13results about How to "Improve execution success rate" patented technology

The invention discloses a cloud computing task storage and computing resource intelligent scheduling method based on an adaptive graph neural network. The method comprises the following steps: S1, constructing a cloud computing basic information set; s2, constructing a graph neural network model of tasks and resources; s3, using the cloud computing basic information set to initialize the graph neural network model; s4, gradually optimizing a resource allocation strategy of the task; s5, updating the embedded vector of the task node based on the optimized graph neural network model; s6, scheduling computing resources and storage resources in real time according to the task priority and the generated resource allocation decision; s7, dynamically updating the embedding vector and the resource allocation decision of the task node; and S8, optimizing the distribution efficiency of storage and computing resources in the cloud computing environment. The resource utilization rate of the system is improved by about 18%, and meanwhile the task execution success rate is improved by about 9%.

The invention discloses a method suitable for adding a control shunt under multiple scenes in a demand response execution process, and the method comprises the steps: obtaining real-time load data, historical load data, environment parameter data and user behavior data, and carrying out the processing through a tensor decomposition method and a support vector machine, and obtaining a scene feature vector and a scene type identifier; real-time load data and system constraint data are combined, a fuzzy comprehensive evaluation method and a graph neural network are adopted for processing, and a shunt response capability matrix and a control constraint matrix are obtained; processing by adopting a multi-objective optimization method to obtain an optimization objective function set, a weight coefficient matrix and a constraint condition set; and processing by adopting a genetic algorithm and a dynamic programming method to obtain a control strategy matrix and an execution time sequence vector. According to the invention, accurate control and dynamic optimization in the demand response process can be realized, and the execution efficiency and reliability of the control strategy are improved.

The invention provides a robot control and model training method and device, equipment and a storage medium, and can be applied to various fields of robots, model training and the like. The method comprises the steps of obtaining description information of a target task to be executed by a robot and a first environment image when the robot is in an initial state; based on the description information of the target task, performing subtask division on the target task to obtain description information of N subtasks; for the ith sub-task in the N sub-tasks, determining three-dimensional voxel movement track information when the robot executes the ith sub-task based on the first environment image and the description information of the ith sub-task; the three-dimensional voxel movement track information of the ith sub-task is processed by controlling the prediction model to obtain the prediction control information of the ith sub-task, and the robot is controlled to execute the ith sub-task based on the prediction control information of the ith sub-task, so that the control effect of the robot is improved.

The embodiment of the invention discloses an instruction execution method, computer equipment and a computer program product, and belongs to the technical field of artificial intelligence. The method comprises the steps that in the recording process of a target instruction, target path information of a target operation path for achieving the target instruction is generated based on an operation track model, and the operation track model comprises node information of a node corresponding to an interface in an application and side information of a behavior side corresponding to an operation behavior in the interface; the target operation path is composed of nodes and behavior edges between the nodes; and in response to an instruction execution operation on the target instruction, simulating an operation behavior based on a target operation path indicated by the target path information. Compared with a complex script file, the space occupied by the storage path information is smaller; and simulation is performed based on the node information indicated by the path information and the side information, so that the accuracy of interface positioning and operation behavior restoration in the simulation process can be improved, and the success rate of instruction execution can be improved.

The invention belongs to the technical field of unmanned aerial vehicle control, and discloses a model prediction contour control system and method based on neural network enhancement, the system processes multi-source sensing data of an unmanned aerial vehicle through an S4-Conv adaptive network architecture, and the system comprises an odometer, a thrust output and a PVDF flexible crash sensor. And accurately predicting the position and attitude of the unmanned aerial vehicle at the next moment. Based on the prediction model, a neural network enhanced model prediction contour control framework is provided for optimizing the thrust output of the unmanned aerial vehicle and performing real-time attitude adjustment. According to the method, external collision and dynamic disturbance in a complex environment can be quickly responded, the trajectory tracking precision of the unmanned aerial vehicle and the stability of task execution are remarkably improved, and the method is suitable for unmanned aerial vehicle flight control tasks in complex environments such as urban space and disaster area search and rescue.

The invention provides a test method and device of smart home equipment, a program and electronic equipment. The method comprises the following steps: acquiring a current equipment state of the smart home equipment; according to the current equipment state and the priority weights, the execution sequence of the multiple test cases in the test case library is determined, so that the smart home equipment executes the multiple test cases, and each test case in the test case library has a preset priority weight. According to the invention, the problem that the test efficiency is affected due to inflexible sorting of the test cases of the smart home equipment in the prior art is solved.

The invention provides an equipment control method and device, equipment and a storage medium, and relates to the technical field of Internet of Things. The method comprises the following steps: determining the space triggering capability of a target space; the space triggering capability is obtained by mapping the device triggering capability of at least one device in the target space; if it is monitored that the space triggering capability meets a space triggering condition in a configured space linkage scheme, determining a space execution action associated with the space triggering condition in the space linkage scheme; the space execution action indicates the space execution capability of the target space under the condition that the space triggering condition is met; and determining at least one target device of which the device execution capability accords with the space execution capability based on a mapping relationship between the device execution capability of each device in the target space and the space execution capability, and controlling each target device to execute a space execution action respectively. According to the invention, the problems of relatively high configuration cost of a user and relatively low execution success rate of equipment control in related technologies are solved.

The present application discloses a control method and system for wireless network equipment, which relates to the field of communication technology, including: collecting user data from different areas in the network; obtaining the coordinate position and transmission power parameters of the network equipment, and calculating the wireless coverage area according to the wireless signal propagation model, which includes the SCHEMA model, the Okumura model and the Hata model; determining the target area by generating a heat map according to the collected user data and the wireless coverage area; calculating the minimum transmission power value that satisfies the wireless coverage and throughput of users in the target area according to the heat map, the wireless coverage area and the user data, and the transmission power parameters; generating a control instruction to adjust the transmission power of the network equipment according to the calculated minimum transmission power value; and adjusting the transmission power of the network equipment according to the generated control instruction. In view of the problem of high energy consumption of network equipment in the prior art, the present application dynamically adjusts the transmission power of the equipment based on the user distribution, etc., thereby reducing energy consumption.

The application relates to the technical field of mobile terminal automatic testing, in particular to a mobile terminal full-scene automatic testing system based on AI visual identification and a testing method thereof, which comprises a backend control layer, an automatic driving layer, an AI visual identification layer, a time execution layer, a cross-platform adaptation layer and a tool support layer. The application reduces the dependence on UI element IDs in the mode of AI visual identification, does not need to modify scripts in iteration, can directly realize version control, improves the maintenance and testing efficiency, and has high positioning accuracy and compatibility.

The application relates to the technical field of unmanned aerial vehicle scheduling and intelligent optimization, and discloses a multi-unmanned aerial vehicle cooperative task allocation method and device for complex task scenarios, which has the technical scheme as follows: global multi-source data acquisition and space-time heterogeneous hypergraph representation, complex task causal emergence decoupling and counterfactual deduction optimization, hierarchical federated heterogeneous matching degree accurate representation, distributed external generalization causal element reinforcement learning dynamic allocation decision, distributed conflict resolution and digital twin closed loop iterative optimization; by adopting frontier technologies such as space-time heterogeneous hypergraph representation, causal emergence reasoning, hierarchical federated learning, causal element reinforcement learning and digital twin closed loop optimization, a causal driving full-link algorithm architecture is constructed to break through the inherent limitations of traditional correlation-based data-driven algorithms, so that efficient cooperative operation of a large-scale heterogeneous unmanned aerial vehicle cluster in a strong coupling task and an extreme dynamic environment can be realized.

A method, apparatus, readable storage medium, and terminal for exporting multi-source heterogeneous data are disclosed. The method includes: upon receiving a data export task, obtaining descriptive information of the data to be exported from the data export task. This descriptive information indicates the attribute information of the data to be exported and the various data sources from which it originates. The attribute information of the data to be exported is set based on preset metadata. A target execution engine is selected based on the various data sources from which the data to be exported originates and the current resource load of the execution engine. The data export task is converted into an executable task adapted to the target execution engine, and the executable task is distributed to the target execution engine. The target execution engine then performs data queries on the corresponding data sources based on the executable task. The export result is obtained based on the data query results and output. This solution enables the fusion and export of heterogeneous data from different data sources, improving the convenience of data export.

The invention provides a signal processing circuit and a memory, and the circuit comprises a pulse broadening circuit which is used for receiving a command signal and an address signal, broadening the pulse width of the address signal through the command signal, and generating an address broadening signal; the input end of the address decoding circuit is coupled to the output end of the pulse broadening circuit, and the address decoding circuit is used for receiving the address broadening signal so as to decode the address broadening signal and generate an address decoding signal corresponding to the address signal. According to the invention, pulse broadening can be carried out on the address signal so as to increase the pulse width of the address signal, more time sequence margin can be provided for address decoding, the command sampling accuracy can be ensured, and the command execution success rate is improved.