1668 results about "Data channel" patented technology

The invention discloses a big language model reasoning optimization method and system based on dynamic video memory compression and memory isomerism, and intelligent management of video memory resources is realized by integrating a dynamic compression strategy of KV Cache and a memory parallel architecture. The method comprises the following steps: 1) analyzing the spatial-temporal characteristics of the KV Cache in real time, adaptively selecting a quantization compression algorithm, a rarefaction algorithm or a low-rank decomposition algorithm, performing hierarchical storage based on attention head importance scores, keeping high precision of a core head, and implementing low-bit quantization on a secondary head; (2) the compressed inactive data are divided into a plurality of data blocks to be stored in a system memory, a parallel data channel group is established according to the number of physical channels, the compressed blocks are concurrently read through multiple channels during loading, and parallel decompression of a sparse matrix is accelerated through a GPU tensor core; and 3) constructing a KV Cache multiplexing mechanism and a parallel channel, and parallelizing a compression / decompression process and model calculation by adopting a hardware acceleration compression and asynchronous pipeline mechanism.

The invention relates to the technical field of data asset sharing, in particular to a data asset sharing method and system based on square and circular clouds, and the method comprises the following steps: obtaining data structure information and establishing a standardized list, counting field matching and jumping conditions to construct a remapping list, extracting an idle path aggregation path group to generate a candidate list, and judging priority conflicts to generate a shunting mapping result, and performing sharing operation on the combined fields and the paths in a butt joint manner. According to the invention, through combination of field name coding and type expression, the unified field identification capability is enhanced, coding difference and type vector difference are adopted to synthesize a score and screen a remapping field pair, the field matching precision is improved, and a path state, service attribution and priority elements are fused to construct a path group; the alternative number and the distribution identifier are configured under the path conflict, and the field and the path number are combined and butted, so that the granularity collaborative sharing is realized, the correspondence between the data channel structure and the access control is enhanced, and the stability of data sharing under multiple environments is improved.

The invention relates to the technical field of fault prediction, in particular to a centrifuge fault prediction system based on machine learning, which comprises a data channel synchronization module, a multi-dimensional feature extraction module, a state evolution index construction module, a trend aggregation trajectory recognition module and a fault section recognition module. According to the method, different types of data are synchronously aligned by a multi-channel signal segmentation processing mechanism based on a periodic state, a state evolution sequence is constructed in combination with a unified sampling structure based on a time scale, a state characteristic track is established through a multi-dimensional parameter set, a trend change index is constructed by means of a difference root-mean-square between adjacent states, and the state evolution sequence is analyzed. According to the method, the aggregation section is recognized and the trajectory deviation frequency is counted by utilizing continuous trend mutation, so that dynamic migration of the trajectory boundary and intelligent recognition of the fault section are realized, the boundary failure problem caused by static preset conditions is avoided, and the continuous prediction stability of long-period equipment and the application range under a non-standard working condition are effectively enhanced.

The invention discloses a retrieval generation method and system based on multi-agent collaboration, a terminal and a medium, and relates to the field of artificial intelligence. Performing semantic analysis on the input word embedding converted by the natural language query instruction through a query analysis agent, and determining a semantic intention vector; performing reinforcement learning and meta learning on the semantic intention vector through a strategy construction agent, and determining a retrieval strategy; performing semantic enhancement on the semantic intention vector according to knowledge graph node embedding to obtain a semantic enhancement vector; determining a data channel according to the semantic enhancement vector, a retrieval strategy and a real-time system load, and calling the data channel for retrieval to obtain candidate documents; and generating a target answer according to each candidate document based on an adaptive reflection feedback mechanism in combination with an auto-encoder and a generative adversarial network. The problems that the prior art depends on a fixed retrieval strategy, has limitation when facing complex query, multi-round interaction and cross-modal data fusion, is easily interfered by noise and is not accurate enough in semantic matching are effectively solved.

The invention provides a multi-platform fusion interaction method and system based on a large touch screen, and relates to the technical field of man-machine interaction, and the method comprises the steps: dividing a main control area and a cooperative display area, building a mapping relation between touch operation and a multi-platform interaction scene, recognizing a gesture intention through a neural network, and generating touch event data. After touch event data are received, a platform control instruction is generated according to the data dependency graph and the interaction rule base, a master control platform instruction is processed preferentially, and an execution result is synchronized to the cooperation platform or vice versa. And finally, establishing an inter-platform real-time data synchronization mechanism, transmitting data through a bidirectional data channel, dynamically adjusting a transmission strategy, and ensuring data consistency of each platform, thereby realizing efficient multi-platform fusion interaction, improving user experience and reducing system delay.

The invention discloses a multi-agent collaborative data authorization operation management method and system, and the method comprises the steps: carrying out the edge grading, edge verification and desensitization of scientific research data, obtaining desensitized shared data, encrypting and transmitting the desensitized shared data to a cloud sharing platform, carrying out the layering encryption, and generating an uploading log, the method comprises the following steps: generating an encrypted request packet, screening data, providing the data for a research and development institution, generating an intelligent contract, carrying out dynamic authorization to generate an authorization instruction and an authorization log, verifying the authorization instruction, opening a data channel permission to determine visited data, carrying out hierarchical decryption on the visited data to obtain collaborative authorization data, and sending the collaborative authorization data to the research and development institution. And carrying out weight sorting on the collaborative authorization data, accessing and downloading the collaborative authorization data, and updating an access log. The method not only can improve the efficiency and accuracy of scientific research data authorization operation management, but also has good interpretability, and can be directly applied to a scientific research data authorization operation management system.

The invention discloses an information security management method and system based on sensitive data, relates to the technical field of data management, and comprises the following steps: realizing quantum encryption transmission of remote high-sensitive data by combining a quantum key distribution link and a dynamic chaotic key stream; the partitioning and synchronization priority is dynamically adjusted according to the node load, the data integrity is verified through CRC, the distributed storage disaster recovery system with extremely high safety and usability is achieved, it is ensured that data can still be recovered even under extreme disasters, a zero-trust data channel based on an IPSec tunnel is constructed in the data transmission process, and the reliability of data transmission is improved. The tamper-proof, anti-counterfeiting and anti-leakage capabilities of a data access link are greatly improved, the access frequency abnormity is detected through the isolated forest model, the behavior trend is detected through the LSTM time sequence prediction model, the baseline deviation analysis is performed through user behavior portrait comparison, and the real-time, multi-modal and chained security monitoring of the user data operation behavior is realized.

The invention provides a GNSS (Global Navigation Satellite System) anti-interference receiver system based on radio frequency front end-INS (Inertial Navigation System) combined assistance, which adopts a dual-core ARM Cortex-A9 and a programmable logic unit for layered decoupling, and realizes real-time receiving and processing of a high-bandwidth data stream, closed-loop control of an interference suppression link and quick response of a navigation path and a strategy decision. All the modules efficiently cooperate through AXI-Lite register mapping, interrupt signals, a shared state buffer area and a timer synchronization mechanism. By controlling bus and state buffer mapping to keep consistency and supporting a module-level independent debugging and system-level soft reset mechanism under hot start, the expandability of subsystem cascade debugging, fault module positioning and online updating is realized. In the aspects of module architecture, data channels and cooperation mechanisms, the structure order, efficient communication and control closed loop are realized, and an embedded multi-module cooperation anti-interference platform which is suitable for a high-interference environment, multi-channel fusion processing and navigation task real-time response is constructed.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

The embodiment of the invention provides an information mapping method for intelligent perception information in a virtual space, perception data is acquired by deploying a multi-sensor array, data features are extracted based on a multi-scale feature pyramid and an attention mechanism, and a space mapping relation is established by using self-supervised learning and geometric constraints. Local data blocks are organized by adopting a hierarchical index tree based on a union search set, an adaptive calculation flow graph is constructed to realize efficient mapping, and data transmission is optimized in combination with predictive caching and a differential updating strategy. And meanwhile, parallel processing of incremental mapping is realized through a pipeline data channel, and the data synchronization efficiency is ensured by a dynamic scheduling mechanism based on load prediction. The method effectively solves the problem of performance bottleneck when a traditional mapping scheme processes large-scale multi-source sensing data.

The invention relates to a data channel state sensing method based on multi-source time sequence data fusion and a topological structure. The method comprises the following steps: collecting multi-source time sequence data; cleaning is conducted; obtaining a multi-source fusion time sequence; constructing a knowledge graph structure, wherein the knowledge graph structure comprises data and operation conditions of an equipment link topological structure, an equipment body and topological association equipment; obtaining a trained long short-term memory network model; and inputting real-time data into the trained long-short-term memory network model, predicting the real-time data by the trained model, and dynamically sensing the running state of the equipment. According to the method, a knowledge graph structure is adopted, modeling is carried out on data and operation conditions of an equipment link topological structure, an equipment body and associated equipment, the complex association relation between the equipment can be expressed in the mode, the sensing capacity of the equipment state is improved in combination with the actual operation data, and the real-time performance of the equipment is improved. Therefore, the monitoring system can perform more comprehensive state analysis by using the existing professional knowledge and the association between the devices.

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker recognition, verification, and / or diarization. The techniques include processing audio data channels (ADCs) using a voice detection model to determine voice activity likelihoods (VALs) that individual ADCs include speech, obtaining, using VALs, a second set of ADC(s), and processing, using an audio processing a neural network (NN) model, the second set of ADCs to obtain association of the speech to the one or more speakers. The techniques also include generating a plurality of embeddings associated with the ADCs, processing the plurality of embeddings to obtain aggregated embedding(s) that represent audio data of multiple ADCs, and processing the aggregated embedding(s), using the audio processing NN model, to obtain association of the speech to the one or more speakers.

The invention discloses a Hall sensor combination system and method for magnetic imaging stability optimization. The system comprises a three-dimensional orthogonal Hall sensor array; the embedded encoder module is used for converting the analog magnetic field signals acquired by the sensor array into digital encoding signals with timestamps; the control module constructs an electromagnetic feature extraction model and is used for carrying out time-frequency domain conjoint analysis on the digital coding signals to obtain magnetic environment disturbance feature parameters; generating a compensation magnetic field parameter opposite to the magnetic environment disturbance characteristic parameter by adopting a closed-loop feedback mechanism; establishing a real-time data channel with a preset electromagnetic imaging module and a preset magnetic field generation module through a preset communication protocol; and constructing a magnetic environment state space model corresponding to the three-dimensional orthogonal Hall sensor array, and obtaining magnetic field distribution data to dynamically adjust and compensate magnetic field parameters so as to realize active magnetic interference cancellation in a three-dimensional space. Problems in the prior art can be effectively solved, and the precision and stability of magnetic imaging are improved.

The embodiment of the invention provides a read-write request processing method, a protocol conversion module, electronic equipment and a storage medium, and relates to the technical field of chips. The method comprises the steps that a protocol conversion module is arranged in a processor cluster, an original read-write request is converted into a first read-write request conforming to a CHI protocol through a first conversion module in the protocol conversion module, and a corresponding transmission channel is distributed to the first read-write request; and the request queue module orderly sends the received first read-write request, and the second conversion module recombines the first read-write request information to a second read-write request conforming to the CHI protocol, and allocates a corresponding channel for the second read-write request. According to the embodiment of the invention, the internal data channel (AXI or similar AXI) is directly converted into the CHI interface in the processor cluster, the internal circuit resources of the processor cluster are reused, the circuit area is saved, and the access path time delay is reduced.

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The present disclosure provides a wireless receiver having a Delay Doppler channel detector. The Delay Doppler channel detector includes a Delay Dopper domain channel estimator and a detector and demodulator. The Delay Doppler domain channel estimator is configured for estimating a channel gain, a delay, and a Doppler frequency shift of a path of a channel of data received via the channel. The detector and demodulator is configured for extracting a symbol sequence of the received data based on the estimated channel gain, the estimated delay, and the estimated Doppler frequency shift of the path of the channel.

The technical scheme of the invention discloses an epilepsy diagnosis system based on electroencephalogram data channel selection and a KAN convolutional spiking neural network, which is characterized by comprising a data processing module, a channel feature extraction module, a self-adaptive channel selection module and a feature classification module. According to the method, an effective solution is provided by introducing KAN convolution, channel selection and a spiking neural network. The introduced attention mechanism allows the model to dynamically pay attention to the most relevant EEG channel and signal characteristics, so that personalized channel selection is realized. Through the mechanism, the model can distribute different weights for different individuals, only key signals most related to epileptic seizure are selected, the calculation burden of redundant channels is avoided, and meanwhile the classification precision is improved. Finally, the artificial neural network is converted into the pulse neural network, an event-driven calculation mode is adopted, energy consumption is effectively reduced, and the method is particularly suitable for being applied to portable equipment.

Disclosed in the present invention is a soft tissue photoacoustic / ultrasonic multi-modal image fusion method based on deep learning. The method comprises the following steps: an ultrasonic-photoacoustic imaging device collecting a human body soft tissue photoacoustic image and ultrasonic image, and performing size normalization processing; inputting the images into a spatial conversion module to convert same into a YCbCr space; inputting the images into a pre-convolutional module to change the number of data channels; inputting the images into a multi-scale feature extraction module to extract salient features of the source images; inputting the features into a filter prediction module to obtain a multi-scale filter; and inputting the filter into a filtering fusion and adaptive enhancement module and combining same with the input source images, so as to obtain a final fusion result. Compared with several traditional fusion methods and deep-learning-based fusion methods, the method provided in the present invention has a greater fusion effect, and more importantly, has an excellent real-time performance. Moreover, multiple modes of photoacoustic / ultrasonic fusion extension experiments are performed on a photoacoustic / ultrasonic multi-modal imaging system, verifying the effectiveness of the method in the present invention.

The invention relates to the technical field of edge computing, and discloses an intelligent edge computing co-processing system based on an integrated circuit, which comprises a heterogeneous computing cluster module, a hardware computing unit set, a multi-core control processor based on RISC-V, a programmable pulse tensor computing array and a reconfigurable engine oriented to streaming processing, according to the intelligent edge computing cooperative processing system based on the integrated circuit, zero-delay data exchange is realized through silicon intermediate layer integration of the heterogeneous computing cluster module and a snakelike data channel, and traditional bus arbitration delay is eliminated through real-time operation code analysis and optimal optical communication path mapping of the hardware task routing matrix (TRF) module; the priority path distribution of the task scheduling subsystem is synchronously coordinated with the time-sensitive task, so that the collaborative efficiency of the computing unit is improved in multiple dimensions, the non-blocking transmission of the high-priority task is ensured, and the effect of enhancing the overall processing capability and response speed of the intelligent edge computing is achieved.

The invention provides an AXI bus interconnection matrix system, which belongs to the field of digital chip design, and comprises m master device interfaces, n slave device interfaces, a configuration module, a decoding module, an arbitration module and a cache module. According to the embodiment of the invention, a channel-level processing mode is provided, an address channel and a data channel are independent, and an arbitration strategy is configured through the register block, so that access limitation modifiability and flexible soft configuration between the AXI bus interconnection matrix master device and the AXI bus interconnection matrix slave device can be realized, and the purpose of access modifiability is achieved; the application scene range of chip information security and function security is effectively expanded, the read-write operation transmission efficiency of the chip is improved, and then the chip performance is improved.

Disclosed are systems and methods for maintaining data integrity (e.g., of a database, a data lake, etc.). In some embodiments, one or more processing circuits identify an update, insertion, or deletion of unstructured data stored in an upstream source. The processing circuits determine a new schema of the unstructured data using an inferring function and further determine a divergence between the new schema and a previous schema of the unstructured data based on comparing the new schema to the previous schema of the unstructured data. In an example, the processing circuits store the structured data in a database. The database includes an established data channel between the database and a downstream source, and the structured data is a unique representation within the database of the update, insertion, or deletion of the unstructured data.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention discloses a multi-brand AGV same-field mixed operation management and control method and device, electronic equipment and a storage medium, and the method comprises the steps: building a standardized data channel with an original scheduling system of each brand AGV, so as to obtain the real-time operation state data and planning path data of each AGV; based on the real-time operation state data and the planned path data, carrying out space intersection judgment on planned paths of all AGVs, and carrying out collision risk initial judgment on AGV pairs with space intersection based on preset limit operation parameters; motion simulation is carried out on the AGV pair with the collision risk through preliminary judgment in the virtual environment based on the actual operation parameters so as to output a quantized collision risk result; and based on the quantized collision risk result, generating an avoidance strategy for the AGV with the low priority in the AGV pair with the collision risk in combination with a preset dynamic priority rule, and issuing and executing a standardized scheduling instruction generated based on the avoidance strategy. According to the invention, safe and efficient cooperative operation of multiple brands of AGVs in a same-field mixed operation environment can be realized.

The invention relates to the technical field of medical information, in particular to a method and a system for processing medical record documents by utilizing artificial intelligence, and particularly relates to a method and a system which can receive and process multi-modal input information including images, videos and voices, can work cooperatively through a multi-agent system and can process the medical record documents. The invention discloses a system for automatically generating, controlling quality and safely inputting medical records in combination with a medical knowledge base and an implementation method thereof. The invention relates to an automatic medical record writing system based on multi-modal input and multi-agent driving. The automatic medical record writing system comprises a multi-modal input module, a multi-agent processing platform and a safety intranet input module. According to the method, establishment of the data channel between the medical intranet and the external AI system is proposed for the first time, the problem of medical intranet isolation is solved, industrial pain points are solved in a breakthrough mode, and a new medical AI landing path is developed.

The invention relates to the field of new energy automobile power supply, and provides an electric automobile interconnection sharing charging operation method and device and a storage medium, the method comprises the following steps: realizing heterogeneous system interconnection of a multi-source charging platform and a supervision platform through a standardized interface protocol, and establishing a cross-platform data channel; a dynamic user authentication system is constructed based on a cross-platform data channel, user registration and authority distribution of the electric vehicle are completed in a multi-mode identity authentication mode to obtain a user authority level, and an authentication result is fed back to the supervision platform; performing intelligent charging pile matching based on the real-time multi-dimensional charging resource atlas according to the user permission level, and generating a dynamic service instruction containing a navigation path and charging parameters; and in a charging service execution process, a bidirectional supervision data flow is created through a cross-platform data channel, full-life-cycle supervision of the charging process is executed in real time, and a service strategy is dynamically adjusted based on a feedback instruction of a supervision platform. According to the technical scheme, the electric vehicle can be cooperatively charged.

The invention discloses a data assimilation method for infrared hyperspectral data of a clear sky channel. The data assimilation method comprises the following steps: acquiring the infrared hyperspectral data of the clear sky channel; a linear or non-linear relationship to convert variables from a mode space to an observation space using a fast radiation transfer mode; satellite infrared hyperspectral atmospheric vertical detection data channel screening is carried out in combination with a principal component method; a channel which is not influenced by cloud pollution is searched by observing the difference between the brightness temperature observed by the infrared and the brightness temperature simulated by the background field; eliminating the statistical deviation between the radiation value observed by the satellite instrument and the radiation value simulated and calculated according to the background field profile; eliminating observation data with errors exceeding a preset threshold value on the mixed earth surface type; and outputting a data assimilation result of the infrared hyperspectral data of the clear sky channel. Analysis fields of all layers of the global atmosphere are improved, the numerical forecasting level is effectively improved, typhoon forecasting intensity errors are remarkably improved, and path errors before landing are superior to conventional observation data tests.

The invention discloses a real estate registration information real-time query system based on graphic data, and relates to the technical field of information real-time query, and the system comprises a knowledge graph generation unit which is used for processing an established multi-modal data set, generating a joint vector, coding the joint vector, and storing the coded joint vector in a preset graph database, synchronously constructing a dual dynamic index mechanism to generate a real estate knowledge graph; the knowledge graph updating unit is used for capturing change events in the multi-modal data set in real time, identifying graph structure differences of a change area and generating a topology correction strategy by positioning the change area and a neighborhood range, and updating a real estate topology knowledge graph in combination with transaction characteristics of a graph database; a query result display unit; and a data synchronization unit. According to the method, the real-time data channel is constructed, capturing and incremental processing of the real estate business change event are achieved, and it is ensured that the real estate knowledge graph and graphic data are dynamically consistent in topological structure and attribute information.

The invention provides an intelligent contract driven workflow engine automatic execution method and system based on a block chain. The method comprises the following steps: constructing a knowledge graph through a historical workflow task, and identifying an association rule between a task type and a resource demand; deploying a monitoring agent at an edge computing node, collecting node load data, and interacting with the block chain smart contract through an encrypted data channel; when a workflow trigger event is detected, a resource pre-allocation strategy is generated in combination with the knowledge graph and the node load data; the block chain intelligent contract verifies the matching degree of the strategy and the current node resource, and after verification is passed, an encryption allocation voucher is generated and written into a block chain account book; the intelligent contract triggers the workflow engine to start the container instance group at the target node according to the voucher, and the task is automatically executed. According to the technical scheme, efficient and safe edge computing resource allocation and workflow automatic execution are achieved, and the resource utilization rate and the task execution efficiency are improved.

The Compliance Commerce Transaction Management Apparatuses, Processes and Systems (“CCTM”) transforms entity onboarding application input, assessment data, authentication data inputs via CCTM components into entity onboarding application output, entity compliance datastructure, transaction compliance datastructure outputs. An entity object identifier of an entity object flagged for compliance monitoring is obtained. A set of compliance monitoring criterion objects and a range of previous compliance datastructures to utilize for the entity object are determined. A set of previously stored compliance datastructures is retrieved and their cryptographic signatures are verified. Updated entity assessment data corresponding to a set of deficient entity assessment data is obtained. Each factor of compliance rule specified in the set of compliance monitoring criterion objects is evaluated. A dimension compliance score is calculated for each dimension of compliance data channel. An overall compliance score is calculated. An entity compliance datastructure for the entity object is generated, cryptographically signed, and stored.

Disclosed are methods and apparatuses for transmitting and receiving data channels in a communication system. An operation method of a terminal in a communication system may comprise receiving, from a base station, resource allocation information of a plurality of physical uplink shared channels (PUSCHs) used for repetitive transmission of a same transport block (TB); identifying a position of each of the plurality of PUSCHs in a time domain based on the resource allocation information; and repeatedly transmitting the same TB to the base station at the position of each of the plurality of PUSCHs. Therefore, performance of the communication system can be improved.