Resource allocation method and device, equipment, medium and program product

Abstract

The invention provides a resource allocation method and device, equipment, a medium and a program product, and relates to the technical field of resource allocation, the method is applied to a first node in a block chain network, and comprises the following steps: obtaining first recommendation information according to calculation demand information of a calculation task of a user, the first recommendation information comprises information of M pieces of first mobile equipment meeting the calculation demand information; obtaining second recommendation information sent by a second node in the block chain network according to the calculation demand information, wherein the second recommendation information comprises information of M second mobile devices meeting the calculation demand information; target recommendation information is obtained according to the first recommendation information and the second recommendation information, the target recommendation information comprises information of M target mobile devices, and the target mobile devices are used for executing P sub-tasks corresponding to the calculation tasks, according to the scheme, the calculation tasks are unloaded to the target mobile devices, and edge network congestion is avoided.

Description

Technical Field

[0001] This application relates to the field of resource allocation technology, specifically to a resource allocation method, apparatus, equipment, medium, and program product. Background Technology

[0002] Currently, thanks to the high-speed, low-latency resources provided by 5G networks, virtual-real convergence applications have achieved seamless integration between the real physical world and the virtual world, enabling people to experience and interact with the virtual environment firsthand. However, with the development of virtual-real convergence applications, the computing demands of virtual-real convergence devices are constantly increasing. Many application scenarios (such as real-time rendering, complex physics simulations, and AI-driven content generation) have extremely high requirements for computing resources. The computing resources of a single node are insufficient to support efficient processing needs, and wireless network resources are limited. Traditional centralized resource allocation schemes are also insufficient to cope with rapidly changing demands and complex service environments.

[0003] While existing technologies can utilize edge computing and cloud computing to offload some computing tasks to edge servers or the cloud to alleviate the problem of insufficient computing resources on a single node, the simultaneous offloading of computing tasks to edge servers or the cloud by a large number of virtual-physical fusion devices in virtual-physical fusion interaction may lead to insufficient resource allocation, resulting in edge network congestion and affecting the overall computing tasks. Summary of the Invention

[0004] At least one embodiment of this application provides a resource allocation method, apparatus, device, medium, and program product to solve the problem of edge network congestion caused by the ever-increasing computing demands in existing virtual-real fusion applications.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a resource allocation method applied to a first node in a blockchain network, the blockchain network comprising N nodes, the first node being one of the N nodes, the method comprising:

[0007] Based on the computational requirements of the user's computational task, first recommendation information is obtained, which includes information on M first mobile devices that meet the computational requirements.

[0008] Obtain second recommendation information sent by a second node in the blockchain network based on the computing demand information, wherein the second recommendation information includes information on M second mobile devices that meet the computing demand information;

[0009] Based on the first recommendation information and the second recommendation information, target recommendation information is obtained. The target recommendation information includes information on M target mobile devices. The target mobile device is one of the first mobile device or the second mobile device. Each target mobile device is used to execute P sub-tasks corresponding to the computing task.

[0010] The computational requirement information includes resource requirement information and / or reputation value requirement information; the second node is any one of the N nodes other than the first node; both the first mobile device and the second mobile device are located within the user's preset range; N, M, and P are all integers greater than 1.

[0011] Optionally, the resource allocation method, wherein obtaining first recommendation information based on the computational requirements of the user's computational task includes:

[0012] Based on the resource information and / or reputation value of each of the X pre-stored mobile devices, obtain the resource requirement information in the computing requirement information that satisfies the user's computing task, and / or, the reputation value satisfies the reputation value requirement information in the computing requirement information of the M first mobile devices, where X is an integer greater than 1;

[0013] Obtain first recommendation information including information about M of the first mobile devices.

[0014] Optionally, the resource allocation method further includes:

[0015] After the user receives the calculation results sent by each target mobile device, the user obtains the reputation evaluation information for each target mobile device.

[0016] The reputation value of the target mobile device is updated based on the reputation evaluation information.

[0017] Optionally, in the resource allocation method, obtaining target recommendation information based on the first recommendation information and the second recommendation information includes:

[0018] Based on the reputation value of each first mobile device in the first recommendation information and the reputation value of each second mobile device in the second recommendation information, sort the M first mobile devices and the M second mobile devices.

[0019] The mobile devices with the highest reputation scores (ranked in the top M) are identified as the target mobile devices.

[0020] Obtain target recommendation information including information about M target mobile devices.

[0021] Optionally, the resource allocation method further includes:

[0022] The data collection device sends a first request via a smart contract, the first request being used to request sensing data from the sensing device.

[0023] The pre-stored sensing data is transmitted to the data collection device, which then transmits the sensing data to the target mobile device. The target mobile device executes P sub-tasks corresponding to the computing task based on the sensing data.

[0024] Optionally, in the resource allocation method, before transmitting the pre-stored sensed data to the data collection device, the method further includes:

[0025] Obtain a second request sent by the sensing device through the smart contract, the second request being used to request resources for storing the sensing data;

[0026] Allocate resources for the sensed data;

[0027] The sensing data transmitted by the sensing device is stored.

[0028] Secondly, this application also provides a resource allocation device applied to a first node in a blockchain network, the blockchain network comprising N nodes, the first node being one of the N nodes, the device comprising:

[0029] The first acquisition module is used to acquire first recommendation information based on the computing requirements information of the user's computing task. The first recommendation information includes information on M first mobile devices that meet the computing requirements information.

[0030] The second acquisition module is used to acquire second recommendation information sent by a second node in the blockchain network according to the computing demand information. The second recommendation information includes information on M second mobile devices that meet the computing demand information.

[0031] The third acquisition module is used to acquire target recommendation information based on the first recommendation information and the second recommendation information. The target recommendation information includes information on M target mobile devices. The target mobile device is one of the first mobile device or the second mobile device. Each target mobile device is used to execute P sub-tasks corresponding to the computing task.

[0032] The computational requirement information includes resource requirement information and / or reputation value requirement information; the second node is any one of the N nodes other than the first node; both the first mobile device and the second mobile device are located within the user's preset range; N, M, and P are all integers greater than 1.

[0033] Thirdly, embodiments of this application also provide a resource allocation device, including: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the processor executes the program or instructions to implement the resource allocation method as described in the first aspect.

[0034] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the resource allocation method as described in the first aspect.

[0035] Fifthly, embodiments of this application also provide a computer program product, including computer instructions, which, when executed by a processor, implement the resource allocation method as described in the first aspect.

[0036] Compared with existing technologies, this application provides a resource allocation method, apparatus, device, medium, and program product. The method is applied to a first node in a blockchain network, which includes N nodes, with the first node being one of the N nodes. The method includes: obtaining first recommendation information based on the computational requirements of a user's computational task, the first recommendation information including information on M first mobile devices that meet the computational requirements; obtaining second recommendation information sent by a second node in the blockchain network based on the computational requirements, the second recommendation information including information on M second mobile devices that meet the computational requirements; and obtaining target recommendation information based on the first and second recommendation information, the target recommendation information including information on M target mobile devices, each target mobile device being one of the first or second mobile devices. Each target mobile device is used to execute P subtasks corresponding to the computational task, dynamically offloading the computational task to the target mobile device. This effectively alleviates the resource pressure on nodes and the edge network congestion problem on the base station side, thereby improving the computational efficiency in virtual-real fusion services. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 This is a schematic diagram of the architecture of the application system for the resource allocation method described in the embodiments of this application;

[0039] Figure 2 This is a flowchart illustrating the resource allocation method described in an embodiment of this application;

[0040] Figure 3 This is a flowchart illustrating one embodiment of the resource allocation method described in this application.

[0041] Figure 4 This is a schematic diagram illustrating the process of sharing perceived data as described in an embodiment of this application;

[0042] Figure 5 This is a schematic diagram of the interaction process of perceived data described in an embodiment of this application;

[0043] Figure 6 This is a schematic diagram of the structure of the resource allocation device described in the embodiments of this application;

[0044] Figure 7 This is a hardware block diagram of the resource allocation device described in the embodiments of this application. Detailed Implementation

[0045] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, "A or B" covers three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0046] Please refer to Figure 1This application provides a resource allocation system, which is an application system of a resource allocation method provided in this application. When a virtual service provider provides services to a virtual service user, the virtual service provider can distribute computing tasks (such as large-scale matrix calculations) to multiple mobile devices located close to the virtual service user for processing. These mobile devices are called computers, and the set of computers is represented as C = {1, 2, 3, ..., c, ... C}. After the computers have completed the corresponding computing tasks, they directly send the calculation results to the virtual service user. The resource allocation system sets the edge server on the base station side as a node in the blockchain network. This node executes the corresponding blockchain consensus algorithm and can therefore be called a consensus node, resulting in a consensus node set {1, 2, 3, ..., d, ... D}. The resource allocation system can realize distributed reputation management and resource interaction records in Coded Distributed Computer (CDC), effectively solving the problems of base station congestion and mobile device computing resource utilization.

[0047] This application provides a resource allocation method applied to a first node in a blockchain network, wherein the blockchain network includes N nodes, the first node is one of the N nodes, and N is an integer greater than 1.

[0048] For example, each of the above nodes can be an edge server on the base station side.

[0049] It should be noted that each node can execute the blockchain consensus algorithm, therefore each node can be called a consensus node.

[0050] Furthermore, such as Figure 2 As shown, the method includes:

[0051] Step 201: Based on the computational requirements of the user's computational task, obtain first recommendation information. The first recommendation information includes information on M first mobile devices that meet the computational requirements. The computational requirements include resource requirements and / or reputation value requirements. The first mobile devices are located within the user's preset range, and M is an integer greater than 1.

[0052] Optionally, the computational tasks include large-scale matrix computation tasks, such as rendering tasks in a virtual-real fusion service.

[0053] For example, the first mobile device is a mobile device capable of participating in large-scale matrix calculations, and can be referred to as a calculator. Furthermore, the first mobile device is located within a preset range of the user, the preset range being set according to actual needs to indicate that the first mobile device is near the user.

[0054] It should be noted that the resource allocation method provided in this application embodiment can be a resource allocation method in virtual-real integration services, and the user can be understood as a virtual service user.

[0055] In some embodiments of this application, when a virtual service provider provides a virtual-real fusion service to the user, i.e., the virtual service user, the virtual service provider can publish the user's computing task computing requirement information on the blockchain network. Each node in the blockchain network receives the computing requirement information, wherein the first node obtains first recommendation information, including information on M first mobile devices that meet the computing requirement information, based on the computing requirement information.

[0056] Step 202: Obtain second recommendation information sent by the second node in the blockchain network based on the computing demand information. The second recommendation information includes information on M second mobile devices that meet the computing demand information. The second node is any one of the N nodes other than the first node, and the second mobile device is located within the user's preset range.

[0057] For example, the second mobile device is a mobile device capable of participating in large-scale matrix calculations, and can be referred to as a calculator. Furthermore, the second mobile device is located within a preset range of the user, the preset range being set according to actual needs to indicate that the second mobile device is near the user.

[0058] In some embodiments of this application, each node in the blockchain network receives the computing demand information, and each node obtains recommendation information including information on M mobile devices that meet the computing demand information based on the computing demand information, and publishes the obtained recommendation information to the blockchain network so that other nodes in the blockchain network can receive it.

[0059] Specifically, the first node can obtain the second recommendation information sent by each of the second nodes.

[0060] Step 203: Based on the first recommendation information and the second recommendation information, obtain target recommendation information. The target recommendation information includes information on M target mobile devices. The target mobile device is one of the first mobile device or the second mobile device. Each target mobile device is used to execute P sub-tasks corresponding to the computing task, where P is an integer greater than 1.

[0061] In some embodiments of this application, the first node uses a blockchain consensus algorithm to obtain target recommendation information, including information on M target mobile devices, based on the first recommendation information obtained by the first node itself and the second recommendation information sent by each of the second nodes.

[0062] Similarly, for any node in the blockchain network other than the first node, i.e. the second node, it can obtain the first recommendation information sent by the first node, and based on the second recommendation information it obtains and the first recommendation information sent by the first node, it can obtain target recommendation information including information on M target mobile devices.

[0063] It is understandable that the first node and the second node obtain the same target recommendation information and jointly recommend M suitable target mobile devices. These nodes that recommend the same M target mobile devices can form an alliance, thereby improving the fairness and accuracy of mobile device recommendations.

[0064] In some embodiments of this application, the information of the M target mobile devices can be recorded in a blockchain distributed ledger in a blockchain network, thereby ensuring the transparency and immutability of the information.

[0065] It should be noted that the virtual service provider can allocate specific computing tasks using a game theory model based on the target recommendation information. First, the virtual service provider, acting as the leader, determines the reward strategy for the computing task. The selected target mobile device, acting as a follower, adjusts its execution strategy based on its computing power and reputation. Then, the virtual service provider decomposes the computing task into P subtasks and uses encoding to generate multiple redundant subtasks, which are then assigned to different target mobile devices. Upon receiving the subtasks, the target mobile devices use Orthogonal Frequency Division Multiple Access (OFDMA) wireless transmission technology to directly transmit the processed computing results to the virtual service user.

[0066] In one implementation method, optionally, first recommendation information is obtained based on the computational requirements of the user's computational task, including:

[0067] Based on the resource information and / or reputation value of each of the X pre-stored mobile devices, obtain the resource requirement information in the computing requirement information that satisfies the user's computing task, and / or, the reputation value satisfies the reputation value requirement information in the computing requirement information of the M first mobile devices, where X is an integer greater than 1;

[0068] Obtain first recommendation information including information about M of the first mobile devices.

[0069] Optionally, the first node obtains resource information and / or reputation value of each of the X pre-stored mobile devices from the blockchain distributed ledger.

[0070] In some embodiments of this application, the first node receives the computing requirement information and evaluates each of the X mobile devices based on the resource requirement information and / or reputation value requirement information in the computing requirement information, thereby obtaining first recommendation information. It is understood that if the resource information of a mobile device does not meet the resource requirement information, and / or the reputation value does not meet the reputation value requirement information, then that mobile device will be excluded from the first recommendation information.

[0071] In one embodiment, optionally, the method further includes:

[0072] After the user receives the calculation results sent by each target mobile device, the user obtains the reputation evaluation information for each target mobile device.

[0073] The reputation value of the target mobile device is updated based on the reputation evaluation information.

[0074] In some embodiments of this application, after the user completes the computation task and receives the computation result, the user evaluates the reputation of each target mobile device, obtains reputation evaluation information, and publishes it to the blockchain network. Each node in the blockchain network obtains the reputation evaluation information of each target mobile device and updates the reputation value of the target mobile device to obtain the updated reputation value. Here, the updated reputation value of the target mobile device can be stored in the blockchain distributed ledger.

[0075] Understandably, after the reputation score is updated, the recommended target devices are all based on the updated reputation score.

[0076] It should be noted that after the user completes the computing task, the target mobile device can obtain corresponding blockchain rewards, such as resource rewards, based on the completion status of the computing task.

[0077] In one implementation, optionally, obtaining target recommendation information based on the first recommendation information and the second recommendation information includes:

[0078] Based on the reputation value of each first mobile device indicated by the first recommendation information and the reputation value of each second mobile device indicated by the second recommendation information, sort the M first mobile devices and the M second mobile devices.

[0079] The mobile devices with the highest reputation scores (ranked in the top M) are identified as the target mobile devices.

[0080] Obtain target recommendation information including information about M target mobile devices.

[0081] In some embodiments of this application, the first node sorts the M first mobile devices it has acquired and the M second mobile devices sent by the second node according to their reputation values ​​from largest to smallest, and selects the mobile devices with the highest reputation values ​​as the target mobile devices.

[0082] It should be noted that for any node in the blockchain network other than the first node, i.e., the second node, the same implementation method is used when obtaining target recommendation information, thereby jointly recommending suitable target mobile devices. These nodes recommending the same target mobile devices can form an alliance.

[0083] Figure 3 This is a flowchart illustrating one embodiment of the resource allocation method described in this application. The resource allocation method includes a computational selection phase and a computational task execution phase. The computational selection phase includes the following steps:

[0084] ① Virtual service providers publish the computing needs information of virtual service users’ computing tasks to the blockchain network in order to recruit computing users, and computing users are mobile devices;

[0085] ② Each node obtains the reputation value of the mobile device from the blockchain network and selects a computer that meets the computing requirements.

[0086] ③ Nodes form an alliance to jointly recommend suitable target mobile devices, and the alliance forms a game;

[0087] ④ The blockchain network sends the information of the selected computers to the virtual service provider;

[0088] ⑤ Virtual service providers use game theory models to assign specific computing tasks to target mobile devices;

[0089] ⑥ All resource interaction records between virtual service providers and virtual service users will be stored in real time on the blockchain network to ensure transparency and data immutability during the resource interaction process.

[0090] ⑦ After the computer performs the computation task, the virtual service user updates the interaction results to the blockchain network.

[0091] Figure 4This is a schematic diagram illustrating the data sharing process described in this application's embodiments. In the virtual-real fusion service process, the construction of part of the virtual space is based on the real space, which requires sensing devices to collect a large amount of physical data, i.e., sensing data. However, current centralized data sharing methods suffer from single points of failure, privacy leaks, and data tampering, making it difficult to meet the needs of virtual-real fusion services. Blockchain, due to its decentralized and immutable characteristics, has become a key technology for next-generation wireless network management. However, nodes participating in the blockchain consensus process need to perform transaction verification and consensus processes, which places high demands on the nodes' computing power. Furthermore, in blockchain-based sensing data sharing, how to effectively store shared data is also a problem. The resource allocation method described in this application's embodiments can effectively store shared data.

[0092] like Figure 4 As shown, in the virtual-real integration service, the resource allocation system includes two layers: a physical domain and a social domain. The physical domain comprises a consensus layer and a device layer, with devices in the device layer corresponding to individuals in the social domain, and these individuals having social relationships.

[0093] Specifically, the consensus layer and device layer are explained as follows:

[0094] (1) Consensus Layer: This is the consensus layer of the blockchain network, including edge servers with high hardware performance. These edge servers participate in the consensus process and are responsible for transaction packaging, block verification, and consensus achievement during the sensing data sharing process. These edge servers are usually deployed near base stations, which are responsible for transmitting the sensing data, thus forming a distributed storage network. The set of edge servers is represented by S = {1, 2, 3, ..., s, ... S}. The edge servers adopt the Proof of Spacetime (PoST) consensus mechanism. In each consensus cycle, the probability of an edge server obtaining a consensus reward is proportional to the amount of storage and transmission resources it provides during the sensing data sharing process. The edge servers participating in the consensus are responsible for storing the sensing data shared by the sensing devices and recording the storage address of the sensing data on the blockchain distributed ledger to ensure the traceability of the sensing data. Finally, the blocks selected through consensus (including block headers and transaction data) are added to the blockchain, forming a reliable and verifiable data sharing record.

[0095] (2) Device Layer: The device layer includes sensing devices and data collection devices of virtual-real fusion service providers. Sensing devices include mobile devices, cameras, etc., and are mainly responsible for collecting various types of data, including GPS coordinates and images. The set of sensing devices is represented by O = {1, 2, 3, ..., o, ... O}, and the set of shared data packets generated by these sensing devices is represented by F = {F1, F2, F3, ..., F...}. o ,…FO The set of data is represented as C = {1, 2, 3, ..., c, ... C}. Furthermore, virtual-real fusion service providers can request the necessary sensing data through the blockchain network, and the data collection devices of these service providers are responsible for receiving this sensing data.

[0096] In one embodiment, optionally, the method further includes:

[0097] The data collection device sends a first request via a smart contract, the first request being used to request sensing data from the sensing device.

[0098] The pre-stored sensing data is transmitted to the data collection device, which then transmits the sensing data to the target mobile device. The target mobile device executes P sub-tasks corresponding to the computing task based on the sensing data.

[0099] Optionally, the data collection device is a data collection device of a virtual service provider.

[0100] In some embodiments of this application, the sensing data of the sensing device is pre-stored in nodes of a blockchain network. The first node receives a first request sent by the data collection device through a smart contract and transmits the sensing data to the data collection device. Thus, the target mobile device can obtain the sensing data from the data collection device and execute P sub-tasks corresponding to the computing task based on the sensing data.

[0101] In one embodiment, optionally, before transmitting the pre-stored sensed data to the data collection device, the method further includes:

[0102] Obtain a second request sent by the sensing device through the smart contract, the second request being used to request resources for storing the sensing data;

[0103] Allocate resources for the sensed data;

[0104] The sensing data transmitted by the sensing device is stored.

[0105] In some embodiments of this application, the virtual service provider interacts with nodes in the blockchain network through the smart contract. The nodes provide storage space for the sensing device, and the sensing device stores the shared sensing data on the node and records the storage address in the blockchain network.

[0106] Specifically, the first node can obtain the second request sent by the sensing device through the smart contract for requesting to store the sensing data, allocate resources for the sensing data, and use the resources to store the sensing data transmitted by the sensing device.

[0107] Figure 5 This is a schematic diagram illustrating the interaction process of perceived data as described in an embodiment of this application. Figure 5 As shown, in the interaction of sensing data in the virtual-real fusion service, the virtual service provider interacts with nodes in the blockchain network by calling smart contracts. Once a node provides storage space for the sensing device, the sensing device stores the shared sensing data on the node and records the storage address in the blockchain network. The virtual service provider's data collection device can then request access to the stored sensing data through a smart contract. After obtaining authorization from the sensing device, the node transmits the stored sensing data to the virtual service provider's data collection device via a base station.

[0108] During the interaction of sensed data, virtual service providers also share data quality information of the sensing devices. If the data quality of a particular sensing device is high, more virtual service providers associated with that device will prioritize requesting access to this sensed data through the blockchain network. This data quality-driven request mechanism not only improves the effective utilization of data but also enhances the intelligence and responsiveness of the resource allocation system. Simultaneously, all sensed data interaction and transmission operations are automatically executed by smart contracts, completely eliminating dependence on third-party platforms. This decentralized architecture greatly improves the system's security and trustworthiness, ensuring transparency and privacy protection in data transmission.

[0109] Therefore, this application embodiment, by combining edge computing with blockchain technology, not only optimizes the sharing and management process of perceived data, but also achieves efficient resource allocation and secure data storage. Edge computing brings data processing closer to the data source, reduces latency, and improves real-time response capabilities. Furthermore, blockchain technology ensures the immutability and traceability of data, thereby significantly enhancing the reliability and user experience of virtual-real fusion services. This provides virtual-real fusion services with greater flexibility, security, and intelligence, enabling them to better meet the needs of future complex application scenarios.

[0110] Next, the smart contracts in the blockchain network will be described as follows:

[0111] Smart contracts are modular, reusable, and automatically executed scripts that run on the blockchain network. They are stored in the blockchain's distributed ledger, and each node in the blockchain network can execute these smart contracts and view their related logs. During the interaction of sensing data, the interaction rules between sensing devices, data collection devices, and nodes, including operations such as the storage, transmission, and sharing of sensing data, are all recorded and automatically executed on the blockchain network through smart contracts.

[0112] In the embodiments of this application, the following two key smart contract algorithms are involved:

[0113] Smart Contract 1, a smart contract for sensing data storage and transmission:

[0114] Smart Contract 1 defines the data storage and transmission rules between sensing devices and nodes. Sensing devices can request storage resources from nodes by invoking the smart contract, and the stored sensing data will be recorded on the blockchain. When a virtual service provider's data collection device requests access to this data, the sensing device invokes the transmission function in the smart contract to transmit the data to the virtual service provider's device via the node. All transactions are verified and recorded on the blockchain, and the node simultaneously collects the corresponding service fee. The algorithm of Smart Contract 1 is shown in Tables 1 and 2 below:

[0115] Table 1: Smart Contract Sensing Data Storage Algorithm

[0116]

[0117] Table 2: Smart Contract Sensing Data Transmission Algorithm

[0118]

[0119] Smart Contract 2, Sensing Data Sharing Smart Contract:

[0120] This smart contract manages the data sharing process between the sensing device and the virtual service provider's data collection device. The virtual service provider's data collection device can request sensing data from the sensing device via the smart contract. The sensing device will then invoke functions in the data storage and transmission contract to share the sensing data with the corresponding data collection device. Once the data is successfully transmitted, the sensing device will receive a reward via the blockchain. The algorithm for this sensing data sharing smart contract is shown in Table 3 below:

[0121] Table 3: Smart Contract Sensing Data Sharing and Incentive Algorithm

[0122]

[0123] Therefore, this application provides a resource allocation method, which is a reliable distributed collaborative resource allocation method based on CDC and blockchain technology. This method dynamically offloads computing tasks to target mobile devices with idle computing resources and / or those meeting reputation value requirements. This distributed collaborative computing approach not only effectively alleviates edge network congestion at the base station side but also fully utilizes dispersed computing resources within the network, thereby improving overall computing efficiency during virtual-real fusion interaction.

[0124] By aggregating M target mobile devices to collaboratively process computing tasks, the key challenge lies in effectively aggregating these distributed resources, given the differences in computing power and available resources among the various mobile devices. In the resource allocation method of this application embodiment, mobile devices are first screened and evaluated to ensure the stability and efficiency of collaborative computing. Secondly, a reputation measurement mechanism is introduced to evaluate the reputation of each participating mobile device. Based on the reputation evaluation results, mobile devices with high reputation and strong reliability are prioritized for collaborative computing, thereby improving the quality of task completion. Then, a cooperative game theory approach is introduced to form a collaborative alliance among multiple mobile devices, ensuring that each mobile device receives a reasonable distribution of benefits during the sharing of computing resources. Finally, the decentralized, tamper-proof, and transparent characteristics of blockchain effectively manage the interactions between entities in virtual-real fusion applications. Furthermore, the system records and manages the distributed reputation value of each device and the resource interaction records between devices on the blockchain, ensuring not only data transparency and trustworthiness but also providing traceable records for future collaborative computing.

[0125] In summary, the resource allocation method described in this application dynamically offloads computing tasks to mobile devices with idle computing resources in the network via CDC, effectively alleviating resource pressure on edge servers and network congestion on the base station side. Compared with the centralized computing mode that relies on edge servers, this application embodiment can more flexibly utilize distributed computing resources, thereby improving overall computing efficiency. Furthermore, by using decentralized reputation management and game theory models to optimize task allocation, it achieves efficient resource utilization, transparent and trustworthy interaction records, and reasonable revenue distribution, thereby improving computing efficiency, system fairness, and reliability in virtual-real fusion services.

[0126] By combining blockchain and edge computing technologies, decentralized sensing data sharing and management are achieved, significantly improving data processing efficiency, storage security, and intelligent task scheduling. Furthermore, edge servers are responsible for storing the sensing data, forming a distributed storage grid that effectively optimizes the storage and management processes. Simultaneously, a data quality-driven request mechanism and the PoST consensus model optimize resource allocation and incentivize edge servers to participate in consensus, ensuring the system's high efficiency, transparency, and reliability.

[0127] By automating data storage, transmission, and sharing operations through smart contracts, reliance on third-party platforms is reduced, enhancing system security and transparency. A blockchain-based reward mechanism encourages more devices and servers to participate in the data sharing process, improving resource utilization and overall system performance. Simultaneously, edge computing reduces data transmission latency, and blockchain technology ensures data immutability and traceability, significantly improving the real-time responsiveness and data security of the virtual-physical integration service.

[0128] Please refer to Figure 6 This application also provides a resource allocation device applied to a first node in a blockchain network, the blockchain network comprising N nodes, the first node being one of the N nodes, the device comprising:

[0129] The first acquisition module 601 is used to acquire first recommendation information based on the computing requirements information of the user's computing task. The first recommendation information includes information on M first mobile devices that meet the computing requirements information.

[0130] The second acquisition module 602 is used to acquire second recommendation information sent by a second node in the blockchain network according to the computing demand information, wherein the second recommendation information includes information on M second mobile devices that meet the computing demand information.

[0131] The third acquisition module 603 is used to acquire target recommendation information based on the first recommendation information and the second recommendation information. The target recommendation information includes information on M target mobile devices. The target mobile device is one of the first mobile device or the second mobile device. Each target mobile device is used to execute P sub-tasks corresponding to the computing task.

[0132] The computational requirement information includes resource requirement information and / or reputation value requirement information; the second node is any one of the N nodes other than the first node; both the first mobile device and the second mobile device are located within the user's preset range; N, M, and P are all integers greater than 1.

[0133] Optionally, in the resource allocation device, the first acquisition module 601 is specifically used for:

[0134] Based on the resource information and / or reputation value of each of the X pre-stored mobile devices, obtain the resource requirement information in the computing requirement information that satisfies the user's computing task, and / or, the reputation value satisfies the reputation value requirement information in the computing requirement information of the M first mobile devices, where X is an integer greater than 1;

[0135] Obtain first recommendation information including information about M of the first mobile devices.

[0136] Optionally, the resource allocation device further includes:

[0137] The fourth acquisition module is used to acquire the reputation evaluation information of each target mobile device after the user receives the calculation results sent by each target mobile device;

[0138] The update module is used to update the reputation value of the target mobile device based on the reputation evaluation information.

[0139] Optionally, in the resource allocation device, the third acquisition module 603 is specifically used for:

[0140] Based on the reputation value of each first mobile device in the first recommendation information and the reputation value of each second mobile device in the second recommendation information, sort the M first mobile devices and the M second mobile devices.

[0141] The mobile devices with the highest reputation scores (ranked in the top M) are identified as the target mobile devices.

[0142] Obtain target recommendation information including information about M target mobile devices.

[0143] Optionally, the resource allocation device further includes:

[0144] The fourth acquisition module is used to acquire the first request sent by the data collection device through a smart contract, wherein the first request is used to request the sensing data of the sensing device;

[0145] A transmission module is used to transmit the pre-stored sensing data to the data collection device, the data collection device is used to transmit the sensing data to the target mobile device, and the target mobile device is used to execute P sub-tasks corresponding to the computing task based on the sensing data.

[0146] Optionally, the resource allocation device further includes:

[0147] The fifth acquisition module is used to acquire a second request sent by the sensing device through the smart contract, wherein the second request is used to request resources for storing the sensing data;

[0148] The allocation module is used to allocate resources for the sensed data;

[0149] A storage module is used to store the sensing data transmitted by the sensing device.

[0150] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above resource allocation method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0151] This application also provides a resource allocation device, such as... Figure 7 As shown, it includes:

[0152] The processor 701, memory 702, transceiver 703, and a program or instructions stored in the memory 702 and executable on the processor 701; when the processor 701 executes the program or instructions, it implements the various processes of the above-described resource allocation method embodiments and achieves the same technical effect. To avoid repetition, these will not be described again here.

[0153] The transceiver 703 is used to receive and send data under the control of the processor 701.

[0154] Among them, Figure 7 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically connecting various circuits of one or more processors represented by processor 701 and memory represented by memory 702. The bus architecture can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 703 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. For different user equipment, the user interface 704 can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0155] The processor 701 is responsible for managing the bus architecture and general processing, while the memory 702 can store the data used by the processor 701 when performing operations.

[0156] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the resource allocation method embodiments described above and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0157] This application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the various processes of the above-described resource allocation method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0158] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0159] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0160] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A resource allocation method, characterized in that, The method, applied to a first node in a blockchain network comprising N nodes, wherein the first node is one of the N nodes, comprises: Based on the computational requirements of the user's computational task, first recommendation information is obtained, which includes information on M first mobile devices that meet the computational requirements. Obtain second recommendation information sent by a second node in the blockchain network based on the computing demand information, wherein the second recommendation information includes information on M second mobile devices that meet the computing demand information; Based on the first recommendation information and the second recommendation information, target recommendation information is obtained. The target recommendation information includes information on M target mobile devices. The target mobile device is one of the first mobile device or the second mobile device. Each target mobile device is used to execute P sub-tasks corresponding to the computing task. The computational requirement information includes resource requirement information and / or reputation value requirement information; the second node is any one of the N nodes other than the first node; both the first mobile device and the second mobile device are located within the user's preset range; N, M, and P are all integers greater than 1.

2. The method according to claim 1, characterized in that, Based on the user's computational task requirements, obtain the first recommendation information, including: Based on the resource information and / or reputation value of each of the X pre-stored mobile devices, obtain the resource requirement information in the computing requirement information that satisfies the user's computing task, and / or, the reputation value satisfies the reputation value requirement information in the computing requirement information of the M first mobile devices, where X is an integer greater than 1; Obtain first recommendation information including information about M of the first mobile devices.

3. The method according to claim 2, characterized in that, The method further includes: After the user receives the calculation results sent by each target mobile device, the user obtains the reputation evaluation information for each target mobile device. The reputation value of the target mobile device is updated based on the reputation evaluation information.

4. The method according to claim 1, characterized in that, Based on the first recommendation information and the second recommendation information, target recommendation information is obtained, including: Based on the reputation value of each first mobile device in the first recommendation information and the reputation value of each second mobile device in the second recommendation information, sort the M first mobile devices and the M second mobile devices. The mobile devices with the highest reputation scores (ranked in the top M) are identified as the target mobile devices. Obtain target recommendation information including information about M target mobile devices.

5. The method according to claim 1, characterized in that, The method further includes: The data collection device sends a first request via a smart contract, the first request being used to request sensing data from the sensing device. The pre-stored sensing data is transmitted to the data collection device, which then transmits the sensing data to the target mobile device. The target mobile device executes P sub-tasks corresponding to the computing task based on the sensing data.

6. The method according to claim 5, characterized in that, Before transmitting the pre-stored sensed data to the data collection device, the method further includes: Obtain a second request sent by the sensing device through the smart contract, the second request being used to request resources for storing the sensing data; Allocate resources for the sensed data; The sensing data transmitted by the sensing device is stored.

7. A resource allocation device, characterized in that, An apparatus for use as a first node in a blockchain network, wherein the blockchain network comprises N nodes, and the first node is one of the N nodes, the apparatus comprising: The first acquisition module is used to acquire first recommendation information based on the computing requirements information of the user's computing task. The first recommendation information includes information on M first mobile devices that meet the computing requirements information. The second acquisition module is used to acquire second recommendation information sent by a second node in the blockchain network according to the computing demand information. The second recommendation information includes information on M second mobile devices that meet the computing demand information. The third acquisition module is used to acquire target recommendation information based on the first recommendation information and the second recommendation information. The target recommendation information includes information on M target mobile devices. The target mobile device is one of the first mobile device or the second mobile device. Each target mobile device is used to execute P sub-tasks corresponding to the computing task. The computational requirement information includes resource requirement information and / or reputation value requirement information; the second node is any one of the N nodes other than the first node; both the first mobile device and the second mobile device are located within the user's preset range; N, M, and P are all integers greater than 1.

8. A resource allocation device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the processor, when executing the program or instructions, implements the resource allocation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the resource allocation method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the resource allocation method as described in any one of claims 1 to 6.

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