36 results about "Classification structure" patented technology

The invention relates to the technical field of data classification management, in particular to a knowledge graph-based foreign trade data classification management system, which comprises a term word order modeling module, a graph path generation module, a path cross analysis module, a semantic category judgment module and a classification structure output module. According to the method, morpheme-level word segmentation and word order extraction are performed on foreign trade terms, an original word order template is constructed, the reduction capability of a semantic expression sequence is enhanced, a directed path is established through morpheme-level label sorting, and the level definition of a term structure and the logic relation between nodes are enhanced; a semantic intersection area is extracted through node intersection and end point frequency analysis, the accuracy of term association judgment is improved, semantic affiliation is selected according to end point label frequency, a node classification closed loop is formed by combining labels and path mapping, links of term graph modeling, path construction, intersection analysis and affiliation output are broken through, and the term association judgment accuracy is improved. And the classification management process of foreign trade data is fully improved.

The invention provides a file classification method, device and equipment and a readable storage medium, and the method comprises the steps: converting a file classification task into a generation task of a large model, determining the classification path description information of each class node in each hierarchy through a preset multi-hierarchy classification structure, and carrying out the classification path description information of each class node in each hierarchy; the information comprises all nodes passing from a hierarchical root node to a category node and description information of classification categories of the nodes, and a hierarchical category result to which the file belongs is generated through reasoning by combining a large model with the classification path description information to represent a hierarchical classification category of the file, so that accumulative errors in a layer-by-layer classification process are reduced; the problem of classification performance reduction caused by information loss is avoided, and the file classification accuracy and classification efficiency are improved.

The invention relates to the technical field of garbage treatment, in particular to a smart city-oriented garbage classification and recognition system, which comprises an image acquisition and restoration module, a multi-modal feature fusion module, a dynamic classification decision module and a classification result output module, wherein the image acquisition and restoration module is used for acquiring and restoring an image of a garbage throwing point and outputting a restored three-dimensional image; the multi-modal feature fusion module is used for fusing the extracted features into multi-modal fusion features through a space-time alignment algorithm; the dynamic classification decision module is used for classifying the parallel double-branch convolutional neural network integrated with the attention mechanism; and the classification result output module is used for triggering a control signal of the garbage recycling equipment. According to the intelligent urban garbage classification system, through collaborative design of multi-modal feature fusion and a parallel classification structure, accurate recognition of garbage materials and purposes in a complex environment is achieved, the recycling equipment can be automatically linked, and the overall intelligent level of the intelligent urban garbage classification system is improved.

The embodiment of the invention provides a user intention recognition method and system for knowledge questions and answers of a hydropower station, and belongs to the technical field of knowledge questions and answers. The method comprises the following steps: performing structured processing on the natural language question to obtain structured question information; the problems are classified, intention information in the structured problem information is extracted based on a classification structure and a BERT model, limiting conditions of the intention information are recognized, and problem analysis information is obtained; key entities in the question analysis information are recognized based on a pre-trained entity recognition model, and the key entities are linked to a knowledge graph through dictionary matching and / or context analysis; and the problem analysis information and the information of the key entity linked to the knowledge graph are constructed into a semantic structure, and a user intention is output. According to the scheme, it is ensured that the finally-output user intention has high accuracy and consistency, and therefore the analysis ability and response accuracy of the question answering system for the problems in the hydropower station field are greatly improved.

The invention discloses a power marketing business key element extraction method and system based on a natural language processing technology. The method provided by the invention comprises the following steps: determining the classification of power marketing business work orders; different types of entities are automatically identified and marked; the method comprises the following steps: constructing a multi-level element classification model by utilizing context embedding representation of RoBERTa and combining a preset service level classification structure, realizing fine-grained extraction of key elements of the power marketing service, and constructing a deep neural network entity relationship extraction model based on an attention mechanism to extract an association relationship of the key elements; and calculating the similarity between the key elements of the power marketing business and the key elements of the policy document, extracting keywords in the key elements of the policy document, generating a policy basis abstract based on an improved Seq2Seq model, and improving the abstract quality in combination with an optimization mechanism. According to the method, the problems of poor key element recognition accuracy, insufficient policy document generation summary information extraction and incomplete semantic expression of the existing power marketing business are solved.

This invention discloses a tracking method based on multi-scale spatial constraints to combat occlusion. It extracts HOG, CN, and grayscale features from candidate target regions and uses PCA to reduce the dimensionality of HOG and CN features, accelerating computation. A channel weight fusion method is employed, training filters separately for each feature layer. Adaptive fusion weights enhance the response of effective feature layers, addressing the problem of insufficient multi-feature response fusion. A stepped spatial constraint method is proposed to optimize the color space constraint model, preventing model errors from obscuring target information and limiting the effectiveness of the spatial domain constraint model. Adaptive learning rate and diffusion search methods reduce irrelevant information learned by the filters and improve tracking accuracy when the target is occluded. A tritree scale acceleration method is proposed, introducing scale filters and transforming the parallel structure of the scale filters into a tritree classification structure.

The invention relates to the technical field of natural language processing, and discloses a natural language processing-based historical text automatic classification method. The method comprises the steps of collecting and preprocessing original historical text data; constructing a text feature map containing a plurality of text entity nodes, a semantic association relationship between entities and feature information of each node; identifying core semantic clusters and discrete semantic segments in the atlas through a text analysis algorithm, and positioning key entity nodes; generating a preliminary classification strategy set containing a semantic remapping scheme, a classification structure adjustment scheme and a weight adjustment scheme according to the core semantic cluster and the key entity node; receiving a user correction instruction, determining a classification correction type and adaptively adjusting the preliminary classification strategy set; and executing the adjusted strategy set, and updating the text feature map according to a classification result. According to the method, the accuracy and flexibility of historical material classification are improved, and efficient sorting and utilization of historical material resources are assisted.

The invention provides a data classification management device for educational management. The data classification management device structurally comprises a power-on control end, an electronic storage main body, a connection frame, a heat dissipation end and a paper storage structure, after a paper storage structure is improved, paper materials are classified and stored in order through the storage grooves of the storage box and the classification structure, then mutual communication of the ventilation end and the heat dissipation end is ensured by combining position positioning of the insertion blocks and the power-on rods, and then the ventilator is started to lead out humid and turbid air in the storage grooves; furthermore, hot air blown out from the heat dissipation end can enable the interior of the storage groove to be in a continuous dry state, so that the condition that fonts are fuzzy due to the fact that paper files are affected with damp due to continuous contact with outside air can be effectively avoided; and meanwhile, the classification structure can improve the classification searching efficiency of the paper files through the ventilation clamping sleeve and the label groove of the supporting block, the empty groove position receives hot air of the heat dissipation end, and classification protection of the paper files is improved.

The application discloses a kind of defect grading method, device, equipment and storage medium.The method comprises: obtaining defect image to be graded;The defect image to be graded is input to the grading model pre-trained;According to the output result of grading model, the defect level of defect image to be graded is determined;Wherein, the grading model is obtained by training the grading network pre-constructed based on defect grading dataset;The grading network includes feature extraction structure, regression structure and classification structure;Defect grading dataset includes at least one group of defect grading data, and each group of defect grading data includes defect image sample and defect image sample matched reference level;The number of reference level is less than the number of defect level.This technical solution solves the problem of low defect grading refinement degree, can realize fine-grained defect grading while effectively improving the accuracy of defect grading to meet the diversified workpiece needs of users.

The present invention relates to the technical field of data classification management, specifically a foreign trade data classification management system based on a knowledge graph. The system includes a term word order modeling module, a graph path generation module, a path intersection analysis module, a semantic category determination module, and a classification structure output module. In the present invention, by performing morpheme-level word segmentation and word order extraction on foreign trade terms, an original word order template is constructed to enhance the restoration ability of semantic expression order. A directed path is established through morpheme-level label sorting to strengthen the hierarchical clarity of the term structure and the logical relationship between nodes. The semantic intersection area is extracted by using node intersection and end point frequency analysis to improve the accuracy of term association determination. The semantic attribution is selected according to the end point label frequency, and a node classification closed loop is formed by combining the label and the path mapping, connecting all links of term graph modeling, path construction, intersection analysis and attribution output, and fully improving the classification management process of foreign trade data.

This invention discloses a method, system, and medium for dynamically defining key process parameters for civil aircraft. The method involves obtaining a classification structure tree; selecting any child node and creating a parameter control object under that node, generating a version number corresponding to the parameter control object according to preset version rules; creating the header content of the parameter control object; creating the body content and termination content of the parameter control object; associating the header content, body content, and termination content with the parameter control object that has a version number; and creating an object instance based on the parameter control object for dynamic use in the manufacturing process. This invention achieves dynamic expansion and control of key process parameters through the body content of the parameter control object, realizing standardized control, free dynamic form, numerical calculation between data, and consistent parameter control, thus connecting various civil aircraft R&D and manufacturing processes such as engineering R&D, process design, manufacturing execution, and quality analysis.

The embodiment of the invention provides a classification model training method based on hierarchical knowledge migration. The method comprises the steps of obtaining a universal baseline model with a semantic understanding capability; according to the hierarchical classification structure of the target domain, an industry domain model containing a plurality of sub-classification output heads is constructed, and initialization is carried out by using a general baseline model parameter; constructing a hierarchical constraint loss function, and constraining the parent class prediction probability to be greater than or equal to the child class prediction probability; obtaining domain classification training samples, and respectively inputting the domain classification training samples into the general baseline model and the industry domain model to obtain first and second prediction category probability distributions; calculating domain cross entropy loss based on the second prediction distribution and the real label, calculating knowledge distillation loss based on the difference between the two distributions, and constructing a target loss function in combination with hierarchical constraint loss; and taking minimization of the target loss function as a target training industry domain model. According to the method, the model training efficiency is effectively improved, efficient migration of domain knowledge is realized, and the hierarchical classification accuracy is remarkably improved.

The application relates to the technical field of natural language processing, and discloses a history material text automatic classification method based on natural language processing. The method comprises the following steps: collecting original history material text data and preprocessing; constructing a text feature graph containing multiple text entity nodes, semantic correlation relationships between entities and feature information of each node; identifying core semantic clusters, discrete semantic fragments and positioning key entity nodes in the graph through a text analysis algorithm; generating a preliminary classification strategy set containing a semantic remapping scheme, a classification structure adjustment scheme and a weight adjustment scheme according to the core semantic clusters and the key entity nodes; receiving a user correction instruction, determining a classification correction type and adaptively adjusting the preliminary classification strategy set; and executing the adjusted strategy set, and updating the text feature graph according to the classification result. The method improves the classification accuracy and flexibility of history materials, and helps efficient arrangement and utilization of history material resources.

The invention discloses a fireproof control chart auxiliary drawing method based on an intelligent model, and the method comprises the steps: manually obtaining all drawing files related to a fireproof control chart, converting the drawing files into a picture format in batches through a Python bitmap conversion method, and keeping the classification structure of the drawing files. The drawing file in the picture format is converted into text data, and prompt words are written based on the text data. And creating a calling variable of the model, and transmitting the calling variable and the cue word to the graphic understanding model to obtain an analysis result in a fixed format. And inputting the analysis result in the fixed format and the cue word into an inference model to extract json key data information, and converting the json key data information into a json key data table by utilizing Python. And establishing connection with the drawing software by utilizing Python, and drawing in the drawing software in sequence according to the json key data table. The working efficiency of drawing is improved, working hours are saved, and the labor cost of designers is reduced.

The utility model discloses a cutter cabinet with a classification structure, which relates to the field of storage cabinets and comprises a cabinet body, a plurality of cutter placing racks are assembled in the cabinet body, and each cutter placing rack comprises a frame, a bottom box and at least one fixing seat. The bottom box is fixedly connected to the lower end of the frame, and the hollow part of the frame is communicated with the inner space of the bottom box; the fixing seats are provided with through holes used for containing cutters, at least one fixing seat is detachably and fixedly arranged at the upper end of the frame, and the through holes are communicated with the space in the box through the hollow-out parts. During use, one or more fixing seats are selectively installed according to needs, and the first cutters are placed in an inserted mode through the through holes of the fixing seats. After the fixing base is installed, the rest inner space of the bottom box is used for storing the second cutters in batches. The storage space of the first cutter is increased or decreased by increasing or decreasing the number of the fixing bases; correspondingly, the storage space of the second cutter is increased or decreased, so that the storage spaces of the two cutters can be mutually converted and can be reasonably distributed according to needs, and the use is flexible.

The invention discloses a digital semantic understanding and conversion system and method based on multi-modal feature fusion, the contribution degrees of three types of features including semantics, mathematical and context in different digital type recognition are displayed through a multi-modal feature importance thermodynamic diagram, a macroscopic-microscopic double-layer classification structure is adopted, error propagation is effectively blocked, and the recognition efficiency is improved. Through three-way cooperation of a rule engine, a statistical model and a semantic engine, high-robustness decision making is realized, and an intelligent conversion routing and grammar adaptation mechanism is developed for a digital semantic conversion link. The method can effectively solve the technical bottlenecks of insufficient accuracy, poor expandability, strong training data dependence and the like of a traditional method in complex number type recognition, and is suitable for multiple application scenes such as TTS voice generation, natural language generation, financial document processing, education science and technology and the like; accurate conversion from digital character strings to semantic expressions conforming to human language habits can be achieved.

This invention discloses a method and apparatus for intelligent identification and classification of industrial assets based on a large language model. The method includes: actively probing the internet address space and collecting web page data; generating page clusters using a two-stage clustering approach that combines structure and semantics; performing semantic summarization and multi-round filtering using a large language model to refine data related to industrial control systems; constructing multi-dimensional feature summaries and generating atomic tags; and generating a hierarchical classification chain based on a dual-encoder retrieval enhancement generative model. This invention achieves automatic discovery and refined classification of industrial control system assets through multi-dimensional semantic fusion and retrieval enhancement reasoning, solving the problems of traditional methods such as reliance on strong rules, low recognition accuracy, and lack of classification structure.

The utility model provides a sample sorting device with a multi-classification structure, which comprises a sorting device body, a conveying assembly and sorting assemblies, the left upper side and the right upper side of the interior of the sorting device body are respectively provided with one sorting assembly which is used for classifying and sorting collecting tubes on the conveying assembly based on mechanical vision. Compared with the prior art, the collecting tube sorting device has the advantages that the sorting windows are arranged on the front side of the shell, the storage boxes with the number corresponding to that of the sorting windows are installed, a large amount of collecting tube sorting work can be processed when necessary, various classification selections are provided, various collecting tubes can be classified and sorted in a more detailed mode, and the sorting efficiency is improved. The conveying assembly and the sorting assembly are arranged, and the visual assembly and the double sorting assemblies are matched, so that a large number of collecting pipes can be processed at the same time, the equipment has the capacity of processing a large number of collecting pipes, and the requirement for sorting the collecting pipes of various classifications can be met.

The present invention relates to the technical field of intelligent data processing, and includes a data analysis system driven by an intelligent algorithm. The system includes a feature screening module, a topological backbone extraction module, an information transmission optimization module, a classification boundary adjustment module, and an adaptive classification hierarchy module. In the present invention, by calculating the conditional entropy of data feature values, analyzing the fluctuation degree of feature information and screening unidirectional dominant features, the interference of low-contribution features is reduced, the topological adjacency relationship of data points is constructed, the main trunk of data classification is optimized, the attribution of data points is adjusted in combination with the change of local information entropy, so that the classification structure is more in line with the data transmission characteristics. By combining the information entropy fluctuation of the classification boundary and the connection strength of core data points, the classification boundary division is optimized. By combining the change rate of hierarchical stability, the data hierarchy division strategy is adjusted, the classification structure is optimized, the stability and calculation efficiency of data classification are improved, the classification boundary adjustment is made more accurate, and it is ensured that the classification hierarchy can be adaptively adjusted.

The present application relates to the technical field of deep fake detection, in particular to a deep fake detection method based on local-global self-supervised contrastive learning; the method comprises the following steps: firstly, collecting unlabelled face images and labelled real-fake data and uniformly preprocessing, including face detection, extension cropping and key point positioning; secondly, dividing two global perspectives and seven local perspectives according to the key points, and constructing multi-view contrastive learning samples; further, designing a self-supervised pre-training process based on a teacher-student framework, learning robust face representation unsupervisedly by comparing the consistency of local and global features and reconstructing mask image blocks; finally, supervisedly fine-tuning the pre-trained model, and optimizing the classification structure by using neural network architecture search. The present application realizes high-precision and strong-generalization deep fake detection.

The application discloses an intrusion detection method and system based on integrated under-sampling and adaptive parameter optimization, randomly selects a hyperparameter combination of a base classifier in a hyperparameter space, obtains a globally optimal parameter combination through quantitative scoring, trains a temporary integrated model composed of temporary base classifiers, and selects an optimal weight combination through performance evaluation; in t rounds of iterations, a current integrated model is constructed by using the base classifiers generated in the previous round of iteration, majority class samples are predicted and divided into bins, the majority class samples are under-sampled according to the misclassification rate, and the under-sampled majority class samples are combined with minority class samples to form a balanced training set of the current round; based on the globally optimal parameter combination, the optimal weight combination and the balanced training set, the base classifiers of the current round are trained, the comprehensive weights are calculated, and the base classifiers are added into the final integrated model; the application can retain boundary majority class sample information, adaptively optimize the base classifier structure, and significantly improve the classification performance and detection rate of a network intrusion detection system.

The application discloses a kind of multi-omics data hierarchical classification structure learning systems based on machine learning, comprising: data import module, for loading multi-omics data, and the data is preprocessed;Hierarchical structure learning module, using the adaptive machine learning method driven by data carries out supervised learning, constructs category similarity matrix, uses bottom-up, top-down unsupervised clustering algorithm to preliminarily construct class label hierarchy, and integrates iterative algorithm into training process, finally obtains optimal class label hierarchy;Hierarchical classification verification module, using optimal class label hierarchy combines multi-omics data to carry out hierarchical classification, and provide result explanation.The application combines supervised, unsupervised machine learning and iterative algorithm, accurately infers the class label hierarchy in multi-omics data, improves the efficient classification and organization of complex multi-omics data, and provides classification result interpretability.

The invention discloses a multi-label data classification method and device based on label noise and hierarchical modeling, and belongs to the technical field of text data processing. The method comprises the following steps: obtaining to-be-classified text data, wherein the to-be-classified text data has a tag semantic hierarchical feature; inputting the to-be-classified text data into the trained classification model to obtain classified data; the trained classification model comprises a feature extraction structure, a classification structure and a three-level label verification structure, the feature extraction structure is used for extracting heterogeneous features of to-be-classified text data in different subspaces, and the classification structure is obtained by training the classification model by using a positive sample-first collaborative noise reduction strategy for noisy label data; the label semantic layering features comprise a first-layer macroscopic label feature, a second-layer specific label feature and a third-layer related label feature, and the three-level label verification structure is used for verifying the first-layer macroscopic label feature, the second-layer specific label feature and the third-layer related label feature.

The invention provides an intelligent management system and method for an equipment model library, equipment and a medium, and belongs to the technical field of computers. The system comprises an equipment model library construction module used for acquiring multi-dimensional data of an equipment model, constructing an industry information tree according to a four-level classification structure, integrating the multi-dimensional data and constructing an equipment model library; the model library management module is used for constructing a metadata knowledge graph of the equipment model specification data by utilizing a graph database, realizing increment storage and tracing of an equipment model specification data version based on a distributed hash table, and performing authority control through an RBAC (Role Based Access Control) mechanism; the open interface module is used for realizing multi-format equipment model data analysis and conversion and recording a calling log at the same time; and the application support module is used for realizing positioning of equipment model standard data by adopting a hybrid index and vectorization retrieval technology, supporting 3D preview and downloading of related equipment twin models, and driving system iterative optimization through user behavior analysis and a real-time feedback mechanism.

The present application proposes an artificial intelligence-based auxiliary diagnosis method, device, electronic device and storage medium for thyroid nodules. The artificial intelligence-based auxiliary diagnosis method for thyroid nodules includes: collecting thyroid ultrasound images with labeled data as a training set; building a first auxiliary diagnosis model based on a control vector set, the first auxiliary diagnosis model including a convolutional structure, a localization structure and a classification structure; training the first auxiliary diagnosis model to update the model parameters to obtain a second auxiliary diagnosis model and a standard vector set, the model parameters including the control vector set; collecting the ultrasound image to be diagnosed and inputting it into the second auxiliary diagnosis model, taking the output of the classification structure as the auxiliary diagnosis result, and constructing an auxiliary feature map based on the output of the convolutional structure and the standard vector set; and displaying the auxiliary feature map and the auxiliary diagnosis result on the terminal screen to assist the doctor in the diagnosis process. The present application can improve the interpretability and accuracy of the auxiliary diagnosis model, thereby improving the doctor's diagnosis efficiency.

The utility model discloses a human resource archive management device, which relates to the technical field of archive management and comprises a base, a protective shell is arranged at the top end of the base, an operation groove is formed in the outer surface of the protective shell, and a plurality of archive storage units are arranged in the protective shell. According to the device, through cooperation of the multiple storage boxes, the fixing frames, the adjusting blocks, the fixing pins, the sliding grooves and the fixing holes, classified storage box searching of human resource files can be conveniently carried out according to different posts and characteristics of personnel, the corresponding storage boxes can be flexibly rotated according to requirements, the file searching and taking efficiency is improved, and the device is convenient to use. And through cooperation of the storage box, the partition plate, the limiting strip and the limiting groove, the size of the storage space in the storage box and the dividing number can be conveniently adjusted according to the file classification requirement, the classification structure can be flexibly adjusted, and classified storage space is provided for a large number of different types of personnel data.

The present application relates to a kind of model-oriented geometric body and three-dimensional mark information extraction and reorganization system, belong to product data management technical field.The system includes: integrated adaptation module, as front-end plug-in is deployed in three-dimensional CAD software, for receiving user instruction and establishing communication with PLM system;Intelligent extraction module, calling CAD kernel interface directly reads the geometric information of model, assembly structure, attribute and three-dimensional mark, and is intelligently identified and classified by rule set;Structured reorganization and incremental synchronization module, the data extracted are encapsulated into composite object according to PLM data model, and only difference part is incrementally transmitted by comparing version or content hash;Lightweight generation and association module, the geometric data containing mark are converted into lightweight model that Web can render, and the metadata associated with PLM business object is embedded.The present application realizes the accurate, efficient, intelligent synchronization of design data to management system.

The embodiments of the present application provide a method and device for identifying fire hazards through AI visual analysis technology, by innovatively constructing a multi-source data acquisition and fusion mechanism, integrating multimodal data such as image video streams, three-dimensional space, thermal imaging, and environmental perception. A feature extraction model based on transfer learning is designed, combined with a hierarchical classification structure and an attention mechanism, to achieve high-precision hazard feature identification through integrated learning. Time series analysis and spatial positioning technology are introduced to construct a hazard feature association network and evolution model to achieve hazard development trend prediction and common hazard discovery. This method effectively solves the shortcomings of traditional technologies in multimodal data processing, feature recognition, and trend prediction, and significantly improves the intelligence level and early warning capabilities of fire hazard identification.

This invention discloses an intent recognition method and system based on a generative large language model, belonging to the field of natural language processing. It solves the problems of insufficient generalization ability, high manual annotation cost, and small context window of the traditional BERT model for intent recognition. This invention retains the traditional NLP supervised classification idea, modifies the Qwen3-0.6B large language model, and adds a classification head to construct an intent classification model. The model is constructed through data processing and fine-tuning during training. In application, user input containing single-turn / multi-turn context is collected. The large model extracts semantic features, the classification head judges and outputs the intent confidence score, and outputs the valid intent after a 0.95 threshold validation. If the score is not met, historical dialogue is concatenated for re-identification. This invention achieves decoupling and fusion of deep semantic understanding and traditional classification structure, improving recognition accuracy, reducing annotation cost, and enhancing system controllability and scalability. It is suitable for multi-turn dialogue scenarios such as intelligent customer service.

The present invention discloses a text classification method and system based on knowledge distillation, belonging to the technical field of natural language processing. The technical problem to be solved by the present invention is how to utilize knowledge distillation and obtain a lightweight model with comparable accuracy by leveraging the accuracy advantage of a complex model. The technical solution adopted is as follows: Specifically, the method is as follows: Obtain an unsupervised corpus and perform data preprocessing on the unsupervised corpus; Train a teacher language model based on a large-scale unsupervised corpus; Use a supervised training corpus for a specific classification task to train the teacher language model through fine-tuning to obtain a trained teacher language model; Construct a student model according to the specific classification task and the trained teacher language model; Construct a loss function based on the intermediate layer output and the final output of the teacher language model, train the student model, and obtain the final student model; Use the final student model to perform text classification prediction: Input new data for classification structure prediction.