15 results about "Swarm computing" patented technology

This disclosure provides a node anomaly detection method, apparatus, electronic device, and storage medium. The method is applied to a computing cluster comprising multiple computing nodes. The method includes: performing risk prediction on a target node to be predicted among the multiple computing nodes; determining the predicted anomaly probability of the target node for multiple anomaly types, including multiple types such as faults, slow nodes, and silent data corruption (SDC); identifying risk nodes from the target nodes based on the predicted anomaly probabilities; performing anomaly detection on the risk nodes based on the predicted anomaly probability of each risk node to obtain anomaly detection results for the risk nodes; and identifying anomaly nodes from the risk nodes based on the anomaly detection results. Embodiments of this disclosure can improve the overall performance of the computing cluster and the proportion of computing nodes in the cluster that are in an available state.

This application provides a cloud platform storage system, method, device, and medium, relating to the field of cloud platform technology. The cloud platform storage system includes a compute node cluster and a storage node cluster. The compute node cluster includes multiple compute nodes, and multiple disks on these nodes constitute a MinIO object storage cluster for storing image resources and / or cloud disk backup resources. The storage node cluster includes multiple storage nodes, and multiple disk arrays on these nodes constitute an NFS storage cluster for storing cloud disk resources. The compute node cluster stores cloud disk resources in the NFS storage cluster through a cloud disk service, stores cloud disk backup resources in the MinIO object storage cluster, and stores image resources in the MinIO object storage cluster through an image management service. This approach meets the storage needs of users in small- to medium-sized cloud platform usage scenarios while reducing the complexity of system deployment and lowering the operation and maintenance costs of using the cloud platform.

In some implementations, a computing device may receive a set of loglines and a problem description associated with the set of loglines. The computing device may identify clusters of loglines, from the set of loglines, based at least in part on structures of the set of loglines. The computing device may identify key indicators associated with a subset of loglines of respective clusters of loglines. The computing device may apply the key indicators to the respective clusters. The computing device may perform error analysis on the set of loglines based at least in part on the key indicators.

This invention relates to the field of heating technology and discloses a method and system for the operation, maintenance, and management of a heating system. The method includes acquiring sensor data, repair work order data, and the location and status of maintenance teams from the heating network; calculating the degradation degree of network sections based on the sensor data, marking source nodes, and generating aggregated fault groups; calculating the time cost and value recovery parameters for each team within the fault group to determine the optimal matching team; based on the estimated arrival time of the optimal team, performing a throttling operation on the upstream electric regulating valve when the triggering conditions are met to proactively intervene in the fault development; terminating the throttling operation after receiving a repair confirmation signal, and disbanding the aggregated fault group after confirming that the degradation degree has decreased and the efficiency has met the standards. This invention integrates multi-source data and optimizes maintenance resource allocation through optimal scheduling and proactive intervention, thereby improving fault response efficiency and system operational safety.

Provided in the present application is a data processing method for improving the efficiency of solving for an eigenvalue and an eigenvector of a matrix. The data processing method comprises: acquiring a target matrix; on the basis of a plurality of characteristic parameters of the target matrix, searching a first database to determine at least one target reference eigenvector, wherein the first database stores a plurality of groups of first reference data, each group of first reference data corresponds to one first reference matrix, each group of first reference data comprises a plurality of characteristic parameters and eigenvectors of the first reference matrix, a plurality of target reference eigenvectors are eigenvectors of a target first reference matrix, and a plurality of characteristic parameters of the target first reference matrix match the plurality of characteristic parameters of the target matrix; and on the basis of the at least one target reference eigenvector, determining an initial reference vector, wherein the initial reference vector is used for solving for eigenvalues and eigenvectors of the target matrix. In addition, further provided in the present application are a corresponding apparatus, a computing device cluster, a computer-readable storage medium and a computer program product.

The application relates to the technical field of file analysis, and discloses a user package file analysis method, system, device and medium based on load balancing, which comprises the following steps: carrying out file splitting on a user package file to obtain a user file list; selecting a target server cluster in a preset backend server cluster according to the user file list; calculating the real-time computing resources of each backend server in the target server cluster, and constructing a file allocation strategy according to the real-time computing resources; carrying out file analysis according to the file allocation strategy, calculating the analysis real-time computing resources after file analysis; and iteratively optimizing the file allocation strategy according to the analysis real-time computing resources until each user file is analyzed to obtain the analysis result of the user package file. According to the application, the user files can be reasonably allocated for file analysis according to the real-time load conditions of the backend servers, so that the computing resources of the backend server cluster can be reasonably utilized, and the stability and accuracy of user file analysis can be effectively improved.

The present application relates to the field of pattern recognition and image processing based on multi-modal sensor data fusion, especially to a wine grape vineyard intelligent picking management system based on multi-modal data fusion, comprising a perception device group, a computing platform and an execution unit; the computing platform is built-in with a perception module, a defect mapping module, a curvature inversion module, a manifold segmentation module and a constraint addressing module. The system synchronously collects color images and point cloud data, extracts optical space bias vectors through a defect nesting model and reversely calculates physical surface normals to fill in missing coordinates and reconstruct closed fruit grain three-dimensional geometric curves. The manifold segmentation module extracts a three-dimensional topological skeleton of the fruit stem, the constraint addressing module locates the morphological bifurcation structure and outputs picking point coordinate parameters, guiding the execution unit to complete the cutting work. The present application effectively solves the positioning deviation problem caused by inconsistent multi-modal perception under strong light.

The application relates to the technical field of oil development, and particularly discloses a carbon dioxide injection-production parameter multi-target optimization method based on Spark distribution, which aims at the problems of large calculation amount of injection-production parameter optimization, target function conflict and the like in the carbon dioxide flooding and storage process, and builds a multi-target optimization model containing cumulative oil production, CO2 storage amount and net present value. By using an Apache Spark distributed computing framework, population individuals are mapped into elastic distributed datasets, and a high-time-consuming oil reservoir numerical simulation task is distributed to cluster computing nodes for execution through a parallel scheduling mechanism; a multi-target aggregation strategy is provided, global non-dominated sorting and congestion calculation are performed on fitness results returned by each node at the Driver end, so that a Pareto optimal solution set is generated. The application can effectively shorten the optimization period, provides diversified injection-production parameter configuration schemes for decision makers on the premise of giving consideration to economic benefits and environmental benefits.

The application discloses a pilot optimization method based on a combination of a genetic algorithm and a random search algorithm for an OTFS system, and the method comprises the following steps: initializing a population; calculating the fitness value of each individual in the population; entering a breeding cycle iteration; performing a cross-recombination operation on offspring; performing a mutation operation on the offspring; performing a reinsertion operation on the offspring; selecting excellent individuals and performing a random search algorithm on the excellent individuals; aiming at a pilot optimization problem in channel estimation of an orthogonal time frequency space (OTFS) system based on compressed sensing, taking minimization of a cross-correlation value of a recovery matrix as a target, and taking the minimum cross-correlation value as a criterion for selecting the best pilot. The method has the advantages of the genetic algorithm, and a part of the population is randomly searched to avoid falling into a local optimum problem. Compared with a conventional genetic algorithm, the method has better effect, and a pilot obtained by using the method can obtain a lower channel estimation mean square error when applied to channel estimation.

This invention discloses a two-level parallel computing method and apparatus for three-dimensional tomographic static correction. The method includes: based on a three-dimensional tomographic static correction algorithm, dividing multiple computing cores of multiple computing nodes into a two-level parallel computing model; determining multiple ray tracing tasks and velocity modeling tasks to be processed by each computing node in the first-level parallel computing model; and determining one ray tracing task to be processed by each computing core in the second-level parallel computing model. The result of the ray tracing task serves as the input to the velocity modeling task, and the output of the velocity modeling task serves as the input to the next ray tracing task. Seismic data is acquired, and three-dimensional tomographic static correction is performed in parallel based on the first and second-level parallel computing models to obtain static correction data, which is then used for further processing. This invention can improve the computing efficiency of a single machine and maximize the computing power of a cluster computing platform.

A multi-core cluster computing method for securely saving and restoring context data includes storing, by a last core in a primary cluster, last core context data in a first memory. The method also includes storing, by a secondary core in a secondary cluster, secondary core context data in a second memory. The method further includes entering, by the secondary core, a deep sleep power state. The method still further includes entering, by the last core, a system suspend state. The method also includes entering a boot state, by the last core, by restoring the last core context data.

This invention provides a method for cogeneration load allocation in cogeneration power plants based on an improved gray wolf algorithm, belonging to the field of cogeneration system optimization and control technology. The method includes: S1: setting parameters; S2: initializing the population based on chaotic mapping; S3: calculating fitness values ​​and selecting superior individuals; S4: stratifying the population based on the fitness calculated in step S3, dividing the population into different levels, and adopting different position update methods according to the characteristics of different levels; S5: iteration and outputting results. This method exhibits strong real-time performance in cogeneration load allocation, possesses a robust multi-objective trade-off mechanism, can balance local and global search capabilities, and effectively solves optimization problems in complex scenarios of cogeneration power plants.

The application discloses a kind of unmanned aerial vehicle production safety quality inspection method and system, the method therein includes obtaining the multiple types quality inspection data of unmanned aerial vehicle production line;Obtain the task priority of several quality inspection tasks in unmanned aerial vehicle production line;Real-time acquisition edge node computing center, local cluster computing center and the computing power load data of cloud computing center;Based on computing power load data and task priority data, the computing power distribution of corresponding quality inspection data of real-time process, corresponding quality inspection data of quality inspection task in local cluster computing center and the quality inspection data of whole production line of unmanned aerial vehicle production line in cloud computing center is completed, respectively calculated to obtain real-time process quality inspection result, quality inspection task quality inspection result and whole production line quality inspection result.The method in the application realizes the balance of real-time, accuracy and energy efficiency of multimodal quality inspection task under the condition of limited computing power.

Provided in the present application is a data generation method, which is used for increasing the number of pieces of sample data for model training while avoiding model bias. The data generation method comprises: acquiring a plurality of pieces of original sample data; on the basis of the plurality of pieces of original sample data, generating a plurality of pieces of candidate sample data by means of a generative model; and on the basis of the plurality of pieces of original sample data, sampling the plurality of pieces of candidate sample data, so as to obtain a plurality of pieces of augmented sample data, wherein the original sample data and the augmented sample data are used for training a target model. In addition, further provided in the present application are a corresponding apparatus, a computing device cluster, a computer-readable storage medium and a computer program product.