Data optimization method, terminal device, and storage medium

Abstract

The application discloses a data optimization method and device and a storage medium, random positions and speeds of node groups in a wireless access network are initialized; fitness values of each node in the wireless access network are calculated, the speeds and positions of the nodes are updated according to the fitness values of the nodes, the speeds and positions of the nodes are adjusted, and a global optimal position of all the nodes is obtained; the obtained global optimal position is compared with a preset fitness value, if the global optimal position reaches the preset fitness value, an optimized wireless access network is obtained, the optimized wireless access network is mapped to an edge cloud and a core cloud through a Cybertwin, and a core network is obtained; a spectrum sharing problem between a primary node and a secondary node in the core network is converted into a global optimization problem, and a dynamic spectrum access architecture based on a blockchain technology is obtained.

Description

Technical Field

[0001] This invention belongs to the field of 6G mobile communication technology, and in particular relates to a data optimization method, device and storage medium based on Cyberwin-driven 6G network architecture. Background Technology

[0002] In the field of mobile communication technology, AMPS, designed by Bell Labs in 1978, represents the first generation of mobile communication systems. With the development of digital modulation technology, 2G networks were officially born. Compared to 2G networks, 3G networks offer better communication quality and security, and can effectively support some multimedia services. However, the existence of various 3G network communication standards accelerated the development of the next generation of networks. In 2010, the cellular network system architecture based on 4G networks was born. 4G effectively increased system capacity and communication transmission efficiency, but due to the rapid development of the Internet of Things (IoT), 4G networks could no longer meet the requirements of ultra-low latency, ultra-high bandwidth, and lower power consumption needed by the digital IoT. In 2015, the fifth-generation mobile communication system was officially established and named IMT-2020. 5G networks, as a new type of network architecture integrating multiple networks and wireless communication technologies, have stronger adaptability and functionality.

[0003] However, with the development of mobile network technology, problems have emerged such as the continuous expansion of network scale, the increasing complexity of network environments, and the growing demand for user collaboration. The architecture design of 5G networks can no longer meet these needs, necessitating research into next-generation mobile network communication technologies. Furthermore, with the continuous development of cloud-network convergence and mobile network technology, the bandwidth and latency requirements of new network applications will increase rapidly. Future networks will not be able to autonomously optimize according to real-world conditions, and the coverage of base stations will not be able to fully cover the explosive proliferation of nodes in future networks. 5G networks can no longer support the multi-dimensional and complex future network resource demands.

[0004] The various new application models of 6G networks are sufficient to meet the network needs in multiple dimensions and will be utilized in areas such as ultra-large-scale mobile broadband, IoE, AI (Artificial Intelligence), and Blockchain. However, the following shortcomings still exist:

[0005] (1) Low spectrum utilization: 3G, 4G and 5G networks have occupied the vast majority of the spectrum, and the available frequency bands are relatively discontinuous. Moreover, the spectrum allocation method used in the design of previous mobile communication networks is exclusive. The exclusive spectrum acquisition method results in a large number of spectrum gaps in the full spectrum sequence for a long time, and nodes cannot obtain a relatively continuous spectrum, which will greatly reduce the spectrum utilization and cause spectrum shortage.

[0006] (2) Communication process is susceptible to interference: As the structure of mobile networks continues to change, the network environment becomes increasingly complex. If the network structure cannot be dynamically adjusted in real time, the communication process between nodes will become unstable, and eventually a certain number of nodes may go offline. With the addition of a large number of nodes, illegal nodes may also participate. Illegal nodes may take improper actions such as attacking or stealing from the entire network, which will ultimately greatly reduce the security and availability of the network.

[0007] (3) Significant differences in node load levels: With the development of cloud-network convergence technology, large-scale computing power will be concentrated in a small number of nodes. Nodes send data and requests to the edge cloud through the RAN (Wireless Access Network), the edge cloud processes the data, and then sends the processed data and requests to the core cloud. The core cloud then sends data and responds to the nodes in the RAN through the edge cloud. The cloud needs to process a large amount of data at all times. Sudden surges in network requests may cause power outages and network fluctuations. If illegal nodes attack or steal data from nodes with large amounts of data, this will lead to large-scale privacy data leaks or network crashes. Summary of the Invention

[0008] The purpose of this invention is to provide a data optimization method, device, and storage medium based on the Cyberwin-driven 6G network architecture to solve the problems of low spectrum utilization, susceptibility to interference during communication, and severe differences in node load in 6G mobile network technology.

[0009] This invention solves the above-mentioned technical problems through the following technical solution: a data optimization method, comprising the following steps:

[0010] S1. Initialize the random positions and speeds of the node group in the wireless access network; the wireless access network includes industrial IoT, vehicle-to-everything (V2X) and broadband access networks;

[0011] S2. Calculate the fitness value of each node in the wireless access network, update the speed and position of the node according to the fitness value, and then adjust the speed and position of the node to obtain the global optimal position of all nodes.

[0012] S3. Compare the global optimal position obtained in step S2 with the preset fitness value. If the global optimal position does not reach the preset fitness value, return to step S2; otherwise, obtain the optimized wireless access network and proceed to step S4.

[0013] S4. Using Cybertwin, the optimized wireless access network is mapped to the edge cloud and the core cloud to obtain the core network;

[0014] S5. The spectrum sharing problem between the master node and the secondary node in the core network is transformed into a global optimization problem, resulting in a dynamic spectrum access architecture based on blockchain technology; the master node is a first-level spectrum acquisition node, and the secondary node is a node that distributes spectrum resources through the master node;

[0015] S6. Use the secondary nodes in the dynamic spectrum access architecture based on blockchain technology as nodes in the blockchain network to perform consensus and authentication operations.

[0016] Furthermore, the specific implementation process of obtaining the globally optimal location of each network node in the wireless access network using optimization algorithms is as follows:

[0017] Step 1.1: Initialize the speed and location of each network node in the wireless access network;

[0018] Step 1.2: Define the objective function, and calculate the fitness value of the corresponding network node based on the objective function and the position of the network node;

[0019] Step 1.3: Compare the current fitness value of the network node with the previous fitness value. If the current fitness value is less than the previous fitness value, then the velocity and position corresponding to the current fitness value are the current velocity and current position of the network node, respectively; otherwise, the velocity and position corresponding to the previous fitness value are the current velocity and current position of the network node, respectively.

[0020] Step 1.4: Calculate the fitness value of the corresponding network node based on the objective function, the current velocity of the network node, and the current position of the network node;

[0021] Step 1.5: Determine if the fitness values ​​of all network nodes are less than the set fitness value. If so, obtain the global optimal position of each network node; otherwise, proceed to step 1.6.

[0022] Step 1.6: Update the velocity and position of the network nodes, and calculate the fitness value of the network nodes, then proceed to Step 1.3; wherein, the update formulas for the current velocity and current position are:

[0023] V i,j (T+1)=V i,j (T)+e1·l 1,j (T)·[P i,j (T)-X i,j (T)]+

[0024] e2·l 2,j (T)·[G j (T)-X i,j (T)]

[0025] X i,j (T+1)=X i,j(T)+V i,j (T+1)

[0026] Among them, V i,j (T+1) represents the velocity of the i-th node at the previous time T-1; V i,j (T) represents the velocity of the i-th node at time T; l 1,j (T) represents a random number uniformly distributed in the interval [0,1] of dimension j at time T; P i,j (T) represents the optimal position of the i-th individual; X i,j (T) represents the particle dimension of the i-th node at time T in an n-dimensional search space; 2,j (T) represents a random number uniformly distributed in the j-th dimension [0,1] at time T.

[0027] Furthermore, the specific implementation process of utilizing nodes in the blockchain network to perceive idle spectrum gaps in the overall network architecture is as follows:

[0028] The signal-to-noise ratio (SNR) of the receiver at the main network node u is calculated using the following formula:

[0029]

[0030] Where, γ u Ω(u) represents the signal-to-noise ratio of the receiver of the main network node u; N is the set of available channels, N = {1, 2, ..., N}; Ω(u) represents the available channels occupied by the main network node u. The transmit power of the main network node u; The signal gain of the main network node u in the channel occupied by node u; The signal gain between the transceivers of the primary network node u and the secondary node v. Let Θ(0) be the transmit power of the secondary node v, and Θ(0) be the noise power of the signal receiver.

[0031] The constraints are defined as follows:

[0032]

[0033]

[0034] Where M is the number of main network nodes, Π u The total interference power received by the master network node u from the node network. This is the maximum transmit frequency of the signal transceiver at the secondary node v;

[0035] The spectrum sharing problem between the primary network node u and the secondary node v is transformed into a global optimization problem, with the objective function being:

[0036]

[0037] Furthermore, the specific implementation process of step S6 includes:

[0038] 1) Initialize the secondary node set V, spectrum resources N, and blockchain BC; initialize the node and blockchain network;

[0039] 2) When spectrum resources N i When the spectrum is not occupied, issue a spectrum sensing acquisition task;

[0040] 3) Generate the corresponding smart contract content and broadcast the available spectrum slots N. i The contract content is shared across the entire network, and a hash puzzle is performed to decrypt all secondary nodes V.

[0041] 4) Verify the perceived data. If the perceived data passes verification, then node V, which successfully solved the hash puzzle, is the correct answer. i If you receive a reward, you will broadcast the available spectrum slots to the entire network; otherwise, you will republish the new contract content.

[0042] Return to step 3).

[0043] As an inventive concept, the present invention also provides a terminal device, which includes:

[0044] One or more processors;

[0045] A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to implement the steps of the method described above.

[0046] As an inventive concept, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0047] Beneficial effects

[0048] Compared with the prior art, the advantages of the present invention are as follows:

[0049] 1. This invention has lower latency and spectrum utilization, and can provide services with more advantageous system response and a more efficient spectrum sharing access method;

[0050] 2. The method proposed in this invention has better system utility. Attached Figure Description

[0051] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart of a data optimization method based on a Cyberwin-driven 6G network architecture according to an embodiment of the present invention;

[0053] Figure 2 This is a flowchart illustrating the implementation of the PSO-SA method in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the dynamic spectrum sharing access process based on blockchain according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram comparing the overhead performance of various methods in embodiments of the present invention;

[0056] Figure 5 This is a schematic diagram illustrating the average time delay of the blockchain system operating dynamic spectrum sharing access according to an embodiment of the present invention;

[0057] Figure 6(a) Comparison of the utility of blockchain systems in the single-chain state; Figure 6(b) Comparison of the utility of blockchain systems in the multi-heterogeneous-chain state;

[0058] Figure 7 This is a schematic diagram comparing the utilization rate of idle spectrum slots using the same algorithm. Detailed Implementation

[0059] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0061] The framework used in the embodiments of the present invention is as follows: Figure 1As shown, Cyberwin-driven 6G consists of three main modules: wireless access network, edge cloud, and core cloud. Nodes in these three modules are aware of and share spectrum, and blockchain technology ensures an efficient and secure dynamic spectrum sharing access solution.

[0062] Example 1

[0063] This embodiment provides a data optimization method, including the following steps:

[0064] Step 1: Incorporate the Industrial Internet of Things (IIoT), Vehicle-to-Everything (V2X) networks, and broadband access networks into the wireless access network; initialize the random positions and speeds of the node groups in the wireless access network.

[0065] Leveraging the low latency, high bandwidth, high collaboration, and security and reliability of 6G networks, they play a role in emerging network applications such as IIoT (Industrial Internet of Things), V2X (Vehicle-to-Everything), AR (Augmented Reality), and VR (Virtual Reality).

[0066] Step 2: Calculate the fitness value of each node; update the velocity and position of the nodes based on their fitness values; adjust the nodes based on their position and velocity to obtain the current global optimal position;

[0067] Suppose we are in an n-dimensional wireless access network with m network nodes forming a node population S (of size m*n). At time T, what is the setting X of the i-th node? i (T)=[X i，1 (T),X i，2 (T),...,X i,n [(T)], In the initial state, the velocity of the i-th node is V. i (T)=[V i，1 (T),V i，2 (T),...,V i,n [T]i=1,2,...,m, where the optimal position of each node is represented by P. i (T)=[P i,1 (T),P i，2 (T),...,P i,n The global optimal position G(T) = [G1(T), G2(T), ..., G(T)] is defined. n (T)],G n Let (T) denote the globally optimal position of the nth node, and let G(T) = P. g (T), where g is the index of the particle at the global optimal position g∈{1,2,...,m}.

[0068] Evolutionary strategy for the Tth generation individual (the node at time T is the Tth generation individual): X T+1 =XT +N(0,δ), where N(0,δ) follows a normal distribution. Evolutionary programming: f(X T As X T The smaller the objective function value, the better the fitness of the population. In this embodiment of the invention, the objective function is an indicator that measures the similarity between the predicted and actual values. The global optimal solution is found iteratively using the objective function.

[0069] The optimal position p of the individual at the i-th node best When the objective function value of a node at time T is less than the objective function value at the previous time T-1, the current individual position is the optimal individual position; conversely, the individual position at the previous time T-1 is the optimal position, which can be expressed as equation (1). The global optimal position g based on all nodes... best =G(T) can be defined by equations (2) and (3).

[0070]

[0071]

[0072] G(T)=P g (T) (3)

[0073] Step 3: Compare the current global optimal position with a pre-set better fitness value, and repeat Step 2 until the current global optimal position reaches the pre-set fitness value, and finally reach the global optimal position.

[0074] The process of node position updating can be roughly summarized as nodes combining historical experience and shared information within the group, continuously adjusting their individual positions according to the actual situation, and ultimately reaching the optimal solution. The entire process of node speed updating can be divided into three modules: the speed of the node's previous iterations, providing initialization conditions; the node's individual cognition, mainly its own thinking, ensuring that the node has a sufficiently strong global search capability to strive for global optimality; and the group's social cognition, providing assurance for confidence sharing among nodes. Therefore, the evolutionary equation for the basic node swarm algorithm can be obtained:

[0075]

[0076] X i,j (T+1)=X i,j (T)+V i,j (T+1) (5)

[0077] Where i = 1, 2, ..., m is the i-th node, and m represents the group size; j = 1, 2, ..., n, m is the i-th dimension of the node group, and n represents the dimension of the group search space. e1 and e2 represent the acceleration factors of the nodes, and l1 and l2 are random numbers uniformly distributed in the interval [0, 1]. It can be seen from equation (4) that the acceleration factor e1 is used as the step size for the node to move to the individual optimal position, and the acceleration factor e2 is used as the step size for the node to adjust to the group optimal position. Usually, it is the node speed V. i,j Define a range, namely V i,j ∈[-V max V max ].

[0078] However, it is quite important to adjust the local optimization and swarm optimization capabilities. [3] proposed a particle swarm optimization algorithm based on inertia weight w. The inertia weight w can control the ratio of local and global search capabilities. Generally, w∈[0.8,1.2]. The smaller w is, the stronger the local search capability; the larger w is, the stronger the global search capability.

[0079]

[0080] Traditional Probability of Search (PSO) algorithms inevitably converge to local optima, failing to guarantee that the final search location is the global optimum. Algorithms like Optimal Search (SA) often employ a single-point search approach, resulting in slower convergence. However, combining PSO and SA algorithms effectively leverages the local search advantages of PSO and the global search advantages of SA, ultimately increasing the probability of reaching the global optimum and improving the overall search speed. Figure 2 As shown.

[0081] The pseudocode of the improved PSO-SA algorithm is shown in Algorithm 1. After running the SA algorithm, the global optimal solution is obtained, and the fitness value of each node in the node network is judged. The node is adjusted according to its position and velocity, and finally the global optimal position is reached.

[0082]

[0083]

[0084] The expression of Algorithm 1 is as follows:

[0085] 1) Initialize the input for the simulated annealing algorithm: population size m; P i =X i ; X i,j Obey [-X] max ,X max Uniform distribution on ], Vi,j Obey [-V] max V max The initial value of the simulated annealing algorithm is P, which is a uniform distribution on the [plane / node]. The number of iterations is T = 0. g ;P i X i Let X be the optimal position and X' be the position of the i-th node, i = 1, 2, ..., m, j = 1, 2, ..., n, where m is the dimension of the node group and n represents the dimension of the group search space. i,j Let X be the position of the i-th node in the j-th dimension. max V represents the upper limit of the position. i,j Let V be the velocity of the i-th node in the j-th dimension. max P is the upper limit of speed. g The position of the particle at the globally optimal position. Represents any node i;

[0086] 2) Run the simulated annealing algorithm to obtain a new solution Y; initialize the positions and velocities of the nodes; calculate the fitness value of each node;

[0087] 3) Let i = 1;

[0088] 4) If f[X] i (T)]<f[P i [(T-1)], then P i (T)=X i (T), otherwise P i (T)=P i (T-1);

[0089] 5) Increment the value of I by 1, return to step 3), until i is not less than m.

[0090] 6) Let i = 1;

[0091] 7) If f[X] i (T)]>f[G(T)], then P g (T)=G(T)

[0092] 8) Increment the value of I by 1, return to step 6), until i is not less than m.

[0093] Step 4: Map the optimized wireless access network to the edge cloud and core cloud via Cybertwin [1].

[0094] Cybertwin is the core function of 6G networks, serving as a digital representation of people or things. Terminal nodes obtain the necessary network services through Cybertwin. Edge clouds can respond to requests more quickly and effectively improve the service quality of the core cloud network. The core cloud is the core of the cloud-native 6G network. After dynamic optimization in various networks using the improved PSO-SA algorithm, it is mapped to the edge cloud and core through Cybertwin. These two parts together form the core network, providing core network services.

[0095] Step 5: Nodes in the blockchain network sense the idle spectrum gaps in the overall network architecture.

[0096] Traditional spectrum sensing technologies rely on a single secondary user (SU) to acquire spectrum data. However, this method suffers from low reliability due to noise interference and communication channel fluctuations, often requiring collaboration among multiple users for intelligent spectrum sensing. Furthermore, centralized spectrum sensing presents several challenges, such as reduced flexibility and spectrum security. It also makes the database vulnerable to malicious attacks or the transmission of erroneous spectrum data, posing significant obstacles to dynamic spectrum access in 6G networks.

[0097] To overcome the shortcomings of preemptive spectrum access methods and single-user spectrum awareness methods, such as low security, low stability, and high cost, blockchain technology is introduced. As a distributed ledger technology, blockchain can store idle spectrum slots in a distributed manner across trusted nodes and allocate them according to user spectrum needs, thus achieving dynamic spectrum access. The blockchain-based dynamic spectrum sharing function provides abundant spectrum slots to each secondary user, effectively improving the utilization rate of spectrum bandwidth and enhancing the stability and security of spectrum sharing.

[0098] In a 6G network at a given moment, the available channels are n = {1, 2, ..., N}, and the set of licensed nodes that can access these channels is u = {1, 2, ..., U}, with U nodes sharing N channels. However, due to practical design limitations, each transceiver can only receive signals from one channel at a time. The signal-to-noise ratio (SNR) of the receiver at node u is shown below:

[0099]

[0100] Where Ω(u) is the channel occupied by node u. Let u be the transmit power. This represents the channel gain of a node in its channel. Let Θ(0) be the channel gain between the signal transceivers of the master node u and the secondary node v, and let Θ(0) represent the noise power of the node signal receiver. Generally, it is assumed that the different channels used by the signal transceivers do not affect the channel gain between the transceivers, and the interference of the gain on the channel can be ignored.

[0101] When the master node u communicates with other secondary nodes, the total interference power received by node u from the node network is Π. u There are the following constraints:

[0102]

[0103] Where the master nodes u = 1, 2, ..., M. Due to noise and transmission channel consumption, the total transmit power constraint of the signal transmitter of the secondary node n is as follows:

[0104]

[0105] in, This indicates the maximum transmit frequency that the transceiver of the secondary node v possesses.

[0106] To address the spectrum sharing problem among secondary nodes and ensure healthy competition for spectrum slots, we model it as a game problem B under cooperative game theory, where the set of strategies adopted by secondary node v in the network is represented by C. v W v This represents the utility produced by a certain strategy adopted by the secondary node v. Utilizing the properties of cooperative game theory, the spectrum sharing problem between the primary and secondary nodes can be transformed into a global optimization problem, as follows:

[0107]

[0108] Step 6: Perform operations such as smart contract processing, consensus algorithm implementation, and data authentication to obtain blockchain rewards.

[0109] In a dynamic spectrum access architecture based on blockchain technology, secondary nodes in the network act as nodes in the blockchain network, performing consensus and authentication operations to achieve better spectrum awareness and sharing services. Each node providing computing power to the blockchain network receives a reward, with different levels of reward distribution based on the node's contribution. The rewards received by the nodes can primarily be used for the use of spectrum gaps within the spectrum bandwidth, such as... Figure 3 As shown.

[0110] Spectrum sharing technology supported by blockchain technology can not only realize spectrum access solutions with low management costs, but also become a high-security, low-cost, and high-efficiency spectrum access solution, as shown in Algorithm 2.

[0111]

[0112] Analysis of experimental results:

[0113] In the Internet of Things (IoT) environment, resource storage overhead, average latency, and idle spectrum utilization can serve as key indicators for evaluating algorithm performance. To investigate the impact of blockchain spectrum sharing access algorithms on various performance aspects, we compared the average latency, system utility, and idle spectrum utilization of the algorithms in references [1-3] with those in this invention. The performance comparisons of each algorithm are detailed in [link to relevant documentation]. Figure 4 The algorithm proposed in this embodiment of the invention reduces storage overhead by half and has lower time complexity compared to other algorithms, indicating that the algorithm proposed in this embodiment of the invention has better storage efficiency. The average latency of the algorithm proposed in this embodiment of the invention reaches the microsecond level, and the spectrum utilization rate of idle spectrum gaps reaches 98%. This model has lower latency and spectrum utilization, indicating that it can provide a more advantageous service in terms of system response and a more efficient spectrum sharing access method.

[0114] Figure 5 The paper demonstrates the average latency of dynamic spectrum sharing access methods in blockchain scenarios with different block lengths. With a block length of 200, the average latency is on the order of 10⁻⁴ seconds, exhibiting excellent latency performance and enabling efficient response services. When the block length increases to 400, the average latency of the blockchain system performing the DSA algorithm reaches the order of 10⁻³ seconds, still sufficient to provide response services to node requests. Finally, when the block length reaches 800, the average latency reaches the order of 10⁻² seconds, and the latency for nodes to obtain DSA services still meets the minimum acceptable response speed for blockchain system operation and nodes. Block length is related to the number of transactions processed in the blockchain system. As the number of transactions processed in the blockchain system increases, the processing time required for the dynamic spectrum sharing access method increases by more than 10 times, but the maximum transaction latency required by the blockchain system is still lower than the worst-case scenario of the experiment and remains within an acceptable range.

[0115] Figures 6(a) and 6(b) show the changes in system utility under different numbers of nodes. While ensuring full utilization of the spectrum, the algorithm proposed in this embodiment has better system utility than that in references [3][4][5]. System utility represents the smooth operation of business and the system surplus. Figure 6(a) shows the results of running the blockchain system in a single-chain state, and Figure 6(b) shows the results of running the blockchain system in a multi-heterogeneous-chain state.

[0116] Figure 7It is shown that under the communication conditions of 36MHz and 48MHz spectrum bandwidth, the spectrum utilization rate of references [3][4] is low, while the algorithm proposed in reference [5] and the embodiment of the present invention both have better spectrum gap utilization. Compared with the other three algorithms, the algorithm of the embodiment of the present invention has a higher spectrum gap utilization rate and can reduce the inefficient use of spectrum gaps.

[0117] Example 2

[0118] Embodiment 2 of the present invention provides a terminal device corresponding to Embodiment 1 above. The terminal device can be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., to execute the method of the above embodiments.

[0119] The terminal device in this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method in Embodiment 1 described above.

[0120] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0121] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0122] Example 3

[0123] Embodiment 3 of the present invention provides a computer-readable storage medium corresponding to Embodiment 1 above, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, they implement the steps of the method of Embodiment 1 above.

[0124] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0125] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0126] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0129] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0130] References

[0131] [1]Yu Q,Ren J,Fu Y,et al.Cybertwin:An Origin of Next GenerationNetwork Architecture[J].IEEE Wireless Communications,2019,26(6):111-117.

[0132] [2]YIYANG PEI,SHISHENG HU,FENG ZHONG,et al.Blockchain-enabled DynamicSpectrum Access:Cooperative Spectrum Sensing,Access and Mining[C]. / / 2019IEEEGlobal Communications Conference:GLOBECOM 2019,Waikoloa,Hawaii,USA,9-13December 2019,[v.2].:Institute of Electrical and Electronics Engineers,1:753-758.

[0133] [3]Kotobi,Khashayar,and Sven G.Bilen."Secure blockchains for dynamicspectrum access:A decentralized database in moving cognitive radio networksenhances security and user access."ieee vehicular technology magazine 13.1(2018):32-39.

[0134] [4]Weiss,Martin BH,et al."On the application of blockchains tospectrum management."IEEE Transactions on Cognitive Communications andNetworking 5.2(2019):193-205.

[0135] [5]Qiu Jing,Zhou Zheng."Distributed dynamic spectrum sharing incognitive radio networks."Journal of Beijing University of Posts andtelecommunications,2009,32(01):69-72.

Claims

1. A data optimization method, characterized in that, Includes the following steps: S1. Initialize the random positions and speeds of the node group in the wireless access network; the wireless access network includes industrial IoT, vehicle-to-everything (V2X) and broadband access networks; S2. Calculate the fitness value of each node in the wireless access network, update the speed and position of the node according to the fitness value, and then adjust the speed and position of the node to obtain the global optimal position of all nodes. S3. Compare the global optimal position obtained in step S2 with the preset fitness value. If the global optimal position does not reach the preset fitness value, return to step S2; otherwise, obtain the optimized wireless access network and proceed to step S4. S4. Using Cybertwin, the optimized wireless access network is mapped to the edge cloud and the core cloud to obtain the core network; S5. The spectrum sharing problem between the master node and the secondary node in the core network is transformed into a global optimization problem, resulting in a dynamic spectrum access architecture based on blockchain technology. The master node is a primary spectrum acquisition node, and the secondary nodes are nodes that distribute spectrum resources through the master node; S6. Use the secondary nodes in the dynamic spectrum access architecture based on blockchain technology as nodes in the blockchain network to perform consensus and authentication operations.

2. The data optimization method according to claim 1, characterized in that, In step S2, the global optimal position G(T) = [G1(T), G2(T), ..., G n [(T)], where 1, 2, 3...n represent the corresponding dimensions, then G1(T),G2(T),...,G n (T) represent the optimal positions in the corresponding dimension at time T, and we have: G(T)=P g (T); Where m is the number of nodes. X i (T) represents the position of the i-th node at time T, and P i (T) represents the optimal position of the i-th node at time T, P g (T) represents the position of the globally optimal particle, g∈{1,2,...,m}, f[X] i [(T)] represents the objective function value at the position of the i-th node at time T.

3. The data optimization method according to claim 1, characterized in that, In step S5, the global optimization problem is expressed as: Where Ω(u) is the channel occupied by the master node u. The transmit power of the main network node u. Let v be the transmit power. Channel gain between the signal transceivers of master node u and secondary node v, Π u Let be the total interference power received by node u from the node network. M represents the maximum transmit frequency of the transceiver of the secondary node v, and M is the total number of master nodes.

4. The data optimization method according to claim 1, characterized in that, The specific implementation process of step S6 includes: 1) Initialize the secondary node set V, spectrum resources N, and blockchain BC; initialize the node and blockchain network; 2) When spectrum resources N i When the spectrum is not occupied, issue a spectrum sensing acquisition task; 3) Generate the corresponding smart contract content and broadcast the available spectrum slots N. i The contract content is shared across the entire network, and a hash puzzle is performed to decrypt all secondary nodes V. 4) Verify the perceived data. If the perceived data passes verification, then node V, which successfully solved the hash puzzle, is the correct answer. i If you receive the reward, you will broadcast the available spectrum slots to the entire network; otherwise, republish the new contract content and return to step 3).

5. A terminal device, characterized in that, include: One or more processors; A memory having stored one or more programs thereon, which, when executed by the one or more processors, cause the one or more processors to perform the steps of the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 4.

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