95 results about "Semantic code" patented technology

The invention provides a multi-round dialogue intention recognition method and system based on self-adaptive semantic understanding, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a natural language dialogue text of a current round of a user, and taking the natural language dialogue text as original input data; based on original input data, multi-level semantic features are extracted through a dynamic semantic coding algorithm, and semantic vector representation of a current round of dialogue is generated; setting three fixed anchor points in a semantic vector space based on a current round semantic vector and a historical dialogue state vector to form a triangular analysis structure; performing gridding segmentation on the triangular analysis structure, and generating a feature adjustment value according to distribution characteristics of segmented grids; and dynamically correcting the extraction process of the context-related features by using the feature adjustment value to obtain the corrected context-related features. According to the method, end-to-end optimization is realized in multiple rounds of interaction scenes such as customer service and intelligent assistants through full-process design.

The invention provides a 3D posture estimation method and system based on space-time double-flow intersection in the technical field of monocular video 3D human body posture estimation, and the method comprises the steps: S1, obtaining a large number of historical human body videos, carrying out the frame-by-frame processing of each historical human body video through a 2D human body posture estimation model, obtaining a 2D coordinate position, and carrying out the frame-by-frame processing of each historical human body video; constructing a joint space-time sequence data set; s2, creating a 3D human body posture estimation model based on a joint semantic coding module, a dynamic graph convolution module, a space-time double-flow cross fusion module and a 3D posture decoding module; s3, training the 3D human body posture estimation model through the joint space-time sequence data set and the loss function; and S4, acquiring a real-time human body video, and reasoning the real-time human body video through the 2D human body posture estimation model and the 3D human body posture estimation model to obtain a 3D human body posture estimation result. The method has the advantages that the precision and robustness of 3D human body posture estimation are greatly improved.

The invention relates to the technical field of artificial intelligence and language learning, in particular to a multi-modal language learning auxiliary system and method based on artificial intelligence, and the system comprises a multi-modal input module, a cross-modal feature fusion module, a dynamic adaptive learning module and an interactive feedback generation module. Wherein the multi-mode input module is used for receiving original text data, original image / video data and original voice data; the cross-modal feature fusion module is used for extracting a visual feature vector and a semantic coding vector and generating a cross-modal joint feature vector; the dynamic adaptive learning module is used for generating dynamic scene parameters; and the interactive feedback generation module is used for outputting a multi-mode feedback data packet through a prompt learning engine. According to the method, by fusing multi-modal feature alignment and dynamic context parameter modeling, linkage generation of grammar, culture and pronunciation feedback is achieved, and the context understanding ability and interaction feedback precision in the language learning process are improved.

The invention relates to the technical field of hydraulic engineering structure safety monitoring, in particular to an intelligent early warning system and method for hydraulic engineering dam body cracks. According to the system, a sensor network is deployed, multi-source image data of a dam body is collected, structural perception attention variation denoising is adopted to fuse gray scale, infrared and thermal imaging channel characteristics, noise is inhibited, and crack details are reserved; modeling a spatio-temporal evolution sequence in combination with a time sequence convolutional network, constructing a dynamic semantic structure map, mapping spatial positions, morphological characteristics and semantic levels of crack nodes, generating a neural network through map enhancement to split structure codes and semantic codes, and fusing a variation generation module to predict a future crack evolution state; a risk scoring function is constructed based on SHAP interpretability analysis, pop-up window early warning is triggered in combination with a dynamic threshold value, scheme generation is automatically responded, and expert collaborative diagnosis is carried out, so that a closed-loop early warning decision-making system is formed. According to the invention, the real-time performance of crack monitoring and the early warning reliability are obviously improved.

The invention provides a multi-modal database query engine and method based on unified semantic representation, the engine associates and stores a vector library of unified semantic vectors of entities, and the core comprises five modules: a query analysis and semantic coding module for processing single-modal and multi-modal queries; analyzing the semantic structure and generating a corresponding semantic vector or a fused semantic vector; the query execution plan generation module generates an optimal plan including a retrieval strategy, weight allocation and resource scheduling according to semantic features, vector library features and the like; a multi-modal query execution module queries a vector library according to a plan to obtain candidate results; the query result fusion module performs weighted fusion on the candidate results to generate weighted scores of the candidate results; and the output module outputs a result according to weighted score sorting. The engine realizes efficient processing of multi-modal query and accurate result return.

The invention provides an intelligent analysis method, system, equipment and program product for automatic code review, which are applied to the field of computer software engineering and development tools, and the method comprises the following steps: obtaining code difference data, preprocessing to generate a semantic code unit, constructing a structured cue word and inputting the structured cue word into a large language model, according to the scheme, the problems that in existing code review, semantic understanding is insufficient, and efficiency is low can be solved, automatic risk identification and analysis of code changes are achieved, and review efficiency and accuracy are improved.

The invention discloses a method for automatically capturing semantic information of a binary code block and a related device, and relates to the technical field of computers.The method comprises the following steps that code disassembling and instruction level analysis are conducted on the binary code block, and a binary stream original instruction sequence is generated; performing semantic intermediate representation conversion and vocabulary overflow standardization on the binary stream original instruction sequence to obtain a binary code standardized instruction sequence; obtaining an instruction data dependency relationship and an instruction control dependency relationship corresponding to the binary code block, and constructing a corresponding binary code program dependency graph; semantic code node embedding is carried out on the binary code program dependency graph to generate a binary code representation vector corresponding to context semantics; and performing automatic semantic capture reasoning on the binary code representation vector corresponding to the context semantics through a pre-trained semantic understanding model to obtain semantic information of the binary code block. According to the method, the semantic information of the binary code block can be efficiently and accurately captured.

The embodiment of the invention discloses a coal blending combustion optimization system and method based on real-time in-furnace coal quality data, and the method comprises the steps: receiving a data set of boiler combustion conditions (a combustion temperature value, an air door opening value, a fan rotating speed value and a combustion chamber pressure value) collected by a sensor assembly, and obtaining the real-time in-furnace coal quality data; carrying out time domain aggregation and multi-scale semantic coding on the boiler combustion condition by adopting an AI-based data processing and coding algorithm, and carrying out embedded coding on the real-time boiler entering coal quality data; therefore, the coal mixing proportion is intelligently recommended according to prompt response interactive representation between the real-time in-furnace coal quality data embedding features and the boiler combustion condition multi-scale time sequence semantic coding features. In this way, real-time monitoring and intelligent control over the combustion process of the coal-fired boiler are achieved.

The invention relates to a diagnosis method based on a cross-modal charging pile maintenance knowledge base, and relates to the technical field of charging pile operation and maintenance. According to the method, the attention-based image encoder, the text encoder and the graph neural network are utilized to process the cross-modal charging pile maintenance knowledge to construct the cross-modal charging pile maintenance knowledge base; vectorized cross-modal charging pile maintenance knowledge in the cross-modal charging pile maintenance knowledge base comprises high-dimensional cross-modal semantic coding and low-dimensional graph embedding; when diagnosis processing is carried out through charging pile diagnosis query, firstly, embedding of entities and relations in the diagnosis query is extracted through a low-dimensional embedding model, and a set number of target graph embedding with the most similar similarity is matched through the embedding of the entities and the relations in the diagnosis query and graph embedding; obtaining a cross-modal semantic code matched with the target image in an embedded manner; the language model generates a diagnosis processing scheme by using the retrieved cross-modal semantic codes and diagnosis queries, and the method supports a small-parameter language model and gives consideration to both accuracy and efficiency.

The invention discloses a multi-modal data quality evaluation method based on deep learning. The method comprises the steps of obtaining first modal record data and second modal record data which are marked to be aligned in a database; respectively carrying out semantic feature extraction on the first modal record data and the second modal record data to obtain a text structured data semantic coding feature tensor and a metadata description semantic coding feature vector; performing cross-modal joint coding on the text structured data semantic coding feature tensor and the metadata description semantic coding feature vector to obtain a structured-metadata cross-modal quality evaluation joint coding feature tensor; generating reconstruction record data based on the structured-metadata cross-modal quality evaluation joint coding feature tensor, and calculating offset features of the reconstruction record data; performing quality evaluation on the offset features through a first deep learning model; according to the method, through semantic coding feature extraction, fine-grained hash cross-modal coding and bidirectional reconstruction offset evaluation, the accuracy of data quality evaluation is improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing visual entity recognition. In particular, semantic codes that represent database entities are used to effectively recognize specific database entities in input images.

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 provides an automatic sensor calibration management system and method based on data visualization, relates to the field of sensor calibration, and adopts a data analysis technology based on artificial intelligence to extract deep implicit features from temperature time sequence deviation data. The method comprises the following steps: firstly, generating a precise small-amplitude adjustment suggestion in combination with semantic coding characteristics of the calibrated temperature sensor, visually displaying the suggestion on a display screen, and finally, automatically calibrating the calibrated temperature sensor based on the calibration adjustment suggestion and a mechanical arm, similarly, referring to a calibration mode of the temperature sensor, and automatically calibrating the calibrated temperature sensor. And the humidity sensor and the pressure sensor are automatically calibrated. Therefore, the calibration efficiency and the intelligent level of the sensor can be effectively improved.

The invention provides an information transmission method and device, and relates to the technical field of communication. According to the specific implementation scheme, semantic features of to-be-transmitted information in a current transmission scene are extracted; based on the current transmission scene and the semantic features, obtaining importance level information of a feature point group set comprising at least one feature point group; performing source-channel joint coding on the semantic features to obtain semantic coding data; based on the current transmission scene, grouping and vectorizing the semantic coded data to obtain a semantic vector group set comprising at least one semantic vector group, the semantic vector groups being in one-to-one correspondence with the feature point groups; obtaining a transmission semantic frame based on the importance level information and the semantic vector group set; and modulating the transmission semantic frame to obtain and adopt a wireless channel to transmit modulation information. According to the embodiment, the information transmission efficiency is improved.

The invention relates to a rapid reasoning semantic communication method and system based on non-autoregressive decoding. The method comprises the following steps: step 1, constructing a semantic communication model of non-autoregressive decoding based on a neural network; 2, sequentially inputting the training batches into a semantic encoder and a source-channel joint encoder to obtain a semantic encoding matrix; step 3, obtaining a disturbed semantic coding matrix; 4, sequentially inputting the obtained disturbed semantic coding matrix into a source-channel joint decoder and a non-autoregressive semantic decoder to obtain a plurality of output probability matrixes; 5, training the semantic communication model of the non-autoregression decoding by using a cross entropy loss function, and finishing the training of the semantic communication model of the non-autoregression decoding when a preset number of times of training is reached; and step 6, reasoning through the trained non-autoregressive decoded semantic communication model, carrying out semantic communication, and obtaining a semantic reconstruction sentence. According to the method, the reasoning speed of the deployed semantic communication system is greatly improved, and the communication time delay is reduced.

The invention relates to the technical field of computers, and provides a video generation method and device, electronic equipment, a storage medium and a computer product, and the method comprises the steps: carrying out the visual semantic feature extraction and content semantic feature extraction of a reference image, and obtaining a semantic feature map and a content semantic coding vector; performing semantic segmentation on the content semantic coding vector to obtain a first sequence containing at least one frame granularity coding vector; on the basis of each frame granularity coding vector in the first sequence, directional modulation of features is carried out on the semantic feature map, and a second sequence containing at least one directional modulation feature map is obtained; and performing video generation based on the second sequence to obtain a target video. According to the method and the device, the target video with variability and semantic coherence in content can be generated, and the availability of the video generated during video generation based on the single image is improved.

The invention discloses an economic data intelligent analysis system and method based on big data, and the method comprises the steps: obtaining background data including public economic data and macrocurrency policy text description; a neural network model based on deep learning is adopted to carry out feature coding on the two features to enhance the understanding ability of the model for the deep meaning of background data, and public economic data item coding features and macrocurrency policy semantic coding features are obtained; performing collaborative analysis on the public economic data item coding characteristics and the macrocurrency policy semantic coding characteristics to mine deep semantic association between the public economic data item coding characteristics and the macrocurrency policy semantic coding characteristics, and capturing a dynamic relationship between the currency policy and an economic index based on the deep semantic association; and outputting an analysis result of the corresponding economic data analysis problem by using the large language model. Therefore, the system can provide more comprehensive and accurate economic data analysis results, and helps users to better understand economic phenomena and policy influences.

The invention discloses a method and a device for minimizing a decoding error probability, and a storage medium. Comprising the following steps: determining a first coded stream and a second coded stream corresponding to a first terminal device, and determining a semantic coded stream corresponding to a second terminal device; according to the first coding stream, the second coding stream and the semantic coding stream, a decoding error probability minimization problem is determined, and the decoding error probability minimization problem comprises a transmitting power optimization problem, a bandwidth allocation optimization problem and a rate allocation factor optimization problem; solving a bandwidth allocation optimization problem and a rate allocation factor optimization problem based on a decoding error probability minimization problem; simplifying a decoding error probability minimization problem, and introducing an auxiliary variable so as to determine a problem equivalent to the decoding error probability minimization problem; solving a transmitting power optimization problem based on the determined problem; and minimizing the decoding error probability based on the optimal solution of the transmitting power optimization problem, the optimal solution of the bandwidth allocation optimization problem and the optimal solution of the rate allocation factor optimization problem.

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 provides a face attribute editing and style control method and system based on a potential guidance and reference guidance diffusion model, and relates to the field of face image synthesis, attribute editing and style control. Firstly, a potential guidance and reference guidance diffusion model comprising a style code generation unit, a style modulation coding unit and a diffusion denoising unit is constructed, the style code generation unit generates style codes of a face image based on a potential guidance or reference guidance strategy, and the expression ability of styles is enhanced; the style modulation and coding unit modulates an input face image by using style codes to generate semantic codes, so that the precision of attribute editing and style control is effectively improved, and the quality of image editing is optimized; the diffusion de-noising unit is used for de-noising the input face image step by step based on semantic coding; a forward and reverse consistency training strategy is adopted to train a style code generation unit and a style modulation coding unit, so that the stability of the training process is improved, and the dependency on training samples in pairs is relieved.

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 present disclosure provides a semantic communication method and device, relates to the technical field of communication, in particular to the technical fields of large model, deep learning, diversity processing and the like. The specific implementation scheme is: obtaining source data; determining a semantic code symbol sequence and an importance ranking sequence number of a semantic code symbol in the semantic code symbol sequence based on the source data; obtaining a target code symbol sequence based on the importance ranking sequence number and the semantic code symbol sequence; and transmitting the target code symbol sequence to a receiving end through different independent fading paths, so that the receiving end obtains target data corresponding to the source data.

The invention discloses a bimodal combined ship electronic chart system and method based on a Beidou satellite, and belongs to the technical field of ship electronic charts. The method comprises the following steps: collecting a ship multivariate time sequence and key equipment image information, and integrating the information in a terminal server; singular term elimination is carried out on the ship multivariate time sequence, and the singular term is provided for the Beidou satellite information transmission terminal module; extracting a key structure and transmitting an image to a ship operation and maintenance image semantic coding module; a PearSGAN model is established; processing the bimodal information by using a Beidou coding structure; and executing an improved Mercator projection transformation method based on an elliptic dynamic equation, and providing a processing result to a centralized control center high-definition display screen module. The ship multivariate time sequence and the image information are deeply combined to form bimodal combined input information, and a high application value and a general paradigm are provided for further development of navigation guarantee, comprehensive management and other technologies of ocean ships in China.

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.