Unmanned cluster data on-demand sharing method and device oriented to maximum utility

Abstract

The invention provides a maximum utility-oriented unmanned cluster data on-demand sharing method and device, and relates to the technical field of unmanned aerial vehicle communication. According to the method, a content distribution model, a bidding model of each content consumer and an offer model of each content producer in an unmanned aerial vehicle naming data network are constructed based on a content sharing incentive mechanism of iterative bilateral auction, and after a current bidding vector of each content consumer and a current offer vector of each content producer are obtained, a content distribution model is established; and solving the current content demand vector of each content consumer, the updated bid vector, the current content supply vector of each content producer and the updated price vector by applying the model, and carrying out iterative updating until the bid vector and the price vector reach a preset convergence condition. The application of the model can effectively encourage the content producer to participate in the data market, thereby alleviating the technical problem of poor data sharing activeness of the existing unmanned aerial vehicle named data network.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle communication, and in particular to an unmanned aerial vehicle cluster data on-demand sharing method and device for maximum utility. Background Art

[0002] Unmanned Aerial Vehicle (UAV) swarm network is a collaborative network system composed of multiple UAVs, which can complete complex tasks through distributed collaboration. In recent years, UAV swarm networks have been widely used in many fields such as communication network expansion and post-disaster rescue due to their advantages in coverage, task decomposition, parallel processing, and rapid deployment. However, due to the high dynamics of UAVs, network topology changes frequently. Traditional IP protocols are inefficient in handling such dynamic changes and are prone to problems such as routing update delays and path instability. In addition, IP networks require complex address resolution and routing selection, which increases communication delays and affects the efficiency of real-time data transmission.

[0003] The emergence of named data networking (NDN) brings solutions to the above challenges. In NDN, nodes are divided into content consumers and content producers. Content consumers request data by sending interest packets, while content producers provide data by returning data packets. Communication between nodes does not rely on fixed IP addresses, but uses data names, eliminating the complexity of frequent address changes and the delays associated with routing updates. The above advantages make NDN very suitable for application in drone cluster networks. NDN's features, such as content naming, stateless communication, data caching, and enhanced security, can provide more efficient, reliable, and secure communication solutions for drone cluster networks. Figure 1 Naming data network communication paradigm diagram for drones.

[0004] However, the existing NDN content sharing scheme adopts a single "request-reply" architecture, that is, the content consumer floods the interest packet with the requested content name into the network, and the node that owns the content returns the data packet as the content producer. However, this content sharing mechanism fails to take into account the inertia of the nodes and it is difficult to effectively encourage the nodes to actively participate. Specifically, due to limited communication resources, drone nodes only want to request the data they need to save their own resources, and lack the motivation to provide data to other nodes as content producers. Therefore, the Drone Named Data Network (UNDN) has the technical problem of poor data sharing activity. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for sharing unmanned cluster data on demand for maximum utility, so as to alleviate the technical problem of poor data sharing activity in existing unmanned aerial vehicle named data networks.

[0006] In a first aspect, the present invention provides an unmanned cluster data on-demand sharing method for maximum utility, including: step S102, constructing a content distribution model in a drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of an iterative bilateral auction; step S104, obtaining a current bidding vector of each content consumer and a current asking price vector of each content producer; step S106, solving the content distribution model based on the current bidding vector and the current asking price vector to obtain a current content demand vector of each content consumer and a current content supply vector of each content producer; step S108, solving the corresponding bidding model based on the current content demand vector of the target consumer to obtain an updated bidding vector of the target consumer, so as to obtain the updated bidding vector of the target consumer. And, based on the current content supply vector of the target producer, the corresponding asking price model is solved to obtain the updated asking price vector of the target producer; wherein, the target consumer represents any content consumer in the drone naming data network; the target producer represents any content producer in the drone naming data network; step S110, when it is determined that the updated bid vector and the updated asking price vector have not reached the preset convergence condition, the updated bid vector is used as the current bid vector, the updated asking price vector is used as the current asking price vector, and the process returns to step S106 until the updated bid vector and the updated asking price vector reach the preset convergence condition, so that each content consumer obtains content data based on the updated bid vector, and each content producer supplies content data based on the updated asking price vector.

[0007] Optionally, before constructing the content allocation model in the drone named data network, the bidding model of each content consumer and the asking price model of each content producer based on the content sharing incentive mechanism of iterative bilateral auction, it also includes: determining the utility function of each content consumer and the cost function of each content producer; constructing a content allocation target model based on the utility functions of all content consumers and the cost functions of all content producers with the goal of maximizing social welfare; converting the content allocation target model into a Lagrangian function to obtain a first Lagrangian function; and determining the KKT condition satisfied by the optimal solution of the content allocation target model based on the first Lagrangian function to obtain the first KKT condition.

[0008] Optionally, a content sharing incentive mechanism based on iterative bilateral auction is used to construct a content distribution model in the drone named data network, including: constructing a content distribution model based on the bid vector and content demand vector of each content consumer, and the asking price vector and content supply vector of each content producer; the content distribution model is expressed as: , ; ; ;in, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i to content producer j, represents the content supply vector of content producer j, represents the amount of content data provided by content producer j to content consumer i, N represents the total number of content producers, M represents the total number of content consumers, represents the bid of content consumer i to content producer j, represents the bid vector of content consumer i, represents the price that content producer j asks content consumer i, represents the asking price vector of content producer j; Indicates the lower limit of the total content data volume requested by content consumer i, represents the upper limit of the total content data volume requested by content consumer i, Indicates the maximum amount of content data of content producer j.

[0009] Optionally, based on the current bid vector and the current asking price vector, the content distribution model is solved to obtain the current content demand vector of each content consumer and the current content supply vector of each content producer, including: converting the content distribution model into a Lagrangian function to obtain a second Lagrangian function; determining the KKT condition satisfied by the optimal solution of the content distribution model based on the second Lagrangian function to obtain a second KKT condition; determining the content demand vector model of each content consumer and the content supply vector model of each content producer based on the second KKT condition; solving the Lagrangian function based on the current bid vector and the current asking price vector to obtain a Lagrangian multiplier; substituting the Lagrangian multiplier and the current bid vector into the content demand vector model to obtain the current content demand vector of each content consumer, and substituting the Lagrangian multiplier and the current asking price vector into the content supply vector model to obtain the current content supply vector of each content producer.

[0010] Optionally, a bidding model for each content consumer is constructed based on a content sharing incentive mechanism of an iterative bilateral auction, including: determining a target bidding model for each content consumer based on a first KKT condition and a second KKT condition; constructing a local optimization model of the target consumer based on the unknown expressions of the target consumer's utility function and settlement function with the goal of maximizing utility; solving the local optimization model of the target consumer based on the target bidding model and the content demand vector model to determine a solution expression of the settlement function; substituting the solution expression of the settlement function into the local optimization model of the target consumer to obtain the bidding model of the target consumer.

[0011] Optionally, a content sharing incentive mechanism based on an iterative bilateral auction is used to construct a charging model for each content producer, including: determining a target charging model for each content producer based on a first KKT condition and a second KKT condition; constructing a local optimization model of the target producer based on unknown expressions of the target producer's cost function and reward function with the goal of maximizing utility; solving the local optimization model of the target producer based on the target charging model and the content supply vector model to determine a solution expression of the reward function; substituting the solution expression of the reward function into the local optimization model of the target producer to obtain the charging model of the target producer.

[0012] Optionally, the content distribution target model is expressed as: , ; ; ;in, represents the utility function of content consumer i, represents the cost function of content producer j; , represents the preset utility weight, Indicates that content consumer i receives content The delay experienced, , Indicates that content consumer i receives content The transmission delay experienced, Indicates that content consumer i receives content The propagation delay experienced, represents the total number of hops transmitted between content producer j and content consumer i, Indicates the content transmission rate, represents the sum of all paths between content consumer i and content producer j, It represents the propagation speed of electromagnetic waves; , , represents the preset cost weight, r represents the next hop in the path from content producer j to content consumer i, represents the transmission energy consumption between content producer j and the next hop r when content producer j sends data to content consumer i, represents the energy consumption per bit of data sent by content producer j, represents the energy dissipation factor of the free space model, represents the energy consumption factor of the multipath fading model, represents the Euclidean distance between content producer j and next hop r, Indicates the preset distance switching threshold, .

[0013] In a second aspect, the present invention provides an unmanned cluster data on-demand sharing device for maximum utility, comprising: a construction module, which is used to construct a content distribution model in a drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of an iterative bilateral auction; an acquisition module, which is used to obtain a current bidding vector of each content consumer and a current asking price vector of each content producer; a first solving module, which is used to solve the content distribution model based on the current bidding vector and the current asking price vector, and obtain a current content demand vector of each content consumer and a current content supply vector of each content producer; a second solving module, which is used to solve the corresponding bidding model based on the current content demand vector of the target consumer, and obtain an updated bidding vector of the target consumer, And, solving the corresponding asking price model based on the current content supply vector of the target producer to obtain the updated asking price vector of the target producer; wherein the target consumer represents any content consumer in the drone naming data network; the target producer represents any content producer in the drone naming data network; an iteration module is used to use the updated bid vector as the current bid vector and the updated asking price vector as the current asking price vector when it is determined that the updated bid vector and the updated asking price vector have not reached the preset convergence condition, and return to call the first solving module until the updated bid vector and the updated asking price vector reach the preset convergence condition, so that each content consumer obtains content data based on the updated bid vector, and each content producer supplies content data based on the updated asking price vector.

[0014] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the on-demand unmanned cluster data sharing method for maximum utility of any one of the aforementioned implementation modes is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the unmanned cluster data on-demand sharing method for maximum utility of any one of the aforementioned implementations.

[0016] The present invention provides an unmanned cluster data on-demand sharing method for maximum utility. The method constructs a content distribution model in a drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of an iterative bilateral auction. After obtaining the current bidding vector of each content consumer and the current asking price vector of each content producer, the above model is applied to solve the current content demand vector of each content consumer, the updated bidding vector, the current content supply vector of each content producer, and the updated asking price vector, and iteratively updates until the bidding vector and the asking price vector reach a preset convergence condition. The application of the above model can effectively encourage content producers to participate in the data market, thereby promoting data sharing between drone nodes, thereby alleviating the technical problem of poor data sharing activity in existing drone named data networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 Naming the data network communication paradigm diagram for drones; Figure 2 A flowchart of an unmanned cluster data on-demand sharing method for maximum utility provided by an embodiment of the present invention; Figure 3 A schematic diagram of the content transaction process in a bilateral auction market; Figure 4 A schematic diagram of a bidding vector of a content consumer provided by an embodiment of the present invention; Figure 5 A schematic diagram of a content demand vector of a content consumer provided by an embodiment of the present invention; Figure 6 A schematic diagram of a price vector of a content producer provided by an embodiment of the present invention; Figure 7 A schematic diagram of a content supply vector of a content producer provided by an embodiment of the present invention; Figure 8 A functional module diagram of an unmanned cluster data on-demand sharing device for maximum utility provided by an embodiment of the present invention; Fig. 9 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0022] Embodiment 1 Figure 2 A flowchart of an unmanned cluster data on-demand sharing method for maximum utility provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the method specifically comprises the following steps: Step S102, constructing a content distribution model in the drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of iterative bilateral auction.

[0023] In order to promote content sharing and encourage content producers to participate in the data market, thereby promoting data sharing between drone nodes, the embodiment of the present invention proposes a content sharing incentive mechanism. In addition, since the "request-reply" architecture of NDN can essentially be regarded as the buyer and seller of content, the embodiment of the present invention constructs a "data trading market" based on bilateral auctions.

[0024] Figure 3 Figure 1 is a schematic diagram of the content trading process in a bilateral auction market. In this market, content can be traded between content consumers and content producers. Content consumers announce their demand for content and the price they are willing to pay (i.e., bid). At the same time, content producers disclose the amount of content they can provide and the price they want in return (i.e., asking price). Then, a virtual central agent (i.e., Figure 3 A market broker in a network matches the two parties to decide how much content each content producer should provide to each content consumer and at what price.

[0025] However, since there is no cooperative relationship between drone nodes, market economic people cannot obtain complete information of all nodes. Therefore, the embodiment of the present invention encourages participants in the trading market to share their hidden information through an incentive mechanism. Generally speaking, the incentive mechanism should meet four economic characteristics: economic benefits, individual rationality, incentive compatibility, and balance of income and expenditure. However, since these four characteristics cannot be met at the same time in the existing bilateral auction mechanism, in order to solve this problem, the embodiment of the present invention adopts an iterative bilateral auction mechanism (IDAA). Under the content sharing incentive mechanism of iterative bilateral auction, the embodiment of the present invention constructs a content distribution model in the drone named data network, a bidding model for each content consumer, and a asking price model for each content producer.

[0026] The content allocation model is used to determine the content demand vector of each content consumer and the content supply vector of each content producer. The content demand vector includes the amount of content data requested by the content consumer from each content producer, and the content supply vector includes the amount of content data provided by the content producer to each content consumer. The content consumer's bidding model is used to determine the bid of the content consumer to each content producer (the content provided). The content producer's asking price model is used to determine the asking price of the content producer to each content consumer (the content provided). The above content allocation model, bidding model and asking price model are all functions that need to be iterated to obtain the optimal solution.

[0027] Step S104, obtaining the current bid vector of each content consumer and the current asking price vector of each content producer.

[0028] In the first stage of the iterative bilateral auction mechanism, each content consumer provides the current bid vector to the virtual market broker, and each content producer provides the current asking price vector to the virtual market broker. At the beginning of the method execution, the current bid vector and the current asking price vector can be obtained by random initialization. represents the bid vector of content consumer i, , represents the bid of content consumer i to content producer j, and N represents the total number of content producers. represents the asking price vector of content producer j, , represents the price charged by content producer j to content consumer i, and M represents the total number of content consumers.

[0029] Step S106: Solve the content distribution model based on the current bid vector and the current asking price vector to obtain the current content demand vector of each content consumer and the current content supply vector of each content producer.

[0030] In the second stage of the iterative bilateral auction mechanism, after obtaining the current bid vector of each content consumer and the current asking price vector of each content producer, the above current bid vector and current asking price vector are substituted into the content allocation model and solved to obtain the current content demand vector corresponding to the current bid vector of each content consumer and the current content supply vector corresponding to the current asking price vector of each content producer.

[0031] The content demand vector of content consumer i is , represents the amount of content data requested by content consumer i to content producer j. The content supply vector of content producer j is , Represents the amount of content data provided by content producer j to content consumer i.

[0032] Step S108, solving the corresponding bidding model based on the current content demand vector of the target consumer to obtain the updated bidding vector of the target consumer, and solving the corresponding asking price model based on the current content supply vector of the target producer to obtain the updated asking price vector of the target producer.

[0033] Among them, the target consumer refers to any content consumer in the drone naming data network; the target producer refers to any content producer in the drone naming data network.

[0034] Step S110, when it is determined that the updated bid vector and the updated asking price vector have not reached the preset convergence condition, the updated bid vector is used as the current bid vector, the updated asking price vector is used as the current asking price vector, and the process returns to step S106 until the updated bid vector and the updated asking price vector reach the preset convergence condition, so that each content consumer obtains content data based on the updated bid vector, and each content producer supplies content data based on the updated asking price vector.

[0035] In order to determine whether the current bid, asking price, and content allocation are the optimal solution, the embodiment of the present invention further uses the bidding model to update the bid vector of each content consumer, and uses the asking price model to update the asking price vector of each content producer. When updating the bid vector of the target consumer, it is necessary to substitute the current content demand vector of the target consumer obtained in the above steps into the bidding model of the target consumer, and by solving the "current optimal solution" of the bidding model, the updated bid vector of the target consumer can be obtained; similarly, when updating the asking price vector of the target producer, it is necessary to substitute the current content supply vector of the target producer obtained in the above steps into the asking price model of the target producer, and by solving the "current optimal solution" of the asking price model, the updated asking price vector of the target producer can be obtained.

[0036] In the embodiment of the present invention, if the difference between the updated bid vector of each content consumer and the current bid vector is less than a preset threshold, and the difference between the updated asking price vector of each content producer and the current asking price vector is also less than the preset threshold, it is determined that the updated bid vector and the updated asking price vector meet the preset convergence condition, that is, the bid and the asking price both fluctuate slightly and reach a convergence state. At this time, each content consumer uses the updated bid vector to obtain content data from the content producer, and each content producer uses the updated asking price vector to supply content data to the content consumer.

[0037] However, if the difference between the updated bid vector of any content consumer and the current bid vector is not less than the preset threshold, or the difference between the updated asking price vector of any content producer and the current asking price vector is not less than the preset threshold, it is determined that the updated bid vector and the updated asking price vector have not reached the preset convergence condition, and it is necessary to use the updated bid vector as the current bid vector and the updated asking price vector as the current asking price vector, and then return to execute the above steps S106 to S108 until the updated bid vector and the updated asking price vector reach the preset convergence condition.

[0038] The embodiment of the present invention provides an unmanned cluster data on-demand sharing method for maximum utility. The method constructs a content distribution model in a drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of an iterative bilateral auction. After obtaining the current bidding vector of each content consumer and the current asking price vector of each content producer, the above model is applied to solve the current content demand vector of each content consumer, the updated bidding vector, the current content supply vector of each content producer, and the updated asking price vector, and iteratively updates until the bidding vector and the asking price vector reach a preset convergence condition. The application of the above model can effectively encourage content producers to participate in the data market, thereby promoting data sharing between drone nodes, thereby alleviating the technical problem of poor data sharing activity in existing drone named data networks.

[0039] In an optional implementation, before constructing a content distribution model in a drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of an iterative bilateral auction, the embodiment of the present invention further includes the following steps: Step S1011, determining the utility function of each content consumer and the cost function of each content producer.

[0040] Specifically, in UNDN, for each specific content, there is a group of content consumers and a group of content producers The embodiment of the present invention regards the process of content sharing between two parties as a data trading market. After each content consumer obtains content data from the content producer, the content consumer can benefit from the above content data. Therefore, each content consumer is configured to have a corresponding utility function. The utility function of content consumer i is expressed as ; Each content producer needs to consume energy to provide content data to content consumers. Therefore, each content producer is configured with a corresponding cost function. The cost function of content producer j is expressed as The utility function and cost function are used to measure the gains and losses of content consumers and content producers in the process of content sharing, respectively.

[0041] In the process of content sharing, content consumers want to request more content from producers to meet their needs. Therefore, the utility function should be positive, increasing, and concave relative to the amount of content. In addition, since content consumers want to obtain content faster, the utility function should be negatively correlated with latency. Therefore, in this embodiment of the present invention, the utility function of content consumer i is expressed as , represents the preset utility weight, Indicates that content consumer i receives content The delay experienced, , Indicates that content consumer i receives content The transmission delay experienced, Indicates that content consumer i receives content The propagation delay experienced, represents the total number of hops transmitted between content producer j and content consumer i, Indicates the content transmission rate, represents the sum of all paths between content consumer i and content producer j, Represents the propagation speed of electromagnetic waves.

[0042] based on From the formula, we can see that the utility function of content consumers is strictly increasing and concave, that is, the utility function of content consumers increases with the increase of the amount of content, while the growth rate of utility decreases with the increase of the amount of content.

[0043] When content producers provide content to content consumers, transmission energy consumption will be generated. The more content a content producer provides, the more energy it consumes, and the greater the value of the cost function. Therefore, the cost function should be positive and positively correlated with the amount of content provided. Therefore, in the embodiment of the present invention, the cost function of content producer j is expressed as: , , represents the preset cost weight, r represents the next hop in the path from content producer j to content consumer i, represents the transmission energy consumption between content producer j and the next hop r when content producer j sends data to content consumer i, represents the energy consumption per bit of data sent by content producer j, represents the energy dissipation factor of the free space model, represents the energy consumption factor of the multipath fading model, represents the Euclidean distance between content producer j and next hop r, Indicates the preset distance switching threshold, .

[0044] As a lightweight flying device, the energy of the drone is limited, so the transmission energy consumption during data transmission must be considered. This consumption is generated by the radio electronics and power amplifier of the transmitting node. Due to the problem of wireless channel fading, the embodiment of the present invention considers both the free space model and the multipath fading model. These two models can be freely switched according to the distance between the content producer j and the next hop r.

[0045] Based on cost function From the formula, we can see that the cost function of content producers is a content supply That is, as the content supply increases, content producers will incur higher transmission costs, leading to higher overall costs.

[0046] Step S1012, taking social welfare maximization as the goal, a content distribution target model is constructed based on the utility functions of all content consumers and the cost functions of all content producers.

[0047] From the above description, it can be seen that content consumers aim to seek more content to improve their utility, while content producers aim to reduce content supply to reduce costs. There is a conflict between the two parties in terms of different goals. Therefore, a fair central market broker is needed to determine the demand and supply vectors to maximize social welfare and thus achieve a balance between utility and cost. Therefore, in the embodiment of the present invention, the content allocation target model is expressed as: , ; ; ;in, represents the utility function of content consumer i, Denotes the cost function of content producer j.

[0048] The above constraint C1 means that the total amount of content data that content consumer i wants to request is and Constraint C2 indicates that the total amount of content data that content producer j can provide will not exceed its maximum amount of content data, and constraint C3 indicates that when the content transaction is completed, the amount of data requested by the content consumer should be equal to the amount of data supplied by the content producer.

[0049] Step S1013: convert the content distribution target model into a Lagrangian function to obtain a first Lagrangian function.

[0050] According to the above analysis of the convexity of the utility function and the cost function, the above content allocation target model is about and is a strictly concave function of , and the constraints are compact and concave. Therefore, the content distribution target model can be described by the Karush-Kuhn-Tucker (KKT) condition. Relaxing the constraints yields the following Lagrangian function: ;in, , , , All represent Lagrange multipliers, , , Represents a column vector whose elements are all 1.

[0051] Step S1014: determining the KKT condition satisfied by the optimal solution of the content distribution target model based on the first Lagrangian function to obtain a first KKT condition.

[0052] Based on the expression of the first Lagrangian function, the KKT conditions that need to be satisfied for the optimal variables generated by the content distribution target model are as follows: ; ; ; ; ; ; ; ; .in, , .

[0053] Obviously, to obtain the optimal solution of the content allocation target model that meets the above conditions, the market broker needs to know the utility function and cost function of all content consumers and content producers. However, since there is no cooperative relationship between drone nodes, the market broker cannot obtain the complete information of all nodes, that is, it is impossible to directly solve the content allocation target model. Therefore, the embodiment of the present invention proposes to construct a content allocation model in the drone named data network based on the content sharing incentive mechanism of iterative bilateral auction.

[0054] In an optional implementation, in the above step S102, a content distribution model in the drone naming data network is constructed based on the content sharing incentive mechanism of iterative bilateral auction, which specifically includes the following contents: A content allocation model is constructed based on the bid vector and content demand vector of each content consumer, and the asking price vector and content supply vector of each content producer.

[0055] The content distribution model is expressed as: , ; ; .

[0056] in, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i to content producer j, represents the content supply vector of content producer j, represents the amount of content data provided by content producer j to content consumer i, N represents the total number of content producers, M represents the total number of content consumers, represents the bid of content consumer i to content producer j, represents the bid vector of content consumer i, represents the price that content producer j asks content consumer i, represents the asking price vector of content producer j; Indicates the lower limit of the total content data volume requested by content consumer i, represents the upper limit of the total content data volume requested by content consumer i, represents the maximum amount of content data of content producer j. In the content distribution model, and is to represent the concave utility function of data consumers and the convex cost function of data producers respectively.

[0057] Based on the current bid vector of content consumers and the current asking price vector of content producers, the market broker determines how to allocate the content volume for buyers and sellers (content consumers and content producers) by solving the content allocation model, that is, determining the current content demand vector of each content consumer and the current content supply vector of each content producer.

[0058] Optionally, the above step S106, based on the current bid vector and the current asking price vector, solves the content allocation model to obtain the current content demand vector of each content consumer and the current content supply vector of each content producer, specifically includes the following steps: Step S1061: convert the content distribution model into a Lagrangian function to obtain a second Lagrangian function.

[0059] By relaxing the constraints, the content allocation model can be transformed into the following Lagrangian function: .

[0060] Step S1062: Determine the KKT condition satisfied by the optimal solution of the content distribution model based on the second Lagrangian function to obtain a second KKT condition.

[0061] Based on the expression of the second Lagrangian function, the KKT conditions that need to be satisfied for the optimal variables generated by the content distribution model are as follows: ; ; ; ; ; ; ; ; .

[0062] Step S1063, determining a content demand vector model of each content consumer and a content supply vector model of each content producer based on the second KKT condition.

[0063] According to the second KKT condition above, the content demand vector model of each content consumer can be obtained: , and the content supply vector model of each content producer .

[0064] Step S1064: Solve the Lagrangian function based on the current bid vector and the current ask price vector to obtain the Lagrangian multiplier.

[0065] After substituting the current bid vector and the current asking price vector into the Lagrangian function, the Lagrangian function solver is used to solve the Lagrangian function to obtain the Lagrangian multiplier: , , and .

[0066] Step S1065, substitute the Lagrange multiplier and the current bid vector into the content demand vector model to obtain the current content demand vector of each content consumer, and substitute the Lagrange multiplier and the current asking price vector into the content supply vector model to obtain the current content supply vector of each content producer.

[0067] That is, and The unknowns on the right side of the equal sign have been determined. Substituting the unknowns into the model, we can calculate and , and then obtain the current content demand vector of each content consumer and the current content supply vector of each content producer.

[0068] In an optional implementation, in the above step S102, a bidding model for each content consumer is constructed based on the content sharing incentive mechanism of iterative bilateral auction, which specifically includes the following steps: Step S201: determining a target bidding model for each content consumer based on the first KKT condition and the second KKT condition.

[0069] By comparing the KKT conditions of the content allocation model and the content allocation target model, it can be seen that the first KKT condition and the second KKT condition only differ in the first two formulas. Therefore, if the first two formulas are equivalent, then the content allocation model and the content allocation target model are equivalent.

[0070] The two formulas related to the content consumer's bid are: and , if the two are equivalent, then: . This is the target bidding model for content consumers.

[0071] The two equations related to the content producer's asking price are: and , if the two are equivalent, then: . This is the target pricing model for content producers.

[0072] Step S202, with the goal of maximizing utility, a local optimization model of the target consumer is constructed based on the unknown expressions of the utility function and settlement function of the target consumer.

[0073] If content consumers and content producers bid and ask prices according to the above two models, the market broker can obtain an optimal solution, and this optimal solution is the same as the optimal solution for maximizing social welfare. However, since content consumers and content producers are selfish and only want to maximize their own utility (local optimization problem), the market broker cannot find the optimal solution for maximizing social welfare. Based on this, the embodiment of the present invention designs some settlement rules for content consumers and some reward rules for content producers to encourage them to submit bids and asks according to the above formula.

[0074] For content consumers, the embodiment of the present invention uses When the market broker receives the bid from content consumer i The settlement rule given when , that is, the unknown expression of the settlement function of content consumer i is Then, with the goal of maximizing utility, the local optimization problem of content consumer i can be expressed using the following local optimization model: , .

[0075] Step S203, solving the local optimization model of the target consumer based on the target bidding model and the content demand vector model, and determining the solution expression of the settlement function.

[0076] By solving the local optimization model, each content consumer can obtain its optimal bid vector. Let the local optimization model objective function be To find the first-order derivative, the optimal bid vector should satisfy the following conditions: .

[0077] The target bidding model is known to be , the content demand vector model is , based on this simplification , we can get: Therefore, the solution expression of the settlement function of the content consumer is: .

[0078] Step S204, substituting the solution expression of the settlement function into the local optimization model of the target consumer to obtain the bidding model of the target consumer.

[0079] That is, in the embodiment of the present invention, the bid model of the target consumer is expressed as .

[0080] In an optional implementation, in the above step S102, the price model of each content producer is constructed based on the content sharing incentive mechanism of iterative bilateral auction, which specifically includes the following steps: Step S301: Determine a target price model for each content producer based on the first KKT condition and the second KKT condition.

[0081] The method for determining the target price model has been described in the description of step S201 above, and will not be repeated here. For details, please refer to the above. In the embodiment of the present invention, the target price model of the content producer is .

[0082] Step S302, with the goal of maximizing utility, a local optimization model of the target producer is constructed based on the unknown expressions of the cost function and reward function of the target producer.

[0083] For content producers, the embodiment of the present invention uses represents the price that the market broker receives from content producer j The reward rule given when . That is, the unknown expression of the reward function of content producer j is , then with the goal of maximizing utility, the local optimization problem of content producer j can be expressed using the following local optimization model: , .

[0084] Step S303, solving the local optimization model of the target producer based on the target asking price model and the content provision vector model, and determining a solution expression of the reward function.

[0085] By solving the local optimization model of the target producer, each content producer can obtain its optimal asking price vector. To find the first-order derivative, the optimal bid vector should satisfy the following conditions: .

[0086] The target asking price model is known to be , the content provision vector model is , based on this simplification , we can get: Therefore, the solution expression of the content producer's reward function is: .

[0087] Step S304, substituting the solution expression of the reward function into the local optimization model of the target producer to obtain the target producer's asking price model.

[0088] That is, in the embodiment of the present invention, the target producer's asking price model is expressed as: .

[0089] In summary, the embodiments of the present invention apply named data networking to unmanned cluster networks, which solves the problem of easily broken link connections caused by the high dynamics of drones, and proposes a content sharing incentive mechanism based on iterative bilateral auctions. Compared with the original content sharing mechanism of named data networking, this mechanism takes into account node inertia and encourages content producers to participate in the data market by designing an iterative bilateral auction algorithm, thereby promoting data sharing between drone nodes.

[0090] In order to verify the convergence and price rules of the method provided by the embodiment of the present invention, Figure 4 A schematic diagram of a bidding vector of a content consumer provided by an embodiment of the present invention, Figure 5 A schematic diagram of a content demand vector of a content consumer provided by an embodiment of the present invention, Figure 6 A schematic diagram of a price vector of a content producer provided in an embodiment of the present invention, Figure 7 A schematic diagram of a content supply vector of a content producer provided by an embodiment of the present invention. That is, Figures 4 to 7 The price strategy and content quantity strategy of content consumers and content producers are shown respectively. It can be observed that after about 10 iterations, the strategies of content producers and content consumers can quickly converge to the maximum point of social welfare. In addition, by observing Figure 5 and Figure 7 , it can be seen that after convergence, the demand vector of content consumers and the supply vector provided by content producers are always equal. This means that the entire content sharing market has successfully converged to an equilibrium point. In addition, Figure 4 The bid vector of the content consumer and Figure 5 The demand vector curves in have similar changing trends, while Figure 6 The curves of the price vector and supply vector of the content producer have opposite changing trends, which is consistent with the content demand vector model and content supply vector model constructed in the embodiment of the present invention.

[0091] Embodiment 2 An embodiment of the present invention also provides an unmanned cluster data on-demand sharing device for maximum utility, which is mainly used to execute the unmanned cluster data on-demand sharing method for maximum utility provided in the above-mentioned embodiment one. The following is a specific introduction to the unmanned cluster data on-demand sharing device for maximum utility provided in an embodiment of the present invention.

[0092] Figure 8 A functional module diagram of an unmanned cluster data on-demand sharing device for maximum utility provided by an embodiment of the present invention, such as Figure 8 As shown, the device mainly includes: a construction module 10, an acquisition module 20, a first solution module 30, a second solution module 40, and an iteration module 50, wherein: Building module 10 is used to build a content distribution model in the drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on the content sharing incentive mechanism of iterative bilateral auction.

[0093] The acquisition module 20 is used to acquire the current bid vector of each content consumer and the current asking price vector of each content producer.

[0094] The first solving module 30 is used to solve the content allocation model based on the current bid vector and the current asking price vector to obtain the current content demand vector of each content consumer and the current content supply vector of each content producer.

[0095] The second solving module 40 is used to solve the corresponding bidding model based on the current content demand vector of the target consumer to obtain the updated bidding vector of the target consumer, and to solve the corresponding asking price model based on the current content supply vector of the target producer to obtain the updated asking price vector of the target producer; wherein the target consumer represents any content consumer in the drone naming data network; the target producer represents any content producer in the drone naming data network.

[0096] The iteration module 50 is used to use the updated bid vector as the current bid vector and the updated asking price vector as the current asking price vector when it is determined that the updated bid vector and the updated asking price vector have not reached a preset convergence condition, and return to call the first solution module until the updated bid vector and the updated asking price vector reach a preset convergence condition, so that each content consumer obtains content data based on the updated bid vector and each content producer supplies content data based on the updated asking price vector.

[0097] The embodiment of the present invention provides an unmanned cluster data on-demand sharing device for maximum utility. The device constructs a content distribution model in a drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of an iterative bilateral auction. After obtaining the current bidding vector of each content consumer and the current asking price vector of each content producer, the above model is applied to solve the current content demand vector of each content consumer, the updated bidding vector, the current content supply vector of each content producer, and the updated asking price vector, and iteratively updates until the bidding vector and the asking price vector reach a preset convergence condition. The application of the above model can effectively encourage content producers to participate in the data market, thereby promoting data sharing between drone nodes, thereby alleviating the technical problem of poor data sharing activity in existing drone named data networks.

[0098] Optionally, the device is also used for: Determine the utility function of each content consumer and the cost function of each content producer.

[0099] With the goal of maximizing social welfare, a content allocation target model is constructed based on the utility function of all content consumers and the cost function of all content producers.

[0100] The content distribution target model is transformed into a Lagrangian function to obtain a first Lagrangian function.

[0101] The KKT condition satisfied by the optimal solution of the content distribution target model is determined based on the first Lagrangian function to obtain a first KKT condition.

[0102] Optionally, the building block 10 includes: The first construction unit is used to construct a content distribution model based on the bid vector and content demand vector of each content consumer and the asking price vector and content supply vector of each content producer.

[0103] The content distribution model is expressed as: , ; ; ;in, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i to content producer j, represents the content supply vector of content producer j, represents the amount of content data provided by content producer j to content consumer i, N represents the total number of content producers, M represents the total number of content consumers, represents the bid of content consumer i to content producer j, represents the bid vector of content consumer i, represents the price that content producer j asks content consumer i, represents the asking price vector of content producer j; Indicates the lower limit of the total content data volume requested by content consumer i, represents the upper limit of the total content data volume requested by content consumer i, Indicates the maximum amount of content data of content producer j.

[0104] Optionally, the first solving module 30 is specifically used for: The content distribution model is transformed into a Lagrangian function to obtain a second Lagrangian function.

[0105] The KKT condition satisfied by the optimal solution of the content distribution model is determined based on the second Lagrangian function to obtain a second KKT condition.

[0106] A content demand vector model for each content consumer and a content supply vector model for each content producer are determined based on the second KKT condition.

[0107] Based on the current bid vector and the current ask price vector, the Lagrangian function is solved to obtain the Lagrangian multiplier.

[0108] Substituting the Lagrange multiplier and the current bid vector into the content demand vector model, the current content demand vector of each content consumer is obtained, and substituting the Lagrange multiplier and the current asking price vector into the content supply vector model, the current content supply vector of each content producer is obtained.

[0109] Optionally, the building block 10 further includes: The first determining unit is used to determine a target bidding model for each content consumer based on the first KKT condition and the second KKT condition.

[0110] The second construction unit is used to construct a local optimization model of the target consumer based on the unknown expressions of the utility function and settlement function of the target consumer with the goal of maximizing utility.

[0111] The first solving unit is used to solve the local optimization model of the target consumer based on the target bidding model and the content demand vector model, and determine the solution expression of the settlement function.

[0112] The first substitution unit is used to substitute the solution expression of the settlement function into the local optimization model of the target consumer to obtain the bidding model of the target consumer.

[0113] Optionally, the building block 10 further includes: The second determining unit is used to determine a target asking price model for each content producer based on the first KKT condition and the second KKT condition.

[0114] The third construction unit is used to construct a local optimization model of the target producer based on unknown expressions of the cost function and reward function of the target producer with the goal of maximizing utility.

[0115] The second solving unit is used to solve the local optimization model of the target producer based on the target asking price model and the content supply vector model, and determine the solution expression of the reward function.

[0116] The second substitution unit is used to substitute the solution expression of the reward function into the local optimization model of the target producer to obtain the asking price model of the target producer.

[0117] Optionally, the content allocation target model is expressed as: , ; ; ;in, represents the utility function of content consumer i, Denotes the cost function of content producer j.

[0118] , represents the preset utility weight, Indicates that content consumer i receives content The delay experienced, , Indicates that content consumer i receives content The transmission delay experienced, Indicates that content consumer i receives content The propagation delay experienced, represents the total number of hops transmitted between content producer j and content consumer i, Indicates the content transmission rate, represents the sum of all paths between content consumer i and content producer j, Represents the propagation speed of electromagnetic waves.

[0119] , , represents the preset cost weight, r represents the next hop in the path from content producer j to content consumer i, represents the transmission energy consumption between content producer j and the next hop r when content producer j sends data to content consumer i, represents the energy consumption per bit of data sent by content producer j, represents the energy dissipation factor of the free space model, represents the energy consumption factor of the multipath fading model, represents the Euclidean distance between content producer j and next hop r, Indicates the preset distance switching threshold, .

[0120] Embodiment 3 See also Fig. 9 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0121] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0122] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0123] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0124] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0125] A computer program product of a method and apparatus for on-demand sharing of unmanned cluster data for maximum utility provided in an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor, wherein the instructions included in the program code can be used to execute the method described in the previous method embodiment. For specific implementation, please refer to the method embodiment, which will not be repeated here.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0127] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0128] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0129] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0130] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0131] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for sharing unmanned cluster data on demand for maximum utility, characterized in that: include: Step S102, constructing a content distribution model in the drone named data network, a bidding model for each content consumer, and a asking price model for each content producer based on a content sharing incentive mechanism of iterative bilateral auction; Step S104, obtaining a current bid vector of each content consumer and a current asking price vector of each content producer; Step S106, solving the content distribution model based on the current bid vector and the current asking price vector to obtain a current content demand vector of each content consumer and a current content supply vector of each content producer; Step S108, solving a corresponding bidding model based on the current content demand vector of the target consumer to obtain an updated bidding vector of the target consumer, and solving a corresponding asking price model based on the current content supply vector of the target producer to obtain an updated asking price vector of the target producer; wherein the target consumer represents any content consumer in the drone naming data network; the target producer represents any content producer in the drone naming data network; Step S110, when it is determined that the updated bid vector and the updated asking price vector have not reached a preset convergence condition, the updated bid vector is used as the current bid vector, the updated asking price vector is used as the current asking price vector, and the process returns to step S106 until the updated bid vector and the updated asking price vector reach the preset convergence condition, so that each content consumer obtains content data based on the updated bid vector, and each content producer supplies content data based on the updated asking price vector.

2. The unmanned cluster data on-demand sharing method for maximum utility according to claim 1 is characterized in that: Before constructing the content distribution model in the drone named data network based on the content sharing incentive mechanism of iterative bilateral auction, the bidding model of each content consumer and the asking price model of each content producer, it also includes: determining a utility function for each of the content consumers and a cost function for each of the content producers; With the goal of maximizing social welfare, a content distribution target model is constructed based on the utility function of all content consumers and the cost function of all content producers. Converting the content distribution target model into a Lagrangian function to obtain a first Lagrangian function; A KKT condition satisfied by an optimal solution of the content distribution target model is determined based on the first Lagrangian function to obtain a first KKT condition.

3. The unmanned cluster data on-demand sharing method for maximum utility according to claim 2, characterized in that: A content distribution model in the drone named data network is constructed based on the content sharing incentive mechanism of iterative bilateral auction, including: Building a content allocation model based on the bid vector and content demand vector of each content consumer and the asking price vector and content supply vector of each content producer; The content distribution model is expressed as: , ; ; ; in, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i to content producer j, represents the content supply vector of content producer j, represents the amount of content data provided by content producer j to content consumer i, N represents the total number of content producers, M represents the total number of content consumers, represents the bid of content consumer i to content producer j, represents the bid vector of content consumer i, represents the price that content producer j asks content consumer i, represents the asking price vector of content producer j; Indicates the lower limit of the total content data volume requested by content consumer i, represents the upper limit of the total content data volume requested by content consumer i, Indicates the maximum amount of content data of content producer j.

4. The unmanned cluster data on-demand sharing method for maximum utility according to claim 3 is characterized in that: Solving the content distribution model based on the current bid vector and the current asking price vector to obtain a current content demand vector of each content consumer and a current content supply vector of each content producer includes: Converting the content distribution model into a Lagrangian function to obtain a second Lagrangian function; Determining a KKT condition satisfied by an optimal solution of the content distribution model based on the second Lagrangian function to obtain a second KKT condition; Determine a content demand vector model for each content consumer and a content supply vector model for each content producer based on the second KKT condition; Solving the Lagrangian function based on the current bid vector and the current ask price vector to obtain a Lagrangian multiplier; Substituting the Lagrangian multiplier and the current bid vector into the content demand vector model, the current content demand vector of each content consumer is obtained; and substituting the Lagrangian multiplier and the current asking price vector into the content supply vector model, the current content supply vector of each content producer is obtained.

5. The unmanned cluster data on-demand sharing method for maximum utility according to claim 4 is characterized in that: The content sharing incentive mechanism based on iterative bilateral auction builds a bidding model for each content consumer, including: Determining a target bidding model for each content consumer based on the first KKT condition and the second KKT condition; Taking utility maximization as the goal, based on the unknown expressions of the utility function and settlement function of the target consumer, a local optimization model of the target consumer is constructed; Solving the local optimization model of the target consumer based on the target bidding model and the content demand vector model to determine a solution expression of the settlement function; Substitute the solution expression of the settlement function into the local optimization model of the target consumer to obtain the bidding model of the target consumer.

6. The unmanned cluster data on-demand sharing method for maximum utility according to claim 4, characterized in that: The content sharing incentive mechanism based on iterative bilateral auction builds the asking price model of each content producer, including: Determining a target asking price model for each content producer based on the first KKT condition and the second KKT condition; Taking utility maximization as the goal, a local optimization model of the target producer is constructed based on unknown expressions of the cost function and reward function of the target producer; Solving the local optimization model of the target producer based on the target asking price model and the content provision vector model to determine a solution expression for the reward function; Substitute the solution expression of the reward function into the local optimization model of the target producer to obtain the asking price model of the target producer.

7. The unmanned cluster data on-demand sharing method for maximum utility according to claim 3 is characterized in that: The content distribution target model is expressed as: , ; ; ;in, represents the utility function of content consumer i, represents the cost function of content producer j; , represents the preset utility weight, Indicates that content consumer i receives content The delay experienced, , Indicates that content consumer i receives content The transmission delay experienced, Indicates that content consumer i receives content The propagation delay experienced, represents the total number of hops transmitted between content producer j and content consumer i, Indicates the content transmission rate, represents the sum of all paths between content consumer i and content producer j, It represents the propagation speed of electromagnetic waves; , , represents the preset cost weight, r represents the next hop in the path from content producer j to content consumer i, represents the transmission energy consumption between content producer j and the next hop r when content producer j sends data to content consumer i, represents the energy consumption per bit of data sent by content producer j, represents the energy dissipation factor of the free space model, represents the energy consumption factor of the multipath fading model, represents the Euclidean distance between content producer j and next hop r, Indicates the preset distance switching threshold, .

8. An unmanned cluster data on-demand sharing device for maximum utility, characterized in that: include: Building modules for constructing the content distribution model in the drone named data network based on the content sharing incentive mechanism of iterative bilateral auction, the bidding model of each content consumer, and the asking price model of each content producer; An acquisition module, used to acquire a current bid vector of each of the content consumers and a current asking price vector of each of the content producers; A first solving module, configured to solve the content allocation model based on the current bid vector and the current asking price vector to obtain a current content demand vector of each content consumer and a current content supply vector of each content producer; The second solving module is used to solve the corresponding bidding model based on the current content demand vector of the target consumer to obtain the updated bidding vector of the target consumer, and solve the corresponding asking price model based on the current content supply vector of the target producer to obtain the updated asking price vector of the target producer; wherein the target consumer represents any content consumer in the drone naming data network; the target producer represents any content producer in the drone naming data network; An iteration module is used for, when it is determined that the updated bid vector and the updated asking price vector have not reached a preset convergence condition, using the updated bid vector as the current bid vector and the updated asking price vector as the current asking price vector, and returning to call the first solution module until the updated bid vector and the updated asking price vector reach the preset convergence condition, so that each content consumer obtains content data based on the updated bid vector and each content producer supplies content data based on the updated asking price vector.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the unmanned cluster data on-demand sharing method for maximum utility according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the unmanned cluster data on-demand sharing method for maximum utility according to any one of claims 1 to 7.

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