21 results about "Resource pricing" patented technology

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention provides a computing power resource pricing method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining resource demand information and identity information of a resource demand side; acquiring historical behavior information corresponding to the resource demander according to the identity information; determining a preference coefficient corresponding to the resource demand side according to resource demand parameters and historical behavior information in the resource demand information; determining a calculation model corresponding to the resource demand side according to a charging mode in the resource demand information; and inputting the tenant level, the preferential coefficient and the resource demand parameter into a calculation model corresponding to the resource demand side to obtain a calculation power resource pricing result corresponding to the resource demand side. According to the technical scheme provided by the embodiment of the invention, the computing power resource requirements of different resource demanders are met through multiple charging modes, and meanwhile, the computing power resource utilization rate is improved.

The embodiment of the invention discloses a data resource pricing method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a data quality evaluation value of a to-be-priced data resource based on a data quality evaluation function according to the attribute of the to-be-priced data resource; based on a data resource utility function, determining a data resource utility value of the to-be-priced data resource according to the data quality evaluation value; and based on an association relationship between a data value and a data resource utility value, determining the value of the to-be-priced data resource according to the data resource utility value, the association relationship being determined when the profit is maximized based on the relationship among the profit, the expected income and the cost. According to the embodiment of the invention, the accuracy of data value determination can be improved.

The invention provides a vehicle-mounted semantic communication task unloading method based on a multi-agent game, and the method comprises the steps: constructing a multi-leader-single follower Stackelber game model for semantic communication task resource transaction between a vehicle and edge calculation, designing an optimal resource pricing strategy and a distribution excitation strategy under multiple constraint conditions, and carrying out the optimal resource pricing strategy and the distribution excitation strategy; the utility functions of the vehicle and the roadside unit are respectively optimized, so that the benefit balance of the two parties and the efficient utilization of computing resources are realized; under the condition that the optimal resource allocation strategy is determined, a multi-agent deep reinforcement learning mode is adopted, a resource pricing strategy is adaptively learned, semantic communication resource transaction is optimized, and efficient and reliable interaction is realized. According to the method, efficient and self-adaptive semantic task transaction can be realized in a vehicle-mounted semantic communication environment with incomplete information, and economic benefit maximization of independent vehicle users and edge nodes can be guaranteed by determining resource pricing and allocation strategies under game equilibrium.

The invention relates to the technical field of edge computing, and discloses a demand-differentiated dynamic edge computing network task unloading method and system.The method comprises the steps that firstly, a model of a vehicle edge computing system is built, problem description and disassembly are conducted according to an optimization target, and a first problem and a second problem are obtained; and the first problem and the second problem are solved in a staged manner. Thus, through joint optimization of the access decision of the two stages, in the first stage, a vehicle user can make an adaptive roadside unit channel access decision under a dynamic network condition. And in the second stage, the interaction between the vehicle user and the roadside unit is used as a Stackelberg game. Therefore, task scheduling and resource pricing are economically, stably and effectively developed in a balanced manner, and the QoE of the vehicle user and the earnings of the service provider can be maximized while efficient task unloading is met.

The invention discloses a multi-commodity-channel dynamic price adjustment system, which comprises a prediction model module, a pricing module and an interface configuration module, and is characterized in that the system automatically selects a pricing mode according to the category and the supply channel of a commodity, and the pricing module realizes the dynamic price adjustment of the commodity; the pricing module is configured to support multiple pricing modes including a traditional pricing mode, an external resource pricing mode, an internal resource pricing mode, a resource-free intelligent pricing mode and a seller quotation intelligent pricing mode. Through cooperative work of the prediction model module, the pricing module, the interface configuration module and the like, corresponding pricing modes are automatically selected for different supply channels and resource conditions, and flexible adjustment of prices is realized.

The invention discloses a slice resource pricing and virtual power plant frequency modulation income optimization method based on a Stackelberg game, and the method comprises the steps: building a Stackelberg game framework between a communication operator and a VPP, wherein the upper operator determines 5G bandwidth resource allocation according to a slice pricing strategy; and the lower VPP optimizes the slice demand and the frequency modulation response under the condition of considering the communication delay, the reliability and the AGC service score. The communication effectiveness is modeled by introducing an effective capacity (EC) theory and is coupled with a frequency modulation revenue function, and a slice-communication performance-frequency modulation revenue closed-loop optimization model is established. According to the method, the problem that communication and frequency modulation decoupling and resource allocation lack an economic mechanism in a traditional scheme can be avoided, balanced improvement of operator income and VPP income is realized, the frequency modulation performance and economy are remarkably improved, and the method has wide application value.

The application discloses an idle user resource auxiliary pricing and distribution method for an Internet of Things edge cloud continuum. First, a multi-agent resource coordination architecture composed of idle users, edge / cloud server resource providers (ECSP) and sensor devices is constructed to cope with the challenges of limited computing resources and prominent supply-demand contradiction in the edge environment. Second, a three-stage evolutionary game model is established to simulate the strategy evolution process of the three types of participating agents in resource pricing and procurement decision-making, thereby realizing dynamic adjustment of pricing and reasonable allocation of resources. In order to improve the rationality of strategy evolution and system stability, the application introduces a reputation incentive mechanism to reward reasonable pricing or resource purchase behavior and punish irrational behavior, and promotes the system evolution to converge to a stable and efficient strategy configuration. By constructing a replicator dynamic equation and combining a stability analysis method, the evolutionary stable strategy (ESS) of the system is identified to ensure the utility optimization of each participant and the maximization of the overall social welfare of the system in a complex dynamic environment.

The application discloses a mobile user task unloading and resource pricing method and system in edge computing, constructs the problem of how the edge server sets the price of resources and how the mobile user unloads tasks to maximize their own long-term benefits into a Markov decision process, and then designs the edge server resource pricing strategy and the mobile user task unloading strategy under dynamic pricing based on the DDPG algorithm and the DQN algorithm. When the edge server is not in the full load state, it will receive the task request, and then the mobile user will send the data required for processing the task to the edge server. After the edge server completes the computing task, the computing result will be immediately returned to the mobile user. The application can take into account the benefits of both the mobile user and the edge server, and can maximize the long-term benefits of both as much as possible, improve the user experience, and also make the edge server obtain more profits.

The present invention proposes a distributed edge-cloud resource leasing decision-making method for the Internet of Things blockchain, including: S1, obtaining the resource pricing of the edge service provider (ESP) and the cloud service provider (CSP) in the network environment, as well as the remaining resource amount, block speed, maximum tolerable delay and the remaining energy, remaining budget, and maximum computing resources of the local device; S2, constructing an optimization objective function; S3, modeling the optimization objective function as a Markov decision process, and then using a policy-based reinforcement learning method to solve the Markov decision process to obtain a resource leasing strategy. The method of the present invention can achieve efficient distributed task scheduling in a dynamic Internet of Things environment. Combined with blockchain technology, it not only improves the security and transparency of data transmission, but also significantly improves the computing efficiency and stability of the system.

The present invention discloses an online matching method and system for reusable resources in a cloud service platform, belonging to the field of cloud service resource matching, including: the cloud service platform generates a resource set I; the client submits the user's current resource request Q to the cloud service platform; the cloud service platform receives the current resource request Q, calculates and informs the user of the resource pricing V; if the user accepts the resource pricing V, the cloud service platform queries the resource set I to obtain a resource set S that meets the current resource request Q and is not occupied; performs a priority evaluation on each resource i in the resource set S, and selects the top n resources with high priority as the resource object I* for request matching. The method of the present invention can take into account the requests of users who have not arrived while meeting the current user's request, avoid subsequent users being unable to obtain suitable resources, improve the utilization rate and efficiency of the cloud service platform, and can realize the reusable use of resources with different attributes.

The present invention discloses an end-to-end network resource allocation method based on Bayesian learning, which belongs to the technical field of calculation, inference or counting. The method adopts a solution based on a game framework, proposes a network resource pricing strategy that reflects the service provider's demand information, and realizes more reasonable network resource pricing and allocation by analyzing the network resource status and service provider behavior. The method first establishes a game model between multiple service providers and operators. The operator selects the unit network slice price in the first stage based on limited demand information; in the second stage, the demand information is updated to determine the price. The simulation results show the existence of the Nash equilibrium point of the system model, and proposes a responsive network resource slice allocation scheme to avoid resource shortages and network congestion caused by service providers' concentrated resource requests, thereby realizing reasonable allocation of network resources.

The invention relates to the technical field of data resource pricing and computers, and discloses a small sample data resource pricing method based on a meta-learning Reptile algorithm, and the method comprises the steps: crawling data resource transaction information from a big data transaction platform through a crawler, and forming an initial training set; performing data preprocessing on the initial training set, and dividing the initial training set into a training set and a test set; using an MLP model as a basic data resource pricing model, and training the MLP model by using the training set and the test set and based on a meta-learning Reptile algorithm to obtain a target data resource pricing model; and performing overall performance evaluation on the target data resource pricing model by calculating a mean absolute error MAE, a mean square error MSE and the goodness of fit R. The problems that small sample data pricing is difficult, pricing standards are fuzzy, transaction rules are missing and the like in an existing data resource pricing method are solved, and the problem that a traditional machine learning pricing method is not suitable for small sample data resource pricing with small sample size and sparse features is solved.

The invention relates to the technical field of resource quantification, and discloses a pricing method, device and equipment of computing power resources and a storage medium, which are used for improving the accuracy of pricing of the computing power resources. The pricing method of the computing power resource comprises the following steps: acquiring key data of multiple dimensions of the computing power resource, wherein the multiple dimensions comprise computing speed, storage performance, network bandwidth and energy consumption; based on the key data of the multiple dimensions, calculation is carried out through a preset calculation power resource value evaluation model, and the unit value of each dimension is obtained; distributing a corresponding weight for each dimension; according to the unit value of each dimension, calculation is carried out in combination with the corresponding weight, and the calculation power unit price of the calculation power resource is obtained; and according to the computing power unit price and the computing power resource quantity actually used by the user, calculating the user cost to obtain the total cost of the user.

The present invention discloses a resource collaboration method for a cloud-edge-end system of intelligent production line control, which belongs to the field of cloud-edge-end technology. By combining economic models and optimization methods, the edge server and terminal devices can cooperate well. The specific contents are as follows: (1) The resource pricing and transaction model of the cloud-edge-end system of intelligent production line control uses economic principles and capital pricing to dynamically quantify the resources of the edge server; (2) A terminal periodic task unloading method based on a multi-armed bandit reinforcement learning method uses reinforcement learning to optimize the resource configuration required for periodic task unloading; (3) A resource collaboration method based on Stackelberg game takes the maximization of the overall benefits of the system as the goal to achieve the purpose of optimizing the overall resource utilization of the system. The present invention adopts the above method, targeting the cloud-edge-end system of intelligent production line control, so that the tasks unloaded by the terminal device can minimize the operating costs within the specified operating time.

This invention relates to an information scene perception multi-task federated learning incentive method and system based on game theory and reinforcement learning. It acquires the utility of clients and servers, determines the type of information scene, and in open information scenes, employs a game theory-based incentive mechanism to model the client-server utility optimization problem as a resource pricing and purchasing game, determining the client's pricing strategy and the server's purchasing strategy. In closed information scenes, it employs a multi-agent reinforcement learning-based incentive mechanism to model the client-server utility optimization problem as a Markov decision process, determining the client's pricing strategy and the server's purchasing strategy. Formal federated learning training is then performed based on the client's pricing strategy and the server's purchasing strategy until a predetermined number of training rounds are reached. Compared with existing technologies, this invention significantly improves the adaptability and overall utility of the federated learning system in different information environments.

The invention discloses a two-way auction resource transaction method based on reputation. The method comprises the following steps: S1, system modeling: establishing a communication and calculation model; designing a reputation evaluation model and a reputation-based resource pricing mechanism; s2, defining utility functions of the UV and the RSU, and optimizing calculation unloading, communication and calculation resource allocation; s3, algorithm design: in a bidirectional auction process, the UV submits a resource request and a corresponding bid to the auctioneer, the RSU submits available resources and a corresponding price to the auctioneer, and the auctioneer collects information from the UV and the RSU, ranks the information according to the bid and the price of the UV and the RSU, and constructs two different tables; establishing a decision process based on a DDPG algorithm; and S4, auction attributes including individual rationality and incentive compatibility. By using the reputation-based bidirectional auction resource transaction method provided by the invention, the market fairness and transaction efficiency are realized, the overall resource allocation is optimized, the operation efficiency is improved, and the overall performance of the VEC network is remarkably improved.

The invention relates to a block chain distributed storage resource allocation method based on the Starburg game, and also relates to a block chain distributed storage resource allocation device based on the Starburg game, and belongs to the technical field of network information security, and the method comprises the steps: constructing constraint strategies of a storage resource price and a storage resource application amount; according to the resource price and the storage resource application amount, respectively establishing utility functions of the storage resource provider and the storage resource user; combining the resource price constraint strategy with the corresponding utility function to obtain a high-level optimization problem; combining the constraint strategy of the storage resource application quantity with the corresponding utility function to obtain a low-layer optimization problem, and constructing a storage resource pricing and allocation optimization problem; and solving a storage resource pricing and allocation optimization problem to obtain a resource price for allocation and a corresponding storage resource application quantity. According to the invention, the resource supply capability can be provided in a balanced manner, and the diversity and timeliness of the demand mode can be considered.

The invention relates to the technical field of edge computing, and discloses a multi-party storage resource transaction and pricing system based on a block chain, and the system comprises an edge alliance layer which is composed of a user, edge nodes of a plurality of edge service providers, a cooperation service layer, and storage resources; the block chain layer comprises a consensus node, a smart contract and a consensus mechanism; the consensus node ensures the consistency of the transaction sequence and the block content through a transaction expectation proof consensus algorithm; the smart contract is used for automatically executing a storage resource transaction rule and recording a transaction process and a transaction result to the block chain; the SGA-MARL framework is used for dynamically optimizing storage resource pricing and a transaction share decision based on a Stackelberg game algorithm of multi-agent reinforcement learning; the framework comprises a leader agent and a follower agent, and resource pricing is coordinated through a reinforcement learning model to realize profit maximization of participants.

The application provides a service offloading method and system based on a multi-level offloading network, and the method comprises the following steps: a resource pricing scheme of a device is proposed, a multi-level task offloading algorithm MOS is designed to maximize the income of a user completing a task; the multi-level task offloading algorithm MOS takes the user as the center and takes the maximum completion delay of the task as the constraint to construct a multi-level task offloading network, and through layer-by-layer pricing, the user can determine the final task offloading scheme. The application solves the technical problems of resource waste, low utilization rate of auxiliary computing devices in the community and low income of task requesters.