417 results about "Structure diagram" patented technology

The invention discloses a transformer fault diagnosis and root cause positioning method based on a space-time diagram neural network, and relates to the field of transformer fault diagnosis, and the method comprises the steps: S1, carrying out the preprocessing of the structure information and DGA time series data of a transformer, and obtaining a topological network structure diagram and a DGA time series; s2, obtaining a spatial vector based on a message passing mechanism of a graph convolutional network; s3, a time sequence vector is obtained in combination with a Transform encoder and multi-head self-attention; s4, obtaining space-time fusion features; s5, constructing a multi-task prediction head based on the space-time fusion feature, the fault type historical data and the fault root cause; and S6, carrying out fault detection and outputting a corresponding fault type and root cause positioning result. According to the application, the accuracy of fault type identification can be remarkably improved, and accurate positioning of the fault root cause is realized.

The invention discloses an FTU-based power distribution network fault positioning method and system, and belongs to the technical field of power distribution automation, and the method comprises the steps: constructing a space-time correlation feature matrix according to a transient current sequence and a voltage drop sequence during a fault period, and extracting the convolution features of a graph to obtain a fault feature graph containing the fault correlation degree between nodes; according to a static topological structure in the power distribution information model, virtual impedance is calculated based on the fault feature graph, and network equivalent topology is dynamically identified to obtain a dynamic virtual topological structure graph; and based on the dynamic virtual topological structure diagram structure and the fault feature diagram, performing fault section confidence competing decision through the intelligent agent unit corresponding to each FTU based on an incomplete information game, and outputting a fault section positioning result. The power distribution network fault positioning method solves the problems that a traditional power distribution network fault positioning method is insufficient in positioning accuracy and poor in fault tolerance and excessively depends on centralized processing and global information synchronization when information is incomplete, fault features are complex and network topology dynamically changes.

The invention discloses a software variation testing system and method based on structure-semantic fusion, and the system comprises a document analysis module, a structure diagram construction module, an agent module, a defect injection module and an influence domain verification module.The method comprises the steps that a function document or natural language description serves as an entry, and after a code structure diagram (such as a function call diagram) is constructed through static analysis, the influence domain is verified; an agent autonomously explores semantic information of a code under the guidance of a structure diagram, precise positioning from function description to a target code position is completed, and defect injection and influence domain verification are carried out.

The invention relates to the field of production scheduling, in particular to an intelligent scheduling method and system for towel production plans. The method comprises the following steps: obtaining towel production order data and a process execution sequence of a towel production information platform, constructing a dependency relationship between process task nodes and tasks according to the process execution sequence and the towel production order data, and generating a towel process task chain set; analyzing resource occupation overlapping conditions among the towel process task chains to construct conflicting resource nodes, analyzing the conflicting resource nodes, constructing conflicting paths, generating a conflicting chain graph, and dividing conflicting release priority areas; analyzing the towel process task chain based on the conflict release priority area and the conflict chain atlas, generating an optimized task chain, and obtaining a schedulable task chain and a path adjustment record; and updating the process task node through the path adjustment record, and generating a towel scheduling table and a towel scheduling structure chart. The method can improve the reasonability of towel production resource scheduling.

The invention discloses an unmanned aerial vehicle wind power blade defect dynamic detection method based on AI vision, and relates to the technical field of JSLYMC, and the method comprises the following steps: S1, collecting an original dynamic image sequence; s2, generating an aligned image sequence; s3, generating a standardized blade surface image sequence; s4, constructing a blade topological structure diagram; s5, inputting the graph structure data into the improved TransGAT model, and outputting a defect candidate set; s6, inputting the defect candidate set into the FairMOT model, and outputting a defect space-time trajectory; and S7, generating a structured detection report. The method overcomes the limitations of dependence on manual inspection, poor dynamic adaptability and insufficient identification precision in a traditional wind power blade defect detection method, and provides an efficient and accurate solution for unmanned aerial vehicle wind power blade automatic inspection and intelligent maintenance decision.

The invention belongs to the technical field of target detection, and particularly relates to a radar image restoration and small target detection method based on adaptive sparse modeling, and the method comprises the steps: collecting original radar signal data, obtaining an initial low-quality radar image through preprocessing, and constructing a signal feature template; carrying out adaptive step size division, sparse decomposition and dynamic threshold judgment to obtain a characteristic coefficient matrix; a double-norm dynamic weighting optimization model and a local noise sensing mechanism are utilized, and alternate iterative optimization is combined, so that a high-quality restored image is obtained; obtaining a signal point enhanced image through a three-scale Gaussian kernel collaborative detection and information entropy quantization weighted fusion strategy; outputting a signal point correlation structure chart and a structured feature matrix; and outputting a small target category probability through training and parallel reasoning by adopting a fusion network of an image path and a graph structure path. The method has the capabilities of small target high-precision detection, cross-equipment efficient adaptation and real-time processing in a complex scene.

The invention discloses a wading concrete micro-crack intelligent evaluation method and system based on a visual depth model. The method comprises the steps that a concrete structure image is collected through an unmanned aerial vehicle and preprocessed; a crack area of the collected concrete structure image is segmented through a GANFormerNet segmentation model, and a segmentation method comprises the following steps: extracting global semantic crack features of the image, optimizing and fusing crack features crossing different scales, modeling a spatial topological relation between crack pixels, and segmenting the crack area of the concrete structure image; based on the output segmentation result, pixel-level quantitative analysis is carried out on the crack area, and an analysis result is integrated into a user interface system; according to the method, refined segmentation of concrete cracks and accurate quantification of physical parameters are achieved, indexes such as mIoU and F1-score are remarkably superior to those of a traditional method, and the method is suitable for automatic detection of surface cracks of hydraulic structures.

The invention provides an automobile TARA analysis method and system based on multi-modal input and hybrid intelligence, and the method comprises the steps: extracting text entity information and image objects from vehicle design data, carrying out the intelligent matching of the text entity information and image objects, and constructing a structured asset list, detailed attribute description and a network topology structure diagram; performing threat identification and attack path deduction on the identified assets, and retrieving most relevant information fragments; and calling a large language model to generate an analysis result based on the multi-stage structured prompt, and outputting a standardized TARA report after post-processing. According to the invention, through the improved target identification model, the detection precision and robustness of tiny icons, slender buses and shielding elements in a complex and intensive automobile framework diagram are obviously improved; according to the method, the accuracy and authority of large language model reasoning are improved through path deduction and most relevant information fragments; the subjective deviation of manual analysis is eliminated while the analysis time is greatly shortened, and the high consistency of analysis results is ensured.

The invention relates to the technical field of knowledge tracking, and discloses a graph enhanced double-memory collaborative knowledge tracking model based on an ACT-R cognitive architecture. Comprising a static knowledge structure coding module based on hypergraph projection, a batch-level dynamic learning track construction and coding module, a cross-graph gating fusion mechanism, a sequence modeling module and an expert hybrid prediction module. According to the method, long-term stable structured semantic association between concepts in declarative memory is modeled through a static knowledge structure diagram, a dynamic learning trajectory diagram based on batch reconstruction is designed to accurately capture an evolution rule of a behavior sequence in programmed memory, and on the basis, a cross-diagram gating fusion mechanism and a hybrid expert mechanism are introduced, so that the evolution rule of the behavior sequence in the programmed memory is accurately captured. And self-adaptive fusion and multi-path decision of double-graph features are realized.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses a structure safety risk reasoning method and system based on a multi-modal graph, and relates to the technical field of pattern recognition, and the method comprises the steps: obtaining the multi-modal information of a to-be-evaluated city structure group, representing the structure units in the structure group as graph nodes, distributing node attributes for each graph node, and obtaining the multi-modal information of the to-be-evaluated city structure group; defining edges according to the relationship among the structural units, and constructing to obtain a multi-modal heterogeneous graph; on the basis of the multi-modal heterogeneous graph, graph nodes are coded by using a graph neural network, the risk state of each graph node is learned, propagation of risks among the graph nodes is simulated, and risk state prediction of each graph node and a risk propagation path among the graph nodes are obtained; and outputting a risk state prediction result and a risk propagation path of each graph node. Through the technical scheme of the invention, a more real and more representative structure diagram is constructed, the physical process of structure performance degradation is better fitted, the adaptability and expansibility are high, and the visualization and interpretability are good.

The invention discloses an image processing method and device for an aerial remote sensing image and a storage medium, and relates to the field of image analysis, and the method comprises the steps: obtaining an aerial image shot by an unmanned plane in real time, and carrying out the image preprocessing of the aerial image, and obtaining a preprocessed aerial image; performing rigid framework extraction on the preprocessed aerial image to obtain an original incomplete framework graph after gravel interference is removed; carrying out fracture framework repair and completion on the original incomplete framework diagram to obtain an unmanned aerial vehicle visual angle topological structure diagram; performing elastic topological mapping matching on the unmanned aerial vehicle visual angle topological structure diagram and a pre-stored satellite reference diagram to obtain a matching result; and carrying out pose calculation according to a matching result, and outputting accurate coordinate information of the unmanned aerial vehicle in the world coordinate system. According to the method and the device, high-precision positioning can be realized in an extreme environment in which the GPS is rejected and the surface texture is subjected to unstructured deformation.

The invention relates to a mining and protecting method and system for a multi-dimensional high-value target of a big data platform, and the method comprises the steps: collecting multi-source heterogeneous data from a plurality of platform data sources, carrying out the information extraction, and forming a fusion triple comprising entity type feature data, attribute type feature data and relation type feature data, constructing a dynamically updated platform structure map based on the fusion triad; performing asset function value evaluation, asset topology importance evaluation and attacker accessibility evaluation on each type of nodes in the platform structure map to obtain a corresponding comprehensive risk score, and identifying a high-value target set; and in response to a security alarm event, associating alarm information with the platform structure map to position an alarm node, thereby extracting a serialized context sub-graph from the platform structure map in combination with the high-value target set, performing attack intention recognition, and outputting an attack intention report. According to the method, a security protection system with perspectiveness and semantic understanding capability can be provided for a big data platform.

The invention belongs to the technical field of task disassembly, and provides an electric power scene tool intelligent complex task disassembly method and system, and the method comprises the steps: carrying out the semantic analysis of an original task text, and extracting an entity, an operation target and a dependency constraint in the original task text; constructing a semantic triple based on the extracted information, and forming a semantic structure chart by taking the constructed semantic triple as an edge of a task atlas and taking a node of the task atlas as task operation; converting and complementing the structure of the semantic structure graph to form a task triple, and fusing the task triple with the current electric power knowledge graph; performing graph path search and logic rule reasoning on the fused electric power knowledge graph by using a pre-trained large language model, and disassembling to generate a subtask sequence conforming to semantic logic and security constraints; and mapping the disassembled subtask sequence chain into a standardized intention instruction, and issuing the standardized intention instruction to each own intelligent control subsystem. According to the method and the device, the accuracy and controllability of task disassembly can be improved.

The invention provides an automobile part defect intelligent detection method and system based on multi-modal data fusion, and the method comprises the steps: collecting image data and three-dimensional point cloud data of an automobile part, and carrying out the space alignment processing, and obtaining a registration modal set; constructing a structure diagram, extracting modal features of a corresponding region from the registration modal set, and generating a structure diagram node embedding vector through a cross-modal fusion strategy; based on the structure diagram and the structure diagram node embedding vector, node-level reasoning is carried out by utilizing a pre-constructed diagram neural network, and a node state vector and a structure diagram node defect state sequence are output; and inputting the structure diagram node defect state sequence of the same automobile part at the plurality of detection time points into a time sequence modeling network guided by a diagram structure, outputting a defect risk score, and outputting a corresponding quality control strategy based on the current defect evolution risk. According to the invention, accurate identification, positioning, grading and trend prediction of automobile part defects can be realized in a complex industrial environment.

The invention discloses a trade commodity dynamic inventory management and control system based on machine learning, and the system comprises the following modules: a structure state module which is used for obtaining the inventory structure state data of each commodity and generating a corresponding inventory structure state vector; the structure diagram module is used for constructing a structure connecting edge and generating a commodity dynamic diagram; the structure aggregation module is used for performing structure feature aggregation on the commodity adjacency relation to generate structure aggregation features; the demand prediction module is used for outputting a demand prediction result and demand plasticity parameters based on the improved iTransform; the inventory decision module is used for executing inventory decision calculation and generating an inventory adjustment track; and the execution feedback module is used for generating an inventory control instruction according to the inventory adjustment track, executing an inventory control operation and updating the inventory structure state data. According to the invention, through machine learning prediction of inventory structure perception, dynamic executable management of trade commodity demands and inventory regulation and control is realized.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical science and technology, and discloses an intelligent decision-making method, device and equipment based on a graph structure and a medium. A visual encoding result and a language encoding result are obtained through encoding of the visual encoder and the language encoder respectively; a graph structure is constructed according to the coding result, nodes in a graph are visual feature vectors in the visual coding result and language feature vectors in the language coding result, and edges are dynamically established based on semantic, spatial or temporal relevance between the visual feature vectors and the language feature vectors; then, the graph structure is input into a large language model for reasoning to obtain feature representation, and finally, an action sequence of the intelligent agent is generated through decoding of an action decoder. According to the method, multi-modal information is fused through a graph structure, reasoning is performed by using a large language model, and more accurate and flexible agent decision is realized.

The invention relates to the technical field of digital supervision, in particular to a digital supervision method and system for a power construction project, and the method comprises the following steps: collecting a surface three-dimensional coordinate point clustering calculation component point cloud cluster, extracting an actual measurement central axis through employing a random sampling consistency algorithm, comparing a design axis net coordinate, calculating an axis net deviation value, and calculating the point cloud cluster. And constructing an axis network topological structure chart, traversing and comparing deviation values, analyzing and marking a primary deviation source, and performing recursive calculation to judge a deviation conduction path. According to the method, three-dimensional coordinate point clusters are collected, actual measurement central axes of components are calculated to restore geometric morphology, a space translation vector and a rotation matrix are calculated by comparing with a designed axis net, an objective deviation value is quantified, manual subjectivity is eliminated, meanwhile, an axis net topological structure diagram is constructed through component connection, and recursive calculation is carried out in combination with the deviation value, so that the precision of the axis net topological structure diagram is improved. A deviation source is effectively analyzed and marked, a deviation conduction path is judged, and closed-loop management from deviation discovery to source diagnosis is achieved.

The invention provides a clothing pattern intelligent generation method and system based on multi-modal visual identification, and relates to the technical field of visual identification. The method comprises the following steps: firstly, acquiring clothing image data and text description data, performing unified preprocessing, obtaining a style category label, an appearance attribute label and a local contour key point set through multi-modal visual identification, generating structure semantic elements and geometric anchor points by utilizing a semantic-to-structure mapping knowledge base, and constructing a plate type structure diagram; further analyzing the structural constraint and executing constraint solution to generate a candidate pattern; and in combination with manufacturability verification and constraint correction, closed-loop optimization is formed, a target plate type file meeting the structure and manufacturing requirements is output, and effective connection from semantic recognition to plate type structure generation is realized.

The invention provides a hydraulic floating bridge ontology-free data reliability analysis method based on a time sequence decoupling network and a Bayesian network, and relates to the technical field of hydraulic floating bridges. Comprising the steps of determining source domain equipment according to a bill of material and a structure diagram of a target hydraulic floating bridge; determining a standardized time sequence monitoring data set of the source domain equipment; training a time sequence decoupling network by using the standardized time sequence monitoring data set to obtain a health probability data set of each key component of the target hydraulic floating bridge; inputting the health probability data set into a four-layer Bayesian network, and recursively calculating the posterior failure probability of each layer from bottom to top by adopting a belief propagation algorithm to obtain failure probability data and posterior probability distribution data; and performing risk assessment according to the failure probability data and the posterior probability distribution data to obtain a risk assessment result. The technical problems that in the prior art, data scarcity and model island exist in operation and maintenance of a hydraulic floating bridge and similar equipment, and reliability evaluation cannot be carried out are solved.

The invention discloses a transformer winding vibration abnormity diagnosis and early warning method based on lightweight edge calculation, and relates to the technical field of transformer monitoring. According to the method, vibration signals and magnetic flux signals of a transformer winding are collected in real time at edge nodes, and a basic data set is obtained after preprocessing; vibration and magnetic flux features are extracted to construct a basic diagnosis feature vector, a three-dimensional tensor is constructed through directional coherence analysis, a boundary enhancement structure map is generated, and a structure enhancement fusion feature vector is formed after fusion. The method comprises the following steps of: realizing abnormal grade and trend prediction by a lightweight Transform diagnosis model based on knowledge distillation and parameter compression optimization, and triggering model adaptive updating by combining risk index modeling and a dynamic risk threshold value. And finally, calculating an abnormal level posterior probability through a Bayesian updating rule, and outputting multi-level early warning. The method has the advantages of high precision, low calculation overhead and self-adaptive capability, and is suitable for online monitoring and intelligent operation and maintenance of the state of the transformer.

An evaluation system, method and program product based on a large language model relate to the technical field of artificial intelligence. The system obtains an evaluation object and an evaluation task configuration, constructs an evaluation structure graph containing theme segments, argument segments, basis segments and reference segments and their support, reference and conflict relations, filters key semantic units, and constructs a coupling representation between key semantic units, evaluation dimensions, dimension rules, candidate evidence segments, evidence positions and unit-evidence relationship markers; input the coupling representation into a large language model to generate an initial evaluation result; construct a statement disturbance set around the key semantic units that keeps the evaluation theme and core facts unchanged, obtain consistency indicators through re-evaluation, and calculate joint confidence by combining evidence coverage, evidence conflict and evidence chain drift; and perform evidence weighting and local calibration on low-confidence target evaluation dimensions. The scheme can improve the accuracy, stability, explainability and traceability of the evaluation result.

The invention relates to the technical field of data retrieval, in particular to a meat detection data management system and method, and the system comprises a data model dynamic adjustment module, a data relation mapping module, a database architecture evolution module, a data merging optimization module, a history tracking and analysis module, a data integrity recovery module and a system performance optimization module. According to the method, the decision tree algorithm and the Bayesian network play a key role in dynamic adjustment of the data model, the optimal data model structure is effectively recognized, the graph database technology and the PageRank algorithm are used for constructing and optimizing the data relation network graph in data relation mapping, the effectiveness of the data storage structure is enhanced, and the data storage efficiency is improved. The clustering analysis method and the K-means algorithm improve the efficiency of data processing, the data recovery algorithm and the transaction log analysis technology ensure the accuracy and integrity of data in data integrity recovery, and the overall performance and stability of the system are remarkably improved through reasonable resource allocation and load balancing of the system monitoring and tuning strategy.

This invention belongs to the field of cashmere raw material sorting and processing technology, specifically disclosing a method for intelligent sorting and processing quality control of cashmere raw materials. The method includes: acquiring multi-dimensional sensing data of the cashmere raw materials to be processed, including fiber scale structure images, surface gloss images, and component spectral data; performing feature-level fusion processing on the multi-dimensional sensing data to generate quantitative feature parameters; inputting the quantitative feature parameters into a machine learning hierarchical decision model to generate sorting instructions; controlling a nozzle array actuator that combines negative pressure adsorption and positive pressure jetting to perform non-contact automatic sorting according to the sorting instructions; and collecting real-time process quality data of subsequent combing and spinning processes through an IoT platform, and dynamically adjusting the sorting threshold and raw material ratio strategy based on this data. This invention achieves quantitative evaluation and closed-loop quality control of cashmere raw material quality, improving the accuracy of sorting, the adaptability of the production system, and the quality stability of the final product.

The application provides a computer vision-based change detection method, system, device and medium, and relates to the technical field of structural health monitoring. The application collects images of a structure to be measured with targets through a visual measuring instrument, identifies and constructs image coordinate groups of the targets; then jointly solves all coordinates to obtain three-dimensional positions of the targets in a unified coordinate system, and further calculates multi-dimensional key parameters reflecting the overall and local states of the structure; then inputs these parameters and environmental data into a space-time propagation model, constructs a structure graph with key parts as nodes according to physical topology; then simulates the space-time propagation process of the structure state on the graph to determine the evolution mode; finally, determines the safety level of the structure according to the mode and executes early warning, which can improve the foresight and accuracy of structure safety early warning.

The application provides a kind of ore grade distribution real-time detection method, system and equipment, it is related to mining technical field, method mainly includes: based on the operation map of mining area, centralized control center determines blast design hole coordinate;Automatic drill travels to a hole position and is in position;Automatic drill carries out drilling operation, obtains drill cuttings and records operation parameters in real time;Automatic detection platform is sampled to drill cuttings by sample collection device continuously by continuous flow;Through drying, tabletting, analysis processing, ore grade content and internal profile structure diagram, and match corresponding hole data;Based on ore grade content, internal profile structure diagram and hole data, single-hole model characteristic curve is obtained by fusion;The above operation is carried out to all hole positions;The average value of the single-hole model characteristic curve of adjacent hole positions is calculated, and curve fitting is carried out to obtain the ore grade spatial distribution imaging map.The scheme can provide intuitive guidance for ore dressing of mine, and can greatly improve the mining efficiency and quality.

The application relates to the technical field of intelligent temperature control, and discloses a pressure roller heating temperature control method and system of a PE packaging line and a storage medium, the method comprising the following steps: taking process variables and environmental variables in high-dimensional time sequence data as nodes and taking dynamic causal relationships as edges to construct a dynamic causal structure diagram of the packaging line; taking the pressure roller temperature as a target node, screening a causal influence relationship to obtain a local network, tracing a key path to obtain a precursor variable; mapping a dynamic behavior mode of the precursor variable to generate a pressure roller temperature feedforward compensation strategy; analyzing the feedforward compensation strategy and a real-time state vector of the precursor variable to obtain a predictive compensation amount; based on the predictive compensation amount, a pressure roller temperature control instruction is cooperatively decided, and a comprehensive control instruction is generated; according to the execution effect of the comprehensive control instruction and topological consistency, the strategy applicability is judged, and a reconstruction trigger signal is obtained; and the application can improve the pressure roller temperature control operation efficiency of the PE packaging line.

The application discloses a steel bar mesh welding defect identification method based on a graph neural network, generates a standardized reference image, forms a welding area candidate set, extracts a steel bar mesh structure graph, marks a suspicious welding point position, forms a steel bar mesh structure graph, executes a topology keeping graph embedding operation on the steel bar mesh structure graph, constructs a low-dimensional embedding space, outputs a node embedding representation and a connected confidence graph, divides the reference image and the steel bar mesh structure graph into a plurality of image blocks and graph structure blocks, outputs a fusion attention feature graph including local details and cross-cell continuity features, judges whether the connectivity of each candidate welding point is abnormal, locates a welding breakpoint, outputs a defect position and a breakpoint type, and performs consistency verification on a judgment result. The application significantly improves the structural consistency and anti-artifact capability of welding breakpoint detection in a complex scene.