A centralized intelligent spectrum management method in unmanned system cluster networks

Abstract

The present invention discloses a centralized intelligent spectrum management method for an unmanned system cluster network. This method constructs a weighted interference graph model and, based on node measurement information and communication performance feedback, employs reinforcement learning methods such as heuristic algorithms and probabilistic rules. This method can autonomously optimize and adjust the network's operating channels, reducing frequency conflicts within the system and the adverse effects of external interference. This method uses the communication performance actually perceived by communication nodes as feedback to guide spectrum decisions, adapts to complex and dynamic spectrum and wireless channel environments, and supports large-scale unmanned system network communication spectrum management. This method allocates channels for service data networks based on reinforcement learning algorithms such as probabilistic rules, and for wireless access networks based on reinforcement learning algorithms such as heuristic algorithms. This method has learning capabilities and low algorithmic complexity.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned system cluster communications, and more particularly, to a centralized intelligent spectrum management method in an unmanned system cluster network. Background Art

[0002] Swarming large numbers of unmanned systems to perform various missions will become a crucial operating model in specific areas in the future. To successfully complete communication missions, unmanned systems must rationally utilize the wireless spectrum. This spectrum usage primarily involves three types of equipment: First, communications equipment, which supports the transmission of control commands from control stations to unmanned systems, the real-time transmission of high-speed data reconnaissance information, and real-time command and control of unmanned systems. These equipment consume the most spectrum and are crucial for successful mission completion. Second, sensor equipment, which provides communication and data transmission support for navigation, positioning, target designation, and friend-or-foe identification. These primarily include satellite navigation, synthetic aperture radar, moving target designators, friend-or-foe identifiers, and remote sensing detectors. Third, electronic countermeasures equipment on vehicles, aircraft, and ships, including jammers and passive jammers, suppresses and disrupts enemy electronic systems or conducts electronic deception. Furthermore, unmanned system swarms typically operate in harsh environments, characterized by complex terrain, wireless channel characteristics, and the threat of enemy jamming signals. This presents multiple challenges for their frequency utilization.

[0003] Coordinating frequency conflicts within unmanned system clusters and adapting to complex external spectrum environments requires efficient and reliable spectrum management. However, existing spectrum management for unmanned systems primarily relies on simple fixed allocations, making it difficult to adapt to dynamic and complex environments and large-scale clusters. Some literature has proposed a spectrum allocation method for unmanned aerial vehicles (UAVs) based on joint task allocation. These methods treat UAVs performing the same task as a coalition, with UAVs within the coalition sharing the same channel, while different channels are used between task alliances. To improve spectrum utilization, some literature has also proposed sharing partially overlapping channels in clustered UAV networks. To overcome the uncertainty of state information caused by channels and UAV motion, a distributed channel allocation algorithm has been designed using fuzzy decision-making and game learning algorithms. Related literature proposes utilizing millimeter wave frequency bands in UAV-based cellular networks. To mitigate interference between base stations and between base stations and wireless backhaul links, a 3D interference graph model is constructed. Spectrum allocation is modeled as a continuous time-segment optimization problem, taking into account constraints such as UAV mobility, energy consumption, and interference. These methods provide references for spectrum management in UAV clusters, but they are primarily theoretical, lack practical protocol and network validation, and are therefore difficult to adapt to large-scale cluster network scenarios.

[0004] There is a lot of research on spectrum management for wireless mesh networks, and existing related research is mainly divided into two types of spectrum management methods: centralized and distributed. However, current centralized spectrum management still mainly relies on traditional optimization methods and lacks the ability to intelligently adapt to dynamic and complex spectrum environments. Summary of the Invention

[0005] In response to at least one defect or improvement need in the existing technology mentioned in the background technology section, the present invention provides a centralized intelligent spectrum management method in an unmanned system cluster network to overcome the technical defects of the existing technology, such as the lack of actual protocols and network verification, and the difficulty in adapting to large-scale cluster communication networks and application scenarios in dynamic and complex spectrum environments.

[0006] To achieve the above objectives, the present invention provides a centralized intelligent spectrum management method in an unmanned system cluster network, comprising:

[0007] S1. Initialize channel selection for the wireless access network and the service data network;

[0008] S2, constructing a weighted interference relationship matrix within the system to describe the potential interference relationship between wireless switching nodes within the system;

[0009] S3. Construct an external weighted interference relationship matrix to measure the number of non-system WiFi networks on each channel;

[0010] S4. In the current time slot, based on the WiFi signals of each channel received by each node, the average link bandwidth of the service data network and the wireless access network are updated respectively; based on the bandwidth measurement process, the average link quality of the service data network and the wireless access network are updated respectively;

[0011] S5. In the current time slot, based on the currently selected channel, the intra-system weighted interference relationship matrix, and the extra-system weighted interference matrix, obtain the actual weighted interference received by the wireless access network and the actual weighted interference received by the service data network;

[0012] S6. In the current time slot, based on the average link bandwidths of the service data network and the radio access network, respectively update the estimated average link bandwidths of the service data network and the radio access network; based on the average link qualities of the service data network and the radio access network, and the actual weighted interference experienced by the service data network and the radio access network, respectively update the estimated average link quality of the service data network and the radio access network;

[0013] S7. Based on the link bandwidth or link quality of the previous time slot, update the channel selection of the service data network according to the corresponding probability rule;

[0014] S8. updating the channel selection of the wireless access network using a heuristic algorithm;

[0015] S9. If the network topology changes, jump to step S2; otherwise, jump to step S3.

[0016] Furthermore, the obtaining of the actual weighted interference received by the wireless access network and the actual weighted interference received by the service data network based on the currently selected channel, the intra-system weighted interference relationship matrix, and the extra-system weighted interference matrix includes the relationship:

[0017]

[0018] Where I(a) represents the actual weighted interference suffered by the wireless access network, represents the weighted interference in the system, represents the weighted interference outside the system, w n1→n2 represents the number of terminals connected to wireless switching node n2 that can receive signals from wireless switching node n1, v n，c It represents the number of WiFi networks outside the system that exist in each channel detected by the terminal belonging to the wireless switching node. δ(a1, a2) is the channel interference function, and its expression is:

[0019] Furthermore, the obtaining of the actual weighted interference received by the wireless access network and the actual weighted interference received by the service data network based on the currently selected channel, the intra-system weighted interference relationship matrix, and the extra-system weighted interference matrix further includes the relationship:

[0020]

[0021] Where I0(a) represents the actual weighted interference received by the service data network, δ(a1, a2) is the channel interference function, and u n，c Indicates the number of WiFi networks outside the system that exist on channel c detected by the wireless switching node n itself.

[0022] Furthermore, updating the estimated average link bandwidths of the service data network and the wireless access network, respectively, based on the average link bandwidths of the service data network and the wireless access network includes updating the estimated average link bandwidth of the service data network based on the average link bandwidth of the service data network. The specific formula includes:

[0023]

[0024] Where D0(a0) represents the estimated average link bandwidth of the service data network, represents the average link bandwidth of the service data network, and T(a0) represents the total number of times the selected channel variable a0 is selected as the service data network channel.

[0025] Furthermore, updating the estimated average link bandwidths of the service data network and the radio access network, respectively, based on the average link bandwidths of the service data network and the radio access network further includes updating the estimated average link bandwidth of the radio access network based on the average link bandwidth of the radio access network. The specific formula includes:

[0026]

[0027] Where D(a0) represents the estimated average link bandwidth of the wireless access network, Indicates the average link bandwidth of the wireless access network.

[0028] Further, the updating of the estimated average link quality values of the service data network and the radio access network based on the average link quality of the service data network and the radio access network, and the actual weighted interference suffered by the service data network and the radio access network, respectively, includes updating the estimated average link quality value of the service data network based on the average link quality of the service data network and the actual weighted interference suffered by the service data network. The specific formula includes:

[0029]

[0030] Where E0(a0) represents the average link quality estimate of the service data network, represents the average link quality of the service data network, and T(a0) represents the total number of times the selected channel variable a0 is selected as the service data network channel.

[0031] Further, updating the estimated average link quality values of the service data network and the radio access network based on the average link quality of the service data network and the radio access network, and the actual weighted interference suffered by the service data network and the radio access network, respectively, further includes updating the estimated average link quality value of the radio access network based on the average link quality of the radio access network and the actual weighted interference suffered by the radio access network. The specific formula includes:

[0032]

[0033] Where E(a0) represents the average link quality estimate of the wireless access network, Indicates the average link quality of the wireless access network.

[0034] Furthermore, updating the channel selection of the service data network according to the corresponding probability rule based on the link bandwidth or link quality of the previous time slot includes updating the channel a0 of the service data network according to the first probability rule based on the link bandwidth of the previous time slot. The specific formula of the first probability rule includes:

[0035] a0 is

[0036] Wherein, D0(c) represents the estimated value of the average link bandwidth of the service data network; D(c) represents the estimated value of the average link bandwidth of the wireless access network; α and β are two predefined parameters, 0<α<1, 0<β<1, α+β=1; ε is a preset probability value that decreases as the number of time slots increases.

[0037] Furthermore, updating the channel selection of the service data network according to the corresponding probability rule based on the link bandwidth or link quality of the previous time slot also includes updating the channel a0 of the service data network according to the second probability rule based on the link quality of the previous time slot. The specific formula of the second probability rule includes:

[0038] a0 is

[0039] Wherein, E0(c) represents the average link quality estimate of the service data network; E(c) represents the average link quality estimate of the wireless access network; α and β are two predefined parameters, 0<α<1, 0<β<1, α+β=1; ε is a preset probability value that decreases as the number of time slots increases.

[0040] Furthermore, the heuristic algorithm includes:

[0041] S81. Randomly select a channel from the optional channels for each wireless access node;

[0042] S82. Randomly select a wireless access node, traverse the actual weighted interference obtained when the wireless access node uses all optional channels, and select the channel that minimizes the actual weighted interference as the channel of the wireless access network;

[0043] S83. If the maximum number of loop times is reached, exit the loop and return to the channel currently selected by each wireless access node; otherwise, return to step S82.

[0044] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0045] The present invention provides a centralized intelligent spectrum management method for unmanned system cluster networks. This method constructs a weighted interference graph model and, based on node measurement information and communication performance feedback, employs reinforcement learning methods such as heuristic algorithms and probabilistic rules. This method can autonomously optimize and adjust the network's operating channels, minimizing internal frequency conflicts and the adverse effects of external interference. This method uses the actual communication performance perceived by communication nodes as feedback to guide spectrum decisions, adapting to complex and dynamic spectrum and wireless channel environments and supporting communication spectrum management for large-scale unmanned system networks. This method allocates channels for service data networks based on reinforcement learning algorithms such as probabilistic rules, and for wireless access networks based on reinforcement learning algorithms such as heuristic algorithms. This method exhibits learning capabilities and exhibits low algorithmic complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flowchart of a centralized intelligent spectrum management method in an unmanned system cluster network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0049] The terms "first," "second," or "third" in the specification, claims, or drawings of this application are used to distinguish different objects, not to describe a specific order. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0050] Targeting future large-scale unmanned system cluster applications, this paper proposes a centralized spectrum management solution based on a layered wireless mesh network architecture. This solution constructs a weighted interference graph model, employs reinforcement learning methods based on node measurement information and communication performance feedback, and autonomously optimizes and adjusts the network's operating channels, minimizing both internal frequency conflicts and the adverse effects of external interference. Through this centralized intelligent spectrum management method, unmanned system clusters can autonomously optimize their operating channels, improving the network's spectrum adaptability to complex environments.

[0051] Some prerequisites for the method of the present invention to work:

[0052] (1) Hierarchical wireless mesh network structure. To support the communication needs of large-scale unmanned system clusters, a two-layer network is used to organize various communications of the unmanned system cluster. Some unmanned system nodes are selected as wireless mesh nodes (called wireless switching nodes). The wireless switching nodes operate in the same frequency band and can build a wireless backbone network through multi-hop. This layer of network is called the business data network. The wireless switching nodes each provide a wireless local area network. Other unmanned systems in the cluster (called wireless terminal nodes) select the wireless local area network with the strongest signal to access the network and realize wireless networking communication. This layer of network is called the wireless access network.

[0053] (2) Random access mode: Both the service data network and the wireless access network adopt the IEEE802.11 wireless LAN mode based on random multiple access.

[0054] (3) Wireless control link: A physically independent control layer network is built between wireless switching nodes, which has low-speed reliable transmission capabilities, collects and processes wireless communication status information, and sends centralized spectrum management results.

[0055] (4) Spectrum Management Controller: The entity that performs centralized spectrum management can be a wireless switching node or a ground control station, which manages the unmanned system cluster communication spectrum through wireless control links.

[0056] Definitions of some nouns and variables:

[0057] (1) Basic variables. The set of M terminal nodes is N wireless switching node sets The set of wireless communication channels available to the system

[0058] (2) Channel selection variables. Indicates the channel used by the business data network, and defines the protection channel set of the business data network as The channel used by the wireless access network of wireless switching node n is denoted as a nIn order to reduce the interference between the wireless access network and the service data network, the channel selection of the wireless switching node is after the service data network channel is determined and protected, that is, the channel can only be randomly selected from the protection channel set. The packet is to ensure sufficient channel spacing. Let the channel set selected by the wireless switching node be vector a=(a1,...,a N ).

[0059] (3) Estimated variables related to link quality. For each single-hop wireless communication link, referring to WiFi, the link signal quality is defined as quality*(A+signal). When A is 200, it is quality*(200+signal), where quality and signal represent the current communication link parameters detected by WiFi. Quality is a score not exceeding 1, and the larger the score, the better the signal. Signal represents the signal strength in dBm, which is usually negative. The 200 added here ensures that (200+signal) is a positive value. The spectrum management controller calculates the average link quality of the service data network separately, that is, the average link quality of all links. The average link quality of the wireless access network, that is, the average link quality of all links (downlinks from wireless switching nodes to each terminal) Then define the corresponding estimated value, define and initialize the all-zero vector E0=(0,...,0) of size 1*C, which represents the average link quality estimated value when the service data network selects each channel. It is an estimate of the average link quality of the service data network and is mainly used to guide the update of the service data network channel. Similarly, define and initialize the all-zero vector E=(0,...,0) of size 1*C, which represents the average link quality estimated value of the wireless access network when the service data network selects each channel. It is an estimate of the average link quality of all wireless access networks and is mainly used to guide the update of the wireless access network channel. Note that the above two variables are long-term estimates of link quality and require iterative estimation of the measurement bandwidth. The specific relationship is shown in the following equations (6) and (7).

[0060] (4) Bandwidth-related estimation variables. Each node implements the bandwidth measurement process, and the spectrum management controller calculates the average link bandwidth of the service data network, that is, the average bandwidth of all links. The average link bandwidth of the wireless access network, that is, the average bandwidth of all links (downlinks from wireless switching nodes to each terminal) Note that the above two variables are the overall average of the bandwidth of the two networks during this measurement process, so they are only single variables, not vectors. Then define the corresponding estimated values, define and initialize the all-zero vector D0 = (0, ..., 0) of size 1*C, which represents the average link bandwidth estimate when the service data network selects each channel. It is an estimate of the average link bandwidth of the service data network and is mainly used to guide the update of the service data network channel. Similarly, define and initialize the all-zero vector D = (0, ..., 0) of size 1*C, which represents the average link bandwidth estimate of the access network when the service data network selects each channel. It is an estimate of the average link bandwidth of all wireless access networks and is mainly used to guide the update of the wireless access network channel. Note that the above two variables are long-term estimates of the link bandwidth and require iterative estimation of the measured bandwidth. The specific relationship is shown in equations (4) and (5) in the algorithm below.

[0061] (5) Channel selection history variable: Define and initialize an all-zero vector T = (0, ..., 0) of size 1*C, recording the cumulative total number of times the service data network selects each channel up to the current time slot.

[0062] (6) Other algorithm parameters: α and β are two predefined parameters that satisfy: 0<α<1, 0<β<1, α+β=1.

[0063] In the present invention, all wireless terminal nodes update their local status information with a time period of T. Simultaneously, the spectrum management controller updates all types of collected network-wide information, including topology information, node information, wireless link information, and service information, maintaining information storage for the most recent K periods. Based on the detected wireless link information, a reinforcement learning mechanism is used to periodically adjust the operating channels of the service data network and the wireless access network. Specifically, the wireless link information includes: the ID, channel, power, quality, signal strength, bandwidth, and latency of the wireless access network currently being accessed, as detected by the terminal; access networks of other switching node networks detected by the terminal, and other identifiable network-related information in the surrounding environment, such as ID, channel, power, quality, and signal strength.

[0064] like Figure 1 As shown, in one embodiment, a centralized intelligent spectrum management method in an unmanned system cluster network is provided, which mainly includes the following steps:

[0065] Step 1: Initialize channel selection. The spectrum management controller randomly selects a channel from the available channels. As a channel of the service data network; all wireless switching nodes randomly select channels from channels outside the protection channel set of the service data network (Initialize channel selection of wireless access network), let the channel set selected by wireless switching node be vector a=(a1, ..., a N ).

[0066] Step 2: Construct a potential intra-system weighted interference relationship matrix to describe the potential interference relationship between wireless switching nodes in the system. Each terminal node can detect the access network signals of other wireless switching nodes working on each channel. These access network signals can be received and processed. If they use the same channel, they will compete with each other, which is a measure of intra-system network interference. The spectrum management controller constructs an intra-system weighted interference matrix W={w n1→n2}, Its element w n1→n2 represents the number of terminals connected to wireless switching node n2 that can receive signals from wireless switching node n1. This is the number of terminals on n2 that are affected by signals from n1 if n2 and n1 operate on the same channel. Note that this matrix is not necessarily symmetric, as the power of different nodes may vary.

[0067] Step 3: Construct the external weighted interference matrix to measure the number of non-system WiFi networks on each channel. There are two external weighted interference matrices: one is for the service data network, U = {u n，c}, the dimension is N*C, and its element u n，c It represents the number of WiFi networks outside the system (non-wireless access network) where the channel c exists, which is detected by the wireless switching node n itself; the other is v = {v n，c}, dimension is N*C, its element v n，c It indicates the number of WiFi networks (non-wireless access networks and service data networks) outside the system that exist on each channel detected by the terminal belonging to the wireless switching node.

[0068] Step 4: Wireless link quality and bandwidth measurement. In the current time slot, based on the WiFi signals of each channel received by each node, the spectrum management controller updates and calculates the average link bandwidth of the service data network and the wireless access network. and Based on the bandwidth measurement process, the spectrum management controller updates and calculates the average link quality of the service data network and the wireless access network and

[0069] Step 5: Update the interference state variables. In the current time slot, calculate the actual weighted interference I(a) experienced by the wireless access network based on the currently selected channel a, the internal weighted interference matrix W, and the external weighted interference matrices U and V.

[0070]

[0071] Among them, δ(a1, a2) is the channel interference function, which can be defined as:

[0072]

[0073] The above weighted interference is the total interference level calculated based on the actual channel conflict situation, including the weighted interference within the system and outside the system. Similarly, the actual weighted interference I0(a) suffered by the service data network can be calculated and defined as:

[0074]

[0075] Step 6: Update the channel selection state variable. In the current time slot, update the total number of times the selected channel variable a0 is selected as the service data network channel T(a0)=T(a0)+1; update the average link bandwidth estimate of the service data network:

[0076]

[0077] Its essence is to iteratively update the average value; update the average link bandwidth estimate of the wireless access network:

[0078]

[0079] Its essence is to iteratively update the average value; update the average link quality estimate of the service data network:

[0080]

[0081] Its essence is to iteratively update the average value; update the average link quality estimate of the wireless access network:

[0082]

[0083] Step 7: Update the channel selection for the service data network. At the beginning of the next time slot, there are two optional decision methods: bandwidth-based and interference-based.

[0084] Bandwidth-based decision-making: The spectrum management controller adjusts channel a0 of the service data network based on the link bandwidth performance of the previous time slot according to the following first probability rule:

[0085]

[0086] The initial value of ε can be set to a small number such as 0.3, and decreases as the number of time slots increases, for example, it is inversely proportional to the number of time slots. t is the current time slot number.

[0087] Interference-based decision-making: The spectrum management controller adjusts channel a0 of the service data network based on the radio link quality of the previous time slot according to the following second probability rule:

[0088]

[0089] The initial value of ε can be set to a small number such as 0.3, and decreases as the number of time slots increases, for example, it is inversely proportional to the number of time slots. t is the current time slot number.

[0090] Step 8: Update the channel selection of the wireless access network. The spectrum management controller uses the following heuristic algorithm to update the channel a of each wireless access network. n ,

[0091] Step S81: select an optional channel for each wireless access node. Randomly select a channel from the .

[0092] Step S82: Randomly select a wireless access node n and traverse all available channels. The actual weighted interference I(a) obtained when , select the channel that minimizes I(a) where a=(a n , a -n ), a -n Indicates that other wireless access nodes except n keep the previous channels unchanged.

[0093] Step S83: If the maximum number of loop times is reached, exit the loop and return to the channel currently selected by each wireless access node; otherwise, return to step S82.

[0094] Step 9: Detect network status changes. If the network topology has changed, jump to step 2; otherwise, jump to step 3.

[0095] The centralized intelligent spectrum management method of this invention is primarily intended for future unmanned combat scenarios, providing an effective intelligent spectrum management approach for large-scale unmanned system cluster networking and communications. Based on their missions and communication capabilities, drones can be divided into wireless switching nodes, spectrum management controllers, and terminal nodes, as described above. The wireless switching nodes and spectrum management controllers coordinate their operation according to the method and solution of this invention to achieve autonomous spectrum allocation and adjustment in the network. This invention is also applicable to spectrum management in large-scale hierarchical wireless mesh networks.

[0096] The present invention discloses a centralized intelligent spectrum management method for unmanned system cluster networks. This method constructs a weighted interference graph model and, based on node measurement information and communication performance feedback, employs reinforcement learning methods such as heuristic algorithms and probabilistic rules. This method can autonomously optimize and adjust the network's operating channels, minimizing internal frequency conflicts and the adverse effects of external interference. This method uses the actual communication performance perceived by communication nodes as feedback to guide spectrum decisions, adapting to complex and dynamic spectrum and wireless channel environments and supporting communication spectrum management for large-scale unmanned system networks. This method allocates channels for service data networks based on reinforcement learning algorithms such as probabilistic rules, and for wireless access networks based on reinforcement learning algorithms such as heuristic algorithms. This method exhibits learning capabilities and exhibits low algorithmic complexity.

[0097] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of other embodiments of the present disclosure. The present invention is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0098] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A centralized intelligent spectrum management method in an unmanned system cluster network, characterized in that: include: S1. Initialize channel selection for the wireless access network and the service data network; S2, constructing a weighted interference relationship matrix within the system to describe the potential interference relationship between wireless switching nodes within the system; S3. Construct an external weighted interference relationship matrix to measure the number of non-system WiFi networks on each channel; S4. In the current time slot, based on the WiFi signals of each channel received by each node, the average link bandwidth of the service data network and the wireless access network are updated respectively; based on the bandwidth measurement process, the average link quality of the service data network and the wireless access network are updated respectively; S5. In the current time slot, based on the currently selected channel, the intra-system weighted interference relationship matrix, and the extra-system weighted interference matrix, obtain the actual weighted interference received by the wireless access network and the actual weighted interference received by the service data network; S6. In the current time slot, based on the average link bandwidths of the service data network and the radio access network, respectively update the estimated average link bandwidths of the service data network and the radio access network; based on the average link qualities of the service data network and the radio access network, and the actual weighted interference experienced by the service data network and the radio access network, respectively update the estimated average link quality of the service data network and the radio access network; S7. Based on the link bandwidth or link quality of the previous time slot, update the channel selection of the service data network according to the corresponding probability rule; S8. updating the channel selection of the wireless access network using a heuristic algorithm; S9. If the network topology changes, jump to step S2; otherwise, jump to step S3.

2. The centralized intelligent spectrum management method according to claim 1, wherein: The method of obtaining the actual weighted interference received by the wireless access network and the actual weighted interference received by the service data network based on the currently selected channel, the intra-system weighted interference relationship matrix, and the extra-system weighted interference matrix includes the following relationship: Where I(a) represents the actual weighted interference suffered by the wireless access network, represents the weighted interference in the system, represents the weighted interference outside the system, w n1→n2 represents the number of terminals connected to wireless switching node n2 that can receive signals from wireless switching node n1, v n，c It represents the number of WiFi networks outside the system that exist in each channel detected by the terminal belonging to the wireless switching node. δ(a1, a2) is the channel interference function, and its expression is:

3. The centralized intelligent spectrum management method according to claim 2, wherein: The method of obtaining the actual weighted interference received by the wireless access network and the actual weighted interference received by the service data network based on the currently selected channel, the intra-system weighted interference relationship matrix, and the extra-system weighted interference matrix further includes the following relationship: Where I0(a) represents the actual weighted interference received by the service data network, δ(a1, a2) is the channel interference function, and u n，c Indicates the number of WiFi networks outside the system that exist on channel c detected by the wireless switching node n itself.

4. The centralized intelligent spectrum management method according to claim 1, wherein: The updating of the estimated average link bandwidths of the service data network and the wireless access network based on the average link bandwidths of the service data network and the wireless access network respectively includes updating the estimated average link bandwidth of the service data network based on the average link bandwidth of the service data network. The specific formula includes: Where D0(a0) represents the estimated average link bandwidth of the service data network, represents the average link bandwidth of the service data network, and T(a0) represents the total number of times the selected channel variable a0 is selected as the service data network channel.

5. The centralized intelligent spectrum management method according to claim 4, wherein: The updating of the estimated average link bandwidths of the service data network and the radio access network based on the average link bandwidths of the service data network and the radio access network respectively further includes updating the estimated average link bandwidth of the radio access network based on the average link bandwidth of the radio access network. The specific formula includes: Where D(a0) represents the estimated average link bandwidth of the wireless access network, Indicates the average link bandwidth of the wireless access network.

6. The centralized intelligent spectrum management method according to claim 3, wherein: The updating of the estimated average link quality values of the service data network and the radio access network based on the average link quality of the service data network and the radio access network, and the actual weighted interference suffered by the service data network and the radio access network, respectively, includes updating the estimated average link quality value of the service data network based on the average link quality of the service data network and the actual weighted interference suffered by the service data network. The specific formula includes: Where E0(a0) represents the average link quality estimate of the service data network, represents the average link quality of the service data network, and T(a0) represents the total number of times the selected channel variable a0 is selected as the service data network channel.

7. The centralized intelligent spectrum management method according to claim 6, wherein: Updating the estimated average link quality values of the service data network and the radio access network based on the average link quality of the service data network and the radio access network, and the actual weighted interference suffered by the service data network and the radio access network, respectively, further includes updating the estimated average link quality value of the radio access network based on the average link quality of the radio access network and the actual weighted interference suffered by the radio access network. The specific formula includes: Where E(a0) represents the average link quality estimate of the wireless access network, Indicates the average link quality of the wireless access network.

8. The centralized intelligent spectrum management method according to claim 5, wherein: The updating of the channel selection of the service data network according to the corresponding probability rule based on the link bandwidth or link quality of the previous time slot includes updating the channel a0 of the service data network according to the first probability rule based on the link bandwidth of the previous time slot. The specific formula of the first probability rule is: include: Wherein, D0(c) represents the estimated value of the average link bandwidth of the service data network; D(c) represents the estimated value of the average link bandwidth of the wireless access network; α and β are two predefined parameters, 0<α<1, 0<β<1, α+β=1; ε is a preset probability value that decreases as the number of time slots increases.

9. The centralized intelligent spectrum management method according to claim 7, wherein: The updating of the channel selection of the service data network according to the corresponding probability rule based on the link bandwidth or link quality of the previous time slot includes updating the channel a0 of the service data network according to the second probability rule based on the link quality of the previous time slot. The specific formula of the second probability rule is: include: Wherein, E0(c) represents the average link quality estimate of the service data network; E(c) represents the average link quality estimate of the wireless access network; α and β are two predefined parameters, 0<α<1, 0<β<1, α+β=1; ε is a preset probability value that decreases as the number of time slots increases.

10. The centralized intelligent spectrum management method according to claim 1, wherein: The heuristic algorithm includes: S81. Randomly select a channel from the optional channels for each wireless access node; S82. Randomly select a wireless access node, traverse the actual weighted interference obtained when the wireless access node uses all optional channels, and select the channel that minimizes the actual weighted interference as the channel of the wireless access network; S83. If the maximum number of loop times is reached, exit the loop and return to the channel currently selected by each wireless access node; otherwise, return to step S82.

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