10 results about "Hierarchical relation" patented technology

The application discloses a complex specification multi-modal understanding method and system based on YOLO two-stage segmentation, comprising the following steps: performing pre-processing on an input specification image; inputting the pre-processed image into a coarse-grained detection stage of an improved YOLOv8 network to generate a coarse-grained candidate region set; inputting the coarse-grained candidate region set into a fine-grained segmentation network based on an improved YOLOv8 feature extraction structure; constructing a layout hierarchical relationship structure based on an accurate layout segmentation result; according to the layout hierarchical relationship structure, performing multi-modal feature extraction on the accurate layout segmentation result, and calculating a matching weight; inputting a graphic-text semantic alignment result and the layout hierarchical relationship structure into a logical structure modeling module together to construct a layout element graph; and generating a knowledge graph semantic output result according to a structured logical reasoning result. The application realizes automatic structured understanding of a complex specification by adopting YOLO two-stage segmentation and multi-modal semantic alignment.

This invention belongs to the technical field of natural language processing and dialogue systems. It discloses a multi-target dialogue recommendation method based on hierarchical prompt optimization, including hierarchical target label modeling, learnable prompt embedding initialization, graph attention enhancement mechanism, multi-task learning framework, and response generation model. Hierarchical target label modeling encodes the hierarchical relationships between target labels into a graph structure. Learnable prompt embedding initialization integrates label semantics and deep hierarchical information into the encoder input space. The graph attention enhancement mechanism enhances the label vector structure perception ability through information transmission and aggregation between label prompts. The multi-task learning framework jointly optimizes masked language modeling and hierarchical multi-label classification. The response generation model uses the predicted target as a control signal to guide generation. This method effectively models the hierarchical dependency relationship between target types and target entities, enhances the structure perception ability of label vectors, maintains the model's language understanding ability, and improves the accuracy of target prediction.

This invention discloses a safety analysis method for ship SOFC systems, belonging to the field of risk prevention and control technology. It includes: identifying the causative factors of the ship SOFC system; constructing a failure fault tree and event tree model; constructing a Bow-Tie model based on the failure fault tree and event tree model; quantitatively analyzing potential causative factors to identify key factors affecting the SOFC system; combining the analyzed ship SOFC system accident causation data and collected ship fuel cell standard and specification data to form a data system and construct a ship SOFC system accident risk prevention knowledge graph; and conducting SOFC system safety analysis based on the ship SOFC system accident risk prevention knowledge graph. This invention clarifies the criticality, hierarchical relationship, and action path of each causative factor; constructs the causative factors into a complex accident causation network; and, combining the semantic information of the knowledge graph and the capabilities of complex network analysis, deeply reveals the accident causation propagation mechanism, providing a comprehensive perspective for understanding the occurrence and development of accidents.

This invention belongs to the field of web page data parsing technology, specifically relating to a web page data parsing method based on a large language model. The method includes: obtaining the target website URL input by the user; performing preliminary parsing of the input URL using a large language model; parsing the DOM structure of each website and extracting HTML elements and their hierarchical relationships; generating corresponding data crawling scripts based on the parsing results; executing the data crawling scripts to extract the required data from each website; preprocessing the extracted data; fusing the preprocessed data from multiple sources; extracting key information from the fused data; and storing the finally processed and extracted information in a database. This invention achieves automatic parsing of complex web page data, intelligent entity extraction and knowledge expansion, as well as efficient data fusion and deduplication.

The application relates to a spacecraft intelligent software virtual simulation test scene intelligent generation method, and belongs to the technical field of spacecraft software testing and simulation, which comprises the following steps: an IR structure with rich semantics is defined in advance; through a preset structured prompt word, a large language model LLM is called to recognize and extract task targets, entities, behaviors, environments, constraints and disturbance elements in natural language instructions; the LLM analyzes the extracted elements into nodes, node attributes and time sequence relationships, logical relationships and hierarchical relationships between the nodes in the predefined IR structure, generates a structured IR; a generated IR JSON file is loaded, and according to a preconfigured conversion rule library of the IR structure to a scene description language, the structured IR is converted into an executable simulation script; a simulation engine analyzes and executes the script, and instantiates a specific and high-fidelity test scene. The problems of low automation, high professional threshold and poor scalability in the simulation test of spacecraft intelligent software are solved.

The application provides a network attack hierarchical detection model training method, a detection method and equipment. The training method comprises: based on a hierarchical label system containing attack scene labels and attack type labels, using a graph attention network to encode the hierarchical relationship; using the encoding result and network traffic data to train a classification model based on prompt learning, outputting a collaborative prediction result of attack scenes and types by calculating the similarity between traffic prompt representation and global label embedding; determining a supervision signal according to real labels, optimizing the classification model through adversarial training, and obtaining a final detection model. The application can realize collaborative identification and hierarchical tracing of attack scenes and types, provide more fine-grained threat perception, significantly improve the robustness, generalization ability and detection stability of the model in a dynamic adversarial environment, thereby improving the network attack identification accuracy, and improving the accuracy and efficiency of network security operation and maintenance.

The application provides a kind of complex equipment system fault propagation analysis method and system based on hierarchical model, it is related to security analysis technical field, first determine analysis purpose and security demand;Then successively establish the hierarchical model of analysis object, function model and behavior model;Again, according to the hierarchical model, function model and behavior model of analysis object, the failure logic of analysis object is analyzed;Failure logic refers to the specific form of behavior deviation caused by analysis object;Finally, by traversing the cause-effect relationship defined in the failure logic of each component, the failure output of the previous component is taken as the failure input of the next component, the output of the next component is obtained by calculation and reasoning according to the logical relationship, and the analysis of fault propagation path is completed through the layer-by-layer transmission of failure deviation.The application is aimed at the complexity, hierarchy and other characteristics of complex equipment system, and comprehensively analyzes the hierarchical relationship of the system from the two-dimensional perspective: longitudinal and transverse.

The application discloses a complex table question and answer method based on a tree structure, an electronic device and a medium, and comprises the following steps: in response to table header information of a complex table, a large language model outputs a table header tree structure; the large language model decomposes a question into a plurality of keywords, aligns the keywords with the table header tree structure, and obtains keyword-tree structures; in response to the keyword-tree structures, the large language model is guided to iteratively reason and search in the complex table by using a React-Style prompting method, and an answer is output. According to the application, the multi-level titles of the table are organized into a tree structure, so that the large language model can more clearly understand the hierarchical relationship of the table, and thus can more accurately locate relevant information when answering questions.

The application discloses a kind of based on the heterogeneous landscape land use mixed ground object classification method of homogeneous patch decomposition, comprising: reading original remote sensing image data and segmentation, generate multiscale patch set and construct patch hierarchical relationship diagram;Calculate patch heterogeneity index, extract gradient structure feature and realize bidirectional feature transmission;Build feature dictionary and based on sparse representation Quantification mixed degree of ground object;Determine the optimal scale by structure consistency index, and according to the heterogeneity degree executes adaptive classification strategy.The application solves the technical problems of optimal scale determination and mixed ground object representation of heterogeneous landscape, improves the classification precision of complex landscape land use, realizes fine land use classification.

The application provides a fault type identification method and device for unbalanced samples, electronic equipment and storage medium, and belongs to the technical field of fault classification identification. The method comprises the following steps: according to a pre-established fault category hierarchical tree, a source domain fault class sample set with the highest similarity to a target domain category in a target domain is screened out from a source domain, and a fault feature extraction network of the source domain fault class sample set is created; support sample sets of the target domain category are input into the fault feature extraction network, fault feature vector sets of the support sample sets are obtained; and a target classifier is trained based on the fault feature vector sets to identify fault categories of the target domain category. The application extracts transferable similarity features of fault samples of the target domain category from the source domain fault class sample set in the source domain by using inter-class hierarchical relationships, thereby realizing fault category identification of the target domain category by using a transfer learning technology, and improving the accuracy of fault classification identification.