10results about How to "Optimize resource scheduling" patented technology

The invention discloses a plan optimization system for distribution network maintenance, which relates to the technical field of power grid management and comprises an intelligent maintenance scheduling optimization module, an equipment monitoring and prediction module, a panoramic maintenance plan simulation module, a dynamic risk assessment module and an inspection robot management module. The intelligent maintenance scheduling optimization module is used for analyzing power distribution network data, and the equipment monitoring and prediction module is used for monitoring the operation state of power distribution network equipment and predicting the time and position of future faults. And the panoramic maintenance plan simulation module dynamically simulates the maintenance scheme. By introducing various models, the defects of the traditional technology in the aspects of flexibility, real-time performance and accuracy are effectively overcome, so that the system has adaptive learning and dynamic adjustment capabilities, can quickly respond to faults and optimize resource scheduling in a complex and changeable working environment, and remarkably reduces the maintenance cost and time; according to the system, the stability and reliability of distribution network equipment are greatly improved, and a solid technical support is provided for intelligent power operation and maintenance.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention discloses a satellite-ground convergence network multi-task unloading method and device, a medium and equipment, and relates to the technical field of satellite-ground convergence networks. According to path loss between each ground user and each low-earth-orbit satellite and transmitting power of each ground user, uplink transmission rate of each ground user during unloading to the low-earth-orbit satellite is determined, and then a communication model of data transmission rate of a multi-modal task of each ground user is constructed by taking a task unloading ratio of each ground user as a variable. Based on this, three multi-modal learning methods are further provided, a centralized Actor-Critic algorithm is used to process and compute intensive tasks to improve the data transmission rate, and a distributed multi-agent depth deterministic strategy gradient is used to process delay sensitive tasks to reduce network delay. The privacy protection task is processed through the quantized federated learning algorithm to protect the privacy data, the requirements of the multi-modal task in different aspects are met, and better resource scheduling is achieved.

The application discloses a kind of multi-radar resource management methods based on risk assessment, belong to radar signal processing technical field.First, the joint state space model of fusing motion state and classification state is constructed, state estimation is carried out using extended Kalman filter, and the misjudgment cost of target classification is modeled by introducing statistical risk function;Subsequently, the expected risk difference before and after observation is calculated, and the expected risk reduction value of the quantitative target current observation value is obtained;Boolean scheduling variable is introduced to construct scheduling optimization model, and the global ERR is maximized as objective function;Finally, local search and conflict resolution strategy are iteratively solved, and the optimal allocation result is quickly converged.The application realizes the scheduling target of key target priority observation, sensor resource efficient utilization and overall risk minimization.

The present invention discloses a method for constructing a large safety emergency model based on artificial intelligence, which relates to the field of safety management technology. The present invention extracts commonalities from a variety of disaster scenarios, and only requires a small amount of data to quickly adjust decisions when facing a new disaster, effectively solving the problem that existing models perform poorly when facing new natural disasters or never-before-seen scenarios, reducing the time required for model retraining, making emergency response more flexible, introducing multimodal data processing, and using convolutional neural networks (CNNs) to perform deep feature extraction on satellite images, sensor data, and social media information, so that the emergency model can more comprehensively and accurately obtain and process information from a variety of data sources, greatly improving the model's perception of disaster status, and reinforcing the learning model (RL), by combining with the graph neural network (GNN), can better handle resource scheduling problems in complex network structures, and dynamically optimize resource allocation and evacuation routes in real time.

The application provides a global data reconstruction method and device based on position mapping and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: for each source data in at least two source data, a mapping relationship between global data and the source data is respectively constructed, and the mapping relationship is stored as index data independent of the source data, wherein the index data corresponding to different source data is different; in response to a reconstruction instruction, the positions of target data segments corresponding to each data point in the global data in the source data are determined based on the index data; and the target data segments are extracted from the source data for fusion to generate the global data. The application realizes the decoupling of the mapping logic and the source data, avoids the residence of full data in the memory, significantly reduces the memory consumption, and improves the calculation efficiency and data reusability.

The invention discloses a performance optimization and stability guarantee method for a multi-calculation-layer real-time control system, and relates to the field of edge calculation and real-time control. The invention provides a new utility function, namely a time / state / utility function, which is used for quantizing the relationship between the control performance and the system state. On the basis, a cross-time-scale task scheduling mechanism is designed, an edge server coordinates a scheduling strategy in a large time scale, and a local controller schedules and controls tasks in real time in a small time scale. The scheduling mechanism divides tasks into stability tasks and performance optimization tasks. And predicting the task value by using a time / state / utility function, and dynamically adjusting the performance optimization task priority to optimize the control performance. By designing a hard real-time stability task model, a stability guarantee problem is converted into a schedulability problem of a stability task. On-line control performance optimization and stability guarantee of the multi-computing-layer edge control system are achieved from the aspects of end-side collaboration and real-time task scheduling.

The invention discloses a transmission processing method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the transmission processing method comprises the steps that a first terminal receives target shared channel occupancy time COT from a second terminal; and the first terminal sends first information to network side equipment based on the target shared COT.

The invention provides a communication method, a communication device, a communication system, a storage medium and a program product. The method comprises: sending a measurement report to a network device, the measurement report comprising a prediction result of a first cell, the first cell being determined based on at least one of the following: a trigger cell list comprising cells whose measurement results satisfy a reporting condition, and a prediction cell list, the prediction cell list comprising cells whose measurement results satisfy a reporting condition, and the trigger cell list comprising cells whose measurement results satisfy a reporting condition; the predicted cell list comprises a cell corresponding to measurement prediction indicated by the network equipment. In the embodiment of the invention, the terminal sends the measurement report containing the prediction result of the first cell to the network equipment, and the first cell is determined based on the trigger cell list meeting the reporting condition and / or the prediction cell list indicated by the network equipment, so that the network equipment accurately obtains the prediction information of the key cell, thereby optimizing the resource scheduling and improving the user experience. And the operation efficiency and service quality of the communication system are improved.

The invention discloses a large-bandwidth power grid service resource optimal configuration method based on low-orbit satellites, and the method comprises the steps: firstly, selecting a plurality of low-orbit satellites as data transmission relays, and guaranteeing the coverage of a target power grid region; secondly, establishing a two-way communication link to support real-time data transmission; dynamically allocating bandwidth according to the flow demand of a power grid communication node by using an orthogonal frequency division multiple access technology, and establishing a mathematical model to maximize the total transmission rate; designing a satellite-ground network architecture, optimizing resource allocation of each satellite, and realizing minimum delay and maximum throughput; and finally, monitoring node states in real time, dynamically adjusting bandwidth allocation, and evaluating network performance indexes by using a data analysis algorithm so as to continuously optimize a communication effect. According to the invention, a reliable solution is provided for efficient scheduling and energy efficiency optimization of power grid communication, and the efficiency and reliability of a power system can be improved.