1006 results about "Text processing" patented technology

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

The invention belongs to the technical field of text processing, and particularly relates to a method and system for retrieving DOCX document content based on keywords, which comprises the following steps: analyzing an Office Open XML structure of a DOCX document, combining with multi-dimensional features such as style names, and utilizing a title classification score model to accurately distinguish a title and a text, so that a semantic hierarchical structure of the document is effectively reserved; and secondly, a multi-level semantic extension mechanism is introduced, and a Sension-BERT, a HowNet knowledge base and a Word2Vec model are fused, so that intelligent extension of synonyms and synonyms of keywords is realized, and the recall rate and semantic understanding ability of retrieval are remarkably improved. And in addition, a BM25 model is combined with paragraph length normalization and structure position weight to calculate a correlation score, so that retrieval results are sorted more accurately and reasonably. The construction of the reverse index is combined with the position coding and compression optimization strategy, and the retrieval efficiency and the storage performance are both considered.

The invention discloses a method for improving long text processing efficiency and accuracy, and relates to the technical field of natural language processing and large language models.According to the method, text word segmentation embedding, sliding block preprocessing, YaRN position code injection, dynamic sparse attention calculation, multi-level attention fusion, graded KV cache management and output generation are sequentially executed; position drift is inhibited through logarithmic scaling, and key contexts are adaptively screened according to the attention activeness, so that the attention calculation complexity is close to linearity; in million-level Token reasoning, the video memory occupation of the method is reduced, the remote dependency recall rate is improved, and the method is suitable for scenes such as document analysis, code auditing and multi-mode streaming understanding.

The invention provides a cue word attack detection method and system of a large language model and electronic equipment, and relates to the technical field of text process.The method comprises the steps that a to-be-processed text block in an original text is determined; performing heuristic semantic analysis on the text block to obtain a first suspicious text block and a first risk score thereof; performing semantic analysis on the first suspicious text block and the context information thereof through a first language model to obtain a second suspicious text block and a second risk score thereof; performing structured verification of the attack intention on the second suspicious text block according to a preset meta prompt word through a second language model to obtain an attack intention verification result; the attack intention verification result comprises a third suspicious text block and a third risk score thereof; and executing a target response action on the original text based on the first risk score, the second risk score and the third risk score. According to the method, the efficient processing requirement of the long text can be considered, and the detection accuracy of complex attacks can be improved.

The invention relates to the field of text processing, in particular to a bidding document information extraction method. Comprising the following steps: segmenting a bidding and tendering file into pages, and identifying the pages to obtain corresponding texts; generating complementary text description for images and tables in the page and adding the complementary text description to the tail of a text corresponding to the page to form an enhanced text block sequence; matching a label from the text block sequence according to a pre-constructed hierarchical label system, and generating a corresponding cue word template according to the label and a pre-constructed cue word template library; inputting the cue word template, the enhanced text block sequence and the context text abstract as a combination into a large language model to obtain a structured extraction result with a hierarchical relationship; and matching the extracted entity content with a local dictionary, carrying out aggregation arrangement on a result after the matching is passed, and outputting a structured data file. On the premise that the model does not need to be retrained, the illusion risk of the generated content is reduced.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention relates to the field of text processing, in particular to a retrieval system for a bidding law document large language model. Comprising a document preprocessing module, a semantic dicing module, an entity and relation recognition module, a knowledge graph construction module, a multi-strategy mixed retrieval module and a retrieval result preferential module. During working, the bidding law document is preprocessed, based on document word number judgment, a chapter-level slicing strategy or a clause-level slicing strategy is adopted for cutting, a LateChunking algorithm is used for determining cutting points, semantic units are extracted, entity elements are recognized, a knowledge graph is constructed, and an optimal retrieval result is obtained through multi-strategy mixed retrieval and preferential processing. According to the method, the limitation of a traditional single retrieval mode in bidding legal chief document processing is overcome, and the provision retrieval accuracy and context coherence are improved, so that the illusion risk caused by information missing or misunderstanding of a large language model is greatly reduced, and the credibility and practicability of a legal intelligent question and answer result are enhanced.

The invention provides a water conservancy knowledge structured extraction and verification method and device, and belongs to the technical field of artificial intelligence, and the method comprises the steps: carrying out the differential text processing of different formats of files, and generating an intermediate file; classifying the intermediate file into a regulation class or a non-regulation class based on a preset rule base; performing hierarchical title identification on the regulatory files to form entry knowledge blocks, and converting table contents into HTML (Hypertext Markup Language) knowledge blocks; performing semantic segmentation on the non-regulation file to generate knowledge blocks; performing knowledge block checking and filing, and marking an abnormal alarm block; converting the table knowledge blocks into natural language description by utilizing a large model; and positioning the context of the original text of the alarm knowledge block, and performing intelligent correction through a large model. According to the method, a traditional semantic analysis model and a large language model are creatively fused, a closed-loop process of preprocessing, extraction, verification and correction is formed, the problems of structured analysis and error correction of complex texts in the water conservancy field are solved, and the knowledge processing efficiency and accuracy are remarkably improved.

The invention relates to the technical field of large language model application, in particular to a fault-tolerant processing method and system for a super-long text in large model service and a storage medium, and the method comprises the following steps: obtaining a super-long text to be processed and a maximum Token threshold of a large model context window, completing Token processing and judging whether the super-long text exceeds the limit or not; starting a multi-level alternative scheme including rapid compression, dynamic compression and chat history compression; starting an error recovery mechanism including hierarchical exception processing, intelligent degradation and parameter verification; outputting the target text of which the Token number is compliant; the system comprises a text acquisition module, a multi-level alternative scheme execution module, an error recovery module and an output module. The storage medium stores a computer program, realizes the method during execution, can adapt to multiple models, and meets industrial-grade super-long text processing requirements; the problems of Token overrun errors and inference service instability caused by lack of fault-tolerant mechanisms and insufficient boundary processing in the prior art are solved.

The invention provides a text processing method and device, electronic equipment, a storage medium and a program product, and relates to the technical fields of artificial intelligence, natural language processing, large language models, automatic driving, intelligent traffic and the like. The method comprises the following steps: determining an initial voice recognition text and phoneme data of a target voice, and generating target prompt information based on the initial voice recognition text and the phoneme data; obtaining a target recognition text through a large language model based on the target prompt information; the large language model can correct the initial speech recognition text based on the phoneme data; as the accuracy of the phoneme data is far greater than the accuracy of the initial speech recognition text, more and more effective information can be provided for reference for the processing process of the large language model by combining the phoneme data, the large language model is helped to obtain a correct text in the processing process, and then the accuracy of text processing is improved.

The invention relates to the technical field of text processing, and discloses an accurate alignment method and system for multilingual terminologies in the nuclear power field, and the method comprises the steps: collecting original terminologies from a nuclear power design document, an operation manual and an international standard database, and constructing an original terminology set in the nuclear power field; encoding the original term set after term cleaning to obtain standardized term blocks; extracting semantic feature vectors according to the standardized term chunks; based on the attention scores of the semantic feature vectors in the sequence, performing weighted fusion on the semantic feature vectors to obtain enhanced semantic representation; constructing candidate term pairs in the nuclear power field according to the similarity of the term pairs in the enhanced semantic representation; performing consistency check on the candidate term pairs through entity relationships and attributes of the knowledge graph, and outputting the candidate term pairs passing the consistency check as target term pairs in the nuclear power field; according to the invention, the efficiency of accurate alignment of multilingual terminologies in the nuclear power field can be improved.

The invention relates to the technical field of artificial intelligence content generation, and provides a video generation method and device based on multi-agent cooperation. The method comprises the following steps: selecting a proper copywriting processing agent according to text information input by a user, so as to use the copywriting processing agent to expand or condense a plurality of sub-mirror descriptions; selecting a proper image generation agent; inputting the picture description, the picture style and the mirror operation method in the mirror splitting description into the selected image generation agent to generate a mirror splitting picture; selecting a proper video generation agent; inputting the split picture and the character action and the mirror moving method in the corresponding split description into a selected video generation agent to generate a split video; and selecting a proper automatic editing agent, and performing post-production on the split video to generate a finished video. The technical problems that in the prior art, the video generation process is fragmented, and multi-element collaboration is difficult are solved, and automatic production of complex video content is achieved.

The invention provides a document element rapid extraction system based on a pre-trained large model, and relates to the technical field of computer software application, the system comprises a parameter field adaptation module used for textualizing a document and constructing an industry standard corpus based on a textualized processing result, adjusting a preset language model by utilizing an industrial standard corpus; the dynamic document partitioning module is used for performing semantic segmentation processing on the industrial standard document to obtain a plurality of text blocks; the entity alignment module is used for carrying out entity and relation extraction on the text blocks and carrying out entity alignment in combination with a uniform manifold approximation and projection method; and the relation reasoning and knowledge graph completion module is used for performing completion processing on the preliminary knowledge graph and storing a completion result. According to the method, the element extraction efficiency can be directly improved without pre-defining a rule template or performing data annotation.

The invention relates to the technical field of text processing, and provides a technical description text generation information system and method. The system comprises a core element extraction module and a hierarchical attention generation module. The core element extraction module comprises a supervised extraction path and an unsupervised extraction path parallel to the supervised extraction path, and is used for extracting a core element set from a text input by a user. And the hierarchical attention generation module is used for calling different attention mechanisms to generate the technical description text based on the core element set and the chapter type of the to-be-generated technical description text. According to the method, the generation efficiency of the technical description text can be remarkably improved while the structuralization and logicality of the generated content are ensured.

The invention relates to the technical field of text processing, in particular to a text classification method and system based on semantic analysis, and the method comprises the following steps: segmenting semantic units, constructing a direction change sequence, positioning mutation nodes, generating a consistency section, forming a convergence section, and outputting a classification result. According to the method, a continuous change sequence is formed by constructing a semantic embedding vector and calculating a direction difference, a semantic mutation point can be anchored and divided into sections by combining mutation intensity identification and local jump tracking, and a semantic closed structure and a convergence section are extracted by means of context direction consistency judgment and generic label comparison; precise recognition of a semantic relation chain is realized, semantic jump and conflict starting points can be dynamically sensed, the semantic boundary recognition capability is improved, and the understanding and classification capability of a model on semantic attribution in a complex context is enhanced on the premise of not depending on a fixed dictionary and shallow statistics. The problems that a traditional model is slow in response to an abrupt change structure and weak in semantic convergence recognition are effectively solved.

The invention provides a key value cache fusion compression method and device, electronic equipment and a storage medium, and the method comprises the steps: selecting an adjacent historical query as an observation window based on the position of a current query, and observing all historical cache key value pairs corresponding to a plurality of historical queries; determining target attention weights of all historical cache key value pairs in the observation window; based on each target attention weight, selecting a key value pair of a reserved cache; and performing compensation reconstruction on the value vector in the to-be-expelled key value pair, and fusing the reconstruction compensation component of the to-be-expelled key value pair and the key value pair with the reserved cache to obtain a fused and compressed key value pair. According to the key value cache fusion compression method provided by the invention, effective screening and fusion compression of historical cache key value pairs are realized, the problem of context information loss is effectively avoided, the calculation efficiency and the expression ability are considered, the overhead of storage and calculation resources is remarkably reduced while the context integrity is ensured, and the method is suitable for popularization and application. The method is especially suitable for long text processing tasks.

This invention relates to the field of text processing technology, and in particular to a method, device, and medium for processing navigational notice text based on semantic enhancement. The method includes: first, acquiring a content carrier to be processed; then, acquiring the semantic vector and glyph feature vector of the content carrier; concatenating the two types of vectors to form an enhanced text representation; extracting temporal features from the enhanced text representation to obtain temporal features containing forward and backward logical relationships within the text; acquiring the weights of words and sentences in the temporal features and performing weighting to obtain weighted word representations and weighted sentence representations; performing correction processing on the weighted representations to generate corrected text; and finally, validating the corrected text and outputting the target text. This invention can improve the accuracy and efficiency of content carrier processing.

The invention relates to the technical field of macro text processing, in particular to a non-macro identifier processing method in a macro reference row, electronic equipment and a medium, the method comprises the following steps: S1, obtaining storage information corresponding to each macro reference of the macro reference row, the storage information corresponding to the macro reference comprises a macro reference identifier, a line number in a macro expanded text of a macro reference line and an intra-macro identifier list corresponding to the line number in the macro expanded text of the macro reference line; step S2, obtaining non-intra-macro identifier positioning information and a corresponding to-be-added macro reference identifier in a macro expanded text of the macro reference line; and S3, adding the non-intra-macro identifier positioning information to an intra-macro identifier list corresponding to the corresponding to-be-added macro reference identifier. According to the method, the non-intra-macro identifier binding object after row expansion can be accurately and quickly referenced for the macro, and a debugging function is realized based on the binding object.

The invention discloses a cross-border digital service multi-language real-time interaction and semantic error correction method and system, and belongs to the technical field of text processing.The method specifically comprises the steps that when cross-border interaction begins, voice, text and auxiliary multi-mode information of a user are collected, fused semantic representation is generated, the semantic representation is input into a cross-language prediction model, and the cross-language prediction model is obtained; a target language candidate result is obtained, a reverse mapping channel is established, when semantic errors or ambiguity occurs in the target language candidate result, collaborative correction is conducted in combination with the scene rule base, user historical preferences and real-time feedback, the corrected result is processed through a predefined target context sensitive model, and the target language candidate result is obtained. Automatically adjusting the culture expression, the terminology and the compliance, and outputting to the cross-border service terminal in real time; according to the method, the target context sensitive model is introduced in the output stage, cultural expression, terminologies and law compliance are automatically adjusted, and multi-language seamless communication and high-reliability semantic transfer in cross-border digital services are achieved.

The invention relates to a self-adaptive retrieval enhancement generation system based on multi-dimensional problem features and an implementation method. The system comprises an RAG initialization module, a text processing module, a model initialization module, a vector processing module, a retriever module and an RAG retrieval generation module. The input end of the retriever module receives the text vector of the vector processing module, the output end of the retriever module is connected with the RAG retrieval generation module, a strategy pool of five types of retrieval strategies and the self-adaptive decision module are arranged in the retriever module, and the retriever module is used for determining retrieval strategies through keyword matching and problem length judgment and constructing RAG cue words. And the RAG retrieval generation module is used for calling the retriever module to input the obtained text segment into the large language model to generate an answer after reconstructing the user question. By adopting the method, the retrieval efficiency and the answer generation quality of the RAG system can be improved.

The invention discloses a picture type PDF document analysis method based on a convolutional neural network, a multi-modal model and a regular expression, and belongs to the technical field of artificial intelligence and text processing. The method comprises the following steps: detecting types of layout elements of a preprocessed PDF document to obtain bounding box coordinates of each layout element; performing content identification on each layout element according to the type of the layout element; using a regular rule engine and a large language model to perform structured information extraction on the identification content, and extracting to obtain a plurality of predefined first business fields corresponding to each layout element; the character recognition result and the table recognition result are combined, and the combined result serves as content needing to be extracted; and taking a proofreading result as an analysis result of the scanned PDF document. According to the method, high-precision structured extraction of paragraphs, tables, formulas and other contents in the PDF document is realized, and the method has good universality, expandability and automation capability.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a state determination method, device, equipment and medium based on audio information. The method comprises the following steps: uniformly performing voice-to-text processing after a preset fixed time length to obtain text information, extracting acoustic features and linguistic features, generating a multi-dimensional feature vector after fusion, inputting a pre-trained analysis model, generating a state probability value, and determining a target state corresponding to an audio signal based on the state probability value. According to the method, the multi-dimensional feature vectors are fused on the basis of acoustic features and linguistic features, and the pre-trained analysis model is introduced to judge the state probability value, so that the problems of incomplete feature extraction, insufficient feature fusion and poor judgment result accuracy and generalization ability are effectively solved; and the accuracy and the stability of audio signal state judgment are improved.

The invention provides a long text processing method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting long text data into a long text processing model, and obtaining a long text processing result; the long text processing model is constructed by adopting a hierarchical mixed attention strategy on the basis of a large language model; according to the hierarchical mixed attention strategy, a first attention mechanism in each network structure of a large language model is replaced by an attention mechanism with a global information retrieval capability, and other attention mechanisms are replaced by attention mechanisms with a local information modeling capability. The hierarchical mixed attention architecture can greatly improve the reasoning efficiency on a long text while ensuring the reasoning effect of the model, reduces the consumption of computing resources, and approaches the modeling effect when only global attention is used.

The embodiment of the invention provides a generative face image quality evaluation method and device, computer equipment, a computer readable storage medium and a computer program product, and belongs to the technical field of image processing. The method comprises the following steps: acquiring input information, wherein the input information comprises a to-be-evaluated generative face image, a reference image and a text prompt; inputting the input information into a pre-trained target quality evaluation model; converting a text prompt into a text embedding vector through a text processing module, and performing feature extraction on an image in the input information through an image feature extraction module to obtain image feature data; mapping the image feature data to a unified language space through a projector module to obtain target image feature data; and outputting an image quality evaluation result based on the text embedding vector and the target image feature data through a large language model module. According to the technical scheme provided by the embodiment of the invention, the quality of the generative face image can be comprehensively and accurately evaluated.

The invention relates to the technical field of internet asset management, and discloses a webpage asset enterprise affiliation identification method based on a BERT language model, which comprises the following steps: S1, acquiring an HTML source code of webpage assets through a crawler technology, and analyzing and extracting a title and body content of a webpage; s2, performing text processing on the title and the body content to obtain a to-be-recognized text; s3, inputting the to-be-recognized text into a trained BERT language model, and outputting an enterprise affiliation recognition result of a webpage through semantic understanding and feature interaction calculation; and S4, carrying out manual verification on the enterprise affiliation identification result, and updating the abnormal text as a new sample to the BERT language model so as to complete the iterative optimization of the model. According to the method and the device, the BERT language model is constructed by training and learning the webpage data, so that the automatic identification of the enterprise attribution of the webpage assets is realized, and the identification efficiency and accuracy are greatly improved.

The invention provides a long text abstract generation method and device, and belongs to the technical field of natural text processing, the method comprises the following steps: using Euclidean norm to carry out importance sorting on Tokens and carrying out compression storage according to a sparse rate, so that global key information is completely reserved and memory occupation is obviously reduced; in the decoding stage, local attention scores and global attention scores are calculated in parallel, entropy differences are mapped into fusion weights through Sigmoid by combining temperature adjusting parameters, and dynamic balance of local details and long-distance dependence is achieved. The local key value pairs and the global key value pairs are subjected to weighted integration based on the fusion weight, a continuous semantic spectrum is formed in a single decoding layer, splicing breakage caused by traditional partitioning is eliminated, the problems of input limitation and semantic splitting are effectively relieved, the context length capable of being processed by a model is expanded under the condition that the calculation amount is not remarkably increased, and the method has the advantages of being simple in structure and convenient to operate. And local and global context information is adaptively fused, so that the accuracy and continuity of the abstract are effectively improved.

The invention relates to the technical field of aeronautical text data processing, and discloses an aeronautical text content cleaning and labeling method, system, device and medium wherein the method comprises: noise filtering: identifying and removing noise in an aeronautical text in combination with static cleaning and a general large model; format standardization: converting the aviation text after noise removal into a standardized format text; duplicate removal and error correction: detecting duplicate contents based on a hash algorithm, and correcting spelling errors and grammar errors based on a general large model to obtain an aviation text subjected to duplicate removal and error correction; entity identification: key entities are extracted based on the general large model, and the extracted key entities are labeled; active learning: screening high-value samples in the marked key entities based on an uncertainty query strategy; and dynamic optimization: performing verification and iterative optimization on the marking result in combination with the aviation knowledge base. According to the method, the automation level, the labeling accuracy and the system self-adaptive capability of aviation text processing can be remarkably improved.

The invention discloses a long text training data generation method, a related device and a computer program product, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining long text source data, and then generating related questions and corresponding answers of the long text source data through the generation capability of a large language model, the method comprises the following steps: generating long text source data, performing answer self-consistency verification on the basis of the similarity between the generated answers, determining the answer with the highest credibility as the final answer, and generating long text training data by utilizing the long text source data, related questions and the corresponding final answer, thereby realizing a long text training data generation task. The training data configuration efficiency and quality suitable for the long text processing task are improved, and a basis is provided for optimizing the model performance of a large model on the long text processing task.

The invention relates to the technical field of artificial intelligence, and discloses a paper text processing method based on intelligent glasses. The method comprises the steps that intelligent glasses are awakened through an awakening word, a user can conduct image recognition through a voice instruction, and whether a textbox is complete or not is judged. If the textbox is complete, character recognition is carried out; if not, the intelligent glasses help the user to adjust through voice guidance until the textbox is completely displayed. If the recognized characters are not the language set by the user, the intelligent glasses automatically translate the recognized characters into the user language, and voice synthesis is carried out to generate a voice file. And after generation, inquiring the user whether to play the voice, and if not, encrypting and storing the voice file. The problems that current text detection precision is not high, the reading process is not natural, user control is complex, and a feedback mechanism is single are solved.