Relay node recommendation method for near field network and related device

Abstract

The present disclosure provides a relay node recommendation method of a near-field network and related equipment, and relates to the technical field of computer and communication. The method comprises the following steps: obtaining near-field network service information between a user node and a relay node, wherein the user node comprises a first user node; obtaining a service quality index of the relay node providing near-field network service for the first user node; determining a first heat index of the first user node for the relay node at a first time according to the near-field network service information and the service quality index; generating relay node recommendation information for the first user node according to the first heat index; and sending the relay node recommendation information to the first user node.

Description

Technical Field

[0001] This disclosure relates to the fields of computer and communication technology, and in particular to a method for recommending relay nodes in a near-field network, a device for recommending relay nodes in a near-field network, a computer device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] With the widespread adoption of various smart terminals, massive access to wireless communications such as 5G (5th Generation Mobile Communication Technology) and 6G (6th Generation Mobile Communication Technology) has led to an explosive growth in data traffic and a surge in demand for communication capacity. How to upgrade cellular frequency reuse to a new stage is an important issue facing operators.

[0003] A near-field network (NFC) refers to a network that connects multiple devices (including user nodes and relay nodes in this disclosure) within a certain range using wireless communication technology. It is a technology that allows terminals to communicate directly via device-to-device (D2D) communication by sharing community network resources, even without basic network infrastructure. It can improve the spectrum efficiency of the communication system, reduce terminal transmission power, and decrease battery consumption.

[0004] For example, Proximity Services (ProSe) provides a way to implement near-field networks. Through ProSe, multiple terminals (e.g., LTE (Long Term Evolution) terminals, 5G terminals, etc.) can communicate directly and share resources within a short distance, forming a temporary, self-organizing network. This network can be temporary, self-organizing, and does not depend on fixed network infrastructure. Summary of the Invention

[0005] This disclosure provides a method for recommending relay nodes in a near-field network. The method includes: obtaining near-field network service information between a user node and a relay node, wherein the user node includes a first user node; obtaining a service quality index (SMI) of the near-field network service provided by the relay node to the first user node; determining a first popularity index of the first user node for the relay node at a first moment based on the near-field network service information and the SMI; generating relay node recommendation information for the first user node based on the first popularity index; and sending the relay node recommendation information to the first user node.

[0006] This disclosure provides a relay node recommendation device for a near-field network. The device includes: a communication module for acquiring near-field network service information between a user node and a relay node, wherein the user node includes a first user node; a processing module for acquiring a service quality index (SMI) of the near-field network service provided by the relay node to the first user node; the processing module is further configured to determine a first popularity index of the first user node towards the relay node at a first moment based on the near-field network service information and the SMI; the processing module is further configured to generate relay node recommendation information for the first user node based on the first popularity index; and the communication module is further configured to send the relay node recommendation information to the first user node.

[0007] This disclosure provides a computer device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute a relay node recommendation method for a near-field network according to any embodiment of this disclosure by executing the executable instructions.

[0008] This disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a relay node recommendation method for a near-field network according to any embodiment of this disclosure.

[0009] This disclosure provides a computer program product, including a computer program that, when executed by a processor, implements a relay node recommendation method for a near-field network according to any embodiment of this disclosure. Attached Figure Description

[0010] Figure 1 This diagram illustrates the overall architecture of a blockchain network according to an embodiment of the present disclosure.

[0011] Figure 2 A flowchart illustrating a relay node recommendation method for a near-field network according to an embodiment of this disclosure is shown.

[0012] Figure 3 This diagram illustrates a structural block diagram of a relay node recommendation device for a near-field network according to an embodiment of the present disclosure.

[0013] Figure 4 A structural block diagram of a relay node recommendation device according to an embodiment of this disclosure is shown.

[0014] Figure 5 This diagram illustrates the structure of a relay node recommendation system for a near-field network according to an embodiment of the present disclosure.

[0015] Figure 6 A structural block diagram of a computer device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0016] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0017] Furthermore, the accompanying drawings are merely illustrative of this disclosure, and the same reference numerals in the drawings denote the same or similar parts, thus repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0018] In this disclosure, at least one item can be described as one item or multiple items, and multiple items can be two, three, four, or more items, without limitation. The " / " sign can indicate that the related objects are in an "or" relationship; for example, A / B can represent A or B. "And / or" can be used to describe three relationships between related objects; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. For ease of description of the technical solutions of this disclosure, terms such as "first," "second," "A," or "B" can be used to distinguish technical features with the same or similar functions. These terms do not limit the quantity or execution order. Furthermore, the terms "first," "second," "A," or "B" are not necessarily different. The words “exemplary” or “for example” are used to indicate examples, illustrations, or explanations. Any design described as “exemplary” or “for example” should not be construed as being superior or more advantageous than other design options. The use of words such as “exemplary” or “for example” is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0019] Figure 1 This diagram illustrates the overall architecture of a blockchain network according to an embodiment of the present disclosure. Figure 1 As shown, the method provided in this embodiment can be applied to a blockchain network 100, that is, the method provided in this embodiment can be implemented in the blockchain network 100. The blockchain network 100 includes a rating and recommendation system 110, at least one relay node, and at least one user node. Figure 1 The example shows three relay nodes (relay node 121, relay node 122 and relay node 123, respectively), but this disclosure does not limit the number of relay nodes, which can be one or more (two or more). Figure 1 The example shows six user nodes, namely user nodes 131, 132, 133, 134, 135 and 136, but this disclosure does not limit the number of user nodes.

[0020] The rating and recommendation system 110 can be implemented by any computer device, such as a terminal and / or server. Both the rating and recommendation system 110 and the relay nodes can initiate registration requests to the blockchain network 100, and after the registration request is approved, they become nodes in the blockchain network 100. Subsequently, if a user node wishes to form a near-field network with the relay nodes, that user node can also initiate a registration request to the blockchain network 100, and after the registration request is approved, it becomes a node in the blockchain network 100.

[0021] Each occurrence of any of the following actions—such as a user node initiating a connection request to a relay node, the relay node responding to the received connection request by providing local area network services to the corresponding user node, or the user node rating the relay node providing local area network services—triggers an on-chain transaction. After consensus is reached, this behavioral data (which can be referred to as local area network service information between user nodes and relay nodes) is stored on the blockchain of the blockchain network, achieving on-chain storage. The rating and recommendation system 110 can obtain the stored local area network service information from the blockchain network 100, calculate the current popularity value (e.g., the first popularity index) of the relay node at the current moment (e.g., the first moment) based on the obtained local area network service information, thereby generating corresponding relay node recommendation information for each user node, and sending the generated relay node recommendation information to the corresponding user node through the blockchain network 100. After receiving relay node recommendation information, a user node can select a suitable relay node based on the relay nodes included in the recommendation information and their corresponding current popularity values, and initiate a connection request to the selected relay node to provide local area network services to the user node.

[0022] When a blockchain network is used to implement the method provided in the embodiments of this disclosure, the solution provided in the embodiments of this disclosure also relates to blockchain technology, that is, it provides a method for recommending relay nodes in a blockchain-based near-field network.

[0023] The relay nodes and / or user nodes in the embodiments of this disclosure can be any computer device, such as a terminal and / or a server. The terminal can also be referred to as a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, etc. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, etc. The embodiments of this disclosure do not limit the specific technology or device form used in the terminal.

[0024] This disclosure applies a relay node recommendation method for near-field networks to a blockchain network, thereby designing a blockchain-based distributed near-field network resource sharing scheme and proposing a near-field network resource recommendation mode to improve the quality of the sharing environment.

[0025] Figure 2 A flowchart illustrating a relay node recommendation method for a near-field network according to an embodiment of this disclosure is shown. Figure 2 The method provided in the embodiments can be derived from... Figure 1 The rating and recommendation system 110 in the document is executed, but this disclosure is not limited to this and can be executed by any computer device. For example... Figure 2 As shown, the method provided in this disclosure embodiment may include the following steps.

[0026] In S210, the near-domain network service information between the user node and the relay node is obtained, wherein the user node includes the first user node.

[0027] In this embodiment of the disclosure, the near-domain network service information refers to any relevant information such as a user node making a request to a relay node, or a relay node providing near-domain network resource sharing or near-domain network services to a user node.

[0028] In an exemplary embodiment, the local area network service information includes historical connection information between the user node and the relay node, and rating information of the user node for the local area network service provided by the relay node.

[0029] In this embodiment of the disclosure, historical connection information refers to information such as the number, frequency, and time of connection requests made by user nodes to relay nodes, and any one or more of the following: connection time (which may include one or more of the following: connection duration, time period for providing connection, time point, etc.), historical connection count, and connection distance. Connection distance refers to the logical number of hops traversed by the relay node to provide local network services to the user node.

[0030] In this embodiment of the disclosure, the rating information refers to any information related to the rating indicators fed back by the user node to the relay node that provides it with the near-field network service. For example, it may include the rating indicators of the user node for the near-field network service provided by the relay node, as well as one or more other information such as the rating mean and rating variance calculated based on the rating indicators.

[0031] In an exemplary embodiment, the historical connection information includes the number of historical connections between the user node and the relay node; the rating information includes the rating index and average rating of the user node for the local network service provided by the relay node.

[0032] In this embodiment of the disclosure, the rating index may include the rating index given by all user nodes to all relay nodes in the system. The average rating can be obtained by averaging the rating index given by a certain user node (e.g., user node 131) to all relay nodes in the system. That is, the number of user nodes in the system corresponds to the number of average ratings for each user node.

[0033] The first user node in this embodiment can be any one of the user nodes in the system, such as the one described above. Figure 1 The user nodes are any one of 131 to 136. The following example uses user node 132 as the first user node, but this disclosure is not limited to this.

[0034] In S220, the service quality index of the relay node providing near-field network services to the first user node is obtained.

[0035] In this embodiment of the disclosure, the service quality index is used to measure the quality of service provided by each relay node in the system to the first user node for the local network service. The higher the service quality, the higher the corresponding service quality index, and vice versa.

[0036] In an exemplary embodiment, obtaining the service quality index (SMI) of the relay node providing local area network services to the first user node includes: obtaining the number of times the relay node was successfully connected to by the first user node; obtaining the number of times the relay node successfully provided local area network services to the first user node; obtaining the bandwidth performance index of the relay node providing local area network services to the first user node; obtaining first rating information of the first user node for the local area network services provided by the relay node; and obtaining the SMI based on the number of times the relay node was successfully connected to by the first user node, the number of times the relay node successfully provided local area network services to the first user node, the bandwidth performance index, and the first rating information.

[0037] In an exemplary embodiment, the service quality metric Q is calculated using the following formula:

[0038] Q = α C Q C +β R Q R +γ B Q B +δ S Q S (1)

[0039] Among them, Q C This represents the number of times the relay node has been successfully connected to by the first user node (also known as the connection count), used to record the number of times a certain relay node (which can be any relay node among all relay nodes in the system, such as relay node 122) has been successfully connected to by the first user node, reflecting the frequency of use of the relay node by the first user node. R This indicates the number of times the relay node successfully provides local area network services to the first user node, that is, the number of times the relay node successfully completes local area network services for the first user node, which can also be called the number of reliable services.

[0040] Q in the above formula B The bandwidth performance metric / bandwidth indicator represents the average bandwidth quality provided by the relay node to the first user node, and measures the service performance provided by the relay node to the first user node. For example, it can be calculated using the following formula:

[0041]

[0042] In the above formula, the theoretical maximum bandwidth refers to the theoretical maximum bandwidth when the relay node provides near-field network services to the first user node, and the actual bandwidth refers to the bandwidth actually provided by the relay node to the first user node when providing near-field network services to the first user node.

[0043] In the above formula, Q S This represents the first rating information. The first rating information can be calculated based on the m rating indicators given by the first user node to the relay node providing it with near-area network services. The average of these m rating indicators is calculated, and the standard deviation of these m rating indicators is then calculated. This standard deviation is used to evaluate service quality indicators, thus eliminating the rating bias of the first user node towards the relay node. Here, m is a positive integer greater than or equal to 1.

[0044] For example, Q can be calculated using the following formula. S :

[0045]

[0046] In the above formula, R uj R represents the j-th rating index given by the first user node to the relay node, where j is a positive integer greater than or equal to 1 and less than or equal to m. uj This represents the average value of the m scoring indicators.

[0047] In this embodiment of the disclosure, α C β R γ B δ S They represent Q respectively C Q R Q B Q S The weighting coefficients are all positive numbers greater than 0 and less than 1. For example, the following formula can be satisfied:

[0048] α C +β R +γ B +δ S =1 (4)

[0049] In this embodiment of the disclosure, by introducing a service quality index Q for the near-field network services provided by different relay nodes to different user nodes (taking the first user node as an example above), the calculation of the first popularity index for each relay node is associated with the corresponding user node. That is, for the same relay node at the same first moment, since the Q values ​​for the same relay node are different for different user nodes, the first popularity index for the same relay node at the same first moment may be different for different user nodes. Thus, different relay node recommendation information can be generated for different user nodes, making the relay node recommendation information more accurate and more targeted.

[0050] In S230, based on the near-domain network service information and the service quality index, the first user node's first popularity index for the relay node at the first moment is determined.

[0051] In this embodiment, based on the near-field network service information between the user node and the relay node and the service quality index of the near-field network service provided by the relay node to the first user node, the connection temperature of each relay node to the first user node at the first moment can be calculated. This can also be referred to as the first popularity index, the first popularity value, or the current popularity value. In subsequent steps, the first popularity indices of different relay nodes to the first user node at the first moment can be compared to determine relay node recommendation information for the first user node.

[0052] In an exemplary embodiment, determining a first popularity index of the first user node for the relay node at a first time point based on the near-domain network service information and the service quality index includes: obtaining a second popularity index of the first user node for the relay node at a second time point, wherein the first time point is after the second time point; obtaining the time difference between the first time point and the second time point; determining a cooling coefficient of the first user node for the relay node based on the service quality index; and determining the first popularity index based on the second popularity index, the historical connection information, the rating information, the time difference, and the cooling coefficient.

[0053] In this embodiment, the second moment refers to the moment preceding the first moment. For example, if we assume the first moment is the current moment, denoted by t, then the second moment could be moment t-1. The time difference between the first and second moments can be a preset fixed time interval (the specific value can be set according to the actual scenario). This means it can periodically retrieve near-domain network service information stored in the blockchain network and periodically update the first popularity index of each user node for each relay node; or it can be a value that dynamically increases over time. Correspondingly, the calculation method for the second popularity index can refer to the calculation method for the first popularity index.

[0054] In other embodiments, the second moment can also be the initial moment when the relay node comes online (e.g., just after successfully registering in the blockchain network). In this case, the time difference between the first moment and the initial moment gradually increases over time. Correspondingly, the second popularity index can be a preset initial popularity value, or its initial popularity value can be determined based on the parameters or performance of the relay node when it first comes online.

[0055] In an exemplary embodiment, the cooling coefficient α of the first user node to the relay node is determined by the following formula:

[0056]

[0057] Where Q represents the quality of service index of the relay node providing near-field network services to the first user node; λ represents the weighting coefficient of the quality of service index, and λ is a positive number greater than 0 and less than 1.

[0058] In this embodiment, a higher Q corresponds to a smaller α, which measures the decay rate of the second popularity index of the first user node towards the relay node. A smaller α indicates a slower decay rate of the second popularity index. The higher the service quality index provided by the relay node to the first user node, the smaller the cooling coefficient α of the relay node relative to the first user node. In the network resource sharing system, for the first user node, the relay node's recommendation ranking in the relay node recommendation information declines more slowly, thereby ensuring that high-quality relay nodes prioritize providing services to user nodes for a longer period, driving users to share network resources.

[0059] In an exemplary embodiment, determining the first popularity index based on the second popularity index, the historical connection information, the rating information, the time difference, and the cooling coefficient includes: determining the first popularity index based on the second popularity index, the number of historical connections, the rating index, the average rating, the time difference, and the cooling coefficient.

[0060] In an exemplary embodiment, determining the first popularity index based on the second popularity index, the historical connection count, the rating index and the average rating, the time difference, and the cooling coefficient includes: obtaining a first weight parameter of the historical connection count, a second weight parameter of the rating information, and a balance parameter; and determining the first popularity index based on the second popularity index, the historical connection count and its first weight parameter, the rating index and the average rating, the second weight parameter, the balance parameter, the time difference, and the cooling coefficient.

[0061] In an exemplary embodiment, the first popularity index is determined by the following formula:

[0062]

[0063] In the above formula, H t This represents the first popularity value of the relay node at the first moment, i.e., the node popularity value at time t, which reflects the recommendation priority of the relay node.

[0064] In the above formula, H0 represents the second popularity value of the relay node at the second time. It can be the historical popularity value of the relay node at the second time. When the second time is the initial time when the relay node went online, H0 represents the initial popularity of the relay node, which can be calculated, for example, based on the historical behavior of the relay node (such as the rating and connection volume at the time of going online).

[0065] In the above formula, βlog(View) M This indicates the impact of the relay node's historical connection count or total historical connection count on its popularity. M This represents the historical connection count or total historical connection count of the relay node. In this embodiment, the historical connection count or total historical connection count of each relay node is calculated using a logarithm to prevent the connection rate of popular relay nodes from having an excessive impact on its first popularity index. β represents the first weighting parameter of the historical connection count, used to adjust the degree of influence of the total historical connection count of the relay node on its first popularity index.

[0066] In the above formula, e -αΔt This represents the natural cooling factor of the second popularity index. The exponential term α represents the cooling coefficient of the relay node. The cooling coefficient α reflects the rate at which the second popularity index of the relay node decays over time. Relay nodes providing high-quality or excellent service to user nodes have lower α values, and their corresponding second popularity index decays more slowly.

[0067] Where Δt represents the time difference between the first time point and the second time point. For example, the time difference between the current time and the previous time point.

[0068] In the above formula, This represents the contribution of all user nodes in the system to the relay node's rating index, specifically its first popularity index. The fairness of the rating for this relay node is improved by subtracting the average rating of each user node across all relay nodes in the system. `n` represents the number of user nodes that have rated the local area network services provided by the relay node, and `n` is an integer greater than or equal to 1. It should be noted that each user node in this embodiment can correspond to one user; when different users log in on the same user node, they can be considered to correspond to different user nodes. Therefore, the number of user nodes here can also refer to the number of users, i.e., the number of users who have rated the local area network services provided by the relay node. `γ` represents the second weighting parameter, used to adjust the degree of influence of the rating on the relay node's first popularity index. ui This represents the rating metric of the i-th user node for the local network service provided by the relay node. i is an integer greater than or equal to 1 and less than or equal to n. This represents the average rating of the i-th user node. We can obtain all the rating metrics given by the i-th user node to all relay nodes in the system, and then calculate the average to obtain the mean. This is used to eliminate rating bias from individual user nodes, eliminate the influence of different users' rating habits, and ensure the fairness of the rating.

[0069] In this embodiment of the disclosure, δ represents the balance parameter. Due to the cooling factor e -αΔt This may cause the first heat index to approach zero. Adding a balancing term or balancing parameter δ can ensure that the value of the first heat index is non-negative, thereby improving the stability of the heat index.

[0070] In this embodiment, β, γ, δ, and α are all positive numbers greater than 0 and less than 1. Their values ​​can be determined based on actual testing. The values ​​of these weighting parameters can be dynamically adjusted according to the actual scenario. In some embodiments, β + λ + γ = 1.

[0071] This disclosure introduces an exponential model that decays over time, adjusting the cooling rate of different relay nodes through a cooling coefficient to ensure that the ranking of high-quality relay nodes declines more slowly. Logarithmic smoothing is applied to the historical connection totals of relay nodes to reduce the impact of excessively amplified historical connection totals of popular relay nodes on their rankings. A scoring bias (the difference from the mean score) is introduced in the scoring section to avoid unfair influences on the results caused by different user scoring standards.

[0072] It is understood that the calculation of the first popularity index is not limited to the above formula. As long as it reflects the gradual decrease in popularity over time, the relatively slow decline in popularity of relay nodes with high service quality, the higher the user rating of the relay node, and the higher the popularity value of the relay node that has historically provided near-area network services to more user nodes, the higher the popularity value. In other embodiments, factors such as connection time and connection distance can also be introduced to calculate the first popularity index.

[0073] In S240, relay node recommendation information is generated for the first user node based on the first popularity index.

[0074] In this embodiment of the disclosure, the first popularity index of all relay nodes in the system relative to the first user node can be calculated using the above method. The first popularity indices of different relay nodes are compared to recommend a list of optimal relay nodes for connection to the first user node, thus generating relay node recommendation information. This information may include each relay node and its corresponding first popularity index, arranged in descending order according to the magnitude of the first popularity index. Alternatively, the relay node recommendation information may include a predetermined number or a predetermined percentage of relay nodes ranked highly.

[0075] The method provided in this disclosure dynamically updates the connection temperature of each user node, i.e., the first heat index of each relay node for each user node, over time. Based on the continuously updated behavior between user nodes and relay nodes, an optimal relay node list is recalculated for each user node.

[0076] The method provided in this disclosure provides a dynamically changing recommendation ranking for relay nodes participating in network resource sharing. At any given time, each relay node participating in network resource sharing has a current popularity value. Relay nodes with higher current popularity values ​​rank higher in the recommendation list (i.e., relay node recommendation information). If a user node in the near-field network connects to, praises, or engages in other positive interactions with a service node / relay node, the current popularity value of that service node / relay node will increase accordingly, and its ranking in the recommendation list will change dynamically. As time passes, the current popularity values ​​of all service nodes will gradually cool down based on their online time, resulting in a lower recommendation ranking. The rate of change in the cooling process of relay nodes is jointly determined by the initial temperature (e.g., a second popularity index), node interaction behavior (e.g., user nodes connecting to relay nodes, user nodes giving ratings to relay nodes, etc.), and time.

[0077] In S250, the relay node recommendation information is sent to the first user node.

[0078] In this embodiment of the disclosure, a list of the best relay nodes available for connection is dynamically recommended to the user or user node (taking the first user node as an example) to ensure the quality of the communication network resources obtained by the user or user node and improve the network service experience of the user or user node.

[0079] The method provided in this disclosure influences the recommendation ranking of relay nodes by incorporating historical connection volume and user node ratings. This ensures that the popularity of newly launched service nodes is boosted, while also guaranteeing that service nodes with high pageviews and service ratings rank higher in the system. On one hand, by using exponential decay of popularity values ​​to simulate the natural decline in relay node recommendation popularity, the recommendation popularity becomes dynamically adaptive, preventing popular relay nodes from occupying the top of the recommendation list for extended periods, thereby improving the fairness of the recommendation mechanism. On the other hand, mapping user node and relay node behaviors (such as connections and ratings) to popularity increments provides a behavior-driven popularity update method, enabling relay node recommendation rankings to reflect user feedback in real time, improving recommendation accuracy and user satisfaction. Furthermore, this disclosure also incorporates multi-dimensional optimizations, dynamically adjusting the cooling coefficient based on multiple indicators such as connection count and ratings to ensure a slower decline in the ranking of high-quality nodes, thus incentivizing high-quality services.

[0080] Near-field network (NAND) D2D communication, a related technology, can increase communication system capacity through frequency reuse, but its application in vertical industries is limited. Due to the lack of effective incentive mechanisms to drive resource sharing between devices, the scalability and sustainability of the solution are poor. Furthermore, it is susceptible to security vulnerabilities such as attacks using fake service nodes. In untrusted or weakly trusted environments, it cannot achieve fair, reliable, and high-quality communication network resource sharing.

[0081] In an exemplary embodiment, the method is performed by a recommending node. The method further includes: the recommending node initiating a registration request to the blockchain network to become a node in the blockchain network. The recommending node in this embodiment may be... Figure 1 After registering with the rating and recommendation system, one becomes a node in the blockchain network.

[0082] In this system, both the relay node and the user node are nodes within the blockchain network. The local area network service information is stored in the blockchain network, and the recommending node obtains this information from the blockchain network. The recommending node then sends the relay node's recommendation information to the first user node via the blockchain network. When a user node wishes to connect to a relay node, it first registers in the blockchain network.

[0083] For example, rating and recommendation systems, relay nodes, and user nodes can authenticate themselves as legitimate users using information such as mobile phone numbers, obtain a public and private key belonging to their respective nodes, and complete the registration process.

[0084] This disclosure presents a blockchain-based distributed near-field network resource sharing scheme, providing a new near-field network resource recommendation model and improving the quality of the sharing environment.

[0085] Terminal users (e.g., mobile phones) can apply for and register as service nodes on the blockchain, directly sharing network resources (e.g., 5G network resources) with other mobile terminals (i.e., user nodes) and providing local network services. By recording user behavior such as connections between user nodes and service nodes, and user ratings of service nodes, this data is incorporated into the service node recommendation function. This dynamically adjusts the recommendation ranking of relay nodes in the system, electing high-quality service nodes (i.e., relay nodes with relatively high popularity) to form a highly reliable blockchain consensus network. This ensures that popular nodes with high service quality rank higher, incentivizing users to participate in network resource sharing. For example, based on the popularity of each service node, a node group that leads the consensus can be elected or voted on.

[0086] Simultaneously, it prevents low-quality relay nodes from providing services for extended periods, encouraging users to share high-quality network resources. It recommends the best available relay nodes to user nodes in real time, ensuring the quality of communication network resources accessed by users and improving their network service experience.

[0087] Once a relay node registers with the blockchain network, it becomes a node within the network. When a user node connects to the relay node and / or provides a rating to it, it triggers an on-chain transaction. After consensus is reached, this transaction is stored in the blockchain.

[0088] In this embodiment of the disclosure, all data such as connections and ratings between user nodes and relay nodes in the near-field network are treated as transaction data. After consensus is reached through the blockchain consensus network, the data is stored in the blockchain, which can prevent data from being tampered with and service nodes from being attacked, thus providing higher credibility.

[0089] In this embodiment, a decentralized incentive mechanism based on blockchain can be organically combined with a recommendation method. The provided decentralized incentive mechanism uses smart contracts to record the service behavior of each service node and distributes on-chain rewards (such as tokens or points) to high-performing service nodes. High-quality service nodes are elected to join the blockchain consensus network through voting or scoring for network consensus. After consensus is reached, the batch of transaction data is stored in the blockchain, improving network reliability.

[0090] In this embodiment, on the one hand, the comprehensive ranking of high-quality service nodes is achieved through the aforementioned recommendation method, incentivizing more users to participate in network resource sharing and provide high-quality services. The shared network system dynamically recommends a list of the best connectable service nodes to user nodes, improving the user's network service experience and further realizing fair, reliable, and high-quality communication network resource sharing. On the other hand, the aforementioned recommendation method is combined with an incentive mechanism. The recommendation method dynamically adjusts the popularity and ranking of service nodes, directly affecting the resource allocation and incentive distribution of service nodes. If a service node provides high-quality services, it can obtain a higher ranking through the aforementioned method, thereby reaching more users and obtaining more incentives. Simultaneously, all user behavior and node status are recorded in the blockchain, preventing scoring fraud and node forgery, providing security guarantees. Furthermore, the consensus mechanism ensures the fairness of the recommendation ranking and avoids single-point control.

[0091] This disclosure can be used for 5G network optimization. During large-scale events (such as concerts and sporting events), dense user traffic leads to network congestion. The method provided in this disclosure can be directly applied to scenarios where operators have low ROI for large-scale events (such as concerts) and need to use a local area network (D2D) mode for frequency reuse. Driving frequency resource reuse through a blockchain incentive mechanism can effectively improve the network service experience at the event venue. Using this solution to dynamically recommend high-quality service nodes improves spectrum reuse rates and alleviates pressure on the main network. The blockchain-based incentive mechanism promotes resource sharing among devices, establishing a low-cost temporary communication infrastructure.

[0092] For example, the method provided in this disclosure can be applied to enhance the capacity of 5G communication systems. It utilizes blockchain smart contracts and consensus mechanisms to drive the establishment of large-scale, low-cost, and scalable near-field network infrastructure, serving scenarios such as 5G IoT, industrial internet, and mobile internet. The solution provided in this disclosure is a universally applicable method.

[0093] For example, the method provided in this disclosure can be applied to IoT and Industrial Internet scenarios. In Industrial Internet scenarios, communication needs between devices are complex, and there is a lack of flexible resource scheduling mechanisms. The solution provided in this disclosure can dynamically recommend highly reliable device nodes, achieve efficient resource allocation, and reduce data latency and transmission failure rates. At the same time, decentralized security ensures the integrity and privacy of data communication.

[0094] For example, the method provided in this disclosure can be applied to improve the mobile internet experience. In related technologies, users often experience poor performance in high-density areas (such as subways and shopping malls). The solution provided in this disclosure can provide real-time optimal node connection services, dynamically optimizing the user experience. It also supports users contributing idle resources through smart contracts, forming a resource-sharing ecosystem.

[0095] The method provided in this disclosure incentivizes more users to participate in network resource sharing and provide high-quality services by comprehensively ranking high-quality service nodes. The shared network system dynamically recommends a list of the best connectable service nodes to user nodes, improving the user's network service experience. By increasing the trust between weakly trusted near-field network nodes, fair, reliable, and high-quality communication network resource sharing can be achieved in untrusted or weakly trusted environments. By protecting the legitimate rights and interests of more users and encouraging more users to become service nodes and share communication network resources, spectrum utilization can be greatly improved. Blockchain-based near-field sharing can effectively defend against traditional Trojan attacks, hacker attacks, relay attacks, and other behaviors that harm system health, improving system robustness and enhancing the enthusiasm and fairness of users participating in the reuse of near-field network resources.

[0096] Figure 3 This diagram illustrates a structural block diagram of a relay node recommendation device for a near-field network according to an embodiment of the present disclosure. Figure 3 As shown, the relay node recommendation device 300 for a near-field network provided in this embodiment may include a communication module 310 and a processing module 320.

[0097] The communication module 310 is used to obtain near-field network service information between user nodes and relay nodes, wherein the user nodes include a first user node.

[0098] The processing module 320 is used to obtain the service quality index of the relay node providing near-field network services to the first user node.

[0099] The processing module 320 is further configured to determine the first heat index of the first user node to the relay node at the first moment based on the near-field network service information and the service quality index.

[0100] The processing module 320 is also used to generate relay node recommendation information for the first user node based on the first popularity index.

[0101] The communication module 310 is also used to send the relay node recommendation information to the first user node.

[0102] Figure 3 Other aspects of the embodiments can be found in the other embodiments described above, and will not be repeated here.

[0103] Figure 4 This diagram illustrates a structural block diagram of a relay node recommendation device according to an embodiment of the present disclosure. Figure 4 As shown, the relay node recommendation device 400 provided in this embodiment may include a relay node recommendation module 410, an interaction module 420, and a blockchain storage module 430.

[0104] The relay node recommendation module 410 may include a communication module 411, a data processing module 412, and a node ranking module 413. The relay node recommendation module 410 in this embodiment can be configured as described above. Figure 1 In the rating and recommendation system 110, a single node can be added to handle the relay node recommendation function, thus implementing the above recommendation method. Figure 3 The communication module 310 in this embodiment has the same function. The data processing module 412 and the node sorting module 413 can be located in... Figure 3 The processing module 320 in this embodiment, i.e., the data processing module 412, can be used to determine the first popularity index of the first user node for the relay node at a first moment based on the near-field network service information and the service quality index. The node ranking module 413 can be used to generate relay node recommendation information for the first user node based on the first popularity index.

[0105] The interaction module 420 may include a node registration module 421 and a node behavior data recording module 422. The interaction module 420 can exist in each node of the blockchain network 100, meaning each node can initiate a registration request to the blockchain network 100 through the node registration module 421. After successfully registering on the blockchain network 100, each node can record near-field network service information between the user node and the relay node through the node behavior data recording module 422.

[0106] The blockchain storage module 430 can store node historical behavior, services, etc., in the form of a database and / or data files. In this embodiment, the database stores data that requires efficient querying, such as storage service quality indicators, rating information, and popularity information (e.g., a second popularity indicator), while the data files store large-scale data such as logs. Frequently accessed data is stored in the database, while archived or cold data is stored in the file system, reducing the database load.

[0107] In this embodiment of the disclosure, the blockchain storage module 430 may also exist in each node of the blockchain network 100. For example, a relay node may store all the data, while other nodes may choose to store all the data or lightweight data.

[0108] Figure 5 This diagram illustrates the structure of a relay node recommendation system for a near-field network according to an embodiment of this disclosure. Figure 5 As shown, the relay node recommendation system 500 for near-field networks provided in this embodiment may include a data display device 510, a data service device 520, a storage device 530, a blockchain network 540, and a management platform 550. Here, the blockchain network 540 can be equivalent to the aforementioned... Figure 1 Blockchain network 100 in China.

[0109] The data display device 510 can display some or all of the data involved in the embodiments of this disclosure through one or more of the following: APP (application), web page, and / or PC (personal computer).

[0110] The data service device 520 can be used for data collection, popularity calculation, dynamic updates, providing recommendation algorithms, implementing routing and forwarding, and anomaly detection. The data service device 520 may include a relay node 521. The relay node 521 can be used for resource forwarding, data recording, log generation, quality monitoring, network monitoring, and route management. The relay node 521 can be equivalent to any one of the relay nodes 121, 122, or 123 mentioned above.

[0111] Storage device 530 can be implemented using one or more of MongoDB, Redis, Kafka, and ES (short for Elasticsearch).

[0112] Blockchain network 540 can be used to provide consensus mechanisms, smart contracts, incentive mechanisms, and key management.

[0113] The data service device 520, storage device 530, and blockchain network 540 can communicate with the management platform 550 through the external interface 560.

[0114] The management platform 550 can be used for configuration management, log storage, system monitoring, data analysis, and user management.

[0115] Furthermore, embodiments of this disclosure also provide a computer device, including: a processor; and a memory for storing executable instructions of the processor. The processor is configured to perform the methods described in any embodiment of this disclosure by executing the executable instructions.

[0116] Furthermore, embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described in any embodiment of this disclosure.

[0117] Furthermore, this disclosure also provides a computer program product, which includes a computer program that, when run, executes the methods described in any embodiment of this disclosure, or executes the methods described in any embodiment of this disclosure, or executes the methods described in any embodiment of this disclosure.

[0118] The following reference Figure 6 To describe a computer device 600 according to such an embodiment of the present disclosure. Figure 6 The computer device 600 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0119] like Figure 6 As shown, the computer device 600 is presented in the form of a general-purpose computing device. The components of the computer device 600 may include, but are not limited to: at least one processing unit 610 (for example, its function may be the same as that of the processing module 320 described above), at least one storage unit 620, and a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610).

[0120] The storage unit 620 stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0121] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0122] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0123] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0124] Computer device 600 can also communicate with one or more external devices 640 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with computer device 600, and / or with any device that enables computer device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, computer device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. As shown, network adapter 660 communicates with other modules of computer device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0125] It should be noted that the above-mentioned modules / units, as part of a device, can be executed in a computer system such as a set of computer-executable instructions.

[0126] Those skilled in the art will understand that various aspects of this disclosure can be implemented as systems, methods, or program products. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0127] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0128] In particular, according to embodiments of this disclosure, the process described above with reference to the flowchart can be implemented as a computer program product, which includes a computer program that, when executed by a processor, implements the above-described relay node recommendation method for a near-field network.

[0129] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. The computer-readable storage medium stores a program product capable of implementing the methods described above. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code that, when run on a terminal / network device, causes the terminal / network device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0130] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0131] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0132] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for recommending relay nodes in a near-field network, characterized in that, include: The system acquires local area network service information between user nodes and relay nodes. The user nodes include a first user node. The local area network service information includes historical connection information between the user nodes and the relay nodes, and rating information of the user nodes on the local area network services provided by the relay nodes. The historical connection information includes the number of historical connections between the user nodes and the relay nodes. The rating information includes the rating index and average rating of the user nodes on the local area network services provided by the relay nodes. Obtain the quality of service (QoS) index of the relay node providing near-field network services to the first user node, and determine the cooling coefficient of the first user node to the relay node based on the QoS index. Obtain the second heat index of the first user node on the relay node at a second time point, where the first time point is after the second time point; Obtain the time difference between the first time point and the second time point; Obtain the first weight parameter of the historical connection count, the second weight parameter of the scoring information, and the balance parameter; Based on the near-domain network service information and the service quality index, a first popularity index for the first user node towards the relay node at a first moment is determined, which includes: obtaining the first popularity index based on a second popularity index, the number of historical connections and its first weight parameter, the cooling coefficient, the time difference, the number of user nodes that score the near-domain network service provided by the relay node, the second weight parameter, the scoring index, the average score, and the balance parameter; Based on the first popularity index, generate relay node recommendation information for the first user node; The relay node recommendation information is sent to the first user node.

2. The method according to claim 1, characterized in that, The first popularity index is determined using the following formula: This indicates the first heat index of the relay node at the first moment; This represents the second heat index of the relay node at the second time point; This indicates the number of historical connections of the relay node; The first weight parameter represents the number of historical connections; This represents the cooling coefficient of the relay node; The first time point represents the time difference between the first time point and the second time point; n represents the number of user nodes that score the near-field network service provided by the relay node, and n is an integer greater than or equal to 1. This represents the second weighting parameter; This represents the rating index of the i-th user node for the relay node, where i is an integer greater than or equal to 1 and less than or equal to n; This represents the average rating of the i-th user node; Indicates the balance parameter; , , , All are positive numbers greater than 0 and less than 1.

3. The method according to claim 1, characterized in that, The cooling coefficient of the first user node to the relay node is determined by the following formula: in, This represents the quality of service (QS) index of the relay node providing near-field network services to the first user node. This represents the weighting coefficient of the service quality indicator. It is a positive number that is greater than 0 and less than 1.

4. The method according to claim 1, characterized in that, Obtaining the quality of service (QoS) indicators of the relay node providing near-field network services to the first user node, including: Obtain the number of times the relay node was successfully connected by the first user node; The number of times the relay node successfully provided near-field network services to the first user node is recorded. Obtain the bandwidth performance metrics of the relay node for providing near-domain network services to the first user node; Obtain the first rating information of the first user node for the near-field network service provided by the relay node; The service quality index is obtained based on the number of times the relay node is successfully connected to by the first user node, the number of times the relay node successfully provides local network services to the first user node, the bandwidth performance index, and the first scoring information.

5. The method according to claim 4, characterized in that, The service quality index is calculated using the following formula. : in, The number of times the relay node was successfully connected by the first user node; This indicates the number of times the relay node successfully provided near-field network services to the first user node; This refers to the bandwidth performance metric; This represents the first scoring information; , , , They represent , , , The weighting coefficients are all positive numbers greater than 0 and less than 1.

6. The method according to claim 1, characterized in that, The method is executed by the recommendation node; the method further includes: The recommending node initiates a registration request to the blockchain network in order to become a node in the blockchain network; In this system, both the relay node and the user node are nodes in the blockchain network; the near-field network service information is stored in the blockchain network, and the recommending node obtains the near-field network service information from the blockchain network; the recommending node sends the relay node's recommendation information to the first user node through the blockchain network.

7. A relay node recommendation device for a near-field network, characterized in that, include: A communication module is used to acquire local area network service information between user nodes and relay nodes. The user nodes include a first user node. The local area network service information includes historical connection information between the user nodes and the relay nodes, and rating information of the user nodes on the local area network services provided by the relay nodes. The historical connection information includes the number of historical connections between the user nodes and the relay nodes. The rating information includes the rating index and average rating of the user nodes on the local area network services provided by the relay nodes. The processing module is used to obtain the service quality index of the relay node providing near-field network services to the first user node, and determine the cooling coefficient of the first user node to the relay node based on the service quality index. Obtain the second popularity index of the first user node to the relay node at a second time point, where the first time point is after the second time point; obtain the time difference between the first time point and the second time point; Obtain the first weight parameter of the historical connection count, the second weight parameter of the scoring information, and the balance parameter; The processing module is further configured to determine a first popularity index of the first user node for the relay node at a first moment based on the near-field network service information and the service quality index, which includes: obtaining the first popularity index based on a second popularity index, the number of historical connections and its first weight parameter, the cooling coefficient, the time difference, the number of user nodes that score the near-field network service provided by the relay node, the second weight parameter, the scoring index, the average score, and the balance parameter; The processing module is also used to generate relay node recommendation information for the first user node based on the first popularity index; The communication module is also used to send the relay node recommendation information to the first user node.

8. A computer device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method described in any one of claims 1 to 6.

10. A computer program product comprising a computer program that, when run, performs the method described in any one of claims 1 to 6.

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