1579 results about "Structure based" patented technology

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

A system and method for latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses spatiotemporal media into navigable latent representations while preserving geometric and semantic relationships through tensor structure maintenance. A latent hyperspace manager organizes compressed representations as geodesic trajectories within a geometric manifold structure based on differential geometry principles. A geodesic trajectory mapper computes optimal navigation paths through the high-dimensional space, while symbolic anchors positioned at semantically significant locations serve as persistent reference points. Spatiotemporal routing protocols manage navigation decisions across multiple temporal scales. A strategy caching system preserves successful navigation patterns for reuse, enabling continuous learning. The system generates synthetic content during navigation to support infinite zoom capability, allowing exploration beyond original media boundaries. Cross-modal fusion combines diverse input modalities into unified representations, applicable to immersive media exploration, scientific visualization, and surveillance analysis.

The invention discloses a cartoon video generation method and system based on a video script, and relates to the technical field of video synthesis, and the method comprises the steps: based on structured script data, combining a predefined lens rule base and a reinforcement learning model, determining the type, duration and lens operation effect of a lens, and generating a lens splitting sequence through a dynamic lens splitting automatic generation mechanism, based on the split mirror sequence, generating an animation style key frame image through a diffusion model, selecting action data matched with the emotion label through a predefined action library, generating a voice waveform matched with the emotion label through a text-to-voice model, selecting a background music audio matched with the emotion label through a music library, and generating a multi-modal content stream; according to the method, the lens type, the duration and the lens operation effect are dynamically optimized through the reinforcement learning model, the defects of a traditional lens scheduling method based on static rule mapping in time continuity and narrative continuity are overcome, and the narrative fluency and the dynamic adaptability of the lens division sequence are obviously improved.

The invention relates to the technical field of detection, in particular to a complex shell detection method and system based on structured light scanning. The method comprises the following steps: calculating a wrapped phase value and a phase modulation amplitude of each pixel point under each frequency, calculating discrete probability distribution containing candidate stripe series and a corresponding probability for each pixel point based on a multi-frequency wrapped phase difference, generating an initial pixel confidence map, and constructing an energy function; the penalty weight of the smooth item is determined according to the phase modulation amplitude and the wrapped phase gradient local variance, obtaining an initial fringe order distribution diagram, updating discrete probability distribution by using an initial pixel confidence map, re-minimizing an energy function to obtain a corrected fringe order distribution diagram, and reconstructing a final three-dimensional shape. And iteratively optimizing a projector principal point and a radial distortion coefficient in system calibration parameters. According to the scheme, the geometric details of the surface can be reserved, and the processing effect and the measurement precision of the complex curved surface and the high-curvature area are improved.

The invention relates to the field of concrete detection, and discloses a concrete structure health real-time monitoring system based on multi-source sensing fusion, and the system comprises a data collection unit which carries out the collection of the physical field data of a concrete structure through a sensor array, and obtains the multi-source sensing data; performing structure response difference analysis on the multi-source sensing data to obtain concrete structure response difference data; according to the response difference data of the concrete structure, performing spatial and temporal distribution density analysis on a microcrack propagation path to obtain microcrack propagation spatial and temporal distribution density data; a data evaluation unit; multi-scale damage evolution path simulation is carried out through the structural damage time-space correlation characteristic data to obtain multi-scale damage evolution path data, the sensor array is used for collecting multi-source sensing data, structural response difference analysis is carried out, tiny changes of a concrete structure can be accurately captured, potential structural problems can be found in time, and the construction efficiency is improved. And a high-quality data basis is provided for subsequent analysis.

The invention discloses an image enhancement method and system based on semantic constraint degradation modeling. The method comprises the steps that semantic masks and multi-scale degradation features are extracted based on a low-resolution image used for training; performing deep fusion on the extracted semantic masks and the multi-scale degradation features based on a double-flow parallel architecture to generate semantic-structure fusion features; forming a multi-modal guide condition, taking the multi-modal guide condition and the semantic-structure fusion feature as input together, and reconstructing a high-resolution prediction image through a diffusion generation model; constructing a structure consistency optimization total loss based on the high-resolution prediction image and the corresponding target image, and optimizing a diffusion generation model based on the structure consistency optimization total loss; and inputting a low-resolution image to be predicted into the optimized diffusion generation model to obtain a high-resolution image corresponding to the low-resolution image. According to the scheme of the invention, comprehensive and refined understanding of low-resolution images is realized through multi-module cooperation and deep fusion.

The invention relates to the technical field of data processing, and discloses a building crack intelligent identification method and system based on multi-source data fusion, and the method comprises the steps: extracting crack initial contour features from a surface optical enhancement feature map, and collecting the features into crack space distribution features; compensating and registering the deformation quantity based on the thermal expansion characteristic of the material to obtain a fusion base image; performing crack trend adaptive frequency domain filtering processing on the fusion base image to obtain a feature enhanced image; performing multi-physical-field probability fusion on visible light texture, infrared temperature and a depth curvature gradient field in the feature enhanced image based on the brittle fracture parameters of the material to obtain a crack probability distribution diagram; the topological connectivity is optimized through multi-scale morphological iteration, and a continuous crack mask is obtained; analyzing the spatial trend of the building structure based on the stress distribution characteristics of the building structure to obtain a crack risk level; according to the invention, the accuracy of building crack intelligent identification based on multi-source data fusion can be improved.

The invention discloses an IOT (Internet of Things) equipment fault prediction method based on GraphRAG. The IOT equipment fault prediction method comprises the following steps: step 1, collecting and preprocessing multi-source heterogeneous data of various IoT equipment terminals; 2, constructing a graph structure based on the preprocessed data, a dynamic edge weight mechanism and a knowledge sub-graph; according to the graph structure, physical connection, functional dependence and communication topology between IOT devices are naturally expressed, so that the relevance between the operation states of the devices is fully captured. Sensor data, historical maintenance records and equipment configuration information are coded into node attributes and edge weights, so that the system can realize unified representation of multi-source heterogeneous data. The GraphRAG framework combines the structure learning ability of a graph neural network and the knowledge fusion characteristic of a retrieval enhancement generation mechanism, and shows stronger generalization ability in the recognition of a non-fault mode. When an abnormal signal occurs in a certain device, the model can trace the potential influence range through the state propagation path of the adjacent node, and the accuracy of early warning is improved.

The invention discloses a cross-activity fracture tunnel full life cycle service state dynamic analysis method and system, relates to the technical field of numerical simulation, and solves the technical problems that the dynamic change of data in a tunnel full life cycle cannot be reflected and the service state analysis is inaccurate in the prior art. The method comprises the following steps: in a design period, establishing a tunnel-surrounding rock-fault three-dimensional refined model considering an internal structure of a fault and creep / stick-slip fault deformation characteristics, inputting collected initial parameters into a model database for numerical simulation, and carrying out preliminary design of seismic resistance and error resistance of the structure on the basis of a numerical simulation result; during the construction period, updating the database, inputting the model for re-simulation, changing the anti-seismic and anti-error design of the structure based on a new numerical simulation result, and preliminarily evaluating the service state of the tunnel; and in an operation period, predicting a dynamic data development trend by using the prediction model, inputting the dynamic data development trend into the numerical model for numerical simulation, and correcting a tunnel service state evaluation and emergency processing scheme according to a numerical simulation result.

The invention belongs to the technical field of additive manufacturing, and particularly relates to a multi-scale parallel optimization method for a lightweight structure based on lattice discrete optimization. According to the method, through multi-scale parallel optimization design, a macro structure and a micro lattice structure are described through geometric parameters of a movable deformation rod piece, an improved two-value coding parameterization method, called a BCP method for short, is adopted to solve discrete optimization of the micro lattice structure, and geometric parameters of an optimization result are easy to extract.

The invention discloses an intelligent timed task configuration and multi-mode feedback system based on natural language interaction, and relates to the field of industrial automation control, enterprise-level task scheduling management and instant messaging. The system comprises a natural language interaction module, a user natural language instruction is converted into structured semantic information through a BERT + BiLSTM + CRF mixed architecture, and lightweight processing is achieved in combination with TinyBERT distillation; the dynamic task arrangement module completes task creation, modification and the like based on structured information, completes missing parameters through historical task mode matching, and supports multi-session real-time synchronization; the rich media report generation module generates a multi-mode report containing charts, tables and the like, and the RAG technology and large model interpretation are combined; and the multi-channel pushing adaptation module adapts multiple platforms by adopting a strategy and factory mode. According to the system, the task configuration efficiency and flexibility are improved, the feedback form is enriched, the information pushing accuracy is guaranteed, and the exception handling capacity is enhanced.

The invention provides a bid invitation file generation method and system and a storage medium, and the method comprises the steps: obtaining original data from a plurality of modalities, the original data comprising a project demand text, a design model file, a law and regulation text and historical bid invitation case data; performing multi-module semantic deep analysis and structured processing on the original data, and respectively generating a project demand sub-graph, a spatial logic sub-graph, a structured regulation knowledge base and a historical case strategy feature library; performing cross-modal dynamic fusion on the project demand sub-graph and the spatial logic sub-graph to construct a unified knowledge graph, and performing dynamic knowledge tracing and strategy injection in the fusion process based on a structured regulation knowledge base and a historical case strategy feature library; and generating a first bid invitation file draft based on the unified knowledge graph, performing compliance verification on the first bid invitation file draft, and determining the first bid invitation file draft passing the verification as a final bid invitation file. The bid invitation file generation accuracy can be improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a complex table intelligent analysis method and system based on multi-modal fusion and semantic analysis. The method comprises the following steps: S1, performing preliminary analysis on a document based on a multi-modal fusion technology, identifying position information of a table area, a text and an image, and outputting the position information of the table area; s2, reconstructing the table structure based on the topological graph structure and the geometrical relationship of the table to obtain a reconstructed table; s3, identifying the content of the reconstructed table by utilizing machine vision, and correcting the identified content of the table and the position information of the table area in combination with semantics to obtain all the content of table analysis; and S4, outputting the analyzed table content according to a required format. According to the method, the complex table can be analyzed and corrected.

The invention belongs to the cross technical field of artificial intelligence and image recognition, and discloses a cursive script image sequence recognition method for modeling by fusing a stroke structure, and the method comprises the steps: preprocessing and normalizing a cursive script image, eliminating interference information, normalizing the form of the cursive script image, and guaranteeing the stability and effectiveness of a subsequent processing flow; based on stroke decomposition and feature coding of structural analysis, stroke-level structural features are extracted through character skeleton modeling, and cursive writing characters are converted into time series data; modeling a cursive script recognition neural network fused with structural semantics, and recognizing a stroke time sequence by adopting a BiLSTM neural network; an identification output and result optimization module is designed to ensure the final identification quality; through application of scene integration and platform deployment interface design, practical deployment of the model and butt joint of a front-end platform are realized, and a complete cursive script recognition ecology is constructed. According to the method, not only is the structured representation capability of the cursive script image improved, but also the recognition precision and generalization performance of the model on complex handwriting are remarkably enhanced.

The invention relates to the field of mines, in particular to a mine ventilation safety intelligent monitoring method. The method comprises the following steps: firstly, constructing a graph structure based on acquired mine information, optimizing spatial distribution of monitoring points in the graph structure through a monitoring point coverage efficiency function, and outputting an optimal monitoring point set; arranging sensors based on the optimal monitoring point position set; then, ventilation parameters are collected based on a sensor, and a state feature matrix is generated; constructing an adjacent matrix based on the state characteristic matrix; based on the state characteristic matrix and the adjacent matrix, calculating the abnormal probability of the monitoring point position; based on the abnormal probability, obtaining an early warning level; based on the abnormal probability, calculating three-dimensional coordinates of the hidden danger position through a gradient-guided air flow propagation positioning algorithm; and finally, based on the early warning level and the three-dimensional coordinate of the hidden danger position, executing a hierarchical automatic processing and response mechanism. The technical problems of incomplete monitoring coverage, poor data acquisition real-time performance, insufficient abnormal recognition capability and untimely early warning response are solved.

The invention discloses a method for quickly calculating transient thermal stress of a ceramic radome along a flight path, which comprises the following steps of: determining calculation input which comprises an aerodynamic thermal environment of the ceramic radome and a to-be-evaluated structure scheme; setting a circumferential discrete strategy of the ceramic radome to determine a plurality of meridian planes of the ceramic radome; constructing an aerodynamic thermal environment interpolation model, and determining a meridian plane where aerodynamic heating of the ceramic antenna housing is most strict in the whole flight process; constructing a two-dimensional feature structure model for transient heat transfer and thermal stress analysis of the ceramic radome, and determining an outer contour line of the two-dimensional feature structure model; constructing an axial thermal response calculation interval of the ceramic radome and performing interpolation calculation to obtain aerodynamic thermal environment parameters of the outer contour line at each flight moment; performing heat transfer calculation grid division on the two-dimensional feature structure model, and calculating structure transient temperature distribution of the two-dimensional feature structure model; calculating the temperature gradient of the structure based on the transient temperature distribution of the structure; and calculating transient thermal stress distribution of the ceramic radome by using the structural temperature gradient.

The invention provides an automatic engineering main body structure construction progress detection method based on unmanned aerial vehicle vision, and the method comprises the steps: collecting a construction site high-resolution image through an unmanned aerial vehicle, constructing a data set, carrying out the labeling preprocessing, and converting the data set into a YOLOv12 recognition format; a YOLOv12 model (including replacing an A2C2f module with an A2C2fMcva module, replacing a C3k2 module with a C3k2-SLBlock module, and replacing a neck network Concat module with a GDSA feature fusion module) is improved, and an optimal detection model is obtained through training; shooting a construction picture in real time by using an unmanned aerial vehicle, segmenting a construction region through a model, judging a current activity based on construction sequence coding, and calculating a construction activity area; and calculating a progress correction coefficient in combination with CAD software, and finally obtaining the construction progress P. The method realizes automatic and accurate detection of the construction progress, and is suitable for complex construction scenes.

The invention discloses an intelligent foundation treatment scheme generation system and method based on a rule engine and a large language model, and belongs to the technical field of civil engineering. According to the method, the accuracy of a rule engine, the professionality of RAG and the flexibility of a large language model are combined, a hierarchical processing mechanism is adopted, tasks with different complexities are subjected to corresponding technical layer processing, verification is carried out in combination with a multiple verification mechanism, and therefore an optimal foundation processing scheme is obtained; and training a large language model by adopting a large model training method based on a structured reasoning process, and dynamically updating standard terms and expert knowledge. According to the method, the accuracy of the rule engine, the professional property of the RAG and the flexibility of the large language model can be combined, safety is ensured, intelligence and efficiency are improved, and quality is guaranteed.

In an embodiment, the following are repeated during a session with a user compute device. A message is received from the user compute device. A determination is made as to whether a workflow portion from a plurality of workflow portions is structured or unstructured. The workflow portion is executed as structured based on the message when the workflow portion is determined to be structured, to produce a structured response. The workflow portion is executed as unstructured based on the message, a task description and a list of structured parameters to be captured, when the workflow portion is determined to be unstructured, to produce an unstructured response via a generative artificial intelligence model.

The invention discloses a land space use control compliance intelligent identification and auxiliary determination system, and relates to the technical field of digital treatment. A data management module receives multi-source heterogeneous time sequence data to construct a dynamic graph structure substrate, and a probability scheduling module predicts global potential conflict probability by using a space-time graph neural network and performs calibration in combination with a historical conflict database; the graph enhancement module positions a key conflict path and a core node by reversely updating a conflict knowledge graph, and the reasoning execution module activates a rule engine to execute accurate matching in a region to be subjected to fine detection and outputs a deterministic conflict identifier; the fusion decision module finally fuses probabilistic prediction and rule judgment results to generate a mixed conflict list, and the feedback module sends the mixed conflict list to the execution terminal and optimizes the model based on feedback. According to the method, multi-level and self-adaptive identification of the land and space purpose conflict from wide-area prediction to accurate judgment is realized, and the supervision efficiency and the decision accuracy are improved.

The invention relates to a training method for providing an underwater vehicle main body structure design model and an underwater vehicle main body structure generation method. The training method of the underwater vehicle main body structure design model comprises the following steps: firstly, generating a noise adding main body structure based on a randomly initialized ordinal number stamp k, a sample main body structure and randomly initialized Gaussian noise generation data; secondly, determining predicted Gaussian noise data based on the ordinal number stamp k, the performance parameters of the sample main body structure and the noise adding main body structure by adopting a main body structure design model; and finally, determining a training loss function based on the generated Gaussian noise data and the predicted Gaussian noise data, and training the main structure design model based on the training loss function. Under the condition that only few sample main body structures exist, enough training sample data used for main body structure design model training can be generated, and model training under a small amount of sample main body structure data is achieved.

The invention provides a box girder defect recognition method and system based on structured light and deep reinforcement learning, and relates to the technical field of box girder defect recognizing.The method comprises the steps that equipment integration and joint calibration are conducted on a to-be-detected box girder component, structured light projection is conducted according to a preset track, and a to-be-detected box girder component is obtained; the method comprises the following steps: triggering an event camera to image by utilizing controlled stroboscopic scanning of structured light, obtaining an event stream and a structured light depth map, carrying out representation conversion and multi-modal alignment, obtaining multi-modal mapping alignment data to carry out primary defect identification, obtaining an initial defect identification result, and triggering a deep reinforcement learning active perceptron to carry out perception strategy analysis. And obtaining a perception strategy analysis result, and carrying out deepening defect identification on the to-be-detected box girder component to obtain a defect identification result. The technical problems that in the prior art, box girder defect detection is low in detection efficiency, not high in accuracy and poor in detection comprehensiveness are solved. The technical effect of improving the efficiency, accuracy and comprehensiveness of box girder defect detection is achieved.

The invention relates to a method, a system and equipment for predicting creep stress at a joint of a concrete structure and a medium. The method comprises the following steps: testing a concrete structure material and a concrete structure joint filling material, and constructing a joint material performance database based on test data; constructing a multi-scale mechanical model comprising a macrostructure model and a microcosmic interface model based on the seam material performance database; constructing an interface constitutive relationship based on the seam material performance database and the multi-scale mechanical model; and predicting the creep stress at the joint of the concrete structure based on the material performance database at the joint, the multi-scale mechanical model and the interface constitutive relationship. The method can fully consider the sudden change of the performance of the material at the joint and the interface effect, provides reliable technical support for the safety evaluation and maintenance of the concrete structure, and is particularly suitable for engineering application of large-span and high-rise buildings and the like with higher requirements on the long-term performance of the structure.

Certain aspects of the disclosure provide for information retrieval that exploits context derived from document structure. Source documents can be preprocessed to identify fields and determine context attributes related to each field based on the structural layout of a source document. Resource documents can also be preprocessed to segment a resource document into passages and determine context related to the passages based on structural layout. Queries pertaining to a field can be enhanced by adding context metadata associated with the field. A query embedding can be generated and compared with previously generated passage embeddings to locate candidate matches based on similarity. A machine learning model can be provided with the top-ranked passages and tasked with re-ranking the passages based on relevancy to the original query. The highest re-ranked passage or set of passages can be output in response to the query.

A method for automatically inserting hyperlinks is provided. In one example, the method includes determining a location for a hyperlink anchor in a document based on a type of structural element identified in the document. A presentation of the hyperlink anchor may be displayed according to a set of rules defining an appearance of the hyperlink anchor according to the type of structural element.

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 discloses a specific sea area water quality change trend prediction method based on time sequence analysis, and relates to the technical field of marine environment monitoring. The method comprises the following steps: firstly, collecting historical water quality data and time-space attributes of a target sea area monitoring station, and constructing a multi-graph structure based on geographical proximity, water quality change similarity and hydrological connectivity to represent a complex spatial dependency relationship; secondly, extracting multi-scale spatio-temporal features, and generating node embedding representation; and then, multi-graph feature aggregation is carried out by using a multi-graph convolutional network and an adaptive weight mechanism. And inputting the fusion features into a deep twin coding-decoding model to carry out multi-step water quality prediction, and finally, generating multiple groups of future scenes by disturbing key external driving factors by adopting a multivariate state estimation method, and outputting a multi-scene prediction sequence under different environmental conditions. According to the method, the accuracy, interpretability and decision support capability of water quality prediction are effectively improved.

The invention provides a multivariable time series prediction method and system based on structural representation learning, and belongs to the technical field of multivariable time series prediction.The method comprises the steps that a similarity matrix between variables is constructed according to multivariable time series data, and variable community tags are obtained through community detection; carrying out weighted binarization to obtain a binary community sensing adjacency matrix; inputting the initial variable feature matrix and the adjacent matrix into a graph auto-encoder to obtain a variable structure embedded matrix; variable value embedding, time step position embedding and structure embedding are fused to obtain a unified input matrix, a time sequence prediction model based on a structure information guiding attention mechanism is input for modeling, and a prediction result is obtained; wherein structure embedding serves as an auxiliary feature to participate in modeling, and explicit structure constraint is not applied. According to the method, variable association is learned and mined through structural representation, and the accuracy and generalization ability of multivariable time sequence prediction are improved.

The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing driven land utilization monitoring system. According to the invention, by extracting a ground feature boundary contour in a remote sensing image and constructing a morphological parameter set with geometric structure proportion characteristics, a path category data system for dividing a land spatial form based on structural characteristics is established, and normalization processing and a Gaussian mixture model are introduced to realize ground feature aspect ratio clustering. Constructing a structural difference index set in combination with the distribution concentration ratio, clearly identifying the path type to track the land parcel direction or the structure change trajectory, and calculating a disturbance factor in the multi-temporal remote sensing image based on the land type number change and the connection direction number of the patch adjacent region; and the main direction, the boundary contour similarity and the spatial disturbance of the land parcel in the specified period are comprehensively judged, so that the dynamic change identification of the land utilization state is realized, and the land utilization form change amplitude and the time period characteristics can be accurately reflected by setting a judgment reference and a change state marking mechanism.

The invention provides an interactive multi-dimensional data visualization display sand table and method for business data analysis, and the method comprises the steps: carrying out the semantic mapping of state parameters in an interaction control operation when the interaction control operation is received, obtaining a plurality of pieces of visualization intention metadata, and generating a structured query instruction; based on the structured query instruction, extracting a target data subset rendered visually by the heterogeneous commercial data source from a multi-dimensional data cube of the commercial data source, performing visual optimization on the visually rendered chart type, the visual coding channel and the initial visual parameter through the cardinal number of the dimension field in the target data subset and the data distribution type and the numerical range of the measurement field to obtain a visual configuration set of the target data subset; and using the visualization configuration set to render and generate a multi-dimensional data visualization view, and outputting and displaying the data visualization view in a graphical user interface. Based on the scheme, automatic closed loop generation from heterogeneous data to interactive views can be realized.