49 results about "Semantic code" patented technology

The invention belongs to the technical field of computers, and provides a video virtual fitting method and device based on a diffusion model and a program product. The method comprises the following steps: firstly, acquiring a target clothing graph and a model video, and extracting depth features, masks and a model latent feature video; meanwhile, semantic codes of the clothes are extracted through a CLIP encoder. The first diffusion model processes the clothing graph and the CLIP code, and outputs a clothing space attention feature vector fusing fine-grained texture and semantics; and the second diffusion model serves as a video diffusion network, receives random noise, character features, masks and depth features, injects garment vectors and CLIP codes into the middle layer, and finally outputs a fitting video with naturally fitted and dynamically coherent garments. According to the method, the high-fidelity virtual fitting video can be efficiently generated according to the new clothing and model video, and the method has wide application value in the fields of electronic commerce and the like.

The invention relates to the technical field of animal behavior analysis, and discloses an animal behavior accurate recording and analyzing system based on multi-mode keyboard coding. A data acquisition and synchronization module of the system acquires original key event data and generates a time sequence behavior data stream with millisecond-level precision; the coding logic engine module is used for converting the physical key sequence into a hierarchical behavior semantic coding sequence through a dynamic mode switching and key mapping technology; the behavior state modeling module is used for constructing an animal behavior state transition probability model, performing state decoding and estimation by adopting a hidden Markov model, and outputting a behavior state sequence with confidence; the behavior analysis and prediction module is used for predicting a behavior evolution trend and identifying an abnormal behavior event; and the threshold value dynamic adjustment module is used for dynamically adjusting an early warning threshold value through interaction between the normal behavior generator and the real-time discriminator aiming at an abnormal behavior event so as to finish accurate recording and risk analysis of animal behaviors.

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 image counting method and device based on natural language guidance, equipment and a medium. Respectively extracting visual features and language features through an image coding module and a text semantic coding module; inputting the two features into a visual language unified embedding module for multi-modal fusion to obtain multi-modal features; performing semantic alignment screening on the multi-modal features through a prompt alignment visual filter to obtain target recognition features; inserting a counting mark into the target recognition feature, and inputting the target recognition feature into a quantity sensing decoding module for decoding to generate a natural language text containing a target quantity; and finally, outputting a natural language quantity answer text conforming to the grammar specification through a language generation header module. According to the method, the counting accuracy is remarkably improved through language-guided visual feature screening and a structured quantity decoding mechanism.

The invention discloses a multi-modal visual language navigation method based on a dynamic environment knowledge graph and related equipment, and can be applied to the technical field of artificial intelligence. After the real-time environment image of the area to which the target robot belongs is captured through the single camera, feature extraction is carried out on the real-time environment image to obtain the image features, the to-be-processed category corresponding to the target object is coded to obtain the semantic code, and after the spatial features of the target object are extracted, the target robot is obtained. Constructing a dynamic environment knowledge graph according to the confidence, semantic coding and spatial features of the target object in combination with an external knowledge base, extracting high-dimensional graph features of the dynamic environment knowledge graph, and performing implicit fusion and display modeling in combination with image features and language features corresponding to the target instruction to obtain a navigation state vector containing long-term memory; therefore, the moving operation of the target robot can be controlled based on the navigation state vector, efficient and explainable navigation reasoning is further realized, and the language navigation accuracy is improved at relatively low cost.

The invention relates to the field of body intelligence, and provides an autonomous decision-making method and system for a body intelligent robot. The autonomous decision-making method comprises the following steps of: acquiring environment and interaction data in real time by adopting an active perception mechanism, and converting the environment and interaction data into a scene and an own semantic code fused with the scene and the own; inputting the subject semantic code into a shunting decision-making layer to output a structured semantic abstract and an execution action, wherein the structured semantic abstract comprises scene parameters, user requirements and confidence; in response to the fact that the confidence is located in the middle confidence interval, determining and executing a non-invasive tentative action instruction used for verifying the user demand based on the execution action and the scene parameters; collecting user feedback data when the non-invasive heuristic action instruction is executed to determine a user feedback result; and determining a decision result based on a user feedback result, and adjusting the range of the dynamic confidence interval and the association weight between the execution action in the shunting decision layer and the user demand.

The application belongs to the technical field of computers, and provides a video virtual try-on method, device and program product based on a diffusion model. First, a target garment image and a model video are acquired, and a deep feature, a mask and a model latent feature video are extracted therefrom; meanwhile, a semantic code of the garment is extracted through a CLIP encoder. A first diffusion model processes the garment image and the CLIP code, and outputs a garment spatial attention feature vector that fuses fine-grained texture and semantics; a second diffusion model serves as a video diffusion network, receives random noise, a person feature, a mask and a deep feature, and injects a garment vector and the CLIP code into an intermediate layer, and finally outputs a try-on video that is natural and dynamically coherent. The application can efficiently generate a high-fidelity virtual try-on video according to a new garment and a model video, and has wide application value in the field of e-commerce.

The application discloses an attention mechanism-based adversarial text defense method and system, which comprises the following steps: inputting to-be-recognized text into a natural language processing model with an encoder-decoder as a basic structure; calculating the importance score of each word in the text by using a word scoring function; taking the reciprocal of the importance score to form a reconstruction score vector; calculating the weight of each hidden layer vector according to an attention formula to obtain an attention weight vector; balancing the reconstruction score vector and the attention weight vector by using the way of multiplying the reconstruction score vector by a hyperparameter, multiplying corresponding elements in the reconstruction score vector and the attention weight vector one by one to obtain a final reconstruction attention vector; and multiplying the reconstruction attention vector by a hidden layer feature vector to obtain a reconstructed semantic code, and obtaining output after decoding. The application has good generalization performance, does not need to retrain a model when coping with new adversarial attacks, and has a certain effect on character-level adversarial attacks and word-level adversarial attacks.

This invention discloses an autonomous data processing system based on a deep mining framework, relating to the field of data processing technology. It includes: a semantic judgment module that extracts the target missing field, associated fields, and historical homologous field sequences from input records; determines the field constraint relationships between the target missing field and associated fields; and constructs a field constraint graph accordingly; calculates the missing position value, constraint break value, and historical offset value; and determines the missing semantic code based on these values; and writes the field constraint graph, the missing semantic code, and the missing position value, constraint break value, and historical offset value into a record-level state payload. This invention constructs a continuous data processing link around the target missing field, associated fields, and historical homologous field sequences; and establishes clear data flow and control flow relationships between vectors such as field constraint relationships, field constraint graphs, missing semantic codes, original data tracks, and completed data tracks.

The invention discloses a cross-platform virtual asset intelligent identification and energy efficiency management system based on the Internet of Things, and the system comprises an Internet of Things edge access and preprocessing module which is used for collecting operation parameter data; the structured Prompt construction module is used for generating a structured Prompt template; the SigLIP model double-tower semantic coding module is used for generating a virtual asset semantic vector set; the virtual asset identification and index mapping module is used for extracting an energy consumption time sequence; the improved TimesNet modeling module is used for executing periodic convolutional coding, energy consumption mode decoupling and cross-period dependence modeling, and outputting a cross-period context feature tensor; the prediction layer module is used for generating an energy consumption prediction result; and the energy efficiency strategy module is used for forming closed-loop control. The method has the advantages that the energy consumption trend is accurately predicted, the cross-cycle energy efficiency optimization strategy is realized, and the intelligent level and response efficiency of energy consumption management are greatly improved.

The application discloses a hydrological remote sensing image target recognition method based on a deep semantic model, relates to the technical field of remote sensing image processing, and comprises the following steps: obtaining a primary semantic code of a hydrological remote sensing image through a deep semantic network, calculating a semantic attribution probability distribution according to the primary semantic code, generating an initial attention guide signal, dynamically adjusting the weight of a specific perception path in the network, realizing first feature re-extraction to obtain refined features, calculating a feature compensation vector based on the confidence deviation generated by matching the features with a standard feature library, correcting the refined features to obtain enhanced features, and inputting the enhanced features into the network again for analysis to obtain a final recognition result. Through the attention adjustment of the semantic guide and the feature compensation based on the confidence feedback, the method effectively improves the recognition accuracy of complex hydrological ground object targets and the model robustness.

Embodiments of the present application provide a text processing method, device and computer storage medium. The method comprises: obtaining a text to be processed; calling a first classification network to process a secondary category code representation, category matching parameters and semantic code representation of the text to be processed, to obtain classification information of a secondary category of the text to be processed; and determining a classification result of the text to be processed based on the classification information of the secondary category and classification information of a primary category of the text to be processed, the classification information of the primary category of the text to be processed being obtained by processing the text to be processed by a second classification network, so that the accuracy of hierarchical classification of the text can be improved.

This invention discloses a behavior anomaly detection method for bank personnel based on behavioral sequences, belonging to the field of behavior anomaly monitoring technology. It addresses the technical problems of existing methods for comprehensively monitoring and identifying high-risk behavioral characteristics of bank employees, which suffer from isolated behavior detection and a lack of semantic association. The method includes: parsing and aligning multi-source heterogeneous data collected from various data sources; performing common baseline analysis of group behavior under standard behavioral sequences on the behavioral path data of bank personnel to obtain standard role behavior baselines; performing semantic code mapping processing on behavioral metadata related to real-time behavior serialization to obtain semantically annotated real-time behavior code strings; performing dual evaluation calculations on the real-time behavior code strings based on path deviation and temporal deviation to obtain a comprehensive risk score; and automatically fixing the evidence chain of high-risk associated behavioral sequences with outliers in the comprehensive risk score to generate anomaly behavior detection results.

The application provides a multi-modal database query engine and method based on unified semantic representation, the engine is associated with a vector library of entity unified semantic vectors, and the core comprises five modules: a query analysis and semantic coding module processes single-modal and multi-modal queries, analyzes semantic structures, and generates corresponding semantic vectors or fused semantic vectors; a query execution plan generation module generates an optimal plan containing a search strategy, weight distribution, and resource scheduling according to semantic characteristics, vector library characteristics, and the like; a multi-modal query execution module obtains candidate results from the vector library according to the plan; a query result fusion module performs weighted fusion on the multiple candidate results, and generates a weighted score of each candidate result; and an output module outputs the results in order of the weighted scores. The engine realizes efficient processing and accurate result returning of multi-modal queries.

This invention discloses an AI-driven DevOps adaptive fault repair method and system. The method specifically includes: extracting semantic features from code and logs generated during system runtime to generate semantic code fingerprints; using the semantic code fingerprints as initial node features to construct an error propagation graph based on the software system's call relationships; utilizing graph convolutional networks to perform high-order topological feature learning on the error propagation graph to accurately deduplicate error information and locate the root cause of the fault; generating elastic scaling strategies and resource configuration optimization schemes for cloud-native clusters based on the deduplicated error information and the system's real-time load pattern; and generating corresponding repair plans and matching the optimal responsible party based on the located root cause of the fault and the real-time status characteristics of team members. This invention automates the entire DevOps fault repair process, significantly reducing the mean time to recovery and improving system stability and operational efficiency.

The invention relates to the technical field of BOM management in a PLM system, in particular to an EBOM and PBOM intelligent matching method based on Tree-LSTM and Transform fusion, which mainly comprises a data preprocessing module, a structure coding module, a semantic coding module, a double-model feature extraction module, a fusion layer training module and a matching output module. All the modules are connected in sequence to form a complete deep learning matching process. The method comprises the following steps: firstly, in a data input stage, a system exports EBOM and PBOM bills of materials from a PLM system; each table comprises typical fields, such as part numbers, part names, father node numbers, child node numbers, the number, units and other information. Each input sample is composed of a pair of nodes to be matched, namely (EBOM node and PBOM node). Each node contains text attribute fields (name, specification, description and the like) and structure attribute fields (hierarchy depth, father-son relation and the like).

The invention discloses an exhibition hall natural language positioning method and system based on a dual-path spatial semantic field and a storage medium. Performing context coding on the geometric layout of the booth through a layout coding network to form a learnable space base; a text encoder and a posture encoder are used for encoding natural language questions input by a user and audience postures respectively, the natural language questions and the audience postures are fused into unified semantic representation through a fusion network, and anchor semantic codes, relation semantic codes and direct semantic codes used for spatial reasoning are generated; then executing two reasoning paths of an anchor path and a direct path in parallel; and finally, fusing the outputs of the two paths in a probability domain to obtain booth probability distribution, and synchronously outputting a target booth ranking, anchor point interpretation information and a continuous thermodynamic diagram visualization result. According to the method, joint modeling is carried out on natural language questions, audience postures and geometric layout of booths in an exhibition hall scene, so that directional, reference and range question positioning is realized.

The invention discloses a proxy dynamic address and identity management system based on cache capability, and the system comprises a capability feature extraction module which is used for obtaining a capability attribute set of a proxy in real time, carrying out the denoising and normalization of the capability attribute set, and generating a standardized capability feature set; and a high-dimensional semantic coding module connected with the capability feature extraction module and used for mapping the standardized capability feature set to a high-dimensional feature space by using a preset semantic coding model to generate a high-dimensional feature vector. Perfect balance is achieved between data timeliness and resource efficiency through the system, the updating strategy periodically scans capacity change through a fixed time window, and the passive updating strategy can respond to model upgrading or hardware recovery emergency state events. The combination of the two ensures that the data in the edge node cache is always kept synchronous with the proxy body state of the physical world, and the storage space is prevented from being fully occupied by invalid historical data through a first-in first-out strategy.

The invention discloses a context-based Chinese intelligent error correction and text optimization system, which belongs to the technical field of natural language processing, and comprises a context semantic coding module, a multi-dimensional error detection module, a literary style perception module, a language authentic degree evaluation module and a self-adaptive error correction optimization module, the context semantic coding module realizes multi-granularity semantic coding through a hierarchical attention mechanism; the multi-dimensional error detection module is used for detecting homophone errors, similar character errors, idiom misuse and grammar errors in parallel; the literary style perception module identifies the literary style type of the text and provides targeted optimization; the language tunnel degree evaluation module evaluates the language tunnel degree and recommends alternative expressions; the self-adaptive error correction optimization module synthesizes the output of each module to generate an error correction result, and realizes closed-loop parameter optimization based on user feedback, the error correction accuracy reaches 96%, and the Chinese writing quality is effectively improved.

The invention relates to the technical field of data communication and processing, and discloses a motion camera real-time data transmission and cloud storage management system, which comprises a semantic coding module used for processing real-time sensor data; the first communication module is used for sending the semantic event to a cloud service platform; the data extraction decision module is used for receiving the semantic event and generating a data extraction trigger signal according to a preset condition; the reverse scheduling module is used for responding to the data extraction trigger signal to generate a data extraction instruction containing information associated with the timestamp; and the second communication module is used for acquiring a corresponding video data block from a local storage medium after the motion camera receives the data extraction instruction. According to the method, the semantic event is reported through the low-power-consumption channel, and the reverse scheduling instruction of the cloud is received, so that high-value video clips are accurately extracted as required under the conditions of low power consumption and low bandwidth consumption.

The invention belongs to the cross technical field of geographic information science and natural language processing, and particularly discloses an open geological space relation extraction method and system based on a dependency graph, and the method comprises the steps: obtaining a sentence text set according to geological text data, screening sentences at least comprising two space entities from the sentence text set to form a subject-object pair, and obtaining a screened sentence text set; constructing an entity relationship triple, obtaining segmented words, part-of-speech and syntactic analysis, and forming a geological space relationship data set; training an extraction model by using the training and evaluation sample set, wherein the model comprises a context semantic coding module, a dependency graph attention network module and a two-stage triple extraction module; and testing by using the trained extraction model, performing a two-stage prediction process on an input test data set, firstly measuring a relationship and then predicting an entity, and generating a structured triple. The method can overcome Chinese word segmentation errors, effectively captures long-distance dependence, and is suitable for open relation extraction in the geological field.

The application discloses a kind of genomic variation atlas visual interactive systems, system includes data access and pretreatment module, multidimensional variation atlas rendering module, interactive control module, dynamic query and filtering module and result output module.Data access and pretreatment module generates standardized variation feature vector set to multiple types of variation data, multidimensional variation atlas rendering module simultaneously renders the position, type, frequency and pathogenicity of variation with position, shape and color semantic coding triple dimensions;Interactive control module realizes continuous seamless zooming from whole genome macroscopic thermal view to single base resolution;Dynamic query and filtering module supports composite condition real-time filtering and atlas synchronous linkage through multidimensional inverted index;The application effectively solves the problems of insufficient multidimensional attribute coding, fragmented interaction levels and missing screening and atlas linkage in existing tools, significantly improves the efficiency of visual analysis of massive genomic variation data.

The invention discloses a child autism auxiliary diagnosis method based on a large model and fNIRS signals. The method comprises three logic chains: (1) a voice-driven large model inquiry link; (2) a brain function collection and semantic coding link based on fNIRS; and (3) reasoning a link based on a large language model and a medical knowledge base. According to the method, a large model inquiry module driven by voice interaction, an fNIRS signal semantic coding module optimized for an autism scene and a diagnosis and intervention suggestion generation module based on RAG (Retrieval Enhanced Generation) are constructed, so that the operation ability of a child guardian is limited, and an fNIRS signal is easily interfered; objective quantitative evaluation and interpretable auxiliary diagnosis of ASD risks are realized, the use threshold is reduced, and the evaluation efficiency and reliability are improved.

The invention discloses an intelligent nursing question-answer decision-making system based on semantic understanding, and the system comprises a semantic preprocessing module which is used for collecting natural language questions and related patient information data, and generating a structured semantic input vector; the semantic coding module is used for carrying out multi-dimensional embedded coding through the improved DeBERTa model and outputting a multi-dimensional semantic representation vector; the multi-dimensional mental atlas construction module is used for constructing a multi-dimensional mental atlas; the semantic resonance reasoning module is used for executing semantic resonance reasoning and outputting a semantic resonance result; the self-consistent reflection and feedback module is used for executing self-consistent reflection and constraint feedback reasoning to generate a consistent nursing scheme; and the decision display module is used for outputting the final nursing question and answer result and generating an interpretable record. According to the method, deep semantic understanding and safety decision optimization of intelligent nursing questions and answers are realized by improving DeBERTa semantic coding and multi-dimensional mental atlas reasoning.

The application discloses a motion posture normative evaluation method based on behavior feature recognition, relates to the technical field of motion posture evaluation, and comprises the following steps: obtaining key point trajectories by processing visual sensor data, extracting space-time features and combining the space-time features into feature vectors. The feature vectors are mapped to a high-dimensional semantic space by a pre-trained posture semantic encoder to obtain semantic codes containing deep motion patterns; three types of internal attributes, i.e., an interpretable motion rhythm, joint coordination and posture stability, are decoded from the codes. Distance measurement is performed on the attributes in the semantic space based on standard templates, an evaluation grade is determined according to rules, and finally, a structured evaluation report combining the grade and each sub-item value is generated. The method realizes a leap from appearance features to semantic understanding, and improves the accuracy of evaluation and the interpretability of results.

The invention discloses a mode-aware black product user detection method and system, and relates to the technical field of artificial intelligence and safety detection. The system comprises a behavior sequence acquisition module, a behavior sequence preprocessing module, a mode mask generation module, a semantic coding module, a user interaction graph construction module, a structure coding module, a mode alignment module and a joint training and classification output module. Firstly, a user behavior sequence is preprocessed, and a mode mask is generated; generating a mode mask vector corresponding to the fixed-length behavior sequence, and reconstructing supervision through a mask; meanwhile, constructing a user interaction graph, processing the user interaction graph by using a frequency perception graph neural network, and performing fusion through a gating mechanism; and finally, extracting a mode induction sub-graph from the user interaction graph, calculating mode alignment loss, and carrying out joint training and classification. According to the method, high-accuracy and fine-grained detection of the black product account is realized, and the method has higher generalization ability and practical application value.

The invention relates to a state information determination method, first equipment, second equipment and a storage medium. The method comprises the following steps: a first device determines channel measurement data; performing semantic coding processing on the channel measurement data to obtain semantic coding data; and sending semantic coded data to a second device, wherein the semantic coded data is used for indicating the motion state information of the perception target in the channel environment.

The invention provides a semantic coding method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: firstly, determining a to-be-processed semantic vector and a semantic granularity level, then obtaining a plurality of sub-vectors with different semantic granularities from the semantic vector based on the semantic granularity level, and then quantifying the sub-vector with each semantic granularity to obtain a code table corresponding to the sub-vector with the semantic granularity; and finally, coding the semantic vector based on the code tables corresponding to the sub-vectors with different semantic granularities to obtain a plurality of semantic codes of the semantic vector, thereby improving generalization and accuracy of semantic coding.

The invention belongs to the technical field of databases, is suitable for the medical field and the financial field, and discloses an insurance service contact attribution method, device, equipment and medium, and the method comprises the steps: constructing a time decay function, and calculating the time decay weight of each contact in a behavior sequence based on the time decay function; splicing the weighted semantic coding vector and the position coding vector of each contact, inputting the spliced coding vector of each contact into a Transform model, and calculating the attention weight of each contact; constructing a covariable matrix of the customer insurance business based on the customer attribute data, the weighted semantic coding vector of each contact and the attention weight, and training a causal forest model based on the constructed covariable matrix; and calculating a condition average processing effect value of each contact based on the trained causal forest model, and generating a contact attribution weight of the customer insurance business. And the accuracy of contribution degree quantification of each contact of the insurance service is effectively improved.

The invention provides a distributed data intelligent retrieval method and system based on cost business dimensionality, and belongs to the technical field of computers.The method comprises the steps that a retrieval request and cost database metadata are processed to obtain a retrieval semantic coding feature vector and a project cost metadata semantic feature index; real-time states of the data nodes are obtained, and an effective data node list is generated after authentication and verification; generating a global metadata routing table based on the index meta-information of the valid data nodes; converting the retrieval semantic coding feature vector into a lightweight retrieval signature; based on the retrieval semantic coding feature vector, a global metadata routing table and an effective data node list, decomposing the retrieval request into sub-retrieval sets; and obtaining an optimal query execution plan based on the multi-dimensional constraint factors of the sub-retrieval sets and a query optimization algorithm, distributing the sub-retrieval sets to corresponding target nodes, executing a two-stage retrieval mechanism to obtain a node local retrieval result set, and outputting a retrieval result after processing.

The invention discloses an efficient positioning method and system fusing a two-dimensional map and visual semantic information, and belongs to the technical field of picture processing, and the method comprises a visual semantic coding module which is used for extracting pixel-level semantic information of an original image, segmenting the original image according to the pixel-level semantic information, and obtaining a semantic segmentation image S; the top view projection module is used for converting the semantic segmentation graph S into a semantic top view and correcting the semantic top view into a final top view semantic graph through a projection model; and the cyclic consistency network is used for re-projecting the final overlook semantic graph back to the visual angle space to obtain a reconstructed semantic graph, calculating consistency loss through the reconstructed semantic graph and the semantic segmentation graph S, and constraining the projection model through a result of the consistency loss. According to the method, the real-time and stable global positioning task is completed by constructing the mapping relation among the view, the top view and the map.