and finance.

The invention relates to the technical field of financial risk control, and discloses a telecommunication fraud risk identification method based on a bank card transfer scene. The method comprises the following steps: a basic feature construction stage: acquiring multi-source data of historical victim users, extracting multi-dimensional features, and processing the multi-source data into standardized time sequence data through feature coding and a DTW algorithm; in the multi-modal fusion and adversarial learning stage, a core layer containing a multi-modal analysis engine, a risk reasoning model and an adversarial generation model is constructed, and multi-modal feature fusion, risk probability output under an RLHF framework and simulated fraud feature generation driven by WGAN-GP are achieved; in the strategy output stage, risk scores are mapped through a double-layer scoring system, and three-level interception is triggered; in the model management stage, model iteration is achieved by means of a monitoring instrument panel and a rolling time window, the online effect is guaranteed by combining gray release and A / B testing, the telecommunication fraud in the transfer scene can be accurately recognized, and the risk control efficiency is improved.

The invention relates to the technical field of aerial video analysis, and discloses a large-scene multi-modal analysis system and method based on an unmanned aerial vehicle. According to the method, an original video frame sequence is obtained from aerial photography equipment, a scene change period and an object motion trend are extracted by calculating inter-frame motion feature vectors, and whether a scene is in a stable or dynamic state is judged by means of a state classification model. Calculating a tracking target position according to preset parameters in a dynamic state, and adjusting and analyzing a target area in combination with a motion trend; generating a camera parameter adjustment instruction according to the deviation value of the target area and the actual object position, and outputting the camera parameter adjustment instruction to an execution mechanism; acquiring a complexity value through the scene complexity feedback signal, updating an analysis strategy by adopting an adaptive algorithm when the complexity value exceeds a preset range, and outputting an adjustment signal; the output time sequence of control and strategy adjustment signals is optimized, meanwhile, the frame sequence is monitored in real time, the parameter change trend is analyzed, the preset parameters are dynamically updated to meet the user requirements, and efficient video analysis processing is achieved.

The invention discloses a lake remote sensing monitoring method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal remote sensing data of a target lake, and carrying out the preprocessing of the multi-modal remote sensing data, so as to obtain the preprocessed standardized data; obtaining a multi-modal visual language model after fine tuning; a geographic coordinate sensing module is embedded in an output layer of the multi-modal visual language model, and a fine tuning data set corresponding to the multi-modal visual language model comprises multi-modal remote sensing data, a multi-modal analysis label and lake boundary labeling data corresponding to a preset lake scene; and inputting the standardized data into a multi-modal visual language model to analyze the standardized data by using the multi-modal visual language model so as to output a multi-modal analysis result and lake boundary vector data. According to the method and the device, the lake change is finely monitored and analyzed by utilizing the multi-modal remote sensing data.

The invention relates to the technical field of monitoring, and discloses a multi-modal video public opinion intelligent monitoring method and system, and the system comprises a multi-modal data collection module, a data processing module, a coefficient generation module, an intelligent analysis module, a dynamic threshold module, and a public opinion visual platform. According to the method, a visual, voice and character cross-modal cooperative computing framework is established, and the one-sidedness problem of traditional single-modal analysis is solved; a recognition-tendency-propagation three-dimensional index system is constructed, and accurate quantitative evaluation of public opinion evolution is achieved; designing a static comprehensive coefficient threshold value and a dynamic growth threshold value of the dual-threshold grading system, and cooperating with an imaginary main body filtering algorithm and KOL polarization value calculation, so that the false alarm rate is remarkably reduced, and meanwhile, rapid early warning response is realized; a three-dimensional propagation map is generated through propagation path concentration analysis, content feature evolution tracking and influence radiation, and a complete public opinion evolution chain and hotspot traceability are provided.

The invention provides a finite element analysis method for a competition-level site cross-country motorcycle frame, and belongs to the technical field of crossing of computer-aided engineering and vehicle structure design. The method comprises the following steps: performing parametric modeling on a frame main bearing structure based on CATIA software, and simplifying non-functional characteristics; importing the model into ANSYS Workbench, generating a high-precision finite element model by adopting a grid division strategy of mixing tetrahedron and hexahedron units, and considering the calculation efficiency and precision; defining material characteristics; a limit load working condition is simulated through statics analysis, a displacement cloud picture and a stress cloud picture are combined to be compared with the yield strength of a material, and topological optimization is conducted on a weak area; meanwhile, the inherent frequency of the frame is extracted through modal analysis, and the resonance risk is avoided. According to the method, statics analysis and modal analysis are carried out on the frame through cross-platform co-simulation, the structural reliability is evaluated, and the method is suitable for efficient development and performance optimization of competition-level cross-country motorcycle frames.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

The invention discloses a document review method based on multi-agent collaboration and retrieval enhancement generation. The method comprises the following steps: firstly, analyzing a document review rule input by a user into a rule semantic intermediate representation through a natural language processing technology, and calling a retrieval enhancement generation (RAG) module to expand related knowledge to form a structured rule library; secondly, multi-modal analysis and chapter segmentation are carried out on a to-be-examined document, and elements such as texts, tables, pictures and formulas are expressed in a unified mode; tasks such as rule analysis, document segmentation, knowledge retrieval, matching comparison and report generation are completed through a multi-agent cooperation mechanism; finally, when ambiguity exists in rule and document matching, an RAG module is introduced to retrieve supplementary evidences from an external knowledge base, explanatory comparison is conducted in combination with the generative model, and the accuracy and authority of judgment are improved.

The invention relates to the technical field of artificial intelligence, and particularly provides an automatic bid invitation file generation method and system, a terminal and a medium, and the method comprises the steps: receiving project demand information input by a user, the project demand information comprising one or more of a structured text, an unstructured text, a technical drawing and a data table; performing multi-modal analysis processing on the project demand information to generate a unified project feature vector; performing matching and reasoning on the item feature vectors and a bid invitation domain knowledge graph; the matching and reasoning process adopts a dynamic correlation threshold strategy based on query complexity and node importance to screen out entity nodes related to current project requirements; and calling a domain large model to receive the item feature vector and the inference result from the knowledge graph to generate a first draft of the bid invitation file, including matching a bid invitation file template, writing bid invitation terms, and adding traceability information to each generated target term. The bid invitation file generation quality and efficiency are improved.

The invention relates to the technical field of data analysis, can be applied to intelligent driving, financial science and technology, medical health and other business scenes, and discloses a multi-modal knowledge graph interpretable analysis method, device, equipment and medium, and the method comprises the steps: obtaining multi-modal original data, carrying out semantic analysis, and extracting structured feature information; constructing an entity-associated knowledge graph based on the structured feature information; performing consistency verification by using redundant information of the knowledge graph to complete data quality calibration; setting an abnormal exposure degree and user experience consistency dimension, and constructing a multi-scale aggregation model for dynamically adjusting an index weight; and generating a rating result based on the calibrated knowledge graph and the aggregation model, and performing reverse attribution analysis to generate a source mapping report. According to the method, multi-modal analysis, knowledge graph modeling and dynamic index aggregation are fused, data self-adaptive evaluation and explainable result output are achieved, and analysis accuracy and reliability are improved.

The invention provides an intelligent number asking method suitable for a sales data query scene, which belongs to the technical field of data processing and comprises the following steps of: (1) inputting a natural language; (2) dialogue type number asking; (3) accessing a plug-in model; (4) carrying out multi-modal analysis; and (5) carrying out platform service. According to the intelligent data asking system architecture, an industry knowledge graph is used as a cognitive footstone, a large language model is used as an interaction engine, and dynamic data association is used as execution blood vessel three-in-one. The core pain point of'water and soil disability 'in vertical industry application of a general AI model is solved, the method is a key for really changing the AI from'chatting' to'drying ', and unprecedented agility and accuracy are provided for data-driven decision making.

The invention discloses a heterogeneous unmanned aerial vehicle cluster communication message processing method and system. The method comprises the following steps that a user uploads a communication protocol document and scene description of an unmanned aerial vehicle; the method comprises the following steps: designing a multi-modal analysis mechanism on the basis of a retrieval enhancement generation (RAG) technology endowed by a large language model (LLM), automatically identifying and processing a communication protocol document, and generating a knowledge graph and a communication message specification library; based on the knowledge graph and the communication message specification library, generating a structured definition according to a scene, and converting the structured definition into a multi-format message instance; and simulating a communication scene, verifying message transmission validity, and outputting an optimization suggestion. By adopting the technical scheme, the heterogeneous unmanned aerial vehicle protocol multi-modal analysis method based on LLM + RAG improves the time for analyzing the standard document and the message instance generation efficiency.

The invention relates to the technical field of mine environment risks, and discloses a mine environment risk multi-modal analysis and early warning decision-making method, which comprises the following steps of: performing time and space reference alignment on multi-source observation data, constructing a unified spatio-temporal data set, and establishing a troposphere disturbance model to realize observation disturbance coupling. And constructing a physical evolution operator and a rainfall-driven external input operator based on a seepage mechanical relationship, and generating a state evolution mechanism. Semantic direction parameters are generated by mapping semantic query information to a target attention dimension, and a lagging propagation operator is constructed by setting a rainstorm ending moment as a starting point. And determining a non-regular spectrum transition index and a peak lag moment through the non-regularity measurement and the dynamic change rate of each lag propagation operator, and setting a reference threshold value based on the spectrum transition index of a historical non-risk time period. Finally, when the early warning intensity meets the condition, early warning is output, and a query result is generated in combination with the lagging propagation operator and the semantic direction parameters.

The invention provides a bridge stay cable video multi-target identification tracking and vibration extraction method based on an unmanned aerial vehicle. The method comprises the following steps: 1, constructing a bridge stay cable refined inclined slender target detection model based on a YOLOv11 model; 2, providing a multi-target tracking algorithm fusing the inclined slender target detection model and a StrongSORT algorithm; step 3, improving a displacement extraction method combining SIFT / ORB feature point matching and a sub-pixel refinement technology; 4, designing an unmanned aerial vehicle motion correction algorithm based on variational mode decomposition and time-frequency domain combined screening; and 5, constructing a joint working modal analysis algorithm for realizing combination of a natural excitation technology and a random subspace recognition algorithm. According to the method, high-precision extraction of the vibration signals of the stay cable and identification of modal parameters of the stay cable are realized, and technical support is provided for health monitoring of the large-span cable-stayed bridge.

The invention discloses an infrared film absorptivity prediction method and system based on machine learning, and relates to the technical field of optical film design and performance prediction.The infrared film absorptivity prediction method comprises the following steps that feature data of a to-be-measured film material and incident light are obtained, electromagnetic field distribution and local absorption power density are output, and according to power integration and boundary conditions, an infrared film absorptivity prediction result is obtained; obtaining initial absorptivity distribution; performing modal analysis on the initial absorptivity distribution, constructing a low-dimensional absorptivity response model, and outputting absorptivity response data after dimension reduction; constructing a physical constraint neural network by taking energy conservation and Fresnel boundary conditions as constraints; on the basis of a physical constraint neural network, absorptivity prediction is carried out on the infrared thin film under different conditions, thin film structure data are optimized through a multi-objective optimization algorithm, and an optimal structure parameter combination of the infrared thin film is obtained; according to the invention, through multi-physics field modeling and the physical constraint neural network, the problems of low infrared film absorptivity prediction precision and poor optimization efficiency are solved.

The invention discloses an automatic auditing method and system based on ship-shore model matching fusion, and belongs to the technical field of data processing.The method comprises the steps that ship-shore matching related documents of a user are interacted, and the ship-shore matching related documents support multiple formats; activating a multi-model fusion processing module to perform preprocessing and multi-modal analysis on the ship-shore matching related document, extracting a text, a visual element and structured data, and generating a structured data object; a preset rule engine and the large language model are called to process the structured data object in parallel, the rule engine executes deterministic rule verification, and the large language model processes uncertainty semantic recognition determination; and fusing output results of the rule engine and the large language model to generate an audit result and a risk prompt. The problems that in the prior art, ship-shore matching auditing depends on labor, the efficiency is low, errors are large, multi-format documents are difficult to process through a single technology, rule and semantic coverage is incomplete, information fragmentation is caused, and auditing accuracy and efficiency are difficult to consider at the same time are solved.

The invention relates to the cross technical field of AI multi-modal analysis and dialect processing, in particular to a dialect content crawling and auditing system and method based on AI analysis, a simulation terminal generates a real person behavior entropy interval event stream through a Markov chain and reinforcement learning hybrid model, a dialect exclusive operation library and a differentiated interest strategy are integrated, and a real person behavior entropy interval event stream is generated through a real person behavior entropy interval event stream; anti-crawling is avoided in combination with a proxy IP pool and a Bezier curve trajectory, a search unit realizes directional crawling of dialect keywords through a hot updateable script, a blind patrol mode locates high-risk content depending on a triple knowledge graph and risk prediction, whole-course block chain evidence storage is performed, and a collaborative analysis unit analyzes dialect audio and video features in a layered architecture. The multi-modal large model is combined with the professional small model, the audio spectrum, the text translation and the video picture are fused to realize cross-modal violation detection, the application service unit alarms violation content in real time, a supervision report containing a violation distribution thermodynamic diagram is generated, and the accuracy and traceability of dialect violation content crawling and auditing are improved.

The invention provides a complex formula intelligent calculation method for the ship field, and the method comprises the steps: obtaining a complex formula calculation file in a ship design document, carrying out the multi-modal analysis of a text, an image, a formula and a table in the document, and converting the text, the image, the formula and the table into structural data; based on the structured data, calling a large language model to fill a standardized formula calculation document template, and generating a standardized formula calculation document including formula expression, parameter definition, calculation steps and unit expression; when a formula is incomplete, variables are missing or conditions are not explained, missing fragments and conditions are automatically complemented by utilizing a knowledge graph and a large model reasoning result; and performing cross-modal error correction and physical consistency verification on the document, and if logic conflicts are found, outputting correction suggestions and automatically updating formula expressions. According to the method, automatic analysis, complementation, inspection and calculation of complex formulas in the ship design field can be realized, and the standardization and reliability of engineering calculation are improved.

The invention belongs to the technical field of steam turbine rotors, discloses a blade synchronous vibration modal analysis method for a steam turbine rotor and a modal analysis method, and can effectively eliminate the modulation influence of a rotation effect on a vibration signal in the technical link of establishing a rotation reference coordinate system. The rotary motion of the blade and the natural vibration of the structure are decoupled, and the periodic interference caused by rotation is separated from the structural response signal, so that the natural vibration characteristics of the blade under the dynamic working condition can be truly reflected. The mode breaks through the limitation that a traditional static coordinate analysis method cannot distinguish rotation and structure coupling components, and lays a theoretical foundation for modal recognition of the high-speed rotor in an operation state. According to the method, system modal identification is carried out in combination with blade natural vibration characteristics, so that modal parameter extraction is more targeted and physically significant.

The invention discloses a method for evaluating forced vibration stress of a blisk, which relates to the technical field of aero-engines and comprises the following steps: constructing a single-sector finite element model of the blisk and a multi-stage single-channel model of a gas compressor, and carrying out modal analysis containing prestress to obtain a Campbell graph of the blisk, a dangerous mode and a dangerous rotating speed; the method comprises the following steps: calculating steady flow fields of different back pressures at dangerous rotating speeds to obtain working points, respectively carrying out nonlinear harmonic method oscillation flow field calculation and nonlinear harmonic method static interference unsteady flow field calculation at the working points to respectively obtain aerodynamic damping and wake excitation, carrying out dot product on the wake excitation and a dangerous mode to obtain modal force, and carrying out nonlinear harmonic method oscillation flow field calculation and nonlinear harmonic method static interference unsteady flow field calculation on the modal force. And calculating forced vibration complex stress response by adopting a modal force method based on the aerodynamic damping and the modal force so as to obtain real stress response and maximum vibration stress thereof. The blisk forced vibration stress assessment method provided by the invention has important engineering significance for improving the design level and test guidance capability of the blisk.

The invention provides a multi-modal ecological environment monitoring element plug-in integration system and method based on unified objectification modeling. Firstly, an object model and an inheritance system of monitoring equipment, data and an algorithm are constructed; on the basis, a unified logic storage and metadata cataloguing mechanism oriented to heterogeneous databases such as relational databases, non-relational databases and file object databases is designed and realized; the plug-and-play and full-life-cycle management of equipment, data and algorithm models is further realized through a dynamic registration module; common APIs (Application Program Interface) such as object-level addition, deletion, modification and query, space-time association query, concurrent task scheduling and aggregation and the like are externally provided by relying on a unified service bus. And the hybrid storage engine cooperatively manages structured and unstructured data and maintains a topological relation between objects. According to the scheme, object dynamic integration and expansion are completed on the premise that operation is not interrupted, the multi-source heterogeneous data management and cross-modal analysis efficiency is remarkably improved, and the method is suitable for various ecological environment monitoring scenes such as natural reserve, drainage basin pollution supervision and disaster emergency response.

The invention discloses a multi-agent multi-modal table question and answer method and system based on a cognitive collaboration mechanism. The method and system are particularly suitable for extracting accurate information from a complex, unstructured or multi-modal data source and answering questions of a user. The system is composed of four core modules, namely a task ontology analysis agent, a multi-modal clue perception agent, a logic verification agent and an arbitration fusion agent, and the task ontology analysis agent, the multi-modal clue perception agent, the logic verification agent and the arbitration fusion agent are integrated by receiving user questions and multi-modal table input. The intelligent agents cooperate to complete the processes of keyword extraction, intention recognition, vision-text cross-modal analysis, clue generation, logic verification, result fusion and the like, and an efficient cognitive closed loop is formed. The system not only supports operation on standard table data, but also can process visual clues such as complex header structures, cell merging modes, font styles and the like, so that the ability of understanding fuzzy or incomplete input is improved. Successful tests in a plurality of practical application scenes prove that the method has strong fault-tolerant capability and accuracy.

The invention relates to a broadband power grid oscillation positioning method and device based on modal analysis. The method comprises the steps of obtaining broadband oscillation data in a power grid, extracting frequency spectrum characteristics of the broadband oscillation data, identifying an oscillation mode based on the frequency spectrum characteristics, determining an oscillation positioning index of each branch node in the power grid based on the broadband oscillation data, and determining the frequency spectrum characteristics of each branch node in the power grid based on the oscillation positioning index of each branch node. And constructing a spatial-temporal feature tensor containing a time feature and a spatial feature, adding the identified oscillation mode as a tag to the spatial-temporal feature tensor, and inputting the spatial-temporal feature tensor to a trained oscillation source positioning model to obtain an oscillation source positioning result. The oscillation source positioning model is obtained by training a long short-term memory network based on a historical spatial-temporal feature tensor carrying an oscillation source position label. By adopting the method, the oscillation source can be efficiently and accurately positioned.

The invention discloses an intelligent context perception English adaptive learning system and method, and belongs to the technical field of acknowledgement science and language education. According to the system, physical scenes, social contexts, emotional physiological states and cognitive load data of a user are comprehensively collected and fused through a multi-dimensional context perception and cross-modal analysis module; constructing a real-time user model containing deep cognitive features by means of a user dynamic cognitive modeling module in combination with a meta-learning algorithm; through a dynamic knowledge graph and a generative content engine, real-time generation and compliance verification of scenarized learning content are realized. According to the method, the problems of one-sided context perception, static content stiffness, single algorithm adaptation and shallow cognitive modeling of an existing system are solved, deep adaptation of learning contents and real-time states of users is realized, the scene practicability of learning is remarkably improved, and an efficient and flexible English learning solution is provided for users with different requirements.