3061 results about "Data encoding" patented technology

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

The invention provides an underwater target detection method based on multi-modal features and domain adaptation. The method comprises the following steps: S11, acquiring a sonar image, an optical image and environmental data; s12, extracting a sonar feature and an optical feature, encoding the environment data into an environment channel weight, and dynamically adjusting a fusion proportion of the sonar feature and the optical feature through the environment channel weight to obtain a fusion feature; s13, performing spatial attention calculation on the sonar features to obtain a spatial weight map, enhancing the optical features by using the spatial weight map, and performing forced alignment with the sonar features at the target edge; and S14, decoupling the fusion feature into a synthetic domain feature, decoupling the fusion feature and the environment data into a real domain feature, and gradually aligning the synthetic domain feature and the real domain feature through asymptotic domain alignment to complete construction of a target detection model. According to the invention, multi-modal data acquisition, dynamic feature fusion and decoupling and embedded real-time detection are combined, so that the precision of underwater target monitoring is remarkably improved.

The invention relates to a deformable object interactive operation control method based on a visual touch-language-action multi-mode model, and the method comprises the steps: carrying out the image, touch and language data coding of a deformable object, and obtaining visual, touch and language features; performing cross-modal feature alignment processing on the visual features, the tactile features and the language features to obtain multi-modal fusion features; inputting the multi-modal fusion features into a large model for environment understanding; iteratively performing action planning and execution by adopting a'thinking-decision 'planning mode; and repeatedly executing the steps until the current deformable object interaction operation task is completed. Compared with the prior art, the multi-modal feature alignment capability, the action planning precision and the task adaptability are improved, efficient recognition and interaction of the robot on the deformable object can be achieved, deformation and state changes of the object can be effectively dealt with especially in a complex environment, the operation strategy can be dynamically adjusted, and the operation efficiency is improved. And more intelligent and accurate deformable object operation is realized.

A neural network system is provided. The system includes an autoencoder configured to encode input data into a latent space representation; a generator neural network configured to receive a noise vector and the latent space representation and output a set of routing coefficients; a discriminator neural network configured to evaluate the effectiveness of the routing coefficients by measuring the performance of a capsule network utilizing said routing coefficients; and a capsule network comprising a first capsule layer and a second capsule layer, wherein the routing coefficients are used to dynamically route outputs from the first capsule layer to the second capsule layer.

The invention discloses a conference memory enhancement method and device for a conference tablet and a medium, and relates to the technical field of intelligent conference equipment, and the method comprises the steps: collecting multi-modal data in real time based on a preset data collection technology, and coding the multi-modal data into a space-time event stream according to the time sequence of the multi-modal data; identifying handwritten characters on the conference tablet, and extracting a key tag in a speech in combination with a pre-trained natural language processing model to construct a conference knowledge graph corresponding to the current conference; comparing the conference knowledge graph with a historical conference knowledge graph, determining conflict content, and superposing a virtual information layer on a physical interface of the conference tablet; and querying query association information corresponding to the semantic query request based on the conference knowledge graph and the time-space time stream to determine semantic contradictions, and displaying a historical conference evidence chain of the semantic contradictions based on the virtual information layer.

Provided in the present invention are a method for training a natural language processing model, and a method for generating subsequent text of a dialogue. The method for training a natural language processing model comprises: acquiring multiple types of heterogeneous sample data and a pre-constructed natural language processing model, wherein the heterogeneous sample data includes structured data, unstructured data, a knowledge graph and expert experience data; performing data encoding fusion on the structured data, the unstructured data and the knowledge graph, so as to obtain encoding fused data; and on the basis of the encoding fused data and the expert experience data, training the pre-constructed natural language processing model, so as to obtain a trained natural language processing model. The present invention enables a knowledge graph to be placed in a model, not as an independent retrieval corpus, but as a method for knowledge enhancement, thereby improving the efficiency of a natural language processing model.

The invention discloses an embedded intelligent visual language large model knowledge base construction and application method and device, a medium and a product, and relates to the field of data intelligent processing. The method comprises the steps that an image encoder and a text encoder are adopted to encode image data and text data into vectors respectively, and image vectors and text vectors are obtained; fusing the image vector and the text vector, and generating a knowledge graph based on a fusion result to obtain an embedded intelligent visual language large model knowledge base; obtaining a user problem, and analyzing the user problem to obtain an analysis result; retrieving in a multi-modal knowledge base based on the analysis result to obtain a plurality of objects and a graph retrieval result; and combining the selected objects with the corresponding graph retrieval results to obtain triple information, converting the triple information into position text descriptions of the objects, inputting the position text descriptions into the multi-modal large language model, and outputting answers corresponding to the user questions. According to the method, the answer generation accuracy can be improved, and illusion phenomena are reduced.

The invention provides a private weight self-adaptive heterogeneous data federated cooperative training method and system in the technical field of federated learning and privacy computing, and the method comprises the steps: S1, enabling each client to carry out the differential privacy operation on a local data set based on a private weight, and obtaining a desensitized data set, encoding the desensitized data set through a heterogeneous data encoding model; s2, performing semantic alignment on each coding vector through a contrast learning model to obtain an aligned vector set; s3, training a local model through the alignment vector set, generating a local gradient, extracting local model parameters, and uploading the privacy weight, the local gradient and local difference parameters to a server; and S4, the server trains the global model based on the local difference parameter and the global gradient, extracts the global model parameter and issues the global model parameter to each client for training. The method has the advantages that the compatibility, the flexibility and the efficiency of heterogeneous data federation cooperative training are greatly improved.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

The invention relates to the technical field of surgical robot motion control, and discloses a surgical robot motion planning method based on a diffusion model, and the method comprises the steps: obtaining obstacle point cloud data, an initial posture and a target posture in a surgical environment; encoding the obstacle point cloud data into a potential space, and combining the encoded information with the initial attitude and the target attitude into a condition code; a Transform structure of an encoder is adopted to replace a U-Net structure in a traditional diffusion model, and the diffusion model is trained through forward diffusion and reverse denoising processes; in training, using a comprehensive loss function to optimize model performance, combining configuration space loss, geometric task space loss and collision loss, and introducing physical constraints to ensure that the trajectory conforms to kinematic characteristics and avoid collision; and in the operation task, generating a motion track based on the trained diffusion model. According to the invention, the motion planning efficiency and safety of the surgical robot in a complex environment can be improved.

A computer-implemented method (200) is disclosed for performing a clinical assessment, the method comprising: providing a first input (206) to a machine learning model (208), the first input comprising template data encoding a template for carrying out a part of the clinical assessment; providing a second input (210) to the machine learning model, the second input comprising assessment data recorded during the clinical assessment; wherein the first input is provided to the machine learning model to condition the machine learning model to provide an output (212) based on the second input for use in the clinical assessment; and using the output from the machine learning model to perform the clinical assessment.

The invention provides a multi-modal automatic driving scene generation method based on autoregressive closed-loop prediction. The method comprises the following steps: encoding an automatic driving scene video, scene understanding prediction questions and answers and trajectory planning data into a unified multi-modal discrete code by using a pre-trained discrete encoder; multi-modal discrete codes are integrated into a coding sequence, a mask strategy is adopted to cover trajectory codes, original codes without masks serve as a supervision target, a model is generated through autoregressive normal form training, and fine tuning is carried out on scene understanding, scene prediction and trajectory planning data sets; during reasoning, an initial scene image collected by a vehicle camera and a user prompt word are input, the model generates a future scene discrete coding sequence according to initial information, and closed-loop prediction is realized through mask track information; the generated codes are decoded into scene images, text questions and answers, and vehicle trajectories using a symmetric decoder. According to the invention, the problems of poor universality and low efficiency of the existing end-to-end automatic driving world model can be solved.

Disclosed are methods and apparatuses for decoding an image. A method includes receiving a bitstream obtained by encoding the image; dividing a first coding block into a plurality of second coding blocks; generating a prediction block of a second coding block based on syntax information obtained from the bitstream; and reconstructing the second coding block based on the prediction block and a residual block of the second coding block, the residual block being obtained by performing a dequantization and an inverse-transform on quantized transform coefficients from the bitstream. The first coding block has a recursive division structure. The first coding block is divided based on at least one of a quad tree division, a binary tree division or a triple tree division.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention provides a communication index prediction method based on a multi-modal large model and related equipment, relates to the technical field of data processing, and can obtain multi-dimensional time sequence data, geographic space data and corresponding text description data in a user communication process; performing time sequence feature extraction processing on the multi-dimensional time sequence data to obtain a time sequence feature vector, and performing spatial feature extraction processing on the geographic spatial data to obtain a spatial feature vector; encoding the text description data based on a large language model word embedding layer to obtain a semantic feature vector; performing cross-modal attention fusion on the three types of feature vectors based on a text prototype to generate joint feature representation; and the joint feature representation is input into the large language model to generate communication index prediction information, so that the coupling relationship among the multi-modal indexes can be effectively modeled, the prediction precision is improved, and the effectiveness of a network optimization decision is ensured.

The invention provides the uses of chemical waste and byproducts in a chemical library to store digital information, and methods for computing with unknown waste chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying change in at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

The invention relates to a large model and small model collaborative robot operation action real-time control method and a large model and small model collaborative robot operation action real-time control system. The method comprises the following steps: firstly, collecting and preprocessing multi-modal data; carrying out data coding on the multi-modal data to obtain a feature vector; aligning the multi-modal data by using a cross-modal token alignment technology to obtain a feature representation; then training a neural network model by using the feature vector and the feature representation to obtain a trained large model; pruning, distilling and quantifying the trained large model to generate a small model; the small model is deployed to an edge computing device, the edge computing device obtains current instruction text data in real time and conducts reasoning on the current instruction text data, and an action plan of the robot is generated; and finally, the robot is controlled to operate and act in real time by means of action planning of the robot and feedback signals of the sensors. Compared with the prior art, the method has the advantages of low delay, high efficiency, capability of improving tactile perception, self-adaptive grabbing and real-time action planning and the like.

The invention discloses a joint denoising method for robot visual motion prediction, and the method comprises the steps: constructing a unified generative model through fusing an image and a depth map collected by a depth camera, motion data collected by CAN line communication of a Piper mechanical arm, and a tactile image collected by a Gelsight Mini tactile sensor; the method comprises two steps of data acquisition and input coding, and joint denoising and generation: firstly, multi-modal data are coded into low-dimensional potential representation, and then future images, depth maps, tactile data and robot actions are cooperatively predicted through a joint denoising framework based on Transform. A mask self-attention mechanism is innovatively introduced, information interaction between modes is dynamically adjusted, action generation is guided through tactile feedback, and the force control precision is improved. The model adopts a de-noising diffusion probability loss function to jointly optimize multi-modal prediction, so that the output consistency is ensured. According to the method, the robustness and the accuracy of flexible operation of the robot are remarkably improved.

The invention discloses a product collaborative design management method and system based on data driving, and relates to the technical field of product design management, and the method comprises the steps: constructing a double-flow heterogeneous quantitative collection network; establishing an incidence matrix and calculating dynamic coupling strength among the parameters; designing a recursive deep belief network based on the incidence matrix and the dynamic coupling strength, encoding product structure parameters and design process data into a probability graph model, and constructing a design knowledge base; dynamically distributing the design rules in the design knowledge base by adopting a swarm intelligent optimization algorithm, generating a collaborative decision-making unit, and establishing a constraint propagation link; and generating a multi-target collaborative optimization scheme group, and screening an optimal scheme group based on Pareto frontier. According to the invention, omnibearing acquisition of product structure parameters and design flow data is realized through the double-flow heterogeneous quantitative acquisition network, and the data integrity is improved.

The invention provides an L1-L4 lumbar vertebra segment-oriented multi-feature fusion diagnosis system and method, and the system comprises an image preprocessing module which is used for receiving a lumbar vertebra CT image sequence of a patient; a centrum anatomy partition module; the multi-dimensional image feature extraction module is used for extracting four types of quantitative features from each sub-region; the clinical multi-modal data coding module is used for independently acquiring and processing three types of clinical data: a multi-modal graph attention fusion network; and the segment-level diagnosis output module outputs diagnosis results of three levels. Through a parallel processing architecture and an optimized feature extraction algorithm, the whole diagnosis process only needs 45 seconds from data input to report generation, time is saved compared with manual film reading, and the consistency of diagnosis results is remarkably improved.

The invention relates to the field of computer vision technology and the like, and discloses a video action recognition method and system based on space-time interaction Transform and object interactivity prediction.The method constructs the video action recognition system by integrating the space-time interaction Transform and the object interactivity prediction.The action in a video can be effectively recognized and analyzed, and the video action recognition efficiency is improved. The method has a good effect especially in a multi-user interaction scene, and comprises the following specific steps: preprocessing an original video, and obtaining necessary video data comprising a video frame sequence and multi-modal features; encoding the preprocessed video data into a discrete token sequence; the token sequence is input into Transform, M-layer self-attention calculation is carried out, and spatial-temporal features are extracted; an object interactivity prediction module is used for judging and modeling the interaction relationship between objects in the video data; and carrying out classification by utilizing the fused multi-modal features, and outputting action categories in the video.

The invention relates to a 2D barcode comprising barcode elements, made up of elementary 2D color modules and of which encoded data are split into a plurality of 2D data blocks made of chunks, each element including a 2D alignment pattern AP of which modules have colors representative of a color palette, a configurable error-correcting code and a locator including an orientation pattern and encoded locator data indicating a size of said barcode element BE, a size of said element, a size of the data blocks DBs of said barcode element, the number of colors of the corresponding color palette, and a level of error correction of the error-correcting code.

The invention relates to the technical field of computer system structures and storage, in particular to a non-volatile memory quick recovery method based on metadata priority and on-demand loading. The method comprises the following steps: in response to a system fault signal detected by a voltage monitoring unit, freezing a processor context and traversing a page table structure to extract system configuration information; writing the system configuration information and business data codes in the volatile memory into a nonvolatile medium to generate a persistent state mirror image; analyzing the persistent state mirror image, extracting address conversion metadata, and reconstructing a mapping relation from a virtual address to a physical page frame in a volatile memory; and generating an address mapping table, wherein the physical page frame pointed by the address mapping table is set to be in an existing state but is not associated with the effective service data. According to the method, decoupling of the control flow and the data flow is realized by constructing a virtual ready state, and quick starting of the system and immediate response of key services are realized on the premise of not depending on the total capacity of a memory.

The invention relates to the technical field of urban earthquake disaster simulation, and provides a visualization system and method for urban building group earthquake disaster simulation, and the system comprises an offline data preprocessing and block organization module and a block dynamic data management and scheduling module. The GPU acceleration rendering module is used for mapping a global floor index to a vertex to endow the vertex with a unique identifier, and finally submitting the combined model to a GPU, and cooperating with a displacement texture and a user-defined shader to realize real-time rendering of a building form; the GPU parallel displacement picture moving module is deeply coupled to the GPU rendering pipeline and is used for resolving the vertex displacement and the color value in real time and injecting the vertex displacement and the color value into the rendering pipeline; and the efficient interaction module dynamically updates label contents and coordinates to realize three-dimensional interaction. According to the method, massive time sequence displacement data are coded into textures, and parallel calculation is performed by using the GPU vertex shader, so that real-time and smooth animation simulation of earthquake responses of all buildings is realized, and the performance bottleneck of a traditional CPU calculation mode is solved.

The invention relates to the field of artificial intelligence and financial science and technology, and discloses an asset assessment method based on a multi-modal large model, comprising the following steps: preprocessing image data, text data and structured data of a to-be-assessed enterprise to generate standardized multi-modal input data; based on the standardized multi-modal input data, respectively processing image data, text data and structured data by using an image encoder, a text encoder and a structured data encoder, and extracting a high-dimensional feature vector of each modal; mapping the obtained high-dimensional feature vector of each mode to the same public embedding space by constructing a mapper network; an optimal transmission method is adopted to measure the embedding distribution difference between each mode, and the difference is minimized. According to the method, the multi-modal encoder, the unified mapper and the optimal transmission alignment mechanism are constructed, so that high-consistency fusion and expression of the multi-modal features in the shared space are realized.

The invention relates to an image data coding method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining ink jet position information corresponding to each ink jet drop point of a swing arm type printer, and enabling a nozzle of the swing arm type printer to swing in a reciprocating manner with one end of a swing arm as a rotating shaft for ink jet; acquiring pixel position information corresponding to each pixel point in the target image; based on the ink jet position information and the pixel position information, performing matching processing on the ink jet drop point and the pixel point to obtain a target ink jet drop point matched with the pixel point; determining ink jet sequence information corresponding to the target ink jet drop point; based on the ink jet sequence information, first coding information corresponding to the pixel points is determined, and the first coding information indicates a printing sequence corresponding to the pixel points when the swing arm type printer prints the target image. According to the invention, the printing sequence corresponding to each pixel point in the printing mode of the swing arm type printer can be determined for each pixel point in the target image, so that the swing arm type printer can correctly print the target image.

The invention discloses a bearing life prediction method and system based on dynamic knowledge embedding, and the method comprises the following steps: S1, encoding bearing field data, expert experience and monitoring data into a structured triple, building a dynamic knowledge graph frame, and designing a sliding window confidence mechanism to achieve the online updating of a graph node relation; s2, extracting knowledge embedding vectors by using a relational graph convolutional network, and extracting vibration signal features by using a hierarchical convolutional network; s3, mapping the knowledge embedding vector and the vibration characteristics to a unified semantic space through a linear projection layer; s4, constructing a Transform encoder based on multi-head self-attention, and establishing a dynamic correlation model between vibration characteristics and knowledge embedding; s5, designing a full-connection network output life prediction result and feeding back the optimized knowledge graph; and S6, adaptively adjusting the size of the sliding window based on the change rate of the working condition, dynamically balancing the contribution weight of new and old knowledge in combination with a gating mechanism, and ensuring the adaptability of the model to the complex working condition.

There is provided a method for a user equipment for detecting a beam failure on at least one transmission / reception point, TRP, the UE being configured to communicate with a plurality of TRPs; based on the detecting, encoding a beam failure indication data to a beam failure recovery, BFR, media access control, MAC, control element, CE, wherein the beam failure indication data indicates the beam failure on the at least one TRP; and transmitting the BFR MAC CE to a network element of a wireless communication network.