878 results about "Code module" patented technology

The invention discloses an intelligent geometric reasoning and semantic understanding method based on a three-dimensional large language model, which comprises the following steps of: acquiring point cloud data of a building component through three-dimensional scanning equipment, associating text information, and constructing a multi-modal three-dimensional large language model comprising a geometric perception coding module, a context semantic understanding module and a parameter efficient fine tuning module; a cross-modal contrast loss and task instruction fine tuning strategy is adopted in model training, and finally semantic recognition, attribute completion and historical background analysis results of the building components are output. The method is suitable for building heritage digital protection, intelligent building process monitoring and three-dimensional digital archive management, component function recognition precision and cultural semantic mining capability in a complex scene can be improved, and real-time semantic updating and interactive response of a dynamic construction environment are supported.

Aspects of the present disclosure relate to automatically updating a software application to ensure compliance with an updated data source. Embodiments include providing an embedding of a first version of a data source and an embedding of a second version of the data source to a comparison engine configured to compare the embedding of the first version of the data source and the embedding of the second version of the data source and generate a data source difference summary. Embodiments further include providing an embedding of a software application code module and the data source difference summary to a code update engine trained to generate an updated version of the software application code module based on the embedding of the software application code module and the data source difference summary. Embodiments further include updating code of the software application using the updated version of the software application code module.

The invention discloses an IG-MambaUNet image segmentation model, a model training method and an application method thereof, the IG-MambaUNet image segmentation model comprises an encoder and a decoder which are symmetrically arranged in parallel, the encoder comprises three first encoding modules and a second encoding module, the three first encoding modules are sequentially connected from top to bottom, the second encoding module is connected with the first encoding module on the bottommost layer, and the third encoding module is connected with the second encoding module on the bottommost layer. The input end of the first coding module on the topmost layer is connected with a first convolution and maximum pooling operation module, the first coding module is formed by coupling an IG-Mama module and a Patch Merging module, the second coding module comprises an IG-Mama module, the IG-Mama module comprises a local feature extraction module, a global feature extraction module and a gating attention module, and the global feature extraction module comprises a local feature extraction module, a global feature extraction module and a gating attention module. The decoder comprises a second coding module and a first coding module which are sequentially connected from bottom to top, and the output end of the first decoding module on the topmost layer is connected with a second convolution and maximum pooling operation module. According to the scheme, fine segmentation is realized, and the method can be suitable for automatic cell body segmentation of histological dyed ganglion images with complex textures and fine structures.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention discloses a knowledge graph enhanced reasoning framework based on a graph neural network and a large language model. The framework comprises a vectorization representation module, a graph neural network coding module, a large language model interaction module and a joint reasoning module. The vectorization representation module is used for aligning the structured knowledge graph and the unstructured text and converting the structured knowledge graph and the unstructured text into unified vectorization representation so as to provide a basis for subsequent processing; the graph neural network coding module obtains graph structure characterization by utilizing low-dimensional embedding of graph neural network learning data; the big language model interaction module performs semantic completion on the knowledge graph entity relationship by means of a big language model to generate an enhanced context vector; and the joint reasoning module fuses the results of the two modules and outputs an enhanced knowledge graph tetrad. Through multi-module cooperation, the accuracy and integrity of knowledge graph reasoning are remarkably improved, and the method has wide application prospects in the fields of intelligent questions and answers, intelligent decisions and the like.

The invention discloses a sentiment analysis method based on prototype guide mode fusion and prompt enhancement, and constructs a multi-mode sentiment analysis network which comprises a multi-mode coding module, a prototype guide mode fusion module, a dynamic mode weight adjustment mechanism and a context prompt generation module. The method comprises the following steps: firstly, extracting semantic features of each mode by using a multi-mode encoder, and constructing a prototype feature library based on a labeled sample to describe typical representations of different modes under each category; and then, dynamically evaluating modal contribution through prototype similarity to realize modal adaptive fusion. Furthermore, a context prompt is generated according to a similarity retrieval result of the input sample and the prototype library, and the pre-training language model is guided to complete sentiment classification. According to the method, the problems of modal inconsistency, information redundancy, weak small sample generalization and the like can be effectively relieved, and the accuracy and robustness of sentiment analysis are improved.

The invention discloses an integrated sensor multi-modal data edge computing system optimized by a large model. The integrated sensor multi-modal data edge computing system comprises a data preprocessing module which obtains pre-training parameters of each modal data through the large model; the modal specific coding module optimizes attention weight through an encoder branch to determine key information of data of each modal, and advanced feature representation of each modal is obtained; the fusion strategy selection and execution module is used for calculating the correlation between the rest modes and the final core mode, and inputting the advanced feature representation of the mode with the strongest correlation in the rest modes and the advanced feature representation of the final core mode into a cross-mode encoder to generate the fusion feature representation of the fused modes; continuing to perform modal fusion operation until all modalities are fused, and generating final fusion feature representation; and the cross-modal coding and task processing module finally generates a multi-modal fusion model prediction result through cross-modal feature representation. According to the invention, the performance and adaptability of multi-modal data processing and the data processing efficiency are improved.

The invention discloses a simulation-oriented multi-agent maneuvering behavior decision-making system and method, and the system comprises a message transmission module which introduces the signal intensity into the communication among multiple agents, simulates a complex simulation environment with interference in a probabilistic communication mode for the discrete quantification of the communication intensity among the agents, and transmits the signal intensity to a decision-making module; obtaining joint state information influencing a maneuvering behavior decision; the state coding module is used for discretely quantifying the obtained joint state information into a unified index and coding the unified index to obtain a joint state code corresponding to each agent; the reward optimization module is used for determining a target reward of each agent in combination with the task target and the historical trajectory of each agent, and the target reward is used for optimizing the deep reinforcement learning network model of the strategy execution module; and the strategy execution module is used for determining and executing a final decision result of each agent by adopting a deep reinforcement learning network model based on an MADDPG algorithm by each agent. According to the invention, the problem of multi-agent collaborative navigation in a complex high-dimensional environment is solved.

A regional extreme rainfall forecasting and early warning method based on a conditional generative adversarial network (cGAN) comprises the following steps: preprocessing multi-source meteorological data, including standardization and space consistency alignment, so as to ensure the availability of the data; and through a coding module of the cGAN, extracting and fusing interannual daily average features and horizontal features, constructing a multi-scale feature pyramid, and enhancing the characterization capability of a rainfall spatial mode. Forecasting data are generated through a cGAN generator and a condition discriminator, rainfall collaborative factors are introduced into the discriminator to serve as physical constraint conditions, and network parameters are optimized through an adversarial loss function and physical consistency loss. The forecasting data is post-processed through a graph neural network (GNN), and spatial dependency modeling and optimization correction are carried out by taking a meteorological station observation value as a true value, so that the forecasting precision and reliability of an extreme rainfall event are remarkably improved.

The invention belongs to the technical field of natural language processing, and particularly relates to an adversarial attack text classification method based on granular ball and metric learning, which comprises the following steps: acquiring a to-be-classified text, and inputting the to-be-classified text into a trained coding module to obtain semantic vector representation of the to-be-classified text; mapping the semantic vector representation of the to-be-classified text to the trained grainball space to obtain a grainball center, and inputting the semantic vector representation corresponding to the grainball center into the classifier to obtain a classification result; according to the method, semantic vector representations of similar texts are aggregated into one granular ball by using granular ball clustering to obtain the granular ball space, then semantic vector representations of to-be-classified texts are mapped into the granular ball space to obtain the corresponding granular ball centers, and the influence of adversarial samples is eliminated by using the corresponding granular ball centers for classification, so that compared with the prior art, the method has the advantages that the classification efficiency is improved, and the classification efficiency is improved. And the accuracy of the classifier under the attack condition of the model is improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention relates to the technical field of artificial intelligence and edge computing crossing technologies, in particular to an edge deployment system and method of a distributed visual language model, a storage medium and a processor. The invention provides an edge deployment system and method for a distributed visual language model, a storage medium and a processor. The edge deployment system comprises a distributed topological structure with a front end and a rear end working cooperatively. The front end is an edge device end, and the rear end is a reasoning device end; the visual coding module is deployed at an edge device end, and the language reasoning module is deployed at a reasoning device end; and the visual coding module and the language reasoning module carry out feature transmission through a dynamic security protocol. The visual coding module and the multi-modal reasoning module are separately deployed in different hardware devices, edge end efficient feature extraction and back end joint reasoning are achieved through modular design, and the calculation load of the edge devices is reduced while privacy security is guaranteed.

A method, computer system, and computer program product are provided for selectively deploying code modules for issue identification and remediation tasks. A plurality of code modules is obtained, wherein each code module includes instructions for issue identification and remediation. A trust score for each code module of the plurality of code modules is determined, wherein the trust score includes a first trust score component for issue identification and a second trust score component for issue remediation, and wherein the trust score is based on a source of each code module selected from a group of a human-generated source and an artificial intelligence model-generated source. A particular code module of the plurality of code modules is deployed based on the trust score of the particular code module satisfying a threshold value. The trust score for the particular code module is updated based on results of deploying the particular code module.

The invention provides a Chinese quintessence art gene decoding method and system based on cross-modal AI, and belongs to the crossing field of artificial intelligence and digital media art, and the method comprises the steps: S1, constructing a Chinese painting-music-text multi-modal data set; s2, inputting the traditional Chinese painting image into a visual encoder improved based on CLIP-ViT, and outputting a 512-dimensional visual Token sequence through a normalization module, a position encoding module and a Transform encoder; s3, inputting the visual Token sequence and the emotion label into a cross-modal adapter, and directly mapping the visual Token to a music hidden space by adopting a self-attention mechanism to obtain a music embedding vector; and S4, inputting the user parameters into the improved high-frequency fidelity generative adversarial network to generate Chinese traditional music audio conforming to five sound orders. According to the method, intelligent semantic communication between visual art and auditory art is realized.

The invention relates to a song conversion model training method, a song timbre conversion method, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of song synthesis, and can improve the efficiency of song timbre conversion by a user. The singing conversion model training method comprises the steps of obtaining a sample song and a singer timbre description text paired with the sample song; obtaining tone characteristics of the sample song by an audio coding module in the singing conversion model, and obtaining text characteristics corresponding to the tone description text of the singer by a text coding module in the singing conversion model; according to the difference between the timbre features and the text features, adjusting model parameters of a singing sound conversion model to obtain a trained singing sound conversion model; the trained song conversion model is used for extracting text features corresponding to the input timbre description text through the text coding module, obtaining target timbre features according to the text features corresponding to the timbre description text, and performing song timbre conversion.

The invention belongs to the field of star flash mouse communication, and discloses a star flash mouse low-power-consumption communication system and method based on Polar code and SLE collaborative optimization, the system adopts a layered architecture design, collaborative optimization of energy efficiency and performance is realized through deep coupling of a physical layer and a link layer, and the performance of the system is improved. The core of the system is composed of three functional units including a Polar code coding module, an SLE control engine and a cross-layer optimization interface, and a complete closed-loop control system is formed. According to the invention, through deep cooperation of the Polar code and the SLE mechanism, the overall power consumption is reduced by 42.7% compared with the traditional scheme; according to the Polar code dynamic construction method based on track prediction, an ultra-low bit error rate of 10 <-5 > magnitude can still be kept in a 2.4 GHz complex interference environment, and in combination with signal-to-noise ratio self-adaptive coding parameter adjustment, it is ensured that transmission delay within 0.5 ms is always kept from a low-speed office scene to a high-speed electronic sports scene; in the aspect of engineering implementation, the method has the capability of quickly adapting to hardware platforms of different manufacturers, and a complete solution with high performance and low cost is provided for large-scale commercial use of the satellite flash technology in the consumer electronics field.

The invention discloses a test risk identification method and system based on artificial intelligence, and particularly relates to the technical field of data processing. By introducing an adaptive learning rate and a feedback mechanism, the improved test risk identification method is provided for the problem of data imbalance existing in the test process of an existing machine learning model, and the test risk identification method is provided by comprehensively collecting test data including test case execution results and test environment states. Extracting a dependency relationship abnormal value and a network delay high-frequency value between code modules, constructing a data prediction model, and comprehensively calculating a deviation of a prediction result based on the data; by dynamically adjusting the deviation of the model to the successful use case, the identification capability of the failed use case and the high-risk area is enhanced, and the high-risk area is effectively prevented from being underestimated or the normal use case is misjudged to be high in risk, so that the prediction precision is improved, the configuration of test resources is optimized, the invalid test reexamination is reduced, the test efficiency is remarkably improved, and the cost and the period are reduced.

The invention discloses a code generation method and device based on a natural language and a medium. The method comprises the following steps: obtaining candidate functions provided by a user based on a target system, and determining function demand information described by a natural language; determining target information from the feature information according to the function demand information; wherein the feature information is determined in advance according to candidate functions; and generating a cue word according to the function demand information and the target information, inputting the cue word into the large language model to generate a code module for executing the function demand information, and integrating the code module into the target system. According to the method, the feature metadata corresponding to the candidate functions of the target system is combined with the natural language information of the user to guide and generate the code module for executing the user-defined function requirements, so that the user is allowed to generate the code module which can be integrated into the target system through the natural language on the scene site; and the processing efficiency of the user function requirements is improved.

The invention discloses an intelligent dialogue system fused with emotional memory retrieval enhancement, and relates to the technical field of artificial intelligence. The system comprises a query coding module, a memory coding module, an emotion retrieval module and a response generation module. The query encoding module encodes semantic and emotional states of queries into vectors by using an embedded model; the memory coding module is used for storing dialogue information of related roles, similar to query coding, and coding memorized semantic and emotional states into vectors; the emotion retrieval module simulates memory recall of human beings, and retrieves a memory fragment most similar to user query from the role memory unit according to semantic similarity and emotion similarity; and the response generation module integrates the query information, the role portrait and the retrieved emotion memory into an emotion prompt and inputs the emotion prompt into the role playing agent to finally generate a response. Experimental verification is carried out on three real data sets of InCharacter, Character Eval and Character-LLM, and the result shows that compared with the prior art, the method has the advantages that the accuracy is higher, the mean square error is smaller, and the interpretability is higher.

The invention relates to a multi-modal feature level fusion method based on a real-time shared Transform, and relates to the technical field of multi-modal data processing. The method comprises the following steps: respectively transmitting image information, text information and voice information to a mapping module to obtain embedded vectors of three coding slices; the embedded vectors of the three kinds of coding slices are respectively transmitted to a common transform coding module, and three kinds of coding tokens are obtained; the three kinds of coding tokens are simultaneously transmitted to a real-time sharing transform coding module, and three kinds of sharing codes are obtained; the three kinds of shared codes are transmitted into an adaptive fusion module to obtain a joint feature representation containing all modal information. According to the method, multi-modal data are converted into embedded vectors, cross-modal information sharing is carried out after feature extraction and coding are carried out, and finally information of all modals is integrated in an adaptive fusion module, so that multi-modal information is fused more comprehensively and organically.

The invention discloses a reference video object segmentation method and system based on motion modeling and multi-modal interaction, and the method comprises the steps: taking a video sequence and natural language description as input, and generating a preliminary segmentation mask through a text coding and mask decoder; a Kalman filtering motion modeling module is introduced to predict the motion trail of the target object, and time sequence consistency optimization is carried out on the preliminary segmentation mask; fusing the historical track of the object and the action semantics in the semantic features on the basis of a key action semantic coding module to realize action semantic alignment and mask dynamic correction; the segmentation quality of the current frame is subjected to multi-dimensional scoring based on a representative frame screening mechanism, the representative frame is screened out to update a memory bank, and the long-term tracking stability is improved. According to the method, the problems of target drift, insufficient semantic alignment and memory pollution in a complex dynamic scene in the prior art are effectively solved, and the segmentation precision, robustness and semantic consistency are remarkably improved while the light weight of the model is kept.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention relates to the field of twitter recognition, in particular to a twitter classification method based on harmonic enhancement and time-frequency semantic joint modeling, which comprises the following steps: collecting twitter samples and carrying out noise reduction and standardized preprocessing, carrying out multi-scale convolution operation on Mel spectrograms by utilizing a layered acoustic encoder, extracting time-frequency features in combination with a channel attention mechanism, and classifying twitter classification results. The method comprises the following steps of: generating adaptive position codes through a dynamic time-frequency joint coding module, carrying out time-frequency mode modeling by combining a global-local interaction mechanism, introducing a semantic fusion module which comprises a frequency band pyramid unit, a harmonic enhancement unit and a time-frequency gating unit, realizing dynamic weighted fusion of multi-layer features, and carrying out time-frequency mode modeling through a global-local interaction mechanism. And inputting the fusion features into a classification layer, training a network by adopting a cross entropy loss function and a gradient descent algorithm, and outputting bird categories through a full connection layer, thereby solving the key problems of insufficient description of a non-stationary time-frequency mode, insufficient modeling of a harmonic structure, reduction of recognition performance in a complex noise environment and the like in the prior art.

The invention discloses a hybrid algorithm protection-based system, a protection method, a BIOS (Basic Input / Output System) and a computer, the system comprises a core code module, and the core code module is configured to store and / or load a core code of the system; protecting the core code by adopting a first protection strategy, wherein the first protection strategy comprises the following steps: calculating an encrypted signature value of the core code by adopting a first encrypted signature algorithm to obtain an original first encrypted signature value; performing digital signature on the original first encrypted signature value by adopting an RSA private key to generate original signature data; encrypting the original signature data by adopting a first symmetric encryption algorithm to generate an encrypted original check code; and storing the original first encrypted signature value and the original check code in a first storage area, wherein the first storage area is a secure storage area. The safety of the system can be improved.

The invention discloses a noise-driven small sample distribution external detection method and system, and the method comprises the following steps: 1, obtaining a data set which comprises a small sample training set and a test set; 2, constructing a detection model, wherein the detection model comprises a data coding module, a noise extraction module and a fusion detection module; 3, performing noise enhancement on the noise region set, constructing a loss function in combination with the clean region set, training the detection model by adopting a gradient back propagation algorithm according to the loss function, and testing the detection model by adopting the test set; 4, performing small sample distribution external detection according to the tested detection model; according to the method, the problem of poor noise robustness of a current small sample distribution external detection method is solved, and the ID classification and OOD detection performance of a small sample learning model is improved.

The invention provides a software code optimization method and system based on big data, equipment and a medium, and relates to the technical field of computers. The method comprises the steps that a target code library and corresponding running log data are acquired, and a first data set is obtained; cleaning, converting and aggregating the first data set from the programming language dimension, the framework dimension and the project specification dimension to obtain a second data set; training a machine learning model by using the second data set, so that the machine learning model can identify code quality, performance bottlenecks and potential defects; analyzing a code module written by a user by using the trained machine learning model to obtain a code analysis result; and according to a code analysis result, generating a corresponding code segment from the target code library, and displaying the code segment by using a visual interface. According to the technical scheme, accurate and comprehensive code optimization suggestions can be given.

The invention discloses a multi-view-angle-based unmanned aerial vehicle visual positioning method and system, and relates to the field of unmanned aerial vehicle vision, and the method comprises the steps: obtaining a satellite map of a target region, and cutting the satellite map into overlapped blocks to construct a satellite library; acquiring images shot by the unmanned aerial vehicle from a plurality of visual angles in the target geographic area range, and constructing a multi-visual-angle unmanned aerial vehicle image based on the shot images; the images are processed through a multi-view-angle unmanned aerial vehicle visual positioning network, and the network comprises an embedded coding module, a view angle consistency feature learning module and a prototype construction and alignment module and is used for extracting single-view-angle and multi-view-angle unmanned aerial vehicle image features and satellite library features; and determining the current position of the unmanned aerial vehicle by calculating the cosine similarity between the multi-view unmanned aerial vehicle image features and the satellite library features. According to the method, DINOv2 feature extraction, adapter design and the view angle invariant attention network are fused, so that efficient information processing and feature extraction are ensured, and the positioning precision and reliability are improved.