Multi-layer heterogeneous unmanned aerial vehicle optimization deployment method and system for safe and rapid transmission of industrial internet

Abstract

The invention discloses a multilayer heterogeneous unmanned aerial vehicle optimization deployment method and system for industrial internet safe and rapid transmission. The method comprises the following steps: step 1, constructing an industrial internet transmission model; the industrial internet transmission model comprises a ground user side, a low-altitude platform, a high-altitude platform, a server and an attack side; 2, constructing an initial optimization problem about cache and unmanned aerial vehicle deployment according to an industrial internet transmission model; 3, decomposing an initial optimization problem about cache and unmanned aerial vehicle deployment into two sub-problems about a cache strategy and a low-altitude platform deployment strategy; and 4, solving the two sub-problems to obtain an optimal caching strategy, a low-altitude platform position and a minimum secure transmission delay. According to the method, deployment of the unmanned aerial vehicle and an active caching strategy are jointly optimized, so that the transmission delay of the secure content is minimized, and secure and rapid transmission of industrial internet data is ensured.

Description

Technical Field

[0001] This invention relates to the field of wireless transmission technology, and in particular to a multi-layer heterogeneous UAV optimized deployment method and system for secure and rapid transmission in the industrial internet. Background Technology

[0002] With the emergence of new industrial scenarios and applications, wireless transmission services have experienced explosive growth, leading to severe network congestion and a shortage of communication resources. Meanwhile, the existence of attack devices also threatens user security. Traditional connection-centric networks cannot meet the growing demand for secure content transmission in the Industrial Internet. How to solve the problem of secure and rapid transmission of massive amounts of content has become an urgent issue to address.

[0003] The rapid development of drone technology has brought new solutions to this problem. First, drones possess excellent flight characteristics and flexible deployment capabilities; by optimizing their deployment strategies, network performance can be significantly improved. For example, optimizing drone deployment locations can provide users with good line-of-sight transmission links, significantly increasing instantaneous transmission rates. Second, by combining drones with edge caching technology, the traditional connection-centric network model can be transformed into a content-centric network model. By using resources during off-peak hours, severe traffic congestion can be alleviated and latency reduced. Therefore, by deploying edge caching devices on drones, leveraging their mobility and high coverage capabilities, efficient content caching and rapid transmission can be achieved, which is an effective way to realize rapid content transmission in industrial internet scenarios. However, in open A2G channels, signals are easily eavesdropped on, interfered with, and tampered with, posing a serious challenge to the secure data transmission of drones. In the industrial internet environment, achieving rapid and secure content transmission through physical layer mechanisms has become a critical issue that urgently needs to be addressed.

[0004] While significant progress has been made in research on active caching for drones, these studies primarily focus on drone deployment and active caching strategies. It's worth noting that the caching capacity and coverage of a single drone are very limited, especially for quadcopter drones. Some works have investigated edge caching strategies using multiple drones, aiming to provide services to a large number of users through multi-drone collaboration. However, research on leveraging multi-drone and active caching technologies to empower secure data transmission in the Industrial Internet is still insufficient, particularly the joint optimization of multi-layered heterogeneous drones and active caching technologies. Summary of the Invention

[0005] To address the severe challenges facing secure data transmission from drones and the insufficient research on combining multi-drone and proactive caching technologies with secure data transmission in the Industrial Internet, this invention provides a multi-layered heterogeneous drone optimized deployment method and system for secure and rapid transmission in the Industrial Internet. First, this invention constructs an Industrial Internet transmission model. Based on this model, it establishes an initial optimization problem concerning caching and drone deployment, dividing this problem into two sub-problems. Solving these sub-problems minimizes the secure transmission latency. This invention jointly optimizes drone deployment and proactive caching strategies to minimize the latency of secure content transmission, ensuring secure and rapid data transmission in the Industrial Internet.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] The first aspect of this invention proposes an optimized deployment method for multi-layer heterogeneous UAVs for secure and rapid transmission in the Industrial Internet, comprising:

[0008] Step 1: Construct an industrial internet transmission model; the industrial internet transmission model includes ground user terminals, low-altitude platforms, high-altitude platforms, servers, and attack terminals, which facilitates secure and rapid information transmission;

[0009] Step 2: Construct an initial optimization problem regarding caching and drone deployment based on the Industrial Internet transmission model to facilitate obtaining the minimum secure transmission latency;

[0010] Step 3: Decompose the initial optimization problem regarding caching and drone deployment into two sub-problems: one regarding caching strategy and the other regarding low-altitude platform deployment strategy. This facilitates the computation of the initial optimization problem regarding caching and drone deployment.

[0011] Step 4: Solve the two sub-problems to obtain the optimal caching strategy, low-altitude platform location, and minimized secure transmission latency.

[0012] Furthermore, the construction of the industrial internet transmission model specifically includes:

[0013] The ground user terminals within the coverage area of ​​the high-altitude platform are divided into multiple clusters. Each cluster is equipped with a low-altitude platform. The low-altitude platform communicates with the ground user terminals and the high-altitude platform within the cluster, respectively. The high-altitude platform communicates with the server.

[0014] The attacking device is used to obtain communication data between ground user terminals and high-altitude platforms, as well as between ground user terminals and low-altitude platforms.

[0015] Furthermore, the transmission methods for obtaining requested content in the industrial internet transmission model include obtaining requested content from a low-altitude platform, obtaining requested content from a high-altitude platform, and obtaining requested content from a server via a high-altitude platform relay.

[0016] The delay in obtaining the requested content from the low-altitude platform is expressed by the following formula:

[0017]

[0018] Where t1 is the latency of obtaining the requested content from the low-altitude platform, and S is the file size of the requested content. d,LAP The transmission rate between the ground user terminal and the low-altitude platform;

[0019] The delay in obtaining the requested content from the high-altitude platform is expressed by the following formula:

[0020]

[0021] Where t2 is the delay in obtaining the requested content from the high-altitude platform, and S d,HAP The transmission rate between the ground user terminal and the high-altitude platform;

[0022] The delay in retrieving the requested content from the server via a high-altitude platform relay is expressed by the following formula:

[0023] t3 = t2 + t fso

[0024] Where t3 represents the latency of retrieving the requested content from the server via the high-altitude platform when neither the low-altitude nor the high-altitude platform caches the content requested by the ground user. fso The latency for the server to transmit files to the high-altitude platform via the FSO link.

[0025] Furthermore, the average transmission delay for obtaining the requested content in the industrial internet transmission model is expressed by the following formula:

[0026]

[0027] in, To obtain the average transmission latency of the requested content, Let ε be the coordinates of the low-altitude platform. y， ∈ y r is an indicator variable n Let r be the hit rate of the nth low-altitude platform in requesting content from the ground user terminal, and r be the hit rate of the high-altitude platform in requesting content from the ground user terminal.

[0028] Furthermore, the initial optimization problem regarding caching and drone deployment is expressed by the following formula:

[0029]

[0030] in, To determine whether content y is cached in the nth low-altitude platform, where Y is the total number of contents. Let C be the cache capacity of the nth low-altitude platform. HAP Let represent the cache capacity of the high-altitude platform, minimize means to minimize, and P represent the initial optimization problem regarding the cache and drone deployment.

[0031] Furthermore, the sub-problem concerning caching strategies is expressed by the following formula:

[0032]

[0033] SP1 is a sub-problem concerning caching strategies.

[0034] Furthermore, in step three, the sub-problem concerning the caching strategy is equivalently transformed to obtain the request loss rate problem SP1-1;

[0035] The request loss rate problem can be expressed by the following formula:

[0036] SP1-1:minimizemax{1-r n -r}

[0037]

[0038] Where minimizemax is the maximum value to be minimized.

[0039] Furthermore, the sub-problem concerning the low-altitude platform deployment strategy is expressed by the following formula:

[0040] SP2:maximizemin{S m -S i}

[0041] SP2 is a subproblem concerning low-altitude platform deployment strategies, where m is the total number of users within a cluster, maximizemin is the minimum value, and S... i S represents the instantaneous transmission rate from the attacking end to the drone. m Let be the instantaneous achievable transmission rate from the UAV to the m-th ground user terminal.

[0042] Furthermore, in step three, the sub-problem concerning the low-altitude platform deployment strategy is iteratively transformed to obtain the iterative sub-problem concerning the low-altitude platform deployment strategy; the iterative sub-problem concerning the low-altitude platform deployment strategy is a standard convex quadratic programming problem.

[0043] The iterative low-altitude platform deployment strategy is expressed by the following formula:

[0044]

[0045] maximizeS *

[0046] stS′ m (k)-S′ i (k)≥S *

[0047] Among them, S * As the lower bound of the security rate, S m (k) represents the legal transmission rate in the k-th iteration, S i (k) represents the eavesdropping rate in the k-th iteration, ζ(k) is the increment in the x-direction, ε(k) is the increment in the y-direction, and S′ m (k) represents the legal transmission rate after the first-order Taylor expansion approximation, S i ′(k) is the eavesdropping rate approximated by the first-order Taylor expansion, SP3 (k) This is a subproblem concerning the deployment strategy of low-altitude platforms after the k-th iteration.

[0048] The second aspect of this invention proposes a multi-layered heterogeneous UAV optimized deployment system for secure and rapid transmission in the Industrial Internet, comprising:

[0049] The model building module is used to construct an industrial internet transmission model; the industrial internet transmission model includes a ground user terminal, a low-altitude platform, a high-altitude platform, a server, and an attack terminal, which facilitates the secure and rapid transmission of information.

[0050] The optimization problem module is used to construct an initial optimization problem about caching and drone deployment based on the industrial internet transmission model, so as to obtain the minimum secure transmission latency;

[0051] The decomposition module is used to break down the initial optimization problem about caching and drone deployment into two sub-problems about caching strategy and low-altitude platform deployment strategy, which facilitates the computation of the initial optimization problem about caching and drone deployment.

[0052] The computation module is used to solve two subproblems to obtain the optimal caching strategy, the low-altitude platform location, and the minimized secure transmission latency.

[0053] The beneficial effects of this invention are:

[0054] This invention studies the optimization of latency for secure content transmission in the Industrial Internet, proposing a multi-layered heterogeneous UAV optimized deployment and proactive caching technology. By designing caching strategies and UAV deployment optimization algorithms, information is transmitted to ground users at the highest confidentiality rate, ensuring that attackers cannot decode and obtain valid information. This approach maximizes content transmission rate while ensuring information security and minimizing transmission latency, ultimately achieving minimal latency for secure content transmission.

[0055] Through simulation experiments and comparative analysis, this invention demonstrates its significant advantages in reducing content transmission latency and improving the reliability and stability of content acquisition. Attached Figure Description

[0056] Figure 1 The flowchart illustrates a multi-layered heterogeneous UAV optimized deployment method for secure and rapid transmission in the industrial internet, as provided in this embodiment of the invention.

[0057] Figure 2 This is a schematic diagram of the industrial internet transmission model provided in an embodiment of the present invention.

[0058] Figure 3 This is a schematic diagram illustrating the performance optimization of LAP deployment provided in an embodiment of the present invention.

[0059] Figure 4 This is a schematic diagram illustrating the impact of the buffer capacity of LAPs on secure transmission latency, as provided in an embodiment of the present invention.

[0060] Figure 5 This is a schematic diagram illustrating the impact of HAP buffer capacity on secure transmission latency, provided in an embodiment of the present invention.

[0061] Figure 6 This is an architecture diagram of a multi-layered heterogeneous UAV optimized deployment system for secure and rapid transmission in the industrial internet, provided as an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0063] Example 1

[0064] like Figure 1 As shown, a multi-layered heterogeneous UAV optimized deployment method for secure and rapid transmission in the Industrial Internet includes:

[0065] S101: Construct an industrial internet transmission model; the industrial internet transmission model includes ground user terminals, low-altitude platforms, high-altitude platforms, servers, and attack terminals.

[0066] Specifically, low-altitude platforms (LAP) include quadcopter drones, while high-altitude platforms (HAP) include airships.

[0067] The ground-based user terminals in the coverage area are divided into multiple clusters. Each cluster is equipped with a low-altitude platform to provide content. Simultaneously, a high-altitude platform is set up to cover all ground-based user terminals and provide greater caching capacity. The overall architecture is shown in the attached figure. Figure 2 As shown. The ground user first sends a request to the Local Access Point (LAP). If the LAP has already cached the content required by the ground user, it provides the service directly; otherwise, the LAP uses a sequential caching strategy, caching content one by one according to the order of the ground user's requests. When the LAP's cache capacity reaches its limit, it replaces the least popular content based on its popularity. At the same time, the ground user also sends a request to the Macro Access Point (HAP), which processes the request in a similar manner to the LAP. If neither the LAP nor the HAP can satisfy the ground user's request, the high-speed content exchange between the high-altitude platform and the server further satisfies the ground user's content needs.

[0068] S102: Construct an initial optimization problem regarding caching and drone deployment based on the Industrial Internet transmission model.

[0069] S103: Decompose the initial optimization problem regarding caching and drone deployment into two sub-problems: one regarding caching strategy and the other regarding low-altitude platform deployment strategy.

[0070] Specifically, the initial optimization problem regarding caching and drone deployment is divided into two sub-problems: caching strategy and low-altitude platform deployment strategy (LAPs deployment strategy).

[0071] S104: Solve the two subproblems to obtain the optimal caching strategy, low-altitude platform location, and minimized secure transmission latency.

[0072] This invention first constructs an industrial internet transmission model and then, based on this model, formulates an initial optimization problem concerning caching and drone deployment. The initial optimization problem is then divided into two sub-problems: an active caching strategy and a low-altitude platform deployment strategy. Solving these two sub-problems yields the optimal caching strategy, the low-altitude platform location, and the minimized secure transmission latency, ensuring the secure and rapid transmission of industrial internet data.

[0073] Example 2

[0074] Based on the above embodiments, this invention proposes an industrial internet transmission model, specifically including:

[0075] Ground user terminals are divided into clusters, each containing a quadcopter drone serving as a low-altitude platform (LAP) to provide content. An additional airship serves as a high-altitude platform (HAP) to assist the LAP. The airship can cover all ground user terminals and cache more content. It also carries a reliable optical communication (FSO) module for high-speed transmission with the server, further meeting the secure and rapid transmission needs of the ground user terminals.

[0076] The wireless channel from the UAV to the ground node is typically a line-of-sight (LOS) channel. This invention employs a free-space path loss model, where the channel gain is primarily determined by the transmission distance. The distance d between the UAV and the ground user terminal at the m-th user is defined as follows: x (m) is represented as:

[0077]

[0078] in, Location of the drone. M represents the location of the ground user terminal, and M represents the total number of clusters.

[0079] Similarly, the distance d from the drone to the attacking end... i It can be represented as:

[0080]

[0081] in, Indicates the location of the attacking end.

[0082] Therefore, the channel gain can be expressed as:

[0083] g m =b0d m -2 m = 1, ..., M

[0084] g i =b0d i -2

[0085] Where b0 is the channel power gain per reference distance, g m Let g be the channel gain between the UAV and the m-th ground user terminal. i Channel gain for drones and attackers.

[0086] The instantaneous achievable transmission rate from the UAV to the ground user terminal within the m-th cluster is:

[0087]

[0088] Among them, S m Let γ be the transmission rate from the UAV to the m-th ground user terminal, γ be the additive white Gaussian noise power (AWGN), and q be the signal transmission power.

[0089] The instantaneous achievable transmission rate from the attacking end to the drone can be expressed as:

[0090]

[0091] Among them, S i This refers to the transmission rate from the attacking device to the drone.

[0092] The secure rate of communication between the terrestrial user terminal and the local access point (LAP) can be expressed as:

[0093] S d (m)=[S m -S i ] +

[0094] Among them, S d (m) represents the secure communication rate from the m-th ground user terminal to the Local Access Point (LAP), [·] + This is a positive operation.

[0095] Preferably, the aforementioned security rate refers to communication between the ground user terminal and the local access point (LAP). The data collection behavior of the attacker on the macro access point (HAP) and the server is essentially the same. Therefore, the same method as calculating the security rate of communication between the HAP and the server and the ground user terminal can be used.

[0096] Preferably, Zipf's law can be used to represent the popularity of content, making it easier for airships and drones to prioritize caching content with high popularity. The preference of ground users for the y-th content in the n-th cluster can be expressed as:

[0097]

[0098] in, Let α represent the preference of a ground user for the y-th content in the n-th cluster. n Y represents the Zipf index and the total number of content preferences.

[0099] Zipf index α n This determines the concentration of requests from ground-based users. Generally speaking, α n Greater than 0, α n The larger the value, the more concentrated the requests. At the same time, LAP and HAP will prioritize caching content with high popularity.

[0100] Assuming all files have the same standardized size, the transmission methods for obtaining requested content in the Industrial Internet transmission model include obtaining requested content from a low-altitude platform, obtaining requested content from a high-altitude platform, and obtaining requested content from a server via a high-altitude platform relay.

[0101] The delay in obtaining the requested content from the low-altitude platform is expressed by the following formula:

[0102]

[0103] Where t1 is the latency of obtaining the requested content from the low-altitude platform, and S is the file size of the requested content. d,LAP The transmission rate between the ground user terminal and the low-altitude platform;

[0104] The delay in obtaining the requested content from the high-altitude platform is expressed by the following formula:

[0105]

[0106] Where t2 is the delay in obtaining the requested content from the high-altitude platform, and S d,HAP The transmission rate between the ground user terminal and the high-altitude platform;

[0107] The delay in retrieving the requested content from the server via a high-altitude platform relay is expressed by the following formula:

[0108] t3 = t2 + t fso

[0109] Where t3 represents the latency of retrieving the requested content from the server via the high-altitude platform when neither the low-altitude nor the high-altitude platform caches the content requested by the ground user. fso The latency for the server to transmit files to the high-altitude platform via the FSO link.

[0110] Assume the latency for the ground user to retrieve content from LAP is less than the latency for the ground user to receive content from HAP. When both LAP and HAP cache the content requested by the ground user, LAP responds to the ground user's request.

[0111] The average transmission delay for obtaining requested content in the Industrial Internet transmission model is expressed by the following formula:

[0112]

[0113] in, To obtain the average transmission latency of the requested content, Let ε be the coordinates of the low-altitude platform. y， ∈ y As indicator variables, r is used to indicate whether the y-th content is cached on the low-altitude platform and whether it is cached on the high-altitude platform, respectively.n Let r be the hit rate of the nth low-altitude platform in requesting content from the ground user terminal, and r be the hit rate of the high-altitude platform in requesting content from the ground user terminal.

[0114] Example 3

[0115] Based on the above embodiments, this invention proposes a process to decompose the initial optimization problem concerning caching and UAV deployment into two sub-problems: one concerning caching strategy and the other concerning low-altitude platform deployment strategy. Specifically, this includes:

[0116] This invention optimizes the proactive caching strategy and the deployment strategy of LAPs to obtain an initial optimization problem regarding caching and drone deployment, in order to minimize content transmission latency. The initial optimization problem regarding caching and drone deployment is expressed by the following formula:

[0117]

[0118] in, To determine whether content y is cached in the nth low-altitude platform, where Y is the total number of contents. Let C be the cache capacity of the nth low-altitude platform. HAP Let represent the cache capacity of the high-altitude platform, minimize means to minimize, and P represent the initial optimization problem regarding the cache and drone deployment.

[0119] and These are the cache capacity constraints for LAP and HAP, respectively. Clearly, both the content caching strategy and the hovering position of LAP affect latency. Therefore, the initial optimization problem regarding caching and drone deployment is a challenging non-convex mixed-integer optimization problem.

[0120] The non-convex mixed-integer optimization problem is decomposed into two subproblems: one concerning caching strategies and the other concerning low-altitude platform deployment strategies. The subproblem concerning caching strategies can be described as a nondeterministic polynomial-hard (NP-hard) knapsack problem. The subproblem concerning low-altitude platform deployment strategies (the hovering position problem of LAP) is a nonlinear non-convex optimization problem.

[0121] Regarding the sub-problem of caching strategies, this invention equates minimizing content transmission latency to minimizing request loss rate, as detailed below:

[0122] The sub-problems concerning caching strategies are expressed by the following formula:

[0123]

[0124] SP1 is a sub-problem concerning caching strategies.

[0125] The caching capacity of LAP and HAP is limited. This is a typical knapsack problem. In the premise of this invention, each file is of the same size. Therefore, we only need to consider how to define the cache value of the files. According to the definition, we know that t3>t2>t1. Combining the average transmission delay of the requested content obtained from the Industrial Internet transmission model, we know that r n (ε y ),r(∈ y The larger the value of D(ε), the greater the value of D(ε). y ,∈ y The smaller the value, the more equivalently minimizing content delivery latency can be transformed into minimizing request loss rate 1-r. n -r.

[0126] The request loss rate issue is as follows:

[0127] SP1-1:minimizemax{1-r n -r}

[0128]

[0129] Among them, minimizemax is to minimize the maximum value, and SP1-1 is the request loss rate problem.

[0130] Therefore, caching the most popular files in LAP and HAP respectively is the optimal solution to the knapsack problem.

[0131] After obtaining the caching strategy, minimizing content transmission latency is equivalent to maximizing the transmission rate between LAP and the ground user terminal.

[0132] The sub-problems concerning low-altitude platform deployment strategies are expressed by the following formula:

[0133] SP2:maximizemin{S m -S i}

[0134] SP2 is a subproblem concerning low-altitude platform deployment strategies, where m is the total number of users within a cluster, maximizemin is the minimum value, and S... i S represents the instantaneous transmission rate from the attacking end to the drone. m Let be the instantaneous achievable transmission rate from the UAV to the m-th ground user terminal.

[0135] Due to the complex form of the subproblem concerning low-altitude platform deployment strategy, it remains a non-convex complex problem. To address this issue, this invention transforms the subproblem concerning low-altitude platform deployment strategy and obtains an approximate optimal solution to the original problem through a convex optimization iterative method.

[0136] This invention first designs a new set of variables to reformulate the original problem, and obtains the rate through Taylor expansion.

[0137] Specifically, the location (LAP) in the low-altitude platform deployment strategy is directly optimized from the complex form of the sub-problem concerning the low-altitude platform deployment strategy. This is quite difficult. To make the original problem easier to solve, the increment of each low-altitude platform can be optimized in each iteration. Where p x and p y Let x and y be the coordinates of the anchor point, respectively. Assume that in the (k-1)th algorithm iteration, the position increment of the low-altitude platform is {ζ(k)≥0, ε(k)≥0, k=2,...,K}, where ζ(k) is the increment in the x-direction of the (k-1)th iteration, and ε(k) is the increment in the y-direction of the (k-1)th iteration. Then, the position of the low-altitude platform in the kth algorithm iteration can be expressed as:

[0138] p x (k)=p x (k-1)+ζ(k-1,)

[0139] p y (k)=p y (k-1)+ε(k-1)

[0140] Where, p x (k) represents the x-axis coordinate of the low-altitude platform during the k-th iteration, p y (k) represents the y-axis coordinate of the low-altitude platform during the k-th iteration, p x (k-1) and p y (k-1) represents the x-axis and y-axis coordinates of the low-altitude platform during the (k-1)th iteration, respectively, and ζ(k-1) and ε(k-1) represent the increments in the x and y directions during the (k-1)th iteration, respectively.

[0141] Combining Taylor expansion, the transmission rate from the UAV to the ground user and the transmission rate from the attacker to the UAV can be approximated as follows:

[0142]

[0143] Among them, S m (k-1) represents the actual transmission rate transmitted by the ground user terminal in the (k-1)th iteration, b m (k-1) is S′ m The coefficients of the first derivative term (x direction) in (k), c m (k-1) is S′ m The coefficients of the first derivative term (y direction) in (k), S i (k-1) represents the actual receiving rate of the attacking end in the (k-1)th iteration, b i (k-1) is Si The coefficients of the first derivative term (x-direction) in ′(k), c i (l-1) is S i The coefficients of the first derivative term (y-direction) in S′(k), S′ m (k) represents the legal transmission rate after the first-order Taylor expansion approximation, S i ′(k) is the eavesdropping rate after the first-order Taylor expansion approximation.

[0144] Preferably, the position increment of the low-altitude platform is known to be non-negative, i.e., {ζ(k)≥0, ε(k)≥0}. Therefore, in each iteration of the algorithm, a pair of positive {ζ(k), ε(k)} can always be found, which increases the achievable security rate until the increment is zero. This means that the achievable security rate is monotonically increasing and bounded with respect to the position increment of the low-altitude platform during the iteration process, and the iterative method proposed in this invention has convergence.

[0145] Furthermore, to make the problem differentiable, it can be solved using standard convex optimization tools. This invention will use subgraph theory to transform SP2 into a standard convex problem, specifically including:

[0146] definition hypof={({S x (k),S i (k)},S * )|f≥S * Clearly, f is a linear function, therefore hypof is a convex set, and problem SP2 can be transformed into:

[0147]

[0148] maximizeS *

[0149] stS′ m (k)-S′ i (k)≥S *

[0150] Among them, S * As the lower bound of the security rate, S m (k) represents the legal transmission rate in the k-th iteration, S i (k) represents the eavesdropping rate in the k-th iteration, ζ(k) is the increment in the x-direction, ε(k) is the increment in the y-direction, and SP3 (k) This is a subproblem concerning the deployment strategy of low-altitude platforms after the k-th iteration.

[0151] SP3 (k) It is a standard convex quadratic programming problem, which can be solved efficiently using existing tools such as CVX.

[0152] Example 4

[0153] Based on the above embodiments, this invention proposes an effectiveness verification process for a multi-layer heterogeneous UAV optimized deployment method for secure and rapid transmission in the Industrial Internet, specifically including:

[0154] This invention simulates the performance of multi-layered UAVs equipped with active caching technology in reducing content transmission latency. The number of ground user terminals is set to N = 500. All ground user terminals are evenly distributed within a square area with sides of 1200m. M = 9, dividing the entire area into 9 smaller areas with sides of 400m, each corresponding to one cluster. The HAP flight altitude is set to h = 400m. The deployment locations of LAPs are determined using the method proposed in this invention, and the impact of file quantity and cache capacity on system latency is observed, verifying that the proposed scheme can effectively reduce the latency of secure content transmission.

[0155] The relevant system parameters for the simulation are summarized in Table 1.

[0156] Table 1 Summary of relevant system parameters from the simulation

[0157]

[0158]

[0159] Experiment 1: Optimizing LAP Deployment Performance

[0160] exist Figure 3 As can be seen, the secure transmission rate converges very quickly with increasing iterations, typically within two to three iterations, demonstrating the effectiveness of the deployment optimization algorithm proposed in this invention. Furthermore, the capacity of the LAP has a significant impact on the secure transmission rate; specifically, the higher the LAP capacity, the greater the achievable secure transmission rate. This is because a higher LAP capacity allows for the transmission of more data via the LAP, increasing the probability of optimizing its location to evade eavesdropping and thus enhancing system security.

[0161] Experiment 2: The impact of LAP and HAP buffer capacity on secure transmission latency

[0162] As attached Figure 4 As shown, the content transmission latency of the proposed solution is significantly lower than that of the random caching strategy. Furthermore, the content security transmission latency decreases with increasing LAP cache capacity.

[0163] As attached Figure 5 As shown, the latency of the proposed solution decreases with the increase of HAP cache capacity and is significantly less than that of the random caching strategy.

[0164] Example 5

[0165] Based on the above embodiments, such as Figure 6 As shown, a multi-layered heterogeneous UAV optimized deployment system for secure and rapid transmission in the Industrial Internet includes:

[0166] The model building module is used to build an industrial internet transmission model; the industrial internet transmission model includes a ground user terminal, a low-altitude platform, a high-altitude platform, a server, and an attack terminal.

[0167] The optimization problem module is used to construct initial optimization problems about caching and drone deployment based on the Industrial Internet transmission model.

[0168] The module is designed to break down the initial optimization problem regarding caching and drone deployment into two sub-problems: one regarding caching strategy and the other regarding low-altitude platform deployment strategy.

[0169] The computation module is used to solve two subproblems to obtain the optimal caching strategy, the low-altitude platform location, and the minimized secure transmission latency.

[0170] It should be noted that the multi-layer heterogeneous UAV optimized deployment system for secure and rapid transmission in the industrial internet provided in this embodiment of the invention is to realize the above-mentioned multi-layer heterogeneous UAV optimized deployment method for secure and rapid transmission in the industrial internet. Its specific functions can be referred to the above-mentioned method embodiments, and will not be repeated here.

[0171] In summary, this invention addresses the optimization problem of content security transmission latency in the Industrial Internet by proposing a multi-layered heterogeneous UAV optimized deployment and proactive caching technology. Through the design of caching strategies and UAV deployment optimization algorithms, it minimizes content security transmission latency. Simulation experiments and comparative analysis demonstrate the significant advantages of this invention in reducing content transmission latency and improving the reliability and stability of content acquisition.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-layer heterogeneous UAV optimized deployment method for secure and rapid transmission in the Industrial Internet, characterized in that, The method comprises the following steps: Step 1: constructing an industrial internet transmission model; the industrial internet transmission model comprises a ground user terminal, a low-altitude platform, a high-altitude platform, a server and an attack terminal; Step 2: constructing an initial optimization problem about cache and unmanned aerial vehicle deployment according to the industrial internet transmission model; Step 3: decomposing the initial optimization problem about cache and unmanned aerial vehicle deployment into two sub-problems about cache strategy and low-altitude platform deployment strategy; Step 4: solving the two sub-problems to obtain an optimal cache strategy, a low-altitude platform position and a minimized safe transmission delay. 2.The method of claim 1, wherein, The construction of the industrial internet transmission model specifically comprises the following steps: dividing the ground user terminals in the coverage area of the high-altitude platform into multiple clusters, setting one low-altitude platform in each cluster, and respectively connecting the low-altitude platforms with the ground user terminals in the clusters and the high-altitude platform, and connecting the high-altitude platform with the server; the attack terminal is used to obtain communication data between the ground user terminals and the high-altitude platform and between the ground user terminals and the low-altitude platform. 3.The method of claim 1, wherein, The transmission mode for obtaining the requested content in the industrial internet transmission model comprises obtaining the requested content from the low-altitude platform, obtaining the requested content from the high-altitude platform and obtaining the requested content from the server through the high-altitude platform relay; The delay for obtaining the requested content from the low-altitude platform is represented by the following formula: wherein t1 is the delay for obtaining the requested content from the low-altitude platform, S is the file size of the requested content, S d,LAP is the transmission rate between the ground user terminal and the low-altitude platform; The delay for obtaining the requested content from the high-altitude platform is represented by the following formula: where t2 is the delay of obtaining the requested content from the high altitude platform, S d,HAP is the transmission rate between the ground user terminal and the high altitude platform; The delay for obtaining the requested content from the server through the high-altitude platform relay is represented by the following formula: t3 = t2 + t fso Wherein, t3 is the delay of the low-altitude platform and the high-altitude platform do not cache the content requested by the ground user terminal, and the high-altitude platform obtains the requested content from the server, t fso is the time delay of the server transmitting the file to the high-altitude platform through the FSO link.

4. The method of claim 3, wherein, The average transmission delay for obtaining the requested content in the industrial internet transmission model is represented by the following formula: wherein, to obtain the average transmission delay of the requested content, is the position coordinate of the low-altitude platform, ε y， ∈ y is the indication variable, r n is the hit rate of the nth low-altitude platform to the ground user terminal requesting content, and r is the hit rate of the high-altitude platform to the ground user terminal request.

5. The method of claim 4, wherein, The initial optimization problem about cache and unmanned aerial vehicle deployment is represented by the following formula: wherein, is whether the yth content is cached in the nth low-altitude platform, Y is the total number of contents, is the cache capacity of the nth low-altitude platform, C HAP is the cache capacity of the high-altitude platform, minimize is to minimize, P is the initial optimization problem regarding the cache and the deployment of the unmanned aerial vehicle. 6.The method of claim 5, wherein, The sub-problem about cache strategy is represented by the following formula: Wherein, SP1 is the sub-problem about cache strategy.

7. The method of claim 6, wherein, In step 3, the sub-problem about cache strategy is equivalently transformed to obtain a request loss rate problem SP1-1; The request loss rate problem is represented by the following formula: SP1-1: minimize max{1 - r n -r} Wherein, minimizemax is the minimized maximum value. 8.The method of claim 1, wherein, The sub-problem about low-altitude platform deployment strategy is represented by the following formula: SP2: maximize min{S m - S i} where SP2 is a sub-problem on low-altitude platform deployment strategy, m is the total number of users within a cluster, maximize min is a maximization minimum, S i is the instantaneous transmission rate from the attacking end to the UAV, S m is the instantaneous achievable transmission rate from the UAV to the mth ground user end. 9.The method of claim 8, wherein, In step 3, the sub-problem about low-altitude platform deployment strategy is iteratively changed to obtain an iterated sub-problem about low-altitude platform deployment strategy; the iterated sub-problem about low-altitude platform deployment strategy is a standard convex quadratic programming problem; The iterated sub-problem about low-altitude platform deployment strategy is represented by the following formula: maximizeS * s.t. S' m (k) - S' i (k) ≥ S * where S * is the secrecy rate lower bound, S m (k) is the legitimate transmission rate in the kth iteration, S i (k) is the eavesdropping rate in the kth iteration, ζ(k) is the increment in the x-direction, ε(k) is the increment in the y-direction, S′ m (k) is the legitimate transmission rate after the first-order Taylor expansion approximation, S i ′(k) is the eavesdropping rate after the first-order Taylor expansion approximation, SP3 (k) is the sub-problem on the low-altitude platform deployment strategy after the kth iteration.

10. A multi-layer heterogeneous unmanned aerial vehicle optimized deployment system for secure and fast transmission of industrial internet, characterized in that, The method comprises the following steps: A model construction module is configured to construct an industrial internet transmission model; the industrial internet transmission model comprises a ground user terminal, a low-altitude platform, a high-altitude platform, a server and an attack terminal; An optimization problem module is configured to construct an initial optimization problem about cache and unmanned aerial vehicle deployment according to the industrial internet transmission model; A splitting module is configured to decompose the initial optimization problem about cache and unmanned aerial vehicle deployment into two sub-problems about cache strategy and low-altitude platform deployment strategy; A calculation module is configured to solve the two sub-problems to obtain an optimal cache strategy, a low-altitude platform position and a minimized safe transmission delay.

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