1652 results about "Generation process" patented technology

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.

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 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 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 an automatic feasibility research report generation method fusing a knowledge graph and a large model, and relates to the technical field of leather inlaying, and the method comprises the following steps: extracting and cleaning information from multi-source heterogeneous data, and carrying out standardization processing on terminologies; constructing a knowledge graph, and converting the knowledge into embedded vectors which can be processed by a computer; according to user information, related sub-graph information is accurately retrieved from the knowledge graph, and cue words containing professional knowledge are dynamically combined; the large model generates a first draft according to cue words, a system automatically checks, and when conflicts are found, the model is required to be regenerated; and integrating the content which is verified to be correct with related standard terms, automatically adding a data reference source, and generating a document which is standard in format and clear in risk point. According to the method, real-time and automatic professional knowledge conformity checking and self-correction can be carried out on technical parameters and logic assertions output by the model in the generation process.

The invention relates to the technical field of natural language processing, in particular to a context-based Prompt generative model optimization system, which comprises a context analysis module, a cue word generation module, a context optimization module, a semantic check module and a structure reconstruction module. According to the method, the context path and the semantic hierarchy information of the semantic unit are introduced, the fine degree of semantic matching degree recognition is improved, semantic guide deviation caused by statement template solidification is avoided, the cue words are recombined in combination with the semantic coherence weight and the logic dependency relationship, and the recognition accuracy is improved. The consistency and expression accuracy of the prompt content in the context are enhanced, the prompt word insertion sequence and connection mode are dynamically adjusted through a semantic conflict detection and structure rechecking mechanism, coherence and stability of a semantic structure and controllable generation of the prompt content are kept, semantic conflicts and expression chaos caused by static matching are effectively avoided in the generation process, and the generation efficiency is improved. And dynamic adaptation of prompt configuration and smooth optimization of language output are integrally realized.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention relates to the technical field of artificial intelligence, and discloses a contract generation system and method based on multiple agents, and the system comprises a user interaction module which is used for receiving a contract demand inputted by a user and displaying a generation result; the knowledge base module is used for storing and retrieving legal terms, industry specifications and contract templates in a distributed manner; the agent cluster module comprises a demand analysis agent, a law expert agent, an industry expert agent, a risk assessment agent, a language optimization agent and a coordination control agent, the agents interact through a standardized communication protocol, and a coordination strategy is dynamically adjusted based on contract complexity calculated in real time; and full-process automation of contract generation is realized. Through cooperative work of multiple agents, the efficiency and quality of contract generation are improved, the law compliance and industry suitability of the contract are ensured, the risk in the contract generation process is reduced, and the user experience is improved.

The invention relates to a signal equipment fault diagnosis rule automatic generation method, system and equipment, and the method comprises the steps: a demand cleaning processing process: carrying out the semantic analysis of an original fault diagnosis demand of signal equipment, and generating a structured output containing a demand pseudo code and a dependency graph based on a recognition result of the demand; the demand searching process is used for constructing a demand vector and a knowledge graph; performing similarity search on the vectorized query demand to obtain similar demand nodes for a user to select a root demand, and performing deep search in the knowledge graph based on the root demand to generate a fault tree structure and fault tree data; and an intelligent rule automatic generation process: generating prompt words based on the fault tree data construction rule, and generating a signal equipment fault diagnosis rule code by using a large language model. Compared with the prior art, the method has the advantages that the signal equipment fault diagnosis rule is automatically generated, and the development efficiency is improved.

The invention discloses a large model fine tuning method based on a causal graph and thinking chain enhancement and a related device, and relates to the technical field of large model fine tuning in the power industry, and the method comprises the steps: carrying out the causal mining of power equipment data, and constructing a power equipment causal graph containing causal weight information; disassembling the input of the large model into a thinking chain, correspondingly generating a chain type causal pair according to the thinking chain, and performing path retrieval matching and causal consistency check through the chain type causal pair and the constructed electrical equipment causal graph to realize alignment of the reasoning process; and exciting a reinforcement learning process through an alignment result of the reasoning process, optimizing a pre-established reinforcement learning reward model, constraining a thinking chain generation process, guiding the large model to generate a thinking chain under a causal constraint condition, and realizing fine tuning of the large model. According to the method, causal reasoning and causality are embedded into a reinforcement learning feedback process of large model fine tuning, so that the large model can learn a basic causal reasoning rule, and the logicality, the interpretability and the robustness of thinking chain reasoning can be improved.

The invention relates to an automatic driving key test scene generation method fusing a visual language large model and a diffusion model, and the method comprises the steps: constructing a diffusion model which is used for learning the data distribution of natural driving; aiming at an original scene, utilizing a visual language large model to analyze and form a guide strategy; and based on a guiding strategy and a guiding diffusion algorithm, guiding intervention is carried out on the diffusion model in a simulation environment so as to calculate a background vehicle track and generate a key scene. Compared with the prior art, the method has the advantages that the original scene is automatically analyzed and potential risks are inferred by utilizing the visual language large model, so that the generation process of the bottom diffusion model is guided, intervened and corrected, and the key test scene with high interactivity, high risk and authenticity can be generated in closed-loop simulation.

The invention discloses an intelligent agent digital image interaction generation method based on multi-modal perception, which comprises the following steps: collecting multi-modal input data of a user, and respectively carrying out preprocessing and feature extraction on the multi-modal input data; inputting to an improved efficient modal cross learning network, and carrying out multi-modal feature fusion processing; constructing a semantic intention map, introducing a time index edge weight and an emotion driving edge weight, and encoding the map by using a structure perception map neural network; a modal style vector is extracted through a cross-modal style contrast learning mechanism, and a personalized style coding vector is generated through a hierarchical nested structure; inputting a personalized regulation and control gating mechanism, and regulating and controlling the middle layer representation in the interaction strategy generation process by adopting a feature channel linear modulation method; inputting the representation vector into a behavior strategy generation module to generate a multi-modal behavior output sequence; and the sequence is output to drive the digital image to perform synchronous response, and natural response generation in the user interaction process is completed.

The invention relates to the technical field of ink-jet printing, and discloses a nozzle driving method, system and device and a storage medium, and the method comprises the steps: generating a digital waveform signal based on a printing demand, and converting the digital waveform signal into an analog voltage waveform through a digital-to-analog conversion module; performing waveform configuration on the analog voltage waveform through an analog switch matrix to generate a target analog waveform; controlling a high-voltage amplification circuit to boost and amplify the target analog waveform to output an initial driving level; driving a nozzle to jet ink droplets based on the initial driving level, and collecting ink droplet state parameters by using an ink droplet monitoring module; comparing the ink droplet state parameters with a preset target value to obtain a comparison result, and generating an adjustment parameter set according to the comparison result; optimizing the initial driving level generation process based on the adjustment parameter set to obtain an optimized driving level; according to the invention, through a closed-loop control process, the problem of poor driving signal adaptability caused by existing open-loop control is effectively solved.

The invention is suitable for the technical field of intelligent customer service, and provides an intelligent question-answering method and system for public rental housing and computer equipment, and the method comprises the steps: receiving a query request input by a user, recognizing a query intention and key parameters through analyzing the query request, and generating an action framework containing the query intention, the key parameters and a preset retrieval strategy; at least one retrieval path is selected and executed according to the action framework, information is retrieved in parallel from heterogeneous data sources at least comprising a vector database, a search engine and a knowledge graph database, and all recalled source information is integrated into an evidence chain capable of being verified; and constraining a generation process of the large language model based on an evidence chain to generate a preliminary answer, calling a rule engine to perform policy compliance verification on the preliminary answer, and after the verification is passed, outputting a final answer with a traceable source label. According to the method and the system, the problem that defects exist when business scenes which are complex, dynamic and high in compliance requirement in the public rental housing field are processed in the prior art is solved.

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention belongs to the technical field of natural language processing, provides a multi-dimensional confidence fusion large language model uncertainty evaluation method and system, designs a multi-dimensional confidence modeling mechanism, and can perform multi-dimensional confidence modeling on the basis of internal information in a large language model generation process without depending on an external knowledge base. And the reliability of the output content is effectively judged. According to the system, on the basis of a semantic clustering mechanism, a Token-level multi-dimensional confidence modeling method is innovatively introduced, a scoring system fusing factors such as probability centrality, context disturbance sensitivity, generation consistency and language rationality is constructed, the confidence structure of each part of content in model output can be evaluated from the Token level, and the evaluation efficiency is improved. And the discrimination capability of the system on the uncertainty difference in the generation process is obviously improved.

The invention discloses an AI video output system and method combined with a text image model, and relates to the technical field of AI video output, and the system comprises a visual angle conversion and multi-visual angle generation module which comprises a visual angle encoder and a multi-visual angle generator based on semantic-style hidden representation and visual angle vectors, the method is used for generating a multi-angle illustration sequence aiming at different camera parameters and keeping structure and style consistency among different visual angles through time domain consistency constraint in the generation process. According to the method, the artistic style is propagated and maintained among multiple frames and multiple visual angles according to a three-dimensional visual angle transformation rule through visual angle perception style propagation, and the generator outputs a consistent multi-angle illustration sequence under the constraint of a visual angle condition during sampling, so that the problem of inconsistent style drift and deformation under multiple visual angles or multiple lenses is solved; the incoherence of stroke, texture and main body structure caused by visual angle change is avoided, the later manual correction is obviously reduced, and the content consistency and the impression professional degree are improved.

The invention provides a target layout data generation method, electronic equipment and a readable storage medium. According to the method, the PCB layout generation process is converted into the iterative optimization task of the neural network model, and dual evaluation on the line length and the density distribution in the total loss function is combined, so that the problems of low layout quality, low optimization efficiency and the like in a traditional layout algorithm are effectively solved. The gradient is calculated through back propagation, the positions of the components are updated, it can be ensured that the lengths of the electrical connecting wires and the space uniformity of the components are optimized at the same time in the layout process, and therefore the overall performance and stability of layout are improved. In addition, in combination with a preset training termination condition, overtraining is avoided, and the calculation efficiency is improved. Finally, the generated optimized layout data has higher quality and consistency, and global optimization can be realized in complex design.

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 10kV cable accessory manufacturing quality inspection report generation method and system, and relates to the technical field of power equipment quality detection and reliability evidence storage. According to the method, original detection data is acquired and structured in multiple dimensions, and data sources, operators and equipment identifiers are bound, so that context integrity and traceability are realized; initial and enhanced data fingerprints are generated through block classification, standardization and Hash operation, and a dynamic fingerprint chain is constructed by using a chain structure, so that whole-course data has content and process dual integrity identifiers; a multi-level verification sequence is embedded in each stage of report generation, finally, a block chain is utilized to record a data packet abstract, an anchor point on the chain is bound with report metadata, a transaction ID is embedded into a report document, and a checking two-dimensional code is generated. According to the method, the non-tampering performance and the verifiability of the content and the generation process of the detection report and the traceability of the whole circulation process are improved.

This disclosure describes a data insights system that implements a framework for generating multimodal insights from large datasets. For example, the data insights system utilizes multiple prompt generation processes paired with one or more generative artificial intelligence (AI) models to efficiently generate accurate visualizations and text insights from a large dataset in response to custom user queries. In particular, the data insights system utilizes different prompt generation processes to intelligently craft targeted generative AI prompts to maximize the efficiency and accuracy of the generative AI insight responses that target answers to custom user queries, rather than providing pre-built dashboards and charts. The data insights system not only provides customized visualizations in response to user queries but also generates and provides insights and summaries that are not intuitively visible. These visual and text insights are presented in a combined interactive interface.

The invention discloses a pre-training language model output lexical element control method and electronic equipment, and relates to the field of computer vision and natural language processing, and the method comprises the steps: introducing a character region visual feature in an input image as a cross-modal constraint basis, positioning the character region of the input image, and extracting the visual feature, the generation process is strongly bound with input vision and character semantics, semantic anchor points are provided for follow-up temperature adjustment, and the semantic imbalance problem caused by indifference temperature control is avoided; the cross-modal consistency score of the output lexical elements and the character region is calculated, so that dynamic quantitative evaluation of the semantic suitability of each lexical element in the generation process is realized; and the sampling temperature is adaptively adjusted based on the cross-modal consistency score, so that the model can optimize the temperature setting in real time according to the semantic alignment condition in the generation process, and finally the dynamic and refined balance of the certainty and diversity / flexibility of the generation result is realized.

The invention relates to the technical field of internet big data and information security, and designs a gearbox fault diagnosis method based on multi-source agent federal field generalization by combining the interaction capability of wide-area data and a local diagnosis agent. According to the method, a dual regularization mechanism is introduced in the training process of each local diagnosis agent, and the problem of negative migration is solved by actively learning domain-invariant fault features, so that the cross-working-condition and cross-equipment generalization diagnosis performance of a final model is remarkably enhanced on the premise of protecting data privacy; meanwhile, a central server is replaced by a decentralized coordination network realized by a block chain technology, and a committee consensus protocol is executed to ensure that the generation process of the global model is open, transparent and non-tampering, so that the single-point fault risk is fundamentally eliminated, the security and robustness guarantee is provided for the whole coordination process, and the method has the advantages of being high in practicability and the like. And the long-term applicability and the fault diagnosis accuracy and reliability of the method in a dynamically changing industrial environment are ensured.

The invention discloses an automatic test case generation method and system based on a large model, and the method comprises the steps: multi-modal data fusion: receiving a natural language demand, a UML design drawing, an API document, a historical defect log and system monitoring data, aligning text and image features through a CLIP model, analyzing the interface constraint through CodeBERT, and carrying out the multi-modal data fusion; feature fusion is realized by adopting a cross-modal attention mechanism; a dynamic test scene modeling step: constructing an environment simulator based on reinforcement learning, and simulating high-concurrency, distributed abnormal and other complex scenes; and a layered test case generation step: generating a function test case, a boundary test case and an increment test case through GPT-4, GAN and LSTM respectively, and introducing an entropy evaluation model to quantify the effectiveness of the cases. According to the method, the multi-modal data is brought into the test case generation process for the first time, the test case generation time is shortened by 80%, the complex scene coverage rate is increased to 95%, the semantic matching degree reaches 92%, and the intelligent level and efficiency of software testing are remarkably improved.

The invention provides a building LOD2 model diffusion generation method and system based on multi-modal feature constraints, and belongs to the field of remote sensing image processing. According to the method, a three-dimensional polygon noise generation module MM2P-Init and a multi-modal feature constraint module MM2D-Control are mainly utilized to construct a diffusion model MMP-DiffNet, a noise reduction generation process from three-dimensional polygon noise to a building roof structure is realized, the influence of extraction precision of low-dimensional features such as points and lines on a building LOD2 model topology reconstruction process is reduced, and the construction efficiency is improved. And the precision of building LOD2 model construction by the satellite image is improved.

The invention discloses a controllable text watermark embedding method based on a reinforcement learning strategy model, and the method comprises the steps: constructing a reinforcement learning strategy model, and controlling the token output of a large language model through the reinforcement learning strategy model; under the strategy control of the reinforcement learning strategy model, watermark embedding is carried out on the output text based on a watermark embedding strategy, and a candidate token sequence with hidden information is formed. According to the method, a policy network based on a reward function is introduced to perform token-level dynamic intervention on a large language model generation process, multiple embedding modes such as vocabulary replacement, grammar disturbance and structure addition are fused, and a watermark detector is used for providing a feedback signal to realize end-to-end closed-loop optimization; therefore, the naturalness, robustness and detectability of the text watermark are remarkably improved on the premise that the main structure of the language model is not modified, and the method has good deployment flexibility and traceability.

The invention discloses an operator generation method, electronic equipment, a storage medium and a program product, and the method comprises the steps: generating a constraint knowledge base according to a current operator generation demand and a preset operator, and generating a candidate operator code; and obtaining a target operator corresponding to the operator generation demand based on the candidate operator code meeting a preset operator verification condition. According to the method, the operator generation process is constrained by setting the operator generation constraint knowledge base, and the candidate operator code is verified by adopting the preset operator verification condition after being generated, so that the finally obtained target operator can be normally compiled; and under the constraint of the operator generation constraint knowledge base, the performance of the target operator can be effectively improved, and the stability of operator generation is ensured.