9 results about "Resource allocation systems" patented technology

This invention relates to the field of maintenance management decision-making technology and discloses an intelligent pavement condition assessment and resource allocation system integrated into a mobile platform. The system includes sequentially connected modules for mobile data acquisition, data preprocessing, pavement condition assessment, resource optimization allocation, and decision support and feedback. The mobile data acquisition module collects multi-source data through a sensor array and dynamic sampling strategy; the preprocessing module generates a high-quality dataset through cleaning, spatiotemporal calibration, and standardization; the assessment module calculates the Pavement Health Index (PHI) based on a three-level model to accurately classify pavement condition levels; the resource optimization allocation module aims to maximize overall maintenance benefits by solving for the optimal resource allocation using a multi-constraint linear programming model; and the decision support and feedback module provides visualized decision display and corrects model parameters based on post-maintenance re-inspection data. The system improves the accuracy of pavement assessment and the scientific nature of resource allocation, adapting to the needs of large-scale road network maintenance management.

This invention discloses a multi-agent air-to-ground network resource allocation method based on federated learning. In the air-to-ground network, the ground network constitutes high-data-rate V2I links; the air network constitutes V2U links for direct communication with ground vehicles; the V2U links share the spectrum resources of the V2I links and use hybrid spectrum access technology for transmission; a network resource allocation system model consisting of M pairs of V2I links and K pairs of V2U links is constructed; a multi-agent resource allocation method is adopted, and a deep reinforcement learning model is constructed with the goal of minimizing the total transmission delay of the V2I link channels; federated learning is used to optimize the deep reinforcement learning model; during the execution phase, the V2U links obtain their current state based on observations, and the optimal resource allocation strategy is obtained using the trained model. This invention exhibits good stability in highly dynamic air-to-ground networks.

This invention discloses a model training and resource scheduling method and system based on LlamaFactory, relating to the field of resource scheduling and distributed model training optimization technology. Specifically, this invention combines the LlamaFactory model training process with a dynamic priority scheduling mechanism to construct an intelligent training and resource allocation system for multi-task, multi-GPU clusters. This achieves adaptive adjustment of task sorting and resource allocation. By combining a comprehensive dynamic priority score function with a cat swarm optimization algorithm, it realizes efficient scheduling solutions under multi-objective constraints, obtaining high-quality scheduling solutions within a limited time, improving the overall energy efficiency and task throughput of the cluster. Furthermore, by combining the LlamaFactory training loop structure with a safe preemption point design, it achieves refined interruption control during the training process, improving the success rate of task migration and recovery.

This invention relates to the technical field of industrial manufacturing and production management, and in particular to a multi-factory tire production collaborative scheduling and resource allocation system. The system includes a unified order management center, a data acquisition unit, a data processing and analysis unit, a factory capability twin model unit, a collaborative scheduling and resource allocation unit, an operation interface unit, and a report generation and query unit. The unified order management center is used to collect, parse, and manage all customer orders, forming a unified global order pool. The data acquisition unit includes equipment sensors, logistics sensors, and a manual input terminal. Equipment sensors are installed on production equipment in each factory to collect real-time information on equipment operating status, production parameters, and production progress. Logistics sensors are installed in warehouses and transport vehicles. This system reduces equipment idle time and production cycles, improves the efficiency of tire production, and optimizes resource allocation and production task distribution.

This invention discloses an AI-based intelligent campus teaching resource allocation system, specifically relating to the field of educational resource management technology. It includes modules for resource monitoring, load status identification, scheduling optimization, feedback synchronization, and resource scheduling execution. The system dynamically adjusts resource allocation schemes by monitoring the status of teaching resources in real time, analyzing load ratios, and comparing them with load standards. Based on demand forecasting and historical data, the system can automatically optimize resource scheduling to ensure the smooth operation of teaching activities. This AI-based intelligent campus teaching resource allocation system, through real-time data monitoring and intelligent optimization scheduling, achieves precise allocation and efficient utilization of teaching resources. The system can promptly identify and adjust overloaded or idle resources, improving the flexibility and accuracy of resource allocation, reducing resource waste, optimizing the teaching environment, and significantly improving the efficiency of educational resource utilization and management coordination.

The application discloses a medical resource allocation system and method based on medical event risk monitoring, equipment and medium, relates to the field of medical information technology, and comprises the following steps: a data acquisition module is used for acquiring a medical event coding set of a target user and time sequence physiological index data corresponding to each medical event; a graph construction module is used for constructing a correlation graph with medical event coding as nodes and correlation strength between events as edges; a feature extraction module is used for extracting a time sequence feature vector from the time sequence physiological index data; a graph attention processing module is used for taking the time sequence feature vector as the initial feature of the corresponding node, and obtaining the updated feature of each node after fusing the neighbor node information through multi-layer message passing; a feature aggregation module is used for aggregating all the updated features to obtain a user-level cross-event joint representation; and a deployment instruction generation module is used for outputting a disease risk score according to the user-level cross-event joint representation and generating a medical resource deployment instruction. Precise resource allocation is realized.

This invention discloses a multi-agent reinforcement learning-based supply chain network resilience optimization and resource allocation system. The system includes: a data preprocessing unit, an agent architecture construction unit, a pattern recognition unit, a non-cooperative game-theoretic allocation unit, a collaborative optimization allocation unit, and a dynamic switching output unit. The data preprocessing unit is configured to acquire the raw operational data and environmental state data of the supply chain network and perform preprocessing operations. The agent architecture construction unit is configured to build a multi-agent architecture for the supply chain. This invention relates to the field of supply chain network management technology. This multi-agent reinforcement learning-based supply chain network resilience optimization and resource allocation system uses a dynamic switching output unit to collect data in real time, update the agent and supply chain network states, and dynamically adjust resource allocation strategies based on a pattern switching mechanism to achieve adaptive switching between efficiency and resilience, adapting to complex and ever-changing supply chain environments.

The application discloses a product resource allocation system based on production and teaching integration, relates to the technical field of information-based education, and comprises a behavior capturing module, a loss analysis module and a resource allocation module. The behavior capturing module is used for image recognition and behavior analysis on the operation behavior of students in an experiment process, identification of misoperation types and frequencies, and establishment of a capability profile. The loss analysis module is used for statistical analysis on the actual use of various materials in an experiment material bag, accurate recording of consumption data through RFID and a weighing mode, and intelligent adjustment of material proportioning based on a student capability file and an experiment task type, so that individualized customization and on-demand replenishment are realized, and resource waste is reduced. The behavior capturing module comprises an image acquisition unit, a behavior recognition module, a misoperation discrimination module, a loss material marking module, a material type distinguishing module and a capability profile establishment module. The application has the characteristics of accurate matching.