155 results about "Text stream" patented technology

A method is provided for estimating the number of distinct tokens in a text stream using a modified text-to-text variational autoencoder (T5VQVAE) model. The method includes receiving a continuous input of a text stream; dynamically maintaining a buffer that stores a probabilistic subset of tokens from the text stream; calculating a sampling probability for each token based on a condition related to the current state of the buffer; updating the buffer based on the sampling probability to include or exclude tokens; encoding the buffered tokens into a latent space using the T5VQVAE model; and estimating the number of distinct tokens in the text stream based on the tokens in the buffer and the corresponding sampling probabilities.

The invention relates to a document processing method and system based on text content extraction. The method comprises the steps that an original document containing text, image and format information is received, the encoding format of the document is automatically detected, character set conversion is executed, and hierarchical indexes including page numbers, paragraphs and tables are established for an unstructured document; the method comprises the following steps: synchronously processing text content and visual layout through a pre-trained visual-language model, extracting word-level and sentence-level semantic features by a text stream embedding layer, analyzing spatial distribution features of document elements by a visual encoder, and fusing text and visual features through a cross-modal attention mechanism; and loading the domain knowledge graph matched with the document type, and executing entity linking to associate the text mentions to the knowledge nodes. According to the document processing method and system based on text content extraction, through the synergistic effect of vision-text joint coding and knowledge enhancement, the accuracy of financial contract key clause recognition tasks is improved, the error rate is lower than that of industry benchmark products, and the semantic understanding precision is remarkably improved.

The invention discloses a short video production method and system based on artificial intelligence, and relates to the technical field of multimedia processing, and the method comprises the steps: inputting a split script into a multi-modal generation engine, employing a lower triangle space-time attention matrix to generate a visual stream, and generating a text stream and an audio stream; calculating a neural conduction delay amount between the visual stream and the audio stream based on an audio-visual perception delay prediction model, and generating a corrected audio stream through time axis forward compensation; calculating a cross-modal attention weight matrix according to the node association strength of the dynamic causal graph, and projecting the text stream and the corrected audio stream to a joint feature space to generate a multi-modal fusion feature; and inputting the multi-modal fusion features into a format adaptation engine, and after time-space consistency verification, embedding a neural implicit watermark and encoding the neural implicit watermark into a short video. According to the invention, the semantic consistency and coordination among the text, the audio and the visual content are improved, so that high-quality and high-intelligence automatic generation of the short video is realized on the whole.

The invention provides a low-delay streaming voice interaction system with an interruption processing function, and relates to the technical field of artificial intelligence. Necessary preprocessing and acoustic feature extraction are performed through a real-time acoustic processing module, and distortion and complex environmental noise introduced by an interaction channel are resisted through a robustness enhancement technology; the streaming acoustic decoding module performs acoustic modeling, language model application and decoding in real time in parallel, and outputs an ultra-low-delay text transfer result stream; the real-time acoustic processing module is combined with a signal processing technology and is responsible for detecting user voice activity in a high-precision and ultra-low-delay manner, and particularly judging the real-time voice activity state of a user through the user voice activity in an AI voice playing period; an efficient and low-delay bidirectional streaming network transmission mode is adopted between modules of the system and between the modules and a communication platform, and it is ensured that audio streams, acoustic feature streams, text streams and control signals can be transmitted and processed in real time with extremely low end-to-end delay.

The invention provides an AI streaming output rich text real-time rendering method and system based on minimum rendering unit recognition. The method comprises the steps that an AI model outputs a text stream to an AI agent; defining a minimum rendering unit (MRU), wherein the minimum rendering unit is self-consistent in format and complete; the AI agent analyzes the text streams in real time, the analysis process continuously judges whether the currently accumulated text streams form a complete MRU or not, and if yes, a rendering trigger mark is added for output; if not, directly outputting; the client receives the output result and the text stream and judges whether current rendering is triggered or not, and if yes, the rendering result is displayed; and if not, continuing to receive the output result until the rendering result is displayed after the rendering is triggered. According to the method, real real-time rendering can be realized, and a user can see a formatted result to appear on a screen step by step and smoothly while AI generates contents, so that response speed perception and interaction experience are greatly improved, and the pain point of blank screen waiting is solved.

The invention relates to a voice recognition processing method, system and device based on a conference scene and a medium, and belongs to the technical field of voice processing. The voice recognition processing method comprises the following steps: acquiring an original conference audio stream collected by a microphone array; performing signal preprocessing on the original conference audio stream collected by the main channel, and outputting a pure voice signal; generating a sound source orientation thermodynamic diagram based on the original conference audio stream; extracting multi-dimensional voiceprint feature vectors from the pure voice signals, performing dynamic grouping, outputting a voice fragment set marked with voiceprint IDs, and generating an initial transcription text; dynamically correcting the initial transliteration text, and outputting a transliteration text stream with an industry term tag; and performing periodic memory enhancement processing on the transliteration text stream, outputting and analyzing a long text, and generating structured conference summary data. According to the invention, the automation level and accuracy of conference voice processing can be improved.

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention discloses a multi-modal fusion short message compliance and security dual auditing method and system, and the method comprises the steps: encrypting account information through employing an irreversible algorithm based on SHA256 and a random salt value, and decomposing the content of a short message into a text stream, a link stream and a symbol stream through employing a regular expression; traversing each character in the short message content by adopting a prefix mode based on a Trie tree in combination with an AC automaton algorithm to detect sensitive words; performing symbol semantic classification mapping and analysis on the special symbol feature data extracted from the symbol stream; carrying out sending behavior analysis on the text feature data extracted from the text stream; performing special detection on link feature data extracted from the link stream; based on the factors corresponding to the sensitive words, the semantics, the behaviors and the links and the weights of the factors, a multi-modal feature fusion decision risk assessment algorithm is adopted to output a risk score value and a risk decision rule of the risk score value so as to execute short message interception operation or short message release operation. According to the invention, full-dimension perception and dynamic defense of risks can be realized.

The invention discloses a lightweight member marketing method and device for real-time conversation, a terminal and a storage medium, and the method comprises the steps: responding to a received user voice stream, continuously generating an incomplete intermediate text stream through streaming voice recognition, and inputting the intermediate text stream into a lightweight marketing response model; the lightweight marketing response model predicts the potential intention of the user in real time based on the intermediate text stream, and pre-assembles one or more candidate response frameworks from the fragmented marketing corpus; when it is detected that the utterance of the user ends, the real intention of the user is determined based on a complete final text stream generated by streaming speech recognition; and arbitrating the candidate reply framework according to the weighted decision factor set to obtain an arbitration result, determining final reply content according to the arbitration result, converting the final reply content into an instant response call, and outputting the instant response call. According to the method, the time delay of the real-time call marketing process can be reduced, and the concurrent processing capability is improved.

The invention discloses a simultaneous interpretation method and system based on a large model and electronic equipment, and the method comprises the steps: extracting bilingual parallel corpora related to terms from professional resources based on a standardized professional dictionary, obtaining qualified corpora through data enhancement processing and manual screening, and constructing a multi-level corpus according to the levels of words, sentences and paragraphs; the method comprises the following steps: receiving an input audio stream in real time, extracting acoustic features through preprocessing, inputting a pre-established large-scale speech recognition model, and carrying out incremental decoding on the acoustic features in a sliding window mode; and calling a sentence boundary prediction network to judge a pause point, and outputting a text stream with a timestamp. Performing fine tuning on the large-scale speech recognition model by using a multi-level corpus, translating a text stream based on the fine-tuned large-scale speech recognition model, and constraining term translation according to a standardized professional dictionary; and synchronously displaying the audio output in the translation result and the subtitles. According to the scheme, the terminology recognition and translation accuracy is improved, and simultaneous interpretation delay is reduced.

The invention discloses an RAG content generation method and system based on dynamic slicing and self-adaptive fusion, and relates to the field of natural language process.The method comprises the steps that in response to a user query instruction, semantic fusion processing is conducted on a plurality of related text blocks, and a preliminary text sequence is traversed; detecting a logic breakpoint between adjacent text blocks through a context association identifier, recalling a middle connection text block corresponding to the logic breakpoint, and inserting the middle connection text block into a sequence to generate a fusion text stream; performing adaptive information density adjustment on the fused text stream to obtain optimized context content; generating structured prompt information based on the optimized context content and the user query instruction; and calling the target large language model to generate a user query result based on the structured prompt information. The answer accuracy and logic continuity of the target large language model for user query are effectively improved, and the utilization efficiency of model computing resources is optimized.

The invention discloses a real-time video processing method and system based on artificial intelligence, and belongs to the technical field of computers. Through deep fusion of multi-source information and a regional adaptive coding mechanism, the problem of collaboration between content generation and network resource allocation in a high-interactivity live broadcast scene is solved. In the feature extraction link, video, audio and text streams are synchronously processed, so that three-dimensional perception of a live broadcast scene is realized; in the multi-mode driving instruction set generation process, audio event features and text interaction features are subjected to semantic association, so that content generation and real-time interaction are highly synchronized, and response delay caused by information splitting in a traditional scheme is avoided; according to the regional differentiation coding processing, resource allocation is dynamically adjusted according to the regional importance of visual semantic feature recognition and the network state, and the coding quality of a digital human region is preferentially guaranteed, so that the stability of core visual elements is maintained under the network fluctuation condition, and the intelligent level of real-time video processing and the user experience are improved.

The invention relates to the technical field of financial science and technology, and discloses a security transaction instruction intelligent analysis method and system based on a large model, and the method comprises the steps: carrying out the compliance screening of an instruction text flow and an instruction feature chart, and obtaining an initial text flow and an initial feature chart; analyzing a transaction feature factor corresponding to the initial feature chart, extracting a space-time structure feature vector from the initial feature chart, and analyzing a structure description text corresponding to the space-time structure feature vector to generate a chart analysis text corresponding to the initial feature chart; calculating segmentation entropy loss corresponding to the initial segmented text, and performing segmentation iterative optimization on the initial text stream to obtain a target segmented text; generating an instruction target text corresponding to the security transaction instruction, calculating a structured likelihood between instruction tags, and determining an association aggregation tag in the instruction tags; and analyzing the transaction intention corresponding to the security transaction instruction, and generating an analysis result corresponding to the security transaction instruction. According to the invention, the accuracy of security transaction instruction analysis can be improved.

The invention relates to a multi-language voice content recognition method and system, and belongs to the technical field of voice signal processing, and the recognition method comprises the steps: collecting original audio stream data, executing the noise reduction filtering processing, and segmenting the original audio stream data into a plurality of audio segments; extracting acoustic feature vectors of the audio clips; inputting the audio clip into the speech recognition model group, and obtaining a text clip and a confidence score; fusing all the text fragments to generate a preliminary recognition text, segmenting the preliminary recognition text into text blocks, and translating the text blocks into a target language text block by block through a streaming translation model in combination with a semantic feedback tag received in real time; executing bidirectional translation verification on the text blocks of which the confidence scores are lower than a set threshold value; inputting the translated text stream and the acoustic feature vector into a semantic analysis model, and fusing an output result to obtain a semantic feedback tag; and constructing a cross-modal association graph through the graph neural network, outputting a key information abstract and triggering an alarm signal. The speech recognition accuracy can be improved while the recognition efficiency is guaranteed, and risk response is carried out.

The invention provides an intelligent conference auxiliary method and system, and belongs to the field of network communication. The method comprises the following steps: excluding target application window data from shared stream data in a process of generating the shared stream data based on collected screen shared data, and sending the target application window data to a conference terminal for visual interface feedback; acquiring voice data of the conference terminal in real time, and transliterating the voice data to generate voice text stream data; in response to the screenshot instruction, performing screenshot analysis on the selected screen image to generate image text stream data; and inputting the generated voice text stream data and / or image text stream data into a pre-training language model, fusing the context information of the current session, and generating a structured response content conforming to a preset rule. According to the intelligent conference assisting method and system, it can be ensured that a user selectively hides a specific application window in screen sharing, and intelligent assistance can be provided for a video conference of the user, so that the personalized and scene-aware interaction experience of the user is improved.

Audio interaction processing methods and system, a server, a client and an electronic device. A method is executed by an audio processing server, and comprises: converting an input audio sent by a client into a text to be replied to (S102); sending to a text processing server the text to be replied to, so as to generate a reply text stream (S104); receiving a request of the client for a reply audio stream corresponding to the reply text stream (S106); on the basis of a message identifier, acquiring the reply text stream from the text processing server (S108); generating a reply audio stream on the basis of the reply text stream (S110); and sending the reply audio stream to the client for playing (S112).

The invention relates to the technical field of retrieval enhancement, in particular to a big language model-based dynamic knowledge retrieval enhancement method, which comprises the following steps of: acquiring theoretical regulations and actual operation data of an unmanned aerial vehicle, and driving a model generation request; monitoring the reasoning text stream in real time, intercepting a logic triple to perform causal verification, and constructing negative feedback prompt blocking and resetting for unsupported logic; executing double-track system dynamic retrieval aiming at regulations and data, evaluating content conformity by utilizing reflection Token and executing conflict resolution correction; and generating a fault hypothesis by adopting tree decoding, constructing an anti-fact query to retrieve the evidence of the security, and pruning the path with the evidence of the security to determine a final path. According to the method, illusion is effectively blocked through real-time logic verification and a negative feedback mechanism, double-track retrieval and anti-fact pruning are combined, strict alignment between the reasoning process and physical facts and theoretical regulations is ensured, and the accuracy and credibility of fault diagnosis are remarkably improved.

The invention discloses a voice real-time question and answer processing method based on a large model and a domain knowledge base, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring a voice input stream of a user, carrying out intelligent sound wave deconstruction on the voice input stream, generating a text stream, and carrying out dependency syntactic analysis and semantic role labeling; generating intention information and key information, dynamically accessing a domain knowledge base, executing predictive loading, generating a preloaded data subset, performing fusion processing by combining the intention information, the key information and the preloaded data subset, generating a fusion result, performing accuracy verification and correction, generating a natural language answer, and converting the natural language answer into voice output. The technical scheme of combining knowledge predictive loading driven by intention analysis, context fusion and answer traceability correction is adopted, and low-delay response, high-precision intention understanding and high-factuality answer of voice questions and answers can be achieved.

The embodiment of the invention discloses a teaching note generation method and device based on a large language model and a medium, belongs to the technical field of artificial intelligence, and solves the problems that classroom notes generated in the prior art are lack of content structuring and knowledge relevance, poor in readability and difficult to be used for effective review. The method comprises the following steps: acquiring a teacher voice signal in a classroom teaching environment in real time through pickup equipment, and converting the teacher voice signal into an initial text stream through a preset voice recognition model; taking the initial text flow as query input, and performing semantic similarity retrieval on the initial text flow and a preset course knowledge base to obtain a plurality of related knowledge fragments; generating an enhanced prompt based on the initial text stream and the plurality of related knowledge fragments, and generating a structured classroom note based on the enhanced prompt and a preset large language model; and outputting the structured classroom note to a user interface, and optimizing a classroom teaching note generation process based on feedback information and modification information of the user.

The invention relates to the field of computer vision and artificial intelligence, in particular to a multi-modal fusion document image classification method based on multi-branch deep convolution and hierarchical semantic modeling. According to the method, visual and text information of a document is co-processed through a double-flow architecture; a visual semantic feature of a document image is efficiently extracted from a visual flow by adopting a lightweight convolution operation and multi-scale feature fusion mechanism; an original text is obtained from a text stream through an optical character recognition technology, text error correction and word embedding extraction are carried out through a fine tuning language model, word-level and sentence-level context modeling is realized by inputting a hierarchical semantic coding network, and document-level features are generated. And high-precision document image classification is realized by fusing the double-flow feature vectors and combining a full-connection layer. Experimental results show that the method has an excellent multi-modal feature fusion effect, relatively high classification precision and strong robustness to complex document scenes in a document image classification task, and is suitable for application scenes of various document image classification.

The invention relates to the technical field of data compression, in particular to a data compression method and system, which comprises an LZ module, a text stream module, a sequence stream module and an interval entropy coding framework, and is characterized in that the LZ module is used for performing repeated data screening on original data to generate a text stream and a sequence stream; the LZ module screens duplicated data through a hash verification method, specifically, hash values are calculated for input data blocks, the duplicated data are determined based on hash value comparison, and direct data content comparison is avoided; and the character flow module is used for carrying out entropy coding on the character flow. According to the method, an interval entropy coding framework is innovatively proposed, a better probability model is constructed through artificial intelligence and a mathematical method, a better finite state machine is trained, the optimal state machine is used in the compression and decompression process, and the problem that the decompression speed of a neural network method is low is efficiently solved.

The invention discloses a natural language processing-based risk public opinion analysis method, which comprises the following steps of: capturing a public opinion text in real time, and desensitizing a user identity field by adopting a Hash pseudo identifier to obtain an original text stream; constructing a double-classification weak supervision framework, calculating a risk confidence coefficient, and when the risk confidence coefficient is greater than or equal to a first threshold value, writing a corresponding text into a labeling pool to form a risk corpus; mapping the risk corpus into a dynamic heterogeneous risk knowledge graph according to an entity-relationship-time slice meta-path rule; applying a multi-head space-time diffusion Transform to a sub-graph intersecting with the current window in the graph to obtain a risk propagation vector of an event node; and inputting the risk propagation vector and the historical baseline vector into a causal comparative analysis model, outputting a risk early warning index, and when the early warning index is greater than or equal to a second threshold value, pushing a risk alarm including an event path to a background management and control center. According to the invention, the automation degree, accuracy and reliability of public opinion risk monitoring and early warning are effectively improved.

The present application relates to the technical field of data compression, in particular to a data compression method and system, comprising: an LZ module, a text stream module, a sequence stream module, an interval entropy encoding framework, the LZ module is used for screening repeated data of original data, generating a text stream and a sequence stream; the LZ module screens repeated data through a hash verification method, specifically: calculating a hash value for the input data block, determining repeated data based on hash value comparison to avoid direct data content comparison; the text stream module is used for entropy encoding the text stream; the present application innovatively proposes an interval entropy encoding framework, constructs a more optimal probability model through artificial intelligence and mathematical methods, trains a more optimal finite state machine, and uses the optimal state machine in the compression and decompression process, efficiently solving the problem of slow decompression speed of neural network type methods.

The invention provides a conference management method and system based on natural language processing and retrieval enhancement, and belongs to the technical field of conference management, and the method comprises the steps: outputting a conference reservation result based on key parameters; if the reservation is successful, acquiring audio stream data in the conference process, and performing voice segmentation, initial voiceprint extraction and initial clustering on the audio stream data to output an original voice segment and speaker reference voice; target speaker extraction, accurate voiceprint extraction and final clustering are carried out based on the original voice segment and the speaker reference voice to obtain a pure voice text stream with a voiceprint label; performing dynamic abstract generation, enhanced retrieval and verification on the pure voice text stream; according to the conference management system and method, the intelligent management of the whole process of the conference can be covered, and meanwhile, the whole process management of the conference from voice-driven reservation, real-time content analysis to automatic task distribution is realized.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for table detection using text streams. One of the methods includes detecting, in a text stream and using column identification data, text for one or more cells in a table; creating, using the text for at least some of the one or more cells in the table, a data structure for the cell a) that associates two or more values from the table and b) for use by a downstream system as part of a natural language analysis process of data from the text stream; and storing, in memory, the data structure.

The invention discloses a live broadcast forbidden word real-time silencing replacement method and device and computer equipment. The method comprises the steps of obtaining an industry to which a live broadcast platform selected by a user belongs; loading a corresponding prohibited word bank according to the industry; obtaining protection sensitivity and strict degree customized by a user to obtain protection strength information; acquiring live broadcast content, and converting the live broadcast content into a text stream; performing prohibited word detection on the text stream according to the protection strength information and a prohibited word library to obtain a detection result; when the detection result is that the forbidden word exists, processing the detection result by adopting a set rule to obtain a processing result; and presenting the real-time protection state, the processing result and the corresponding detection result in a visualized manner. By implementing the method provided by the invention, prohibited vocabularies can be eliminated, semantic replacement can be provided, the fluency and coherence of live broadcast contents are ensured, the cost is low, the application is easy, and the content compliance of small and medium-sized platforms and independent creators can be improved.

The invention relates to a material processing method and system based on sensitive content detection and a medium, and belongs to the technical field of multimedia content security. The material processing method comprises the following steps: receiving an input original material, and analyzing the input original material into an image stream, a video stream and a text stream; executing HSV color space conversion, and extracting texture features; performing semantic analysis on the text stream to generate a word segmentation sequence and a named entity recognition result; random frequency noise is injected based on the saturation channel, and local binary pattern features are extracted from the saturation channel after noise injection; generating a semantic vector according to the word segmentation sequence and a corresponding named entity recognition result, and calculating a risk entropy value; and establishing a space-time mapping relationship between the image stream / video stream and the text stream, constructing a cross-modal incidence matrix, executing a grading decision according to an output result, executing a material processing operation, and feeding back a processing result to the cross-modal incidence matrix for weight updating. The sensitive content identification accuracy and timeliness can be improved.

The invention provides an intelligent voice interaction method and device based on a large language model, and relates to the technical field of computers. The method comprises the following steps: converting a received target voice input by a user into a target question text based on a voice recognition module; inputting the target question text and a predefined intention prompt word template into a first large language model to obtain an intention rating of the target question text; matching the second large language model with the third large language model; inputting the target question text and the keyword cue word template into a third language model to obtain an abstract and a keyword; and inputting the target question text and the historical context related to the target question text into the second large language model to obtain a high-quality text stream. Dynamic balance among resource consumption, generation quality and response speed is realized through division of labor and cooperation of a multi-level model.