2621 results about "Generation process" patented technology

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention relates to the technical field of artificial intelligence, and discloses a bidding document generation method based on retrieval enhancement generation and a large language model, and the method comprises the steps: analyzing technical parameters, legal terms and score weights in a bidding demand document; retrieving matched historical bidding document fragments and technical specifications from the industry knowledge base, and generating a retrieval enhanced data set; fusing the data through a dynamic weight distribution algorithm and generating an initial bidding document draft; checking conflict terms based on the legal semantic knowledge graph and marking correction suggestions; and optimizing the bidding document structure and the key content according to the score weight. According to the method, an online learning mechanism driven by sentence vector matching, timeliness weight calculation and user feedback is adopted, so that the problems of inaccurate technical parameter extraction, low legal conflict detection efficiency and non-optimized scoring rules in the bidding document generation process are solved.

The invention discloses a multi-level information security policy generation method based on a knowledge graph, and the method comprises the following steps: S1, collecting multi-modal data of a plurality of security data sources, and generating a standardized data set; s2, constructing and dynamically updating a multi-level knowledge graph based on the standardized data set; s3, based on the multi-level knowledge graph, a graph neural network is adopted to identify security threats and generate an evaluation report; s4, analyzing time-space characteristics of the security event, and predicting a threat evolution trend; s5, optimizing a protection strategy based on the graph attention network, and adjusting in combination with a real-time feedback mechanism; s6, simulating an attack scene by using a generative adversarial network, and optimizing a security protection strategy generation process; and S7, based on the optimized protection strategy, further adjusting and optimizing by adopting a meta-learning algorithm. Through combination of meta learning, the graph neural network and the generative adversarial network, an intelligent and dynamically adjusted network security protection strategy generation and optimization scheme is provided.

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

The invention discloses an electric power knowledge system construction and intelligent question and answer method based on a lightweight large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an electric power field knowledge system, and building a reasoning logic classification system to carry out the classification marking of a knowledge base in the electric power field knowledge system; based on the power domain knowledge system, a multi-dimensional similarity matching strategy is adopted to process a problem input by a user; generating a reasoning link based on a matching result of the multi-dimensional similarity matching strategy; the reasoning link is used for extracting and combining answers from the power domain knowledge system, and the generation process of the reasoning link comprises question decomposition and answer integration. According to the invention, systematic management of electric power professional knowledge is realized, and the matching accuracy of professional problems is improved; by generating the self-adaptive multi-hop reasoning link based on the question type and combining the question decomposition and answer integration technology, the intelligent question and answer ability of the system under the scenes of power equipment fault diagnosis, maintenance scheme recommendation, risk assessment and the like is improved.

The invention discloses a self-adaptive learning question answering system and method based on multi-modal interaction, and particularly relates to the technical field of self-adaptive learning question answering. By constructing a modal recognition and preprocessing module, standardization and structuralization of multi-modal input of texts, voices, images and the like are realized; establishing a cross-round modal memory map through time sequence coding and map modeling; dynamically updating a user portrait in combination with user interaction history and current modal characteristics; in the question and answer generation process, current input, a user portrait and a historical graph are fused, intermediate semantic representation is generated through a context enhancement module, and the intermediate semantic representation is combined with a knowledge base to generate answers; meanwhile, a modal confidence degree dynamic evaluation mechanism is introduced, and weights are distributed according to the input quality, the user adaptation degree and the context correlation; and finally, incremental optimization is carried out on the graph structure, the user portrait and the question and answer strategy through a user feedback driving system, and multi-round, multi-mode and self-adaptive intelligent question and answer interaction is realized.

The invention discloses a method for generating and constructing a database test knowledge base based on an open source large language model and retrieval enhancement, and relates to the technical field of database testing, and the method comprises the steps: building a local deployment system on a local computer system or server, and achieving the efficient local operation of the open source large language model and the stable output of language processing service; on a local computer system or a server, a retrieval enhancement generation framework is constructed, and accurate knowledge support and reliable content output are provided for local language processing service by integrating multi-modal knowledge, optimizing retrieval and generation processes and guaranteeing safety and compliance; on a local computer system or a server, a personalized database knowledge base is built, database related information is collected and processed from multiple channels and is output in an adaptive form, and the specified information requirement of local personalized language processing service is met. According to the method, efficient and stable operation of the open source large language model is realized, accurate and reliable language processing output is realized, and personalized services meet specific requirements.

The embodiment of the invention provides a document generation method and device based on artificial intelligence, and the method comprises the steps: extracting multi-modal data features of a historical multi-modal file through natural language processing and a computer vision algorithm, and constructing a corresponding knowledge graph according to the multi-modal data features, converting the multi-modal data subjected to feature extraction into high-dimensional vectors by using a vectorization algorithm, and storing the high-dimensional vectors to form a vector library; splitting a current document generation task into a plurality of sub-tasks by adopting a task planning algorithm, and executing the sub-tasks at the same time to obtain a first document; performing iterative optimization of document content according to the document first draft, the cue word and the language model to obtain an optimized first draft, acquiring related information from the vector library by using an attention mechanism-based retrieval algorithm, and fusing the related information and the optimized first draft through a text generation algorithm to obtain a final document; according to the invention, the accuracy and intelligence in the document generation process can be effectively improved.

A computer program comprising instructions which, when executed by a computer, cause the computer to perform the following operations: Receiving an input document (104), wherein the input document (104) contains content elements including at least one text element; Performing a lossless hierarchical text processing process (106) in which the input document (104) is reproduced as a hierarchical representation (108) in which all text elements from the input document (104) are preserved verbatim and organized according to a structure of the input document (104), the lossless hierarchical text processing process (106) comprising: Prompting a generative language model to generate the hierarchical representation (108), wherein the prompt instructs the generative language model to generate the hierarchical representation (108) without summarizing or omitting any of the text elements; and Performing a retrieval unit generation process (110) in which retrieval units (112) are generated based on the hierarchical representation (108) and stored in a memory accessible by a generative client language model for retrieval-enhanced generation.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

The invention belongs to the technical field of steel production equipment fault diagnosis, and particularly relates to a steel production equipment fault diagnosis system based on a large language model. The system comprises a data processing module, a large language model training module and a retrieval enhancement generation module. Through domain data processing and model training, an intelligent diagnosis platform specially used for steel production equipment is constructed. The data processing module is responsible for performing feature extraction on the sampled data and constructing a vector database to ensure that the model can perform efficient semantic matching; the training module performs field adaptive adjustment on the large language model to improve the reasoning ability of the large language model in a specific field; the retrieval enhancement generation module realizes fault analysis and technical guidance with strong pertinence and high accuracy by integrating a vector database and a generative answer mode; the system runs locally or on a privatized server, all data processing, model calculation and generation processes are ensured to be completed in a controlled environment, and the privacy of user data is fully protected.

Training a mixed language speech recognition model, and speech recognition performed by the model, can be enhanced. Model manager can control training and / or operation of the model. Mixed language data generation manager can generate an enhanced mixed language dataset that can enhance training of the model. Fine tuner can facilitate configuring hyperparameters of the model. Audio-based information and transcript that are representative of the dataset can be applied to the model to facilitate model training. FAL evaluator can determine fidelity, accuracy, and latency of performance of speech recognition on the audio-based information and transcript by the model. Based on such determination, mixed language data generation process and / or hyperparameters can be updated to enhance further training of the model to enhance fidelity, accuracy, and / or latency regarding performance of speech recognition by the model. Model manager can control one or more iterations of model training.

The invention discloses a small sample industrial defect detection system based on a multi-stage diffusion model. The small sample industrial defect detection system comprises a multi-stage diffusion generation module and a defect detection module based on multi-scale attention and physical constraint. The multi-stage diffusion generation module divides the diffusion generation process into three stages of global structure reconstruction, local detail refinement and texture feature synthesis through a stage control mechanism, and gradually guides feature evolution and improves the generation effect aiming at the quality and diversity problems of defect image generation under the small sample condition; the defect detection module based on multi-scale attention and physical constraint adopts a coding structure fusing local window attention and global attention, through multi-scale feature extraction and fusion, significant features of a defect area are effectively captured, gradient smoothing loss and edge energy consistency loss are introduced at a decoder end, and the defect detection accuracy is improved. Therefore, high-quality reconstruction of a normal area and effective suppression of an abnormal area are realized.

The invention discloses an image registration and segmentation joint optimization method, system, device and medium, and relates to the technical field of image registration and segmentation, and the method comprises the steps: obtaining an original medical image, and employing a dynamic pairing strategy to obtain a floating image and a fixed image; the method comprises the following steps: constructing an image registration and segmentation joint optimization model, wherein the model comprises an encoder sharing weight, a segmentation decoder, a registration decoder and an uncertainty estimator; and inputting the floating image and the fixed image into an image registration and segmentation joint optimization model to obtain a registered image and a tag thereof, inputting the fixed image and the registered image into an uncertainty estimator to predict the uncertainty of registration, and minimizing a loss function to obtain an updated image registration and segmentation joint optimization model. According to the method, the problem of class imbalance of different anatomical structure labels can be relieved, and the forward action of the layered segmentation feature map in the deformation subfield generation process can be promoted.

The invention discloses a personalized digital human generation method based on a single video, and relates to the field of virtual digital human modeling and driving, and the method aims at the video and voice data of a target person, through introducing a multi-modal alignment constrained voice driving synchronization mechanism and combining semantic understanding and an emotion label expression generation model, a personalized digital human model is generated. High synchronization, nature and vividness of digital human facial expressions and voice contents are realized. According to the method, a self-supervised style consistency constraint is added in model training to ensure that the style of a generated character image is stable, and a 3D semantic mask is added in an image fusion stage to improve the fusion precision and realistic effect of a synthetic facial expression and a reference face. According to the method, the digital human can be rapidly cloned and driven to synthesize the expression through the voice only through a single video sample, the generation process is efficient, and the obtained digital human video has excellent sense of reality and interactivity.

The invention relates to the technical field of semantic processing, in particular to a context-aware SQL (structured query language) generation method based on table field semantic enhancement, which comprises the following steps of: acquiring financial management and investment combination data of a user and mode information of a background database, constructing a mixed semantic index database, receiving natural language query input by the user, and generating a query result. Performing intention judgment on the query by utilizing the lightweight intention classification model, and if the intention is judged to be matched, calling a parameterized SQL template bound with the query intention and filling parameters to generate a final SQL query; if it is judged that the query intention is an exploration type intention, executing a two-stage SQL generation process, including generating an SQL skeleton according to the query intention, performing target type field recall based on the SQL skeleton, and filling the recalled field into the SQL skeleton to generate a final SQL query; and monitoring the execution performance of the final SQL query, and updating the query popularity metadata and the calculation cost metadata. According to the method, the SQL generation efficiency is improved through intelligent shunting and self-optimization.

The invention discloses a system and a method for security enhancement of generated content information of a large model, and the method comprises the steps: firstly, carrying out the bidirectional LoRA fine tuning technology and knowledge distillation technology on the large model, and enabling the large model to adapt to a factual output task from the illusion dimension and the factual dimension; performing extraction of intermediate layer representation in a fact space on the fine-tuned large model, and obtaining different mappings from fine-grained illusion types to a fact direction through a comparative learning method; and finally, in the reasoning generation process of the large model, extracting the representation of the middle layer of the large model to judge the potential illusion type, then applying the learned mapping corresponding to different illusions to the middle representation of the reasoning process, correcting factual representation, reducing the illusion output probability, and in the pre-training stage and the reasoning generation stage, obtaining the illusion type of the large model. Parameters and generated contents of the large model are controlled, and the factuality and reliability of the generated contents of the large model are greatly improved while the generation capability of the large model is guaranteed.

The present application provides a code testing method, applied to a code testing system. When a test code is generated, context information of a code under test is extracted, and the test code is generated on the basis of the context information of the code under test. The context information often has a service meaning and can embody a test scenario, so that the prior knowledge of the service and the test scenario is achieved in the generation process of test codes, thereby generating test codes having higher quality.

The invention relates to the technical field of AI dialogue generation, and discloses a child AI dialogue generation method and system based on IP role personalization, and a storage medium. The method comprises the following steps: acquiring IP role personal setting data and constructing a character feature sub-vector, a style feature sub-vector and a knowledge feature sub-vector; performing man-set weight attention calculation on the text input by the user to obtain an attention weight distribution matrix; joint optimization is carried out through a multi-target man-set constraint loss function, and intermediate dialogue content meeting an IP role man-set constraint condition is obtained; and carrying out education content adaptation and role identity consistency verification based on the intermediate dialogue content to obtain a pre-generated dialogue text, and carrying out real-time man-set deviation correction to obtain a target dialogue generation result. The method can continuously monitor and correct the human setting deviation phenomenon in the dialogue generation process, ensures that the generated content always conforms to the IP role setting, and effectively solves the technical problem of role identity drift in multiple rounds of dialogues.

The invention provides a database query training method and device based on RAG and a large model, and the method comprises the steps: obtaining DDL annotation data and SQL annotation data, standardizing the data into a training document, vectorizing the training document into a retrieval knowledge vector, and storing the retrieval knowledge vector into a preset vector database; in response to a query operation of a user, vectorizing the query operation into a query vector, and retrieving matched associated DDL and associated SQL in a preset vector database according to the query vector; constructing a model cue word according to a query problem corresponding to the query operation, the associated DDL and the associated SQL, inputting the model cue word into a preset large language model, and converting the query problem into a corresponding target SQL; and determining a query result of the target SQL in the target business database, and feeding back the query result to the user. According to the method, intelligent conversion from the natural language to the SQL can be realized, and the vector retrieval is introduced to enhance the generation process, so that the illusion problem of a large model is remarkably reduced, and the accuracy and safety of generating the SQL are improved.

The invention relates to the field of artificial intelligence (AI), in particular to an electrical stimulation method, an electrical stimulation device, electrical stimulation equipment and a storage medium. The method comprises the steps of obtaining a to-be-perceived image of a target object, inputting the to-be-perceived image into a trained electrical stimulation model to obtain electrical stimulation parameters output by the electrical stimulation model, and performing corresponding electrical stimulation operation on the target object to recover visual perception of the target object on the to-be-perceived image. An electrical stimulation generator in electrical stimulation is obtained by performing first link training and second link training based on a plurality of sample images and corresponding sample neural signals, the first link represents a verification link among electrical stimulation parameters, analog neural signals and reconstructed images, and the second link represents a verification link among the reconstructed images. The second link represents a verification link among the electrical stimulation parameters, the light magical view image and the sample image, so that the electrical stimulation model can greatly optimize the generation process of the electrical stimulation parameters, and the accuracy and generalization of restoring visual perception through electrical stimulation are improved.

The invention provides a publicly verifiable large language model watermark detection method and system. The method comprises the following steps: S1, embedding watermark information into a large language model by utilizing a watermark embedding model; s2, extracting watermark information from the text generated by the large language model by using a watermark extraction model, and generating a corresponding correctness proof; according to the watermark embedding model, logits and probability distribution of candidate words in a large language model generation process are dynamically modified, so that the large language model is more likely to select candidate words in a green word set as output words, and watermark information is embedded in an output text; the watermark extraction model is used for judging whether the text contains watermark information or not by counting the number of green words and the number of red words contained in the text and utilizing a statistical test method.

The invention belongs to the technical field of artificial intelligence, and provides a target pose sensing estimation method and system for mechanical arm grabbing operation and a robot. According to the technical scheme, multi-modal data of a target object in the mechanical arm grabbing process under a historical language instruction is obtained; based on the obtained multi-modal data of the target object in the historical mechanical arm grabbing process under the historical language instruction, the end-to-end large model is trained, and a trained large model is obtained; according to real-time environment information and task requirements, the weight of each sub-target in multi-target optimization in the large model is dynamically adjusted to obtain an inference model, an action sequence is generated based on a vector field predicted by the inference model, the generation process is optimized, and meanwhile, multi-modal information is fused for real-time decision making; and the action sequence of the mechanical arm executing the grabbing task in the current environment is determined. And the grabbing efficiency and safety of the mechanical arm are improved.

According to the method and device for generating the SQL statement based on the natural language, the equipment and the storage medium provided by the invention, the query instruction input by the user is received, and the query instruction is the natural language; performing semantic analysis on the query instruction; performing semantic vector coding on the query instruction, and performing similarity retrieval in a preset vector database to obtain semantic enhancement information related to the query instruction; filling a preset structured prompt template with the semantic analysis result and the semantic enhancement information to obtain prompt input information; and inputting the prompt input information into a large language model to generate an SQL statement. According to the method, semantic analysis and vector coding are performed on the natural language instruction of the user, and semantic enhancement is performed in combination with the preset vector database, so that the generation process has context semantic support, and the integrating degree of the generation result and the intention of the user is effectively improved.

The invention discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention belongs to the technical field of three-dimensional scene modeling, and discloses a dual-branch diffusion three-dimensional scene generation method based on a multi-modal semantic graph, which comprises the following steps of: firstly, receiving multi-modal data such as sketches, texts, automatic completion instructions and scene general knowledge, extracting features and fusing the features into a unified multi-modal semantic graph; utilizing a graph neural network and an attention mechanism to enhance semantic graph features, and optimizing physical constraints through a physical engine; complementing the missing visual modality and graph structure relationship; performing quality scoring on the scene based on semantics, spatial relationships and physical constraints; and finally, respectively generating a spatial layout and a geometric shape through a double-branch diffusion model, and ensuring the coordination of the layout and the shape. The method has the advantages of multi-modal information fusion, physical rationality guarantee, high structure complementation capability, high-quality score optimization and efficient generation process, and is suitable for three-dimensional scene modeling requirements in the fields of virtual reality, augmented reality, robots and the like.

The invention discloses a legal instrument generation method and device, electronic equipment and a storage medium, and relates to the technical field of legal instrument generation or other related fields, and the method comprises the steps: receiving first language case information and a legal instrument generation demand; analyzing the first language case information through a semantic analyzer corresponding to the first language to obtain case factors of the target case, and translating the first language case information through a translator to obtain second language case information; querying a target legal knowledge base based on the case factors to obtain a query result; and determining a target document template based on the legal document generation demand, and generating a target legal document based on the target document template, the second language case information, the case factors and the query result. According to the method and the device, the technical problem that the generated legal document is inaccurate and even not compliant due to the fact that professional legal terms and contexts cannot be accurately understood and converted in the cross-language generation process of the legal document in related technologies is solved.

The invention discloses a parcel logistics multimodal transport cooperative scheduling method based on big data, which relates to the technical field of big data, and comprises the following steps: when an information monitoring terminal monitors an abnormal condition, triggering dynamic correction of a transport efficiency coefficient, a cost fluctuation coefficient and a service risk coefficient; and when the absolute value of the difference value between the updated evaluation index and the original evaluation index exceeds a preset threshold value, the scheduling decision terminal re-executes the scheme generation process to generate an emergency scheme. Through a built-in evaluation model, according to scheduling modes such as cost priority, timeliness priority and safety priority selected by clients, weights are dynamically allocated, evaluation indexes are output, a transportation mode combination is generated in combination with a genetic algorithm, time connection verification and equipment matching verification are carried out, multi-target collaborative optimization is realized, different client requirements can be flexibly adapted, and the method is suitable for large-scale popularization and application. And the transportation efficiency, the cost and the safety are balanced, and the multimodal transport resource allocation efficiency is improved.

The invention discloses a PCB production process management system and method based on intelligent tracing, and relates to the technical field of intelligent tracing. Production links and a parallel line of each link are obtained in a process line by using production characteristics; constructing a flow line framework of a PCB generation process; the correlation degree between the production data in the adjacent production links is calculated, the correlation degree of different production data in all the adjacent production links is judged, and the production data with the maximum correlation degree serves as transmission data of the corresponding production link; automatically learning a Bayesian network structure through maximum likelihood estimation to obtain a potential causal path of each piece of transmission data; generating a chained quality evaluation model; and after the real-time PCB quality is judged to be unqualified by utilizing a qualified quality threshold, performing reverse tracing by utilizing a potential causal path of each production link in the chain type quality evaluation model.