1045 results about "Data encoding" patented technology

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.

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.

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.

A computing system is disclosed for generating fulfillment-ready coordination sessions based on tokenized item data and user-submitted participation parameters. The system ingests structured, semi-structured, or unstructured item data from merchant sources and applies schema-aligned transformation logic to normalize the data into structured item representations. The normalized records are tokenized into machine-readable item tokens that encode fulfillment constraints and canonical attributes. The system receives user item selections and associated participation parameters, encodes the item tokens and participation data into structured vector embeddings, and applies compatibility scoring logic using vector comparison and rule-based threshold evaluation. Compatibility scores are evaluated against session eligibility constraints derived from the item tokens. When eligibility conditions are met, the system generates a coordination session payload comprising match outcomes, proportional pricing, and fulfillment metadata, and issues orchestration instructions to external fulfillment systems for group-based delivery or preparation execution.

The invention discloses a pulse neural network hardware accelerator and a data processing method, and the accelerator is characterized in that a low-power-consumption three-stage pipeline CPU module is used for receiving input data, scheduling an SNN network acceleration instruction, and sending the input data to an asynchronous edge SNN hardware accelerator module through a coprocessor interface; the asynchronous edge SNN hardware accelerator module comprises a pulse data encoding and decoding module, L neuromorphic kernels and an on-chip network, the pulse data encoding and decoding module encodes input data into a pulse form and sends the pulse form into the neuromorphic kernels, and the neuromorphic kernels are used for performing calculation based on the data in the pulse form; the network-on-chip is used for communication between the neuromorphic kernels, and the connection between neurons before and after synapses in the neuromorphic kernels is realized by adopting a synaptic cross array. According to the invention, the data processing acceleration performance can be greatly improved.

The invention discloses a building electrical safety protection method, which comprises the following steps of performing real-time data acquisition on a building electrical system to obtain multi-modal time sequence data; constructing the multi-modal time sequence data into graph structure data with a node-edge topological relation, and encoding the multi-modal original observation data corresponding to each node into a multi-modal initial feature vector of the node; graph neural network feature extraction is carried out on the graph structure data, and a global feature vector sequence used for representing the operation state of the electrical system is obtained in combination with an attention mechanism; inputting the global feature vector sequence into a pre-constructed time sequence prediction model to perform operation state prediction, and judging whether potential abnormality exists or not; and when the residual error exceeds a preset threshold value, fault backtracking positioning is carried out on the key node according to the attention weight, and an electrical fault point is generated in combination with node characteristics. According to the invention, the fault response speed and the emergency disposal efficiency can be effectively improved, and the safety accident rate is reduced.

The invention discloses a multi-tenant adaptive cooperative defense method and system in a hybrid cloud scene, and the method comprises the following steps: obtaining a remote credible proof of hardware, verifying the credibility of a tenant agent and the integrity of a code based on the remote credible proof, granting a mark, and detecting cloud information to generate a machine readable portrait containing key parameters; collecting local multi-mode log data coding embedding vectors of tenants, and after privacy processing, calculating that the abnormal confidence exceeds a threshold value by a local model to trigger current limiting or blocking; and constructing a hierarchical federated architecture containing local nodes of tenants, a regional cloud and a global control plane, encrypting aggregation parameters to generate a cross-tenant attack recognition global model and issuing the cross-tenant attack recognition global model to each tenant on the premise that log data is not out of a domain. According to the invention, integrity verification is carried out on the tenant side security agent through the trusted access mechanism, and the tenant portrait is constructed based on the trusted mark, so that the real and verifiable cooperative defense capability among a plurality of tenants is realized, and the cooperative process has dependency.

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.

Systems, methods, and apparatuses are described for verifying a user identity in a video conference. A computing device may receive user data and a plurality of security parameters associated with accessing a video conference based on a confidentiality level of the video conference. The computing device may generate a security code that is encoded with user data. The computing device might cause the security code to be displayed on the mobile device for a predetermined time period. The computing device may receive an indication that the first device scanned the security code by using a camera. To verify the identity of a user, the computing device may decode the security code, compare the decoded user data of the decoded security code and expected user data associated with the video conference. The computing device may determine the authenticity of a user video and allow access to the video conference.

The invention discloses a software test expected result prediction method and system based on deep learning, and the method comprises the steps: obtaining multi-source heterogeneous data of a tested system, and carrying out the preprocessing and correlation fusion based on the multi-source heterogeneous data, so as to determine a multi-modal data set; performing feature extraction on the multi-modal data set through a text encoder, a structured data encoder and a time sequence feature encoder to obtain a plurality of feature vectors, and performing fusion processing on the feature vectors to generate a feature vector set; performing deep learning reasoning on the feature vector set, and generating a multi-dimensional prediction result through multi-head self-attention mechanism and cross-modal attention fusion; and carrying out dynamic verification and conflict resolution processing on the multi-dimensional prediction result to generate a test expectation result conforming to system constraints and business logic. According to the method, the multi-modal features are automatically extracted and fused through the deep learning technology, the accurate test expected result is generated, manual intervention is reduced, and the software test efficiency and accuracy are improved.

The invention discloses a voice data compression and index storage method based on a voiceprint template, and relates to the technical field of voice compression processing. The voice data compression and index storage method based on the voiceprint template comprises the following steps: S1, collecting and preprocessing acoustic state data, coded data and edge synchronization data, and constructing a standardized voice input data set; s2, evaluating the individual sound channel difference degree, and stripping non-universal components highly related to individuals in the voice; s3, analyzing the audio compression content, and constructing a structured total compression amount; s4, performing joint evaluation on real-time transmission regulation and control requirements of the compressed data, and dynamically adjusting an uploading priority and a continuous transmission strategy; and S5, constructing a multi-dimensional reverse index system at the cloud. The problems that for a high-noise far-field pickup scene, an existing compression method does not consider that individual sound channel characteristics are stripped from original signals, so that compression redundancy is high, and retrieval interference is large are solved.

An end-cloud cooperative data mining method, comprising: determining a target text and a task configuration file according to a business requirement by a cloud end; encoding the target text by a text encoder to obtain a text feature; placing the text feature in the task configuration file and issuing it to a vehicle end together; encoding image data by a first picture encoder to obtain an image feature by the vehicle end; calculating a value of a similarity of the text feature and the image feature; determining a target picture according to the value of the similarity and the task configuration file; and uploading the target picture to the cloud end; wherein the first picture encoder is obtained by compressing and optimizing a second picture encoder; and the text encoder and the second picture encoder are two modules of a picture-text multimodal large model. The application is applied to an automatic driving shadow mode, can complete data mining of any interesting target text by using one large model, and does not need to design and develop detection rules for each type of interesting target. The picture encoder of the vehicle end can be continuously updated and optimized, and the model iteration efficiency is improved.

The invention provides an opencast coal mine VR safety training dynamic difficulty regulation and control method, which comprises the steps of collecting multi-dimensional interaction behavior data of a target student in a VR training scene in real time, and encoding the multi-dimensional interaction behavior data into a behavior vector sequence; inputting the behavior vector sequence into a pre-trained prediction model, and outputting a potential risk behavior and a risk occurrence probability; when the risk occurrence probability is higher than a preset threshold value, generating a risk event continuously caused by the potential risk behavior through a risk deduction model; dynamically generating a negative guide plot based on the risk event, and implanting the negative guide plot into the VR training scene; and dynamically adjusting a subsequent generation strategy of the negative guide plot according to a response result of the target student to the negative guide plot. The method has the technical effects of deeply understanding the behavior intention of the student, early predicting the potential risk and performing dynamic intervention.

The invention relates to a multi-mode large model and light-weight small model collaborative road surface ice condensation state prediction method, and belongs to the field of road traffic safety monitoring and prediction. The method aims at solving the problem that real-time early warning and accurate prevention and control are difficult in the prior art. According to the invention, a multi-modal data coding system is constructed, road surface monitoring images, meteorological time sequence data and historical ice condensation text cases are integrated, and feature fusion is realized by adopting visual-physical feature joint coding, meteorological time sequence feature enhancement and text semantic mining; a pseudo label is generated through large model zero sample reasoning, and a lightweight small model is trained through cross-modal knowledge distillation; and finally, on the basis of a dynamic trigger type double-model reasoning framework, calling a cloud large model for fine judgment when the small model is low in confidence coefficient or high in scene complexity, and outputting the icing starting moment and thickness through confidence coefficient weighted fusion. According to the method, the prediction accuracy and real-time performance are improved, the model generalization ability is enhanced, and reliable support is provided for road traffic control in winter.

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 relates to the technical field of control adjustment, and discloses a humanoid robot control method based on image segmentation, and the method comprises the steps: carrying out the data cleaning of the original environment image data of a humanoid robot, and obtaining a standard environment data frame; performing pixel semantic segmentation on the standard environment data frame to obtain a semantic segmentation region mask; analyzing and identifying a passable area and an interactive object of the humanoid robot to obtain a two-dimensional path navigation route and interactive object information; performing parameter quantization on the two-dimensional path navigation route, and encoding quantized data into an executable leg instruction of the humanoid robot; performing action sequence instruction conversion on the joint rotation angle and the grabbing force of the humanoid robot to obtain an arm control instruction; performing association fusion on the executable leg instruction and the arm control instruction to obtain a comprehensive control instruction; according to the invention, the efficiency of humanoid robot control based on image segmentation can be improved.

The invention relates to the technical field of IT support, and provides a time sequence processing method, device and equipment adopting a quantum pulse neural network, and the method comprises the steps: obtaining time sequence data which comprises network alarm data, network equipment performance index data and network operation and maintenance work order data; encoding the time sequence data into a first quantum state by using a quantum preprocessing layer, inputting the first quantum state into a pulse neural network layer, converting the first quantum state into a time sequence pulse sequence, and processing the time sequence pulse sequence to obtain an output result; by utilizing a quantum attention enhancement mechanism, calculating attention weight of an output result in a quantum state space, and weighting to obtain a second quantum state; and decoding the second quantum state by using the hybrid decoding layer to obtain a final prediction result. The final prediction result is used for realizing fault root cause positioning, abnormal work order identification or network service quality prediction. According to the method, the parallelism of quantum calculation and the superposition characteristic of the quantum state are utilized, and the calculation efficiency can be improved when large-scale time sequence data are processed.

Provided is an audio recognition method. The method includes that audio data is encoded to an audio encoding feature; the audio encoding feature is decoded to a first decoded feature; a preset word text and the first decoded feature are encoded to a first text encoding feature, the first text encoding feature including a feature representing semantics of the preset word text and a feature representing semantics of the audio data; and the first text encoding feature is decoded to a predicted audio text, where the predicted audio text represents the semantics of the preset word text and the semantics of the audio data. An audio recognition apparatus and a storage medium are also provided.

The invention discloses a causal decoupling method and device based on multi-scale noise and adversarial supervision, and the method comprises the steps: carrying out the simulation of the causal relationship of variables in a causal graph, so as to generate observation image data, and constructing a training set and a test set according to the observation image data, the corresponding causal label information and the causal graph; constructing a causal decoupling model, and performing adversarial supervision training under multi-scale noise by using the training set; and obtaining anti-fact intervention data by using the test set and the trained causal graph matrix and utilizing the trained observation data coding module and observation data decoding module. According to the method, an auto-encoder and causal acyclic constraints are fully combined, the discrimination module is trained under multi-scale noise, and high-quality adversarial supervision is performed, so that the model representation learning ability is improved, the representation understanding of the model on data with causal relationships is enhanced, the accuracy of implicit causal network prediction is improved, and the prediction efficiency is improved. And the causal decoupling accuracy is improved.

The invention provides an agricultural data processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source agricultural data, and coding the multi-source agricultural data into a multi-source time sequence feature vector; performing intra-modal time sequence feature extraction on the multi-source time sequence feature vectors to generate a multi-modal feature sequence aligned with the unified time axis; performing time step-by-step cross-modal fusion on the multi-modal feature sequence through a gated cross attention mechanism to generate a fused feature sequence; and performing global context coding on the fused feature sequence, and compressing to generate a full-growth-cycle feature vector. According to the method, the problem of effective fusion of multi-source heterogeneous data can be solved, the multi-source heterogeneous agricultural data is converted into the global feature vector which is highly concentrated in information and rich in spatio-temporal context and causal semantics, and the global feature vector can improve the accuracy and reliability of downstream agricultural intelligent tasks; and a high-quality data basis is provided for precise decision-making of intelligent agriculture.

Systems and methods for implementing mobility foundation models in accordance with some embodiments of the invention are illustrated. One embodiment includes a method for operating a mobile device. The method receives initial state tokens, wherein each corresponds to data reflecting a previous state of a mobile device. The method determines a sub-task for the mobile device by applying an LLM to the initial state tokens. The method encodes sensor data into patch tokens. Each of the patch tokens reflects a recent state of the mobile device. The method updates the initial state tokens into updated state tokens, based on the patch tokens and the sub-task. The method produces navigation waypoints from the updated state tokens, wherein each of the navigation waypoints represents a distinct destination for the mobile device. The method controlling the mobile device according to the navigation waypoints.

Embodiments can relate to systems and methods for generating hyperspectro-polarimetric images. The system can include a metasurface having a plurality of super pixels configured to encode spectral and polarization image data into spatial intensity distributions. The system can include an image capture device having at least one image sensor and a plurality of subsets of pixels configured to capture the spatial intensity distributions encoded by the metasurface. The system can include a processor in communicative connection with the image capture device. The processor can be configured to receive pixel data from the image capture device, generate captured image input data based on the pixel data, and access at least one computational reconstruction model configured to decode the captured image input data, and generate image output data based on the decoded captured image input data.

A system comprises a Universal Physics Transformer implemented on a data processing apparatus. The Universal Physics Transformer comprises an encoder configured to encode input data into a unified latent representation of the input data in a latent space, an approximator configured to propagate the latent representation of the input data forward in time, and a decoder configured to query the latent representation of the input data at arbitrary query positions.

A method of encoding or decoding video data includes storing a block vector (BV) for a current block in a history BV list for encoding or decoding a subsequent block, wherein the current block is encoded or decoded in intra temporal motion vector prediction (IntraTMP) mode, and wherein the history BV list includes BVs for blocks that do not neighbor the subsequent block; deriving a candidate list of BVs for the subsequent block based on BVs from the history BV list that includes the BV for the current block; and encoding or decoding the subsequent block based on the candidate list of BVs.

The invention provides a training method of an aircraft maintenance simulation model and an aircraft maintenance simulation method, relates to the technical field of aircraft maintenance simulation, and aims to predict future evolution of aircraft maintenance so as to improve authenticity of aircraft maintenance simulation. The method comprises the following steps: acquiring training data related to maintenance simulation; training a maintenance simulation model based on the training data; the aircraft maintenance simulation model comprises a video word segmentation device, a multi-modal input encoder, a multi-modal token sequence and a multi-modal output encoder, wherein the video word segmentation device is used for encoding videos related to aircraft maintenance simulation into a video token sequence; the multi-modal input encoder is used for encoding multi-modal input data related to aircraft maintenance into a multi-modal token sequence; the potential action model is used for determining a potential action representation of a maintenance action based on the video token sequence and the multi-modal token sequence, and the dynamic prediction model is used for predicting a prediction token at the next moment based on the video token sequence, the multi-modal token sequence and the potential action representation so as to simulate a maintenance scene at the next moment.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.