7results about How to "Low execution efficiency" patented technology

The invention discloses a Beidou navigation unmanned aerial vehicle inspection path planning method based on a simulated annealing algorithm. The method comprises the following steps: S1, reading latitude and longitude data: reading latitude and longitude data of an inspection target point and a base from a file or a database; s2, calculating a distance matrix: calculating an actual distance between any two points according to the longitude and latitude data, and constructing the distance matrix; s3, constructing a solution space: constructing all path sets; s4, selecting an initial solution: selecting an initial path from the path set as a current solution; s5, setting an initial temperature: setting the initial temperature of a simulated annealing algorithm; s6, loop iteration: performing an iteration process of a simulated annealing algorithm; and S7, generating a new solution: generating a new path as the new solution by using a transformation method 2 or a transformation method 3 according to the current solution. The method is used for solving the problem that a genetic algorithm and a particle swarm optimization algorithm are relatively weak in theoretical convergence guarantee, and particularly are possibly difficult to meet theoretical conditions in practical application.

The invention provides a training fault tolerance method and device applied to a distributed training system and a chip product, and relates to the technical field of distributed training. The method comprises the steps that under the condition that execution of a distributed training task is interrupted, a fault node is determined from a plurality of computing nodes, and the fault node refers to a computing node which cannot continue to participate in execution of the distributed training task; determining a first network domain based on the network domains to which the plurality of computing nodes belong, the first network domain being the network domain to which the fault node belongs; and under the condition that the standby node exists in the first network domain, selecting the standby node from the first network domain to replace the fault node. According to the embodiment of the invention, the standby node belonging to the same network domain as the fault node is adopted to replace the fault node, so that the execution stability and the execution efficiency of the distributed training task can be maintained.

This application provides a directed acyclic graph (DAG) scheduling method and system for multiple toolchains in intelligent agents, solving the technical problems of existing scheduling methods that rely on manual orchestration, have high topological redundancy, and low execution efficiency. First, it receives multi-tool collaboration task instructions and parses them to obtain a set of tool identifiers to be orchestrated and corresponding tool capability declaration texts. Then, it extracts historical call topology patterns with time-stamped edges; next, it retrieves candidate directed edges that meet capability connection conditions and combines them to generate an initial DAG skeleton; then, it eliminates redundant edges to obtain a simplified DAG structure; finally, it generates a tool call execution sequence containing time-series tool identifiers and a predecessor dependency list. This invention can achieve automated and efficient scheduling of multiple toolchains, adapt to dynamic task requirements, and reduce the overall task execution time.

The present invention belongs to the field of software testing technology, and discloses a method and system for scene-based automated continuous testing of microservice continuous integration. The method includes: API interface management, test data preparation management, storage management of parameters of different deployment environments and test data of each environment, including deployment address information and global parameter configuration of test data; scene-based automated script management, test strategy management, continuous integration test execution, monitoring and reporting. The present invention reduces labor costs, automates scenarios for business use cases, and conducts online scheduled inspections around automation or implements automated access control. Coding efficiency, readability, and visual reporting; microservice landing quality assurance strategy, reducing later regression testing and maintenance costs.

The invention relates to a test case scheduling method and device, computer equipment and a storage medium, and relates to the technical field of cloud computing testing. The method comprises the following steps: obtaining a dependency relationship among a plurality of use cases, and constructing a use case network based on the dependency relationship; based on root use cases in the use case network and the number of dependent use cases of each use case, splitting the use case network to obtain at least two use case sets of a use case independent set, a use case tree set and a use case graph set; the use cases in the use case tree set form a first use case tree, and the use cases in the use case graph set form a second use case tree; determining the total execution time corresponding to the use cases in each use case set, and determining an execution machine allocated to each use case set based on the total execution time; for each use case set, scheduling the use cases in the use case set through the corresponding execution machine and the calling strategy corresponding to the use case set; wherein different use case sets correspond to different calling strategies. By adopting the method, the test efficiency of the test case can be improved.

The embodiment of the invention provides a command processing method, a controller and a computer system.The method comprises the steps that an analysis result of a data processing command is obtained, and the analysis result comprises all operations used for completing interaction between the data processing command and a memory; determining the operation type of each operation, and setting a loose sorting value for each operation based on the out-of-order execution definition corresponding to each operation type, so as to realize out-of-order execution of each operation interacting with the memory among different data processing commands according to the loose sorting value set by the out-of-order execution definition; the out-of-order execution definition is used for indicating whether out-of-order execution of the operation is allowed or not according to conditions for correct execution of the data processing command; and performing packing processing on each operation for completing the setting of the loose sorting value to obtain a data packet corresponding to the data processing command. According to the technical scheme provided by the embodiment of the invention, the execution efficiency of the data processing command can be improved while the execution correctness of the data processing command is ensured.

This application relates to a test case scheduling method, apparatus, computer device, and storage medium, belonging to the field of cloud computing testing technology. The method includes: obtaining the dependencies between multiple test cases; constructing a test case network based on the dependencies; splitting the test case network based on the root test case and the number of dependent test cases for each test case, obtaining at least two types of test case sets: a test case set without dependencies, a test case tree set, and a test case graph set; the test cases in the test case tree set constitute a first test case tree, and the test cases in the test case graph set constitute a second test case tree; determining the total execution time corresponding to the test cases in each test case set; and determining the execution machine allocated to each test case set based on the total execution time; scheduling the test cases in each test case set according to the corresponding execution machine and the calling strategy corresponding to the test case set; wherein different test case sets correspond to different calling strategies. This method can improve the testing efficiency of test cases.