11results about How to "Improve processing throughput" patented technology

The application relates to a kind of automatic garbage transfer control method and system based on multi-agency cooperative scheduling, wherein, method includes: according to multi-source data, dynamically allocate unloading port positioning parameter, vehicle unloading window period and vehicle operation time beat, to guide corresponding vehicle unloading;Through push mechanism, the material retained in unloading port is transported to the operation area of lower mechanism;Through laser scanning device, the three-dimensional profile data inside unloading pit before and after unloading is monitored, the motion parameters of lower mechanism are synchronously collected, and the matching state of lower mechanism and vehicle operation time beat is judged;Based on three-dimensional profile data and motion parameters, dynamic volume model is established, and single unloading amount is calculated in real time in combination with material density parameter;When detecting that single unloading amount exceeds tolerance range or beat is out of synchronization, chain plate machine variable speed gradient adjustment, push mechanism intervention compensation and unloading port redistribution adjustment are carried out. Therefore, the automatic operation efficiency of garbage transfer is significantly improved.

The application discloses an anchor point guided clustering based data classification method, device and equipment, relates to the technical field of digital data processing, and comprises the following steps: obtaining an original data set to be classified, converting each sample data into a numerical vector, and constructing a data matrix; initializing a clustering center matrix and an anchor point matrix, and setting a fuzzy coefficient; obtaining the anchor point matrix and the clustering center matrix after joint iterative optimization; calculating the fuzzy membership degree of each sample vector; the category with the maximum fuzzy membership degree is taken as the final category label of the sample vector, and the classification results of all sample vectors are output. The application solves the problems that the existing fuzzy clustering method is sensitive to initial conditions, is easy to fall into a suboptimal solution, leads to unstable and inaccurate classification results, and cannot be directly solved by using a gradient descent algorithm, and is difficult to be applied to large-scale data sets, and realizes the enhancement of complex data classification precision and the applicability in different data scale scenes.

The invention provides an image coding method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps that: a CPU (Central Processing Unit) analyzes an image to be coded in a wavelet transform coding process to obtain original pixel data, and transmits the original pixel data to a GPU (Graphics Processing Unit); the GPU performs preprocessing operation on the original pixel data in parallel by using a plurality of threads to obtain standard pixel data meeting the wavelet transform requirement; wherein one thread processes one pixel column in the original pixel data; the GPU performs forward wavelet transform on the standard pixel data to obtain a sub-band coefficient; and the GPU performs entropy coding processing on the sub-band coefficients to obtain coded data. According to the scheme, image analysis is completed in the CPU, and preprocessing, wavelet transform and entropy coding processing are executed in parallel in the GPU, so that the coding throughput rate and the resource utilization rate in the image coding process can be improved.

This invention provides a method and apparatus for inserting semi-structured data into a database, comprising: acquiring an insertion request; parsing the semi-structured data to be processed; determining whether the semi-structured data to be processed contains a predefined identifier field; if it contains a predefined identifier field, using the field value as a target identifier; if it does not contain a predefined identifier field, generating a target identifier; and performing an insertion operation of the target identifier and the semi-structured data to be processed based on pre-set index rules. The solution of this invention has dual logic of identifier fields and the generation of unique identifiers, ensuring that each piece of semi-structured data to be processed corresponds to a unique target identifier. Combined with pre-set index rules, it can prevent the writing of duplicate identifiers from the insertion stage. Performing the insertion operation based on preset index rules avoids the cumbersome process of additional full data traversal verification required when inserting semi-structured data.

The application discloses a coal mine safety risk inference method, system, device and storage medium, which are one-to-one corresponding solutions, and the solutions aim to improve the safety of knowledge graph storage, the traceability of data, the maximum throughput of data, and the efficiency of inference operation, specifically: the knowledge graph storage and optimized transaction processing scheme based on the blockchain can improve the safety of the knowledge graph storage, the traceability of data, and the maximum throughput of data; the entity embedding decomposition scheme can reduce parameter redundancy, improve reasoning efficiency by using refined type perception reasoning, and combine a self-supervised entity classification model and an efficient graph neural network processing to optimize the construction and query processing of the knowledge graph, so that efficient reasoning and application of a large-scale knowledge graph are realized.

The application provides a data feedback task flow dynamic optimization method and system, comprising: obtaining process feedback data, determining process execution time consumption and processing results through a production monitoring system log, identifying current process processing capacity, and simultaneously obtaining downstream assembly node pending workpiece quantity and upstream process output workpiece frequency fluctuation amplitude; obtaining each process queuing workpiece quantity and real-time load quantity corresponding to the rearranged process processing execution sequence, analyzing to obtain a process processing capacity matching degree index, and determining a global workpiece flow transfer stability evaluation result; obtaining total workpiece quantity change data of each process from a dynamic optimization implementation scheme, judging system processing throughput improvement degree, and determining a workpiece flow transfer process load balancing state.

A method and system for image similarity recognition based on multi-feature fusion, relating to the field of image processing technology, is disclosed. The method includes: calculating the hash features of an input image and generating hash similarity and hash confidence values; when the hash confidence value is less than a hash confidence reference value, performing local feature extraction on the input image to generate local similarity and local confidence values; when the local confidence value is less than the local confidence reference value, performing deep learning feature extraction on the input image to obtain deep features and deep confidence values; determining the weight coefficients of the hash features, local features, and deep features based on the hash confidence value, local confidence value, and deep confidence value, respectively; performing feature fusion to generate a fused feature vector; and calculating the final similarity between the input image and the image to be compared based on the fused feature vector. Implementing this application can improve processing efficiency while ensuring the accuracy of image similarity recognition.

This invention discloses a bidding processing method, system, electronic device, and storage medium. The method includes: responding to a bidding request to obtain a user identifier and bidding score; freezing the associated target asset and recording the operation flow; sorting users based on their bidding scores and associated time parameters to update the user bidding leaderboard; after detecting a bidding end signal, determining the top N users and using the Nth bid as the settlement benchmark score; generating a settlement task based on the settlement benchmark score and sending it to an asynchronous message queue, where a message consumer performs asset deduction and residual release processing for the top users according to the operation flow, and performs asset release processing for users not in the top N. This invention directly targets users by pre-setting a leaderboard quota and combines it with asynchronous flow calculation, avoiding centralized traversal calculation of all data during the settlement stage, reducing computational complexity, and improving processing efficiency, consistency, and system stability in high-concurrency scenarios.

The invention discloses a data pre-labeling method and device, electronic equipment and a storage medium, and the data pre-labeling method can be applied to the technical field of automatic driving, and comprises the steps: obtaining a to-be-processed data frame; converting the to-be-processed data frame into tensor data which can be processed by a graphics processor based on memory mapping; encapsulating the tensor data into a co-processing task; the co-processing task comprises a preprocessing task of the central processor and a reasoning task of the graphics processor; the co-processing task defines an output result of the preprocessing task as input data of a reasoning task, and the reasoning task is configured to perform reasoning on the input data to generate a pre-labeling result; and scheduling the co-processing task to the target graphics processor, so that the target graphics processor and a central processing unit associated with the target graphics processor execute the co-processing task. According to the method and the device, the parallel cooperation of the CPU and the GPU is realized while the data flow delay is reduced, so that the processing efficiency of data pre-labeling and the resource utilization rate are remarkably improved.

The application discloses a tandem repeat expansion detection method for third-generation sequencing sequences. The application is committed to breaking away from the constraint of a predefined catalog in the background of third-generation sequencing, especially Oxford Nanopore Technologies (ONT) sequencing data, realizing high-sensitivity de novo detection of short tandem repeat expansion, designing a consensus sequence generation algorithm of edge typing and edge correction under GPU acceleration, realizing high-precision haplotype sequence analysis, and giving consideration to algorithm efficiency and clinical-level detection accuracy.

This application discloses a method, apparatus, electronic device, and readable storage medium for determining k target values. First, the input dataset is divided into multiple data blocks. Based on the local extrema of each data block obtained through parallel computation, k candidate data blocks are selected. According to the block indices of the k candidate data blocks, their internal data rows are obtained, and based on the local extrema of each data row obtained through parallel computation, k candidate data rows are selected. Finally, the data is obtained according to the row indices of the k candidate data rows, and k target values ​​are determined. This scheme transforms traditional serial heap operations into fully parallelizable extremum comparison operations through block division and a two-level selection mechanism, reducing computational complexity. This design enables efficient parallel execution of computational tasks on multiple processors, improving processing throughput. Simultaneously, the index supports batch data retrieval mechanisms, optimizing memory access efficiency and reducing bandwidth requirements, thereby achieving excellent performance in scenarios with data volumes much larger than k.