15 results about "Parallel computing architecture" patented technology

The present disclosure provides a kind of income processing method and system based on event-driven and multilayer parallel architecture, the method comprises: receiving and processing multi-source trigger signal, generates the standardized trigger context of income calculation task;According to the standardized trigger context, the income calculation task is split into multiple independent subtasks, encapsulated as a standardized income event message and published;Subscription and processing standardized income event message, split each independent subtask into atomic subtask, execute distributed income calculation, generate customer income raw results;Customer income raw results are converged and persisted to unified income accounting table.The present disclosure effectively solves the bottleneck of traditional system in performance and expansibility through event-driven and parallel computing architecture, and further through the innovative initiative audit repair mechanism, builds endogenous, automated data quality guarantee system, realizes the overall upgrade of reliability, accuracy and operation efficiency of income processing system.

This invention discloses a method, apparatus, device, and storage medium for predicting transformer overload. By calculating the multi-dimensional dynamically coupled adjacency matrix of the transformer, this adjacency matrix can be used in conjunction with a spatiotemporal attention bidirectional feedback network to effectively improve the accuracy of overload prediction and reduce the false judgment rate under extreme conditions. Furthermore, both the multi-dimensional dynamically coupled adjacency matrix and the spatiotemporal attention bidirectional feedback network are lightweight parallel computing architectures, resulting in a short prediction time for the target transformer's load rate and low resource consumption, which can meet the real-time dispatching requirements of the power grid. The invention also obtains overload and overload benchmark thresholds based on the historical number of overloads in the target transformer's static equipment dataset. These thresholds are used to predict whether the target transformer is overloaded or overloaded, allowing for threshold settings based on the specific conditions of the target transformer to reflect individual differences and further improve prediction accuracy.

The application discloses a kind of multi-objective collaborative optimization method and device of coal blending of thermal power plant, belong to energy management and intelligent control technical field.Integrating the coal quality of thermal power plant, operation, emission and scheduling data, construct unified feature library, and based on this, set the initial weight of economic, safety, environmental protection target, establish multi-objective optimization model;Adopt parallel computing architecture to solve model, generate multiple candidate blending scheme;Subsequently, combined with the artificial adjustment result of operator and actual combustion feedback data, carry out secondary prediction and comparative analysis;Finally, through continuous learning adjustment behavior and operation effect, drive model automatically update weight and optimization algorithm combination, form closed loop self-learning mechanism, to realize multi-objective collaborative optimization and adaptive decision of thermal power plant.The application improves the intelligence, economy and environmental protection of system operation.

This invention discloses a graph computing optimization method and system based on multi-centrality metrics and globally optimal ranking. The method first calculates the multi-centrality metric of vertices based on an industry knowledge graph, generating candidate ranking sequences adapted to different business operations, and then filters out efficient sequence subsets through performance testing. Subsequently, a parallel computing architecture and a business-region combined partitioning strategy are employed to perform a breadth-first search in parallel, starting from key nodes, generating locally optimal sequences. By incorporating sequence similarity calculations with industry chain weights, the filtered efficient sequence subsets are merged with the locally optimal sequences to form a globally optimal sequence. This effectively overcomes the limitations of traditional single ranking in adaptability across multiple business scenarios and significantly reduces computational complexity. The constructed globally optimal sequence demonstrates higher accuracy and adaptability in businesses such as supply chain risk identification and precise investment attraction, providing an efficient and stable graph computing solution for industrial cloud platforms in the equipment manufacturing field.

This invention relates to the field of aerospace technology, and particularly to a method and apparatus for multimodal fault diagnosis of momentum exchange devices based on trajectory sensitivity analysis. The method includes: connecting multiple types of sensors on the momentum exchange device to acquire multimodal monitoring data in real time; running a trajectory sensitivity analysis algorithm to calculate the residuals of each channel and generate normalized multi-channel residual signals; performing fusion analysis on the multi-channel residuals based on a parallel computing architecture, outputting anomaly scores and comparing them with thresholds to determine the fault type and location. This invention uses a sensitivity analysis algorithm to calculate the derivative of the influence of parameter changes on the system's output trajectory in real time, and guides the generation and normalization of residual signals. This amplifies and highlights minute deviations caused by slight increases in friction, bearing wear, etc., thereby achieving timely detection of faults in their early stages and overcoming the shortcomings of traditional methods in terms of insufficient sensitivity to initial weak anomalies.

This invention discloses a feature-preserving mesh processing method and system based on high-performance parallel computing. The input is an unlabeled point cloud dataset, used to train a point cloud feature generator and a prediction head MLP. α The input is a grid dataset with semantic segmentation labels. The grid surface is sampled to generate a sparse point cloud, and noise is added to the grid to represent the redundant structure of the grid geometry clipping task. Then, the trained prediction head MLP is removed. α Train prediction head MLPs separately β With MLP γ The user inputs the mesh to be processed, all vertices are converted into a sparse point cloud, and the point cloud feature generator and prediction head MLP are used. β MLP γ The invention generates semantic segmentation labels and redundant structure category labels for the sparse point cloud, respectively; finally, it performs a culling operation on the points marked as redundant structures to complete the mesh pruning. This invention can effectively understand the features in the mesh data, and the semantic segmentation results are more accurate. The noise addition process of this invention uses a CUDA parallel computing architecture to ensure the high efficiency of the entire computation process.

This invention discloses a method, apparatus, medium, and product for a multi-engine asynchronous parallel computing architecture. The method includes: acquiring interval marker pairs, which indicate the start and end of a target program segment in a preset program; in response to the interval marker pairs, determining the boundary notification corresponding to the target program from among multiple notifications sent to the control core when each asynchronous engine related to the target program segment performs an operation; and determining the actual execution time of the target program segment based on the sending times of all boundary notifications sent by all asynchronous engines related to the target program segment.

The present application relates to the technical field of power system relay protection, in particular to a hydropower and new energy integration grid-related setting value checking method and system, the system comprises: a panoramic modeling module, which performs parameter input of hydropower station and new energy station equipment; a fault simulation module, which performs fault current simulation under multiple working conditions based on a distributed parallel computing architecture; a setting value calculation module, which generates setting parameters of protection devices; and a grid-related checking module, which constructs a coordination graph of power generation equipment capacity curve and protection action characteristics and outputs checking conclusions. In the present application, by constructing a high-precision mathematical model of multi-energy integration and a parallel computing mechanism, the problem that the traditional single-computer calculation mode is difficult to cope with complex topological changes and dynamic characteristics after new energy is connected is solved, accurate matching and visual verification of grid-related protection setting values and equipment capacity boundaries are realized, protection misoperation or refusal to operate risks are effectively avoided, and the safety defense level and operation and maintenance efficiency of new-type power systems are significantly improved.

The application discloses an aviation part round hole and end face cooperative detection method, device, equipment and medium, and relates to the aviation part detection field.The method comprises the following steps: performing outlier elimination on the surface three-dimensional point cloud data of the target aviation part by using the 3σ criterion, and obtaining a round hole effective point set and an end face effective point set; based on a double-thread parallel computing architecture, a first thread is used to perform iterative optimization on the round hole effective point set based on the linear least square method and the 3σ criterion, a round hole fitting result is obtained, a second thread is used to extract an end face plane normal vector based on the end face effective point set and the minimum eigenvalue method of the covariance matrix, and an end face fitting result is obtained; based on the fitting result, a normal error component in a three-dimensional round center vector is eliminated by using a vector projection method, so that the projection round center distance and the eccentricity of the nested round hole in the end face plane are calculated, and the concentricity of the nested round hole is evaluated.The application can realize the aviation part round hole and end face cooperative detection in a non-damage, accurate and efficient manner.

A method for accelerating Scloud+ algorithm based on multi-thread matrix multiplication. S1, a coarse-grained parallel architecture is constructed; S2, the AES-128 encryption module of the matrix A generation link in Scloud+.KEM is optimized by GPU native; S3, a local variable storage reuse strategy is adopted, and the local matrix variables with non-overlapping life cycle are stored in the storage space; S4, a pre-computation and persistence mechanism is introduced, and the pre-computation of the index data required for the generation of the matrix A is completed in the program initialization stage; S5, instruction level deep optimization; S6, a warp level fine-grained synchronization mechanism is constructed; S7, a double-thread cooperation fine-grained parallel implementation based on the coarse-grained architecture. Through the multi-dimensional collaborative innovation of architecture, memory hierarchy and instruction set, the computing, storage and communication bottlenecks of the post-quantum cryptography algorithm Scloud+ on the general parallel computing architecture are systematically solved.

The present application relates to the technical field of image data processing and three-dimensional reconstruction, and particularly relates to a multi-stage pipeline parallel computing architecture and method based on 3D video sequence data. In the process of continuously collecting images of a target to be measured, multiple image data at the same collection time are organized as processing data units and used as basic processing objects for three-dimensional reconstruction calculation. A multi-stage pipeline processing flow including multiple processing stages connected in sequence is constructed, so that the continuously formed processing data units can be executed in parallel on different processing stages, wherein the output of a previous processing stage for the same processing data unit is used as the input of a subsequent processing stage for the processing data unit. In the case of continuous image collection by a 3D camera, continuous real-time output of three-dimensional reconstruction results is achieved. The present application reconstructs the continuous three-dimensional reconstruction calculation flow into a multi-stage pipeline parallel processing, reduces the synchronization overhead, improves the hardware computing power utilization rate, and realizes continuous real-time output of the reconstruction results.

Described are methods, platforms, systems, and media for providing passive data transparency in a distributed market, comprising: aggregating or using aggregated ETF data at least including ownership data to generate a first database; applying, in a distributed parallel computing architecture, a first algorithm utilizing a vectorized calculation over time series data to generate a multi-dimensional representation of relationship graphs including pass through variables between nodes within or across the Primary Markets and Secondary Markets; generating a database, searchable by any dimension of the calculated data or any component of the relationship graph; and providing an analytics layer for synthesizing any component of the relationship graph including pass through variables, and the database to perform a task. Wherein a task can comprise providing information, analysis, recommendation, predictions, projections, and / or performing actions to manage an investment portfolio or a combination thereof.

The application discloses a kind of ocean sound field parallel simulation methods based on DR-SETD, belong to the technical field of simulation calculation.This application includes: three-dimensional fluid-structure interaction model is simplified to axisymmetric model to reduce the scale of calculation;Adopt high-order spectral element method based on orthogonal polynomial to carry out spatial dispersion, and numerical singularity is handled for axisymmetric property;The weak form of time-domain wave equation of fluid domain and solid domain is respectively constructed, and is solved by interface coupling condition;Efficient parallelization of solving process is realized using explicit time integration format and shared memory parallel computing architecture.This application realizes the high-precision, high-efficiency full-wave simulation of fluid-structure interaction sound field containing complex terrain.

This invention discloses a human brain-based element-sharing data processing architecture and its implementation method. Based on the perception, memory, and neuronal connectivity characteristics of the human brain's biological neural network, this architecture decomposes the target processing object into indivisible and indispensable basic core elements according to their inherent attributes. These elements are then combined according to a preset hierarchical relationship to construct a multi-level sub-element structure. The basic core elements carry real attribute information and are fixedly stored in the bottom-level module. Sub-elements introduce an attention mechanism to filter and adapt element combinations, recording only the temporal, spatial, and logical relationships with the basic core elements / other sub-elements, without storing specific attribute information. All event elements exist in a distributed manner based on the hierarchical structure and are processed and expressed through relationships. This invention constructs a global sharing, multi-level bidirectional traceability and association mechanism between basic core elements and sub-elements. Information import naturally maps the inherent relationships of things, abandoning the rigid dependence on large-scale sample training and ultra-large-scale computing power. It adopts a distributed element-sharing and hierarchical parallel computing architecture, enabling efficient operation in conventional computing environments.