158 results about "Hybrid computation" patented technology

A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.

The invention provides a power grid dispatching strategy optimization method and system, and the method comprises the steps: deploying a device based on a quantum entanglement technology between transformer substations, and collecting the state information of a power grid, and encrypting and transmitting the state information to a control center through a quantum network; a pulse neural network processor is used in a control center to extract spatial-temporal characteristics, and a quantum game theory model is used to generate an optimized scheduling strategy. Multi-modal verification data is collected, including visual deformation, abnormal sound monitoring, and operational resistance data. A genetic algorithm and deep reinforcement learning are utilized to optimize a decision model, and a dynamic weight distribution mechanism is included. And through a photon-quantum hybrid computing architecture execution model, a scheduling strategy is collaboratively optimized, and a result is fed back to a physical power grid. According to the method, the quantum technology and the spiking neural network are combined, new energy fluctuation is captured in real time, the multi-target weight is dynamically adjusted through the quantum game theory model, and the scheduling strategy robustness is improved. And a photon-quantum hybrid computing architecture is adopted, so that the feature extraction speed and the optimization efficiency are improved.

The invention provides an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

The invention discloses a quantum classical hybrid computing task dynamic scheduling system and method based on SLURM. The system comprises a quantum task feature extraction module, a dynamic priority evaluation module, a dependency analysis module, a quantum perception backfilling module and a uniform resource abstraction layer module. The method comprises the following steps: extracting quantum features of a to-be-processed task and a computing resource to form a quantum feature set and caching the quantum feature set; the feature set is obtained in real time, task priorities are output through multi-dimensional evaluation, and a real-time priority sequence is generated; based on the task type and the feature set, forming a dependency relationship between the classic task and the quantum task, and converting the dependency relationship into a dependency constraint and / or resource reservation instruction; according to the task priority, the resource reservation instruction and the real-time resource state, a future idle period is predicted, and a short-time quantum task is inserted for backfilling; computing resources are distributed according to the task priority and the dependency constraint, and the resource utilization state is fed back to the backfill and priority evaluation module in real time. According to the method, efficient scheduling of hybrid computing tasks can be realized, and the overall performance of the system is remarkably improved.

The invention relates to the field of integrated circuit and chip architecture design, and discloses a storage and calculation integrated chip dynamic reconstruction system supporting multi-precision hybrid calculation, which comprises a fuzzy precision sensing module, a heterogeneous multi-precision calculation array module, a data reconstruction distribution module, an energy consumption prediction control module, a multi-target intelligent scheduling module and a calculation storage mapping module, the system carries out modeling through precision analysis and computing power requirements, calls a heterogeneous array to carry out dynamic precision matching, completes rearrangement and multi-level storage mapping, carries out calculation and storage path joint optimization, evaluates a power consumption load in advance, and carries out balanced scheduling among performance, power consumption and resources through a multi-target scheduling module. Dynamic adaptation of tasks under different precisions, efficient data reconstruction and distribution, intelligent mapping of storage paths and energy consumption control can be achieved, the calculation efficiency and the energy efficiency ratio of the system under a multi-task heterogeneous load are improved, and the method is suitable for edge AI, embedded reasoning and other high-performance low-power-consumption application scenes.

The invention provides a task scheduling execution method and system based on a hybrid computing environment. The task scheduling execution method comprises the steps of receiving a task, performing task type division and quantum coding on the task to form quantum coding information for representing the task, and determining an initial quantum state of the task; evaluating the quantum enhancement priority of the task based on the quantum coding information, and converting a quantum enhancement priority evaluation result of the task into task priority information for a classical task scheduling execution method; and scheduling and executing the task based on the task priority information. According to the method, the dual advantages of fusion quantum computing and classical computing are utilized, effective management of complex tasks is achieved, the isomerized computing environment and task requirements are met, the problem of low-efficiency transfer caused by insufficient integration in the current hybrid computing environment is solved, and the efficiency is improved. And the comprehensive performance, the resource use efficiency and the task scheduling capability of the hybrid computing environment are effectively improved.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention relates to the technical field of edge cloud and distributed storage hybrid computing, and discloses an edge cloud and distributed storage hybrid computing task scheduling system, which comprises an edge computing module, a distributed storage module, a task scheduling module, a network communication module, a monitoring and management module, a security and privacy module, a cloud edge collaboration module and an application layer interface module. According to the hybrid computing task scheduling system based on the edge cloud and the distributed storage, computing and storage resources are collaboratively optimized, a computing task is sunk to an edge node close to a data source, the time for transmitting data to the cloud is shortened, the data are fragmented and stored in a plurality of edge nodes, parallel reading and writing are supported, the single-point bottleneck is avoided, and the computing efficiency is improved. And the real-time response capability is improved, and decision lag caused by network delay is avoided, so that the resource scheduling efficiency is improved.

The invention provides a sparse and dense state calculation method, device and equipment and a readable storage medium, and the method comprises the steps: receiving an input data stream, analyzing the feature information of the input data stream, and marking a sensitivity level for the data stream; according to the sensitivity level of the data stream, a corresponding calculation strategy is called, and the calculation strategy at least comprises secret state calculation, hybrid calculation and plaintext calculation; and executing a calculation strategy for calculation associated with the data stream according to the calculation strategy corresponding to the data stream in AI training and / or AI reasoning. Through the technical scheme of the specification, the sensitivity level is marked for the data stream, and the secret state, mixing or plaintext calculation strategy is dynamically matched, so that the overall calculation overhead is remarkably reduced while the high-sensitivity data security is ensured, and the AI training and reasoning efficiency is improved.

The invention discloses a machine room operation and maintenance management automatic inspection system based on intellectualization, and belongs to the technical field of intellectualization. Comprising a multi-source data fusion processing module, a hidden fault probability distribution module, a fault root cause analysis module, a dynamic adaptive baseline generation module, a multivariate strategy self-healing decision module, a cooperative control execution module and a feedback optimization module. According to the method, a full-link intelligent system of multi-source data fusion, quantum neural network prediction, graph neural network traceability, dynamic baseline generation, reinforcement learning decision, transactional scheduling execution and closed-loop optimization is constructed, the hidden fault recognition capability is improved through quantum classical hybrid calculation, and accurate root cause positioning is realized in combination with a causal atlas; and an optimal repair scheme is generated based on a dynamic baseline and a reinforcement learning strategy library, and finally, the reliability of cross-system operation is ensured through transactional scheduling, so that the fault prediction accuracy is remarkably improved.

The invention provides an execution system and method for a hybrid measurement and power calculation task and electronic equipment, and the system comprises a measurement and power calculation service platform which is used for analyzing the hybrid measurement and power calculation task to generate a sub-task set, and generating a task work queue corresponding to the sub-task set according to a dependency relationship between sub-tasks; and the scheduling adapter feeds back the task execution progress in the task work queue in real time, and the electricity measuring calculation service platform selects to issue the sub-tasks in the task queue to the calculation task scheduling execution system according to the task execution progress. The invention provides an execution system and method for a power measurement hybrid calculation task and electronic equipment, and the system employs a distributed task scheduling execution system, enables a power measurement calculation service platform to interact with the task scheduling execution system through employing a scheduling adapter as a middle layer, and distributes a calculation task to the corresponding task scheduling execution system. The resource allocation is more reasonable, the coupling degree of different task scheduling execution systems is reduced, and a new quantum computing cluster can be conveniently expanded.

According to the method for designing the reasonable arch axis of the large-span arch bridge based on the hybrid calculation algorithm and the self-adaptive load distribution, an analytic equation method, a curve fitting method and an intelligent optimization algorithm are combined, and by selecting the initial arch axis, introducing a dynamic load distribution model and applying a genetic algorithm and a particle swarm optimization algorithm, the reasonable arch axis of the large-span arch bridge is designed. Global and local optimization is carried out, and the optimal shape and load distribution of the arch axis are ensured. And finally outputting design parameters, such as a control point, a bending moment, thrust and the like. According to the method, the design precision, flexibility and safety can be effectively improved, the material utilization rate is optimized, the cost is reduced, and the method is particularly suitable for design of large-span bridges and has high practical value.

A method of performing computation using a hybrid quantum-classical computing system comprising a classical computer and a quantum processor includes computing, by use of a classical computer, short-range inter-particle interaction energies and self-energies of a group of interacting particles, transforming the quantum processor from an initial state to a charge-position encoded state, applying Quantum Fourier transformation to the quantum processor, measuring an estimated amplitude of the Fourier transformed superposition state on the quantum processor, computing long-range inter-particle interaction energies based on the measured estimated amplitude of the Fourier transformed superposition state, and computing and outputting a sum of the short-range inter-particle interaction energies, the self-energies of the system, and the long-range inter-particle interaction energies as a total inter-particle interaction energies of the system.

The invention relates to a hybrid computing resource optimization model. Network optimization for arranging computing subtasks in a hybrid computing environment is provided. The method includes receiving an input of a network of nodes and edges representing computational processes and composition information thereof, wherein the nodes are grouped depending on whether they use classical computing resources or quantum computing resources. The method generates workflow constraints, scheduling constraints, and computing resource allocation constraints. The method generates an objective function. And solving the optimization problem according to the objective function and all the constraints. The solution determines the best computational goal achieved, computational workflow through selection of nodes, computational job scheduling, and allocation of computational processes between classic and quantum computational resources. The computing workflow is then executed to achieve the best computing goal according to the computed job schedule and the allocation of the computing process between the classical computing resources and the quantum computing resources.

A hybrid quantum-classical computing system designed to enhance AI applications by integrating classical computational methods with quantum computing power. The system incorporates a classical computing unit and multiple quantum computing units, each specialized for distinct algorithmic tasks. A control module within the classical unit, using predetermined decision logic, manages the distribution of tasks, selectively engaging quantum units for their unique capabilities to optimize efficiency. This scalable architecture allows for significant advances in AI, tackling complex, resource-intensive tasks by effectively harnessing the strengths of both quantum superposition and classical processing.

The invention discloses an acceleration method and accelerator for a video generation model using a 3D attention mechanism, and the method comprises the following steps: employing a protocol speculation mode to check an important part in attention calculation, employing an FP-FP mode to calculate a matrix multiplication for the important part, and employing an FP-FP mode to calculate a matrix multiplication for the important part; for the non-important part, a matrix multiplication is calculated in an FP-INT mode; in the process of calculating the matrix multiplication by adopting an FP-INT mode, the hybrid calculation engine obtains the product of the mantissa and the shaping of the floating-point number in a table look-up mode. Through a speculation-based similarity detection method and a cache lookup table architecture, redundant operation of attention calculation is remarkably reduced, higher than 65% of high-overhead FP-FP calculation in the original attention calculation process is replaced with low-overhead FP-INT calculation, efficiency is approximately improved, energy consumption in the video generation process is reduced, and the video generation efficiency is improved. And the large-scale video generation task is more economical and efficient.

The invention relates to the technical field of numerical calculation and data analysis, in particular to a data interpolation and fitting analysis system which comprises a data processing module, a depth generation interpolation module, an uncertainty quantification module, an interpretability optimization module, a multi-source heterogeneous data processing module and a hybrid calculation acceleration module. In the prior art, a traditional interpolation method is easy to generate over-fitting or under-fitting in a complex data distribution and high noise scene, and a single deep learning model is insufficient in generalization ability in a small sample or data sparse region; through the dynamic fusion architecture of the depth generation interpolation module, the advantages of a traditional numerical method and deep learning are combined, the weight is automatically adjusted based on data characteristics, the prediction error under noise data is remarkably reduced, the adaptability to complex distribution is improved, and the stability of a data sparse region is enhanced; the problems of insufficient precision and weak generalization ability of a single model of a traditional method are effectively solved.

The invention provides an electric bicycle intelligent network connection system based on cloud-side hybrid computing, belongs to the technical field of electric bicycle intelligent network connection, and is used for solving the problems of data transmission redundancy, high response delay and inaccurate anomaly detection in a traditional cloud centralized processing mode. The system comprises an edge computing node, a cloud platform and a communication module, the edge computing node is deployed on a terminal side, the uploaded data content is determined based on historical data intelligent analysis, and key abnormal data or original data are uploaded in a grading mode according to needs; the cloud platform is responsible for global data aggregation, model training and strategy issuing. Through cooperation of the edge layer and the cloud, the system can reduce the bandwidth cost of the cloud by more than 40%, shorten the abnormal response time to be within 100ms, reduce the false alarm rate of abnormal detection by 65% in combination with an isolated forest algorithm and mahalanobis distance detection, and improve the real-time performance and safety of intelligent management of the electric bicycle.

The invention discloses an underwater robot imaging algorithm optimization method based on deep learning, and relates to the technical field of underwater robots, and the method comprises the steps: firstly carrying out the dynamic capture of an underwater light field through employing a curved surface CMOS array sensor, and adaptively adjusting the focal length of the curved surface CMOS array sensor according to the change of the light intensity of a scene, and activating the local pixels when the scene changes; designing a quantum convolution kernel, mapping classical convolution operation into a quantum circuit, constructing a hybrid calculation pipeline, and performing quantum state joint coding and cross-domain feature fusion on optical and acoustic data to obtain fused data; through quantum Fourier transform and quantum convolution, data of different modalities are fused, the accuracy and robustness of imaging are improved, weights of optical images and sonar data can be dynamically adjusted under different water turbidity and illumination conditions, the fusion effect is improved, model errors are reduced, metasurface parameters can be adjusted in real time, sub-wavelength wavefront regulation and control are achieved, and the method is suitable for large-scale popularization and application. And the adaptability and robustness of the imaging system are improved.

The invention discloses an intelligent article selection scoring system based on fusion of a rule engine and machine learning. The system comprises a data preprocessing unit, a feature engineering unit, a hybrid calculation unit and a dynamic weighted fusion unit. The feature engineering unit is used for generating a feature data set, a statistical feature data set, a time sequence feature data set and a composite feature data set from the preprocessed intelligent product selection data; the hybrid calculation unit is used for synchronously executing preset business rule calculation and machine learning prediction; preset business rule calculation is completed through a business rule calculation function, and machine learning prediction is completed through a multi-dimensional linkage machine learning prediction model; and the dynamic weight fusion unit is used for fusing the preset business rule score and the machine learning prediction score according to the dynamic business rule weight factor, and finally obtaining an intelligent product selection score. According to the invention, scientific, automatic and high-precision commodity potential evaluation is realized, and a breakthrough technical framework is provided for the field of e-commerce commodity selection.

The invention discloses a high-precision quantum sensing environment physical quantity real-time monitoring system and method, and belongs to the technical field of environment monitoring. The system comprises a quantum sensing front end, a signal excitation and regulation module, a quantum classical hybrid computing engine, an edge cloud collaborative transmission module and a terminal application module. The quantum sensing front end adopts a quantum entanglement sensing array integrated by NV color center diamond and a rubidium atom gas chamber to synchronously sense a magnetic field, temperature, pressure and vibration; the signal excitation and regulation module optimizes regulation parameters and inhibits noise through a quantum optimal control algorithm; a quantum-classical hybrid calculation engine is combined with a quantum neural network and classical multi-algorithm collaboration to realize efficient resolving and data fusion of quantum state features. According to the invention, the monitoring precision, the real-time response speed and the anti-interference capability are obviously improved, synchronous monitoring of multiple physical quantities is supported, and the system is suitable for various scenes such as industry, scientific research and deep space exploration, and has wide application value.

The invention relates to the technical field of earphone control, and discloses a multi-scene adaptive wireless earphone dynamic control method, which comprises the following steps: identifying environmental noise through phonon entangled state density and phase coherence length by adopting a quantum sensor array; physiological signals of a user are collected through the anti-interference electroencephalogram electrode, and the auditory state is analyzed; a millimeter wave radar is used to construct an environmental acoustic model, and quantum-classical hybrid calculation is combined to realize rapid sound field simulation; establishing a multi-objective optimization function to dynamically adjust noise reduction, equalization and spatial sound effect parameters; and realizing smooth transition control based on scene change prediction. According to the invention, the control accuracy of the wireless earphone during multi-scene adaptation can be improved.

A system and method are disclosed for transforming at least one foundation artificial intelligence (AI) model into one or more embedded micro-models configured for deployment in constrained or hybrid computing environments. Each micro-model is packaged within a structured container that includes inference logic, metadata, telemetry thresholds, and fallback logic. A runtime engine monitors execution conditions and adaptively switches among inference logics or activates fallback behavior based on telemetry signals such as CPU load, memory usage, latency, or confidence score. The system supports operation on OS-less or minimal-runtime platforms and enables distributed deployment across mesh networks using multiple communication protocols. Micro-models may coordinate autonomously or under supervisory guidance and are executed within multi-dimensional or non-topological configurations. The invention enables scalable, resilient AI inference in embedded systems with limited resources.

The invention relates to the technical field of neural networks and vehicle professional language processing, in particular to a vehicle professional language text classification method and system based on a quantum recurrent neural network, and the method comprises the steps: enabling a word to be embedded into a pre-trained hybrid calculation model with a vector as an input feature, the hybrid calculation model comprises a quantum recurrent neural network uploaded in batches and a classical neural network, and the quantum recurrent neural network uploaded in batches is fused into the classical neural network; the quantum neural network uploaded in batches is characterized in that an input word embedding vector is divided into a plurality of batches according to a predefined quantum bit number, and the batches are connected through a variable component sub-circuit, so that a coding-variable layering hybrid model is formed; and running the hybrid calculation model, and outputting the text classification corresponding to the to-be-processed corpus data. According to the method, limited data resources are better utilized to carry out classification tasks, and meanwhile, the understanding ability of the model for complex vehicle professional language expression is improved.

The invention provides an uncorrelated parallel machine scheduling method, system and device, a medium and a product. The method comprises the steps of obtaining a main problem and a sub-problem corresponding to a scheduling task; obtaining a binary representation model of the main question; the scheduling quantum computer analyzes the binary representation model of the main problem to obtain a plurality of optional scheduling strategies; analyzing the sub-problem based on the plurality of selectable scheduling strategies to obtain a plurality of candidate sub-problem feedback information; screening the plurality of candidate sub-problem feedback information to obtain target sub-problem feedback information, and performing a next iterative analysis process based on the target sub-problem feedback information to obtain a plurality of new selectable scheduling strategies; and obtaining a target scheduling strategy until an iteration ending condition is met. Through the quantum and classical hybrid computing system, the quality and generation efficiency of the scheduling strategy are improved.

The application discloses an optoelectronic computer based on time domain all-optical signal serial-parallel hybrid computing, which is composed of an electrical transceiving module, a light source module, a linear matrix vector computing module, a summation separation module and a nonlinear activation module.The application adopts time sequence encoding serial input, and multiple input signals are simultaneously and parallelly calculated, so that all computing steps of a full connection neural network, i.e., linear calculation, summation operation and nonlinear activation, can be completed in the optical domain, and the host end only needs to send input data and receive the final computing result.The application has the following advantages compared with other comparative applications: multiple matrix-vector multiplication operation can be completed in parallel; only one optical element is used to complete summation and nonlinear activation operation on multiple multiplication vectors, thereby reducing the hardware deployment pressure; and the bandwidth loss of a pure serial time domain computing method is relieved through serial-parallel hybrid encoding in the time domain, and the computer computing speed is improved.

The invention provides a hybrid computing architecture and method based on cloud computing and edge computing, and the method comprises the steps: a business scene analyzer determines a processing mode of a task, a resource allocation module executes resource allocation by adopting a genetic algorithm according to the processing mode, and dynamically allocates cloud computing resources and edge computing resources, the decision-making center predicts future resource requirements based on a machine learning algorithm and feeds back a prediction result to the resource allocation module, the data management unit performs block processing on business data and allocates storage positions based on hash values, the edge node processing module executes a quick response task, the cloud processing center executes a compute-intensive task, and the cloud processing center executes a cloud computing task. And finally, feeding back a processing result to the edge node for further optimization. According to the method, the advantages of cloud computing and edge computing are integrated, flexible distribution and efficient utilization of computing power are achieved, and the requirements for the computing power in different scenes are met.

The invention relates to the technical field of high-speed aircraft ground simulation and electromagnetic property calculation, and provides a high-speed aircraft electromagnetic calculation method and system based on a quantum classification algorithm. A variable component sub-classifier is used as an intelligent scheduling core to perform feature recognition and region division on a whole flow field, and a transmission matrix method and a WKB approximation method are adaptively called according to a division result to perform hybrid calculation, so that the technical problem that a traditional single calculation strategy is difficult to collaboratively optimize in precision and efficiency is solved; the method has the advantages that global computing resources can be distributed according to needs, and the overall solving speed is greatly increased while high simulation precision of the key area is guaranteed.

A server farm with at least one hybrid computing module operating at clock speed optimally matching intrinsic clock speed of a related semiconductor die related thereto.

The application relates to the technical field of Internet of Things, and discloses a STAR-RIS assisted hybrid NOMA computing offloading method, which comprises the following steps: S1, before the start of the offloading process, a main base station collects and caches hyper-surface and auxiliary base station basic information in a network; S2, when a user needs to offload a task, the user first sends an offloading request to the main base station; S3, the main base station receives the user offloading requirement; S4, after the optimization of the offloading strategy is completed, the main base station can notify the user, the auxiliary base station and RIS to prepare for task offloading; and S5, after the hyper-surface and the auxiliary base station are configured, the main base station sends the strategy to the user. Compared with the traditional only-reflection RIS technology, the STAR-RIS technology can transmit the offloading task to both sides of the hyper-surface through reflection and refraction signals, the offloading range is expanded, and for the Internet of Things equipment, computing nodes can be selected for computing offloading in a wider communication range.