151 results about "Structure matching" patented technology

The invention discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

The invention discloses a computer interactive programming platform automatic evaluation system based on deep learning, and particularly relates to the technical field of deep learning, the system comprises five modules, namely a structure diagram construction module, a semantic diagram generation module, a dynamic evaluation trigger module, a semantic solution identification module and an exponential deduction feedback module, and full-process evaluation from code structure abstraction to semantic modeling is realized. The system dynamically controls a deep evaluation process through a structure matching rate and a logic deviation degree, identifies a potential innovation solution, generates a semantic equivalence degree and a path innovation index, outputs a grade in combination with a neural network score, and embeds a high-innovation sample into a training buffer area to update a model, so as to realize intelligent evaluation and adaptive optimization; according to the method, the modeling precision of a program structure and a control flow is improved, dynamic regulation and control of semantic understanding and an evaluation process are realized, a potential innovation solution can be identified, and model self-updating is realized through scoring grade feedback, so that the intelligent analysis and adaptive evolution capability of an evaluation system on complex programming behaviors is enhanced.

The invention relates to the technical field of data processing, and provides an AI fool-proof algorithm and digital model optimization method and system, and the method comprises the steps: collecting multi-modal process data, carrying out the time alignment and event segmentation, obtaining a standard behavior sequence and an abnormal behavior sequence, inputting a preset deep learning behavior prediction model according to the standard behavior sequence, and carrying out the prediction of an abnormal behavior. And carrying out structure matching on the behavior prediction result and the abnormal behavior sequence to obtain an optimized instruction set and semantic prompt information, and then optimizing a preset deep learning behavior prediction model to obtain an optimized fool-proof intelligent prediction model. By optimizing an original deep learning behavior prediction model, the adaptive recognition capability and risk avoidance capability of the model for abnormal behaviors are improved, and the problem that in a complex practical application environment, high-frequency operation environment changes cannot be flexibly processed, so that part of high-risk behaviors cannot be recognized or corrected in time, and the risk of abnormal behaviors is reduced is solved. And the problems of low accuracy and poor adaptability exist.

The invention discloses a Web interface element recognition method, system and device and a medium, belongs to the technical field of artificial intelligence and Web interface development, and aims to solve the technical problem of how to improve the recognition rate of dynamic elements and CSS hidden elements so as to enable Web interface element positioning to be more accurate. Performing screenshot by using a playwright screenshot function, converting the screenshot into a Base64 coded character string by using a function after the screenshot is completed, inputting the character string and a url into an intelligent agent, calling a DeepSeek-VL2 visual model by the intelligent agent, analyzing the screenshot and outputting identified structured element data; dOM structure analysis: inputting a page url address, and generating DOM structure data by using a DOM analysis function; dOM tree analysis and visual element matching: calling an element matching function, performing visual element and DOM structure matching, and obtaining all successfully matched element data; interface element extraction and positioning generation; and element application.

The invention discloses a graph mapping method based on precise synthesis, and the method comprises the steps: constructing a precise synthesis structure library based on NPN equivalence classes, and inputting the precise synthesis structure library into a Boolean network; k-Cut cutting enumeration is carried out on the input Boolean network, a truth table is calculated, cutting is matched with a structure in a precise comprehensive structure library through Boolean matching, and rapid search is realized in combination with NPN classification; the overlay network is generated through multi-round mapping optimization, logic sharing nodes are mined in combination with structural hash, and the multi-round mapping optimization comprises delay-oriented mapping, global area topological optimization and local accurate area optimization; and a new target network is generated based on the optimized overlay network, redundant nodes are removed, and final network construction is completed. According to the method, manual intervention is not needed through full-process automatic mapping and a redundancy removal mechanism, the labor cost of a technical mapping link in chip design is remarkably reduced, and the method is particularly suitable for efficient design of complex circuits and emerging majority of logic base technologies.

The invention belongs to the technical field of character pattern recognition, and particularly provides an oracle structure recognition method of a font pattern isomorphic network, which comprises three stages of font pattern structure feature extraction, font pattern isomorphic network model and oracle font structure recognition. Through oracle font skeleton extraction, oracle font skeleton singular point detection, skeleton burr and bifurcation optimization, and key point extraction as a node communication path as an edge, an oracle font graph structure is established, and graph structure nodes, edges and graph level features are calculated. The font graph isomorphic network integrates the advantages of a graph isomorphic network and dynamic edge and graph embedding, and learns vector representation of multi-level features such as nodes, edges and graphs of the oracle font graph structure so as to train and identify the font structure of the oracle. The method can be used for structure matching recognition of oracle character pattern images, has the advantage of being high in recognition accuracy, and is suitable for recognition of character pattern structures such as gold texts and seal scripts and recognition of social networks, biological information and molecular structures.

The invention provides an enterprise business-oriented flow node intelligent optimization and scheduling method and system, and relates to the technical field of business processing, and the method comprises the steps: obtaining a business target description and an executed flow step sequence, retrieving historical flow nodes based on depth vector embedding, and generating business intention features through a bidirectional long-short term memory network; and constructing context state representation to perform structure matching, and finally constructing a dynamic heterogeneous graph structure to generate a weight recommendation sequence. According to the invention, accurate recommendation and intelligent scheduling of the enterprise process are realized, and the process execution efficiency and the business objective achievement rate are improved.

The invention relates to the technical field of data acquisition, in particular to an AI-based electric power big data acquisition and processing method. According to the method, numbering extraction is carried out on field contents and upper and lower positions of the power maintenance log and the transaction contract data, a propagation path between fields is constructed on a semantic level, and field groups with semantic offset and structural dislocation are subjected to bidirectional alignment after difference identification; according to the method, field semantic expressions and sequence positions are synchronously corrected in the association process, the accuracy of field connection between different types of data is improved, continuous judgment is conducted in combination with time parameters, connection between maintenance and contract fields better fits the actual business process, field contents and connection relations are further archived after field combinations are aligned, and the efficiency is improved. And a structured basis is provided for data reconciliation and scheduling collection, and the semantic consistency, the structure matching degree and the application availability of an electric power big data acquisition processing result are remarkably improved through the processing flow.

The invention discloses a spectrum and molecular structure comparison matching system based on machine learning, and relates to the field of spectrum intelligent analysis, and the system comprises a model construction module, a training optimization module, a calculation candidate module, and an interpretation visualization module. According to the method, the deep semantic features of the spectrum and the structure are automatically extracted by constructing the multi-modal deep learning model, and the accuracy and robustness of spectrum analysis and molecular structure matching are greatly improved. A shared embedding space learning strategy is adopted, different modes are projected to the same feature space, and unified matching and similarity scoring are achieved. An attention mechanism is introduced, visual mapping between spectrum peaks and molecular structure fragments is achieved, understanding of corresponding chemical groups or atoms behind each spectrum peak is facilitated, and interpretability of a matching result is enhanced.

The embodiment of the invention relates to reference image structure matching using a diffusion model. The invention discloses a method, a device, a non-transitory computer readable medium and a system for image processing. The method comprises the following steps: acquiring a structure input indicating a target space structure; encoding the structural input using a conditional encoder to obtain a structural code representing the target spatial structure; and generating a composite image based on the structure encoding using the image generation model, wherein the composite image depicts the object having the target spatial structure.

The invention provides a target detection credibility estimation method fusing data and model uncertainty, and belongs to the field of data processing, and the method comprises the steps: obtaining an image quality score through an image quality evaluation model; outputting a target frame by using a two-dimensional detection model, and performing Bayesian correction of confidence in combination with an image quality score and a weather perception model result; meanwhile, outputting a three-dimensional target frame by using a laser radar point cloud detection model, and correcting three-dimensional detection confidence in combination with a target distance and weather conditions; according to the statistical relationship between the number of point clouds in the detection frame and the detection accuracy, constructing the point number confidence coefficient based on Bayesian reasoning; and performing structural consistency analysis according to the spatial size of the detection frame and the prior size distribution of the target category, and outputting structural matching confidence. And finally, the five indexes are fused, and the overall credibility of the sensing result is calculated. According to the method, the credible judgment capability of a sensing system in a complex environment can be improved, and the method has good practicability and engineering value.

The invention relates to the field of mechanical engineering, in particular to a hydraulic-structure collaborative optimization design method for a shield pump, and aims to solve the problems of load distortion and structure matching imbalance caused by traditional staged optimization. The method comprises the following steps: constructing a fluid-solid bidirectional coupling simulation model, and extracting transient hydraulic loads under multiple working conditions; establishing a structure dynamic response agent model based on modal superposition and Gaussian process regression; key coupling parameters such as the impeller outlet width and the volute base circle diameter are recognized through a Sobol method; and the efficiency, the lift fluctuation rate, the stress and the vibration are taken as multiple targets, and a Pareto optimal solution is searched through an improved genetic algorithm. Manufacturing constraints are embedded in the optimization process, the single period is shorter than 48 hours, and fatigue life evaluation and knowledge base intelligent recommendation are supported. According to the method, closed-loop cooperation of hydraulic excitation and structural response is achieved, the vibration prediction error is lower than 10%, the optimization dimension is compressed to be within 5 dimensions, the efficiency is improved by 20 times or above, and the performance reliability and the design intelligence level of the shield pump are remarkably improved.

The invention discloses a literature metrology and clinical data fusion lung cancer knowledge graph construction method and device, which can solve the problems of research and clinical disjunction, data fragmentation and AI'black box ', and realize systematic mining, verification and interpretable application of lung cancer diagnosis factors. The method comprises the following steps: (1) mining high-frequency and emerging diagnosis factors from scientific research literatures through a multi-dimensional algorithm, and generating academic prior weights; (2) enabling the literature candidate factors to correspond to real patient data, and performing structured matching to obtain an aligned clinical candidate factor set; (3) screening out a core factor having a reliable causal relationship with the lung cancer outcome from the clinical candidate factors, and providing a causal-driven skeleton for the knowledge graph; (4) establishing an initial knowledge graph based on D3 and G, and dynamically updating an edge weight based on newly added data; and (5) outputting a clinical interpretable report which comprises a puncture score Pbiopsy, driving factor contribution degree ranking, an evidence chain and a recommended path.

The invention discloses a method for improving code writing efficiency. The method comprises the following steps: S1, acquiring and preprocessing a user programming instruction sequence; s2, constructing a behavior feature model, and generating a semantic map sequence in combination with big data analysis; s3, analyzing a user code and a project structure, and extracting context features of the code; s4, based on the semantic map sequence and code context features, using Transform to predict a current position code sequence under grammar constraints, and generating candidate segments; s5, grading and sorting the candidate segments according to the context structure matching degree, the semantic similarity and the confidence coefficient of the behavior feature model; s6, presenting the candidate code snippet with the highest score to the user and recording the response behavior of the user; and S7, storing the candidate code snippets and user feedback data, and updating model parameters and generation logic in real time. According to the method, through behavior-driven modeling, context fusion prediction and a multi-factor dynamic scoring mechanism, the code writing efficiency and quality are remarkably improved.

The invention discloses a large-burial-depth pipeline nondestructive detection method and system, and relates to the technical field of pipeline positioning. The system combines a region feasibility identification module, an initial positioning module, a multi-type nondestructive detection unit, a multi-path anomaly identification module, an underground structure matching module, an intelligent obstacle avoidance compensation module and a signal fusion module, and adopts a multi-source information fusion and intelligent path planning technology. And high-precision and non-intrusive nondestructive detection of the metal pipeline with the burial depth exceeding 5 meters is realized. Through comprehensive evaluation of ground obstacles and underground interference sources, the detection path layout is optimized, and the problems of signal interference, multi-path reflection and detection blind areas in the urban complex underground environment are effectively solved. The system can accurately reject non-target structure reflection signals, dynamically generate an obstacle avoidance compensation path, and improve the detection coverage rate and the signal integrity. And finally, three-dimensional space positioning and digital map coordinate output are realized, and the accuracy of pipeline positioning and the reliability of depth estimation are remarkably improved.

The invention relates to the technical field of electric connectors, in particular to an electric connector detection method and system based on machine vision, and the method comprises the steps: building triple template data and a template structure topological graph of an electric connector, and obtaining a binary mask of the electric connector; generating a topological graph structure matching value according to the binary mask of the electric connector and a template structure topological graph; generating an electric connector foreground image, and generating a material attribute matching degree; and performing matching degree calculation on the topological graph structure matching value and the material attribute matching degree, obtaining an electric connector change image, and generating an electric connector detection result according to the electric connector change image. According to the method, the comprehensive matching degree of the electric connector is calculated by combining the topological graph structure matching value and the material attribute matching degree, double constraints of structure topology and material attributes can be fused, comprehensive evaluation of the type attribution and the quality level of the electric connector is formed, and the detection accuracy of the electric connector is improved.

The invention discloses a heterogeneous computing power resource collaborative management system and method based on deep learning, and belongs to the technical field of computing power resource management. The method comprises the steps of receiving task description, performing semantic analysis to extract computing power core features to form a feature set, converting the feature set into task vectors, inputting the task vectors into a pre-training model, and outputting a computing power demand range; extracting to-be-selected host parameters and states to establish a feature library, obtaining an adaptation list through core computing power hard matching, heterogeneous scene adaptation and state filtering, and updating state binding after a user selects a host; extracting storage features from the task vectors, calculating initial capacity and combining host calibration, abstracting storage resources and demands into graph structure matching to obtain a partitioning scheme, calculating redundant capacity and integrating the redundant capacity into storage configuration; and extracting network demand indexes from task vectors, hosts and storage configurations, establishing a network feature library, defining screening rules, and determining an optimal network scheme by using a path optimization algorithm after matching adaptive resources.

The invention discloses an agricultural large model evaluation method and system based on a knowledge graph, relates to the field of machine learning, and solves the problems that an existing agricultural large model evaluation method lacks a fact verification basis, scores cannot be explained and the structure matching capability is weak. S2, constructing an agricultural knowledge graph based on the agricultural knowledge base; s3, performing intention expansion and entity expansion on original user query by utilizing the agricultural knowledge graph to obtain expanded natural language query; s4, inputting the expanded natural language query to a to-be-tested large model to obtain output; and S5, generating a comprehensive score in combination with the factuality verification and the text similarity verification, and realizing automatic testing and feedback through a scoring platform. The knowledge graph-driven interpretable evaluation method has a remarkable application prospect in the field of intelligent agriculture, and the knowledge graph-driven interpretable evaluation method provides core support for agricultural intellectualization.

Apparatus for matching user data with third-party data structures include a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive user data, create a query data structure as a function of the user data, query a data repository using the query data structure to identify a match, and generate a recommended course of action as a function of the match. The query data structure includes a query attribute and a first temporal attribute. The data repository includes a plurality of third-party data structures, wherein each third-party data structure includes a data feature and a second temporal attribute. Identifying the matches includes computing an overlap score and comparing the overlap score with a matching threshold.

The invention relates to the technical field of bioinformatics, in particular to an esophageal cancer RNA-protein interaction space prediction method and device based on a graph neural network, and can solve the problem that it is difficult to accurately distinguish the difference of cancer tissue and normal tissue on RPI in a traditional method to a certain extent. The method comprises the steps of obtaining multi-stage space transcriptome data related to esophageal cancer, and performing preprocessing to obtain a multi-modal feature tensor and a canceration stage tag; using the multi-modal feature tensor and the canceration stage tag, using RNA and protein as nodes, defining a static edge based on sequence complementarity and structure matching degree, defining a dynamic edge based on spatial co-expression correlation, and constructing a space-time dynamic graph updated along with the canceration stage; the space-time dynamic graph serves as input, interaction existence, site coordinates and binding energy are predicted through space-time double-branch coding and attention mechanism fusion features, and an interaction prediction result is output.

The invention relates to the technical field of data query, in particular to a data query method based on a graph convolutional network. The data query method based on the graph convolutional network comprises the following steps: constructing an extraction and multi-layer semantic network of heterogeneous data; generating a multi-layer topology network conversion and layered topology sequence; performing semantic matching; matching sub-graphs embedded based on a GCN path; and carrying out structure matching and isomorphism verification. According to the method, heterogeneous data is organized into hierarchical network representation of an entity relation, an attribute relation and a time sequence relation through construction of a multi-layer semantic network, original semantic information of the data is reserved to the maximum extent, meanwhile, in combination with a semantic matching and topology verification technology based on a graph convolutional network, the method can flexibly capture complex semantic association, and the method has the advantages of being simple in structure and convenient to operate. According to the method, the expression ability and the query precision of the data are remarkably improved, and a brand new technical path is provided for efficient management and utilization of the heterogeneous data.

The invention relates to a method and a system for capturing and analyzing industrial network protocol data packets, which solve the technical problem that the existing industrial protocol analysis technology is poor in adaptability to unknown, private or non-standard protocols, and the method comprises the following steps: capturing and screening network data packets for protocol identification. If the protocol is the known protocol, calling a preset analyzer for analysis; if the protocol is an unknown protocol, extracting a protocol feature vector of the protocol, obtaining a candidate protocol and confidence through reasoning of a neural mapping model, and dynamically judging whether to start interactive reasoning to obtain user feedback, calibrating a parameterized protocol template and instantiating to generate an executable analysis rule according to the structural matching degree of an existing analyzer; and finally, registering and using the new analyzer to realize adaptive learning and analysis. The method has the following effects: intelligent identification of an unknown protocol, dynamic construction of an analyzer and system self-expansion are autonomously realized through an adaptive learning mechanism, and normal form upgrading from manual reverse analysis to system autonomous evolution is completed.

A Cybersecurity Event Handling Processor (CEHP) and method for processing security alerts includes: a File System containing a Universal Target Schema (UTS) of target language representations (UTS JSONs); a Normalizer running Feature Extraction and Word Embeddings algorithms; a Tree Converter; and a Transformer running linguistic and structural matching algorithms. The CEHP: (a) captures threat events in one or more native formats generated by cybersecurity tools; (b) runs Feature Extraction and Word Embeddings algorithms for tokenization and categorization of the captured events to create normalized events; (c) converts the normalized events into trees and then translates the trees into event representations in JSON (or XML) format (Event JSONs); and (d) runs nearest neighbor and / or linguistic and structural matching algorithms to compare the Event JSONs to the UTS JSONs to generate output JSONs (Translation JSONs) from the UTS corresponding to the captured events.

The invention discloses a multi-element perception fusion positioning system and method based on vehicle-road cooperation, and belongs to the technical field of intelligent network connection automobiles, and the method comprises the steps: collecting multi-mode sensor data at a vehicle-mounted end and a road side end, and constructing a local semantic map; cross-source alignment and fusion of semantic graphs are realized through a graph structure matching algorithm and a time window dynamic alignment mechanism, and collaborative semantic graphs are generated; on the basis of data source redundancy, observation angle difference and historical credibility distribution characteristics of nodes, a nonlinear confidence regulation and control model is constructed, and self-adaptive adjustment of multi-source data fusion weights is achieved; key semantic events are extracted on the basis of the fused semantic map, and semantic-driven position state reasoning is carried out in combination with a prior map and historical trajectory data; on the basis of real-time positioning error feedback, updating a confidence model structure and parameters by using an online sample mining and edge cloud collaborative optimization algorithm; the method has high precision and self-learning capability, and is suitable for an intelligent vehicle positioning system in a complex traffic environment.

The invention provides a passive multi-sensor multi-target angular trajectory association method, and relates to the technical field of optical target detection, and the method specifically comprises the steps: obtaining angular trajectories obtained by observing all targets through two observation platforms, and obtaining an angular trajectory association relationship set; according to each association matching relationship in the angular trajectory association relationship set, obtaining a target three-dimensional position estimation result; obtaining a target spatial position according to the target three-dimensional position estimation result; obtaining a first observed quantity according to the target spatial position; obtaining a second observed quantity, and carrying out topological structure matching according to the first observed quantity and the second observed quantity to obtain topological structure matching error statistics; obtaining inclination angle difference statistics, and obtaining association cost statistics of each association matching relation according to the topological structure matching error statistics and the inclination angle difference statistics; and obtaining an angle trajectory association result according to the association cost statistics and a preset judgment threshold. According to the invention, the accuracy of angle trajectory association in a dense target scene can be improved.

The present invention relates to the field of three-dimensional model interaction technology, and discloses a method and system for making an interactive electronic manual of a three-dimensional model, including: obtaining three-dimensional structure data and function data of a target object; constructing a structure manual according to the three-dimensional structure data, and constructing a function manual according to the function data; performing structure matching on the structure manual, and establishing function interaction on the function manual; during the interaction process, generating an interaction guidance strategy through a knowledge graph, and guiding the interaction process according to the interaction guidance strategy. It improves the organization and visualization effect of the electronic manual, enabling users to more intuitively understand the structural relationship and functional logic of the product. By using a knowledge graph to construct an interaction guidance strategy, it realizes intelligent recommendation based on the user operation path and dynamic interaction optimization, effectively reducing the learning cost and improving the information acquisition efficiency.

This invention discloses a risk quantification method for AI large-scale models in engineering safety production, relating to the field of engineering safety technology. The method includes: S1, acquiring a domain causal template library constructed based on preset engineering safety standards, the domain causal template library including multiple standardized causal chain structure templates; S2, parsing the inference text generated by the main model in real time, and constructing an instance graph reflecting the current safety inference logic; S3, performing structural matching between the instance graph and the templates in the domain causal template library, and determining whether there is a structural deviation in the inference chain of the main model based on the matching result; if so, triggering a backup model to take over; S4, extracting key safety commitments from the inference context or instance graph of the main model, converting the key safety commitments into formal logical constraints, and injecting the formal logical constraints into the backup model; S5, verifying the response generated by the backup model and determining whether it satisfies the formal logical constraints.

The application discloses an image retrieval method based on graph structure matching, and inputs the graph structure of a query image into a trained graph neural network to obtain the node features of the query image and the relative and absolute positions between the nodes, wherein the graph neural network comprises a feature extraction module, a relative position prediction head and an absolute position prediction head; the feature extraction module is used for extracting the node features of the graph structure; the relative position prediction head and the absolute position prediction head are respectively used for predicting the relative and absolute positions between the nodes; and the training of the network is completed by calculating a classification loss and an auxiliary loss. The application improves the training effect of the network and greatly improves the performance of image retrieval.