119 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 discloses an intelligent product recommendation system based on a cloud platform, and the system comprises the steps: obtaining the user portrait information of a target user object, and obtaining the material information of a target candidate medical beauty product from a plurality of candidate medical beauty products; semantic coding is carried out on the user portrait of the target user object and the material information of the target candidate medical beauty product by utilizing a data analysis and processing mode based on artificial intelligence; and intelligently generating a recommendation probability value of the target candidate medical beauty product based on the semantic flow field alignment matching representation between the target user portrait semantic coding feature and the target candidate medical beauty product material semantic coding feature, and determining whether to use the target candidate medical beauty product as the target medical beauty product. In this way, the consistency and objectivity of recommendation results can be ensured. Meanwhile, the latest behavior and preference change of the user can be captured in real time, the recommendation model is updated in time, and it is ensured that the recommendation result always meets the current requirement of the user.

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 invention relates to the technical field of article recommendation, in particular to an article recommendation method and device based on multi-modal representation, equipment and a medium. The method comprises the following steps: firstly, carrying out alignment coding processing on a text and an image to obtain a text semantic coding matrix and an image semantic coding matrix, then predicting the preference degree of a user for each article according to the text semantic coding matrix, the image semantic coding matrix and an article representation matrix, and finally, determining the preference degree of the user for each article according to the preference degree of the user. And obtaining the target recommended article. According to the method, alignment is carried out to enable the same row corresponding to the text semantic coding matrix and the image semantic coding matrix to point to the same article so as to effectively represent the same article. Due to the fact that the text semantics and the image semantics corresponding to the same article can be accurately matched through alignment, the preference degree of the user for each article can be calculated subsequently according to the text semantics and the image semantics of the articles, and then recommendation accuracy is improved.

The invention relates to the technical field of machine learning, and discloses a sample data screening method and device, computer equipment and a storage medium. The method comprises the steps of obtaining multiple pieces of first text data, and inputting the first text data into a to-be-trained model to obtain a first semantic code and first probability distribution; according to the current corpus and the first semantic code, matching the first text data to obtain a plurality of second text data with similar semantics, and determining to obtain a plurality of second annotation data; calculating a probability difference between the first text data and the plurality of second text data according to the first probability distribution and the plurality of second annotation data to obtain a value score of the first text data; and processing the first text data according to the value score of the first text data to obtain sample data. The probability difference is used for screening, so that the sample data beneficial to training the to-be-trained model can be obtained more efficiently and conveniently.

The invention discloses a large model semantic coding BIM data and engineering entity matching system, which comprises a knowledge graph construction module used for establishing a knowledge graph according to building project historical data; the BIM data and engineering entity coding module is used for coding the current BIM data or the current engineering entity into a vector by using a Chinese pre-training language model; the BIM data and engineering entity similar semantic retrieval module is used for retrieving a plurality of similar vectors of the current BIM data vector or the current engineering entity vector in the knowledge graph by using a retrieval enhancement generation model; and the BIM data and engineering entity matching module is used for matching the current BIM data vector or the current engineering entity vector with the corresponding similar vector by using cosine similarity. The invention further discloses a BIM data and engineering entity matching method for large model semantic coding. Compared with manual operation, the technical scheme of the invention has the advantages of high accuracy, high efficiency and high flexibility.

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 discloses an intelligent analysis system and method for rehabilitation treatment of lumbar intervertebral disc herniation, and relates to the field of intelligent analysis. Abstract clinical information amounts such as lesion part features, pain perception description and movement function requirements are converted into high-dimensional vectors by adopting a semantic coding technology based on deep learning; the system can more accurately understand and simulate the condition of a patient in a multi-dimensional space, further, a dynamic mapping mechanism between a rehabilitation starting point and a target is established to capture non-linear correlation between the current state of the patient and an ideal rehabilitation target, and intelligent derivation of a rehabilitation path is achieved based on the non-linear correlation. Through the dynamic analysis of the multi-dimensional information of the patient, the system can better understand the unique demand of each patient, the matching precision of the rehabilitation treatment scheme is improved, and the treatment is more personalized and effective.

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.

The invention discloses a knowledge question-answering method and device based on vehicle information data. The method comprises the following steps: acquiring a knowledge graph constructed based on the vehicle information data; determining a node semantic coding vector of each node according to the knowledge graph; inputting the adjacent matrix of the knowledge graph and the semantic coding vector of each node into a pre-constructed graph convolutional neural network model to obtain an optimized semantic coding vector corresponding to each node; and according to the received user question and the optimized semantic coding vector corresponding to each node, based on a preset generative large model, obtaining a user question and answer result. By constructing the knowledge graph and inputting the adjacency matrix and the node semantic coding vector of the knowledge graph into the graph convolutional neural network model, the semantic coding vector is optimized, the information recall accuracy is improved, the information omission and misjudgment are reduced, the recall result is more accurate, and the question and answer result generated by the generative large model is more detailed and accurate.

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 relates to a generative semantic communication system and method based on frame interpolation, and belongs to the field of communication and artificial intelligence. The system comprises a semantic coding module and a channel coding module at a sending end, and a channel decoding module, a semantic decoding module and a frame interpolation module at a receiving end. The sending end extracts key semantic information of the dynamic image through semantic coding, and compresses and transmits at least two key frames; and a receiving end reconstructs the key frame through a decoding module, and generates an intermediate frame by means of a frame interpolation module, thereby restoring a complete dynamic image. In the aspect of system design, the semantic encoding and decoding module is based on a Swin Transform structure, and the frame interpolation module is based on an optical flow decoder, so that efficient semantic compression and decoding generation are realized. A training strategy adopts transfer learning, and an image transmission and frame generation module is gradually optimized, so that the model is quickly converged to an optimal state. The system structure and the implementation method are described in detail.

The invention provides a distributed photovoltaic grid-connected security protection system and method, and belongs to the field of network security protection. The system comprises an information acquisition terminal, a semantic coding module and a distributed photovoltaic grid-connected monitoring master station, the information acquisition terminal is used for acquiring operation data of the distributed photovoltaic power station and the equipment formed by the distributed photovoltaic power station; the semantic coding module is used for receiving the operation data, carrying out semantic coding and encryption on the operation data, and sending the encrypted coded data to the distributed photovoltaic grid-connected monitoring master station based on a wireless transmission mode; and the distributed photovoltaic grid-connected monitoring master station is used for receiving the encrypted coded data, decrypting and decoding the encrypted coded data to obtain decoded data, and monitoring and analyzing the operation of the distributed photovoltaic power station based on the decoded data. According to the invention, both the transmission real-time performance and safety of distributed photovoltaic grid-connected monitoring can be considered.

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 a multi-modal coding alignment method and device based on a diffusion model, and belongs to the technical field of multi-modal coding alignment. The method comprises the following steps: acquiring multi-modal data to be subjected to semantic coding, performing conditional diffusion on the multi-modal data to be subjected to semantic coding by using a pre-trained multi-modal coding alignment model to generate a text code; the text codes are input into a large model decoder or used as RAG codes for downstream tasks; wherein the generated text code is used as a multi-modal data semantic code, and is in semantic alignment with the text code. According to the method, the pre-trained multi-modal coding alignment model utilizes potential multi-modal data coding information which can be learned by a diffusion model, multi-modal semantic coding is completed at the same time through a diffusion model structure, and meanwhile, large language model coding is used as a multi-task optimization direction; and a pre-trained multi-modal model coding alignment model result is more suitable for downstream tasks.

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 provides a video content similarity calculation method and device, equipment, a medium and a product, and the method comprises the steps: carrying out the key frame sampling of an obtained target video and a to-be-recognized video, and obtaining a first key frame sequence of the target video and a second key frame sequence of the to-be-recognized video; converting the first key frame sequence and the second key frame sequence into text description to obtain a first semantic coding text sequence and a second semantic coding text sequence; encoding the first semantic encoding text sequence and the second semantic encoding text sequence to obtain a first semantic encoding vector and a second semantic encoding vector; and calculating the similarity between the target video and the to-be-identified video according to the first semantic coding vector and the second semantic coding vector. According to the scheme, the semantic features of the video content are extracted through semantic conversion, the semantic similarity of the video can be better captured, and calculation of the video content correlation can be more efficiently and accurately achieved.

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 relates to the technical field of place name data processing, and particularly discloses an automatic association processing method based on open source place name data, which comprises the following steps: firstly, carrying out entity detection on natural language text content containing place names to extract candidate place names, and taking other content in the text as supplement; the semantic coding technology based on deep learning is adopted to carry out semantic coding and compensation type interactive fusion on candidate place names and supplementary content thereof, and the supplementary content is used as a context background to optimize semantic feature expression of the candidate place names. And constructing a place name alternative list by querying associated entity data of the candidate place names in the geographic database, and realizing automatic association of the place name data based on semantic similarity between each alternative place name in the list and the candidate place names. According to the application, the accuracy of place name data association can be effectively improved, meanwhile, the dependence on manual annotation is reduced, and the data processing efficiency is improved.