79 results about "Structure matching" patented technology

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 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 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 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 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 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.

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.

The application discloses a Text-to-SQL-oriented SQL generation quality evaluation method and system, and the evaluation method comprises the following steps: obtaining to-be-evaluated generated SQL, reference SQL and execution environment information; performing standardized processing on the generated SQL and the reference SQL; splitting the standardized generated SQL and the standardized reference SQL according to preset key clauses, and calculating a structure matching score according to preset weights of the key clauses to obtain an optimized accurate matching rate EM+; executing the generated SQL and the reference SQL, calculating an execution deviation score based on column mapping and result set comparison to obtain an optimized execution accuracy EX+; and outputting at least one of EM+ and EX+ as a SQL generation quality evaluation result. The application can solve the problems that existing evaluation indexes are not sensitive to aliases, equivalent expressions and partial errors or are too rough, and can realize fine-grained, interpretable and extensible evaluation of SQL generation quality.

The invention discloses a full-process automatic tracking method and system for property work order processing, particularly relates to the technical field of service scheduling, and is used for solving the problem of unreasonable property work order task allocation. According to the method, a standardized work order feature set and a service resource set are constructed, and an adaptation relation matrix is constructed in combination with task skill integrating degree, arrival accessibility, time window overlapping degree and conflict indexes, so that structured matching and path screening of work orders and personnel are realized, and resource binding conflicts and path overlapping are effectively avoided; in the execution process, node changes are dynamically tracked through a state mapping graph, abnormity is recognized in real time, task path reconstruction is executed, the self-healing capacity of a scheduling system is improved, behavior tracks and feedback information are further collected, a behavior feature data set is constructed, strategy deviation groups are recognized through a clustering algorithm, scheduling parameters are updated, and a closed-loop optimization mechanism is formed; therefore, the property work order processing efficiency and the user satisfaction degree are improved.

This invention provides a method for simulating the effects of facial micro-plastic surgery, comprising: acquiring facial image data of a target user to construct a facial structure model, calculating its similarity with a preset celebrity sample library to determine a target style model, and extracting corresponding style structure parameters; determining structural offsets based on style structure parameters, mapping them to the corresponding regional positions of the facial structure model, and generating structural adjustment parameters; identifying and sorting aging features in the facial image data to generate an aging feature sequence, and constructing facial structure deformation rules in conjunction with the structural adjustment parameters to perform regional deformation calculations on the facial structure model, obtaining an adjusted facial structure model, and generating a corresponding simulated facial image. This method improves the structural matching degree and simulation naturalness between the simulated results of facial micro-plastic surgery and the target style model, overcoming the problem of insufficient adaptation of facial bone structure during facial micro-plastic surgery simulation.

This invention provides a method and system for intelligent optimization and scheduling of process nodes for enterprise business, relating to the field of business processing technology. The method includes obtaining a description of business objectives and a sequence of executed process steps, retrieving historical process nodes based on deep vector embedding, generating business intent features using a bidirectional long short-term memory network, constructing a context state representation for structural matching, and finally constructing a dynamic heterogeneous graph structure to generate a weighted recommendation sequence. This invention achieves accurate recommendation and intelligent scheduling of enterprise processes, improving process execution efficiency and the achievement rate of business objectives.

The invention belongs to the technical field of time sequence anomaly detection, and provides a semantic and structure collaborative dual-path time sequence anomaly detection method and system. Comprising the following steps: constructing a dual-path anomaly detection model which comprises a frequency domain enhanced time domain reconstruction path and a semantic guided structure modeling path; inputting the input sequence into the frequency domain enhanced time domain reconstruction path to obtain a time domain reconstruction error; inputting the input sequence into a semantic-guided structure modeling path to obtain a structure matching deviation; performing standardization and adaptive weighted fusion processing on the time domain reconstruction error and the structure matching deviation to obtain an adaptive fusion score as a final abnormal score; and obtaining to-be-predicted server cluster time sequence data, calculating an anomaly score by adopting the trained dual-path anomaly detection model, and outputting potential anomaly. According to the method, the technical problem that a model in the prior art is insufficient in discrimination capability, stability and generalization of a time sequence abnormal mode of a complex system is solved.

The invention relates to a structure period segment division method based on structure dynamic response similarity, which is characterized in that on the basis of structure dynamic response under seismic action, objective division of structure period segments is realized by constructing response characteristic matrixes corresponding to different structure periods and introducing a fuzzy clustering mechanism to perform unsupervised classification on the structure periods. According to the method, linear and non-linear structure response characteristics are considered at the same time, the optimal period segmentation number and the boundary threshold are determined in combination with the clustering effectiveness evaluation index, and a more accurate thought and method are provided for more rapidly evaluating seismic damage of the structure in the specific period range and matching input vibration of the structure in the specific period range.

The invention discloses a query response and code generation method and system based on power field document analysis, and the method comprises the steps: carrying out the analysis and preprocessing of an input field document, and constructing a document text logic diagram; constructing a domain knowledge graph; receiving user query, and executing structure matching retrieval based on a document text logic diagram and association reasoning retrieval based on a domain knowledge graph to form a traceable evidence packet; constructing a structure enhancement context based on the evidence packet, inputting the structure enhancement context into a domain language large model, and generating a response containing a natural language description and a structured domain language code; and performing instant compilation and tensor simulation verification on the structured domain language code, triggering retrieval and generated closed-loop correction based on deviation information when the verification is not passed, and outputting an analysis result and the code when the verification is passed. According to the method, the accuracy, interpretability and output reliability of long document processing in the electric power field are improved.

The invention discloses a test paper detection system based on computer vision, and relates to the technical field of computer vision, during operation of the system, digital images of original test paper are obtained, standardized preprocessing operation is executed, and standardized input of test paper images is achieved through high-precision imaging and multi-step preprocessing; and then finishing test paper region extraction, layout key point detection, structural relationship matching and character and handwriting content identification through a co-processing engine comprising a pixel-level segmentation network, a layout adaptive analysis unit and a multi-modal information fusion center. According to the system, multi-format adaptation is achieved through dynamic graph structure matching and a self-evolution knowledge base, structural analysis of the content level is completed in combination with an OCR assembly line and handwriting neatness analysis, when the recognition result is lower than a threshold value, a directional feedback loop is triggered to improve reliability, a multi-scale reasoning strategy and distributed deployment based on micro-service are supported, and the system is suitable for being applied to the field of information processing. And finally, generating a structured detection report conforming to the standard.

The application relates to the field of natural language processing, and discloses a cross-language entity alignment method and system based on relationship semantic enhancement, which comprises the following steps: constructing entity initial embedding and relationship embedding representation of a cross-language knowledge graph; a key neighbor screening stage; screening key neighbor entities with structural matching and semantic matching based on an entity similarity matrix and a relationship similarity matrix and in combination with a preset semantic correlation threshold; neighbor-enhanced entity embedding updating; adopting a graph attention network to fuse semantic features of target entities and key neighbors thereof, and generating context-aware entity embedding; semantic consistency alignment propagation: first-order entity feature alignment and second-order relationship structure alignment are jointly optimized through a shared permutation matrix; dynamic seed expansion iteration: high-confidence unaligned entity pairs are dynamically identified according to Manhattan distances of entity embedding, entity pairs with distances lower than a threshold are added to a seed set, and the foregoing steps are iteratively executed until convergence.

The present application relates to a kind of metabolic pathway extension network based on structural feature automatical establishment method, belong to bioinformatics and computational chemistry technical field.The purpose is to solve the problem of existing KEGG database incomplete, annotation lag of metabolite.Method includes: obtaining KEGG skeleton and modified group construction seed library;Through substructure matching and set difference calculation to determine the attribution of derivative;Iterative expansion forms structure association set;Combining stereochemistry rule and mass difference verification screening derivative;Merging KEGG reaction and extended relationship constructs network, and remove redundant path by graph theory method, finally output extended pathway network.The method greatly improves the annotation rate of metabolite pathway, supports in-depth functional analysis of metabolomics data.

The invention relates to the technical field of energy storage equipment monitoring, in particular to an energy storage cell pile three-dimensional heating power real-time rendering method and system based on multi-field coupling, and the method comprises the steps: firstly preparing parameters such as MaxDistance, Center List, ColList, Mesh and the like and configuration items such as CiLiao, Radius and the like, and carrying out the preprocessing, thereby obtaining a ColLength and world coordinate system vertex set MPlist; the hot spot temperature is converted into a Radius of [-1, 1] through a thermal radius control function, a drawCircle function is called to calculate a vertex thermal value, and the thermal value is mapped into a color through a colStep function; according to the energy storage cell pile three-dimensional thermal real-time rendering method and system based on multi-field coupling, millisecond-level response is achieved, spatial distribution of multiple parameters such as temperature can be reflected at the same time, the rendering precision and the structure matching degree are improved, the GPU load is reduced, and the real-time rendering efficiency is improved. The problems of poor real-time performance, multi-parameter coupling deficiency, insufficient precision and the like in the prior art are solved.

The application discloses a graph mapping method based on exact synthesis, comprising: constructing an exact synthesis structure library based on NPN equivalence class, and inputting a Boolean network; performing k-Cut cutting enumeration on the input Boolean network and calculating a truth table, matching the cutting with a structure in the exact synthesis structure library through Boolean matching, and realizing fast searching in combination with NPN classification; generating a cover network through multi-round mapping optimization, and mining a logic shared node in combination with structure hashing, wherein the multi-round mapping optimization comprises delay-oriented mapping, global area topology optimization and local exact area optimization; generating a new target network based on the optimized cover network, removing redundant nodes and completing final network construction. Through the automatic mapping and redundant removal mechanism of the whole process, the application does not need manual intervention, significantly reduces the manual cost of the technical mapping link in chip design, and is especially suitable for efficient design of complex circuits and emerging majority logic base technologies.

This invention relates to the field of intelligent process control technology in industrial production, specifically to an AI-based intelligent prediction method and system for electrolytic manganese production quality, comprising: a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module, and a deviation verification module. In this invention, by utilizing the response relationship between inputs and outputs at each stage, dynamic compensation is achieved for the drift of input parameters and the deviation between output parameters. Based on the correlation and matching between the cyclical changes in the production process and the fluctuation frequency between target control parameters, the actual impact of fluctuations in each parameter on target control parameters and product quality is effectively identified. Potential interference from key process parameters on the stability of each process step is promptly identified. By determining the path between residual liquid concentration trends and raw material addition locations, the spatial and temporal origin of impurity diffusion is clearly traced. Furthermore, migration path parameters are adjusted based on the ion retention behavior at boundary detection points.

This invention discloses a query response and code generation method and system based on document parsing in the power industry. The method includes: parsing and preprocessing the input domain document to construct a document text logic graph; constructing a domain knowledge graph; receiving user queries and performing structure matching retrieval based on the document text logic graph and association reasoning retrieval based on the domain knowledge graph to form a traceable evidence package; constructing a structure-enhanced context based on the evidence package and inputting it into a large domain language model to generate a response containing natural language descriptions and structured domain language code; performing just-in-time compilation and tensor simulation verification on the structured domain language code; if verification fails, triggering closed-loop correction of retrieval and generation based on deviation information; if verification passes, outputting the parsing result and code. This invention improves the accuracy, interpretability, and output reliability of long document processing in the power industry.

The invention discloses a motor fault diagnosis method and system based on Chladni image feature matching, and the method comprises the steps: firstly converting a vibration signal into Chladni images, then carrying out the feature extraction of each fault Chladni image through a SuperPoint model, carrying out the feature matching of the fault Chladni images through a SuperGlue model, and forming a fault image dictionary; in addition, preferably, the image dictionary is updated by using a farthest point sampling strategy based on similarity, and finally, the dictionary is matched with an unknown vibration signal to obtain a final diagnosis result. According to the technical scheme of the invention, semantic-level features extracted by SuperPoint and a graph structure matching mechanism of SuperGlue cooperate with each other, a mapping relation between a fault mode and a feature dictionary can be adaptively established, a fault graph dictionary with higher reliability is obtained, and the description limitation of traditional manual features on a complex vibration mode is overcome.

The application relates to the technical field of data processing, and provides an AI fool-proof algorithm and a digital model optimization method and system, which comprises the following steps: after collecting multi-modal process data, time alignment and event segmentation are performed to obtain a standard behavior sequence and an abnormal behavior sequence; a preset deep learning behavior prediction model is input according to the standard behavior sequence to obtain a behavior prediction result; structure matching is performed on the behavior prediction result and the abnormal behavior sequence to obtain an optimization instruction set and semantic prompt information; the preset deep learning behavior prediction model is optimized to obtain an optimized fool-proof intelligent prediction model. Through optimization of the original deep learning behavior prediction model, the self-adaptive recognition ability and the risk avoidance ability of the model to abnormal behaviors are improved, and the problem that in a complex actual application environment, high-frequency operation environment changes cannot be flexibly handled, resulting in that part of high-risk behaviors cannot be timely recognized or corrected, and the accuracy is low and the adaptability is poor is solved.

The invention discloses a complex event processing method based on an extended regular tree mode, and relates to the technical field of complex event processing. According to the method, the ERTP is used as a unified query mode description mode and comprises a node group, a regular type group, a structural relation axis group, a regular type assignment axis group, a conditional expression group and a return requirement, so that a structure, a time sequence and attribute constraints are expressed in a single mode at the same time, and query processing based on the ERTP comprises the steps that structure matching is executed on a tree mode part; partial matching results are stored in an instance set; when the root node list is updated, recursively enumerating and checking the condition expression, and outputting an event corresponding to a return node; performing symbol storage automaton (SMA)-based incremental timing matching on the regular portion; according to the method and the device, intermediate result generation and overhead can be reduced, and streaming complex event processing efficiency and accuracy are improved.