13results about How to "Reduced execution time" patented technology

A method for facilitating playing a legacy game is described. The method includes receiving user input during the playing of the legacy game, determining whether one or more code blocks for servicing the user input are cached, and accessing one or more instructions of legacy game code upon determining that the one or more code blocks are not cached. The method also includes compiling the one or more code blocks from the one or more instructions of the legacy game code, caching the one or more code blocks, and executing the one or more code blocks to display a virtual environment.

This invention discloses a cloud platform computing power resource allocation system and method based on load balancing, relating to the field of computing power resource allocation technology. The application includes: constructing a computing power supply and demand matching platform, connecting with idle computing power resource providers, and receiving computing power requests from computing power resource lessors; based on the computing power requests from the lessors, determining the cluster time, and allocating idle computing power resources to the lessors in the cluster; the lessors setting up key communication, and after key verification, the system accessing the cloud platform of the allocated lessor, achieving intelligent decision-making and optimizing the scheduling of allocated idle computing power resources in the cloud platform. This invention can maximize overall resource utilization, reduce operating costs, minimize task execution latency, and improve user experience. Simultaneously, dynamic allocation based on computing power sharing and cluster processing according to different time nodes improves the overall utilization efficiency of computing power resources.

This utility model discloses a gripping and rotating coordinated apple-picking end effector, installed at the end of a picking robotic arm, comprising a cylindrical housing, a linear motor, a transmission mechanism, and a gripper assembly. The transmission mechanism converts the linear motion output by the linear motor into linear and rotational motions. The gripper assembly opens and rotates via an upward motion, and closes and rotates in the opposite direction via a downward motion to twist and break the fruit stem. The transmission mechanism uses a pin sleeve in cooperation with a central groove shaft, and achieves step-by-step movement through a spiral groove and a limiting groove structure. The gripper assembly completes clamping and twisting through a guide ridge and a hinge seat. This design simplifies the drive structure, reduces costs, improves reliability and picking efficiency, and is suitable for apple picking scenarios with short stems and dense branches.

The invention discloses a construction method of an application algorithm execution cost prediction cost model and an end-to-end application algorithm perception graph division method. The method comprises the following steps: firstly, constructing a calculation cost prediction model through machine learning so as to accurately quantify the real calculation and communication cost of processing graph data by a specific application algorithm on a calculation node; then, a cost sensitive graph neural network model named CoSTA-SAGE is designed, and the predicted cost is used as a direct optimization target; through end-to-end learning, a load balancing (variance minimization) and efficient (bottleneck minimization) division scheme on real cost is generated. In order to realize the target, the method comprises two core parts: cost model construction for predicting the execution cost of the application algorithm and a graph neural network division process for performing end-to-end optimization based on the prediction cost.

The invention discloses a model training method and system based on artificial intelligence, a medium and a product, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes. Tasks with the same preposed continuous logic units are aggregated into a practical training task group, the preposed units serve as common baseline workflows to be separated from the tasks, and remaining independent training logics of the tasks are converted into personalized derivative workflows. A first computing resource is allocated to perform single centralized execution on the common baseline workflow to generate a reference context slice. And distributing a second computing resource for each derivative workflow, distributing the reference context slice for environment reconstruction, and executing each personalized derivative workflow in parallel, thereby completing concurrent training of multiple tasks. According to the method, the computing power redundancy consumption caused by high-similarity training tasks is reduced, and the overall efficiency of task scheduling is improved.

The application discloses a workflow execution optimization method based on a random time Petri net, which comprises the following steps: S1, a service time model is constructed, total average service time of activities under different structures is calculated, and current load is updated to obtain a target function of minimum workflow service time; S2, according to the target model of minimum load balancing service time, executor capacity and actual task quantity allocated to the executor are converted into executor load to perform a traversal cycle to find an optimal candidate executor; S3, a waiting time model is constructed, and a target function of minimum workflow execution time is calculated based on the target function of minimum workflow service time; and S4, according to the target function of minimum workflow execution time, optimization of the random time Petri net execution time is performed. The application constructs a workflow execution time model based on the random time Petri net, which is used for analyzing, calculating and reducing workflow process execution time.

The invention provides an automatic regression test method and system for process step files of battery test software. The method comprises the following steps: establishing a golden sample library containing a process step file set passing historical verification and corresponding standard output time sequence data; starting to-be-tested software and loading a virtual battery simulation engine, wherein the virtual battery simulation engine is configured to simulate battery response characteristics and respond to a hardware control instruction of the to-be-tested software; obtaining a to-be-tested step file associated with the to-be-tested software, loading the to-be-tested step file in batches, driving the virtual battery simulation engine to execute a test, and collecting actual measurement output time sequence data; and comparing the actual measurement output time sequence data corresponding to the part, contained in the gold sample library, in the to-be-tested work step file with standard output time sequence data to judge a regression test result and output a report. According to the method, the automatic regression test of the battery test software working step file which runs at an ultra-real-time speed and automatically performs high-precision data comparison is realized.

The invention discloses a data query method, device and equipment based on a two-fusion scene, a medium and a product. The method comprises the steps of obtaining a two-fusion data query problem text of a target user, performing text analysis on the two-fusion data query problem text, and determining query problem time dimension features; if it is determined that the two-fusion data query problem text does not meet preset data query judgment conditions of the day according to the text initiation time and query problem time dimension features of the two-fusion data query problem text, obtaining a prompt word template; according to the two-fusion data query problem text and the prompt word template, based on a pre-selected intelligent question and answer model, obtaining a target SQL query statement corresponding to the two-fusion data query problem text; and executing the target SQL query statement, performing data query on a pre-constructed and stored summary statistical table or a pre-acquired and stored transaction flow table, generating a two-fusion data query result, and feeding back the two-fusion data query result to the target user.

The utility model discloses a plasma operation electrode for treating rhinitis, and relates to the technical field of medical instruments. In order to solve the problems that solution instillation and waste suction are inconvenient and the ablation effect is poor in the existing rhinitis plasma ablation electrode, the following technical scheme is provided: the rhinitis plasma ablation electrode comprises an electrode tip, an electrode rod, a handle and a cable joint which are connected in sequence, the electrode rod comprises an inner tube and an outer tube, the tail end of the electrode tip is arranged between the inner tube and the outer tube, and the cable joint is connected with the handle. The inner tube and the outer tube extend into the handle; the tail end of the handle is connected with an instillation tube and a suction tube; an instillation hole is formed in an insulating head, close to the electrode tip, of the front end of the outer tube, and the instillation hole is communicated with the instillation tube through a gap between the inner tube and the outer tube; a suction hole is formed in the middle or the rear portion of the upper end face of the insulating head and communicated with the inner tube, and tissue generated in the operation is sucked out through the suction tube. According to the utility model, the performance of an operation tool is improved, the safety, efficiency and effect of an operation are improved, and a more effective and safer method is provided for treating rhinitis.

The present application belongs to the technical field of multi-target tracking application, and particularly relates to a nonlinear multi-target tracking method based on a conversion state probability hypothesis density filter. The method reconstructs a nonlinear system of multi-targets into a linear system by using a conversion state, and obtains a Gaussian mixture probability hypothesis density algorithm of the conversion state by combining with a GM-PHD filter. Compared with a traditional nonlinear multi-target tracking method, such as a GM-PHD of an extended Kalman filter and a GM-PHD of an unscented Kalman filter, the present application has higher tracking precision and lower execution time.

The application provides a database query optimization method and system based on large model enhancement, and belongs to the technical field of database management systems. The application deeply embeds a large model into key processes of database query optimization, including SQL rewriting, cardinality estimation, cost estimation and execution plan enumeration, so as to improve the understanding and optimization ability of an optimizer for complex SQL. Through deep analysis of SQL semantics, large-scale statistical pattern recognition and pruning of a complex plan search space, the application significantly improves the quality of a query plan, reduces cardinality estimation errors, reduces a plan search space and improves the overall execution performance of a database. The application can be applied to any relational database system and has universality and high practical value.

The application provides a SQL script segmentation and parallel method and system based on a dependent directed graph, comprising splitting a SQL script into several SQL statements, parsing the SQL statements to determine target tables and dependent tables, defining node versions based on the order of repeated target tables in the SQL statements, determining the inter-target-table dependency based on the SQL statements, determining the inter-version dependency based on the order of the node versions, constructing a dependent directed graph with the target tables as nodes and the inter-table dependency as connection keys; grabbing nodes with a parent node of 0 in the dependent directed graph to form a node combination included in a running stage, and then grabbing the remaining nodes in the dependent directed graph in sequence until the number of the grabbed nodes is 0; and based on the SQL statements corresponding to several nodes in the node combination corresponding to the stage, generating several SQL sub-files that can be synchronously run in a single stage. The application can accurately reflect the inter-table dependency in an ETL process, divide the script into stages in which the SQL statements can be executed in parallel, and significantly shorten the total execution time.

The application discloses an operator execution method and device, equipment, a storage medium and a product. The method comprises the following steps: applying for a preset number of buffer zones for executing a thread of the operator; wherein the preset number is greater than or equal to 3; for any buffer zone, target data elements in a thread local register array are sequentially sent to the buffer zone by the thread to be accumulated, and an accumulated result in the buffer zone is obtained; wherein the target data elements are data elements corresponding to thread local register indexes in the thread local register array and spaced by the preset number; and the accumulated results of the corresponding buffer zones are accumulated by the thread to obtain a row accumulated result of each row or a column accumulated result of each column in the thread local register array. The embodiment of the application can reduce the execution time of the operator on a chip and improve the performance of the operator.