85 results about "Resource allocation systems" patented technology

The invention discloses a sales resource distribution system based on a dynamic customer portrait, and relates to the technical field of artificial intelligence and big data application, and the system comprises an event modeling module, an abnormity monitoring module, a copy management module, a trial investment evaluation module, an unfreezing control module and an income backflow module. According to the method, a distribution offset map is generated by playing back customer behavior data in historical burst periods, and customer groups and key interaction contacts with easily distorted behaviors are identified as data monitoring references. According to the invention, through dynamic monitoring, label copy management, trial investment feedback evaluation, segmented unfreezing control and a negative pressure backflow mechanism, accurate identification and verification recovery of customer behavior abnormity are realized, the label stability is guaranteed, the sales resource distribution is dynamically optimized, and the response capability and configuration efficiency in an emergent environment are improved.

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

The invention relates to the technical field of communication networks, in particular to a resource allocation system and method in a satellite-ground fusion network, and the system comprises a satellite-ground topology and channel evolution predictor which is used for obtaining satellite ephemeris, user GNSS positioning and moving vectors, ground base station topology and a real-time CQI / RSRP measurement report; generating future link state information; the business QoE impact and switching cost modeling device is used for calculating transient interruption time of a switching action; generating switching cost information; the continuity risk quantification engine is used for generating continuity risk information containing the link maintaining risk probability, the instantaneous impact collapse probability and the link switching risk probability; the risk evasion type resource decision-making device is used for calculating maintenance link comprehensive cost and switching link comprehensive cost; calculating a dynamic switching damping threshold value; generating risk avoidance decision information; according to the invention, the high-frequency ping-pong switching near the edge state point is effectively inhibited, and the signaling overhead caused by invalid switching is reduced.

The invention relates to the technical field of network communication, in particular to a 5G-A network slice dynamic adjustment and resource allocation system oriented to low-altitude economy. The system comprises a mobility management feature integration module, a network slice service level protocol evaluation module, a mobility management demand prediction benchmark construction module, a multi-dimensional trend prediction module, a joint optimization decision generation module and a strategy verification and execution module, and is used for acquiring a mobility management feature data set of a wireless network; performing network slice service level protocol evaluation on the wireless network to obtain a network slice state evaluation result; constructing a mobility management demand prediction benchmark; performing multi-dimensional trend analysis on the network slice state evaluation result to obtain a prediction result; performing joint optimization decision on the standard network slice configuration strategy to obtain a network slice adjustment strategy; and executing the verified slice adjustment strategy to realize dynamic adjustment and resource allocation of the network slice. According to the invention, the efficiency of network slice dynamic adjustment and resource allocation can be improved.

The invention relates to a hierarchical diagnosis and treatment resource allocation system based on an AI priority pre-auditing mechanism, and belongs to the technical field of medical resource allocation, and the system comprises a user input module which is used for receiving initial information of a patient based on an AI applet, and the initial information at least comprises a symptom description, a medical history record, examination data and a diagnosis and treatment stage; the AI intelligent pre-inquiry module is used for performing multiple rounds of questions and answers on the patient according to the initial information to extract an illness state index, inputting the illness state index into a preset risk layering model, and determining an illness state severity / urgency index of the patient; the resource distribution module comprises a special number source pool reserved for experts, and the resource distribution module is used for opening expert numbers for the patients whose illness condition severity / emergency index is greater than an illness condition threshold value; and the referral guidance module is used for recommending a replacement doctor seeing scheme for the patient who does not accord with the open expert number. According to the invention, the technical problem of insufficient fairness and efficiency of an illness state emergency degree registration distribution mechanism caused by structural mismatching of expert resources is solved.

The invention relates to a point cloud multi-scale feature segmentation method and device based on an attention mechanism, equipment and a medium. The method comprises the following steps: constructing a multi-scale feature group by extracting geometric and semantic features of point cloud data, and calculating local geometric complexity and semantic importance based on a real-time strategy to generate a dynamic weight coefficient; the time sequence activation sequence of the multi-scale branches is controlled according to the weight priority, and fine segmentation of the key area is achieved; and finally, strategy parameters are optimized through a closed-loop feedback mechanism, and a self-adaptive scene evolution resource allocation system is formed. According to the method, the problems that a static attention mechanism cannot adapt to scene changes and multi-scale computing resource allocation is unbalanced in the prior art are solved through a dynamic allocation strategy, the segmentation precision and the computing efficiency in a complex dynamic scene are remarkably improved, and reliable technical support is provided for real-time three-dimensional perception scenes such as automatic driving.

A resource allocation system may comprise a prediction engine configured to generate a prediction record in a database and a history module configured to store a service record for a service. The resource allocation system may comprise a comparison engine configured to compare the prediction against the service records. The resource allocation system may comprise a service improvement module configured to: use the prediction record and generate a recommendation for a settings change to the resource allocation system. The resource allocation system configured to implement a change to the service to improve performance of services based on the recommendation.

System device for real-time data orchestration, comprehensive at least one processing unit, a data acquisition unit for recording data streams from a plurality of technically different data sources, an orchestration unit, and a computing resource allocation unit, wherein the system device is designed to capture and process data streams system-side in real time, characterized by that the computing resource allocation unit controls a predictive allocation of computing resources on a hardware or firmware level, depending on detected data load and processing states of the data streams.

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.

The invention discloses a non-cellular wireless access network multi-hop relay resource allocation system and method for offshore long-distance coverage. The system comprises a plurality of wireless relays deployed on the sea surface, an edge distributed unit (EDU), an access point (AP) equipped with a root antenna and a central processing unit (CPU), the AP connected with the EDU provides services for each single-antenna user on the opposite bank through a hop network, wherein each hop network comprises a wireless relay; the system comprises node clusters including an AP cluster, a relay cluster and a user cluster. Considering that each wireless relay is equipped with a root transmitting antenna and a single receiving antenna, each transmission is operated in a half-duplex mode. Reasonable deployment of the relay on the sea surface can be realized, and reachability and rate maximization of the system are realized.

The application belongs to the field of computer system resource allocation based on specific calculation model, and discloses a display resource allocation system based on fuzzy logic, which comprises: a system obtains an acceleration vector of user interaction, and performs authenticity discrimination based on the direction consistency of the acceleration vector to generate a weighted interaction acceleration; a fuzzy logic reasoning engine maps the weighted acceleration to a resource allocation priority, and the running period of the engine is dynamically adjusted according to the system core temperature to compensate for the calculation delay; finally, resource allocation is performed according to the priority, the application changes the resource scheduling basis from the application state to the physical intention of the user verified by authenticity, realizes the deep synchronization of system response and user attention, avoids the delay and lag of key interaction, and makes the human-computer interaction experience reach the qualitative change of smoothness and harmony.

The invention discloses a task unloading and resource allocation method based on average field deep reinforcement learning, and belongs to the technical field of large-scale smart grid equipment, and the method comprises the steps: building a task scene of a cloud-side cooperative system, constructing a task unloading and resource allocation model, considering the transmission and execution of a task, and calculating the time delay of completing the task and the energy consumption of completing the task; based on the time delay and the energy consumption of task completion, establishing an optimization target for minimizing the energy consumption value of the intensive task unloading and resource allocation system; based on the optimization target, establishing a cloud edge collaborative intensive task unloading optimization problem model based on average field deep reinforcement learning; and on the basis of the optimization problem model, an MFTDDPG algorithm is adopted for the task unloading problem, and task unloading and resource allocation are completed.

The embodiment of the invention provides a resource allocation system and method, electronic equipment and a storage medium, and relates to the technical field of computers. The system comprises a publishing module, a data acquisition module, a data processing module and a resource allocation module. The publishing module is used for acquiring attribute information of to-be-allocated resources and publishing the attribute information to a plurality of resource demanders; the data acquisition module is used for acquiring first information and second information of a plurality of demand sides; the data processing module is used for determining a first score of the demand side according to the first information and determining a second score according to the second information; the resource allocation module is used for acquiring current popularity information of the to-be-allocated resources, and fusing the first scores and the second scores of the plurality of demand sides to obtain a comprehensive score of each demand side; and determining a target demand side according to the comprehensive score, and allocating the to-be-allocated resource to the target demand side. The method and the device can be used in a resource bidding allocation process, and are used for solving the problems of stiffness and low efficiency of an existing resource allocation mode.

The application relates to the technical field of communication networks, in particular to a resource allocation system and method in a satellite-ground integrated network, which comprises a satellite-ground topology and channel evolution predictor, acquires satellite ephemeris, user GNSS positioning and movement vectors, ground base station topology and real-time CQI / RSRP measurement reports, generates future link state information, a service QoE impact and switching cost modeler, calculates transient interruption time of a switching action, generates switching cost information, a continuity risk quantization engine, generates continuity risk information containing the holding link risk probability, the instantaneous impact collapse probability and the switching link risk probability, a risk-avoiding resource decision maker, calculates holding link comprehensive cost and switching link comprehensive cost, calculates a dynamic switching damping threshold, and generates risk-avoiding decision information; the application effectively suppresses high-frequency ping-pong switching near edge state points and reduces signaling overhead caused by invalid switching.

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.

The embodiment of the application provides a resource allocation method of a communication network and a resource allocation system of the communication network, wherein the method comprises the following steps: analyzing a service request of a specified service to obtain a target parameter requirement, the target parameter requirement being a parameter requirement of a group of quality of service parameters of the communication network for the specified service; obtaining network state information of a first network domain in a plurality of network domains, the network state information of the first network domain being used for indicating a network state of the first network domain; based on the network state information of the first network domain, decomposing the target parameter requirement into a sub-parameter requirement corresponding to each network domain in the plurality of network domains, and respectively issuing a resource allocation instruction carrying the sub-parameter requirement corresponding to each network domain to a controller of each network domain, so as to instruct the controller of each network domain to allocate resources for the specified service in each network domain, and the sub-parameter requirement corresponding to each network domain is a parameter requirement of a group of quality of service parameters of each network domain for the specified service.

Systems and techniques are described for on-demand traffic resource allocation. A system determines, for a traffic resource allocation request for a vehicle, a tracking capability for the vehicle. The tracking capability can be one of multiple different tracking capabilities, and tracking capability enables a tracking of a vehicle by a tracking method that is unique to the tracking capability and different from each other tracking capability. The system generates, for the vehicle, a tracking token that includes data that describes the tracking capability determined for the vehicle and that enables the traffic resource allocation system to track the vehicle using the tracking token and according to the tracking capability. The system then enables tracking of the vehicle according to the tracking capability described by the data in the tracking token.

The application relates to a resource allocation system, method, device, computer equipment and storage medium. The system comprises an allocator configured to determine a resource container corresponding to a resource allocation request, and determine a target resource identifier corresponding to the resource container from a candidate resource identifier set; a resource plug-in configured to trigger, in response to an allocation instruction message, establishment of a resource association relationship of a resource container corresponding to the target resource identifier and a container identifier; and a resource container configured to acquire, in a business processing process, the target resource identifier according to the resource association relationship, determine a target installation position corresponding to the target resource identifier according to a position correspondence relationship, and call a target business processor installed in the target installation position to process business data. According to the above scheme, even if the business processor in the target installation position is replaced, the resource container can still call a new target business processor to process business, so that the order of resource use can be ensured, and the use efficiency of computing resources is improved.

The application discloses a resource allocation method, device and system. The method obtains state data and / or resource usage data of an existing database instance through a database node; sends the data to a central management node; the central management node obtains load change characteristic data of the existing database instance in a first period according to data in the first period; sends the load change characteristic data to the database node; and the database node obtains resource allocation data of the existing database instance at a future time according to the load change characteristic data and data of the existing database instance in a second period. The processing manner can transform the resource allocation controlled by the central management node into the resource allocation decision made by the central management node and the database node in combination, and can effectively improve resource allocation efficiency, accuracy and robustness.

System and methods for forecasting resource allocations are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical data associated with a transportation carrier, receiving, from the database, journey data associated with the transportation carrier, parsing and extracting, from the journey data, a plurality of segments, training a plurality of models based on the historical data, identifying a best model from the plurality of models based on performance metrics over a predetermined period of time, and applying the best model to the plurality of segments to identify an optimal route associated with the transportation carrier.

The invention belongs to the technical field of intelligent communication, and particularly relates to a factory digital twinning synchronization-oriented multi-dimensional resource allocation system and method, the multi-dimensional resource allocation system is realized through a digital twinning synchronization model, the digital twinning synchronization model comprises a physical entity layer and a digital twinning layer, the physical entity layer is responsible for acquiring and transmitting data information required for constructing a digital twinborn model, and the digital twinborn layer is deployed in an edge server and is responsible for calculating and synchronizing the data information required for the digital twinborn model and dynamically adjusting a resource allocation strategy according to an evaluation result of an AoI-DT evaluation mechanism so as to realize optimal allocation of multi-dimensional resources. According to the method, an information age evaluation mechanism of the digital twinning model is introduced, the freshness of the digital twinning synchronization model is reflected by quantizing the data synchronization time delay and the unit data perception time delay, and the resource allocation effect is fed back in real time, so that the requirements of multi-dimensional resource dynamic allocation and real-time data synchronous updating are fully met.

The invention relates to an LTE-V dual-mode communication optimization and resource allocation system, which belongs to the technical field of communication, and comprises a mode selection module for selecting communication modes according to service types, vehicle distances and channel quality, the communication modes comprising a PC5 mode, a Uu mode and a dual-mode communication mode; the dual-mode communication mode is a combined communication mode of a PC5 mode and a Uu mode, and the resource allocation module is used for allocating communication resources of the PC5 mode or the Uu mode and improving the frequency efficiency; the interference coordination module is used for reducing dual-mode interference by controlling transmission power and resource isolation when the mode selection module selects the dual-mode communication mode; and the reinforcement learning optimization module is used for guiding and optimizing control parameters of the interference coordination module, a decision threshold value of the mode selection module and a resource allocation proportion of the resource allocation module based on a Q learning algorithm. The optimal communication mode is dynamically selected by combining the service type, the distance and the channel state, the reliability is improved, and resource waste is avoided.

The invention discloses a low-orbit giant constellation space-ground integrated resource allocation system modeling method based on MBSE, and the method eliminates the demand ambiguity through a structured demand expression and tracing mechanism, and improves the accuracy of system design. Through a unified modeling platform of MBSE, consistency among design stages is ensured based on a traceability matrix, and design deviation is avoided; complex resource allocation requirements are met, and effective scheduling and management of multi-source heterogeneous tasks and resources are ensured; based on a simulation verification framework of a unified model, verification efficiency and accuracy are greatly improved.

The invention provides a resource allocation method, device and system based on collaborative agents, and the method comprises the steps: obtaining the resource request information of each agent in at least one preset dimension under the condition that a plurality of agents request the same resource at the same time; calculating a weighted average value of each piece of resource request information to obtain a resource request score of each agent; and allocating the same requested resource to the current agent with the highest resource request score, so that the agent executes a task based on the allocated resource. According to the scheme, the problem that in the prior art, multiple Agents may request the same resource at the same time, and due to unreasonable resource allocation, the performance of the whole system is poor is solved.

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.

The invention relates to the technical field of education, and discloses a dynamic educational resource allocation system and method based on a multi-target game, and the system comprises a data collection module, a multi-target game model module, a dynamic weight adjustment module, a resource allocation decision engine, and a real-time feedback and optimization module. Wherein the data acquisition module is used for acquiring real-time education demand data and user behavior data, the real-time education demand data contains education resource supply and demand dynamic change information, and the user behavior data comprises a resource use state and education effect feedback information; and the multi-target game model module is used for constructing a game model of multiple participants. By constructing a multi-target game model and a dynamic weight adjustment mechanism, the inherent contradiction between fairness and efficiency in educational resource allocation is effectively solved. Wherein the multi-objective game model takes Gini coefficient minimization, resource utilization rate maximization and financial investment minimization as core objectives, and the strategy stability is ensured in combination with the Nash equilibrium theory.

The invention discloses a job scheduling and resource allocation system in a high-performance computing cluster, and relates to the technical field of high-performance computing. The system comprises a job scheduler, a resource manager, a newly-added job DAG (directed acyclic graph) analyzer, a cluster data distribution monitor, a cache preheating priority calculator, a collaborative decision engine, a distributed cache preheating actuator and a performance optimization module, and the job DAG analyzer constructs a job dependence directed acyclic graph and analyzes a critical path; the cache preheating priority calculator adopts a dynamic priority drift algorithm to determine a data preheating priority, the collaborative decision engine coordinates scheduling and preheating resource allocation, the performance optimization module dynamically adjusts system parameters, the system realizes deep collaboration of job scheduling and cache preheating, the job access delay is reduced, and the system performance is improved. The method improves the cluster computing efficiency and the resource utilization rate, and is suitable for processing a high-performance computing cluster of complex operation workflow.

This invention provides a method for constructing a wireless network resource allocation system. The system is used to obtain a wireless network resource allocation strategy based on the wireless network state. The method includes: S1, obtaining a non-convex optimization objective with interruption probability constraints corresponding to wireless communication requirements under an imperfect global channel state information (CSI) environment; S2, transforming the obtained non-convex optimization objective to obtain a non-convex optimization objective without interruption probability constraints; S3, obtaining imperfect global channel state information of the wireless network; S4, using the obtained non-convex optimization objective as the training target and the imperfect global channel state information from step S3 as input, training the initial resource allocation system to convergence using reinforcement learning. This invention uses more realistic CSI to train the learning-based initial allocation system, improving the convergence rate of the wireless network resource allocation system and enhancing its performance in achieving the optimization objective.

The invention discloses a quantum key distribution network resource allocation system and method based on cloud edge collaboration, and belongs to the technical field of quantum communication and network resource scheduling, and the method comprises the steps that a terminal layer generates a service request and a service demand label and sends the service request and the service demand label to an edge layer; the edge layer determines a weight coefficient, constructs a resource state matrix of the system, determines an execution strategy of edge node cache based on the resource state matrix, determines a candidate computational node set based on the resource state matrix and the execution strategy, determines a candidate path set based on the resource state matrix and the candidate computational node set, and constructs an edge node cache; determining a target path based on the candidate path set and the weight coefficient of the three-dimensional target function, and performing resource allocation on computing nodes belonging to an edge layer in the target path; and the cloud layer allocates resources to the computing nodes belonging to the cloud layer in the target path. According to the application, a three-level architecture design of terminal layer-edge layer-cloud layer is adopted, and key forwarding loss and link resource waste are significantly reduced.

The embodiment of the invention provides a resource allocation processing method and device.The resource allocation processing method comprises the steps that firstly, a resource request sent by a resource processing system in a resource link is obtained, and a processing assembly deployed by a resource allocation system is called to conduct field analysis on the resource request to obtain at least one field type; a resource allocation identifier is generated according to the at least one field type, matching detection is carried out on the resource allocation identifier and resource allocation templates in a resource allocation template library, the resource allocation templates are in one-to-one correspondence with allocation scenes of resource allocation of the resource allocation system, and if the matching detection is not passed, the resource allocation templates are not matched with the allocation scenes of resource allocation of the resource allocation system; and calling the processing component to intercept the resource request so as to carry out risk detection processing on the resource request, thereby carrying out resource allocation based on the resource request on the basis of the risk detection processing.