A Method and Device for On-Demand Sharing of Unmanned Cluster Data Oriented to Maximum Utility

Abstract

The present invention provides a method and device for on-demand data sharing of unmanned clusters oriented to maximum utility, which relates to the technical field of unmanned aerial vehicle communication. The method constructs a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in the named data network of unmanned aerial vehicles based on the content sharing incentive mechanism of iterative bilateral auction. After obtaining the current bid vector of each content consumer and the current asking price vector of each content producer, the above models are applied to solve 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 asking price vector. Through iterative update, until the bid vector and the asking price vector reach the preset convergence condition. The application of the above models can effectively encourage content producers to participate in the data market, thereby alleviating the technical problem of poor data sharing activity existing in the existing named data network of unmanned aerial vehicles.

Description

Technical Field

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

[0002] A UAV cluster network is a collaborative network system composed of multiple UAVs, which can complete complex tasks through distributed cooperation. In recent years, due to the advantages of UAV cluster networks in terms of coverage, task decomposition, parallel processing, rapid deployment, etc., they have been widely used in many fields such as communication network expansion and post-disaster rescue. However, due to the high dynamicity of UAVs, the 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 latency and affects the efficiency of real-time data transmission.

[0003] The emergence of Named Data Networking (NDN) provides a solution to the above challenges. In NDN, nodes are divided into content consumers and content producers. Content consumers send interest packets to request data, while content producers provide data by returning data packets. Communication between nodes does not rely on fixed IP addresses but uses data names, thus eliminating the complexity of frequent address changes and the latency associated with routing updates. The above advantages make NDN very suitable for application in UAV cluster networks. The functions of NDN, such as content naming, stateless communication, data caching, and enhanced security, can provide a more efficient, reliable, and secure communication solution for UAV cluster networks. Figure 1 Schematic diagram of the communication paradigm for UAV named data networking.

[0004] However, existing NDN content sharing schemes adopt a single "request - reply" architecture, that is, content consumers flood interest packets with the name of the requested content into the network, and the nodes that have the content act as content producers and return data packets. However, this content sharing mechanism fails to consider the laziness of nodes and is difficult to effectively encourage nodes to actively participate. Specifically, due to limited communication resources, UAV 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, there is a technical problem of poor data sharing activity in the UAV named data network (UNDN). Summary of the Invention

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

[0006] In a first aspect, the present invention provides a method for on-demand sharing of unmanned cluster data for maximum utility, including: Step S102, constructing a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in the unmanned aerial vehicle named data network based on an iterative bilateral auction-based content sharing incentive mechanism; Step S104, obtaining the current bid vector of each content consumer and the current asking price vector of each content producer; Step S106, solving 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; Step S108, solving the corresponding bid model based on the current content demand vector of the target consumer to obtain the updated bid 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 unmanned aerial vehicle named data network; the target producer represents any content producer in the unmanned aerial vehicle named data network; Step S110, in the case where it is determined that the updated bid vector and the updated asking price vector do not meet the preset convergence condition, taking the updated bid vector as the current bid vector, the updated asking price vector as the current asking price vector, and returning to Step S106 until the updated bid vector and the updated asking price vector meet 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 a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in the unmanned aerial vehicle named data network based on an iterative bilateral auction-based content sharing incentive mechanism, it further 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; transforming the content allocation target model into a Lagrangian function to obtain a first Lagrangian function; determining the KKT conditions satisfied by the optimal solution of the content allocation target model based on the first Lagrangian function to obtain a first KKT condition.

[0008] Optionally, constructing a content allocation model in the unmanned aerial vehicle named data network based on an iterative bilateral auction-based content sharing incentive mechanism includes: constructing 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 allocation model is expressed as: , ; ; ; wherein, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i from 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, and M represents the total number of content consumers. represents the bid price of content consumer i for content producer j, represents the bid vector of content consumer i, represents the asking price of content producer j to content consumer i, represents the asking price vector of content producer j; represents the lower limit of the total amount of content data requested by content consumer i, represents the upper limit of the total amount of content data requested by content consumer i, represents the maximum amount of content data of content producer j.

[0009] Optionally, based on the current bid vector and the current asking price vector, solve 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, including: transforming the content allocation model into a Lagrangian function to obtain the second Lagrangian function; determining the KKT conditions satisfied by the optimal solution of the content allocation model based on the second Lagrangian function to obtain the second KKT conditions; 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 conditions; solving the Lagrangian function based on the current bid vector and the current asking price vector to obtain the Lagrange multipliers; substituting the Lagrange multipliers 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 Lagrange multipliers 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, construct the bid model of each content consumer based on the content sharing incentive mechanism of iterative bilateral auction, including: determining the target bid model of each content consumer based on the first KKT condition and the second KKT condition; constructing the local optimization model of the target consumer with the goal of maximizing utility based on the unknown expressions of the utility function and the settlement function of the target consumer; solving the local optimization model of the target consumer based on the target bid model and the content demand vector model to determine the 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 bid model of the target consumer.

[0011] Optionally, an asking price model for each content producer is constructed based on an iterative bilateral auction-based content sharing incentive mechanism, including: determining the target asking price model for each content producer based on the first KKT condition and the second KKT condition; constructing a local optimization model for the target producer with the goal of maximizing utility, based on the unknown expressions of the cost function and the 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 supply vector model to determine the 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 asking price model of the target producer.

[0012] Optionally, the content allocation target model is expressed as: , ; ; ; where, represents the utility function of content consumer i, represents the cost function of content producer j; , represents the preset utility weight, represents the delay experienced by content consumer i in receiving content , , represents the transmission delay experienced by content consumer i in receiving content , represents the propagation delay experienced by content consumer i in receiving content , represents the total number of hops transmitted between content producer j and content consumer i, represents the content transmission rate, represents the sum of all paths passed between content consumer i and content producer j, 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 of content producer j for sending each bit of data, represents the energy consumption 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 the next hop r, represents the preset distance switching threshold, .

[0013] In a second aspect, the present invention provides a device for on-demand sharing of unmanned cluster data for maximum utility, including: a construction module for constructing a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in a drone named data network based on an iterative bilateral auction-based content sharing incentive mechanism; an acquisition module for acquiring the current bid vector of each content consumer and the current asking price vector of each content producer; a first solution module for solving 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; a second solution module for solving the corresponding bid model based on the current content demand vector of a target consumer to obtain the updated bid vector of the target consumer, and for solving the corresponding asking price model based on the current content supply vector of a target producer to obtain the updated asking price vector of the target producer; where the target consumer represents any content consumer in the drone named data network; the target producer represents any content producer in the drone named data network; an iteration module for, in the case where it is determined that the updated bid vector and the updated asking price vector do not reach a preset convergence condition, taking the updated bid vector as the current bid vector, 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.

[0014] In a third aspect, the present invention provides an electronic device, including a memory and a processor, where a computer program that can run on the processor is stored on the memory, and when the processor executes the computer program, it implements the method for on-demand sharing of unmanned cluster data for maximum utility according to any one of the foregoing embodiments.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, and when the computer instructions are executed by a processor, they implement the method for on-demand sharing of unmanned cluster data for maximum utility according to any one of the foregoing embodiments.

[0016] The present invention provides a method for on-demand data sharing of unmanned clusters oriented to maximum utility. Based on the content sharing incentive mechanism of iterative bilateral auctions, this method constructs a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in the named data network of unmanned aerial vehicles (UAVs). After obtaining the current bid vector of each content consumer and the current asking price vector of each content producer, the above models are applied to solve 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 asking price vector. Through iterative updates, it continues until the bid vector and the asking price vector reach the preset convergence condition. The application of the above models can effectively encourage content producers to participate in the data market, thereby promoting data sharing among UAV nodes, and further alleviating the technical problem of poor data sharing activity in the existing named data network of UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the communication paradigm of the named data network of UAVs;

[0019] Figure 2 It is a flowchart of a method for on-demand data sharing of unmanned clusters oriented to maximum utility provided by an embodiment of the present invention;

[0020] Figure 3 It is a schematic diagram of the content trading process in the bilateral auction market;

[0021] Figure 4 It is a schematic diagram of the bid vector of a content consumer provided by an embodiment of the present invention;

[0022] Figure 5 It is a schematic diagram of the content demand vector of a content consumer provided by an embodiment of the present invention;

[0023] Figure 6 It is a schematic diagram of the asking price vector of a content producer provided by an embodiment of the present invention;

[0024] Figure 7 It is a schematic diagram of the content supply vector of a content producer provided by an embodiment of the present invention;

[0025] Figure 8Function module diagram of a device for on-demand sharing of unmanned cluster data for maximum utility provided by an embodiment of the present invention;

[0026] Figure 9 Schematic diagram of an electronic device provided by an embodiment of the present invention. Specific embodiments

[0027] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0028] 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 claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0029] The following will describe in detail some embodiments of the present invention with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0030] Embodiment 1

[0031] Figure 2 Flowchart of a method for on-demand sharing of unmanned cluster data for maximum utility provided by an embodiment of the present invention. As Figure 2 shown, the method specifically includes the following steps:

[0032] Step S102, construct a content distribution model, a bid model for each content consumer, and an asking price model for each content producer in the named data network of unmanned aerial vehicles based on an iterative bilateral auction-based content sharing incentive mechanism.

[0033] To promote content sharing, encourage content producers to participate in the data market, and thus promote data sharing among unmanned aerial vehicle nodes, an embodiment of the present invention proposes a content sharing incentive mechanism. Moreover, since the "request - response" architecture of NDN can essentially be regarded as the buyer and seller of content, an embodiment of the present invention constructs a "data trading market" based on bilateral auction.

[0034] Figure 3It 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., the bid). At the same time, content producers disclose the quantity of content they can provide and the return price they want (i.e., the asking price). Then, a virtual central agent (i.e., the market broker in Figure 3 ) matches the two parties to determine how much content each content producer should provide to each content consumer at what price. Figure 3 However, since there is no cooperation relationship between drone nodes, the market broker cannot obtain the complete information of all nodes. Therefore, the embodiments of the present invention use an incentive mechanism to encourage the participants in the trading market to share their hidden information. Generally speaking, the incentive mechanism should satisfy four economic characteristics: economic efficiency, individual rationality, incentive compatibility, and budget balance. However, since these four characteristics cannot be simultaneously satisfied in the existing bilateral auction mechanism, to solve this problem, the embodiments of the present invention adopt an iterative bilateral auction mechanism (IDAA). Under the content sharing incentive mechanism of the iterative bilateral auction, the embodiments of the present invention construct a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in the drone named data network.

[0035] Among them, 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 bid model of the content consumer is used to determine the bid of the content consumer for each content producer (the provided content). The asking price model of the content producer is used to determine the asking price of the content producer for each content consumer (the provided content). The above content allocation model, bid model, and asking price model are all functions that need to obtain the optimal solution through iteration.

[0036] Step S104, obtain the current bid vector of each content consumer and the current asking price vector of each content producer.

[0037] 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 both be obtained through random initialization.

[0038] Let \(\mathbf{b}_i\) represent the bid vector of content consumer \(i\), \(\mathbf{b}_{ij}\) represents the bid of content consumer \(i\) for content producer \(j\), and \(N\) represents the total number of content producers. Let \(\mathbf{b}_i\) represent the bid vector of content consumer \(i\), \(\mathbf{b}_{ij}\), \(\mathbf{b}_{ij}\) represents the bid of content consumer \(i\) for content producer \(j\), and \(N\) represents the total number of content producers. Denote the asking price vector of content producer j, , denote the asking price from content producer j to content consumer i, and M denote the total number of content consumers.

[0039] Step S106: Based on the current bid vector and the current asking price vector, solve 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.

[0040] In the second stage of the iterative double auction mechanism, after obtaining the current bid vector of each content consumer and the current asking price vector of each content producer, substitute the above current bid vector and current asking price vector into the content allocation model and solve it 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.

[0041] The content demand vector of content consumer i is , denote the amount of content data requested by content consumer i from content producer j, and the content supply vector of content producer j is , denote the amount of content data provided by content producer j to content consumer i.

[0042] Step S108: Based on the current content demand vector of the target consumer, solve the corresponding bid model to obtain the updated bid vector of the target consumer, and based on the current content supply vector of the target producer, solve the corresponding asking price model to obtain the updated asking price vector of the target producer.

[0043] Among them, the target consumer represents any content consumer in the drone named data network; the target producer represents any content producer in the drone named data network.

[0044] Step S110: In the case that it is determined that the updated bid vector and the updated asking price vector do not reach the preset convergence condition, use the updated bid vector as the current bid vector, the updated asking price vector as the current asking price vector, and return 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.

[0045] To determine whether the current bid, asking price, and content allocation are the optimal solutions, the embodiments of the present invention further use the bid model to update the bid vector of each content consumer and use 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 bid model of the target consumer. By solving the "current optimal solution" of the bid 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. By solving the "current optimal solution" of the asking price model, the updated asking price vector of the target producer can be obtained.

[0046] In the embodiments of the present invention, if the difference between the updated bid vector of each content consumer and the current bid vector is less than the 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, both the bid and the asking price fluctuate slightly and reach the 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.

[0047] 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 do not meet the preset convergence condition. 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 meet the preset convergence condition.

[0048] An embodiment of the present invention provides a method for on-demand data sharing of an unmanned cluster for maximum utility. Based on the content sharing incentive mechanism of iterative bilateral auction, a content allocation model, a bidding model for each content consumer, and an asking price model for each content producer are constructed in the unmanned aerial vehicle named data network. After obtaining the current bidding vector of each content consumer and the current asking price vector of each content producer, the above models are 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. Through iterative update, until the bidding vector and the asking price vector reach the preset convergence condition. The application of the above models can effectively encourage content producers to participate in the data market, thereby promoting data sharing between unmanned aerial vehicle nodes, and further alleviating the technical problem of poor data sharing activity in the existing unmanned aerial vehicle named data network.

[0049] In an alternative embodiment, before constructing a content allocation model, a bidding model for each content consumer, and an asking price model for each content producer based on the content sharing incentive mechanism of iterative bilateral auction, the embodiment of the present invention further includes the following steps:

[0050] Step S1011, determine the utility function of each content consumer and the cost function of each content producer.

[0051] Specifically, in the 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 the 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 with a corresponding utility function, and the utility function of content consumer i is expressed as ; and 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, and the cost function of content producer j is expressed as . The utility function and the cost function are respectively used to measure the benefits and losses of content consumers and content producers in the content sharing process.

[0052] 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 with respect to the quantity of content. In addition, since content consumers hope to obtain content faster, the utility function should be negatively correlated with delay. Therefore, in the embodiment of the present invention, the utility function of content consumer i is expressed as , represents the preset utility weight, Denote the latency experienced by content consumer \(i\) when receiving the content , , Denote the transmission delay experienced by content consumer \(i\) when receiving the content , Denote the propagation delay experienced by content consumer \(i\) when receiving the content , Denote the total number of hops between content producer \(j\) and content consumer \(i\), Denote the content transmission rate, Denote the sum of all paths between content consumer \(i\) and content producer \(j\), Denote the propagation speed of electromagnetic waves.

[0053] Based on the formula, it can be seen that the utility function of the content consumer is strictly increasing and concave, that is, the utility function of the content consumer increases with the increase of the content quantity, while the growth rate of the utility decreases with the increase of the content quantity.

[0054] When the content producer provides content to the content consumer, transmission energy consumption will be generated. The more content the content producer provides, the more energy it consumes, and the larger the value of the cost function. Therefore, the cost function should be positive and positively correlated with the content supply quantity. Therefore, in the embodiment of the present invention, the cost function of content producer \(j\) is expressed as: , , Denote the preset cost weight, \(r\) denotes the next hop in the path from content producer \(j\) to content consumer \(i\), Denote the transmission energy consumption between content producer \(j\) and the next hop \(r\) when content producer \(j\) sends data to content consumer \(i\), Denote the energy consumption of content producer \(j\) for sending each bit of data, Denote the energy consumption factor of the free space model, Denote the energy consumption factor of the multipath fading model, Denote the Euclidean distance between content producer \(j\) and the next hop \(r\), Denote the preset distance switching threshold, .

[0055] As a lightweight flying device, the energy of the UAV is limited. Therefore, it is necessary to consider the transmission energy consumption during the data transmission process. 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 simultaneously considers the free space model and the multipath fading model. These two models can be freely switched according to the distance between content producer \(j\) and the next hop \(r\).

[0056] Based on the cost function in the formula, the cost function of the content producer is a strictly increasing convex function of the content supply volume . That is to say, as the content supply increases, the content producer will incur higher transmission costs, resulting in higher overall costs.

[0057] Step S1012: With the goal of maximizing social welfare, based on the utility functions of all content consumers and the cost functions of all content producers, construct a content allocation target model.

[0058] From the description above, 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 lower costs. There is a conflict between the two parties on 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 embodiments of the present invention, the content allocation target model is expressed as: , ; ; ; where represents the utility function of content consumer i, represents the cost function of content producer j.

[0059] The above constraint condition C1 means that the total amount of content data that content consumer i wants to request is between and . The constraint condition C2 means that the total amount of content data that content producer j can provide will not exceed its maximum content data volume. The constraint condition C3 means 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.

[0060] Step S1013: Transform the content allocation target model into a Lagrangian function to obtain the first Lagrangian function.

[0061] According to the analysis of the convexity and concavity of the utility function and the cost function above, the above content allocation target model is a strictly concave function with respect to and , and the constraint conditions are compact and concave. Therefore, the content allocation target model can be described by the Karush-Kuhn-Tucker (KKT) conditions. Relaxing the constraints can obtain the following Lagrangian function: ; where , , , all represent Lagrange multipliers, , , Denote a column vector with all elements being 1.

[0062] Step S1014: Determine the KKT conditions satisfied by the optimal solution of the content allocation target model based on the first Lagrangian function, and obtain the first KKT conditions.

[0063] Based on the expression of the first Lagrangian function, the KKT conditions that the optimal variables generated by the content allocation target model need to satisfy are as follows: ; ; ; ; ; ; ; ; . Among them, , .

[0064] Obviously, to obtain the optimal solution of the content allocation target model that satisfies the above conditions, the market broker needs to know the utility functions and cost functions of all content consumers and content producers. However, since there is no cooperation relationship among the UAV nodes, the market economy person 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 a content sharing incentive mechanism based on iterative double-sided auction to construct a content allocation model in the UAV named data network.

[0065] In an optional implementation manner, in the above step S102, constructing a content allocation model in the UAV named data network based on the content sharing incentive mechanism of iterative double-sided auction specifically includes the following contents:

[0066] Construct 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.

[0067] The content allocation model is expressed as: , ; ; .

[0068] Among them, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i from 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, and M represents the total number of content consumers. Denote the bid of content consumer \(i\) for content producer \(j\). Denote the bid vector of content consumer \(i\). Denote the asking price of content producer \(j\) for content consumer \(i\). Denote the asking price vector of content producer \(j\). Denote the lower limit of the total content data volume requested by content consumer \(i\). Denote the upper limit of the total content data volume requested by content consumer \(i\). Denote the maximum content data volume of content producer \(j\). In the content allocation model, and are used to represent the concave utility function of data consumers and the convex cost function of data producers respectively.

[0069] 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 both the buyer and seller (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.

[0070] Optionally, in step S106, based on the current bid vector and the current asking price vector, solve 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, which specifically includes the following steps:

[0071] Step S1061, transform the content allocation model into a Lagrangian function to obtain the second Lagrangian function.

[0072] By relaxing the constraints, the content allocation model can be transformed into the following Lagrangian function: .

[0073] Step S1062, determine the KKT conditions satisfied by the optimal solution of the content allocation model based on the second Lagrangian function to obtain the second KKT conditions.

[0074] Based on the expression of the second Lagrangian function, the KKT conditions that the optimal variables generated by the content allocation model need to satisfy are as follows: ; ; ; ; ; ; ; ; .

[0075] Step S1063: Determine 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.

[0076] According to the above second KKT condition, the content demand vector model of each content consumer can be obtained: , and the content supply vector model of each content producer .

[0077] Step S1064: Solve the Lagrangian function based on the current bid vector and the current asking price vector to obtain the Lagrange multipliers.

[0078] After substituting the current bid vector and the current asking price vector into the Lagrangian function, use the Lagrangian function solver to solve the Lagrangian function to obtain the Lagrange multipliers , , and .

[0079] Step S1065: Substitute the Lagrange multipliers 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 multipliers and the current asking price vector into the content supply vector model to obtain the current content supply vector of each content producer.

[0080] That is, and The unknowns on the right side of the equal sign have been determined. Substitute the unknowns into the model to calculate and , and then obtain the current content demand vector of each content consumer and the current content supply vector of each content producer.

[0081] In an optional implementation manner, in the above step S102, constructing the bid model of each content consumer based on the content sharing incentive mechanism of the iterative bilateral auction specifically includes the following steps:

[0082] Step S201: Determine the target bid model of each content consumer based on the first KKT condition and the second KKT condition.

[0083] By comparing the KKT conditions of the content allocation model and the content allocation target model, it can be seen that only the first two equations of the first KKT condition and the second KKT condition are different. Therefore, if the first two equations are equivalent, then the content allocation model and the content allocation target model are equivalent.

[0084] The two equations related to the bid of the content consumer are: and . If the two are equivalent, then: 。 This is the target bid model for content consumers.

[0085] The two equations related to the asking price of content producers are: and , if the two are equivalent, then there is: 。 This is the target asking price model for content producers.

[0086] Step S202: With the goal of maximizing utility, based on the unknown expressions of the utility function and the settlement function of the target consumers, construct the local optimization model of the target consumers.

[0087] If content consumers and content producers submit bids and asking prices respectively 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 of maximizing social welfare. However, since both 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 to maximize social welfare. Based on this, the embodiments of the present invention design some settlement rules for content consumers and some reward rules for content producers to encourage them to submit bids and asking prices according to the above formulas.

[0088] For content consumers, the embodiments of the present invention use to represent the settlement rule given when the market broker receives the bid of content consumer i, 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 represented by the following local optimization model: , .

[0089] Step S203: Based on the target bid model and the content demand vector model, solve the local optimization model of the target consumers to determine the solution expression of the settlement function.

[0090] By solving the local optimization model, each content consumer can obtain its optimal bid vector. Let the objective function of the local optimization model be differentiated with respect to , and the optimal bid vector should satisfy the following conditions: .

[0091] Given that the target bid model is , and the content demand vector model is , based on this, simplify , and we can get: . Therefore, the solution expression of the settlement function of content consumers is: 。

[0092] Step S204: Substitute the solution expression of the settlement function into the local optimization model of the target consumer to obtain the bid model of the target consumer.

[0093] That is, in the embodiment of the present invention, the bid model of the target consumer is expressed as 。

[0094] In an optional embodiment, in the above step S102, based on the content sharing incentive mechanism of the iterative bilateral auction, the asking price model of each content producer is constructed, which specifically includes the following steps:

[0095] Step S301: Determine the target asking price model of each content producer based on the first KKT condition and the second KKT condition.

[0096] The method for determining the target asking price model has been described in the relevant description of the above step S201, and will not be repeated here. For details, please refer to the above text. In the embodiment of the present invention, the target asking price model of the content producer is 。

[0097] Step S302: With the goal of maximizing utility, based on the unknown expressions of the cost function and the reward function of the target producer, construct the local optimization model of the target producer.

[0098] For the content producer, in the embodiment of the present invention, is used to represent the reward rule given when the market broker receives the asking price of content producer j. 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 represented by the following local optimization model: , 。

[0099] Step S303: Solve the local optimization model of the target producer based on the target asking price model and the content supply vector model to determine the solution expression of the reward function.

[0100] By solving the local optimization model of the target producer, each content producer can obtain its optimal asking price vector. Let the local optimization model take the first derivative of , and the optimal bid vector should satisfy the following conditions: 。

[0101] Given that the target asking price model is , and the content supply vector model is , based on this, simplify , and we can get: Therefore, the solution expression of the reward function for the content producer is as follows: .

[0102] Step S304: 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.

[0103] That is, in the embodiment of the present invention, the asking price model of the target producer is expressed as: .

[0104] In summary, the embodiment of the present invention applies the named data network to the unmanned cluster network, solves the problem of easy disconnection of link connections caused by the high dynamicity of unmanned aerial vehicles, and proposes a content sharing incentive mechanism based on iterative bilateral auctions. Compared with the original content sharing mechanism of the named data network, 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 among unmanned aerial vehicle nodes.

[0105] To verify the convergence and price rules of the method provided in the embodiment of the present invention, Figure 4 FIG. is a schematic diagram of the bid vector of a content consumer provided in the embodiment of the present invention. Figure 5 FIG. is a schematic diagram of the content demand vector of a content consumer provided in the embodiment of the present invention. Figure 6 FIG. is a schematic diagram of the asking price vector of a content producer provided in the embodiment of the present invention. Figure 7 FIG. is a schematic diagram of the content supply vector of a content producer provided in the embodiment of the present invention. That is, Figures 4 to 7 respectively show the price strategies and content quantity strategies of content consumers and content producers. It can be observed that after about 10 iterations, the strategies of both 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 the demand vector of the content consumer and the supply vector provided by the content producer are always equal after convergence. 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 in Figure 5 and the demand vector curve in Figure 6 have similar changing trends, while

[0106] Embodiment 2

[0107] The embodiment of the present invention also provides a device for on-demand sharing of unmanned cluster data for maximum utility. This device is mainly used to execute the method for on-demand sharing of unmanned cluster data for maximum utility provided in the first embodiment above. The following is a specific introduction to the device for on-demand sharing of unmanned cluster data for maximum utility provided by the embodiment of the present invention.

[0108] Figure 8 It is a functional module diagram of a device for on-demand sharing of unmanned cluster data for maximum utility provided by an embodiment of the present invention. As Figure 8 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, where:

[0109] The construction module 10 is used to construct a content allocation model, a bid model for each content consumer, and an asking price model for each content producer in the drone named data network based on the content sharing incentive mechanism of iterative double auction.

[0110] 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.

[0111] The first solution 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.

[0112] The second solution module 40 is used to solve the corresponding bid model based on the current content demand vector of the target consumer to obtain the updated bid 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; where the target consumer represents any content consumer in the drone named data network; the target producer represents any content producer in the drone named data network.

[0113] The iteration module 50 is used to, when it is determined that the updated bid vector and the updated asking price vector do not reach the preset convergence condition, use the updated bid vector as the current bid vector, the updated asking price vector as the current asking price vector, and return 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.

[0114] An embodiment of the present invention provides a device for on-demand data sharing of an unmanned cluster for maximum utility. The device constructs a content distribution model, a bidding model for each content consumer, and an asking price model for each content producer in a named data network of unmanned aerial vehicles based on a content sharing incentive mechanism of 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 models are 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. Through iterative update until the bidding vector and the asking price vector reach a preset convergence condition. The application of the above models can effectively encourage content producers to participate in the data market, thereby promoting data sharing among unmanned aerial vehicle nodes, and further alleviating the technical problem of poor data sharing activity in the existing named data network of unmanned aerial vehicles.

[0115] Optionally, the device is further configured to:

[0116] Determine the utility function of each content consumer and the cost function of each content producer.

[0117] With the goal of maximizing social welfare, based on the utility functions of all content consumers and the cost functions of all content producers, construct a content distribution target model.

[0118] Convert the content distribution target model into a Lagrangian function to obtain the first Lagrangian function.

[0119] Based on the first Lagrangian function, determine the KKT conditions satisfied by the optimal solution of the content distribution target model to obtain the first KKT conditions.

[0120] Optionally, the construction module 10 includes:

[0121] The first construction unit is configured to construct a content distribution model based on the bidding vector and content demand vector of each content consumer, and the asking price vector and content supply vector of each content producer.

[0122] The content distribution model is expressed as: , ; ; ; where, represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i from 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, Denote the bid of content consumer \(i\) for content producer \(j\). Denote the bid vector of content consumer \(i\). Denote the asking price of content producer \(j\) for content consumer \(i\). Denote the asking price vector of content producer \(j\). Denote the lower limit of the total content data volume requested by content consumer \(i\). Denote the upper limit of the total content data volume requested by content consumer \(i\). Denote the maximum content data volume of content producer \(j\).

[0123] Optionally, the first solving module 30 is specifically configured to:

[0124] Convert the content allocation model into a Lagrangian function to obtain the second Lagrangian function.

[0125] Based on the second Lagrangian function, determine the KKT conditions satisfied by the optimal solution of the content allocation model to obtain the second KKT conditions.

[0126] Based on the second KKT conditions, determine the content demand vector model of each content consumer and the content supply vector model of each content producer.

[0127] Based on the current bid vector and the current asking price vector, solve the Lagrangian function to obtain the Lagrange multipliers.

[0128] Substitute the Lagrange multipliers 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 multipliers and the current asking price vector into the content supply vector model to obtain the current content supply vector of each content producer.

[0129] Optionally, the construction module 10 further includes:

[0130] The first determination unit is used to determine the target bid model of each content consumer based on the first KKT conditions and the second KKT conditions.

[0131] The second construction unit is used to construct the local optimization model of the target consumer with the goal of maximizing utility, based on the utility function of the target consumer and the unknown expression of the settlement function.

[0132] The first solving unit is used to solve the local optimization model of the target consumer based on the target bid model and the content demand vector model to determine the solution expression of the settlement function.

[0133] 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 bid model of the target consumer.

[0134] Optionally, the construction module 10 further includes:

[0135] A second determination unit, configured to determine the target asking price model of each content producer based on the first KKT condition and the second KKT condition.

[0136] A third construction unit, configured to construct a local optimization model of the target producer with the goal of maximizing utility, based on the unknown expressions of the cost function and the reward function of the target producer.

[0137] A second solving unit, configured 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.

[0138] A second substitution unit, configured 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.

[0139] Optionally, the content allocation target model is expressed as: , ; ; ; where represents the utility function of content consumer i, represents the cost function of content producer j.

[0140] , represents the preset utility weight, represents the delay experienced by content consumer i when receiving content , , represents the transmission delay experienced by content consumer i when receiving content , represents the propagation delay experienced by content consumer i when receiving content , represents the total number of hops transmitted between content producer j and content consumer i, represents the content transmission rate, represents the sum of all paths passed between content consumer i and content producer j, represents the propagation speed of electromagnetic waves.

[0141] , , 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, Indicates the energy consumption of content producer j for sending each bit of data. Indicates the energy consumption factor of the free space model. Indicates the energy consumption factor of the multipath fading model. Indicates the Euclidean distance between content producer j and the next hop r. Indicates the preset distance switching threshold. 。

[0142] Embodiment III

[0143] See Figure 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. The processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is configured to execute an executable module stored in the memory 61, such as a computer program.

[0144] Among them, the memory 61 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 63 (which can be wired or wireless), a communication connection between this system network element and at least one other network element can be realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0145] The bus 62 may be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 9 only a bidirectional arrow is used in [description] to represent it, but it does not mean that there is only one bus or one type of bus.

[0146] Among them, the memory 61 is used to store a program. After receiving an execution instruction, the processor 60 executes the program. The method executed by the device defined by the process disclosed in any embodiment of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0147] The processor 60 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 60 or the instructions in the form of software. The above-mentioned processor 60 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art 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. This storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and combines its hardware to complete the steps of the above method.

[0148] A computer program product of a method and device for on-demand sharing of unmanned cluster data for maximum utility provided by an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the foregoing method embodiments. For specific implementation, reference can be made to the method embodiments and will not be elaborated here.

[0149] In addition, in each embodiment of the present invention, each functional unit may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0150] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0151] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0152] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0153] In addition, the terms "horizontal", "vertical", "hanging", etc. do not mean that the components are required to be absolutely horizontal or hanging, but can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0154] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connect", "couple" 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 directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0155] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and 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 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 iterative bilateral auction; the drone named data network represents the application of a named data network in a drone cluster network, and both content consumers and content producers are drone nodes in the drone cluster network; 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 do not meet the preset convergence condition, use the updated bid vector as the current bid vector and the updated asking price vector as the current asking price vector, and return to step S106 until the updated bid vector and the updated asking price vector meet 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; Before step S102, the method further 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. Among them, the utility function of content consumer i is expressed as: , , represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i to content producer j, N represents the total number of content producers, 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.

2. The unmanned cluster data on-demand sharing method for maximum utility according to claim 1 is characterized in that: Before step S102, the method further includes: 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 supply vector of content producer j, represents the amount of content data provided by content producer j to content consumer i, 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 allocation target model is expressed as: , ; ; ;in, represents the cost function of content producer j; , , 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: A building module is used to build a content distribution model, a bidding model for each content consumer, and a asking price model for each content producer in a drone named data network based on a content sharing incentive mechanism of iterative bilateral auction; the drone named data network represents the application of a named data network in a drone cluster network, and both content consumers and content producers are drone nodes in the drone cluster network; 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, configured to, if it is determined that the updated bid vector and the updated asking price vector do not meet a preset convergence condition, use the updated bid vector as a current bid vector and the updated asking price vector as a current asking price vector, and return to call the first solution module until the updated bid vector and the updated asking price vector meet 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; The device is also used to: 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. Among them, the utility function of content consumer i is expressed as: , , represents the content demand vector of content consumer i, represents the amount of content data requested by content consumer i to content producer j, N represents the total number of content producers, 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.

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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